diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..f709d5407802e935e6e760d3864d8bf18fbca5a9 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +pretrain/nsf_hifigan/model filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..28bac26685278dcadb12f316bf4664395345c4f8 --- /dev/null +++ b/LICENSE @@ -0,0 +1,28 @@ +BSD 3-Clause License + +Copyright (c) 2023, SVC Develop Team + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 29c7f58a843c055c1727f1d635a4a3d65abe4534..d6461a8a9189c76069b9d514907b3a2561cf2e36 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@ --- title: So Vits 4.1 Matikanefukukitaru -emoji: 🚀 -colorFrom: green -colorTo: indigo +emoji: 🐨 +colorFrom: pink +colorTo: red sdk: gradio -sdk_version: 3.40.1 +sdk_version: 3.38.0 app_file: app.py +python_version: 3.8.10 pinned: false -license: bsd-3-clause --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/README_zh_CN.md b/README_zh_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..1dea560c83b117613bbc4cecd7e3ee5e21240a4f --- /dev/null +++ b/README_zh_CN.md @@ -0,0 +1,496 @@ +# 马上要高考了，SvcDevelopTeam在此助各位考生高考旗开得胜，超常发挥。 + +# SoftVC VITS Singing Voice Conversion + +[**English**](./README.md) | [**中文简体**](./README_zh_CN.md) + +#### ✨ 带有F0曲线编辑器，角色混合时间轴编辑器的推理端 (Onnx模型的用途) : [MoeVoiceStudio(即将到来)](https://github.com/NaruseMioShirakana/MoeVoiceStudio) + +#### ✨ 改善了交互的一个分支推荐：[34j/so-vits-svc-fork](https://github.com/34j/so-vits-svc-fork) + +#### ✨ 支持实时转换的一个客户端：[w-okada/voice-changer](https://github.com/w-okada/voice-changer) + +**本项目与Vits有着根本上的不同。Vits是TTS，本项目是SVC。本项目无法实现TTS，Vits也无法实现SVC，这两个项目的模型是完全不通用的。** + +## 重要通知 + +这个项目是为了让开发者最喜欢的动画角色唱歌而开发的，任何涉及真人的东西都与开发者的意图背道而驰。 + +## 声明 + +本项目为开源、离线的项目，SvcDevelopTeam的所有成员与本项目的所有开发者以及维护者（以下简称贡献者）对本项目没有控制力。本项目的贡献者从未向任何组织或个人提供包括但不限于数据集提取、数据集加工、算力支持、训练支持、推理等一切形式的帮助；本项目的贡献者不知晓也无法知晓使用者使用该项目的用途。故一切基于本项目训练的AI模型和合成的音频都与本项目贡献者无关。一切由此造成的问题由使用者自行承担。 + +此项目完全离线运行，不能收集任何用户信息或获取用户输入数据。因此，这个项目的贡献者不知道所有的用户输入和模型，因此不负责任何用户输入。 + +本项目只是一个框架项目，本身并没有语音合成的功能，所有的功能都需要用户自己训练模型。同时，这个项目没有任何模型，任何二次分发的项目都与这个项目的贡献者无关。 + +## 📏 使用规约 + +# Warning：请自行解决数据集授权问题，禁止使用非授权数据集进行训练！任何由于使用非授权数据集进行训练造成的问题，需自行承担全部责任和后果！与仓库、仓库维护者、svc develop team 无关！ + +1. 本项目是基于学术交流目的建立，仅供交流与学习使用，并非为生产环境准备。 +2. 任何发布到视频平台的基于 sovits 制作的视频，都必须要在简介明确指明用于变声器转换的输入源歌声、音频，例如：使用他人发布的视频 / 音频，通过分离的人声作为输入源进行转换的，必须要给出明确的原视频、音乐链接；若使用是自己的人声，或是使用其他歌声合成引擎合成的声音作为输入源进行转换的，也必须在简介加以说明。 +3. 由输入源造成的侵权问题需自行承担全部责任和一切后果。使用其他商用歌声合成软件作为输入源时，请确保遵守该软件的使用条例，注意，许多歌声合成引擎使用条例中明确指明不可用于输入源进行转换！ +4. 禁止使用该项目从事违法行为与宗教、政治等活动，该项目维护者坚决抵制上述行为，不同意此条则禁止使用该项目。 +5. 继续使用视为已同意本仓库 README 所述相关条例，本仓库 README 已进行劝导义务，不对后续可能存在问题负责。 +6. 如果将此项目用于任何其他企划，请提前联系并告知本仓库作者，十分感谢。 + +## 📝 模型简介 + +歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 [NSF HiFiGAN](https://github.com/openvpi/DiffSinger/tree/refactor/modules/nsf_hifigan)解决断音问题。 + +### 🆕 4.1-Stable 版本更新内容 + ++ 特征输入更换为 [Content Vec](https://github.com/auspicious3000/contentvec) 的第12层Transformer输出，并兼容4.0分支 ++ 更新浅层扩散，可以使用浅层扩散模型提升音质 ++ 增加whisper语音编码器的支持 ++ 增加静态/动态声线融合 ++ 增加响度嵌入 ++ 增加特征检索,来自于[RVC](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) + +### 🆕 关于兼容4.0模型的问题 + ++ 可通过修改4.0模型的config.json对4.0的模型进行支持，需要在config.json的model字段中添加speech_encoder字段，具体见下 + +``` + "model": { + ......... + "ssl_dim": 256, + "n_speakers": 200, + "speech_encoder":"vec256l9" + } +``` + +### 🆕 关于浅扩散 +![Diagram](shadowdiffusion.png) + +## 💬 关于 Python 版本问题 + +在进行测试后，我们认为`Python 3.8.9`能够稳定地运行该项目 + +## 📥 预先下载的模型文件 + +#### **必须项** + +**以下编码器需要选择一个使用** + +##### **1. 若使用contentvec作为声音编码器（推荐）** + +`vec768l12`与`vec256l9` 需要该编码器 + ++ contentvec ：[checkpoint_best_legacy_500.pt](https://ibm.box.com/s/z1wgl1stco8ffooyatzdwsqn2psd9lrr) + + 放在`pretrain`目录下 + +或者下载下面的ContentVec，大小只有199MB，但效果相同: ++ contentvec ：[hubert_base.pt](https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt) + + 将文件名改为`checkpoint_best_legacy_500.pt`后，放在`pretrain`目录下 + +```shell +# contentvec +wget -P pretrain/ http://obs.cstcloud.cn/share/obs/sankagenkeshi/checkpoint_best_legacy_500.pt +# 也可手动下载放在pretrain目录 +``` + +##### **2. 若使用hubertsoft作为声音编码器** ++ soft vc hubert：[hubert-soft-0d54a1f4.pt](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt) + + 放在`pretrain`目录下 + +##### **3. 若使用Whisper-ppg作为声音编码器** ++ 下载模型 [medium.pt](https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt), 该模型适配`whisper-ppg` ++ 下载模型 [large-v2.pt](https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt), 该模型适配`whisper-ppg-large` + + 放在`pretrain`目录下 + +##### **4. 若使用cnhubertlarge作为声音编码器** ++ 下载模型 [chinese-hubert-large-fairseq-ckpt.pt](https://huggingface.co/TencentGameMate/chinese-hubert-large/resolve/main/chinese-hubert-large-fairseq-ckpt.pt) + + 放在`pretrain`目录下 + +##### **5. 若使用dphubert作为声音编码器** ++ 下载模型 [DPHuBERT-sp0.75.pth](https://huggingface.co/pyf98/DPHuBERT/resolve/main/DPHuBERT-sp0.75.pth) + + 放在`pretrain`目录下 + +##### **6. 若使用WavLM作为声音编码器** ++ 下载模型 [WavLM-Base+.pt](https://valle.blob.core.windows.net/share/wavlm/WavLM-Base+.pt?sv=2020-08-04&st=2023-03-01T07%3A51%3A05Z&se=2033-03-02T07%3A51%3A00Z&sr=c&sp=rl&sig=QJXmSJG9DbMKf48UDIU1MfzIro8HQOf3sqlNXiflY1I%3D), 该模型适配`wavlmbase+` + + 放在`pretrain`目录下 + +##### **7. 若使用OnnxHubert/ContentVec作为声音编码器** ++ 下载模型 [MoeSS-SUBModel](https://huggingface.co/NaruseMioShirakana/MoeSS-SUBModel/tree/main) + + 放在`pretrain`目录下 + +#### **编码器列表** +- "vec768l12" +- "vec256l9" +- "vec256l9-onnx" +- "vec256l12-onnx" +- "vec768l9-onnx" +- "vec768l12-onnx" +- "hubertsoft-onnx" +- "hubertsoft" +- "whisper-ppg" +- "cnhubertlarge" +- "dphubert" +- "whisper-ppg-large" +- "wavlmbase+" + +#### **可选项(强烈建议使用)** + ++ 预训练底模文件： `G_0.pth` `D_0.pth` + + 放在`logs/44k`目录下 + ++ 扩散模型预训练底模文件： `model_0.pt ` + + 放在`logs/44k/diffusion`目录下 + +从svc-develop-team(待定)或任何其他地方获取Sovits底模 + +扩散模型引用了[Diffusion-SVC](https://github.com/CNChTu/Diffusion-SVC)的Diffusion Model，底模与[Diffusion-SVC](https://github.com/CNChTu/Diffusion-SVC)的扩散模型底模通用，可以去[Diffusion-SVC](https://github.com/CNChTu/Diffusion-SVC)获取扩散模型的底模 + +虽然底模一般不会引起什么版权问题，但还是请注意一下，比如事先询问作者，又或者作者在模型描述中明确写明了可行的用途 + +#### **可选项(根据情况选择)** + +如果使用`NSF-HIFIGAN增强器`或`浅层扩散`的话，需要下载预训练的NSF-HIFIGAN模型，如果不需要可以不下载 + ++ 预训练的NSF-HIFIGAN声码器 ：[nsf_hifigan_20221211.zip](https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-v1/nsf_hifigan_20221211.zip) + + 解压后，将四个文件放在`pretrain/nsf_hifigan`目录下 + +```shell +# nsf_hifigan +wget -P pretrain/ https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-v1/nsf_hifigan_20221211.zip +unzip -od pretrain/nsf_hifigan pretrain/nsf_hifigan_20221211.zip +# 也可手动下载放在pretrain/nsf_hifigan目录 +# 地址：https://github.com/openvpi/vocoders/releases/tag/nsf-hifigan-v1 +``` + +## 📊 数据集准备 + +仅需要以以下文件结构将数据集放入dataset_raw目录即可 + +``` +dataset_raw +├───speaker0 +│ ├───xxx1-xxx1.wav +│ ├───... +│ └───Lxx-0xx8.wav +└───speaker1 + ├───xx2-0xxx2.wav + ├───... + └───xxx7-xxx007.wav +``` + +可以自定义说话人名称 + +``` +dataset_raw +└───suijiSUI + ├───1.wav + ├───... + └───25788785-20221210-200143-856_01_(Vocals)_0_0.wav +``` + +## 🛠️ 数据预处理 + +### 0. 音频切片 + +将音频切片至`5s - 15s`, 稍微长点也无伤大雅，实在太长可能会导致训练中途甚至预处理就爆显存 + +可以使用[audio-slicer-GUI](https://github.com/flutydeer/audio-slicer)、[audio-slicer-CLI](https://github.com/openvpi/audio-slicer) + +一般情况下只需调整其中的`Minimum Interval`，普通陈述素材通常保持默认即可，歌唱素材可以调整至`100`甚至`50` + +切完之后手动删除过长过短的音频 + +**如果你使用Whisper-ppg声音编码器进行训练，所有的切片长度必须小于30s** + +### 1. 重采样至44100Hz单声道 + +```shell +python resample.py +``` + +#### 注意 + +虽然本项目拥有重采样、转换单声道与响度匹配的脚本resample.py，但是默认的响度匹配是匹配到0db。这可能会造成音质的受损。而python的响度匹配包pyloudnorm无法对电平进行压限，这会导致爆音。所以建议可以考虑使用专业声音处理软件如`adobe audition`等软件做响度匹配处理。若已经使用其他软件做响度匹配，可以在运行上述命令时添加`--skip_loudnorm`跳过响度匹配步骤。如： + +```shell +python resample.py --skip_loudnorm +``` + +### 2. 自动划分训练集、验证集，以及自动生成配置文件 + +```shell +python preprocess_flist_config.py --speech_encoder vec768l12 +``` + +speech_encoder拥有以下选择 + +``` +vec768l12 +vec256l9 +hubertsoft +whisper-ppg +whisper-ppg-large +cnhubertlarge +dphubert +wavlmbase+ +``` + +如果省略speech_encoder参数，默认值为vec768l12 + +**使用响度嵌入** + +若使用响度嵌入，需要增加`--vol_aug`参数，比如： + +```shell +python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug +``` + +使用后训练出的模型将匹配到输入源响度，否则为训练集响度。 + +#### 此时可以在生成的config.json与diffusion.yaml修改部分参数 + +* `keep_ckpts`：训练时保留最后几个模型，`0`为保留所有，默认只保留最后`3`个 + +* `all_in_mem`,`cache_all_data`：加载所有数据集到内存中，某些平台的硬盘IO过于低下、同时内存容量 **远大于** 数据集体积时可以启用 + +* `batch_size`：单次训练加载到GPU的数据量，调整到低于显存容量的大小即可 + +* `vocoder_name` : 选择一种声码器，默认为`nsf-hifigan`. + +##### **声码器列表** + +``` +nsf-hifigan +nsf-snake-hifigan +``` + +### 3. 生成hubert与f0 + +```shell +python preprocess_hubert_f0.py --f0_predictor dio +``` + +f0_predictor拥有四个选择 + +``` +crepe +dio +pm +harvest +``` + +如果训练集过于嘈杂，请使用crepe处理f0 + +如果省略f0_predictor参数，默认值为dio + +尚若需要浅扩散功能（可选），需要增加--use_diff参数，比如 + +```shell +python preprocess_hubert_f0.py --f0_predictor dio --use_diff +``` + +执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除 dataset_raw 文件夹了 + +## 🏋️‍♀️ 训练 + +### 扩散模型（可选） + +尚若需要浅扩散功能，需要训练扩散模型，扩散模型训练方法为: + +```shell +python train_diff.py -c configs/diffusion.yaml +``` + +### 主模型训练 + +```shell +python train.py -c configs/config.json -m 44k +``` + +模型训练结束后，模型文件保存在`logs/44k`目录下，扩散模型在`logs/44k/diffusion`下 + +## 🤖 推理 + +使用 [inference_main.py](inference_main.py) + +```shell +# 例 +python inference_main.py -m "logs/44k/G_30400.pth" -c "configs/config.json" -n "君の知らない物語-src.wav" -t 0 -s "nen" +``` + +必填项部分： ++ `-m` | `--model_path`：模型路径 ++ `-c` | `--config_path`：配置文件路径 ++ `-n` | `--clean_names`：wav 文件名列表，放在 raw 文件夹下 ++ `-t` | `--trans`：音高调整，支持正负（半音） ++ `-s` | `--spk_list`：合成目标说话人名称 ++ `-cl` | `--clip`：音频强制切片，默认0为自动切片，单位为秒/s + +可选项部分：部分具体见下一节 ++ `-lg` | `--linear_gradient`：两段音频切片的交叉淡入长度，如果强制切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒 ++ `-f0p` | `--f0_predictor`：选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器) ++ `-a` | `--auto_predict_f0`：语音转换自动预测音高，转换歌声时不要打开这个会严重跑调 ++ `-cm` | `--cluster_model_path`：聚类模型或特征检索索引路径，如果没有训练聚类或特征检索则随便填 ++ `-cr` | `--cluster_infer_ratio`：聚类方案或特征检索占比，范围0-1，若没有训练聚类模型或特征检索则默认0即可 ++ `-eh` | `--enhance`：是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭 ++ `-shd` | `--shallow_diffusion`：是否使用浅层扩散，使用后可解决一部分电音问题，默认关闭，该选项打开时，NSF_HIFIGAN增强器将会被禁止 ++ `-usm` | `--use_spk_mix`：是否使用角色融合/动态声线融合 ++ `-lea` | `--loudness_envelope_adjustment`：输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络 ++ `-fr` | `--feature_retrieval`：是否使用特征检索，如果使用聚类模型将被禁用，且cm与cr参数将会变成特征检索的索引路径与混合比例 + +浅扩散设置： ++ `-dm` | `--diffusion_model_path`：扩散模型路径 ++ `-dc` | `--diffusion_config_path`：扩散模型配置文件路径 ++ `-ks` | `--k_step`：扩散步数，越大越接近扩散模型的结果，默认100 ++ `-od` | `--only_diffusion`：纯扩散模式，该模式不会加载sovits模型，以扩散模型推理 ++ `-se` | `--second_encoding`：二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，有时候效果好，有时候效果差 + +### 注意！ + +如果使用`whisper-ppg` 声音编码器进行推理，需要将`--clip`设置为25，`-lg`设置为1。否则将无法正常推理。 + +## 🤔 可选项 + +如果前面的效果已经满意，或者没看明白下面在讲啥，那后面的内容都可以忽略，不影响模型使用(这些可选项影响比较小，可能在某些特定数据上有点效果，但大部分情况似乎都感知不太明显) + +### 自动f0预测 + +4.0模型训练过程会训练一个f0预测器，对于语音转换可以开启自动音高预测，如果效果不好也可以使用手动的，但转换歌声时请不要启用此功能！！！会严重跑调！！ ++ 在inference_main中设置auto_predict_f0为true即可 + +### 聚类音色泄漏控制 + +介绍：聚类方案可以减小音色泄漏，使得模型训练出来更像目标的音色（但其实不是特别明显），但是单纯的聚类方案会降低模型的咬字（会口齿不清）（这个很明显），本模型采用了融合的方式，可以线性控制聚类方案与非聚类方案的占比，也就是可以手动在"像目标音色" 和 "咬字清晰" 之间调整比例，找到合适的折中点 + +使用聚类前面的已有步骤不用进行任何的变动，只需要额外训练一个聚类模型，虽然效果比较有限，但训练成本也比较低 + ++ 训练过程： + + 使用cpu性能较好的机器训练，据我的经验在腾讯云6核cpu训练每个speaker需要约4分钟即可完成训练 + + 执行`python cluster/train_cluster.py`，模型的输出会在`logs/44k/kmeans_10000.pt` + + 聚类模型目前可以使用gpu进行训练，执行`python cluster/train_cluster.py --gpu` ++ 推理过程： + + `inference_main.py`中指定`cluster_model_path` + + `inference_main.py`中指定`cluster_infer_ratio`，`0`为完全不使用聚类，`1`为只使用聚类，通常设置`0.5`即可 + +### 特征检索 + +介绍：跟聚类方案一样可以减小音色泄漏，咬字比聚类稍好，但会降低推理速度，采用了融合的方式，可以线性控制特征检索与非特征检索的占比， + ++ 训练过程： + 首先需要在生成hubert与f0后执行： + +```shell +python train_index.py -c configs/config.json +``` + +模型的输出会在`logs/44k/feature_and_index.pkl` + ++ 推理过程： + + 需要首先制定`--feature_retrieval`，此时聚类方案会自动切换到特征检索方案 + + `inference_main.py`中指定`cluster_model_path` 为模型输出文件 + + `inference_main.py`中指定`cluster_infer_ratio`，`0`为完全不使用特征检索，`1`为只使用特征检索，通常设置`0.5`即可 + +### [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/svc-develop-team/so-vits-svc/blob/4.1-Stable/sovits4_for_colab.ipynb) [sovits4_for_colab.ipynb](https://colab.research.google.com/github/svc-develop-team/so-vits-svc/blob/4.1-Stable/sovits4_for_colab.ipynb) + +## 🗜️ 模型压缩 + +生成的模型含有继续训练所需的信息。如果确认不再训练，可以移除模型中此部分信息，得到约 1/3 大小的最终模型。 + +使用 [compress_model.py](compress_model.py) + +```shell +# 例 +python compress_model.py -c="configs/config.json" -i="logs/44k/G_30400.pth" -o="logs/44k/release.pth" +``` + +## 👨‍🔧 声线混合 + +### 静态声线混合 + +**参考`webUI.py`文件中，小工具/实验室特性的静态声线融合。** + +介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线 +**注意：** + +1. 该功能仅支持单说话人的模型 +2. 如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音 +3. 保证所有待混合模型的config.json中的model字段是相同的 +4. 输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用 +5. 批量上传模型的时候最好把模型放到一个文件夹选中后一起上传 +6. 混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果 +7. 混合完毕后，文件将会保存在项目根目录中，文件名为output.pth +8. 凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会 + +### 动态声线混合 + +**参考`spkmix.py`文件中关于动态声线混合的介绍** + +角色混合轨道 编写规则： + +角色ID : \[\[起始时间1, 终止时间1, 起始数值1, 起始数值1], [起始时间2, 终止时间2, 起始数值2, 起始数值2]] + +起始时间和前一个的终止时间必须相同，第一个起始时间必须为0，最后一个终止时间必须为1 （时间的范围为0-1） + +全部角色必须填写，不使用的角色填\[\[0., 1., 0., 0.]]即可 + +融合数值可以随便填，在指定的时间段内从起始数值线性变化为终止数值，内部会自动确保线性组合为1（凸组合条件），可以放心使用 + +推理的时候使用`--use_spk_mix`参数即可启用动态声线混合 + +## 📤 Onnx导出 + +使用 [onnx_export.py](onnx_export.py) + ++ 新建文件夹：`checkpoints` 并打开 ++ 在`checkpoints`文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称，比如`aziplayer` ++ 将你的模型更名为`model.pth`，配置文件更名为`config.json`，并放置到刚才创建的`aziplayer`文件夹下 ++ 将 [onnx_export.py](onnx_export.py) 中`path = "NyaruTaffy"` 的 `"NyaruTaffy"` 修改为你的项目名称，`path = "aziplayer" (onnx_export_speaker_mix，为支持角色混合的onnx导出)` ++ 运行 [onnx_export.py](onnx_export.py) ++ 等待执行完毕，在你的项目文件夹下会生成一个`model.onnx`，即为导出的模型 + +注意：Hubert Onnx模型请使用MoeSS提供的模型，目前无法自行导出（fairseq中Hubert有不少onnx不支持的算子和涉及到常量的东西，在导出时会报错或者导出的模型输入输出shape和结果都有问题） + +## ☀️ 旧贡献者 + +因为某些原因原作者进行了删库处理，本仓库重建之初由于组织成员疏忽直接重新上传了所有文件导致以前的contributors全部木大，现在在README里重新添加一个旧贡献者列表 + +*某些成员已根据其个人意愿不将其列出* + + + + + + + + + + + +

MistEO

XiaoMiku01

しぐれ

TomoGaSukunai

Plachtaa

zd小达

凍聲響世
+ +## 📚 一些法律条例参考 + +#### 任何国家，地区，组织和个人使用此项目必须遵守以下法律 + +#### 《民法典》 + +##### 第一千零一十九条 + +任何组织或者个人不得以丑化、污损，或者利用信息技术手段伪造等方式侵害他人的肖像权。未经肖像权人同意，不得制作、使用、公开肖像权人的肖像，但是法律另有规定的除外。未经肖像权人同意，肖像作品权利人不得以发表、复制、发行、出租、展览等方式使用或者公开肖像权人的肖像。对自然人声音的保护，参照适用肖像权保护的有关规定。 + +##### 第一千零二十四条 + +【名誉权】民事主体享有名誉权。任何组织或者个人不得以侮辱、诽谤等方式侵害他人的名誉权。 + +##### 第一千零二十七条 + +【作品侵害名誉权】行为人发表的文学、艺术作品以真人真事或者特定人为描述对象，含有侮辱、诽谤内容，侵害他人名誉权的，受害人有权依法请求该行为人承担民事责任。行为人发表的文学、艺术作品不以特定人为描述对象，仅其中的情节与该特定人的情况相似的，不承担民事责任。 + +#### 《[中华人民共和国宪法](http://www.gov.cn/guoqing/2018-03/22/content_5276318.htm)》 + +#### 《[中华人民共和国刑法](http://gongbao.court.gov.cn/Details/f8e30d0689b23f57bfc782d21035c3.html?sw=中华人民共和国刑法)》 + +#### 《[中华人民共和国民法典](http://gongbao.court.gov.cn/Details/51eb6750b8361f79be8f90d09bc202.html)》 + +## 💪 感谢所有的贡献者 + + + diff --git a/Runtime.tar.gz b/Runtime.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..aba229605c818df865b0935d9b28dcdfd13cf219 --- /dev/null +++ b/Runtime.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4454cc1c6296aaff11d08a57afd49c3f94c662122a6e733622a862b57362a2de +size 4768696452 diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..174b4b26cd9997bf0fa108fbde14ac236481d6dd --- /dev/null +++ b/app.py @@ -0,0 +1,1066 @@ +import multiprocessing +import os +import re +import torch +import glob +import gradio as gr +import librosa +import numpy as np +import soundfile as sf +from inference.infer_tool import Svc +import logging +import json +import yaml +import time +import subprocess +import shutil +import utils +import datetime +import traceback +from utils import mix_model +from onnxexport.model_onnx import SynthesizerTrn +from itertools import chain +from compress_model import removeOptimizer +from auto_slicer import AutoSlicer + +logging.getLogger('numba').setLevel(logging.WARNING) +logging.getLogger('markdown_it').setLevel(logging.WARNING) +logging.getLogger('urllib3').setLevel(logging.WARNING) +logging.getLogger('matplotlib').setLevel(logging.WARNING) + +workdir = "logs/44k" +diff_workdir = "logs/44k/diffusion" +config_dir = "configs/" +raw_path = "dataset_raw" +raw_wavs_path = "raw" +models_backup_path = 'models_backup' +root_dir = "checkpoints" +debug = False +sovits_params = {} +diff_params = {} + +loaded = None + +def debug_change(): + global debug + debug = debug_button.value + +def get_default_settings(): + global sovits_params, diff_params + yaml_path = "settings.yaml" + with open(yaml_path, 'r') as f: + default_settings = yaml.safe_load(f) + sovits_params = default_settings['sovits_params'] + diff_params = default_settings['diff_params'] + return sovits_params, diff_params + +def save_default_settings(log_interval,eval_interval,keep_ckpts,batch_size,learning_rate,fp16_run,all_in_mem,num_workers,cache_all_data,cache_device,amp_dtype,diff_batch_size,diff_lr,diff_interval_log,diff_interval_val,diff_force_save): + yaml_path = "settings.yaml" + with open(yaml_path, 'r') as f: + default_settings = yaml.safe_load(f) + default_settings['sovits_params']['log_interval'] = int(log_interval) + default_settings['sovits_params']['eval_interval'] = int(eval_interval) + default_settings['sovits_params']['keep_ckpts'] = int(keep_ckpts) + default_settings['sovits_params']['batch_size'] = int(batch_size) + default_settings['sovits_params']['learning_rate'] = float(learning_rate) + default_settings['sovits_params']['fp16_run'] = fp16_run + default_settings['sovits_params']['all_in_mem'] = all_in_mem + default_settings['diff_params']['num_workers'] = int(num_workers) + default_settings['diff_params']['cache_all_data'] = cache_all_data + default_settings['diff_params']['cache_device'] = str(cache_device) + default_settings['diff_params']['amp_dtype'] = str(amp_dtype) + default_settings['diff_params']['diff_batch_size'] = int(diff_batch_size) + default_settings['diff_params']['diff_lr'] = float(diff_lr) + default_settings['diff_params']['diff_interval_log'] = int(diff_interval_log) + default_settings['diff_params']['diff_interval_val'] = int(diff_interval_val) + default_settings['diff_params']['diff_force_save'] = int(diff_force_save) + with open(yaml_path, 'w') as y: + yaml.safe_dump(default_settings, y, default_flow_style=False, sort_keys=False) + return "成功保存默认配置" + +def get_model_info(choice_ckpt): + pthfile = os.path.join(workdir, choice_ckpt) + net = torch.load(pthfile, map_location=torch.device('cpu')) #cpu load + spk_emb = net["model"].get("emb_g.weight") + if spk_emb is None: + return "所选模型缺少emb_g.weight，你可能选择了一个底模" + _dim, _layer = spk_emb.size() + model_type = { + 768: "Vec768-Layer12", + 256: "Vec256-Layer9 / HubertSoft", + 1024: "Whisper-PPG" + } + return model_type.get(_layer, "不受支持的模型") + +def load_json_encoder(config_choice): + config_file = os.path.join(config_dir + config_choice) + with open(config_file, 'r') as f: + config = json.load(f) + try: + config_encoder = str(config["model"]["speech_encoder"]) + return config_encoder + except Exception as e: + if "speech_encoder" in str(e): + return "你的配置文件似乎是未作兼容的旧版，请根据文档指示对你的配置文件进行修改" + else: + return f"出错了: {e}" + +def load_model_func(ckpt_name,cluster_name,config_name,enhance,diff_model_name,diff_config_name,only_diffusion,encoder,using_device): + global model + config_path = os.path.join(config_dir, config_name) + diff_config_path = os.path.join(config_dir, diff_config_name) if diff_config_name != "no_diff_config" else "configs/diffusion.yaml" + with open(config_path, 'r') as f: + config = json.load(f) + spk_dict = config["spk"] + spk_name = config.get('spk', None) + spk_choice = next(iter(spk_name)) if spk_name else "未检测到音色" + ckpt_path = os.path.join(workdir, ckpt_name) + _, _suffix = os.path.splitext(cluster_name) + fr = True if _suffix == ".pkl" else False #如果是pkl后缀就启用特征检索 + cluster_path = os.path.join(workdir, cluster_name) + diff_model_path = os.path.join(diff_workdir, diff_model_name) + shallow_diffusion = True if diff_model_name != "no_diff" else False + use_spk_mix = False + device = None if using_device == "Auto" else using_device + model = Svc(ckpt_path, + config_path, + device, + cluster_path, + enhance, + diff_model_path, + diff_config_path, + shallow_diffusion, + only_diffusion, + use_spk_mix, + fr) + spk_list = list(spk_dict.keys()) + clip = 25 if encoder == "Whisper-PPG" else 0 #Whisper必须强制切片25秒 + device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev) + index_or_kmeans = "特征索引" if fr is True else "聚类模型" + clu_load = "未加载" if cluster_name == "no_clu" else cluster_name + diff_load = "未加载" if diff_model_name == "no_diff" else diff_model_name + output_msg = f"模型被成功加载到了{device_name}上\n{index_or_kmeans}：{clu_load}\n扩散模型：{diff_load}" + return output_msg, gr.Dropdown.update(choices=spk_list, value=spk_choice), clip + +def Newload_model_func(ckpt_name,cluster_name,config_name2,enhance2,diff_model_name2,diff_config_name2,only_diffusion2,encoder2,using_device2): + global model, loaded + config_name = config_name2.value + enhance = enhance2.value + diff_model_name = diff_model_name2.value + diff_config_name = (diff_config_name2).value + only_diffusion = (only_diffusion2).value + encoder = (encoder2).value + using_device = (using_device2).value + config_path = os.path.join(config_dir, config_name) + diff_config_path = os.path.join(config_dir, diff_config_name) if diff_config_name != "no_diff_config" else "configs/diffusion.yaml" + with open(config_path, 'r') as f: + config = json.load(f) + spk_dict = config["spk"] + spk_name = config.get('spk', None) + spk_choice = next(iter(spk_name)) if spk_name else "未检测到音色" + ckpt_path = os.path.join(workdir, ckpt_name) + _, _suffix = os.path.splitext(cluster_name) + fr = True if _suffix == ".pkl" else False #如果是pkl后缀就启用特征检索 + cluster_path = os.path.join(workdir, cluster_name) + diff_model_path = os.path.join(diff_workdir, diff_model_name) + shallow_diffusion = True if diff_model_name != "no_diff" else False + use_spk_mix = False + device = None if using_device == "Auto" else using_device + model = Svc(ckpt_path, + config_path, + device, + cluster_path, + enhance, + diff_model_path, + diff_config_path, + shallow_diffusion, + only_diffusion, + use_spk_mix, + fr) + spk_list = list(spk_dict.keys()) + clip = 25 if encoder == "Whisper-PPG" else 0 #Whisper必须强制切片25秒 + device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev) + index_or_kmeans = "特征索引" if fr is True else "聚类模型" + clu_load = "未加载" if cluster_name == "no_clu" else cluster_name + diff_load = "未加载" if diff_model_name == "no_diff" else diff_model_name + loaded = cluster_name + #output_msg = f"模型被成功加载到了{device_name}上\n{index_or_kmeans}：{clu_load}\n扩散模型：{diff_load}" + #return output_msg, gr.Dropdown.update(choices=spk_list, value=spk_choice), clip + +def get_file_options(directory, extension): + return [file for file in os.listdir(directory) if file.endswith(extension)] + +def load_options(): + ckpt_list = [file for file in get_file_options(workdir, ".pth") if not file.startswith("D_")] + config_list = get_file_options(config_dir, ".json") + cluster_list = ["no_clu"] + get_file_options(workdir, ".pt") + get_file_options(workdir, ".pkl") # 聚类和特征检索模型 + diff_list = ["no_diff"] + get_file_options(diff_workdir, ".pt") + diff_config_list = get_file_options(config_dir, ".yaml") + return ckpt_list, config_list, cluster_list, diff_list, diff_config_list + +def refresh_options(): + ckpt_list, config_list, cluster_list, diff_list, diff_config_list = load_options() + return ( + choice_ckpt.update(choices=ckpt_list), + config_choice.update(choices=config_list), + cluster_choice.update(choices=cluster_list), + diff_choice.update(choices=diff_list), + diff_config_choice.update(choices=diff_config_list) + ) + +def vc_infer(sid, input_audio, input_audio_path, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment): + if np.issubdtype(input_audio.dtype, np.integer): + input_audio = (input_audio / np.iinfo(input_audio.dtype).max).astype(np.float32) + if len(input_audio.shape) > 1: + input_audio = librosa.to_mono(input_audio.transpose(1, 0)) + _audio = model.slice_inference( + input_audio_path, + sid, + vc_transform, + slice_db, + cluster_ratio, + auto_f0, + noise_scale, + pad_seconds, + cl_num, + lg_num, + lgr_num, + f0_predictor, + enhancer_adaptive_key, + cr_threshold, + k_step, + use_spk_mix, + second_encoding, + loudness_envelope_adjustment + ) + model.clear_empty() + timestamp = str(int(time.time())) + if not os.path.exists("results"): + os.makedirs("results") + output_file_name = os.path.splitext(os.path.basename(input_audio_path))[0] + "_" + sid + "_" + timestamp + ".wav" + output_file_path = os.path.join("results", output_file_name) + sf.write(output_file_path, _audio, model.target_sample, format="wav") + return output_file_path + +def vc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment): + global model + try: + if input_audio is None: + return "You need to upload an audio", None + if model is None: + return "You need to upload an model", None + sampling_rate, audio = input_audio + temp_path = "temp.wav" + sf.write(temp_path, audio, sampling_rate, format="wav") + output_file_path = vc_infer(sid, audio, temp_path, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment) + os.remove(temp_path) + return "Success", output_file_path + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def vc_batch_fn(sid, input_audio_files, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment): + global model + try: + if input_audio_files is None or len(input_audio_files) == 0: + return "You need to upload at least one audio file" + if model is None: + return "You need to upload a model" + for file_obj in input_audio_files: + input_audio_path = file_obj.name + audio, sampling_rate = sf.read(input_audio_path) + vc_infer(sid, audio, input_audio_path, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment) + return "批量推理完成，音频已经被保存到results文件夹" + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def tts_fn(_text, _speaker, sid, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold, k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment): + global model + try: + subprocess.run([r"python", "tts.py", _text, _speaker]) + sr = 44100 + y, sr = librosa.load("tts.wav") + resampled_y = librosa.resample(y, orig_sr=sr, target_sr=sr) + sf.write("tts.wav", resampled_y, sr, subtype = "PCM_16") + input_audio = "tts.wav" + audio, sampling_rate = sf.read(input_audio) + if model is None: + return "You need to upload a model", None + output_file_path = vc_infer(sid, audio, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment) + return "Success", output_file_path + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def load_raw_dirs(): + illegal_files = [] + #检查文件名 + allowed_pattern = re.compile(r'^[a-zA-Z0-9_@#$%^&()_+\-=\s\.]*$') + for root, dirs, files in os.walk(raw_path): + if root != raw_path: # 只处理子文件夹内的文件 + for file in files: + file_name, _ = os.path.splitext(file) + if not allowed_pattern.match(file_name): + illegal_files.append(file) + if len(illegal_files)!=0: + return f"数据集文件名只能包含数字、字母、下划线，以下文件不符合要求，请改名后再试：{illegal_files}" + #检查有没有小可爱不用wav文件当数据集 + for root, dirs, files in os.walk(raw_path): + if root != raw_path: # 只处理子文件夹内的文件 + for file in files: + if not file.lower().endswith('.wav'): + illegal_files.append(file) + if len(illegal_files)!=0: + return f"以下文件为非wav格式文件，请删除后再试：{illegal_files}" + spk_dirs = [] + with os.scandir(raw_path) as entries: + for entry in entries: + if entry.is_dir(): + spk_dirs.append(entry.name) + if len(spk_dirs) != 0: + return raw_dirs_list.update(value=spk_dirs) + else: + return raw_dirs_list.update(value="未找到数据集，请检查dataset_raw文件夹") + +def dataset_preprocess(encoder, f0_predictor, use_diff, vol_aug, skip_loudnorm, num_processes): + diff_arg = "--use_diff" if use_diff else "" + vol_aug_arg = "--vol_aug" if vol_aug else "" + skip_loudnorm_arg = "--skip_loudnorm" if skip_loudnorm else "" + preprocess_commands = [ + r"python resample.py %s" % (skip_loudnorm_arg), + r"python preprocess_flist_config.py --speech_encoder %s %s" % (encoder, vol_aug_arg), + r"python preprocess_hubert_f0.py --num_processes %s --f0_predictor %s %s" % (num_processes ,f0_predictor, diff_arg) + ] + accumulated_output = "" + #清空dataset + dataset = os.listdir("dataset/44k") + if len(dataset) != 0: + for dir in dataset: + dataset_dir = "dataset/44k/" + str(dir) + if os.path.isdir(dataset_dir): + shutil.rmtree(dataset_dir) + accumulated_output += f"Deleting previous dataset: {dir}\n" + for command in preprocess_commands: + try: + result = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True, text=True) + accumulated_output += f"Command: {command}, Using Encoder: {encoder}, Using f0 Predictor: {f0_predictor}\n" + yield accumulated_output, None + progress_line = None + for line in result.stdout: + if r"it/s" in line or r"s/it" in line: #防止进度条刷屏 + progress_line = line + else: + accumulated_output += line + if progress_line is None: + yield accumulated_output, None + else: + yield accumulated_output + progress_line, None + result.communicate() + except subprocess.CalledProcessError as e: + result = e.output + accumulated_output += f"Error: {result}\n" + yield accumulated_output, None + if progress_line is not None: + accumulated_output += progress_line + accumulated_output += '-' * 50 + '\n' + yield accumulated_output, None + config_path = "configs/config.json" + with open(config_path, 'r') as f: + config = json.load(f) + spk_name = config.get('spk', None) + yield accumulated_output, gr.Textbox.update(value=spk_name) + +def regenerate_config(encoder, vol_aug): + vol_aug_arg = "--vol_aug" if vol_aug else "" + cmd = r"python preprocess_flist_config.py --speech_encoder %s %s" % (encoder, vol_aug_arg) + output = "" + try: + result = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True, text=True) + for line in result.stdout: + output += line + output += "Regenerate config file successfully." + except subprocess.CalledProcessError as e: + result = e.output + output += f"Error: {result}\n" + return output + +def clear_output(): + return gr.Textbox.update(value="Cleared!>_<") + +def read_config(config_path): + with open(config_path, 'r') as config_file: + config_data = json.load(config_file) + return config_data + +def config_fn(log_interval, eval_interval, keep_ckpts, batch_size, lr, fp16_run, all_in_mem, diff_num_workers, diff_cache_all_data, diff_batch_size, diff_lr, diff_interval_log, diff_interval_val, diff_cache_device, diff_amp_dtype, diff_force_save): + config_origin = "configs/config.json" + diff_config = "configs/diffusion.yaml" + config_data = read_config(config_origin) + config_data['train']['log_interval'] = int(log_interval) + config_data['train']['eval_interval'] = int(eval_interval) + config_data['train']['keep_ckpts'] = int(keep_ckpts) + config_data['train']['batch_size'] = int(batch_size) + config_data['train']['learning_rate'] = float(lr) + config_data['train']['fp16_run'] = fp16_run + config_data['train']['all_in_mem'] = all_in_mem + with open(config_origin, 'w') as config_file: + json.dump(config_data, config_file, indent=4) + with open(diff_config, 'r') as diff_yaml: + diff_config_data = yaml.safe_load(diff_yaml) + diff_config_data['train']['num_workers'] = int(diff_num_workers) + diff_config_data['train']['cache_all_data'] = diff_cache_all_data + diff_config_data['train']['batch_size'] = int(diff_batch_size) + diff_config_data['train']['lr'] = float(diff_lr) + diff_config_data['train']['interval_log'] = int(diff_interval_log) + diff_config_data['train']['interval_val'] = int(diff_interval_val) + diff_config_data['train']['cache_device'] = str(diff_cache_device) + diff_config_data['train']['amp_dtype'] = str(diff_amp_dtype) + diff_config_data['train']['interval_force_save'] = int(diff_force_save) + with open(diff_config, 'w') as diff_yaml: + yaml.safe_dump(diff_config_data, diff_yaml, default_flow_style=False, sort_keys=False) + return "配置文件写入完成" + +def check_dataset(dataset_path): + if not os.listdir(dataset_path): + return "数据集不存在，请检查dataset文件夹" + no_npy_pt_files = True + for root, dirs, files in os.walk(dataset_path): + for file in files: + if file.endswith('.npy') or file.endswith('.pt'): + no_npy_pt_files = False + break + if no_npy_pt_files: + return "数据集中未检测到f0和hubert文件，可能是预处理未完成" + return None + +def training(gpu_selection, encoder): + config_data = read_config("configs/config.json") + vol_emb = config_data["model"]["vol_embedding"] + dataset_warn = check_dataset("dataset/44k") + if dataset_warn is not None: + return dataset_warn + encoder_models = { #编码器好多，要塞不下了 + "vec256l9": ("D_0.pth", "G_0.pth", "pre_trained_model"), + "vec768l12": ("D_0.pth", "G_0.pth", "pre_trained_model/768l12/vol_emb" if vol_emb else "pre_trained_model/768l12"), + "hubertsoft": ("D_0.pth", "G_0.pth", "pre_trained_model/hubertsoft"), + "whisper-ppg": ("D_0.pth", "G_0.pth", "pre_trained_model/whisper-ppg"), + "cnhubertlarge": ("D_0.pth", "G_0.pth", "pre_trained_model/cnhubertlarge"), + "dphubert": ("D_0.pth", "G_0.pth", "pre_trained_model/dphubert"), + "whisper-ppg-large": ("D_0.pth", "G_0.pth", "pre_trained_model/whisper-ppg-large") + } + if encoder not in encoder_models: + return "未知编码器" + d_0_file, g_0_file, encoder_model_path = encoder_models[encoder] + d_0_path = os.path.join(encoder_model_path, d_0_file) + g_0_path = os.path.join(encoder_model_path, g_0_file) + timestamp = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M') + new_backup_folder = os.path.join(models_backup_path, str(timestamp)) + if os.listdir(workdir) != ['diffusion']: + os.makedirs(new_backup_folder, exist_ok=True) + for file in os.listdir(workdir): + if file != "diffusion": + shutil.move(os.path.join(workdir, file), os.path.join(new_backup_folder, file)) + shutil.copy(d_0_path, os.path.join(workdir, "D_0.pth")) + shutil.copy(g_0_path, os.path.join(workdir, "G_0.pth")) + cmd = r"set CUDA_VISIBLE_DEVICES=%s && python train.py -c configs/config.json -m 44k" % (gpu_selection) + subprocess.Popen(["cmd", "/c", "start", "cmd", "/k", cmd]) + return "已经在新的终端窗口开始训练，请监看终端窗口的训练日志。在终端中按Ctrl+C可暂停训练。" + +def continue_training(gpu_selection, encoder): + dataset_warn = check_dataset("dataset/44k") + if dataset_warn is not None: + return dataset_warn + if encoder == "": + return "请先选择预处理对应的编码器" + all_files = os.listdir(workdir) + model_files = [f for f in all_files if f.startswith('G_') and f.endswith('.pth')] + if len(model_files) == 0: + return "你还没有已开始的训练" + cmd = r"set CUDA_VISIBLE_DEVICES=%s && python train.py -c configs/config.json -m 44k" % (gpu_selection) + subprocess.Popen(["cmd", "/c", "start", "cmd", "/k", cmd]) + return "已经在新的终端窗口开始训练，请监看终端窗口的训练日志。在终端中按Ctrl+C可暂停训练。" + +def kmeans_training(kmeans_gpu): + if not os.listdir(r"dataset/44k"): + return "数据集不存在，请检查dataset文件夹" + cmd = r"python cluster/train_cluster.py --gpu" if kmeans_gpu else r"python cluster/train_cluster.py" + subprocess.Popen(["cmd", "/c", "start", "cmd", "/k", cmd]) + return "已经在新的终端窗口开始训练，训练聚类模型不会输出日志，CPU训练一般需要5-10分钟左右" + +def index_training(): + if not os.listdir(r"dataset/44k"): + return "数据集不存在，请检查dataset文件夹" + cmd = r"python train_index.py -c configs/config.json" + subprocess.Popen(["cmd", "/c", "start", "cmd", "/k", cmd]) + return "已经在新的终端窗口开始训练" + +def diff_training(encoder): + if not os.listdir(r"dataset/44k"): + return "数据集不存在，请检查dataset文件夹" + pre_trained_model_768l12 = "pre_trained_model/diffusion/768l12/model_0.pt" + pre_trained_model_hubertsoft = "pre_trained_model/diffusion/hubertsoft/model_0.pt" + timestamp = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M') + new_backup_folder = os.path.join(models_backup_path, "diffusion", str(timestamp)) + if len(os.listdir(diff_workdir)) != 0: + os.makedirs(new_backup_folder, exist_ok=True) + for file in os.listdir(diff_workdir): + shutil.move(os.path.join(diff_workdir, file), os.path.join(new_backup_folder, file)) + if encoder == "vec256l9" or encoder == "whisper-ppg": + return "你所选的编码器暂时不支持训练扩散模型" + elif encoder == "vec768l12": + shutil.copy(pre_trained_model_768l12, os.path.join(diff_workdir, "model_0.pt")) + elif encoder == "hubertsoft": + shutil.copy(pre_trained_model_hubertsoft, os.path.join(diff_workdir, "model_0.pt")) + else: + return "请先选择编码器" + subprocess.Popen(["cmd", "/c", "start", "cmd", "/k", r"python train_diff.py -c configs/diffusion.yaml"]) + return "已经在新的终端窗口开始训练，请监看终端窗口的训练日志。在终端中按Ctrl+C可暂停训练。" + +def diff_continue_training(encoder): + if not os.listdir(r"dataset/44k"): + return "数据集不存在，请检查dataset文件夹" + if encoder == "": + return "请先选择预处理对应的编码器" + all_files = os.listdir(diff_workdir) + model_files = [f for f in all_files if f.endswith('.pt')] + if len(model_files) == 0: + return "你还没有已开始的训练" + subprocess.Popen(["cmd", "/c", "start", "cmd", "/k", r"python train_diff.py -c configs/diffusion.yaml"]) + return "已经在新的终端窗口开始训练，请监看终端窗口的训练日志。在终端中按Ctrl+C可暂停训练。" + +def upload_mix_append_file(files,sfiles): + try: + if(sfiles == None): + file_paths = [file.name for file in files] + else: + file_paths = [file.name for file in chain(files,sfiles)] + p = {file:100 for file in file_paths} + return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2)) + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def mix_submit_click(js,mode): + try: + assert js.lstrip()!="" + modes = {"凸组合":0, "线性组合":1} + mode = modes[mode] + data = json.loads(js) + data = list(data.items()) + model_path,mix_rate = zip(*data) + path = mix_model(model_path,mix_rate,mode) + return f"成功，文件被保存在了{path}" + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def updata_mix_info(files): + try: + if files == None : return mix_model_output1.update(value="") + p = {file.name:100 for file in files} + return mix_model_output1.update(value=json.dumps(p,indent=2)) + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def pth_identify(): + if not os.path.exists(root_dir): + return f"未找到{root_dir}文件夹，请先创建一个{root_dir}文件夹并按第一步流程操作" + model_dirs = [d for d in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, d))] + if not model_dirs: + return f"未在{root_dir}文件夹中找到模型文件夹，请确保每个模型和配置文件都被放置在单独的文件夹中" + valid_model_dirs = [] + for path in model_dirs: + pth_files = glob.glob(f"{root_dir}/{path}/*.pth") + json_files = glob.glob(f"{root_dir}/{path}/*.json") + if len(pth_files) != 1 or len(json_files) != 1: + return f"错误: 在{root_dir}/{path}中找到了{len(pth_files)}个.pth文件和{len(json_files)}个.json文件。应当确保每个文件夹内有且只有一个.pth文件和.json文件" + valid_model_dirs.append(path) + + return f"成功识别了{len(valid_model_dirs)}个模型：{valid_model_dirs}" + +def onnx_export(): + model_dirs = [d for d in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, d))] + try: + for path in model_dirs: + pth_files = glob.glob(f"{root_dir}/{path}/*.pth") + json_files = glob.glob(f"{root_dir}/{path}/*.json") + model_file = pth_files[0] + json_file = json_files[0] + with open(json_file, 'r') as config_file: + config_data = json.load(config_file) + channels = config_data["model"]["gin_channels"] + if str(channels) == "256": + para1 = 1 + if str(channels) == "768": + para1 = 192 + device = torch.device("cpu") + hps = utils.get_hparams_from_file(json_file) + SVCVITS = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + **hps.model) + _ = utils.load_checkpoint(model_file, SVCVITS, None) + _ = SVCVITS.eval().to(device) + for i in SVCVITS.parameters(): + i.requires_grad = False + n_frame = 10 + test_hidden_unit = torch.rand(para1, n_frame, channels) + test_pitch = torch.rand(1, n_frame) + test_mel2ph = torch.arange(0, n_frame, dtype=torch.int64)[None] # torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0) + test_uv = torch.ones(1, n_frame, dtype=torch.float32) + test_noise = torch.randn(1, 192, n_frame) + test_sid = torch.LongTensor([0]) + input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"] + output_names = ["audio", ] + onnx_file = os.path.splitext(model_file)[0] + ".onnx" + torch.onnx.export(SVCVITS, + ( + test_hidden_unit.to(device), + test_pitch.to(device), + test_mel2ph.to(device), + test_uv.to(device), + test_noise.to(device), + test_sid.to(device) + ), + onnx_file, + dynamic_axes={ + "c": [0, 1], + "f0": [1], + "mel2ph": [1], + "uv": [1], + "noise": [2], + }, + do_constant_folding=False, + opset_version=16, + verbose=False, + input_names=input_names, + output_names=output_names) + return "转换成功，模型被保存在了checkpoints下的对应目录" + except Exception as e: + if debug: traceback.print_exc() + return "转换错误："+str(e) + +def load_raw_audio(audio_path): + if not os.path.isdir(audio_path): + return "请输入正确的目录", None + files = os.listdir(audio_path) + wav_files = [file for file in files if file.lower().endswith('.wav')] + if not wav_files: + return "未在目录中找到.wav音频文件", None + return "成功加载", wav_files + +def slicer_fn(input_dir, output_dir, process_method, max_sec, min_sec): + if output_dir == "": + return "请先选择输出的文件夹" + slicer = AutoSlicer() + if not os.path.exists(output_dir): + os.makedirs(output_dir) + for filename in os.listdir(input_dir): + if filename.lower().endswith(".wav"): + slicer.auto_slice(filename, input_dir, output_dir, max_sec) + if process_method == "丢弃": + for filename in os.listdir(output_dir): + if filename.endswith(".wav"): + filepath = os.path.join(output_dir, filename) + audio, sr = librosa.load(filepath, sr=None, mono=False) + if librosa.get_duration(y=audio, sr=sr) < min_sec: + os.remove(filepath) + elif process_method == "将过短音频整合为长音频": + slicer.merge_short(output_dir, max_sec, min_sec) + file_count, max_duration, min_duration, orig_duration, final_duration = slicer.slice_count(input_dir, output_dir) + hrs = int(final_duration / 3600) + mins = int((final_duration % 3600) / 60) + sec = format(float(final_duration % 60), '.2f') + rate = format(100 * (final_duration / orig_duration), '.2f') + return f"成功将音频切分为{file_count}条片段，其中最长{max_duration}秒，最短{min_duration}秒，切片后的音频总时长{hrs:02d}小时{mins:02d}分{sec}秒，为原始音频时长的{rate}%" + +def model_compression(_model): + if _model == "": + return "请先选择要压缩的模型" + else: + model_path = os.path.join(workdir, _model) + filename, extension = os.path.splitext(_model) + output_model_name = f"{filename}_compressed{extension}" + output_path = os.path.join(workdir, output_model_name) + removeOptimizer(model_path, output_path) + return f"模型已成功被保存在了{output_path}" + +# read ckpt list +ckpt_list, config_list, cluster_list, diff_list, diff_config_list = load_options() + +#read GPU info +ngpu=torch.cuda.device_count() +gpu_infos=[] +if(torch.cuda.is_available()==False or ngpu==0):if_gpu_ok=False +else: + if_gpu_ok = False + for i in range(ngpu): + gpu_name=torch.cuda.get_device_name(i) + if("MX"in gpu_name):continue + if("10"in gpu_name or "16"in gpu_name or "20"in gpu_name or "30"in gpu_name or "40"in gpu_name or "A50"in gpu_name.upper() or "70"in gpu_name or "80"in gpu_name or "90"in gpu_name or "M4"in gpu_name or"P4"in gpu_name or "T4"in gpu_name or "TITAN"in gpu_name.upper()):#A10#A100#V100#A40#P40#M40#K80 + if_gpu_ok=True#至少有一张能用的N卡 + gpu_infos.append("%s\t%s"%(i,gpu_name)) +gpu_info="\n".join(gpu_infos)if if_gpu_ok==True and len(gpu_infos)>0 else "很遗憾您这没有能用的显卡来支持您训练" +gpus="-".join([i[0]for i in gpu_infos]) + +#read default params +sovits_params, diff_params = get_default_settings() + +app = gr.Blocks() + +def Newget_model_info(choice_ckpt2): + choice_ckpt = str(choice_ckpt2) + pthfile = os.path.join(workdir, choice_ckpt) + net = torch.load(pthfile, map_location=torch.device('cpu')) #cpu load + spk_emb = net["model"].get("emb_g.weight") + if spk_emb is None: + return "所选模型缺少emb_g.weight，你可能选择了一个底模" + _dim, _layer = spk_emb.size() + model_type = { + 768: "Vec768-Layer12", + 256: "Vec256-Layer9 / HubertSoft", + 1024: "Whisper-PPG" + } + return gr.Textbox(visible=False, value=model_type.get(_layer, "不受支持的模型")) + +with app: + gr.Markdown(value=""" + ### So-VITS-SVC 4.1-Stable + + 修改自原项目及bilibili@麦哲云 + + 仅供个人娱乐和非商业用途，禁止用于血腥、暴力、性相关、政治相关内容 + + weiui来自：bilibili@羽毛布団，交流③群:416656175 + + 镜像作者：bilibili@kiss丿冷鸟鸟,交流群:829974025 + + """) + with gr.Tabs(): + with gr.TabItem("FC"): + #with gr.Row(): + # choice_ckpt = gr.Dropdown(label="模型选择", choices=ckpt_list, value="no_model") + # model_branch = gr.Textbox(label="模型编码器", placeholder="请先选择模型", interactive=False) + #choice_ckpt = gr.Dropdown(value="G_388000.pth", visible=False) + #with gr.Row(): + # config_choice = gr.Dropdown(label="配置文件", choices=config_list, value="no_config") + # config_info = gr.Textbox(label="配置文件编码器", placeholder="请选择配置文件") + config_choice = gr.Dropdown(value="config.json", visible=False) + #gr.Markdown(value="""**请检查模型和配置文件的编码器是否匹配**""") + #with gr.Row(): + # diff_choice = gr.Dropdown(label="（可选）选择扩散模型", choices=diff_list, value="no_diff", interactive=True) + # diff_config_choice = gr.Dropdown(label="扩散模型配置文件", choices=diff_config_list, value="no_diff_config", interactive=True) + diff_choice = gr.Dropdown(value="no_diff", visible=False) + diff_config_choice = gr.Dropdown(value="no_diff_config", visible=False) + with gr.Row(): + cluster_choice = gr.Dropdown(label="（可选）选择聚类模型/特征检索模型", choices=cluster_list, value="no_clu") + with gr.Row(): + enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强，该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False) + #only_diffusion = gr.Checkbox(label="是否使用全扩散推理，开启后将不使用So-VITS模型，仅使用扩散模型进行完整扩散推理，默认关闭", value=False) + only_diffusion = gr.Checkbox(value=False, visible=False) + #using_device = gr.Dropdown(label="推理设备，默认为自动选择", choices=["Auto","cuda","cpu"], value="Auto") + using_device = gr.Dropdown(value='Auto', visible=False) + #refresh = gr.Button("刷新选项") + #loadckpt = gr.Button("加载模型", variant="primary") + #with gr.Row(): + # model_message = gr.Textbox(label="Output Message") + # sid = gr.Dropdown(label="So-VITS说话人", value="speaker0") + sid = gr.Dropdown(value="1056", visible=False) + + #choice_ckpt.change(get_model_info, [choice_ckpt], [model_branch]) + model_branch = Newget_model_info("G_388000.pth") + #config_choice.change(load_json_encoder, [config_choice], [config_info]) + #refresh.click(refresh_options,[],[choice_ckpt,config_choice,cluster_choice,diff_choice,diff_config_choice]) + + gr.Markdown(value=""" + 请稍等片刻，模型加载大约需要10秒。后续操作不需要重新加载模型 + """) + with gr.Tabs(): + with gr.TabItem("单个音频上传"): + vc_input3 = gr.Audio(label="单个音频上传") + with gr.TabItem("批量音频上传"): + vc_batch_files = gr.Files(label="批量音频上传", file_types=["audio"], file_count="multiple") + with gr.TabItem("文字转语音（实验性）"): + gr.Markdown(""" + 文字转语音（TTS）说明：使用edge_tts服务生成音频，并转换为So-VITS模型音色。可以在输入文字中使用标点符号简单控制情绪 + zh-CN-XiaoyiNeural：中文女声 + zh-CN-YunxiNeural: 中文男声 + ja-JP-NanamiNeural：日文女声 + ja-JP-KeitaNeural：日文男声 + zh-CN-liaoning-XiaobeiNeural：东北话女声 + zh-CN-shaanxi-XiaoniNeural: 陕西话女声 + zh-HK-HiuMaanNeural: 粤语女声 + zh-HK-WanLungNeural: 粤语男声 + """) + with gr.Row(): + text_input = gr.Textbox(label = "在此输入需要转译的文字（建议打开自动f0预测）",) + tts_spk = gr.Dropdown(label = "选择原始音频音色（来自微软TTS）", choices=["zh-CN-XiaoyiNeural", "zh-CN-YunxiNeural", "zh-CN-liaoning-XiaobeiNeural", "zh-CN-shaanxi-XiaoniNeural", "zh-HK-HiuMaanNeural", "zh-HK-WanLungNeural", "ja-JP-NanamiNeural", "ja-JP-KeitaNeural"], value = "zh-CN-XiaoyiNeural") + #with gr.Row(): + # tts_rate = gr.Slider(label = "TTS语音变速（倍速）", minimum = 0, maximum = 3, value = 1) + # tts_volume = gr.Slider(label = "TTS语音音量（相对值）", minimum = 0, maximum = 1.5, value = 1) + + with gr.Row(): + auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会跑调）", value=False) + f0_predictor = gr.Radio(label="f0预测器选择（如遇哑音可以更换f0预测器解决，crepe为原F0使用均值滤波器）", choices=["pm","crepe","harvest","dio"], value="pm") + cr_threshold = gr.Number(label="F0过滤阈值，只有使用crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05) + with gr.Row(): + vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0) + cluster_ratio = gr.Number(label="聚类模型/特征检索混合比例，0-1之间，默认为0不启用聚类或特征检索，能提升音色相似度，但会导致咬字下降", value=0) + k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000) + with gr.Row(): + enhancer_adaptive_key = gr.Number(label="使NSF-HIFIGAN增强器适应更高的音域(单位为半音数)|默认为0", value=0,interactive=True) + slice_db = gr.Number(label="切片阈值", value=-50) + cl_num = gr.Number(label="音频自动切片，0为按默认方式切片，单位为秒/s，爆显存可以设置此处强制切片", value=0) + with gr.Accordion("高级设置（一般不需要动）", open=False): + noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4) + pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5) + lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=1) + lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75,interactive=True) + second_encoding = gr.Checkbox(label = "二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，效果时好时差，默认关闭", value=False) + loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络", value = 0) + use_spk_mix = gr.Checkbox(label="动态声线融合，暂时没做完", value=False, interactive=False) + with gr.Row(): + vc_submit = gr.Button("音频转换", variant="primary") + vc_batch_submit = gr.Button("批量转换", variant="primary") + vc_tts_submit = gr.Button("文本转语音", variant="primary") + vc_output1 = gr.Textbox(label="Output Message") + vc_output2 = gr.Audio(label="Output Audio") + + def Newvc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment, clus2): + global model, loaded + if loaded != clus2: + Newload_model_func("G_388000.pth",clus2,config_choice,enhance,diff_choice,diff_config_choice,only_diffusion,model_branch,using_device) + loaded = clus2 + try: + if input_audio is None: + return "You need to upload an audio", None + if model is None: + return "You need to upload an model", None + sampling_rate, audio = input_audio + temp_path = "temp.wav" + sf.write(temp_path, audio, sampling_rate, format="wav") + output_file_path = vc_infer(sid, audio, temp_path, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment) + os.remove(temp_path) + return "Success", output_file_path + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + + #loadckpt.click(load_model_func,[choice_ckpt,cluster_choice,config_choice,enhance,diff_choice,diff_config_choice,only_diffusion,model_branch,using_device],[model_message, sid, cl_num]) + vc_submit.click(Newvc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment,cluster_choice], [vc_output1, vc_output2]) + vc_batch_submit.click(vc_batch_fn, [sid, vc_batch_files, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1]) + vc_tts_submit.click(tts_fn, [text_input, tts_spk, sid, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2]) + ''' + with gr.TabItem("训练"): + gr.Markdown(value="""请将数据集文件夹放置在dataset_raw文件夹下，确认放置正确后点击下方获取数据集名称""") + raw_dirs_list=gr.Textbox(label="Raw dataset directory(s):") + get_raw_dirs=gr.Button("识别数据集", variant="primary") + gr.Markdown(value="""确认数据集正确识别后请选择训练使用的特征编码器和f0预测器，**如果要训练扩散模型，请选择Vec768l12或hubertsoft，并确保So-VITS和扩散模型使用同一个编码器**""") + with gr.Row(): + gr.Markdown(value="""**vec256l9**: ContentVec(256Layer9)，旧版本叫v1，So-VITS-SVC 4.0的基础版本，**暂不支持扩散模型** + **vec768l12**: 特征输入更换为ContentVec的第12层Transformer输出，模型理论上会更加还原训练集音色 + **hubertsoft**: So-VITS-SVC 3.0使用的编码器，咬字更为准确，但可能存在多说话人音色泄露问题 + **whisper-ppg**: 来自OpenAI，咬字最为准确，但和Hubertsoft一样存在多说话人音色泄露，且显存占用和训练时间有明显增加。**暂不支持扩散模型** + """) + gr.Markdown(value="""**crepe**: 抗噪能力最强，但预处理速度慢（不过如果你的显卡很强的话速度会很快） + **pm**: 预处理速度快，但抗噪能力较弱 + **dio**: 先前版本预处理默认使用的f0预测器 + **harvest**: 有一定抗噪能力，预处理显存占用友好，速度比较慢 + """) + with gr.Row(): + branch_selection = gr.Radio(label="选择训练使用的编码器", choices=["vec256l9","vec768l12","hubertsoft","whisper-ppg"], value="vec768l12", interactive=True) + f0_predictor_selection = gr.Radio(label="选择训练使用的f0预测器", choices=["crepe","pm","dio","harvest"], value="crepe", interactive=True) + use_diff = gr.Checkbox(label="是否使用浅扩散模型，如要训练浅扩散模型请勾选此项", value=True) + vol_aug=gr.Checkbox(label="是否启用响度嵌入和音量增强，启用后可以根据输入源控制输出响度，但对数据集质量的要求更高。**仅支持vec768l12编码器**", value=False) + with gr.Row(): + skip_loudnorm = gr.Checkbox(label="是否跳过响度匹配，如果你已经用音频处理软件做过响度匹配，请勾选此处") + num_processes = gr.Slider(label="预处理使用的CPU线程数，可以大幅加快预处理速度，但线程数过大容易爆显存，建议12G显存设置为2", minimum=1, maximum=multiprocessing.cpu_count(), value=1, step=1) + with gr.Row(): + raw_preprocess=gr.Button("数据预处理", variant="primary") + regenerate_config_btn=gr.Button("重新生成配置文件", variant="primary") + preprocess_output=gr.Textbox(label="预处理输出信息，完成后请检查一下是否有报错信息，如无则可以进行下一步", max_lines=999) + clear_preprocess_output=gr.Button("清空输出信息") + with gr.Group(): + gr.Markdown(value="""填写训练设置和超参数""") + with gr.Row(): + gr.Textbox(label="当前使用显卡信息", value=gpu_info) + gpu_selection=gr.Textbox(label="多卡用户请指定希望训练使用的显卡ID（0,1,2...）", value=gpus, interactive=True) + with gr.Row(): + log_interval=gr.Textbox(label="每隔多少步(steps)生成一次评估日志", value=sovits_params['log_interval']) + eval_interval=gr.Textbox(label="每隔多少步(steps)验证并保存一次模型", value=sovits_params['eval_interval']) + keep_ckpts=gr.Textbox(label="仅保留最新的X个模型，超出该数字的旧模型会被删除。设置为0则永不删除", value=sovits_params['keep_ckpts']) + with gr.Row(): + batch_size=gr.Textbox(label="批量大小，每步取多少条数据进行训练，大batch有助于训练但显著增加显存占用。6G显存建议设定为4", value=sovits_params['batch_size']) + lr=gr.Textbox(label="学习率，一般不用动，批量大小较大时可以适当增大学习率，但强烈不建议超过0.0002，有炸炉风险", value=sovits_params['learning_rate']) + fp16_run=gr.Checkbox(label="是否使用fp16混合精度训练，fp16训练可能降低显存占用和训练时间，但对模型质量的影响尚未查证", value=sovits_params['fp16_run']) + all_in_mem=gr.Checkbox(label="是否加载所有数据集到内存中，硬盘IO过于低下、同时内存容量远大于数据集体积时可以启用，能显著加快训练速度", value=sovits_params['all_in_mem']) + with gr.Row(): + gr.Markdown("请检查右侧的说话人列表是否和你要训练的目标说话人一致，确认无误后点击写入配置文件，然后就可以开始训练了") + speakers=gr.Textbox(label="说话人列表") + with gr.Accordion(label = "扩散模型配置（训练扩散模型需要写入此处）", open=True): + with gr.Row(): + diff_num_workers = gr.Number(label="num_workers, 如果你的电脑配置较高，可以将这里设置为0加快训练速度", value=diff_params['num_workers']) + diff_cache_all_data = gr.Checkbox(label="是否缓存数据，启用后可以加快训练速度，关闭后可以节省显存或内存，但会减慢训练速度", value=diff_params['cache_all_data']) + diff_cache_device = gr.Radio(label="若启用缓存数据，使用显存(cuda)还是内存(cpu)缓存，如果显卡显存充足，选择cuda以加快训练速度", choices=["cuda","cpu"], value=diff_params['cache_device']) + diff_amp_dtype = gr.Radio(label="训练数据类型，fp16可能会有更快的训练速度，前提是你的显卡支持", choices=["fp32","fp16"], value=diff_params['amp_dtype']) + with gr.Row(): + diff_batch_size = gr.Number(label="批量大小(batch_size)，根据显卡显存设置，小显存适当降低该项，6G显存可以设定为48，但该数值不要超过数据集总数量的1/4", value=diff_params['diff_batch_size']) + diff_lr = gr.Number(label="学习率（一般不需要动）", value=diff_params['diff_lr']) + diff_interval_log = gr.Number(label="每隔多少步(steps)生成一次评估日志", value = diff_params['diff_interval_log']) + diff_interval_val = gr.Number(label="每隔多少步(steps)验证并保存一次模型，如果你的批量大小较大，可以适当减少这里的数字，但不建议设置为1000以下", value=diff_params['diff_interval_val']) + diff_force_save = gr.Number(label="每隔多少步强制保留模型，只有该步数的倍数保存的模型会被保留，其余会被删除。设置为与验证步数相同的值则每个模型都会被保留", value=diff_params['diff_force_save']) + with gr.Row(): + save_params=gr.Button("将当前设置保存为默认设置", variant="primary") + write_config=gr.Button("写入配置文件", variant="primary") + write_config_output=gr.Textbox(label="输出信息") + + gr.Markdown(value="""**点击从头开始训练**将会自动将已有的训练进度保存到models_backup文件夹，并自动装载预训练模型。 + **继续上一次的训练进度**将从上一个保存模型的进度继续训练。继续训练进度无需重新预处理和写入配置文件。 + 关于扩散、聚类和特征检索的详细说明请看[此处](https://www.yuque.com/umoubuton/ueupp5/kmui02dszo5zrqkz)。 + """) + with gr.Row(): + with gr.Column(): + start_training=gr.Button("从头开始训练", variant="primary") + training_output=gr.Textbox(label="训练输出信息") + with gr.Column(): + continue_training_btn=gr.Button("继续上一次的训练进度", variant="primary") + continue_training_output=gr.Textbox(label="训练输出信息") + with gr.Row(): + with gr.Column(): + diff_training_btn=gr.Button("从头训练扩散模型", variant="primary") + diff_training_output=gr.Textbox(label="训练输出信息") + with gr.Column(): + diff_continue_training_btn=gr.Button("继续训练扩散模型", variant="primary") + diff_continue_training_output=gr.Textbox(label="训练输出信息") + with gr.Accordion(label = "聚类、特征检索训练", open=False): + with gr.Row(): + with gr.Column(): + kmeans_button=gr.Button("训练聚类模型", variant="primary") + kmeans_gpu = gr.Checkbox(label="使用GPU训练", value=True) + kmeans_output=gr.Textbox(label="训练输出信息") + with gr.Column(): + index_button=gr.Button("训练特征检索模型", variant="primary") + index_output=gr.Textbox(label="训练输出信息") + ''' + with gr.TabItem("小工具/实验室特性"): + gr.Markdown(value=""" + ### So-vits-svc 4.1 小工具/实验室特性 + 提供了一些有趣或实用的小工具，可以自行探索 + """) + with gr.Tabs(): + with gr.TabItem("静态声线融合"): + gr.Markdown(value=""" + 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线 + 注意： + 1.该功能仅支持单说话人的模型 + 2.如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音 + 3.保证所有待混合模型的config.json中的model字段是相同的 + 4.输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用 + 5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传 + 6.混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果 + 7.混合完毕后，文件将会保存在项目根目录中，文件名为output.pth + 8.凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会 + + """) + mix_model_path = gr.Files(label="选择需要混合模型文件") + mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple") + mix_model_output1 = gr.Textbox( + label="混合比例调整，单位/%", + interactive = True + ) + mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式",value="凸组合",interactive = True) + mix_submit = gr.Button("声线融合启动", variant="primary") + mix_model_output2 = gr.Textbox( + label="Output Message" + ) + with gr.TabItem("onnx转换"): + gr.Markdown(value=""" + 提供了将.pth模型（批量）转换为.onnx模型的功能 + 源项目本身自带转换的功能，但不支持批量，操作也不够简单，这个工具可以支持在WebUI中以可视化的操作方式批量转换.onnx模型 + 有人可能会问，转.onnx模型有什么作用呢？相信我，如果你问出了这个问题，说明这个工具你应该用不上 + + ### Step 1: + 在整合包根目录下新建一个"checkpoints"文件夹，将pth模型和对应的json配置文件按目录分别放置到checkpoints文件夹下 + 看起来应该像这样： + checkpoints + ├───xxxx + │ ├───xxxx.pth + │ └───xxxx.json + ├───xxxx + │ ├───xxxx.pth + │ └───xxxx.json + └───…… + """) + pth_dir_msg = gr.Textbox(label="识别待转换模型", placeholder="请将模型和配置文件按上述说明放置在正确位置") + pth_dir_identify_btn = gr.Button("识别", variant="primary") + gr.Markdown(value=""" + ### Step 2: + 识别正确后点击下方开始转换，转换一个模型可能需要一分钟甚至更久 + """) + pth2onnx_btn = gr.Button("开始转换", variant="primary") + pth2onnx_msg = gr.Textbox(label="输出信息") + + with gr.TabItem("智能音频切片"): + gr.Markdown(value=""" + 该工具可以实现对音频的切片，无需调整参数即可完成符合要求的数据集制作。 + 数据集要求的音频切片约在2-15秒内，用传统的Slicer-GUI切片工具需要精准调参和二次切片才能符合要求，该工具省去了上述繁琐的操作，只要上传原始音频即可一键制作数据集。 + """) + with gr.Row(): + raw_audio_path = gr.Textbox(label="原始音频文件夹", placeholder="包含所有待切片音频的文件夹，示例: D:\干声\speakers") + load_raw_audio_btn = gr.Button("加载原始音频", variant = "primary") + load_raw_audio_output = gr.Textbox(label = "输出信息") + raw_audio_dataset = gr.Textbox(label = "音频列表", value = "") + slicer_output_dir = gr.Textbox(label = "输出目录", placeholder = "选择输出目录") + with gr.Row(): + process_method = gr.Radio(label = "对过短音频的处理方式", choices = ["丢弃","将过短音频整合为长音频"], value = "丢弃") + max_sec = gr.Number(label = "切片的最长秒数", value = 15) + min_sec = gr.Number(label = "切片的最短秒数", value = 2) + slicer_btn = gr.Button("开始切片", variant = "primary") + slicer_output_msg = gr.Textbox(label = "输出信息") + + mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1]) + mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1]) + mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2]) + pth_dir_identify_btn.click(pth_identify, [], [pth_dir_msg]) + pth2onnx_btn.click(onnx_export, [], [pth2onnx_msg]) + load_raw_audio_btn.click(load_raw_audio, [raw_audio_path], [load_raw_audio_output, raw_audio_dataset]) + slicer_btn.click(slicer_fn, [raw_audio_path, slicer_output_dir, process_method, max_sec, min_sec], [slicer_output_msg]) + + with gr.TabItem("模型压缩工具"): + gr.Markdown(value=""" + 该工具可以实现对模型的体积压缩，在**不影响模型推理功能**的情况下，将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。 + **注意：压缩后的模型将无法继续训练，请在确认封炉后再压缩。** + 将模型文件放置在logs/44k下，然后选择需要压缩的模型 + """) + model_to_compress = gr.Dropdown(label="模型选择", choices=ckpt_list, value="") + compress_model_btn = gr.Button("压缩模型", variant="primary") + compress_model_output = gr.Textbox(label="输出信息", value="") + + compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output]) + """ + get_raw_dirs.click(load_raw_dirs,[],[raw_dirs_list]) + raw_preprocess.click(dataset_preprocess,[branch_selection, f0_predictor_selection, use_diff, vol_aug, skip_loudnorm, num_processes],[preprocess_output, speakers]) + regenerate_config_btn.click(regenerate_config,[branch_selection, vol_aug],[preprocess_output]) + clear_preprocess_output.click(clear_output,[],[preprocess_output]) + save_params.click(save_default_settings, [log_interval,eval_interval,keep_ckpts,batch_size,lr,fp16_run,all_in_mem,diff_num_workers,diff_cache_all_data,diff_cache_device,diff_amp_dtype,diff_batch_size,diff_lr,diff_interval_log,diff_interval_val,diff_force_save], [write_config_output]) + write_config.click(config_fn,[log_interval, eval_interval, keep_ckpts, batch_size, lr, fp16_run, all_in_mem, diff_num_workers, diff_cache_all_data, diff_batch_size, diff_lr, diff_interval_log, diff_interval_val, diff_cache_device, diff_amp_dtype, diff_force_save],[write_config_output]) + start_training.click(training,[gpu_selection, branch_selection],[training_output]) + diff_training_btn.click(diff_training,[branch_selection],[diff_training_output]) + continue_training_btn.click(continue_training,[gpu_selection, branch_selection],[continue_training_output]) + diff_continue_training_btn.click(diff_continue_training,[branch_selection],[diff_continue_training_output]) + kmeans_button.click(kmeans_training,[kmeans_gpu],[kmeans_output]) + index_button.click(index_training, [], [index_output]) + """ + with gr.Tabs(): + with gr.Row(variant="panel"): + with gr.Column(): + gr.Markdown(value=""" + WebUI设置 + """) + debug_button = gr.Checkbox(label="Debug模式，反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug) + + debug_button.change(debug_change,[],[]) + + app.queue(concurrency_count=1022, max_size=2044).launch() diff --git a/auto_slicer.py b/auto_slicer.py new file mode 100644 index 0000000000000000000000000000000000000000..090d913455f8153b7f39ee85aba068b3ba28230a --- /dev/null +++ b/auto_slicer.py @@ -0,0 +1,106 @@ +import os +import numpy as np +import librosa +import soundfile as sf +from modules.slicer2 import Slicer + +class AutoSlicer: + def __init__(self): + self.slicer_params = { + "threshold": -40, + "min_length": 5000, + "min_interval": 300, + "hop_size": 10, + "max_sil_kept": 500, + } + self.original_min_interval = self.slicer_params["min_interval"] + + def auto_slice(self, filename, input_dir, output_dir, max_sec): + audio, sr = librosa.load(os.path.join(input_dir, filename), sr=None, mono=False) + slicer = Slicer(sr=sr, **self.slicer_params) + chunks = slicer.slice(audio) + files_to_delete = [] + for i, chunk in enumerate(chunks): + if len(chunk.shape) > 1: + chunk = chunk.T + output_filename = f"{os.path.splitext(filename)[0]}_{i}" + output_filename = "".join(c for c in output_filename if c.isascii() or c == "_") + ".wav" + output_filepath = os.path.join(output_dir, output_filename) + sf.write(output_filepath, chunk, sr) + #Check and re-slice audio that more than max_sec. + while True: + new_audio, sr = librosa.load(output_filepath, sr=None, mono=False) + if librosa.get_duration(y=new_audio, sr=sr) <= max_sec: + break + self.slicer_params["min_interval"] = self.slicer_params["min_interval"] // 2 + if self.slicer_params["min_interval"] >= self.slicer_params["hop_size"]: + new_chunks = Slicer(sr=sr, **self.slicer_params).slice(new_audio) + for j, new_chunk in enumerate(new_chunks): + if len(new_chunk.shape) > 1: + new_chunk = new_chunk.T + new_output_filename = f"{os.path.splitext(output_filename)[0]}_{j}.wav" + sf.write(os.path.join(output_dir, new_output_filename), new_chunk, sr) + files_to_delete.append(output_filepath) + else: + break + self.slicer_params["min_interval"] = self.original_min_interval + for file_path in files_to_delete: + if os.path.exists(file_path): + os.remove(file_path) + + def merge_short(self, output_dir, max_sec, min_sec): + short_files = [] + for filename in os.listdir(output_dir): + filepath = os.path.join(output_dir, filename) + if filename.endswith(".wav"): + audio, sr = librosa.load(filepath, sr=None, mono=False) + duration = librosa.get_duration(y=audio, sr=sr) + if duration < min_sec: + short_files.append((filepath, audio, duration)) + short_files.sort(key=lambda x: x[2], reverse=True) + merged_audio = [] + current_duration = 0 + for filepath, audio, duration in short_files: + if current_duration + duration <= max_sec: + merged_audio.append(audio) + current_duration += duration + os.remove(filepath) + else: + if merged_audio: + output_audio = np.concatenate(merged_audio, axis=-1) + if len(output_audio.shape) > 1: + output_audio = output_audio.T + output_filename = f"merged_{len(os.listdir(output_dir))}.wav" + sf.write(os.path.join(output_dir, output_filename), output_audio, sr) + merged_audio = [audio] + current_duration = duration + os.remove(filepath) + if merged_audio and current_duration >= min_sec: + output_audio = np.concatenate(merged_audio, axis=-1) + if len(output_audio.shape) > 1: + output_audio = output_audio.T + output_filename = f"merged_{len(os.listdir(output_dir))}.wav" + sf.write(os.path.join(output_dir, output_filename), output_audio, sr) + + def slice_count(self, input_dir, output_dir): + orig_duration = final_duration = 0 + for file in os.listdir(input_dir): + if file.endswith(".wav"): + _audio, _sr = librosa.load(os.path.join(input_dir, file), sr=None, mono=False) + orig_duration += librosa.get_duration(y=_audio, sr=_sr) + wav_files = [file for file in os.listdir(output_dir) if file.endswith(".wav")] + num_files = len(wav_files) + max_duration = -1 + min_duration = float("inf") + for file in wav_files: + file_path = os.path.join(output_dir, file) + audio, sr = librosa.load(file_path, sr=None, mono=False) + duration = librosa.get_duration(y=audio, sr=sr) + final_duration += float(duration) + if duration > max_duration: + max_duration = float(duration) + if duration < min_duration: + min_duration = float(duration) + return num_files, max_duration, min_duration, orig_duration, final_duration + + diff --git a/cluster/__init__.py b/cluster/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f1b9bde04e73e9218a5d534227caa4c25332f424 --- /dev/null +++ b/cluster/__init__.py @@ -0,0 +1,29 @@ +import numpy as np +import torch +from sklearn.cluster import KMeans + +def get_cluster_model(ckpt_path): + checkpoint = torch.load(ckpt_path) + kmeans_dict = {} + for spk, ckpt in checkpoint.items(): + km = KMeans(ckpt["n_features_in_"]) + km.__dict__["n_features_in_"] = ckpt["n_features_in_"] + km.__dict__["_n_threads"] = ckpt["_n_threads"] + km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"] + kmeans_dict[spk] = km + return kmeans_dict + +def get_cluster_result(model, x, speaker): + """ + x: np.array [t, 256] + return cluster class result + """ + return model[speaker].predict(x) + +def get_cluster_center_result(model, x,speaker): + """x: np.array [t, 256]""" + predict = model[speaker].predict(x) + return model[speaker].cluster_centers_[predict] + +def get_center(model, x,speaker): + return model[speaker].cluster_centers_[x] diff --git a/cluster/__pycache__/__init__.cpython-38.pyc b/cluster/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4d2a93ae1c065086941c5c28cea54f78e6b2d86e Binary files /dev/null and b/cluster/__pycache__/__init__.cpython-38.pyc differ diff --git a/cluster/__pycache__/__init__.cpython-39.pyc b/cluster/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d1a54e818cac554e18e0875787fdc3364ec87ae9 Binary files /dev/null and b/cluster/__pycache__/__init__.cpython-39.pyc differ diff --git a/cluster/__pycache__/kmeans.cpython-38.pyc b/cluster/__pycache__/kmeans.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5a2e93054a79bd5ce57dda855b23dc4c87439a4f Binary files /dev/null and b/cluster/__pycache__/kmeans.cpython-38.pyc differ diff --git a/cluster/kmeans.py b/cluster/kmeans.py new file mode 100644 index 0000000000000000000000000000000000000000..6111ea45e66a15d41b5b904be6f75affd3c4369f --- /dev/null +++ b/cluster/kmeans.py @@ -0,0 +1,201 @@ +import math,pdb +import torch,pynvml +from torch.nn.functional import normalize +from time import time +import numpy as np +# device=torch.device("cuda:0") +def _kpp(data: torch.Tensor, k: int, sample_size: int = -1): + """ Picks k points in the data based on the kmeans++ method. + + Parameters + ---------- + data : torch.Tensor + Expect a rank 1 or 2 array. Rank 1 is assumed to describe 1-D + data, rank 2 multidimensional data, in which case one + row is one observation. + k : int + Number of samples to generate. + sample_size : int + sample data to avoid memory overflow during calculation + + Returns + ------- + init : ndarray + A 'k' by 'N' containing the initial centroids. + + References + ---------- + .. [1] D. Arthur and S. Vassilvitskii, "k-means++: the advantages of + careful seeding", Proceedings of the Eighteenth Annual ACM-SIAM Symposium + on Discrete Algorithms, 2007. + .. [2] scipy/cluster/vq.py: _kpp + """ + batch_size=data.shape[0] + if batch_size>sample_size: + data = data[torch.randint(0, batch_size,[sample_size], device=data.device)] + dims = data.shape[1] if len(data.shape) > 1 else 1 + init = torch.zeros((k, dims)).to(data.device) + r = torch.distributions.uniform.Uniform(0, 1) + for i in range(k): + if i == 0: + init[i, :] = data[torch.randint(data.shape[0], [1])] + else: + D2 = torch.cdist(init[:i, :][None, :], data[None, :], p=2)[0].amin(dim=0) + probs = D2 / torch.sum(D2) + cumprobs = torch.cumsum(probs, dim=0) + init[i, :] = data[torch.searchsorted(cumprobs, r.sample([1]).to(data.device))] + return init +class KMeansGPU: + ''' + Kmeans clustering algorithm implemented with PyTorch + + Parameters: + n_clusters: int, + Number of clusters + + max_iter: int, default: 100 + Maximum number of iterations + + tol: float, default: 0.0001 + Tolerance + + verbose: int, default: 0 + Verbosity + + mode: {'euclidean', 'cosine'}, default: 'euclidean' + Type of distance measure + + init_method: {'random', 'point', '++'} + Type of initialization + + minibatch: {None, int}, default: None + Batch size of MinibatchKmeans algorithm + if None perform full KMeans algorithm + + Attributes: + centroids: torch.Tensor, shape: [n_clusters, n_features] + cluster centroids + ''' + def __init__(self, n_clusters, max_iter=200, tol=1e-4, verbose=0, mode="euclidean",device=torch.device("cuda:0")): + self.n_clusters = n_clusters + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.mode = mode + self.device=device + pynvml.nvmlInit() + gpu_handle = pynvml.nvmlDeviceGetHandleByIndex(device.index) + info = pynvml.nvmlDeviceGetMemoryInfo(gpu_handle) + self.minibatch=int(33e6/self.n_clusters*info.free/ 1024 / 1024 / 1024) + print("free_mem/GB:",info.free/ 1024 / 1024 / 1024,"minibatch:",self.minibatch) + + @staticmethod + def cos_sim(a, b): + """ + Compute cosine similarity of 2 sets of vectors + + Parameters: + a: torch.Tensor, shape: [m, n_features] + + b: torch.Tensor, shape: [n, n_features] + """ + return normalize(a, dim=-1) @ normalize(b, dim=-1).transpose(-2, -1) + + @staticmethod + def euc_sim(a, b): + """ + Compute euclidean similarity of 2 sets of vectors + Parameters: + a: torch.Tensor, shape: [m, n_features] + b: torch.Tensor, shape: [n, n_features] + """ + return 2 * a @ b.transpose(-2, -1) -(a**2).sum(dim=1)[..., :, None] - (b**2).sum(dim=1)[..., None, :] + + def max_sim(self, a, b): + """ + Compute maximum similarity (or minimum distance) of each vector + in a with all of the vectors in b + Parameters: + a: torch.Tensor, shape: [m, n_features] + b: torch.Tensor, shape: [n, n_features] + """ + if self.mode == 'cosine': + sim_func = self.cos_sim + elif self.mode == 'euclidean': + sim_func = self.euc_sim + sim = sim_func(a, b) + max_sim_v, max_sim_i = sim.max(dim=-1) + return max_sim_v, max_sim_i + + def fit_predict(self, X): + """ + Combination of fit() and predict() methods. + This is faster than calling fit() and predict() seperately. + Parameters: + X: torch.Tensor, shape: [n_samples, n_features] + centroids: {torch.Tensor, None}, default: None + if given, centroids will be initialized with given tensor + if None, centroids will be randomly chosen from X + Return: + labels: torch.Tensor, shape: [n_samples] + + mini_=33kk/k*remain + mini=min(mini_,fea_shape) + offset=log2(k/1000)*1.5 + kpp_all=min(mini_*10/offset,fea_shape) + kpp_sample=min(mini_/12/offset,fea_shape) + """ + assert isinstance(X, torch.Tensor), "input must be torch.Tensor" + assert X.dtype in [torch.half, torch.float, torch.double], "input must be floating point" + assert X.ndim == 2, "input must be a 2d tensor with shape: [n_samples, n_features] " + # print("verbose:%s"%self.verbose) + + offset = np.power(1.5,np.log(self.n_clusters / 1000))/np.log(2) + with torch.no_grad(): + batch_size= X.shape[0] + # print(self.minibatch, int(self.minibatch * 10 / offset), batch_size) + start_time = time() + if (self.minibatch*10//offset< batch_size): + x = X[torch.randint(0, batch_size,[int(self.minibatch*10/offset)])].to(self.device) + else: + x = X.to(self.device) + # print(x.device) + self.centroids = _kpp(x, self.n_clusters, min(int(self.minibatch/12/offset),batch_size)) + del x + torch.cuda.empty_cache() + # self.centroids = self.centroids.to(self.device) + num_points_in_clusters = torch.ones(self.n_clusters, device=self.device, dtype=X.dtype)#全1 + closest = None#[3098036]#int64 + if(self.minibatch>=batch_size//2 and self.minibatch=batch_size): + X=X.to(self.device) + for i in range(self.max_iter): + iter_time = time() + if self.minibatch= 2: + print('iter:', i, 'error:', error.item(), 'time spent:', round(time()-iter_time, 4)) + if error <= self.tol: + break + + if self.verbose >= 1: + print(f'used {i+1} iterations ({round(time()-start_time, 4)}s) to cluster {batch_size} items into {self.n_clusters} clusters') + return closest diff --git a/cluster/train_cluster.py b/cluster/train_cluster.py new file mode 100644 index 0000000000000000000000000000000000000000..8644566388a4107c4442da14c0de090bcd4a91b8 --- /dev/null +++ b/cluster/train_cluster.py @@ -0,0 +1,84 @@ +import time,pdb +import tqdm +from time import time as ttime +import os +from pathlib import Path +import logging +import argparse +from kmeans import KMeansGPU +import torch +import numpy as np +from sklearn.cluster import KMeans,MiniBatchKMeans + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) +from time import time as ttime +import pynvml,torch + +def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False,use_gpu=False):#gpu_minibatch真拉，虽然库支持但是也不考虑 + logger.info(f"Loading features from {in_dir}") + features = [] + nums = 0 + for path in tqdm.tqdm(in_dir.glob("*.soft.pt")): + # for name in os.listdir(in_dir): + # path="%s/%s"%(in_dir,name) + features.append(torch.load(path,map_location="cpu").squeeze(0).numpy().T) + # print(features[-1].shape) + features = np.concatenate(features, axis=0) + print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype) + features = features.astype(np.float32) + logger.info(f"Clustering features of shape: {features.shape}") + t = time.time() + if(use_gpu==False): + if use_minibatch: + kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features) + else: + kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features) + else: + kmeans = KMeansGPU(n_clusters=n_clusters, mode='euclidean', verbose=2 if verbose else 0,max_iter=500,tol=1e-2)# + features=torch.from_numpy(features)#.to(device) + labels = kmeans.fit_predict(features)# + + print(time.time()-t, "s") + + x = { + "n_features_in_": kmeans.n_features_in_ if use_gpu==False else features.shape[1], + "_n_threads": kmeans._n_threads if use_gpu==False else 4, + "cluster_centers_": kmeans.cluster_centers_ if use_gpu==False else kmeans.centroids.cpu().numpy(), + } + print("end") + + return x + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--dataset', type=Path, default="./dataset/44k", + help='path of training data directory') + parser.add_argument('--output', type=Path, default="logs/44k", + help='path of model output directory') + parser.add_argument('--gpu',action='store_true', default=False , + help='to use GPU') + + + args = parser.parse_args() + + checkpoint_dir = args.output + dataset = args.dataset + use_gpu = args.gpu + n_clusters = 10000 + + ckpt = {} + for spk in os.listdir(dataset): + if os.path.isdir(dataset/spk): + print(f"train kmeans for {spk}...") + in_dir = dataset/spk + x = train_cluster(in_dir, n_clusters,use_minibatch=False,verbose=False,use_gpu=use_gpu) + ckpt[spk] = x + + checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt" + checkpoint_path.parent.mkdir(exist_ok=True, parents=True) + torch.save( + ckpt, + checkpoint_path, + ) + diff --git a/compress_model.py b/compress_model.py new file mode 100644 index 0000000000000000000000000000000000000000..9c7a8c4aa765edb65658aa62db50f14174503f36 --- /dev/null +++ b/compress_model.py @@ -0,0 +1,69 @@ +from collections import OrderedDict + +import torch + +import utils +from models import SynthesizerTrn + + +def copyStateDict(state_dict): + if list(state_dict.keys())[0].startswith('module'): + start_idx = 1 + else: + start_idx = 0 + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + name = ','.join(k.split('.')[start_idx:]) + new_state_dict[name] = v + return new_state_dict + + +def removeOptimizer(config: str, input_model: str, output_model: str): + hps = utils.get_hparams_from_file(config) + + net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + **hps.model) + + optim_g = torch.optim.AdamW(net_g.parameters(), + hps.train.learning_rate, + betas=hps.train.betas, + eps=hps.train.eps) + + state_dict_g = torch.load(input_model, map_location="cpu") + new_dict_g = copyStateDict(state_dict_g) + keys = [] + for k, v in new_dict_g['model'].items(): + keys.append(k) + + new_dict_g = {k: new_dict_g['model'][k] for k in keys} + + torch.save( + { + 'model': new_dict_g, + 'iteration': 0, + 'optimizer': optim_g.state_dict(), + 'learning_rate': 0.0001 + }, output_model) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("-c", + "--config", + type=str, + default='configs/config.json') + parser.add_argument("-i", "--input", type=str) + parser.add_argument("-o", "--output", type=str, default=None) + + args = parser.parse_args() + + output = args.output + + if output is None: + import os.path + filename, ext = os.path.splitext(args.input) + output = filename + "_release" + ext + + removeOptimizer(args.config, args.input, output) diff --git a/configs/.ipynb_checkpoints/config-checkpoint.json b/configs/.ipynb_checkpoints/config-checkpoint.json new file mode 100644 index 0000000000000000000000000000000000000000..ffc320967fd7eeb8a475f6f5ef6590a8f3609739 --- /dev/null +++ b/configs/.ipynb_checkpoints/config-checkpoint.json @@ -0,0 +1,96 @@ +{ + "train": { + "log_interval": 200, + "eval_interval": 200, + "seed": 1234, + "epochs": 10000, + "learning_rate": 0.0001, + "betas": [ + 0.8, + 0.99 + ], + "eps": 1e-09, + "batch_size": 6, + "fp16_run": false, + "lr_decay": 0.999875, + "segment_size": 10240, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0, + "use_sr": true, + "max_speclen": 512, + "port": "8001", + "keep_ckpts": 20, + "all_in_mem": true + }, + "data": { + "training_files": "filelists/train.txt", + "validation_files": "filelists/val.txt", + "max_wav_value": 32768.0, + "sampling_rate": 44100, + "filter_length": 2048, + "hop_length": 512, + "win_length": 2048, + "n_mel_channels": 80, + "mel_fmin": 0.0, + "mel_fmax": 22050 + }, + "model": { + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "upsample_rates": [ + 8, + 8, + 2, + 2, + 2 + ], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [ + 16, + 16, + 4, + 4, + 4 + ], + "n_layers_q": 3, + "use_spectral_norm": false, + "gin_channels": 768, + "ssl_dim": 768, + "n_speakers": 1, + "speech_encoder": "vec768l12", + "speaker_embedding": false + }, + "spk": { + "renge": 0 + } +} \ No newline at end of file diff --git a/configs/config.json b/configs/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b837c77d3e4fa7707a05b014c7223849807d1e9 --- /dev/null +++ b/configs/config.json @@ -0,0 +1,100 @@ +{ + "train": { + "log_interval": 200, + "eval_interval": 800, + "seed": 1234, + "epochs": 10000, + "learning_rate": 0.0001, + "betas": [ + 0.8, + 0.99 + ], + "eps": 1e-09, + "batch_size": 6, + "fp16_run": false, + "lr_decay": 0.999875, + "segment_size": 10240, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0, + "use_sr": true, + "max_speclen": 512, + "port": "8001", + "keep_ckpts": 3, + "all_in_mem": false, + "vol_aug": true + }, + "data": { + "training_files": "filelists/train.txt", + "validation_files": "filelists/val.txt", + "max_wav_value": 32768.0, + "sampling_rate": 44100, + "filter_length": 2048, + "hop_length": 512, + "win_length": 2048, + "n_mel_channels": 80, + "mel_fmin": 0.0, + "mel_fmax": 22050, + "unit_interpolate_mode": "nearest" + }, + "model": { + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "upsample_rates": [ + 8, + 8, + 2, + 2, + 2 + ], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [ + 16, + 16, + 4, + 4, + 4 + ], + "n_layers_q": 3, + "use_spectral_norm": false, + "gin_channels": 768, + "ssl_dim": 768, + "n_speakers": 1, + "vocoder_name": "nsf-hifigan", + "speech_encoder": "vec768l12", + "speaker_embedding": false, + "vol_embedding": true + }, + "spk": { + "1056": 0 + } +} \ No newline at end of file diff --git a/configs/diffusion.yaml b/configs/diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2818175eebe90e847c8352e5f3ab1cdfe2402c2 --- /dev/null +++ b/configs/diffusion.yaml @@ -0,0 +1,49 @@ +data: + block_size: 512 + cnhubertsoft_gate: 10 + duration: 2 + encoder: vec768l12 + encoder_hop_size: 320 + encoder_out_channels: 768 + encoder_sample_rate: 16000 + extensions: + - wav + sampling_rate: 44100 + training_files: filelists/train.txt + unit_interpolate_mode: nearest + validation_files: filelists/val.txt +device: cuda +env: + expdir: logs/44k/diffusion + gpu_id: 0 +infer: + method: dpm-solver + speedup: 10 +model: + n_chans: 512 + n_hidden: 256 + n_layers: 20 + n_spk: 1 + type: Diffusion + use_pitch_aug: true +spk: + '1056': 0 +train: + amp_dtype: fp32 + batch_size: 48 + cache_all_data: true + cache_device: cpu + cache_fp16: true + decay_step: 100000 + epochs: 100000 + gamma: 0.5 + interval_force_save: 10000 + interval_log: 10 + interval_val: 2000 + lr: 0.0002 + num_workers: 2 + save_opt: false + weight_decay: 0 +vocoder: + ckpt: pretrain/nsf_hifigan/model + type: nsf-hifigan diff --git a/configs_template/config_template.json b/configs_template/config_template.json new file mode 100644 index 0000000000000000000000000000000000000000..670329cfa719fb3a8981dcc9998f825c2f7ac416 --- /dev/null +++ b/configs_template/config_template.json @@ -0,0 +1,72 @@ +{ + "train": { + "log_interval": 200, + "eval_interval": 800, + "seed": 1234, + "epochs": 10000, + "learning_rate": 0.0001, + "betas": [ + 0.8, + 0.99 + ], + "eps": 1e-09, + "batch_size": 6, + "fp16_run": false, + "lr_decay": 0.999875, + "segment_size": 10240, + "init_lr_ratio": 1, + "warmup_epochs": 0, + "c_mel": 45, + "c_kl": 1.0, + "use_sr": true, + "max_speclen": 512, + "port": "8001", + "keep_ckpts": 3, + "all_in_mem": false, + "vol_aug":false + }, + "data": { + "training_files": "filelists/train.txt", + "validation_files": "filelists/val.txt", + "max_wav_value": 32768.0, + "sampling_rate": 44100, + "filter_length": 2048, + "hop_length": 512, + "win_length": 2048, + "n_mel_channels": 80, + "mel_fmin": 0.0, + "mel_fmax": 22050, + "unit_interpolate_mode":"nearest" + }, + "model": { + "inter_channels": 192, + "hidden_channels": 192, + "filter_channels": 768, + "n_heads": 2, + "n_layers": 6, + "kernel_size": 3, + "p_dropout": 0.1, + "resblock": "1", + "resblock_kernel_sizes": [3,7,11], + "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], + "upsample_rates": [ 8, 8, 2, 2, 2], + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [16,16, 4, 4, 4], + "n_layers_q": 3, + "use_spectral_norm": false, + "gin_channels": 768, + "ssl_dim": 768, + "n_speakers": 200, + "vocoder_name":"nsf-hifigan", + "speech_encoder":"vec768l12", + "speaker_embedding":false, + "vol_embedding":false + }, + "spk": { + "nyaru": 0, + "huiyu": 1, + "nen": 2, + "paimon": 3, + "yunhao": 4 + } +} \ No newline at end of file diff --git a/configs_template/diffusion_template.yaml b/configs_template/diffusion_template.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c636de379615a28ec8cf12adb0fca6e0c8b04f76 --- /dev/null +++ b/configs_template/diffusion_template.yaml @@ -0,0 +1,49 @@ +data: + sampling_rate: 44100 + block_size: 512 # Equal to hop_length + duration: 2 # Audio duration during training, must be less than the duration of the shortest audio clip + encoder: 'vec768l12' # 'hubertsoft', 'vec256l9', 'vec768l12' + cnhubertsoft_gate: 10 + encoder_sample_rate: 16000 + encoder_hop_size: 320 + encoder_out_channels: 768 # 256 if using 'hubertsoft' + training_files: "filelists/train.txt" + validation_files: "filelists/val.txt" + extensions: # List of extension included in the data collection + - wav + unit_interpolate_mode: "nearest" +model: + type: 'Diffusion' + n_layers: 20 + n_chans: 512 + n_hidden: 256 + use_pitch_aug: true + n_spk: 1 # max number of different speakers +device: cuda +vocoder: + type: 'nsf-hifigan' + ckpt: 'pretrain/nsf_hifigan/model' +infer: + speedup: 10 + method: 'dpm-solver' # 'pndm' or 'dpm-solver' +env: + expdir: logs/44k/diffusion + gpu_id: 0 +train: + num_workers: 2 # If your cpu and gpu are both very strong, set to 0 may be faster! + amp_dtype: fp32 # fp32, fp16 or bf16 (fp16 or bf16 may be faster if it is supported by your gpu) + batch_size: 48 + cache_all_data: true # Save Internal-Memory or Graphics-Memory if it is false, but may be slow + cache_device: 'cpu' # Set to 'cuda' to cache the data into the Graphics-Memory, fastest speed for strong gpu + cache_fp16: true + epochs: 100000 + interval_log: 10 + interval_val: 2000 + interval_force_save: 10000 + lr: 0.0002 + decay_step: 100000 + gamma: 0.5 + weight_decay: 0 + save_opt: false +spk: + 'nyaru': 0 \ No newline at end of file diff --git a/data_utils.py b/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2539519fde3efddd749e46a76257ebe25125adca --- /dev/null +++ b/data_utils.py @@ -0,0 +1,185 @@ +import time +import os +import random +import numpy as np +import torch +import torch.utils.data + +import modules.commons as commons +import utils +from modules.mel_processing import spectrogram_torch, spec_to_mel_torch, spectrogram_torch +from utils import load_wav_to_torch, load_filepaths_and_text + +# import h5py + + +"""Multi speaker version""" + + +class TextAudioSpeakerLoader(torch.utils.data.Dataset): + """ + 1) loads audio, speaker_id, text pairs + 2) normalizes text and converts them to sequences of integers + 3) computes spectrograms from audio files. + """ + + def __init__(self, audiopaths, hparams, all_in_mem: bool = False, vol_aug: bool = True): + self.audiopaths = load_filepaths_and_text(audiopaths) + self.hparams = hparams + self.max_wav_value = hparams.data.max_wav_value + self.sampling_rate = hparams.data.sampling_rate + self.filter_length = hparams.data.filter_length + self.hop_length = hparams.data.hop_length + self.win_length = hparams.data.win_length + self.unit_interpolate_mode = hparams.data.unit_interpolate_mode + self.sampling_rate = hparams.data.sampling_rate + self.use_sr = hparams.train.use_sr + self.spec_len = hparams.train.max_speclen + self.spk_map = hparams.spk + self.vol_emb = hparams.model.vol_embedding + self.vol_aug = hparams.train.vol_aug and vol_aug + random.seed(1234) + random.shuffle(self.audiopaths) + + self.all_in_mem = all_in_mem + if self.all_in_mem: + self.cache = [self.get_audio(p[0]) for p in self.audiopaths] + + def get_audio(self, filename): + filename = filename.replace("\\", "/") + audio, sampling_rate = load_wav_to_torch(filename) + if sampling_rate != self.sampling_rate: + raise ValueError("{} SR doesn't match target {} SR".format( + sampling_rate, self.sampling_rate)) + audio_norm = audio / self.max_wav_value + audio_norm = audio_norm.unsqueeze(0) + spec_filename = filename.replace(".wav", ".spec.pt") + + # Ideally, all data generated after Mar 25 should have .spec.pt + if os.path.exists(spec_filename): + spec = torch.load(spec_filename) + else: + spec = spectrogram_torch(audio_norm, self.filter_length, + self.sampling_rate, self.hop_length, self.win_length, + center=False) + spec = torch.squeeze(spec, 0) + torch.save(spec, spec_filename) + + spk = filename.split("/")[-2] + spk = torch.LongTensor([self.spk_map[spk]]) + + f0, uv = np.load(filename + ".f0.npy",allow_pickle=True) + + f0 = torch.FloatTensor(np.array(f0,dtype=float)) + uv = torch.FloatTensor(np.array(uv,dtype=float)) + + c = torch.load(filename+ ".soft.pt") + c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0], mode=self.unit_interpolate_mode) + if self.vol_emb: + volume_path = filename + ".vol.npy" + volume = np.load(volume_path) + volume = torch.from_numpy(volume).float() + else: + volume = None + + lmin = min(c.size(-1), spec.size(-1)) + assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename) + assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length + spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin] + audio_norm = audio_norm[:, :lmin * self.hop_length] + if volume!= None: + volume = volume[:lmin] + return c, f0, spec, audio_norm, spk, uv, volume + + def random_slice(self, c, f0, spec, audio_norm, spk, uv, volume): + # if spec.shape[1] < 30: + # print("skip too short audio:", filename) + # return None + + if random.choice([True, False]) and self.vol_aug and volume!=None: + max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5 + max_shift = min(1, np.log10(1/max_amp)) + log10_vol_shift = random.uniform(-1, max_shift) + audio_norm = audio_norm * (10 ** log10_vol_shift) + volume = volume * (10 ** log10_vol_shift) + spec = spectrogram_torch(audio_norm, + self.hparams.data.filter_length, + self.hparams.data.sampling_rate, + self.hparams.data.hop_length, + self.hparams.data.win_length, + center=False)[0] + + if spec.shape[1] > 800: + start = random.randint(0, spec.shape[1]-800) + end = start + 790 + spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end] + audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length] + if volume !=None: + volume = volume[start:end] + return c, f0, spec, audio_norm, spk, uv,volume + + def __getitem__(self, index): + if self.all_in_mem: + return self.random_slice(*self.cache[index]) + else: + return self.random_slice(*self.get_audio(self.audiopaths[index][0])) + + def __len__(self): + return len(self.audiopaths) + + +class TextAudioCollate: + + def __call__(self, batch): + batch = [b for b in batch if b is not None] + + input_lengths, ids_sorted_decreasing = torch.sort( + torch.LongTensor([x[0].shape[1] for x in batch]), + dim=0, descending=True) + + max_c_len = max([x[0].size(1) for x in batch]) + max_wav_len = max([x[3].size(1) for x in batch]) + + lengths = torch.LongTensor(len(batch)) + + c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len) + f0_padded = torch.FloatTensor(len(batch), max_c_len) + spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len) + wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) + spkids = torch.LongTensor(len(batch), 1) + uv_padded = torch.FloatTensor(len(batch), max_c_len) + volume_padded = torch.FloatTensor(len(batch), max_c_len) + + c_padded.zero_() + spec_padded.zero_() + f0_padded.zero_() + wav_padded.zero_() + uv_padded.zero_() + volume_padded.zero_() + + for i in range(len(ids_sorted_decreasing)): + row = batch[ids_sorted_decreasing[i]] + + c = row[0] + c_padded[i, :, :c.size(1)] = c + lengths[i] = c.size(1) + + f0 = row[1] + f0_padded[i, :f0.size(0)] = f0 + + spec = row[2] + spec_padded[i, :, :spec.size(1)] = spec + + wav = row[3] + wav_padded[i, :, :wav.size(1)] = wav + + spkids[i, 0] = row[4] + + uv = row[5] + uv_padded[i, :uv.size(0)] = uv + volume = row[6] + if volume != None: + volume_padded[i, :volume.size(0)] = volume + else : + volume_padded = None + return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded, volume_padded diff --git a/diffusion/__init__.py b/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffusion/__pycache__/__init__.cpython-38.pyc b/diffusion/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..67d36d685ed6fad96c5e3841629f1bb7b24f0685 Binary files /dev/null and b/diffusion/__pycache__/__init__.cpython-38.pyc differ diff --git a/diffusion/__pycache__/__init__.cpython-39.pyc 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utils import repeat_expand_2d +from tqdm import tqdm +from torch.utils.data import Dataset + +def traverse_dir( + root_dir, + extensions, + amount=None, + str_include=None, + str_exclude=None, + is_pure=False, + is_sort=False, + is_ext=True): + + file_list = [] + cnt = 0 + for root, _, files in os.walk(root_dir): + for file in files: + if any([file.endswith(f".{ext}") for ext in extensions]): + # path + mix_path = os.path.join(root, file) + pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path + + # amount + if (amount is not None) and (cnt == amount): + if is_sort: + file_list.sort() + return file_list + + # check string + if (str_include is not None) and (str_include not in pure_path): + continue + if (str_exclude is not None) and (str_exclude in pure_path): + continue + + if not is_ext: + ext = pure_path.split('.')[-1] + pure_path = pure_path[:-(len(ext)+1)] + file_list.append(pure_path) + cnt += 1 + if is_sort: + file_list.sort() + return file_list + + +def get_data_loaders(args, whole_audio=False): + data_train = AudioDataset( + filelists = args.data.training_files, + waveform_sec=args.data.duration, + hop_size=args.data.block_size, + sample_rate=args.data.sampling_rate, + load_all_data=args.train.cache_all_data, + whole_audio=whole_audio, + extensions=args.data.extensions, + n_spk=args.model.n_spk, + spk=args.spk, + device=args.train.cache_device, + fp16=args.train.cache_fp16, + unit_interpolate_mode = args.data.unit_interpolate_mode, + use_aug=True) + loader_train = torch.utils.data.DataLoader( + data_train , + batch_size=args.train.batch_size if not whole_audio else 1, + shuffle=True, + num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0, + persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False, + pin_memory=True if args.train.cache_device=='cpu' else False + ) + data_valid = AudioDataset( + filelists = args.data.validation_files, + waveform_sec=args.data.duration, + hop_size=args.data.block_size, + sample_rate=args.data.sampling_rate, + load_all_data=args.train.cache_all_data, + whole_audio=True, + spk=args.spk, + extensions=args.data.extensions, + unit_interpolate_mode = args.data.unit_interpolate_mode, + n_spk=args.model.n_spk) + loader_valid = torch.utils.data.DataLoader( + data_valid, + batch_size=1, + shuffle=False, + num_workers=0, + pin_memory=True + ) + return loader_train, loader_valid + + +class AudioDataset(Dataset): + def __init__( + self, + filelists, + waveform_sec, + hop_size, + sample_rate, + spk, + load_all_data=True, + whole_audio=False, + extensions=['wav'], + n_spk=1, + device='cpu', + fp16=False, + use_aug=False, + unit_interpolate_mode = 'left' + ): + super().__init__() + + self.waveform_sec = waveform_sec + self.sample_rate = sample_rate + self.hop_size = hop_size + self.filelists = filelists + self.whole_audio = whole_audio + self.use_aug = use_aug + self.data_buffer={} + self.pitch_aug_dict = {} + self.unit_interpolate_mode = unit_interpolate_mode + # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item() + if load_all_data: + print('Load all the data filelists:', filelists) + else: + print('Load the f0, volume data filelists:', filelists) + with open(filelists,"r") as f: + self.paths = f.read().splitlines() + for name_ext in tqdm(self.paths, total=len(self.paths)): + name = os.path.splitext(name_ext)[0] + path_audio = name_ext + duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate) + + path_f0 = name_ext + ".f0.npy" + f0,_ = np.load(path_f0,allow_pickle=True) + f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device) + + path_volume = name_ext + ".vol.npy" + volume = np.load(path_volume) + volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device) + + path_augvol = name_ext + ".aug_vol.npy" + aug_vol = np.load(path_augvol) + aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device) + + if n_spk is not None and n_spk > 1: + spk_name = name_ext.split("/")[-2] + spk_id = spk[spk_name] if spk_name in spk else 0 + if spk_id < 0 or spk_id >= n_spk: + raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ') + else: + spk_id = 0 + spk_id = torch.LongTensor(np.array([spk_id])).to(device) + + if load_all_data: + ''' + audio, sr = librosa.load(path_audio, sr=self.sample_rate) + if len(audio.shape) > 1: + audio = librosa.to_mono(audio) + audio = torch.from_numpy(audio).to(device) + ''' + path_mel = name_ext + ".mel.npy" + mel = np.load(path_mel) + mel = torch.from_numpy(mel).to(device) + + path_augmel = name_ext + ".aug_mel.npy" + aug_mel,keyshift = np.load(path_augmel, allow_pickle=True) + aug_mel = np.array(aug_mel,dtype=float) + aug_mel = torch.from_numpy(aug_mel).to(device) + self.pitch_aug_dict[name_ext] = keyshift + + path_units = name_ext + ".soft.pt" + units = torch.load(path_units).to(device) + units = units[0] + units = repeat_expand_2d(units,f0.size(0),unit_interpolate_mode).transpose(0,1) + + if fp16: + mel = mel.half() + aug_mel = aug_mel.half() + units = units.half() + + self.data_buffer[name_ext] = { + 'duration': duration, + 'mel': mel, + 'aug_mel': aug_mel, + 'units': units, + 'f0': f0, + 'volume': volume, + 'aug_vol': aug_vol, + 'spk_id': spk_id + } + else: + path_augmel = name_ext + ".aug_mel.npy" + aug_mel,keyshift = np.load(path_augmel, allow_pickle=True) + self.pitch_aug_dict[name_ext] = keyshift + self.data_buffer[name_ext] = { + 'duration': duration, + 'f0': f0, + 'volume': volume, + 'aug_vol': aug_vol, + 'spk_id': spk_id + } + + + def __getitem__(self, file_idx): + name_ext = self.paths[file_idx] + data_buffer = self.data_buffer[name_ext] + # check duration. if too short, then skip + if data_buffer['duration'] < (self.waveform_sec + 0.1): + return self.__getitem__( (file_idx + 1) % len(self.paths)) + + # get item + return self.get_data(name_ext, data_buffer) + + def get_data(self, name_ext, data_buffer): + name = os.path.splitext(name_ext)[0] + frame_resolution = self.hop_size / self.sample_rate + duration = data_buffer['duration'] + waveform_sec = duration if self.whole_audio else self.waveform_sec + + # load audio + idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1) + start_frame = int(idx_from / frame_resolution) + units_frame_len = int(waveform_sec / frame_resolution) + aug_flag = random.choice([True, False]) and self.use_aug + ''' + audio = data_buffer.get('audio') + if audio is None: + path_audio = os.path.join(self.path_root, 'audio', name) + '.wav' + audio, sr = librosa.load( + path_audio, + sr = self.sample_rate, + offset = start_frame * frame_resolution, + duration = waveform_sec) + if len(audio.shape) > 1: + audio = librosa.to_mono(audio) + # clip audio into N seconds + audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size] + audio = torch.from_numpy(audio).float() + else: + audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size] + ''' + # load mel + mel_key = 'aug_mel' if aug_flag else 'mel' + mel = data_buffer.get(mel_key) + if mel is None: + mel = name_ext + ".mel.npy" + mel = np.load(mel) + mel = mel[start_frame : start_frame + units_frame_len] + mel = torch.from_numpy(mel).float() + else: + mel = mel[start_frame : start_frame + units_frame_len] + + # load f0 + f0 = data_buffer.get('f0') + aug_shift = 0 + if aug_flag: + aug_shift = self.pitch_aug_dict[name_ext] + f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len] + + # load units + units = data_buffer.get('units') + if units is None: + path_units = name_ext + ".soft.pt" + units = torch.load(path_units) + units = units[0] + units = repeat_expand_2d(units,f0.size(0),self.unit_interpolate_mode).transpose(0,1) + + units = units[start_frame : start_frame + units_frame_len] + + # load volume + vol_key = 'aug_vol' if aug_flag else 'volume' + volume = data_buffer.get(vol_key) + volume_frames = volume[start_frame : start_frame + units_frame_len] + + # load spk_id + spk_id = data_buffer.get('spk_id') + + # load shift + aug_shift = torch.from_numpy(np.array([[aug_shift]])).float() + + return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext) + + def __len__(self): + return len(self.paths) \ No newline at end of file diff --git a/diffusion/diffusion.py b/diffusion/diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..decc1d31503e93e6611b02ced7b9c6f00b95db58 --- /dev/null +++ b/diffusion/diffusion.py @@ -0,0 +1,317 @@ +from collections import deque +from functools import partial +from inspect import isfunction +import torch.nn.functional as F +import librosa.sequence +import numpy as np +import torch +from torch import nn +from tqdm import tqdm + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def extract(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def linear_beta_schedule(timesteps, max_beta=0.02): + """ + linear schedule + """ + betas = np.linspace(1e-4, max_beta, timesteps) + return betas + + +def cosine_beta_schedule(timesteps, s=0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = np.linspace(0, steps, steps) + alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return np.clip(betas, a_min=0, a_max=0.999) + + +beta_schedule = { + "cosine": cosine_beta_schedule, + "linear": linear_beta_schedule, +} + + +class GaussianDiffusion(nn.Module): + def __init__(self, + denoise_fn, + out_dims=128, + timesteps=1000, + k_step=1000, + max_beta=0.02, + spec_min=-12, + spec_max=2): + super().__init__() + self.denoise_fn = denoise_fn + self.out_dims = out_dims + betas = beta_schedule['linear'](timesteps, max_beta=max_beta) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.k_step = k_step + + self.noise_list = deque(maxlen=4) + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + self.register_buffer('spec_min', torch.FloatTensor([spec_min])[None, None, :out_dims]) + self.register_buffer('spec_max', torch.FloatTensor([spec_max])[None, None, :out_dims]) + + def q_mean_variance(self, x_start, t): + mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract(1. - self.alphas_cumprod, t, x_start.shape) + log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, cond): + noise_pred = self.denoise_fn(x, t, cond=cond) + x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) + + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): + """ + Use the PLMS method from + [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). + """ + + def get_x_pred(x, noise_t, t): + a_t = extract(self.alphas_cumprod, t, x.shape) + a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / ( + a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x + x_delta + + return x_pred + + noise_list = self.noise_list + noise_pred = self.denoise_fn(x, t, cond=cond) + + if len(noise_list) == 0: + x_pred = get_x_pred(x, noise_pred, t) + noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond) + noise_pred_prime = (noise_pred + noise_pred_prev) / 2 + elif len(noise_list) == 1: + noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 + elif len(noise_list) == 2: + noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 + else: + noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 + + x_prev = get_x_pred(x, noise_pred_prime, t) + noise_list.append(noise_pred) + + return x_prev + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def p_losses(self, x_start, t, cond, noise=None, loss_type='l2'): + noise = default(noise, lambda: torch.randn_like(x_start)) + + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + x_recon = self.denoise_fn(x_noisy, t, cond) + + if loss_type == 'l1': + loss = (noise - x_recon).abs().mean() + elif loss_type == 'l2': + loss = F.mse_loss(noise, x_recon) + else: + raise NotImplementedError() + + return loss + + def forward(self, + condition, + gt_spec=None, + infer=True, + infer_speedup=10, + method='dpm-solver', + k_step=300, + use_tqdm=True): + """ + conditioning diffusion, use fastspeech2 encoder output as the condition + """ + cond = condition.transpose(1, 2) + b, device = condition.shape[0], condition.device + + if not infer: + spec = self.norm_spec(gt_spec) + t = torch.randint(0, self.k_step, (b,), device=device).long() + norm_spec = spec.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] + return self.p_losses(norm_spec, t, cond=cond) + else: + shape = (cond.shape[0], 1, self.out_dims, cond.shape[2]) + + if gt_spec is None: + t = self.k_step + x = torch.randn(shape, device=device) + else: + t = k_step + norm_spec = self.norm_spec(gt_spec) + norm_spec = norm_spec.transpose(1, 2)[:, None, :, :] + x = self.q_sample(x_start=norm_spec, t=torch.tensor([t - 1], device=device).long()) + + if method is not None and infer_speedup > 1: + if method == 'dpm-solver': + from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver + # 1. Define the noise schedule. + noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t]) + + # 2. Convert your discrete-time `model` to the continuous-time + # noise prediction model. Here is an example for a diffusion model + # `model` with the noise prediction type ("noise") . + def my_wrapper(fn): + def wrapped(x, t, **kwargs): + ret = fn(x, t, **kwargs) + if use_tqdm: + self.bar.update(1) + return ret + + return wrapped + + model_fn = model_wrapper( + my_wrapper(self.denoise_fn), + noise_schedule, + model_type="noise", # or "x_start" or "v" or "score" + model_kwargs={"cond": cond} + ) + + # 3. Define dpm-solver and sample by singlestep DPM-Solver. + # (We recommend singlestep DPM-Solver for unconditional sampling) + # You can adjust the `steps` to balance the computation + # costs and the sample quality. + dpm_solver = DPM_Solver(model_fn, noise_schedule) + + steps = t // infer_speedup + if use_tqdm: + self.bar = tqdm(desc="sample time step", total=steps) + x = dpm_solver.sample( + x, + steps=steps, + order=3, + skip_type="time_uniform", + method="singlestep", + ) + if use_tqdm: + self.bar.close() + elif method == 'pndm': + self.noise_list = deque(maxlen=4) + if use_tqdm: + for i in tqdm( + reversed(range(0, t, infer_speedup)), desc='sample time step', + total=t // infer_speedup, + ): + x = self.p_sample_plms( + x, torch.full((b,), i, device=device, dtype=torch.long), + infer_speedup, cond=cond + ) + else: + for i in reversed(range(0, t, infer_speedup)): + x = self.p_sample_plms( + x, torch.full((b,), i, device=device, dtype=torch.long), + infer_speedup, cond=cond + ) + else: + raise NotImplementedError(method) + else: + if use_tqdm: + for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + else: + for i in reversed(range(0, t)): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + x = x.squeeze(1).transpose(1, 2) # [B, T, M] + return self.denorm_spec(x) + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min diff --git a/diffusion/diffusion_onnx.py b/diffusion/diffusion_onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..1c1e80321de162b5233801efa3423739f7f92bdc --- /dev/null +++ b/diffusion/diffusion_onnx.py @@ -0,0 +1,612 @@ +from collections import deque +from functools import partial +from inspect import isfunction +import torch.nn.functional as F +import librosa.sequence +import numpy as np +from torch.nn import Conv1d +from torch.nn import Mish +import torch +from torch import nn +from tqdm import tqdm +import math + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def extract(a, t): + return a[t].reshape((1, 1, 1, 1)) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def linear_beta_schedule(timesteps, max_beta=0.02): + """ + linear schedule + """ + betas = np.linspace(1e-4, max_beta, timesteps) + return betas + + +def cosine_beta_schedule(timesteps, s=0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = np.linspace(0, steps, steps) + alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return np.clip(betas, a_min=0, a_max=0.999) + + +beta_schedule = { + "cosine": cosine_beta_schedule, + "linear": linear_beta_schedule, +} + + +def extract_1(a, t): + return a[t].reshape((1, 1, 1, 1)) + + +def predict_stage0(noise_pred, noise_pred_prev): + return (noise_pred + noise_pred_prev) / 2 + + +def predict_stage1(noise_pred, noise_list): + return (noise_pred * 3 + - noise_list[-1]) / 2 + + +def predict_stage2(noise_pred, noise_list): + return (noise_pred * 23 + - noise_list[-1] * 16 + + noise_list[-2] * 5) / 12 + + +def predict_stage3(noise_pred, noise_list): + return (noise_pred * 55 + - noise_list[-1] * 59 + + noise_list[-2] * 37 + - noise_list[-3] * 9) / 24 + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + self.half_dim = dim // 2 + self.emb = 9.21034037 / (self.half_dim - 1) + self.emb = torch.exp(torch.arange(self.half_dim) * torch.tensor(-self.emb)).unsqueeze(0) + self.emb = self.emb.cpu() + + def forward(self, x): + emb = self.emb * x + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class ResidualBlock(nn.Module): + def __init__(self, encoder_hidden, residual_channels, dilation): + super().__init__() + self.residual_channels = residual_channels + self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation) + self.diffusion_projection = nn.Linear(residual_channels, residual_channels) + self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1) + self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) + + def forward(self, x, conditioner, diffusion_step): + diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) + conditioner = self.conditioner_projection(conditioner) + y = x + diffusion_step + y = self.dilated_conv(y) + conditioner + + gate, filter_1 = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) + + y = torch.sigmoid(gate) * torch.tanh(filter_1) + y = self.output_projection(y) + + residual, skip = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) + + return (x + residual) / 1.41421356, skip + + +class DiffNet(nn.Module): + def __init__(self, in_dims, n_layers, n_chans, n_hidden): + super().__init__() + self.encoder_hidden = n_hidden + self.residual_layers = n_layers + self.residual_channels = n_chans + self.input_projection = Conv1d(in_dims, self.residual_channels, 1) + self.diffusion_embedding = SinusoidalPosEmb(self.residual_channels) + dim = self.residual_channels + self.mlp = nn.Sequential( + nn.Linear(dim, dim * 4), + Mish(), + nn.Linear(dim * 4, dim) + ) + self.residual_layers = nn.ModuleList([ + ResidualBlock(self.encoder_hidden, self.residual_channels, 1) + for i in range(self.residual_layers) + ]) + self.skip_projection = Conv1d(self.residual_channels, self.residual_channels, 1) + self.output_projection = Conv1d(self.residual_channels, in_dims, 1) + nn.init.zeros_(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + x = spec.squeeze(0) + x = self.input_projection(x) # x [B, residual_channel, T] + x = F.relu(x) + # skip = torch.randn_like(x) + diffusion_step = diffusion_step.float() + diffusion_step = self.diffusion_embedding(diffusion_step) + diffusion_step = self.mlp(diffusion_step) + + x, skip = self.residual_layers[0](x, cond, diffusion_step) + # noinspection PyTypeChecker + for layer in self.residual_layers[1:]: + x, skip_connection = layer.forward(x, cond, diffusion_step) + skip = skip + skip_connection + x = skip / math.sqrt(len(self.residual_layers)) + x = self.skip_projection(x) + x = F.relu(x) + x = self.output_projection(x) # [B, 80, T] + return x.unsqueeze(1) + + +class AfterDiffusion(nn.Module): + def __init__(self, spec_max, spec_min, v_type='a'): + super().__init__() + self.spec_max = spec_max + self.spec_min = spec_min + self.type = v_type + + def forward(self, x): + x = x.squeeze(1).permute(0, 2, 1) + mel_out = (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + if self.type == 'nsf-hifigan-log10': + mel_out = mel_out * 0.434294 + return mel_out.transpose(2, 1) + + +class Pred(nn.Module): + def __init__(self, alphas_cumprod): + super().__init__() + self.alphas_cumprod = alphas_cumprod + + def forward(self, x_1, noise_t, t_1, t_prev): + a_t = extract(self.alphas_cumprod, t_1).cpu() + a_prev = extract(self.alphas_cumprod, t_prev).cpu() + a_t_sq, a_prev_sq = a_t.sqrt().cpu(), a_prev.sqrt().cpu() + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / ( + a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x_1 + x_delta.cpu() + + return x_pred + + +class GaussianDiffusion(nn.Module): + def __init__(self, + out_dims=128, + n_layers=20, + n_chans=384, + n_hidden=256, + timesteps=1000, + k_step=1000, + max_beta=0.02, + spec_min=-12, + spec_max=2): + super().__init__() + self.denoise_fn = DiffNet(out_dims, n_layers, n_chans, n_hidden) + self.out_dims = out_dims + self.mel_bins = out_dims + self.n_hidden = n_hidden + betas = beta_schedule['linear'](timesteps, max_beta=max_beta) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.k_step = k_step + + self.noise_list = deque(maxlen=4) + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + self.register_buffer('spec_min', torch.FloatTensor([spec_min])[None, None, :out_dims]) + self.register_buffer('spec_max', torch.FloatTensor([spec_max])[None, None, :out_dims]) + self.ad = AfterDiffusion(self.spec_max, self.spec_min) + self.xp = Pred(self.alphas_cumprod) + + def q_mean_variance(self, x_start, t): + mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract(1. - self.alphas_cumprod, t, x_start.shape) + log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, cond): + noise_pred = self.denoise_fn(x, t, cond=cond) + x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) + + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): + """ + Use the PLMS method from + [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). + """ + + def get_x_pred(x, noise_t, t): + a_t = extract(self.alphas_cumprod, t) + a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t))) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / ( + a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x + x_delta + + return x_pred + + noise_list = self.noise_list + noise_pred = self.denoise_fn(x, t, cond=cond) + + if len(noise_list) == 0: + x_pred = get_x_pred(x, noise_pred, t) + noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond) + noise_pred_prime = (noise_pred + noise_pred_prev) / 2 + elif len(noise_list) == 1: + noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 + elif len(noise_list) == 2: + noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 + else: + noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 + + x_prev = get_x_pred(x, noise_pred_prime, t) + noise_list.append(noise_pred) + + return x_prev + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def p_losses(self, x_start, t, cond, noise=None, loss_type='l2'): + noise = default(noise, lambda: torch.randn_like(x_start)) + + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + x_recon = self.denoise_fn(x_noisy, t, cond) + + if loss_type == 'l1': + loss = (noise - x_recon).abs().mean() + elif loss_type == 'l2': + loss = F.mse_loss(noise, x_recon) + else: + raise NotImplementedError() + + return loss + + def org_forward(self, + condition, + init_noise=None, + gt_spec=None, + infer=True, + infer_speedup=100, + method='pndm', + k_step=1000, + use_tqdm=True): + """ + conditioning diffusion, use fastspeech2 encoder output as the condition + """ + cond = condition + b, device = condition.shape[0], condition.device + if not infer: + spec = self.norm_spec(gt_spec) + t = torch.randint(0, self.k_step, (b,), device=device).long() + norm_spec = spec.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] + return self.p_losses(norm_spec, t, cond=cond) + else: + shape = (cond.shape[0], 1, self.out_dims, cond.shape[2]) + + if gt_spec is None: + t = self.k_step + if init_noise is None: + x = torch.randn(shape, device=device) + else: + x = init_noise + else: + t = k_step + norm_spec = self.norm_spec(gt_spec) + norm_spec = norm_spec.transpose(1, 2)[:, None, :, :] + x = self.q_sample(x_start=norm_spec, t=torch.tensor([t - 1], device=device).long()) + + if method is not None and infer_speedup > 1: + if method == 'dpm-solver': + from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver + # 1. Define the noise schedule. + noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t]) + + # 2. Convert your discrete-time `model` to the continuous-time + # noise prediction model. Here is an example for a diffusion model + # `model` with the noise prediction type ("noise") . + def my_wrapper(fn): + def wrapped(x, t, **kwargs): + ret = fn(x, t, **kwargs) + if use_tqdm: + self.bar.update(1) + return ret + + return wrapped + + model_fn = model_wrapper( + my_wrapper(self.denoise_fn), + noise_schedule, + model_type="noise", # or "x_start" or "v" or "score" + model_kwargs={"cond": cond} + ) + + # 3. Define dpm-solver and sample by singlestep DPM-Solver. + # (We recommend singlestep DPM-Solver for unconditional sampling) + # You can adjust the `steps` to balance the computation + # costs and the sample quality. + dpm_solver = DPM_Solver(model_fn, noise_schedule) + + steps = t // infer_speedup + if use_tqdm: + self.bar = tqdm(desc="sample time step", total=steps) + x = dpm_solver.sample( + x, + steps=steps, + order=3, + skip_type="time_uniform", + method="singlestep", + ) + if use_tqdm: + self.bar.close() + elif method == 'pndm': + self.noise_list = deque(maxlen=4) + if use_tqdm: + for i in tqdm( + reversed(range(0, t, infer_speedup)), desc='sample time step', + total=t // infer_speedup, + ): + x = self.p_sample_plms( + x, torch.full((b,), i, device=device, dtype=torch.long), + infer_speedup, cond=cond + ) + else: + for i in reversed(range(0, t, infer_speedup)): + x = self.p_sample_plms( + x, torch.full((b,), i, device=device, dtype=torch.long), + infer_speedup, cond=cond + ) + else: + raise NotImplementedError(method) + else: + if use_tqdm: + for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + else: + for i in reversed(range(0, t)): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + x = x.squeeze(1).transpose(1, 2) # [B, T, M] + return self.denorm_spec(x).transpose(2, 1) + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + def get_x_pred(self, x_1, noise_t, t_1, t_prev): + a_t = extract(self.alphas_cumprod, t_1) + a_prev = extract(self.alphas_cumprod, t_prev) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / ( + a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x_1 + x_delta + return x_pred + + def OnnxExport(self, project_name=None, init_noise=None, hidden_channels=256, export_denoise=True, export_pred=True, export_after=True): + cond = torch.randn([1, self.n_hidden, 10]).cpu() + if init_noise is None: + x = torch.randn((1, 1, self.mel_bins, cond.shape[2]), dtype=torch.float32).cpu() + else: + x = init_noise + pndms = 100 + + org_y_x = self.org_forward(cond, init_noise=x) + + device = cond.device + n_frames = cond.shape[2] + step_range = torch.arange(0, self.k_step, pndms, dtype=torch.long, device=device).flip(0) + plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device) + noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device) + + ot = step_range[0] + ot_1 = torch.full((1,), ot, device=device, dtype=torch.long) + if export_denoise: + torch.onnx.export( + self.denoise_fn, + (x.cpu(), ot_1.cpu(), cond.cpu()), + f"{project_name}_denoise.onnx", + input_names=["noise", "time", "condition"], + output_names=["noise_pred"], + dynamic_axes={ + "noise": [3], + "condition": [2] + }, + opset_version=16 + ) + + for t in step_range: + t_1 = torch.full((1,), t, device=device, dtype=torch.long) + noise_pred = self.denoise_fn(x, t_1, cond) + t_prev = t_1 - pndms + t_prev = t_prev * (t_prev > 0) + if plms_noise_stage == 0: + if export_pred: + torch.onnx.export( + self.xp, + (x.cpu(), noise_pred.cpu(), t_1.cpu(), t_prev.cpu()), + f"{project_name}_pred.onnx", + input_names=["noise", "noise_pred", "time", "time_prev"], + output_names=["noise_pred_o"], + dynamic_axes={ + "noise": [3], + "noise_pred": [3] + }, + opset_version=16 + ) + + x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev) + noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond) + noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev) + + elif plms_noise_stage == 1: + noise_pred_prime = predict_stage1(noise_pred, noise_list) + + elif plms_noise_stage == 2: + noise_pred_prime = predict_stage2(noise_pred, noise_list) + + else: + noise_pred_prime = predict_stage3(noise_pred, noise_list) + + noise_pred = noise_pred.unsqueeze(0) + + if plms_noise_stage < 3: + noise_list = torch.cat((noise_list, noise_pred), dim=0) + plms_noise_stage = plms_noise_stage + 1 + + else: + noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0) + + x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev) + if export_after: + torch.onnx.export( + self.ad, + x.cpu(), + f"{project_name}_after.onnx", + input_names=["x"], + output_names=["mel_out"], + dynamic_axes={ + "x": [3] + }, + opset_version=16 + ) + x = self.ad(x) + + print((x == org_y_x).all()) + return x + + def forward(self, condition=None, init_noise=None, pndms=None, k_step=None): + cond = condition + x = init_noise + + device = cond.device + n_frames = cond.shape[2] + step_range = torch.arange(0, k_step.item(), pndms.item(), dtype=torch.long, device=device).flip(0) + plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device) + noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device) + + ot = step_range[0] + ot_1 = torch.full((1,), ot, device=device, dtype=torch.long) + + for t in step_range: + t_1 = torch.full((1,), t, device=device, dtype=torch.long) + noise_pred = self.denoise_fn(x, t_1, cond) + t_prev = t_1 - pndms + t_prev = t_prev * (t_prev > 0) + if plms_noise_stage == 0: + x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev) + noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond) + noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev) + + elif plms_noise_stage == 1: + noise_pred_prime = predict_stage1(noise_pred, noise_list) + + elif plms_noise_stage == 2: + noise_pred_prime = predict_stage2(noise_pred, noise_list) + + else: + noise_pred_prime = predict_stage3(noise_pred, noise_list) + + noise_pred = noise_pred.unsqueeze(0) + + if plms_noise_stage < 3: + noise_list = torch.cat((noise_list, noise_pred), dim=0) + plms_noise_stage = plms_noise_stage + 1 + + else: + noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0) + + x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev) + x = self.ad(x) + return x diff --git a/diffusion/dpm_solver_pytorch.py b/diffusion/dpm_solver_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..dee5e280661b61e0a99038ce0bd240db51344ead --- /dev/null +++ b/diffusion/dpm_solver_pytorch.py @@ -0,0 +1,1201 @@ +import math + +import torch + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError( + "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format( + schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * ( + 1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), + self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0 ** 2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), + torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * ( + 1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * noise_schedule.total_N + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class DPM_Solver: + def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.): + """Construct a DPM-Solver. + + We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0"). + If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver). + If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++). + In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True. + The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales. + + Args: + model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]): + `` + def model_fn(x, t_continuous): + return noise + `` + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model. + thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1]. + max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding. + + [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + + Args: + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + N: A `int`. The total number of the spacing of the time steps. + device: A torch device. + Returns: + A pytorch tensor of the time steps, with the shape (N + 1,). + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError( + "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + + We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast". + Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is: + - If order == 1: + We take `steps` of DPM-Solver-1 (i.e. DDIM). + - If order == 2: + - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of DPM-Solver-2. + - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If order == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2. + + ============================================ + Args: + order: A `int`. The max order for the solver (2 or 3). + steps: A `int`. The total number of function evaluations (NFE). + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + device: A torch device. + Returns: + orders: A list of the solver order of each step. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3, ] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3, ] * (K - 1) + [1] + else: + orders = [3, ] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2, ] * K + else: + K = steps // 2 + 1 + orders = [2, ] * (K - 1) + [1] + elif order == 1: + K = 1 + orders = [1, ] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[ + torch.cumsum(torch.tensor([0, ] + orders), dim=0).to(device)] + return timesteps_outer, orders + + def denoise_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False): + """ + DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + if self.predict_x0: + phi_1 = torch.expm1(-h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + else: + phi_1 = torch.expm1(h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + + def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, + solver_type='dpm_solver'): + """ + Singlestep solver DPM-Solver-2 from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + r1: A `float`. The hyperparameter of the second-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 0.5 + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + s1 = ns.inverse_lambda(lambda_s1) + log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff( + s1), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t) + alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t) + + if self.predict_x0: + phi_11 = torch.expm1(-r1 * h) + phi_1 = torch.expm1(-h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + expand_dims(sigma_s1 / sigma_s, dims) * x + - expand_dims(alpha_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * ( + model_s1 - model_s) + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_1 = torch.expm1(h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x + - expand_dims(sigma_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s) + ) + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1} + else: + return x_t + + def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None, + return_intermediate=False, solver_type='dpm_solver'): + """ + Singlestep solver DPM-Solver-3 from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + r1: A `float`. The hyperparameter of the third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`). + If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 1. / 3. + if r2 is None: + r2 = 2. / 3. + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + lambda_s2 = lambda_s + r2 * h + s1 = ns.inverse_lambda(lambda_s1) + s2 = ns.inverse_lambda(lambda_s2) + log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff( + s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std( + s2), ns.marginal_std(t) + alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t) + + if self.predict_x0: + phi_11 = torch.expm1(-r1 * h) + phi_12 = torch.expm1(-r2 * h) + phi_1 = torch.expm1(-h) + phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1. + phi_2 = phi_1 / h + 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + expand_dims(sigma_s1 / sigma_s, dims) * x + - expand_dims(alpha_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + expand_dims(sigma_s2 / sigma_s, dims) * x + - expand_dims(alpha_s2 * phi_12, dims) * model_s + + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + expand_dims(alpha_t * phi_2, dims) * D1 + - expand_dims(alpha_t * phi_3, dims) * D2 + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_12 = torch.expm1(r2 * h) + phi_1 = torch.expm1(h) + phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1. + phi_2 = phi_1 / h - 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x + - expand_dims(sigma_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x + - expand_dims(sigma_s2 * phi_12, dims) * model_s + - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - expand_dims(sigma_t * phi_2, dims) * D1 + - expand_dims(sigma_t * phi_3, dims) * D2 + ) + + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2} + else: + return x_t + + def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"): + """ + Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + ns = self.noise_schedule + dims = x.dim() + model_prev_1, model_prev_0 = model_prev_list + t_prev_1, t_prev_0 = t_prev_list + lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda( + t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0 = h_0 / h + D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) + if self.predict_x0: + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0 + ) + else: + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0 + ) + return x_t + + def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'): + """ + Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + model_prev_2, model_prev_1, model_prev_0 = model_prev_list + t_prev_2, t_prev_1, t_prev_0 = t_prev_list + lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda( + t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_1 = lambda_prev_1 - lambda_prev_2 + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0, r1 = h_0 / h, h_1 / h + D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) + D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2) + D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1) + D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1) + if self.predict_x0: + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1 + - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2 + ) + else: + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1 + - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2 + ) + return x_t + + def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, + r2=None): + """ + Singlestep DPM-Solver with the order `order` from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + r1: A `float`. The hyperparameter of the second-order or third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate) + elif order == 2: + return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, + solver_type=solver_type, r1=r1) + elif order == 3: + return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, + solver_type=solver_type, r1=r1, r2=r2) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'): + """ + Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1]) + elif order == 2: + return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + elif order == 3: + return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, + solver_type='dpm_solver'): + """ + The adaptive step size solver based on singlestep DPM-Solver. + + Args: + x: A pytorch tensor. The initial value at time `t_T`. + order: A `int`. The (higher) order of the solver. We only support order == 2 or 3. + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + h_init: A `float`. The initial step size (for logSNR). + atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1]. + rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05. + theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1]. + t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the + current time and `t_0` is less than `t_err`. The default setting is 1e-5. + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_0: A pytorch tensor. The approximated solution at time `t_0`. + + [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021. + """ + ns = self.noise_schedule + s = t_T * torch.ones((x.shape[0],)).to(x) + lambda_s = ns.marginal_lambda(s) + lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x)) + h = h_init * torch.ones_like(s).to(x) + x_prev = x + nfe = 0 + if order == 2: + r1 = 0.5 + lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, + solver_type=solver_type, + **kwargs) + elif order == 3: + r1, r2 = 1. / 3., 2. / 3. + lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, + return_intermediate=True, + solver_type=solver_type) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, + solver_type=solver_type, + **kwargs) + else: + raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order)) + while torch.abs((s - t_0)).mean() > t_err: + t = ns.inverse_lambda(lambda_s + h) + x_lower, lower_noise_kwargs = lower_update(x, s, t) + x_higher = higher_update(x, s, t, **lower_noise_kwargs) + delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev))) + norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True)) + E = norm_fn((x_higher - x_lower) / delta).max() + if torch.all(E <= 1.): + x = x_higher + s = t + x_prev = x_lower + lambda_s = ns.marginal_lambda(s) + h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s) + nfe += order + print('adaptive solver nfe', nfe) + return x + + def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', denoise=False, solver_type='dpm_solver', atol=0.0078, + rtol=0.05, + ): + """ + Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. + + ===================================================== + + We support the following algorithms for both noise prediction model and data prediction model: + - 'singlestep': + Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. + We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps). + The total number of function evaluations (NFE) == `steps`. + Given a fixed NFE == `steps`, the sampling procedure is: + - If `order` == 1: + - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2. + - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If `order` == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2. + - 'multistep': + Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`. + We initialize the first `order` values by lower order multistep solvers. + Given a fixed NFE == `steps`, the sampling procedure is: + Denote K = steps. + - If `order` == 1: + - We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2. + - If `order` == 3: + - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3. + - 'singlestep_fixed': + Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3). + We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE. + - 'adaptive': + Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper). + We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`. + You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs + (NFE) and the sample quality. + - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2. + - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3. + + ===================================================== + + Some advices for choosing the algorithm: + - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs: + Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False) + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3, + skip_type='time_uniform', method='singlestep') + - For **guided sampling with large guidance scale** by DPMs: + Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True) + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2, + skip_type='time_uniform', method='multistep') + + We support three types of `skip_type`: + - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images** + - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**. + - 'time_quadratic': quadratic time for the time steps. + + ===================================================== + Args: + x: A pytorch tensor. The initial value at time `t_start` + e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution. + steps: A `int`. The total number of function evaluations (NFE). + t_start: A `float`. The starting time of the sampling. + If `T` is None, we use self.noise_schedule.T (default is 1.0). + t_end: A `float`. The ending time of the sampling. + If `t_end` is None, we use 1. / self.noise_schedule.total_N. + e.g. if total_N == 1000, we have `t_end` == 1e-3. + For discrete-time DPMs: + - We recommend `t_end` == 1. / self.noise_schedule.total_N. + For continuous-time DPMs: + - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15. + order: A `int`. The order of DPM-Solver. + skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'. + method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'. + denoise: A `bool`. Whether to denoise at the final step. Default is False. + If `denoise` is True, the total NFE is (`steps` + 1). + solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`. + atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + Returns: + x_end: A pytorch tensor. The approximated solution at time `t_end`. + + """ + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + if method == 'adaptive': + with torch.no_grad(): + x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, + solver_type=solver_type) + elif method == 'multistep': + assert steps >= order + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + with torch.no_grad(): + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + # Init the first `order` values by lower order multistep DPM-Solver. + for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, + solver_type=solver_type) + model_prev_list.append(self.model_fn(x, vec_t)) + t_prev_list.append(vec_t) + # Compute the remaining values by `order`-th order multistep DPM-Solver. + for step in range(order, steps + 1): + vec_t = timesteps[step].expand(x.shape[0]) + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, order, + solver_type=solver_type) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + model_prev_list[-1] = self.model_fn(x, vec_t) + elif method in ['singlestep', 'singlestep_fixed']: + if method == 'singlestep': + timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, + skip_type=skip_type, + t_T=t_T, t_0=t_0, + device=device) + elif method == 'singlestep_fixed': + K = steps // order + orders = [order, ] * K + timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device) + for i, order in enumerate(orders): + t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1] + timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), + N=order, device=device) + lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner) + vec_s, vec_t = t_T_inner.repeat(x.shape[0]), t_0_inner.repeat(x.shape[0]) + h = lambda_inner[-1] - lambda_inner[0] + r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h + r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h + x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2) + if denoise: + x = self.denoise_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,) * (dims - 1)] diff --git a/diffusion/how to export onnx.md b/diffusion/how to export onnx.md new file mode 100644 index 0000000000000000000000000000000000000000..6d22719fd1a8e9d034e6224cc95f4b50d44a0320 --- /dev/null +++ b/diffusion/how to export onnx.md @@ -0,0 +1,4 @@ +- Open [onnx_export](onnx_export.py) +- project_name = "dddsp" change "project_name" to your project name +- model_path = f'{project_name}/model_500000.pt' change "model_path" to your model path +- Run \ No newline at end of file diff --git a/diffusion/infer_gt_mel.py b/diffusion/infer_gt_mel.py new file mode 100644 index 0000000000000000000000000000000000000000..033b821a5d21a1232f1786bce5616b12e01488ad --- /dev/null +++ b/diffusion/infer_gt_mel.py @@ -0,0 +1,74 @@ +import numpy as np +import torch +import torch.nn.functional as F +from diffusion.unit2mel import load_model_vocoder + + +class DiffGtMel: + def __init__(self, project_path=None, device=None): + self.project_path = project_path + if device is not None: + self.device = device + else: + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.model = None + self.vocoder = None + self.args = None + + def flush_model(self, project_path, ddsp_config=None): + if (self.model is None) or (project_path != self.project_path): + model, vocoder, args = load_model_vocoder(project_path, device=self.device) + if self.check_args(ddsp_config, args): + self.model = model + self.vocoder = vocoder + self.args = args + + def check_args(self, args1, args2): + if args1.data.block_size != args2.data.block_size: + raise ValueError("DDSP与DIFF模型的block_size不一致") + if args1.data.sampling_rate != args2.data.sampling_rate: + raise ValueError("DDSP与DIFF模型的sampling_rate不一致") + if args1.data.encoder != args2.data.encoder: + raise ValueError("DDSP与DIFF模型的encoder不一致") + return True + + def __call__(self, audio, f0, hubert, volume, acc=1, spk_id=1, k_step=0, method='pndm', + spk_mix_dict=None, start_frame=0): + input_mel = self.vocoder.extract(audio, self.args.data.sampling_rate) + out_mel = self.model( + hubert, + f0, + volume, + spk_id=spk_id, + spk_mix_dict=spk_mix_dict, + gt_spec=input_mel, + infer=True, + infer_speedup=acc, + method=method, + k_step=k_step, + use_tqdm=False) + if start_frame > 0: + out_mel = out_mel[:, start_frame:, :] + f0 = f0[:, start_frame:, :] + output = self.vocoder.infer(out_mel, f0) + if start_frame > 0: + output = F.pad(output, (start_frame * self.vocoder.vocoder_hop_size, 0)) + return output + + def infer(self, audio, f0, hubert, volume, acc=1, spk_id=1, k_step=0, method='pndm', silence_front=0, + use_silence=False, spk_mix_dict=None): + start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size) + if use_silence: + audio = audio[:, start_frame * self.vocoder.vocoder_hop_size:] + f0 = f0[:, start_frame:, :] + hubert = hubert[:, start_frame:, :] + volume = volume[:, start_frame:, :] + _start_frame = 0 + else: + _start_frame = start_frame + audio = self.__call__(audio, f0, hubert, volume, acc=acc, spk_id=spk_id, k_step=k_step, + method=method, spk_mix_dict=spk_mix_dict, start_frame=_start_frame) + if use_silence: + if start_frame > 0: + audio = F.pad(audio, (start_frame * self.vocoder.vocoder_hop_size, 0)) + return audio diff --git a/diffusion/logger/__init__.py b/diffusion/logger/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffusion/logger/__pycache__/__init__.cpython-38.pyc b/diffusion/logger/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e76e7b408ce5d6fcec8a8e095f2d30112c5c88d6 Binary files /dev/null and b/diffusion/logger/__pycache__/__init__.cpython-38.pyc differ diff --git a/diffusion/logger/__pycache__/utils.cpython-38.pyc b/diffusion/logger/__pycache__/utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..533461a6f90e4f19af3a7849c3d3a7ffa041166e Binary files /dev/null and b/diffusion/logger/__pycache__/utils.cpython-38.pyc differ diff --git a/diffusion/logger/saver.py b/diffusion/logger/saver.py new file mode 100644 index 0000000000000000000000000000000000000000..ef78b52b6bcd32106f962b731d3784d72d5f0cce --- /dev/null +++ b/diffusion/logger/saver.py @@ -0,0 +1,150 @@ +''' +author: wayn391@mastertones +''' + +import os +import json +import time +import yaml +import datetime +import torch +import matplotlib.pyplot as plt +from . import utils +from torch.utils.tensorboard import SummaryWriter + +class Saver(object): + def __init__( + self, + args, + initial_global_step=-1): + + self.expdir = args.env.expdir + self.sample_rate = args.data.sampling_rate + + # cold start + self.global_step = initial_global_step + self.init_time = time.time() + self.last_time = time.time() + + # makedirs + os.makedirs(self.expdir, exist_ok=True) + + # path + self.path_log_info = os.path.join(self.expdir, 'log_info.txt') + + # ckpt + os.makedirs(self.expdir, exist_ok=True) + + # writer + self.writer = SummaryWriter(os.path.join(self.expdir, 'logs')) + + # save config + path_config = os.path.join(self.expdir, 'config.yaml') + with open(path_config, "w") as out_config: + yaml.dump(dict(args), out_config) + + + def log_info(self, msg): + '''log method''' + if isinstance(msg, dict): + msg_list = [] + for k, v in msg.items(): + tmp_str = '' + if isinstance(v, int): + tmp_str = '{}: {:,}'.format(k, v) + else: + tmp_str = '{}: {}'.format(k, v) + + msg_list.append(tmp_str) + msg_str = '\n'.join(msg_list) + else: + msg_str = msg + + # dsplay + print(msg_str) + + # save + with open(self.path_log_info, 'a') as fp: + fp.write(msg_str+'\n') + + def log_value(self, dict): + for k, v in dict.items(): + self.writer.add_scalar(k, v, self.global_step) + + def log_spec(self, name, spec, spec_out, vmin=-14, vmax=3.5): + spec_cat = torch.cat([(spec_out - spec).abs() + vmin, spec, spec_out], -1) + spec = spec_cat[0] + if isinstance(spec, torch.Tensor): + spec = spec.cpu().numpy() + fig = plt.figure(figsize=(12, 9)) + plt.pcolor(spec.T, vmin=vmin, vmax=vmax) + plt.tight_layout() + self.writer.add_figure(name, fig, self.global_step) + + def log_audio(self, dict): + for k, v in dict.items(): + self.writer.add_audio(k, v, global_step=self.global_step, sample_rate=self.sample_rate) + + def get_interval_time(self, update=True): + cur_time = time.time() + time_interval = cur_time - self.last_time + if update: + self.last_time = cur_time + return time_interval + + def get_total_time(self, to_str=True): + total_time = time.time() - self.init_time + if to_str: + total_time = str(datetime.timedelta( + seconds=total_time))[:-5] + return total_time + + def save_model( + self, + model, + optimizer, + name='model', + postfix='', + to_json=False): + # path + if postfix: + postfix = '_' + postfix + path_pt = os.path.join( + self.expdir , name+postfix+'.pt') + + # check + print(' [*] model checkpoint saved: {}'.format(path_pt)) + + # save + if optimizer is not None: + torch.save({ + 'global_step': self.global_step, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict()}, path_pt) + else: + torch.save({ + 'global_step': self.global_step, + 'model': model.state_dict()}, path_pt) + + # to json + if to_json: + path_json = os.path.join( + self.expdir , name+'.json') + utils.to_json(path_params, path_json) + + def delete_model(self, name='model', postfix=''): + # path + if postfix: + postfix = '_' + postfix + path_pt = os.path.join( + self.expdir , name+postfix+'.pt') + + # delete + if os.path.exists(path_pt): + os.remove(path_pt) + print(' [*] model checkpoint deleted: {}'.format(path_pt)) + + def global_step_increment(self): + self.global_step += 1 + + diff --git a/diffusion/logger/utils.py b/diffusion/logger/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..485681ced897980dc0bf5b149308245bbd708de9 --- /dev/null +++ b/diffusion/logger/utils.py @@ -0,0 +1,126 @@ +import os +import yaml +import json +import pickle +import torch + +def traverse_dir( + root_dir, + extensions, + amount=None, + str_include=None, + str_exclude=None, + is_pure=False, + is_sort=False, + is_ext=True): + + file_list = [] + cnt = 0 + for root, _, files in os.walk(root_dir): + for file in files: + if any([file.endswith(f".{ext}") for ext in extensions]): + # path + mix_path = os.path.join(root, file) + pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path + + # amount + if (amount is not None) and (cnt == amount): + if is_sort: + file_list.sort() + return file_list + + # check string + if (str_include is not None) and (str_include not in pure_path): + continue + if (str_exclude is not None) and (str_exclude in pure_path): + continue + + if not is_ext: + ext = pure_path.split('.')[-1] + pure_path = pure_path[:-(len(ext)+1)] + file_list.append(pure_path) + cnt += 1 + if is_sort: + file_list.sort() + return file_list + + + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def get_network_paras_amount(model_dict): + info = dict() + for model_name, model in model_dict.items(): + # all_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + info[model_name] = trainable_params + return info + + +def load_config(path_config): + with open(path_config, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + # print(args) + return args + +def save_config(path_config,config): + config = dict(config) + with open(path_config, "w") as f: + yaml.dump(config, f) + +def to_json(path_params, path_json): + params = torch.load(path_params, map_location=torch.device('cpu')) + raw_state_dict = {} + for k, v in params.items(): + val = v.flatten().numpy().tolist() + raw_state_dict[k] = val + + with open(path_json, 'w') as outfile: + json.dump(raw_state_dict, outfile,indent= "\t") + + +def convert_tensor_to_numpy(tensor, is_squeeze=True): + if is_squeeze: + tensor = tensor.squeeze() + if tensor.requires_grad: + tensor = tensor.detach() + if tensor.is_cuda: + tensor = tensor.cpu() + return tensor.numpy() + + +def load_model( + expdir, + model, + optimizer, + name='model', + postfix='', + device='cpu'): + if postfix == '': + postfix = '_' + postfix + path = os.path.join(expdir, name+postfix) + path_pt = traverse_dir(expdir, ['pt'], is_ext=False) + global_step = 0 + if len(path_pt) > 0: + steps = [s[len(path):] for s in path_pt] + maxstep = max([int(s) if s.isdigit() else 0 for s in steps]) + if maxstep >= 0: + path_pt = path+str(maxstep)+'.pt' + else: + path_pt = path+'best.pt' + print(' [*] restoring model from', path_pt) + ckpt = torch.load(path_pt, map_location=torch.device(device)) + global_step = ckpt['global_step'] + model.load_state_dict(ckpt['model'], strict=False) + if ckpt.get('optimizer') != None: + optimizer.load_state_dict(ckpt['optimizer']) + return global_step, model, optimizer diff --git a/diffusion/onnx_export.py b/diffusion/onnx_export.py new file mode 100644 index 0000000000000000000000000000000000000000..5deda785cf22b341f7d2e6399ef5fcdad6fe129e --- /dev/null +++ b/diffusion/onnx_export.py @@ -0,0 +1,226 @@ +from diffusion_onnx import GaussianDiffusion +import os +import yaml +import torch +import torch.nn as nn +import numpy as np +from wavenet import WaveNet +import torch.nn.functional as F +import diffusion + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_model_vocoder( + model_path, + device='cpu'): + config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml') + with open(config_file, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + + # load model + model = Unit2Mel( + args.data.encoder_out_channels, + args.model.n_spk, + args.model.use_pitch_aug, + 128, + args.model.n_layers, + args.model.n_chans, + args.model.n_hidden) + + print(' [Loading] ' + model_path) + ckpt = torch.load(model_path, map_location=torch.device(device)) + model.to(device) + model.load_state_dict(ckpt['model']) + model.eval() + return model, args + + +class Unit2Mel(nn.Module): + def __init__( + self, + input_channel, + n_spk, + use_pitch_aug=False, + out_dims=128, + n_layers=20, + n_chans=384, + n_hidden=256): + super().__init__() + self.unit_embed = nn.Linear(input_channel, n_hidden) + self.f0_embed = nn.Linear(1, n_hidden) + self.volume_embed = nn.Linear(1, n_hidden) + if use_pitch_aug: + self.aug_shift_embed = nn.Linear(1, n_hidden, bias=False) + else: + self.aug_shift_embed = None + self.n_spk = n_spk + if n_spk is not None and n_spk > 1: + self.spk_embed = nn.Embedding(n_spk, n_hidden) + + # diffusion + self.decoder = GaussianDiffusion(out_dims, n_layers, n_chans, n_hidden) + self.hidden_size = n_hidden + self.speaker_map = torch.zeros((self.n_spk,1,1,n_hidden)) + + + + def forward(self, units, mel2ph, f0, volume, g = None): + + ''' + input: + B x n_frames x n_unit + return: + dict of B x n_frames x feat + ''' + + decoder_inp = F.pad(units, [0, 0, 1, 0]) + mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, units.shape[-1]]) + units = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H] + + x = self.unit_embed(units) + self.f0_embed((1 + f0.unsqueeze(-1) / 700).log()) + self.volume_embed(volume.unsqueeze(-1)) + + if self.n_spk is not None and self.n_spk > 1: # [N, S] * [S, B, 1, H] + g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] + g = g * self.speaker_map # [N, S, B, 1, H] + g = torch.sum(g, dim=1) # [N, 1, B, 1, H] + g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] + x = x.transpose(1, 2) + g + return x + else: + return x.transpose(1, 2) + + + def init_spkembed(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None, + gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True): + + ''' + input: + B x n_frames x n_unit + return: + dict of B x n_frames x feat + ''' + x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume) + if self.n_spk is not None and self.n_spk > 1: + if spk_mix_dict is not None: + spk_embed_mix = torch.zeros((1,1,self.hidden_size)) + for k, v in spk_mix_dict.items(): + spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device) + spk_embeddd = self.spk_embed(spk_id_torch) + self.speaker_map[k] = spk_embeddd + spk_embed_mix = spk_embed_mix + v * spk_embeddd + x = x + spk_embed_mix + else: + x = x + self.spk_embed(spk_id - 1) + self.speaker_map = self.speaker_map.unsqueeze(0) + self.speaker_map = self.speaker_map.detach() + return x.transpose(1, 2) + + def OnnxExport(self, project_name=None, init_noise=None, export_encoder=True, export_denoise=True, export_pred=True, export_after=True): + hubert_hidden_size = 768 + n_frames = 100 + hubert = torch.randn((1, n_frames, hubert_hidden_size)) + mel2ph = torch.arange(end=n_frames).unsqueeze(0).long() + f0 = torch.randn((1, n_frames)) + volume = torch.randn((1, n_frames)) + spk_mix = [] + spks = {} + if self.n_spk is not None and self.n_spk > 1: + for i in range(self.n_spk): + spk_mix.append(1.0/float(self.n_spk)) + spks.update({i:1.0/float(self.n_spk)}) + spk_mix = torch.tensor(spk_mix) + spk_mix = spk_mix.repeat(n_frames, 1) + orgouttt = self.init_spkembed(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks) + outtt = self.forward(hubert, mel2ph, f0, volume, spk_mix) + if export_encoder: + torch.onnx.export( + self, + (hubert, mel2ph, f0, volume, spk_mix), + f"{project_name}_encoder.onnx", + input_names=["hubert", "mel2ph", "f0", "volume", "spk_mix"], + output_names=["mel_pred"], + dynamic_axes={ + "hubert": [1], + "f0": [1], + "volume": [1], + "mel2ph": [1], + "spk_mix": [0], + }, + opset_version=16 + ) + + self.decoder.OnnxExport(project_name, init_noise=init_noise, export_denoise=export_denoise, export_pred=export_pred, export_after=export_after) + + def ExportOnnx(self, project_name=None): + hubert_hidden_size = 768 + n_frames = 100 + hubert = torch.randn((1, n_frames, hubert_hidden_size)) + mel2ph = torch.arange(end=n_frames).unsqueeze(0).long() + f0 = torch.randn((1, n_frames)) + volume = torch.randn((1, n_frames)) + spk_mix = [] + spks = {} + if self.n_spk is not None and self.n_spk > 1: + for i in range(self.n_spk): + spk_mix.append(1.0/float(self.n_spk)) + spks.update({i:1.0/float(self.n_spk)}) + spk_mix = torch.tensor(spk_mix) + orgouttt = self.orgforward(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks) + outtt = self.forward(hubert, mel2ph, f0, volume, spk_mix) + + torch.onnx.export( + self, + (hubert, mel2ph, f0, volume, spk_mix), + f"{project_name}_encoder.onnx", + input_names=["hubert", "mel2ph", "f0", "volume", "spk_mix"], + output_names=["mel_pred"], + dynamic_axes={ + "hubert": [1], + "f0": [1], + "volume": [1], + "mel2ph": [1] + }, + opset_version=16 + ) + + condition = torch.randn(1,self.decoder.n_hidden,n_frames) + noise = torch.randn((1, 1, self.decoder.mel_bins, condition.shape[2]), dtype=torch.float32) + pndm_speedup = torch.LongTensor([100]) + K_steps = torch.LongTensor([1000]) + self.decoder = torch.jit.script(self.decoder) + self.decoder(condition, noise, pndm_speedup, K_steps) + + torch.onnx.export( + self.decoder, + (condition, noise, pndm_speedup, K_steps), + f"{project_name}_diffusion.onnx", + input_names=["condition", "noise", "pndm_speedup", "K_steps"], + output_names=["mel"], + dynamic_axes={ + "condition": [2], + "noise": [3], + }, + opset_version=16 + ) + + +if __name__ == "__main__": + project_name = "dddsp" + model_path = f'{project_name}/model_500000.pt' + + model, _ = load_model_vocoder(model_path) + + # 分开Diffusion导出（需要使用MoeSS/MoeVoiceStudio或者自己编写Pndm/Dpm采样） + model.OnnxExport(project_name, export_encoder=True, export_denoise=True, export_pred=True, export_after=True) + + # 合并Diffusion导出（Encoder和Diffusion分开，直接将Encoder的结果和初始噪声输入Diffusion即可） + # model.ExportOnnx(project_name) + diff --git a/diffusion/solver.py b/diffusion/solver.py new file mode 100644 index 0000000000000000000000000000000000000000..aaf0b21591b42fa903424f8d44fef88d7d791e57 --- /dev/null +++ b/diffusion/solver.py @@ -0,0 +1,195 @@ +import os +import time +import numpy as np +import torch +import librosa +from diffusion.logger.saver import Saver +from diffusion.logger import utils +from torch import autocast +from torch.cuda.amp import GradScaler + +def test(args, model, vocoder, loader_test, saver): + print(' [*] testing...') + model.eval() + + # losses + test_loss = 0. + + # intialization + num_batches = len(loader_test) + rtf_all = [] + + # run + with torch.no_grad(): + for bidx, data in enumerate(loader_test): + fn = data['name'][0].split("/")[-1] + speaker = data['name'][0].split("/")[-2] + print('--------') + print('{}/{} - {}'.format(bidx, num_batches, fn)) + + # unpack data + for k in data.keys(): + if not k.startswith('name'): + data[k] = data[k].to(args.device) + print('>>', data['name'][0]) + + # forward + st_time = time.time() + mel = model( + data['units'], + data['f0'], + data['volume'], + data['spk_id'], + gt_spec=None, + infer=True, + infer_speedup=args.infer.speedup, + method=args.infer.method) + signal = vocoder.infer(mel, data['f0']) + ed_time = time.time() + + # RTF + run_time = ed_time - st_time + song_time = signal.shape[-1] / args.data.sampling_rate + rtf = run_time / song_time + print('RTF: {} | {} / {}'.format(rtf, run_time, song_time)) + rtf_all.append(rtf) + + # loss + for i in range(args.train.batch_size): + loss = model( + data['units'], + data['f0'], + data['volume'], + data['spk_id'], + gt_spec=data['mel'], + infer=False) + test_loss += loss.item() + + # log mel + saver.log_spec(f"{speaker}_{fn}.wav", data['mel'], mel) + + # log audi + path_audio = data['name_ext'][0] + audio, sr = librosa.load(path_audio, sr=args.data.sampling_rate) + if len(audio.shape) > 1: + audio = librosa.to_mono(audio) + audio = torch.from_numpy(audio).unsqueeze(0).to(signal) + saver.log_audio({f"{speaker}_{fn}_gt.wav": audio,f"{speaker}_{fn}_pred.wav": signal}) + # report + test_loss /= args.train.batch_size + test_loss /= num_batches + + # check + print(' [test_loss] test_loss:', test_loss) + print(' Real Time Factor', np.mean(rtf_all)) + return test_loss + + +def train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_test): + # saver + saver = Saver(args, initial_global_step=initial_global_step) + + # model size + params_count = utils.get_network_paras_amount({'model': model}) + saver.log_info('--- model size ---') + saver.log_info(params_count) + + # run + num_batches = len(loader_train) + model.train() + saver.log_info('======= start training =======') + scaler = GradScaler() + if args.train.amp_dtype == 'fp32': + dtype = torch.float32 + elif args.train.amp_dtype == 'fp16': + dtype = torch.float16 + elif args.train.amp_dtype == 'bf16': + dtype = torch.bfloat16 + else: + raise ValueError(' [x] Unknown amp_dtype: ' + args.train.amp_dtype) + saver.log_info("epoch|batch_idx/num_batches|output_dir|batch/s|lr|time|step") + for epoch in range(args.train.epochs): + for batch_idx, data in enumerate(loader_train): + saver.global_step_increment() + optimizer.zero_grad() + + # unpack data + for k in data.keys(): + if not k.startswith('name'): + data[k] = data[k].to(args.device) + + # forward + if dtype == torch.float32: + loss = model(data['units'].float(), data['f0'], data['volume'], data['spk_id'], + aug_shift = data['aug_shift'], gt_spec=data['mel'].float(), infer=False) + else: + with autocast(device_type=args.device, dtype=dtype): + loss = model(data['units'], data['f0'], data['volume'], data['spk_id'], + aug_shift = data['aug_shift'], gt_spec=data['mel'], infer=False) + + # handle nan loss + if torch.isnan(loss): + raise ValueError(' [x] nan loss ') + else: + # backpropagate + if dtype == torch.float32: + loss.backward() + optimizer.step() + else: + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + scheduler.step() + + # log loss + if saver.global_step % args.train.interval_log == 0: + current_lr = optimizer.param_groups[0]['lr'] + saver.log_info( + 'epoch: {} | {:3d}/{:3d} | {} | batch/s: {:.2f} | lr: {:.6} | loss: {:.3f} | time: {} | step: {}'.format( + epoch, + batch_idx, + num_batches, + args.env.expdir, + args.train.interval_log/saver.get_interval_time(), + current_lr, + loss.item(), + saver.get_total_time(), + saver.global_step + ) + ) + + saver.log_value({ + 'train/loss': loss.item() + }) + + saver.log_value({ + 'train/lr': current_lr + }) + + # validation + if saver.global_step % args.train.interval_val == 0: + optimizer_save = optimizer if args.train.save_opt else None + + # save latest + saver.save_model(model, optimizer_save, postfix=f'{saver.global_step}') + last_val_step = saver.global_step - args.train.interval_val + if last_val_step % args.train.interval_force_save != 0: + saver.delete_model(postfix=f'{last_val_step}') + + # run testing set + test_loss = test(args, model, vocoder, loader_test, saver) + + # log loss + saver.log_info( + ' --- --- \nloss: {:.3f}. '.format( + test_loss, + ) + ) + + saver.log_value({ + 'validation/loss': test_loss + }) + + model.train() + + diff --git a/diffusion/unit2mel.py b/diffusion/unit2mel.py new file mode 100644 index 0000000000000000000000000000000000000000..52293b13da8e1afeef6fa5586aeaf01cbcc27fb7 --- /dev/null +++ b/diffusion/unit2mel.py @@ -0,0 +1,147 @@ +import os +import yaml +import torch +import torch.nn as nn +import numpy as np +from .diffusion import GaussianDiffusion +from .wavenet import WaveNet +from .vocoder import Vocoder + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_model_vocoder( + model_path, + device='cpu', + config_path = None + ): + if config_path is None: config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml') + else: config_file = config_path + + with open(config_file, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + + # load vocoder + vocoder = Vocoder(args.vocoder.type, args.vocoder.ckpt, device=device) + + # load model + model = Unit2Mel( + args.data.encoder_out_channels, + args.model.n_spk, + args.model.use_pitch_aug, + vocoder.dimension, + args.model.n_layers, + args.model.n_chans, + args.model.n_hidden) + + print(' [Loading] ' + model_path) + ckpt = torch.load(model_path, map_location=torch.device(device)) + model.to(device) + model.load_state_dict(ckpt['model']) + model.eval() + return model, vocoder, args + + +class Unit2Mel(nn.Module): + def __init__( + self, + input_channel, + n_spk, + use_pitch_aug=False, + out_dims=128, + n_layers=20, + n_chans=384, + n_hidden=256): + super().__init__() + self.unit_embed = nn.Linear(input_channel, n_hidden) + self.f0_embed = nn.Linear(1, n_hidden) + self.volume_embed = nn.Linear(1, n_hidden) + if use_pitch_aug: + self.aug_shift_embed = nn.Linear(1, n_hidden, bias=False) + else: + self.aug_shift_embed = None + self.n_spk = n_spk + if n_spk is not None and n_spk > 1: + self.spk_embed = nn.Embedding(n_spk, n_hidden) + + self.n_hidden = n_hidden + # diffusion + self.decoder = GaussianDiffusion(WaveNet(out_dims, n_layers, n_chans, n_hidden), out_dims=out_dims) + self.input_channel = input_channel + + def init_spkembed(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None, + gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True): + + ''' + input: + B x n_frames x n_unit + return: + dict of B x n_frames x feat + ''' + x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume) + if self.n_spk is not None and self.n_spk > 1: + if spk_mix_dict is not None: + spk_embed_mix = torch.zeros((1,1,self.hidden_size)) + for k, v in spk_mix_dict.items(): + spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device) + spk_embeddd = self.spk_embed(spk_id_torch) + self.speaker_map[k] = spk_embeddd + spk_embed_mix = spk_embed_mix + v * spk_embeddd + x = x + spk_embed_mix + else: + x = x + self.spk_embed(spk_id - 1) + self.speaker_map = self.speaker_map.unsqueeze(0) + self.speaker_map = self.speaker_map.detach() + return x.transpose(1, 2) + + def init_spkmix(self, n_spk): + self.speaker_map = torch.zeros((n_spk,1,1,self.n_hidden)) + hubert_hidden_size = self.input_channel + n_frames = 10 + hubert = torch.randn((1, n_frames, hubert_hidden_size)) + mel2ph = torch.arange(end=n_frames).unsqueeze(0).long() + f0 = torch.randn((1, n_frames)) + volume = torch.randn((1, n_frames)) + spks = {} + for i in range(n_spk): + spks.update({i:1.0/float(self.n_spk)}) + orgouttt = self.init_spkembed(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks) + + def forward(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None, + gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True): + + ''' + input: + B x n_frames x n_unit + return: + dict of B x n_frames x feat + ''' + + x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume) + if self.n_spk is not None and self.n_spk > 1: + if spk_mix_dict is not None: + for k, v in spk_mix_dict.items(): + spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device) + x = x + v * self.spk_embed(spk_id_torch) + else: + if spk_id.shape[1] > 1: + g = spk_id.reshape((spk_id.shape[0], spk_id.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] + g = g * self.speaker_map # [N, S, B, 1, H] + g = torch.sum(g, dim=1) # [N, 1, B, 1, H] + g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] + x = x + g + else: + x = x + self.spk_embed(spk_id) + if self.aug_shift_embed is not None and aug_shift is not None: + x = x + self.aug_shift_embed(aug_shift / 5) + x = self.decoder(x, gt_spec=gt_spec, infer=infer, infer_speedup=infer_speedup, method=method, k_step=k_step, use_tqdm=use_tqdm) + + return x + diff --git a/diffusion/vocoder.py b/diffusion/vocoder.py new file mode 100644 index 0000000000000000000000000000000000000000..bbaa47f64fd5a3191a24dfaa054c423fa86e5bae --- /dev/null +++ b/diffusion/vocoder.py @@ -0,0 +1,94 @@ +import torch +from vdecoder.nsf_hifigan.nvSTFT import STFT +from vdecoder.nsf_hifigan.models import load_model,load_config +from torchaudio.transforms import Resample + + +class Vocoder: + def __init__(self, vocoder_type, vocoder_ckpt, device = None): + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = device + + if vocoder_type == 'nsf-hifigan': + self.vocoder = NsfHifiGAN(vocoder_ckpt, device = device) + elif vocoder_type == 'nsf-hifigan-log10': + self.vocoder = NsfHifiGANLog10(vocoder_ckpt, device = device) + else: + raise ValueError(f" [x] Unknown vocoder: {vocoder_type}") + + self.resample_kernel = {} + self.vocoder_sample_rate = self.vocoder.sample_rate() + self.vocoder_hop_size = self.vocoder.hop_size() + self.dimension = self.vocoder.dimension() + + def extract(self, audio, sample_rate, keyshift=0): + + # resample + if sample_rate == self.vocoder_sample_rate: + audio_res = audio + else: + key_str = str(sample_rate) + if key_str not in self.resample_kernel: + self.resample_kernel[key_str] = Resample(sample_rate, self.vocoder_sample_rate, lowpass_filter_width = 128).to(self.device) + audio_res = self.resample_kernel[key_str](audio) + + # extract + mel = self.vocoder.extract(audio_res, keyshift=keyshift) # B, n_frames, bins + return mel + + def infer(self, mel, f0): + f0 = f0[:,:mel.size(1),0] # B, n_frames + audio = self.vocoder(mel, f0) + return audio + + +class NsfHifiGAN(torch.nn.Module): + def __init__(self, model_path, device=None): + super().__init__() + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = device + self.model_path = model_path + self.model = None + self.h = load_config(model_path) + self.stft = STFT( + self.h.sampling_rate, + self.h.num_mels, + self.h.n_fft, + self.h.win_size, + self.h.hop_size, + self.h.fmin, + self.h.fmax) + + def sample_rate(self): + return self.h.sampling_rate + + def hop_size(self): + return self.h.hop_size + + def dimension(self): + return self.h.num_mels + + def extract(self, audio, keyshift=0): + mel = self.stft.get_mel(audio, keyshift=keyshift).transpose(1, 2) # B, n_frames, bins + return mel + + def forward(self, mel, f0): + if self.model is None: + print('| Load HifiGAN: ', self.model_path) + self.model, self.h = load_model(self.model_path, device=self.device) + with torch.no_grad(): + c = mel.transpose(1, 2) + audio = self.model(c, f0) + return audio + +class NsfHifiGANLog10(NsfHifiGAN): + def forward(self, mel, f0): + if self.model is None: + print('| Load HifiGAN: ', self.model_path) + self.model, self.h = load_model(self.model_path, device=self.device) + with torch.no_grad(): + c = 0.434294 * mel.transpose(1, 2) + audio = self.model(c, f0) + return audio \ No newline at end of file diff --git a/diffusion/wavenet.py b/diffusion/wavenet.py new file mode 100644 index 0000000000000000000000000000000000000000..3d48c7eaaa0e8191b27a5d1890eb657cbcc0d143 --- /dev/null +++ b/diffusion/wavenet.py @@ -0,0 +1,108 @@ +import math +from math import sqrt + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Mish + + +class Conv1d(torch.nn.Conv1d): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + nn.init.kaiming_normal_(self.weight) + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class ResidualBlock(nn.Module): + def __init__(self, encoder_hidden, residual_channels, dilation): + super().__init__() + self.residual_channels = residual_channels + self.dilated_conv = nn.Conv1d( + residual_channels, + 2 * residual_channels, + kernel_size=3, + padding=dilation, + dilation=dilation + ) + self.diffusion_projection = nn.Linear(residual_channels, residual_channels) + self.conditioner_projection = nn.Conv1d(encoder_hidden, 2 * residual_channels, 1) + self.output_projection = nn.Conv1d(residual_channels, 2 * residual_channels, 1) + + def forward(self, x, conditioner, diffusion_step): + diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) + conditioner = self.conditioner_projection(conditioner) + y = x + diffusion_step + + y = self.dilated_conv(y) + conditioner + + # Using torch.split instead of torch.chunk to avoid using onnx::Slice + gate, filter = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) + y = torch.sigmoid(gate) * torch.tanh(filter) + + y = self.output_projection(y) + + # Using torch.split instead of torch.chunk to avoid using onnx::Slice + residual, skip = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) + return (x + residual) / math.sqrt(2.0), skip + + +class WaveNet(nn.Module): + def __init__(self, in_dims=128, n_layers=20, n_chans=384, n_hidden=256): + super().__init__() + self.input_projection = Conv1d(in_dims, n_chans, 1) + self.diffusion_embedding = SinusoidalPosEmb(n_chans) + self.mlp = nn.Sequential( + nn.Linear(n_chans, n_chans * 4), + Mish(), + nn.Linear(n_chans * 4, n_chans) + ) + self.residual_layers = nn.ModuleList([ + ResidualBlock( + encoder_hidden=n_hidden, + residual_channels=n_chans, + dilation=1 + ) + for i in range(n_layers) + ]) + self.skip_projection = Conv1d(n_chans, n_chans, 1) + self.output_projection = Conv1d(n_chans, in_dims, 1) + nn.init.zeros_(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + :param spec: [B, 1, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, M, T] + :return: + """ + x = spec.squeeze(1) + x = self.input_projection(x) # [B, residual_channel, T] + + x = F.relu(x) + diffusion_step = self.diffusion_embedding(diffusion_step) + diffusion_step = self.mlp(diffusion_step) + skip = [] + for layer in self.residual_layers: + x, skip_connection = layer(x, cond, diffusion_step) + skip.append(skip_connection) + + x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers)) + x = self.skip_projection(x) + x = F.relu(x) + x = self.output_projection(x) # [B, mel_bins, T] + return x[:, None, :, :] diff --git a/filelists/test.txt b/filelists/test.txt new file mode 100644 index 0000000000000000000000000000000000000000..be640cffb48b3bc39126f9d1b83a3c992fe6e30d --- /dev/null +++ b/filelists/test.txt @@ -0,0 +1,4 @@ +./dataset/44k/taffy/000562.wav +./dataset/44k/nyaru/000011.wav +./dataset/44k/nyaru/000008.wav +./dataset/44k/taffy/000563.wav diff --git a/filelists/train.txt b/filelists/train.txt new file mode 100644 index 0000000000000000000000000000000000000000..238cb7236ab3bf12a643520ba18ca5c6909c6620 --- /dev/null +++ b/filelists/train.txt @@ -0,0 +1,924 @@ +./dataset/44k/1001/story_data_04_1001_storytimeline_041001004_56.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006211_35.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100100_25.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006221_19.wav +./dataset/44k/1001/story_data_04_1052_storytimeline_041052002_35.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006081_35.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001003_33.wav +./dataset/44k/1001/story_data_09_0005_storytimeline_090005003_26.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006051_47.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001001_14.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100101_17.wav +./dataset/44k/1001/story_data_10_0003_storytimeline_100003001_41.wav +./dataset/44k/1001/story_data_04_1004_storytimeline_041004004_0.wav 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+./dataset/44k/1001/character_system_text_1001_snd_voi_home_100100_14.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006101_34.wav +./dataset/44k/1001/story_data_04_1004_storytimeline_041004004_41.wav +./dataset/44k/1001/story_data_02_0005_storytimeline_020005051_29.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_training_100103_5.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100100_10.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_event_090001_10.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006031_5.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006231_18.wav +./dataset/44k/1001/story_data_02_0005_storytimeline_020005071_26.wav +./dataset/44k/1001/story_data_09_0005_storytimeline_090005003_37.wav +./dataset/44k/1001/story_data_09_0005_storytimeline_090005004_52.wav +./dataset/44k/1001/story_data_10_0000_storytimeline_100000008_5.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001002_0.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006191_47.wav +./dataset/44k/1001/story_data_09_0021_storytimeline_090021007_21.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006011_41.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001007_18.wav +./dataset/44k/1001/story_data_09_0011_storytimeline_090011006_38.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001004_0.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001004_32.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006101_35.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100102_13.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001008_18.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006081_25.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001001_28.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006213_6.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001002_29.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_training_100103_6.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006101_55.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001005_21.wav +./dataset/44k/1001/story_data_02_0005_storytimeline_020005071_25.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001002_30.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100101_2.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006191_36.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006213_25.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100102_14.wav +./dataset/44k/1001/story_data_02_0005_storytimeline_020005101_4.wav +./dataset/44k/1001/story_data_09_0005_storytimeline_090005004_54.wav +./dataset/44k/1001/story_data_09_0021_storytimeline_090021005_41.wav +./dataset/44k/1001/story_data_02_0005_storytimeline_020005041_10.wav +./dataset/44k/1001/story_data_10_0000_storytimeline_100000008_8.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001006_18.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006141_36.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_training_100100_13.wav +./dataset/44k/1001/story_data_09_0005_storytimeline_090005004_21.wav +./dataset/44k/1001/story_data_10_0002_storytimeline_100002001_4.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_training_100100_14.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001008_42.wav +./dataset/44k/1001/story_data_09_0011_storytimeline_090011001_3.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006063_28.wav +./dataset/44k/1001/story_data_09_0005_storytimeline_090005005_9.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001002_1.wav +./dataset/44k/1001/story_data_09_0021_storytimeline_090021006_39.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006231_23.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_event_090001_5.wav +./dataset/44k/1001/story_data_09_0001_storytimeline_090001001_39.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006101_29.wav +./dataset/44k/1001/story_data_04_1001_storytimeline_041001001_36.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_home_100100_9.wav +./dataset/44k/1001/character_system_text_1001_snd_voi_training_100100_21.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006151_11.wav diff --git a/filelists/val.txt b/filelists/val.txt new file mode 100644 index 0000000000000000000000000000000000000000..43bcbf25ecd0b624d6b87a4b1ef4a35dac98d297 --- /dev/null +++ b/filelists/val.txt @@ -0,0 +1,2 @@ +./dataset/44k/1001/story_data_09_0001_storytimeline_090001004_19.wav +./dataset/44k/1001/story_data_02_0006_storytimeline_020006211_88.wav diff --git a/flask_api.py b/flask_api.py new file mode 100644 index 0000000000000000000000000000000000000000..b3f1e06847b2711a8e5841a4c95375445470d2ee --- /dev/null +++ b/flask_api.py @@ -0,0 +1,60 @@ +import io +import logging + +import soundfile +import torch +import torchaudio +from flask import Flask, request, send_file +from flask_cors import CORS + +from inference.infer_tool import Svc, RealTimeVC + +app = Flask(__name__) + +CORS(app) + +logging.getLogger('numba').setLevel(logging.WARNING) + + +@app.route("/voiceChangeModel", methods=["POST"]) +def voice_change_model(): + request_form = request.form + wave_file = request.files.get("sample", None) + # 变调信息 + f_pitch_change = float(request_form.get("fPitchChange", 0)) + # DAW所需的采样率 + daw_sample = int(float(request_form.get("sampleRate", 0))) + speaker_id = int(float(request_form.get("sSpeakId", 0))) + # http获得wav文件并转换 + input_wav_path = io.BytesIO(wave_file.read()) + + # 模型推理 + if raw_infer: + # out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path) + out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0, + auto_predict_f0=False, noice_scale=0.4, f0_filter=False) + tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample) + else: + out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0, + auto_predict_f0=False, noice_scale=0.4, f0_filter=False) + tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample) + # 返回音频 + out_wav_path = io.BytesIO() + soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav") + out_wav_path.seek(0) + return send_file(out_wav_path, download_name="temp.wav", as_attachment=True) + + +if __name__ == '__main__': + # 启用则为直接切片合成，False为交叉淡化方式 + # vst插件调整0.3-0.5s切片时间可以降低延迟，直接切片方法会有连接处爆音、交叉淡化会有轻微重叠声音 + # 自行选择能接受的方法，或将vst最大切片时间调整为1s，此处设为Ture，延迟大音质稳定一些 + raw_infer = True + # 每个模型和config是唯一对应的 + model_name = "logs/32k/G_174000-Copy1.pth" + config_name = "configs/config.json" + cluster_model_path = "logs/44k/kmeans_10000.pt" + svc_model = Svc(model_name, config_name, cluster_model_path=cluster_model_path) + svc = RealTimeVC() + # 此处与vst插件对应，不建议更改 + app.run(port=6842, host="0.0.0.0", debug=False, threaded=False) diff --git a/flask_api_full_song.py b/flask_api_full_song.py new file mode 100644 index 0000000000000000000000000000000000000000..9dbf66a17114c7f9679717e2938759ae4a371c34 --- /dev/null +++ b/flask_api_full_song.py @@ -0,0 +1,55 @@ +import io +import numpy as np +import soundfile +from flask import Flask, request, send_file + +from inference import infer_tool +from inference import slicer + +app = Flask(__name__) + + +@app.route("/wav2wav", methods=["POST"]) +def wav2wav(): + request_form = request.form + audio_path = request_form.get("audio_path", None) # wav文件地址 + tran = int(float(request_form.get("tran", 0))) # 音调 + spk = request_form.get("spk", 0) # 说话人(id或者name都可以,具体看你的config) + wav_format = request_form.get("wav_format", 'wav') # 范围文件格式 + infer_tool.format_wav(audio_path) + chunks = slicer.cut(audio_path, db_thresh=-40) + audio_data, audio_sr = slicer.chunks2audio(audio_path, chunks) + + audio = [] + for (slice_tag, data) in audio_data: + print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======') + + length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample)) + if slice_tag: + print('jump empty segment') + _audio = np.zeros(length) + else: + # padd + pad_len = int(audio_sr * 0.5) + data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])]) + raw_path = io.BytesIO() + soundfile.write(raw_path, data, audio_sr, format="wav") + raw_path.seek(0) + out_audio, out_sr = svc_model.infer(spk, tran, raw_path) + svc_model.clear_empty() + _audio = out_audio.cpu().numpy() + pad_len = int(svc_model.target_sample * 0.5) + _audio = _audio[pad_len:-pad_len] + + audio.extend(list(infer_tool.pad_array(_audio, length))) + out_wav_path = io.BytesIO() + soundfile.write(out_wav_path, audio, svc_model.target_sample, format=wav_format) + out_wav_path.seek(0) + return send_file(out_wav_path, download_name=f"temp.{wav_format}", as_attachment=True) + + +if __name__ == '__main__': + model_name = "logs/44k/G_60000.pth" # 模型地址 + config_name = "configs/config.json" # config地址 + svc_model = infer_tool.Svc(model_name, config_name) + app.run(port=1145, host="0.0.0.0", debug=False, threaded=False) diff --git a/inference/__init__.py b/inference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/inference/__pycache__/__init__.cpython-38.pyc b/inference/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..51ab07f66fe017778f157a26f1cf9ff81ea9f55a Binary files /dev/null and b/inference/__pycache__/__init__.cpython-38.pyc differ diff --git a/inference/__pycache__/__init__.cpython-39.pyc b/inference/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a0faf65bc21f0aa7b4ee90f978ecf870f5b9b83e Binary files 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a/inference/__pycache__/slicer.cpython-39.pyc b/inference/__pycache__/slicer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e153a1eadd1243157e38c0db40f2e48c19880b9b Binary files /dev/null and b/inference/__pycache__/slicer.cpython-39.pyc differ diff --git a/inference/infer_tool.py b/inference/infer_tool.py new file mode 100644 index 0000000000000000000000000000000000000000..442342c95210668c0ff1eda3e3d45f4750e0fbe6 --- /dev/null +++ b/inference/infer_tool.py @@ -0,0 +1,529 @@ +import hashlib +import io +import json +import logging +import os +import time +from pathlib import Path +from inference import slicer +import gc + +import librosa +import numpy as np +# import onnxruntime +import soundfile +import torch +import torchaudio + +import cluster +import utils +from models import SynthesizerTrn +import pickle + +from diffusion.unit2mel import load_model_vocoder +import yaml + +logging.getLogger('matplotlib').setLevel(logging.WARNING) + + +def read_temp(file_name): + if not os.path.exists(file_name): + with open(file_name, "w") as f: + f.write(json.dumps({"info": "temp_dict"})) + return {} + else: + try: + with open(file_name, "r") as f: + data = f.read() + data_dict = json.loads(data) + if os.path.getsize(file_name) > 50 * 1024 * 1024: + f_name = file_name.replace("\\", "/").split("/")[-1] + print(f"clean {f_name}") + for wav_hash in list(data_dict.keys()): + if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600: + del data_dict[wav_hash] + except Exception as e: + print(e) + print(f"{file_name} error,auto rebuild file") + data_dict = {"info": "temp_dict"} + return data_dict + + +def write_temp(file_name, data): + with open(file_name, "w") as f: + f.write(json.dumps(data)) + + +def timeit(func): + def run(*args, **kwargs): + t = time.time() + res = func(*args, **kwargs) + print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t)) + return res + + return run + + +def format_wav(audio_path): + if Path(audio_path).suffix == '.wav': + return + raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None) + soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate) + + +def get_end_file(dir_path, end): + file_lists = [] + for root, dirs, files in os.walk(dir_path): + files = [f for f in files if f[0] != '.'] + dirs[:] = [d for d in dirs if d[0] != '.'] + for f_file in files: + if f_file.endswith(end): + file_lists.append(os.path.join(root, f_file).replace("\\", "/")) + return file_lists + + +def get_md5(content): + return hashlib.new("md5", content).hexdigest() + +def fill_a_to_b(a, b): + if len(a) < len(b): + for _ in range(0, len(b) - len(a)): + a.append(a[0]) + +def mkdir(paths: list): + for path in paths: + if not os.path.exists(path): + os.mkdir(path) + +def pad_array(arr, target_length): + current_length = arr.shape[0] + if current_length >= target_length: + return arr + else: + pad_width = target_length - current_length + pad_left = pad_width // 2 + pad_right = pad_width - pad_left + padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0)) + return padded_arr + +def split_list_by_n(list_collection, n, pre=0): + for i in range(0, len(list_collection), n): + yield list_collection[i-pre if i-pre>=0 else i: i + n] + + +class F0FilterException(Exception): + pass + +class Svc(object): + def __init__(self, net_g_path, config_path, + device=None, + cluster_model_path="logs/44k/kmeans_10000.pt", + nsf_hifigan_enhance = False, + diffusion_model_path="logs/44k/diffusion/model_0.pt", + diffusion_config_path="configs/diffusion.yaml", + shallow_diffusion = False, + only_diffusion = False, + spk_mix_enable = False, + feature_retrieval = False + ): + self.net_g_path = net_g_path + self.only_diffusion = only_diffusion + self.shallow_diffusion = shallow_diffusion + self.feature_retrieval = feature_retrieval + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + self.net_g_ms = None + if not self.only_diffusion: + self.hps_ms = utils.get_hparams_from_file(config_path,True) + self.target_sample = self.hps_ms.data.sampling_rate + self.hop_size = self.hps_ms.data.hop_length + self.spk2id = self.hps_ms.spk + self.unit_interpolate_mode = self.hps_ms.data.unit_interpolate_mode if self.hps_ms.data.unit_interpolate_mode is not None else 'left' + self.vol_embedding = self.hps_ms.model.vol_embedding if self.hps_ms.model.vol_embedding is not None else False + self.speech_encoder = self.hps_ms.model.speech_encoder if self.hps_ms.model.speech_encoder is not None else 'vec768l12' + + self.nsf_hifigan_enhance = nsf_hifigan_enhance + if self.shallow_diffusion or self.only_diffusion: + if os.path.exists(diffusion_model_path) and os.path.exists(diffusion_model_path): + self.diffusion_model,self.vocoder,self.diffusion_args = load_model_vocoder(diffusion_model_path,self.dev,config_path=diffusion_config_path) + if self.only_diffusion: + self.target_sample = self.diffusion_args.data.sampling_rate + self.hop_size = self.diffusion_args.data.block_size + self.spk2id = self.diffusion_args.spk + self.speech_encoder = self.diffusion_args.data.encoder + self.unit_interpolate_mode = self.diffusion_args.data.unit_interpolate_mode if self.diffusion_args.data.unit_interpolate_mode!=None else 'left' + if spk_mix_enable: + self.diffusion_model.init_spkmix(len(self.spk2id)) + else: + print("No diffusion model or config found. Shallow diffusion mode will False") + self.shallow_diffusion = self.only_diffusion = False + + # load hubert and model + if not self.only_diffusion: + self.load_model(spk_mix_enable) + self.hubert_model = utils.get_speech_encoder(self.speech_encoder,device=self.dev) + self.volume_extractor = utils.Volume_Extractor(self.hop_size) + else: + self.hubert_model = utils.get_speech_encoder(self.diffusion_args.data.encoder,device=self.dev) + self.volume_extractor = utils.Volume_Extractor(self.diffusion_args.data.block_size) + + if os.path.exists(cluster_model_path): + if self.feature_retrieval: + with open(cluster_model_path,"rb") as f: + self.cluster_model = pickle.load(f) + self.big_npy = None + self.now_spk_id = -1 + else: + self.cluster_model = cluster.get_cluster_model(cluster_model_path) + else: + self.feature_retrieval=False + + if self.shallow_diffusion : self.nsf_hifigan_enhance = False + if self.nsf_hifigan_enhance: + from modules.enhancer import Enhancer + self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev) + + def load_model(self, spk_mix_enable=False): + # get model configuration + self.net_g_ms = SynthesizerTrn( + self.hps_ms.data.filter_length // 2 + 1, + self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, + **self.hps_ms.model) + _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None) + if "half" in self.net_g_path and torch.cuda.is_available(): + _ = self.net_g_ms.half().eval().to(self.dev) + else: + _ = self.net_g_ms.eval().to(self.dev) + if spk_mix_enable: + self.net_g_ms.EnableCharacterMix(len(self.spk2id), self.dev) + + def get_unit_f0(self, wav, tran, cluster_infer_ratio, speaker, f0_filter ,f0_predictor,cr_threshold=0.05): + + f0_predictor_object = utils.get_f0_predictor(f0_predictor,hop_length=self.hop_size,sampling_rate=self.target_sample,device=self.dev,threshold=cr_threshold) + + f0, uv = f0_predictor_object.compute_f0_uv(wav) + if f0_filter and sum(f0) == 0: + raise F0FilterException("No voice detected") + f0 = torch.FloatTensor(f0).to(self.dev) + uv = torch.FloatTensor(uv).to(self.dev) + + f0 = f0 * 2 ** (tran / 12) + f0 = f0.unsqueeze(0) + uv = uv.unsqueeze(0) + + wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000) + wav16k = torch.from_numpy(wav16k).to(self.dev) + c = self.hubert_model.encoder(wav16k) + c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode) + + if cluster_infer_ratio !=0: + if self.feature_retrieval: + speaker_id = self.spk2id.get(speaker) + if speaker_id is None: + raise RuntimeError("The name you entered is not in the speaker list!") + if not speaker_id and type(speaker) is int: + if len(self.spk2id.__dict__) >= speaker: + speaker_id = speaker + feature_index = self.cluster_model[speaker_id] + feat_np = c.transpose(0,1).cpu().numpy() + if self.big_npy is None or self.now_spk_id != speaker_id: + self.big_npy = feature_index.reconstruct_n(0, feature_index.ntotal) + self.now_spk_id = speaker_id + print("starting feature retrieval...") + score, ix = feature_index.search(feat_np, k=8) + weight = np.square(1 / score) + weight /= weight.sum(axis=1, keepdims=True) + npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) + c = cluster_infer_ratio * npy + (1 - cluster_infer_ratio) * feat_np + c = torch.FloatTensor(c).to(self.dev).transpose(0,1) + print("end feature retrieval...") + else: + cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T + cluster_c = torch.FloatTensor(cluster_c).to(self.dev) + c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c + + c = c.unsqueeze(0) + return c, f0, uv + + def infer(self, speaker, tran, raw_path, + cluster_infer_ratio=0, + auto_predict_f0=False, + noice_scale=0.4, + f0_filter=False, + f0_predictor='pm', + enhancer_adaptive_key = 0, + cr_threshold = 0.05, + k_step = 100, + frame = 0, + spk_mix = False, + second_encoding = False, + loudness_envelope_adjustment = 1 + ): + wav, sr = librosa.load(raw_path, sr=self.target_sample) + if spk_mix: + c, f0, uv = self.get_unit_f0(wav, tran, 0, None, f0_filter,f0_predictor,cr_threshold=cr_threshold) + n_frames = f0.size(1) + sid = speaker[:, frame:frame+n_frames].transpose(0,1) + else: + speaker_id = self.spk2id.get(speaker) + if not speaker_id and type(speaker) is int: + if len(self.spk2id.__dict__) >= speaker: + speaker_id = speaker + if speaker_id is None: + raise RuntimeError("The name you entered is not in the speaker list!") + sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0) + c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold) + n_frames = f0.size(1) + if "half" in self.net_g_path and torch.cuda.is_available(): + c = c.half() + with torch.no_grad(): + start = time.time() + vol = None + if not self.only_diffusion: + vol = self.volume_extractor.extract(torch.FloatTensor(wav).to(self.dev)[None,:])[None,:].to(self.dev) if self.vol_embedding else None + audio,f0 = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale,vol=vol) + audio = audio[0,0].data.float() + audio_mel = self.vocoder.extract(audio[None,:],self.target_sample) if self.shallow_diffusion else None + else: + audio = torch.FloatTensor(wav).to(self.dev) + audio_mel = None + if self.only_diffusion or self.shallow_diffusion: + vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev) if vol==None else vol[:,:,None] + if self.shallow_diffusion and second_encoding: + audio16k = librosa.resample(audio.detach().cpu().numpy(), orig_sr=self.target_sample, target_sr=16000) + audio16k = torch.from_numpy(audio16k).to(self.dev) + c = self.hubert_model.encoder(audio16k) + c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1],self.unit_interpolate_mode) + f0 = f0[:,:,None] + c = c.transpose(-1,-2) + audio_mel = self.diffusion_model( + c, + f0, + vol, + spk_id = sid, + spk_mix_dict = None, + gt_spec=audio_mel, + infer=True, + infer_speedup=self.diffusion_args.infer.speedup, + method=self.diffusion_args.infer.method, + k_step=k_step) + audio = self.vocoder.infer(audio_mel, f0).squeeze() + if self.nsf_hifigan_enhance: + audio, _ = self.enhancer.enhance( + audio[None,:], + self.target_sample, + f0[:,:,None], + self.hps_ms.data.hop_length, + adaptive_key = enhancer_adaptive_key) + if loudness_envelope_adjustment != 1: + audio = utils.change_rms(wav,self.target_sample,audio,self.target_sample,loudness_envelope_adjustment) + use_time = time.time() - start + print("vits use time:{}".format(use_time)) + return audio, audio.shape[-1], n_frames + + def clear_empty(self): + # clean up vram + torch.cuda.empty_cache() + + def unload_model(self): + # unload model + self.net_g_ms = self.net_g_ms.to("cpu") + del self.net_g_ms + if hasattr(self,"enhancer"): + self.enhancer.enhancer = self.enhancer.enhancer.to("cpu") + del self.enhancer.enhancer + del self.enhancer + gc.collect() + + def slice_inference(self, + raw_audio_path, + spk, + tran, + slice_db, + cluster_infer_ratio, + auto_predict_f0, + noice_scale, + pad_seconds=0.5, + clip_seconds=0, + lg_num=0, + lgr_num =0.75, + f0_predictor='pm', + enhancer_adaptive_key = 0, + cr_threshold = 0.05, + k_step = 100, + use_spk_mix = False, + second_encoding = False, + loudness_envelope_adjustment = 1 + ): + if use_spk_mix: + if len(self.spk2id) == 1: + spk = self.spk2id.keys()[0] + use_spk_mix = False + wav_path = Path(raw_audio_path).with_suffix('.wav') + chunks = slicer.cut(wav_path, db_thresh=slice_db) + audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks) + per_size = int(clip_seconds*audio_sr) + lg_size = int(lg_num*audio_sr) + lg_size_r = int(lg_size*lgr_num) + lg_size_c_l = (lg_size-lg_size_r)//2 + lg_size_c_r = lg_size-lg_size_r-lg_size_c_l + lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0 + + if use_spk_mix: + assert len(self.spk2id) == len(spk) + audio_length = 0 + for (slice_tag, data) in audio_data: + aud_length = int(np.ceil(len(data) / audio_sr * self.target_sample)) + if slice_tag: + audio_length += aud_length // self.hop_size + continue + if per_size != 0: + datas = split_list_by_n(data, per_size,lg_size) + else: + datas = [data] + for k,dat in enumerate(datas): + pad_len = int(audio_sr * pad_seconds) + per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) + a_length = per_length + 2 * pad_len + audio_length += a_length // self.hop_size + audio_length += len(audio_data) + spk_mix_tensor = torch.zeros(size=(len(spk), audio_length)).to(self.dev) + for i in range(len(spk)): + last_end = None + for mix in spk[i]: + if mix[3]<0. or mix[2]<0.: + raise RuntimeError("mix value must higer Than zero!") + begin = int(audio_length * mix[0]) + end = int(audio_length * mix[1]) + length = end - begin + if length<=0: + raise RuntimeError("begin Must lower Than end!") + step = (mix[3] - mix[2])/length + if last_end is not None: + if last_end != begin: + raise RuntimeError("[i]EndTime Must Equal [i+1]BeginTime!") + last_end = end + if step == 0.: + spk_mix_data = torch.zeros(length).to(self.dev) + mix[2] + else: + spk_mix_data = torch.arange(mix[2],mix[3],step).to(self.dev) + if(len(spk_mix_data)0 or p_len - len(f0) - pad_size>0): + f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') + + f0 *= pow(2, f0_up_key / 12) + f0_mel = 1127 * np.log(1 + f0 / 700) + f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1 + f0_mel[f0_mel <= 1] = 1 + f0_mel[f0_mel > 255] = 255 + f0_coarse = np.rint(f0_mel).astype(np.int) + return f0_coarse, f0 + +def clean_pitch(input_pitch): + num_nan = np.sum(input_pitch == 1) + if num_nan / len(input_pitch) > 0.9: + input_pitch[input_pitch != 1] = 1 + return input_pitch + + +def plt_pitch(input_pitch): + input_pitch = input_pitch.astype(float) + input_pitch[input_pitch == 1] = np.nan + return input_pitch + + +def f0_to_pitch(ff): + f0_pitch = 69 + 12 * np.log2(ff / 440) + return f0_pitch + + +def fill_a_to_b(a, b): + if len(a) < len(b): + for _ in range(0, len(b) - len(a)): + a.append(a[0]) + + +def mkdir(paths: list): + for path in paths: + if not os.path.exists(path): + os.mkdir(path) + + +class VitsSvc(object): + def __init__(self): + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.SVCVITS = None + self.hps = None + self.speakers = None + self.hubert_soft = utils.get_hubert_model() + + def set_device(self, device): + self.device = torch.device(device) + self.hubert_soft.to(self.device) + if self.SVCVITS != None: + self.SVCVITS.to(self.device) + + def loadCheckpoint(self, path): + self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json") + self.SVCVITS = SynthesizerTrn( + self.hps.data.filter_length // 2 + 1, + self.hps.train.segment_size // self.hps.data.hop_length, + **self.hps.model) + _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None) + _ = self.SVCVITS.eval().to(self.device) + self.speakers = self.hps.spk + + def get_units(self, source, sr): + source = source.unsqueeze(0).to(self.device) + with torch.inference_mode(): + units = self.hubert_soft.units(source) + return units + + + def get_unit_pitch(self, in_path, tran): + source, sr = torchaudio.load(in_path) + source = torchaudio.functional.resample(source, sr, 16000) + if len(source.shape) == 2 and source.shape[1] >= 2: + source = torch.mean(source, dim=0).unsqueeze(0) + soft = self.get_units(source, sr).squeeze(0).cpu().numpy() + f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran) + return soft, f0 + + def infer(self, speaker_id, tran, raw_path): + speaker_id = self.speakers[speaker_id] + sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0) + soft, pitch = self.get_unit_pitch(raw_path, tran) + f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device) + stn_tst = torch.FloatTensor(soft) + with torch.no_grad(): + x_tst = stn_tst.unsqueeze(0).to(self.device) + x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2) + audio,_ = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float() + return audio, audio.shape[-1] + + def inference(self,srcaudio,chara,tran,slice_db): + sampling_rate, audio = srcaudio + audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) + if len(audio.shape) > 1: + audio = librosa.to_mono(audio.transpose(1, 0)) + if sampling_rate != 16000: + audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) + soundfile.write("tmpwav.wav", audio, 16000, format="wav") + chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db) + audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks) + audio = [] + for (slice_tag, data) in audio_data: + length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate)) + raw_path = io.BytesIO() + soundfile.write(raw_path, data, audio_sr, format="wav") + raw_path.seek(0) + if slice_tag: + _audio = np.zeros(length) + else: + out_audio, out_sr = self.infer(chara, tran, raw_path) + _audio = out_audio.cpu().numpy() + audio.extend(list(_audio)) + audio = (np.array(audio) * 32768.0).astype('int16') + return (self.hps.data.sampling_rate,audio) diff --git a/inference/slicer.py b/inference/slicer.py new file mode 100644 index 0000000000000000000000000000000000000000..b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462 --- /dev/null +++ b/inference/slicer.py @@ -0,0 +1,142 @@ +import librosa +import torch +import torchaudio + + +class Slicer: + def __init__(self, + sr: int, + threshold: float = -40., + min_length: int = 5000, + min_interval: int = 300, + hop_size: int = 20, + max_sil_kept: int = 5000): + if not min_length >= min_interval >= hop_size: + raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size') + if not max_sil_kept >= hop_size: + raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size') + min_interval = sr * min_interval / 1000 + self.threshold = 10 ** (threshold / 20.) + self.hop_size = round(sr * hop_size / 1000) + self.win_size = min(round(min_interval), 4 * self.hop_size) + self.min_length = round(sr * min_length / 1000 / self.hop_size) + self.min_interval = round(min_interval / self.hop_size) + self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) + + def _apply_slice(self, waveform, begin, end): + if len(waveform.shape) > 1: + return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)] + else: + return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)] + + # @timeit + def slice(self, waveform): + if len(waveform.shape) > 1: + samples = librosa.to_mono(waveform) + else: + samples = waveform + if samples.shape[0] <= self.min_length: + return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} + rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) + sil_tags = [] + silence_start = None + clip_start = 0 + for i, rms in enumerate(rms_list): + # Keep looping while frame is silent. + if rms < self.threshold: + # Record start of silent frames. + if silence_start is None: + silence_start = i + continue + # Keep looping while frame is not silent and silence start has not been recorded. + if silence_start is None: + continue + # Clear recorded silence start if interval is not enough or clip is too short + is_leading_silence = silence_start == 0 and i > self.max_sil_kept + need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length + if not is_leading_silence and not need_slice_middle: + silence_start = None + continue + # Need slicing. Record the range of silent frames to be removed. + if i - silence_start <= self.max_sil_kept: + pos = rms_list[silence_start: i + 1].argmin() + silence_start + if silence_start == 0: + sil_tags.append((0, pos)) + else: + sil_tags.append((pos, pos)) + clip_start = pos + elif i - silence_start <= self.max_sil_kept * 2: + pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin() + pos += i - self.max_sil_kept + pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start + pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept + if silence_start == 0: + sil_tags.append((0, pos_r)) + clip_start = pos_r + else: + sil_tags.append((min(pos_l, pos), max(pos_r, pos))) + clip_start = max(pos_r, pos) + else: + pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start + pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept + if silence_start == 0: + sil_tags.append((0, pos_r)) + else: + sil_tags.append((pos_l, pos_r)) + clip_start = pos_r + silence_start = None + # Deal with trailing silence. + total_frames = rms_list.shape[0] + if silence_start is not None and total_frames - silence_start >= self.min_interval: + silence_end = min(total_frames, silence_start + self.max_sil_kept) + pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start + sil_tags.append((pos, total_frames + 1)) + # Apply and return slices. + if len(sil_tags) == 0: + return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} + else: + chunks = [] + # 第一段静音并非从头开始，补上有声片段 + if sil_tags[0][0]: + chunks.append( + {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"}) + for i in range(0, len(sil_tags)): + # 标识有声片段（跳过第一段） + if i: + chunks.append({"slice": False, + "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"}) + # 标识所有静音片段 + chunks.append({"slice": True, + "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"}) + # 最后一段静音并非结尾，补上结尾片段 + if sil_tags[-1][1] * self.hop_size < len(waveform): + chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"}) + chunk_dict = {} + for i in range(len(chunks)): + chunk_dict[str(i)] = chunks[i] + return chunk_dict + + +def cut(audio_path, db_thresh=-30, min_len=5000): + audio, sr = librosa.load(audio_path, sr=None) + slicer = Slicer( + sr=sr, + threshold=db_thresh, + min_length=min_len + ) + chunks = slicer.slice(audio) + return chunks + + +def chunks2audio(audio_path, chunks): + chunks = dict(chunks) + audio, sr = torchaudio.load(audio_path) + if len(audio.shape) == 2 and audio.shape[1] >= 2: + audio = torch.mean(audio, dim=0).unsqueeze(0) + audio = audio.cpu().numpy()[0] + result = [] + for k, v in chunks.items(): + tag = v["split_time"].split(",") + if tag[0] != tag[1]: + result.append((v["slice"], audio[int(tag[0]):int(tag[1])])) + return result, sr diff --git a/inference_main.py b/inference_main.py new file mode 100644 index 0000000000000000000000000000000000000000..58aa176dd5315604b03dad7a0560df1e2b7f566a --- /dev/null +++ b/inference_main.py @@ -0,0 +1,149 @@ +import io +import logging +import time +from pathlib import Path +from spkmix import spk_mix_map +import librosa +import matplotlib.pyplot as plt +import numpy as np +import soundfile +from inference import infer_tool +from inference import slicer +from inference.infer_tool import Svc + +logging.getLogger('numba').setLevel(logging.WARNING) +chunks_dict = infer_tool.read_temp("inference/chunks_temp.json") + + + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='sovits4 inference') + + # 一定要设置的部分 + parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_37600.pth", help='模型路径') + parser.add_argument('-c', '--config_path', type=str, default="logs/44k/config.json", help='配置文件路径') + parser.add_argument('-cl', '--clip', type=float, default=0, help='音频强制切片，默认0为自动切片，单位为秒/s') + parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下') + parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）') + parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['buyizi'], help='合成目标说话人名称') + + # 可选项部分 + parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False, help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调') + parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型或特征检索索引路径，如果没有训练聚类或特征检索则随便填') + parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案或特征检索占比，范围0-1，若没有训练聚类模型或特征检索则默认0即可') + parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两段音频切片的交叉淡入长度，如果强制切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒') + parser.add_argument('-f0p', '--f0_predictor', type=str, default="pm", help='选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)') + parser.add_argument('-eh', '--enhance', action='store_true', default=False, help='是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭') + parser.add_argument('-shd', '--shallow_diffusion', action='store_true', default=False, help='是否使用浅层扩散，使用后可解决一部分电音问题，默认关闭，该选项打开时，NSF_HIFIGAN增强器将会被禁止') + parser.add_argument('-usm', '--use_spk_mix', action='store_true', default=False, help='是否使用角色融合') + parser.add_argument('-lea', '--loudness_envelope_adjustment', type=float, default=1, help='输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络') + parser.add_argument('-fr', '--feature_retrieval', action='store_true', default=False, help='是否使用特征检索，如果使用聚类模型将被禁用，且cm与cr参数将会变成特征检索的索引路径与混合比例') + + # 浅扩散设置 + parser.add_argument('-dm', '--diffusion_model_path', type=str, default="logs/44k/diffusion/model_0.pt", help='扩散模型路径') + parser.add_argument('-dc', '--diffusion_config_path', type=str, default="logs/44k/diffusion/config.yaml", help='扩散模型配置文件路径') + parser.add_argument('-ks', '--k_step', type=int, default=100, help='扩散步数，越大越接近扩散模型的结果，默认100') + parser.add_argument('-se', '--second_encoding', action='store_true', default=False, help='二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，有时候效果好，有时候效果差') + parser.add_argument('-od', '--only_diffusion', action='store_true', default=False, help='纯扩散模式，该模式不会加载sovits模型，以扩散模型推理') + + + # 不用动的部分 + parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50') + parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu') + parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别，会影响咬字和音质，较为玄学') + parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现') + parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式') + parser.add_argument('-lgr', '--linear_gradient_retain', type=float, default=0.75, help='自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭') + parser.add_argument('-eak', '--enhancer_adaptive_key', type=int, default=0, help='使增强器适应更高的音域(单位为半音数)|默认为0') + parser.add_argument('-ft', '--f0_filter_threshold', type=float, default=0.05,help='F0过滤阈值，只有使用crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音') + + + args = parser.parse_args() + + clean_names = args.clean_names + trans = args.trans + spk_list = args.spk_list + slice_db = args.slice_db + wav_format = args.wav_format + auto_predict_f0 = args.auto_predict_f0 + cluster_infer_ratio = args.cluster_infer_ratio + noice_scale = args.noice_scale + pad_seconds = args.pad_seconds + clip = args.clip + lg = args.linear_gradient + lgr = args.linear_gradient_retain + f0p = args.f0_predictor + enhance = args.enhance + enhancer_adaptive_key = args.enhancer_adaptive_key + cr_threshold = args.f0_filter_threshold + diffusion_model_path = args.diffusion_model_path + diffusion_config_path = args.diffusion_config_path + k_step = args.k_step + only_diffusion = args.only_diffusion + shallow_diffusion = args.shallow_diffusion + use_spk_mix = args.use_spk_mix + second_encoding = args.second_encoding + loudness_envelope_adjustment = args.loudness_envelope_adjustment + + svc_model = Svc(args.model_path, + args.config_path, + args.device, + args.cluster_model_path, + enhance, + diffusion_model_path, + diffusion_config_path, + shallow_diffusion, + only_diffusion, + use_spk_mix, + args.feature_retrieval) + + infer_tool.mkdir(["raw", "results"]) + + if len(spk_mix_map)<=1: + use_spk_mix = False + if use_spk_mix: + spk_list = [spk_mix_map] + + infer_tool.fill_a_to_b(trans, clean_names) + for clean_name, tran in zip(clean_names, trans): + raw_audio_path = f"raw/{clean_name}" + if "." not in raw_audio_path: + raw_audio_path += ".wav" + infer_tool.format_wav(raw_audio_path) + for spk in spk_list: + kwarg = { + "raw_audio_path" : raw_audio_path, + "spk" : spk, + "tran" : tran, + "slice_db" : slice_db, + "cluster_infer_ratio" : cluster_infer_ratio, + "auto_predict_f0" : auto_predict_f0, + "noice_scale" : noice_scale, + "pad_seconds" : pad_seconds, + "clip_seconds" : clip, + "lg_num": lg, + "lgr_num" : lgr, + "f0_predictor" : f0p, + "enhancer_adaptive_key" : enhancer_adaptive_key, + "cr_threshold" : cr_threshold, + "k_step":k_step, + "use_spk_mix":use_spk_mix, + "second_encoding":second_encoding, + "loudness_envelope_adjustment":loudness_envelope_adjustment + } + audio = svc_model.slice_inference(**kwarg) + key = "auto" if auto_predict_f0 else f"{tran}key" + cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}" + isdiffusion = "sovits" + if shallow_diffusion : isdiffusion = "sovdiff" + if only_diffusion : isdiffusion = "diff" + if use_spk_mix: + spk = "spk_mix" + res_path = f'results/{clean_name}_{key}_{spk}{cluster_name}_{isdiffusion}.{wav_format}' + soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format) + svc_model.clear_empty() + +if __name__ == '__main__': + main() diff --git a/logs/44k/D_388000.pth b/logs/44k/D_388000.pth new file mode 100644 index 0000000000000000000000000000000000000000..4ecca0cee49d1f16d7ca3565a0b155bbcb772643 --- /dev/null +++ b/logs/44k/D_388000.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c30462e26ac843b5f2c32eb722e1550adcd0a5fb9bb7fcacca451730bb7392f9 +size 561099143 diff --git a/logs/44k/G_388000.pth b/logs/44k/G_388000.pth new file mode 100644 index 0000000000000000000000000000000000000000..c5658e4e421e551769d19a86606380a587102d15 --- /dev/null +++ b/logs/44k/G_388000.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6faddfbd1e7ed849a6168bd8c5ff83dd1f6151942a607bca997b18dfad191259 +size 627915739 diff --git a/logs/44k/diffusion/.gitkeep b/logs/44k/diffusion/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/logs/44k/diffusion/model_0.pt b/logs/44k/diffusion/model_0.pt new file mode 100644 index 0000000000000000000000000000000000000000..e355af7b1d4acfef44ea54911d5cf77d2a727abc --- /dev/null +++ b/logs/44k/diffusion/model_0.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:409452a27ab310f7a5897844d003d372a7357cc91c4a43562584a1714518cdf9 +size 220895384 diff --git a/logs/44k/feature_and_index.pkl b/logs/44k/feature_and_index.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a6a130d1de595eef5fb3dfe7e1490af3bdb760c6 --- /dev/null +++ b/logs/44k/feature_and_index.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ec985174f563fe862d5cb0a3fade796b893065cc7e0d7074afd26a7b67eefa8 +size 487742865 diff --git a/logs/44k/kmeans_10000.pt b/logs/44k/kmeans_10000.pt new file mode 100644 index 0000000000000000000000000000000000000000..6a49e2279f8195a85eee4491bb6da41374968aed --- /dev/null +++ b/logs/44k/kmeans_10000.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6a10368385154f29532851b53db382e6402ce695cf48266ad997df52a15b1ef +size 33452857 diff --git a/models.py b/models.py new file mode 100644 index 0000000000000000000000000000000000000000..ac40c3cda6b5ef351049b0348711f90e2985ce1e --- /dev/null +++ b/models.py @@ -0,0 +1,469 @@ +import copy +import math +import torch +from torch import nn +from torch.nn import functional as F + +import modules.attentions as attentions +import modules.commons as commons +import modules.modules as modules + +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +import utils +from modules.commons import init_weights, get_padding +from utils import f0_to_coarse + +class ResidualCouplingBlock(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + n_flows=4, + gin_channels=0): + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for i in range(n_flows): + self.flows.append( + modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=gin_channels, mean_only=True)) + self.flows.append(modules.Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x + + +class Encoder(nn.Module): + def __init__(self, + in_channels, + out_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + + self.pre = nn.Conv1d(in_channels, hidden_channels, 1) + self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, g=None): + # print(x.shape,x_lengths.shape) + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + x = self.pre(x) * x_mask + x = self.enc(x, x_mask, g=g) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class TextEncoder(nn.Module): + def __init__(self, + out_channels, + hidden_channels, + kernel_size, + n_layers, + gin_channels=0, + filter_channels=None, + n_heads=None, + p_dropout=None): + super().__init__() + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.gin_channels = gin_channels + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + self.f0_emb = nn.Embedding(256, hidden_channels) + + self.enc_ = attentions.Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + + def forward(self, x, x_mask, f0=None, noice_scale=1): + x = x + self.f0_emb(f0).transpose(1, 2) + x = self.enc_(x * x_mask, x_mask) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask + + return z, m, logs, x_mask + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + self.use_spectral_norm = use_spectral_norm + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 16, 15, 1, padding=7)), + norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), + norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(MultiPeriodDiscriminator, self).__init__() + periods = [2, 3, 5, 7, 11] + + discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] + discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] + self.discriminators = nn.ModuleList(discs) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + y_d_gs.append(y_d_g) + fmap_rs.append(fmap_r) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class SpeakerEncoder(torch.nn.Module): + def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): + super(SpeakerEncoder, self).__init__() + self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) + self.linear = nn.Linear(model_hidden_size, model_embedding_size) + self.relu = nn.ReLU() + + def forward(self, mels): + self.lstm.flatten_parameters() + _, (hidden, _) = self.lstm(mels) + embeds_raw = self.relu(self.linear(hidden[-1])) + return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) + + def compute_partial_slices(self, total_frames, partial_frames, partial_hop): + mel_slices = [] + for i in range(0, total_frames - partial_frames, partial_hop): + mel_range = torch.arange(i, i + partial_frames) + mel_slices.append(mel_range) + + return mel_slices + + def embed_utterance(self, mel, partial_frames=128, partial_hop=64): + mel_len = mel.size(1) + last_mel = mel[:, -partial_frames:] + + if mel_len > partial_frames: + mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) + mels = list(mel[:, s] for s in mel_slices) + mels.append(last_mel) + mels = torch.stack(tuple(mels), 0).squeeze(1) + + with torch.no_grad(): + partial_embeds = self(mels) + embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) + # embed = embed / torch.linalg.norm(embed, 2) + else: + with torch.no_grad(): + embed = self(last_mel) + + return embed + +class F0Decoder(nn.Module): + def __init__(self, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + spk_channels=0): + super().__init__() + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.spk_channels = spk_channels + + self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1) + self.decoder = attentions.FFT( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1) + self.cond = nn.Conv1d(spk_channels, hidden_channels, 1) + + def forward(self, x, norm_f0, x_mask, spk_emb=None): + x = torch.detach(x) + if (spk_emb is not None): + x = x + self.cond(spk_emb) + x += self.f0_prenet(norm_f0) + x = self.prenet(x) * x_mask + x = self.decoder(x * x_mask, x_mask) + x = self.proj(x) * x_mask + return x + + +class SynthesizerTrn(nn.Module): + """ + Synthesizer for Training + """ + + def __init__(self, + spec_channels, + segment_size, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + gin_channels, + ssl_dim, + n_speakers, + sampling_rate=44100, + vol_embedding=False, + vocoder_name = "nsf-hifigan", + **kwargs): + + super().__init__() + self.spec_channels = spec_channels + self.inter_channels = inter_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.resblock = resblock + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.upsample_rates = upsample_rates + self.upsample_initial_channel = upsample_initial_channel + self.upsample_kernel_sizes = upsample_kernel_sizes + self.segment_size = segment_size + self.gin_channels = gin_channels + self.ssl_dim = ssl_dim + self.vol_embedding = vol_embedding + self.emb_g = nn.Embedding(n_speakers, gin_channels) + if vol_embedding: + self.emb_vol = nn.Linear(1, hidden_channels) + + self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2) + + self.enc_p = TextEncoder( + inter_channels, + hidden_channels, + filter_channels=filter_channels, + n_heads=n_heads, + n_layers=n_layers, + kernel_size=kernel_size, + p_dropout=p_dropout + ) + hps = { + "sampling_rate": sampling_rate, + "inter_channels": inter_channels, + "resblock": resblock, + "resblock_kernel_sizes": resblock_kernel_sizes, + "resblock_dilation_sizes": resblock_dilation_sizes, + "upsample_rates": upsample_rates, + "upsample_initial_channel": upsample_initial_channel, + "upsample_kernel_sizes": upsample_kernel_sizes, + "gin_channels": gin_channels, + } + + + if vocoder_name == "nsf-hifigan": + from vdecoder.hifigan.models import Generator + self.dec = Generator(h=hps) + elif vocoder_name == "nsf-snake-hifigan": + from vdecoder.hifiganwithsnake.models import Generator + self.dec = Generator(h=hps) + else: + print("[?] Unkown vocoder: use default(nsf-hifigan)") + from vdecoder.hifigan.models import Generator + self.dec = Generator(h=hps) + + self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) + self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) + self.f0_decoder = F0Decoder( + 1, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + spk_channels=gin_channels + ) + self.emb_uv = nn.Embedding(2, hidden_channels) + self.character_mix = False + + def EnableCharacterMix(self, n_speakers_map, device): + self.speaker_map = torch.zeros((n_speakers_map, 1, 1, self.gin_channels)).to(device) + for i in range(n_speakers_map): + self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]).to(device)) + self.speaker_map = self.speaker_map.unsqueeze(0).to(device) + self.character_mix = True + + def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None, vol = None): + g = self.emb_g(g).transpose(1,2) + + # vol proj + vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol!=None and self.vol_embedding else 0 + + # ssl prenet + x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) + x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) + vol + + # f0 predict + lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 + norm_lf0 = utils.normalize_f0(lf0, x_mask, uv) + pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) + + # encoder + z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0)) + z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) + + # flow + z_p = self.flow(z, spec_mask, g=g) + z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size) + + # nsf decoder + o = self.dec(z_slice, g=g, f0=pitch_slice) + + return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 + + def infer(self, c, f0, uv, g=None, noice_scale=0.35, seed=52468, predict_f0=False, vol = None): + + if c.device == torch.device("cuda"): + torch.cuda.manual_seed_all(seed) + else: + torch.manual_seed(seed) + + c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) + + if self.character_mix and len(g) > 1: # [N, S] * [S, B, 1, H] + g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] + g = g * self.speaker_map # [N, S, B, 1, H] + g = torch.sum(g, dim=1) # [N, 1, B, 1, H] + g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] + else: + if g.dim() == 1: + g = g.unsqueeze(0) + g = self.emb_g(g).transpose(1, 2) + + x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) + # vol proj + vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol!=None and self.vol_embedding else 0 + + x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) + vol + + if predict_f0: + lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 + norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False) + pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) + f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1) + + z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale) + z = self.flow(z_p, c_mask, g=g, reverse=True) + o = self.dec(z * c_mask, g=g, f0=f0) + return o,f0 + diff --git a/modules/F0Predictor/CrepeF0Predictor.py b/modules/F0Predictor/CrepeF0Predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..e0052881b9b7b3aa373ebf69eb553815a564f610 --- /dev/null +++ b/modules/F0Predictor/CrepeF0Predictor.py @@ -0,0 +1,31 @@ +from modules.F0Predictor.F0Predictor import F0Predictor +from modules.F0Predictor.crepe import CrepePitchExtractor +import torch + +class CrepeF0Predictor(F0Predictor): + def __init__(self,hop_length=512,f0_min=50,f0_max=1100,device=None,sampling_rate=44100,threshold=0.05,model="full"): + self.F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device,threshold=threshold,model=model) + self.hop_length = hop_length + self.f0_min = f0_min + self.f0_max = f0_max + self.device = device + self.threshold = threshold + self.sampling_rate = sampling_rate + + def compute_f0(self,wav,p_len=None): + x = torch.FloatTensor(wav).to(self.device) + if p_len is None: + p_len = x.shape[0]//self.hop_length + else: + assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error" + f0,uv = self.F0Creper(x[None,:].float(),self.sampling_rate,pad_to=p_len) + return f0 + + def compute_f0_uv(self,wav,p_len=None): + x = torch.FloatTensor(wav).to(self.device) + if p_len is None: + p_len = x.shape[0]//self.hop_length + else: + assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error" + f0,uv = self.F0Creper(x[None,:].float(),self.sampling_rate,pad_to=p_len) + return f0,uv \ No newline at end of file diff --git a/modules/F0Predictor/DioF0Predictor.py b/modules/F0Predictor/DioF0Predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..4ab27de23cae4dbc282e30f84501afebd1a37518 --- /dev/null +++ b/modules/F0Predictor/DioF0Predictor.py @@ -0,0 +1,85 @@ +from modules.F0Predictor.F0Predictor import F0Predictor +import pyworld +import numpy as np + +class DioF0Predictor(F0Predictor): + def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100): + self.hop_length = hop_length + self.f0_min = f0_min + self.f0_max = f0_max + self.sampling_rate = sampling_rate + + def interpolate_f0(self,f0): + ''' + 对F0进行插值处理 + ''' + + data = np.reshape(f0, (f0.size, 1)) + + vuv_vector = np.zeros((data.size, 1), dtype=np.float32) + vuv_vector[data > 0.0] = 1.0 + vuv_vector[data <= 0.0] = 0.0 + + ip_data = data + + frame_number = data.size + last_value = 0.0 + for i in range(frame_number): + if data[i] <= 0.0: + j = i + 1 + for j in range(i + 1, frame_number): + if data[j] > 0.0: + break + if j < frame_number - 1: + if last_value > 0.0: + step = (data[j] - data[i - 1]) / float(j - i) + for k in range(i, j): + ip_data[k] = data[i - 1] + step * (k - i + 1) + else: + for k in range(i, j): + ip_data[k] = data[j] + else: + for k in range(i, frame_number): + ip_data[k] = last_value + else: + ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝 + last_value = data[i] + + return ip_data[:,0], vuv_vector[:,0] + + def resize_f0(self,x, target_len): + source = np.array(x) + source[source<0.001] = np.nan + target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source) + res = np.nan_to_num(target) + return res + + def compute_f0(self,wav,p_len=None): + if p_len is None: + p_len = wav.shape[0]//self.hop_length + f0, t = pyworld.dio( + wav.astype(np.double), + fs=self.sampling_rate, + f0_floor=self.f0_min, + f0_ceil=self.f0_max, + frame_period=1000 * self.hop_length / self.sampling_rate, + ) + f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) + for index, pitch in enumerate(f0): + f0[index] = round(pitch, 1) + return self.interpolate_f0(self.resize_f0(f0, p_len))[0] + + def compute_f0_uv(self,wav,p_len=None): + if p_len is None: + p_len = wav.shape[0]//self.hop_length + f0, t = pyworld.dio( + wav.astype(np.double), + fs=self.sampling_rate, + f0_floor=self.f0_min, + f0_ceil=self.f0_max, + frame_period=1000 * self.hop_length / self.sampling_rate, + ) + f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) + for index, pitch in enumerate(f0): + f0[index] = round(pitch, 1) + return self.interpolate_f0(self.resize_f0(f0, p_len)) diff --git a/modules/F0Predictor/F0Predictor.py b/modules/F0Predictor/F0Predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..69d8a9bd28729e33d092a5af8e2ce544c1330c3b --- /dev/null +++ b/modules/F0Predictor/F0Predictor.py @@ -0,0 +1,16 @@ +class F0Predictor(object): + def compute_f0(self,wav,p_len): + ''' + input: wav:[signal_length] + p_len:int + output: f0:[signal_length//hop_length] + ''' + pass + + def compute_f0_uv(self,wav,p_len): + ''' + input: wav:[signal_length] + p_len:int + output: f0:[signal_length//hop_length],uv:[signal_length//hop_length] + ''' + pass \ No newline at end of file diff --git a/modules/F0Predictor/HarvestF0Predictor.py b/modules/F0Predictor/HarvestF0Predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..122bdbb4c736feb4a8d974eca03df71aede76f69 --- /dev/null +++ b/modules/F0Predictor/HarvestF0Predictor.py @@ -0,0 +1,81 @@ +from modules.F0Predictor.F0Predictor import F0Predictor +import pyworld +import numpy as np + +class HarvestF0Predictor(F0Predictor): + def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100): + self.hop_length = hop_length + self.f0_min = f0_min + self.f0_max = f0_max + self.sampling_rate = sampling_rate + + def interpolate_f0(self,f0): + ''' + 对F0进行插值处理 + ''' + + data = np.reshape(f0, (f0.size, 1)) + + vuv_vector = np.zeros((data.size, 1), dtype=np.float32) + vuv_vector[data > 0.0] = 1.0 + vuv_vector[data <= 0.0] = 0.0 + + ip_data = data + + frame_number = data.size + last_value = 0.0 + for i in range(frame_number): + if data[i] <= 0.0: + j = i + 1 + for j in range(i + 1, frame_number): + if data[j] > 0.0: + break + if j < frame_number - 1: + if last_value > 0.0: + step = (data[j] - data[i - 1]) / float(j - i) + for k in range(i, j): + ip_data[k] = data[i - 1] + step * (k - i + 1) + else: + for k in range(i, j): + ip_data[k] = data[j] + else: + for k in range(i, frame_number): + ip_data[k] = last_value + else: + ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝 + last_value = data[i] + + return ip_data[:,0], vuv_vector[:,0] + + def resize_f0(self,x, target_len): + source = np.array(x) + source[source<0.001] = np.nan + target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source) + res = np.nan_to_num(target) + return res + + def compute_f0(self,wav,p_len=None): + if p_len is None: + p_len = wav.shape[0]//self.hop_length + f0, t = pyworld.harvest( + wav.astype(np.double), + fs=self.hop_length, + f0_ceil=self.f0_max, + f0_floor=self.f0_min, + frame_period=1000 * self.hop_length / self.sampling_rate, + ) + f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs) + return self.interpolate_f0(self.resize_f0(f0, p_len))[0] + + def compute_f0_uv(self,wav,p_len=None): + if p_len is None: + p_len = wav.shape[0]//self.hop_length + f0, t = pyworld.harvest( + wav.astype(np.double), + fs=self.sampling_rate, + f0_floor=self.f0_min, + f0_ceil=self.f0_max, + frame_period=1000 * self.hop_length / self.sampling_rate, + ) + f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) + return self.interpolate_f0(self.resize_f0(f0, p_len)) diff --git a/modules/F0Predictor/PMF0Predictor.py b/modules/F0Predictor/PMF0Predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..ccf4128436c5b7e5a3e720d4597bad0c622d0920 --- /dev/null +++ b/modules/F0Predictor/PMF0Predictor.py @@ -0,0 +1,83 @@ +from modules.F0Predictor.F0Predictor import F0Predictor +import parselmouth +import numpy as np + +class PMF0Predictor(F0Predictor): + def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100): + self.hop_length = hop_length + self.f0_min = f0_min + self.f0_max = f0_max + self.sampling_rate = sampling_rate + + + def interpolate_f0(self,f0): + ''' + 对F0进行插值处理 + ''' + + data = np.reshape(f0, (f0.size, 1)) + + vuv_vector = np.zeros((data.size, 1), dtype=np.float32) + vuv_vector[data > 0.0] = 1.0 + vuv_vector[data <= 0.0] = 0.0 + + ip_data = data + + frame_number = data.size + last_value = 0.0 + for i in range(frame_number): + if data[i] <= 0.0: + j = i + 1 + for j in range(i + 1, frame_number): + if data[j] > 0.0: + break + if j < frame_number - 1: + if last_value > 0.0: + step = (data[j] - data[i - 1]) / float(j - i) + for k in range(i, j): + ip_data[k] = data[i - 1] + step * (k - i + 1) + else: + for k in range(i, j): + ip_data[k] = data[j] + else: + for k in range(i, frame_number): + ip_data[k] = last_value + else: + ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝 + last_value = data[i] + + return ip_data[:,0], vuv_vector[:,0] + + def compute_f0(self,wav,p_len=None): + x = wav + if p_len is None: + p_len = x.shape[0]//self.hop_length + else: + assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error" + time_step = self.hop_length / self.sampling_rate * 1000 + f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac( + time_step=time_step / 1000, voicing_threshold=0.6, + pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency'] + + pad_size=(p_len - len(f0) + 1) // 2 + if(pad_size>0 or p_len - len(f0) - pad_size>0): + f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') + f0,uv = self.interpolate_f0(f0) + return f0 + + def compute_f0_uv(self,wav,p_len=None): + x = wav + if p_len is None: + p_len = x.shape[0]//self.hop_length + else: + assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error" + time_step = self.hop_length / self.sampling_rate * 1000 + f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac( + time_step=time_step / 1000, voicing_threshold=0.6, + pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency'] + + pad_size=(p_len - len(f0) + 1) // 2 + if(pad_size>0 or p_len - len(f0) - pad_size>0): + f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant') + f0,uv = self.interpolate_f0(f0) + return f0,uv diff --git a/modules/F0Predictor/__init__.py b/modules/F0Predictor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/F0Predictor/__pycache__/CrepeF0Predictor.cpython-38.pyc b/modules/F0Predictor/__pycache__/CrepeF0Predictor.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..55f9c364cac598befa6bbf6fc1b3c0658b17f7ae Binary files /dev/null and b/modules/F0Predictor/__pycache__/CrepeF0Predictor.cpython-38.pyc differ diff --git a/modules/F0Predictor/__pycache__/DioF0Predictor.cpython-38.pyc b/modules/F0Predictor/__pycache__/DioF0Predictor.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..55dff5c936f460fddf8c8d6701d14524067924ea Binary files /dev/null and b/modules/F0Predictor/__pycache__/DioF0Predictor.cpython-38.pyc differ diff --git a/modules/F0Predictor/__pycache__/F0Predictor.cpython-38.pyc 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typing_extensions import Literal +import numpy as np +import torch +import torchcrepe +from torch import nn +from torch.nn import functional as F +import scipy + +#from:https://github.com/fishaudio/fish-diffusion + +def repeat_expand( + content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest" +): + """Repeat content to target length. + This is a wrapper of torch.nn.functional.interpolate. + + Args: + content (torch.Tensor): tensor + target_len (int): target length + mode (str, optional): interpolation mode. Defaults to "nearest". + + Returns: + torch.Tensor: tensor + """ + + ndim = content.ndim + + if content.ndim == 1: + content = content[None, None] + elif content.ndim == 2: + content = content[None] + + assert content.ndim == 3 + + is_np = isinstance(content, np.ndarray) + if is_np: + content = torch.from_numpy(content) + + results = torch.nn.functional.interpolate(content, size=target_len, mode=mode) + + if is_np: + results = results.numpy() + + if ndim == 1: + return results[0, 0] + elif ndim == 2: + return results[0] + + +class BasePitchExtractor: + def __init__( + self, + hop_length: int = 512, + f0_min: float = 50.0, + f0_max: float = 1100.0, + keep_zeros: bool = True, + ): + """Base pitch extractor. + + Args: + hop_length (int, optional): Hop length. Defaults to 512. + f0_min (float, optional): Minimum f0. Defaults to 50.0. + f0_max (float, optional): Maximum f0. Defaults to 1100.0. + keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True. + """ + + self.hop_length = hop_length + self.f0_min = f0_min + self.f0_max = f0_max + self.keep_zeros = keep_zeros + + def __call__(self, x, sampling_rate=44100, pad_to=None): + raise NotImplementedError("BasePitchExtractor is not callable.") + + def post_process(self, x, sampling_rate, f0, pad_to): + if isinstance(f0, np.ndarray): + f0 = torch.from_numpy(f0).float().to(x.device) + + if pad_to is None: + return f0 + + f0 = repeat_expand(f0, pad_to) + + if self.keep_zeros: + return f0 + + vuv_vector = torch.zeros_like(f0) + vuv_vector[f0 > 0.0] = 1.0 + vuv_vector[f0 <= 0.0] = 0.0 + + # 去掉0频率, 并线性插值 + nzindex = torch.nonzero(f0).squeeze() + f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy() + time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy() + time_frame = np.arange(pad_to) * self.hop_length / sampling_rate + + if f0.shape[0] <= 0: + return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device) + + if f0.shape[0] == 1: + return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device) + + # 大概可以用 torch 重写? + f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1]) + vuv_vector = vuv_vector.cpu().numpy() + vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0)) + + return f0,vuv_vector + + +class MaskedAvgPool1d(nn.Module): + def __init__( + self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0 + ): + """An implementation of mean pooling that supports masked values. + + Args: + kernel_size (int): The size of the median pooling window. + stride (int, optional): The stride of the median pooling window. Defaults to None. + padding (int, optional): The padding of the median pooling window. Defaults to 0. + """ + + super(MaskedAvgPool1d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride or kernel_size + self.padding = padding + + def forward(self, x, mask=None): + ndim = x.dim() + if ndim == 2: + x = x.unsqueeze(1) + + assert ( + x.dim() == 3 + ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)" + + # Apply the mask by setting masked elements to zero, or make NaNs zero + if mask is None: + mask = ~torch.isnan(x) + + # Ensure mask has the same shape as the input tensor + assert x.shape == mask.shape, "Input tensor and mask must have the same shape" + + masked_x = torch.where(mask, x, torch.zeros_like(x)) + # Create a ones kernel with the same number of channels as the input tensor + ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device) + + # Perform sum pooling + sum_pooled = nn.functional.conv1d( + masked_x, + ones_kernel, + stride=self.stride, + padding=self.padding, + groups=x.size(1), + ) + + # Count the non-masked (valid) elements in each pooling window + valid_count = nn.functional.conv1d( + mask.float(), + ones_kernel, + stride=self.stride, + padding=self.padding, + groups=x.size(1), + ) + valid_count = valid_count.clamp(min=1) # Avoid division by zero + + # Perform masked average pooling + avg_pooled = sum_pooled / valid_count + + # Fill zero values with NaNs + avg_pooled[avg_pooled == 0] = float("nan") + + if ndim == 2: + return avg_pooled.squeeze(1) + + return avg_pooled + + +class MaskedMedianPool1d(nn.Module): + def __init__( + self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0 + ): + """An implementation of median pooling that supports masked values. + + This implementation is inspired by the median pooling implementation in + https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598 + + Args: + kernel_size (int): The size of the median pooling window. + stride (int, optional): The stride of the median pooling window. Defaults to None. + padding (int, optional): The padding of the median pooling window. Defaults to 0. + """ + + super(MaskedMedianPool1d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride or kernel_size + self.padding = padding + + def forward(self, x, mask=None): + ndim = x.dim() + if ndim == 2: + x = x.unsqueeze(1) + + assert ( + x.dim() == 3 + ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)" + + if mask is None: + mask = ~torch.isnan(x) + + assert x.shape == mask.shape, "Input tensor and mask must have the same shape" + + masked_x = torch.where(mask, x, torch.zeros_like(x)) + + x = F.pad(masked_x, (self.padding, self.padding), mode="reflect") + mask = F.pad( + mask.float(), (self.padding, self.padding), mode="constant", value=0 + ) + + x = x.unfold(2, self.kernel_size, self.stride) + mask = mask.unfold(2, self.kernel_size, self.stride) + + x = x.contiguous().view(x.size()[:3] + (-1,)) + mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device) + + # Combine the mask with the input tensor + #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf"))) + x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device)) + + # Sort the masked tensor along the last dimension + x_sorted, _ = torch.sort(x_masked, dim=-1) + + # Compute the count of non-masked (valid) values + valid_count = mask.sum(dim=-1) + + # Calculate the index of the median value for each pooling window + median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0) + + # Gather the median values using the calculated indices + median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1) + + # Fill infinite values with NaNs + median_pooled[torch.isinf(median_pooled)] = float("nan") + + if ndim == 2: + return median_pooled.squeeze(1) + + return median_pooled + + +class CrepePitchExtractor(BasePitchExtractor): + def __init__( + self, + hop_length: int = 512, + f0_min: float = 50.0, + f0_max: float = 1100.0, + threshold: float = 0.05, + keep_zeros: bool = False, + device = None, + model: Literal["full", "tiny"] = "full", + use_fast_filters: bool = True, + decoder="viterbi" + ): + super().__init__(hop_length, f0_min, f0_max, keep_zeros) + if decoder == "viterbi": + self.decoder = torchcrepe.decode.viterbi + elif decoder == "argmax": + self.decoder = torchcrepe.decode.argmax + elif decoder == "weighted_argmax": + self.decoder = torchcrepe.decode.weighted_argmax + else: + raise "Unknown decoder" + self.threshold = threshold + self.model = model + self.use_fast_filters = use_fast_filters + self.hop_length = hop_length + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + if self.use_fast_filters: + self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device) + self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device) + + def __call__(self, x, sampling_rate=44100, pad_to=None): + """Extract pitch using crepe. + + + Args: + x (torch.Tensor): Audio signal, shape (1, T). + sampling_rate (int, optional): Sampling rate. Defaults to 44100. + pad_to (int, optional): Pad to length. Defaults to None. + + Returns: + torch.Tensor: Pitch, shape (T // hop_length,). + """ + + assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor." + assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels." + + x = x.to(self.dev) + f0, pd = torchcrepe.predict( + x, + sampling_rate, + self.hop_length, + self.f0_min, + self.f0_max, + pad=True, + model=self.model, + batch_size=1024, + device=x.device, + return_periodicity=True, + decoder=self.decoder + ) + + # Filter, remove silence, set uv threshold, refer to the original warehouse readme + if self.use_fast_filters: + pd = self.median_filter(pd) + else: + pd = torchcrepe.filter.median(pd, 3) + + pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512) + f0 = torchcrepe.threshold.At(self.threshold)(f0, pd) + + if self.use_fast_filters: + f0 = self.mean_filter(f0) + else: + f0 = torchcrepe.filter.mean(f0, 3) + + f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0] + + if torch.all(f0 == 0): + rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to) + return rtn,rtn + + return self.post_process(x, sampling_rate, f0, pad_to) diff --git a/modules/__init__.py b/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/__pycache__/__init__.cpython-38.pyc b/modules/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a6c2500dad02ff1867d09d5c801b88bea60f9f8d Binary files /dev/null and b/modules/__pycache__/__init__.cpython-38.pyc differ diff --git a/modules/__pycache__/__init__.cpython-39.pyc b/modules/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7732e52a91874300687605f997a74151a26fbf37 Binary files /dev/null and b/modules/__pycache__/__init__.cpython-39.pyc differ diff --git a/modules/__pycache__/__pycache__.rar b/modules/__pycache__/__pycache__.rar new file mode 100644 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100644 index 0000000000000000000000000000000000000000..f9c11ca4a3acb86bf1abc04d9dcfa82a4ed4061f --- /dev/null +++ b/modules/attentions.py @@ -0,0 +1,349 @@ +import copy +import math +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +import modules.commons as commons +import modules.modules as modules +from modules.modules import LayerNorm + + +class FFT(nn.Module): + def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0., + proximal_bias=False, proximal_init=True, **kwargs): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + + self.drop = nn.Dropout(p_dropout) + self.self_attn_layers = nn.ModuleList() + self.norm_layers_0 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + for i in range(self.n_layers): + self.self_attn_layers.append( + MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, + proximal_init=proximal_init)) + self.norm_layers_0.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + """ + x: decoder input + h: encoder output + """ + self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) + x = x * x_mask + for i in range(self.n_layers): + y = self.self_attn_layers[i](x, x, self_attn_mask) + y = self.drop(y) + x = self.norm_layers_0[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + x = x * x_mask + return x + + +class Encoder(nn.Module): + def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + + self.drop = nn.Dropout(p_dropout) + self.attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + x = x * x_mask + for i in range(self.n_layers): + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class Decoder(nn.Module): + def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + + self.drop = nn.Dropout(p_dropout) + self.self_attn_layers = nn.ModuleList() + self.norm_layers_0 = nn.ModuleList() + self.encdec_attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init)) + self.norm_layers_0.append(LayerNorm(hidden_channels)) + self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True)) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask, h, h_mask): + """ + x: decoder input + h: encoder output + """ + self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype) + encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + x = x * x_mask + for i in range(self.n_layers): + y = self.self_attn_layers[i](x, x, self_attn_mask) + y = self.drop(y) + x = self.norm_layers_0[i](x + y) + + y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class MultiHeadAttention(nn.Module): + def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.p_dropout = p_dropout + self.window_size = window_size + self.heads_share = heads_share + self.block_length = block_length + self.proximal_bias = proximal_bias + self.proximal_init = proximal_init + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = nn.Conv1d(channels, channels, 1) + self.conv_k = nn.Conv1d(channels, channels, 1) + self.conv_v = nn.Conv1d(channels, channels, 1) + self.conv_o = nn.Conv1d(channels, out_channels, 1) + self.drop = nn.Dropout(p_dropout) + + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels**-0.5 + self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) + self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) + + nn.init.xavier_uniform_(self.conv_q.weight) + nn.init.xavier_uniform_(self.conv_k.weight) + nn.init.xavier_uniform_(self.conv_v.weight) + if proximal_init: + with torch.no_grad(): + self.conv_k.weight.copy_(self.conv_q.weight) + self.conv_k.bias.copy_(self.conv_q.bias) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + # reshape [b, d, t] -> [b, n_h, t, d_k] + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + + scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) + if self.window_size is not None: + assert t_s == t_t, "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings) + scores_local = self._relative_position_to_absolute_position(rel_logits) + scores = scores + scores_local + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + if self.block_length is not None: + assert t_s == t_t, "Local attention is only available for self-attention." + block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) + scores = scores.masked_fill(block_mask == 0, -1e4) + p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) + output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] + return output, p_attn + + def _matmul_with_relative_values(self, x, y): + """ + x: [b, h, l, m] + y: [h or 1, m, d] + ret: [b, h, l, d] + """ + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + """ + x: [b, h, l, d] + y: [h or 1, m, d] + ret: [b, h, l, m] + """ + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + max_relative_position = 2 * self.window_size + 1 + # Pad first before slice to avoid using cond ops. + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = F.pad( + relative_embeddings, + commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + """ + x: [b, h, l, 2*l-1] + ret: [b, h, l, l] + """ + batch, heads, length, _ = x.size() + # Concat columns of pad to shift from relative to absolute indexing. + x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) + + # Concat extra elements so to add up to shape (len+1, 2*len-1). + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]])) + + # Reshape and slice out the padded elements. + x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] + return x_final + + def _absolute_position_to_relative_position(self, x): + """ + x: [b, h, l, l] + ret: [b, h, l, 2*l-1] + """ + batch, heads, length, _ = x.size() + # padd along column + x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) + x_flat = x.view([batch, heads, length**2 + length*(length -1)]) + # add 0's in the beginning that will skew the elements after reshape + x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) + x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] + return x_final + + def _attention_bias_proximal(self, length): + """Bias for self-attention to encourage attention to close positions. + Args: + length: an integer scalar. + Returns: + a Tensor with shape [1, 1, length, length] + """ + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.activation = activation + self.causal = causal + + if causal: + self.padding = self._causal_padding + else: + self.padding = self._same_padding + + self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) + self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) + self.drop = nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(self.padding(x * x_mask)) + if self.activation == "gelu": + x = x * torch.sigmoid(1.702 * x) + else: + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(self.padding(x * x_mask)) + return x * x_mask + + def _causal_padding(self, x): + if self.kernel_size == 1: + return x + pad_l = self.kernel_size - 1 + pad_r = 0 + padding = [[0, 0], [0, 0], [pad_l, pad_r]] + x = F.pad(x, commons.convert_pad_shape(padding)) + return x + + def _same_padding(self, x): + if self.kernel_size == 1: + return x + pad_l = (self.kernel_size - 1) // 2 + pad_r = self.kernel_size // 2 + padding = [[0, 0], [0, 0], [pad_l, pad_r]] + x = F.pad(x, commons.convert_pad_shape(padding)) + return x diff --git a/modules/commons.py b/modules/commons.py new file mode 100644 index 0000000000000000000000000000000000000000..074888006392e956ce204d8368362dbb2cd4e304 --- /dev/null +++ b/modules/commons.py @@ -0,0 +1,188 @@ +import math +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +def slice_pitch_segments(x, ids_str, segment_size=4): + ret = torch.zeros_like(x[:, :segment_size]) + for i in range(x.size(0)): + idx_str = ids_str[i] + idx_end = idx_str + segment_size + ret[i] = x[i, idx_str:idx_end] + return ret + +def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4): + b, d, t = x.size() + if x_lengths is None: + x_lengths = t + ids_str_max = x_lengths - segment_size + 1 + ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) + ret = slice_segments(x, ids_str, segment_size) + ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size) + return ret, ret_pitch, ids_str + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def intersperse(lst, item): + result = [item] * (len(lst) * 2 + 1) + result[1::2] = lst + return result + + +def kl_divergence(m_p, logs_p, m_q, logs_q): + """KL(P||Q)""" + kl = (logs_q - logs_p) - 0.5 + kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) + return kl + + +def rand_gumbel(shape): + """Sample from the Gumbel distribution, protect from overflows.""" + uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 + return -torch.log(-torch.log(uniform_samples)) + + +def rand_gumbel_like(x): + g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) + return g + + +def slice_segments(x, ids_str, segment_size=4): + ret = torch.zeros_like(x[:, :, :segment_size]) + for i in range(x.size(0)): + idx_str = ids_str[i] + idx_end = idx_str + segment_size + ret[i] = x[i, :, idx_str:idx_end] + return ret + + +def rand_slice_segments(x, x_lengths=None, segment_size=4): + b, d, t = x.size() + if x_lengths is None: + x_lengths = t + ids_str_max = x_lengths - segment_size + 1 + ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) + ret = slice_segments(x, ids_str, segment_size) + return ret, ids_str + + +def rand_spec_segments(x, x_lengths=None, segment_size=4): + b, d, t = x.size() + if x_lengths is None: + x_lengths = t + ids_str_max = x_lengths - segment_size + ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) + ret = slice_segments(x, ids_str, segment_size) + return ret, ids_str + + +def get_timing_signal_1d( + length, channels, min_timescale=1.0, max_timescale=1.0e4): + position = torch.arange(length, dtype=torch.float) + num_timescales = channels // 2 + log_timescale_increment = ( + math.log(float(max_timescale) / float(min_timescale)) / + (num_timescales - 1)) + inv_timescales = min_timescale * torch.exp( + torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) + scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) + signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) + signal = F.pad(signal, [0, 0, 0, channels % 2]) + signal = signal.view(1, channels, length) + return signal + + +def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): + b, channels, length = x.size() + signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) + return x + signal.to(dtype=x.dtype, device=x.device) + + +def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): + b, channels, length = x.size() + signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) + return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) + + +def subsequent_mask(length): + mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) + return mask + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def shift_1d(x): + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] + return x + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def generate_path(duration, mask): + """ + duration: [b, 1, t_x] + mask: [b, 1, t_y, t_x] + """ + device = duration.device + + b, _, t_y, t_x = mask.shape + cum_duration = torch.cumsum(duration, -1) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path.unsqueeze(1).transpose(2,3) * mask + return path + + +def clip_grad_value_(parameters, clip_value, norm_type=2): + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + parameters = list(filter(lambda p: p.grad is not None, parameters)) + norm_type = float(norm_type) + if clip_value is not None: + clip_value = float(clip_value) + + total_norm = 0 + for p in parameters: + param_norm = p.grad.data.norm(norm_type) + total_norm += param_norm.item() ** norm_type + if clip_value is not None: + p.grad.data.clamp_(min=-clip_value, max=clip_value) + total_norm = total_norm ** (1. / norm_type) + return total_norm diff --git a/modules/enhancer.py b/modules/enhancer.py new file mode 100644 index 0000000000000000000000000000000000000000..37676311f7d8dc4ddc2a5244dedc27b2437e04f5 --- /dev/null +++ b/modules/enhancer.py @@ -0,0 +1,105 @@ +import numpy as np +import torch +import torch.nn.functional as F +from vdecoder.nsf_hifigan.nvSTFT import STFT +from vdecoder.nsf_hifigan.models import load_model +from torchaudio.transforms import Resample + +class Enhancer: + def __init__(self, enhancer_type, enhancer_ckpt, device=None): + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = device + + if enhancer_type == 'nsf-hifigan': + self.enhancer = NsfHifiGAN(enhancer_ckpt, device=self.device) + else: + raise ValueError(f" [x] Unknown enhancer: {enhancer_type}") + + self.resample_kernel = {} + self.enhancer_sample_rate = self.enhancer.sample_rate() + self.enhancer_hop_size = self.enhancer.hop_size() + + def enhance(self, + audio, # 1, T + sample_rate, + f0, # 1, n_frames, 1 + hop_size, + adaptive_key = 0, + silence_front = 0 + ): + # enhancer start time + start_frame = int(silence_front * sample_rate / hop_size) + real_silence_front = start_frame * hop_size / sample_rate + audio = audio[:, int(np.round(real_silence_front * sample_rate)) : ] + f0 = f0[: , start_frame :, :] + + # adaptive parameters + adaptive_factor = 2 ** ( -adaptive_key / 12) + adaptive_sample_rate = 100 * int(np.round(self.enhancer_sample_rate / adaptive_factor / 100)) + real_factor = self.enhancer_sample_rate / adaptive_sample_rate + + # resample the ddsp output + if sample_rate == adaptive_sample_rate: + audio_res = audio + else: + key_str = str(sample_rate) + str(adaptive_sample_rate) + if key_str not in self.resample_kernel: + self.resample_kernel[key_str] = Resample(sample_rate, adaptive_sample_rate, lowpass_filter_width = 128).to(self.device) + audio_res = self.resample_kernel[key_str](audio) + + n_frames = int(audio_res.size(-1) // self.enhancer_hop_size + 1) + + # resample f0 + f0_np = f0.squeeze(0).squeeze(-1).cpu().numpy() + f0_np *= real_factor + time_org = (hop_size / sample_rate) * np.arange(len(f0_np)) / real_factor + time_frame = (self.enhancer_hop_size / self.enhancer_sample_rate) * np.arange(n_frames) + f0_res = np.interp(time_frame, time_org, f0_np, left=f0_np[0], right=f0_np[-1]) + f0_res = torch.from_numpy(f0_res).unsqueeze(0).float().to(self.device) # 1, n_frames + + # enhance + enhanced_audio, enhancer_sample_rate = self.enhancer(audio_res, f0_res) + + # resample the enhanced output + if adaptive_factor != 0: + key_str = str(adaptive_sample_rate) + str(enhancer_sample_rate) + if key_str not in self.resample_kernel: + self.resample_kernel[key_str] = Resample(adaptive_sample_rate, enhancer_sample_rate, lowpass_filter_width = 128).to(self.device) + enhanced_audio = self.resample_kernel[key_str](enhanced_audio) + + # pad the silence frames + if start_frame > 0: + enhanced_audio = F.pad(enhanced_audio, (int(np.round(enhancer_sample_rate * real_silence_front)), 0)) + + return enhanced_audio, enhancer_sample_rate + + +class NsfHifiGAN(torch.nn.Module): + def __init__(self, model_path, device=None): + super().__init__() + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = device + print('| Load HifiGAN: ', model_path) + self.model, self.h = load_model(model_path, device=self.device) + + def sample_rate(self): + return self.h.sampling_rate + + def hop_size(self): + return self.h.hop_size + + def forward(self, audio, f0): + stft = STFT( + self.h.sampling_rate, + self.h.num_mels, + self.h.n_fft, + self.h.win_size, + self.h.hop_size, + self.h.fmin, + self.h.fmax) + with torch.no_grad(): + mel = stft.get_mel(audio) + enhanced_audio = self.model(mel, f0[:,:mel.size(-1)]).view(-1) + return enhanced_audio, self.h.sampling_rate \ No newline at end of file diff --git a/modules/losses.py b/modules/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..cd21799eccde350c3aac0bdd661baf96ed220147 --- /dev/null +++ b/modules/losses.py @@ -0,0 +1,61 @@ +import torch +from torch.nn import functional as F + +import modules.commons as commons + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + rl = rl.float().detach() + gl = gl.float() + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + dr = dr.float() + dg = dg.float() + r_loss = torch.mean((1-dr)**2) + g_loss = torch.mean(dg**2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + dg = dg.float() + l = torch.mean((1-dg)**2) + gen_losses.append(l) + loss += l + + return loss, gen_losses + + +def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): + """ + z_p, logs_q: [b, h, t_t] + m_p, logs_p: [b, h, t_t] + """ + z_p = z_p.float() + logs_q = logs_q.float() + m_p = m_p.float() + logs_p = logs_p.float() + z_mask = z_mask.float() + #print(logs_p) + kl = logs_p - logs_q - 0.5 + kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p) + kl = torch.sum(kl * z_mask) + l = kl / torch.sum(z_mask) + return l diff --git a/modules/mel_processing.py b/modules/mel_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..99c5b35beb83f3b288af0fac5b49ebf2c69f062c --- /dev/null +++ b/modules/mel_processing.py @@ -0,0 +1,112 @@ +import math +import os +import random +import torch +from torch import nn +import torch.nn.functional as F +import torch.utils.data +import numpy as np +import librosa +import librosa.util as librosa_util +from librosa.util import normalize, pad_center, tiny +from scipy.signal import get_window +from scipy.io.wavfile import read +from librosa.filters import mel as librosa_mel_fn + +MAX_WAV_VALUE = 32768.0 + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global hann_window + dtype_device = str(y.dtype) + '_' + str(y.device) + wnsize_dtype_device = str(win_size) + '_' + dtype_device + if wnsize_dtype_device not in hann_window: + hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + return spec + + +def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): + global mel_basis + dtype_device = str(spec.dtype) + '_' + str(spec.device) + fmax_dtype_device = str(fmax) + '_' + dtype_device + if fmax_dtype_device not in mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) + spec = torch.matmul(mel_basis[fmax_dtype_device], spec) + spec = spectral_normalize_torch(spec) + return spec + + +def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global mel_basis, hann_window + dtype_device = str(y.dtype) + '_' + str(y.device) + fmax_dtype_device = str(fmax) + '_' + dtype_device + wnsize_dtype_device = str(win_size) + '_' + dtype_device + if fmax_dtype_device not in mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) + if wnsize_dtype_device not in hann_window: + hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + + spec = torch.matmul(mel_basis[fmax_dtype_device], spec) + spec = spectral_normalize_torch(spec) + + return spec diff --git a/modules/modules.py b/modules/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..54290fd207b25e93831bd21005990ea137e6b50e --- /dev/null +++ b/modules/modules.py @@ -0,0 +1,342 @@ +import copy +import math +import numpy as np +import scipy +import torch +from torch import nn +from torch.nn import functional as F + +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm + +import modules.commons as commons +from modules.commons import init_weights, get_padding + + +LRELU_SLOPE = 0.1 + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-5): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + x = x.transpose(1, -1) + x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) + return x.transpose(1, -1) + + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + assert n_layers > 1, "Number of layers should be larger than 0." + + self.conv_layers = nn.ModuleList() + self.norm_layers = nn.ModuleList() + self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = nn.Sequential( + nn.ReLU(), + nn.Dropout(p_dropout)) + for _ in range(n_layers-1): + self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + +class DDSConv(nn.Module): + """ + Dialted and Depth-Separable Convolution + """ + def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): + super().__init__() + self.channels = channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + + self.drop = nn.Dropout(p_dropout) + self.convs_sep = nn.ModuleList() + self.convs_1x1 = nn.ModuleList() + self.norms_1 = nn.ModuleList() + self.norms_2 = nn.ModuleList() + for i in range(n_layers): + dilation = kernel_size ** i + padding = (kernel_size * dilation - dilation) // 2 + self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, + groups=channels, dilation=dilation, padding=padding + )) + self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) + self.norms_1.append(LayerNorm(channels)) + self.norms_2.append(LayerNorm(channels)) + + def forward(self, x, x_mask, g=None): + if g is not None: + x = x + g + for i in range(self.n_layers): + y = self.convs_sep[i](x * x_mask) + y = self.norms_1[i](y) + y = F.gelu(y) + y = self.convs_1x1[i](y) + y = self.norms_2[i](y) + y = F.gelu(y) + y = self.drop(y) + x = x + y + return x * x_mask + + +class WN(torch.nn.Module): + def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): + super(WN, self).__init__() + assert(kernel_size % 2 == 1) + self.hidden_channels =hidden_channels + self.kernel_size = kernel_size, + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + self.p_dropout = p_dropout + + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + self.drop = nn.Dropout(p_dropout) + + if gin_channels != 0: + cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + + for i in range(n_layers): + dilation = dilation_rate ** i + padding = int((kernel_size * dilation - dilation) / 2) + in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, + dilation=dilation, padding=padding) + in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') + self.in_layers.append(in_layer) + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2 * hidden_channels + else: + res_skip_channels = hidden_channels + + res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') + self.res_skip_layers.append(res_skip_layer) + + def forward(self, x, x_mask, g=None, **kwargs): + output = torch.zeros_like(x) + n_channels_tensor = torch.IntTensor([self.hidden_channels]) + + if g is not None: + g = self.cond_layer(g) + + for i in range(self.n_layers): + x_in = self.in_layers[i](x) + if g is not None: + cond_offset = i * 2 * self.hidden_channels + g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] + else: + g_l = torch.zeros_like(x_in) + + acts = commons.fused_add_tanh_sigmoid_multiply( + x_in, + g_l, + n_channels_tensor) + acts = self.drop(acts) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + res_acts = res_skip_acts[:,:self.hidden_channels,:] + x = (x + res_acts) * x_mask + output = output + res_skip_acts[:,self.hidden_channels:,:] + else: + output = output + res_skip_acts + return output * x_mask + + def remove_weight_norm(self): + if self.gin_channels != 0: + torch.nn.utils.remove_weight_norm(self.cond_layer) + for l in self.in_layers: + torch.nn.utils.remove_weight_norm(l) + for l in self.res_skip_layers: + torch.nn.utils.remove_weight_norm(l) + + +class ResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x, x_mask=None): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c2(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x, x_mask=None): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + if x_mask is not None: + xt = xt * x_mask + xt = c(xt) + x = xt + x + if x_mask is not None: + x = x * x_mask + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Log(nn.Module): + def forward(self, x, x_mask, reverse=False, **kwargs): + if not reverse: + y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask + logdet = torch.sum(-y, [1, 2]) + return y, logdet + else: + x = torch.exp(x) * x_mask + return x + + +class Flip(nn.Module): + def forward(self, x, *args, reverse=False, **kwargs): + x = torch.flip(x, [1]) + if not reverse: + logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) + return x, logdet + else: + return x + + +class ElementwiseAffine(nn.Module): + def __init__(self, channels): + super().__init__() + self.channels = channels + self.m = nn.Parameter(torch.zeros(channels,1)) + self.logs = nn.Parameter(torch.zeros(channels,1)) + + def forward(self, x, x_mask, reverse=False, **kwargs): + if not reverse: + y = self.m + torch.exp(self.logs) * x + y = y * x_mask + logdet = torch.sum(self.logs * x_mask, [1,2]) + return y, logdet + else: + x = (x - self.m) * torch.exp(-self.logs) * x_mask + return x + + +class ResidualCouplingLayer(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + p_dropout=0, + gin_channels=0, + mean_only=False): + assert channels % 2 == 0, "channels should be divisible by 2" + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.half_channels = channels // 2 + self.mean_only = mean_only + + self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) + self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) + self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) + self.post.weight.data.zero_() + self.post.bias.data.zero_() + + def forward(self, x, x_mask, g=None, reverse=False): + x0, x1 = torch.split(x, [self.half_channels]*2, 1) + h = self.pre(x0) * x_mask + h = self.enc(h, x_mask, g=g) + stats = self.post(h) * x_mask + if not self.mean_only: + m, logs = torch.split(stats, [self.half_channels]*2, 1) + else: + m = stats + logs = torch.zeros_like(m) + + if not reverse: + x1 = m + x1 * torch.exp(logs) * x_mask + x = torch.cat([x0, x1], 1) + logdet = torch.sum(logs, [1,2]) + return x, logdet + else: + x1 = (x1 - m) * torch.exp(-logs) * x_mask + x = torch.cat([x0, x1], 1) + return x diff --git a/modules/slicer2.py b/modules/slicer2.py new file mode 100644 index 0000000000000000000000000000000000000000..606b07be14eb9769b10a9a8f78cc1580334a2076 --- /dev/null +++ b/modules/slicer2.py @@ -0,0 +1,186 @@ +import numpy as np + + +# This function is obtained from librosa. +def get_rms( + y, + *, + frame_length=2048, + hop_length=512, + pad_mode="constant", +): + padding = (int(frame_length // 2), int(frame_length // 2)) + y = np.pad(y, padding, mode=pad_mode) + + axis = -1 + # put our new within-frame axis at the end for now + out_strides = y.strides + tuple([y.strides[axis]]) + # Reduce the shape on the framing axis + x_shape_trimmed = list(y.shape) + x_shape_trimmed[axis] -= frame_length - 1 + out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) + xw = np.lib.stride_tricks.as_strided( + y, shape=out_shape, strides=out_strides + ) + if axis < 0: + target_axis = axis - 1 + else: + target_axis = axis + 1 + xw = np.moveaxis(xw, -1, target_axis) + # Downsample along the target axis + slices = [slice(None)] * xw.ndim + slices[axis] = slice(0, None, hop_length) + x = xw[tuple(slices)] + + # Calculate power + power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) + + return np.sqrt(power) + + +class Slicer: + def __init__(self, + sr: int, + threshold: float = -40., + min_length: int = 5000, + min_interval: int = 300, + hop_size: int = 20, + max_sil_kept: int = 5000): + if not min_length >= min_interval >= hop_size: + raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size') + if not max_sil_kept >= hop_size: + raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size') + min_interval = sr * min_interval / 1000 + self.threshold = 10 ** (threshold / 20.) + self.hop_size = round(sr * hop_size / 1000) + self.win_size = min(round(min_interval), 4 * self.hop_size) + self.min_length = round(sr * min_length / 1000 / self.hop_size) + self.min_interval = round(min_interval / self.hop_size) + self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) + + def _apply_slice(self, waveform, begin, end): + if len(waveform.shape) > 1: + return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)] + else: + return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)] + + # @timeit + def slice(self, waveform): + if len(waveform.shape) > 1: + samples = waveform.mean(axis=0) + else: + samples = waveform + if samples.shape[0] <= self.min_length: + return [waveform] + rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) + sil_tags = [] + silence_start = None + clip_start = 0 + for i, rms in enumerate(rms_list): + # Keep looping while frame is silent. + if rms < self.threshold: + # Record start of silent frames. + if silence_start is None: + silence_start = i + continue + # Keep looping while frame is not silent and silence start has not been recorded. + if silence_start is None: + continue + # Clear recorded silence start if interval is not enough or clip is too short + is_leading_silence = silence_start == 0 and i > self.max_sil_kept + need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length + if not is_leading_silence and not need_slice_middle: + silence_start = None + continue + # Need slicing. Record the range of silent frames to be removed. + if i - silence_start <= self.max_sil_kept: + pos = rms_list[silence_start: i + 1].argmin() + silence_start + if silence_start == 0: + sil_tags.append((0, pos)) + else: + sil_tags.append((pos, pos)) + clip_start = pos + elif i - silence_start <= self.max_sil_kept * 2: + pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin() + pos += i - self.max_sil_kept + pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start + pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept + if silence_start == 0: + sil_tags.append((0, pos_r)) + clip_start = pos_r + else: + sil_tags.append((min(pos_l, pos), max(pos_r, pos))) + clip_start = max(pos_r, pos) + else: + pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start + pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept + if silence_start == 0: + sil_tags.append((0, pos_r)) + else: + sil_tags.append((pos_l, pos_r)) + clip_start = pos_r + silence_start = None + # Deal with trailing silence. + total_frames = rms_list.shape[0] + if silence_start is not None and total_frames - silence_start >= self.min_interval: + silence_end = min(total_frames, silence_start + self.max_sil_kept) + pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start + sil_tags.append((pos, total_frames + 1)) + # Apply and return slices. + if len(sil_tags) == 0: + return [waveform] + else: + chunks = [] + if sil_tags[0][0] > 0: + chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0])) + for i in range(len(sil_tags) - 1): + chunks.append(self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0])) + if sil_tags[-1][1] < total_frames: + chunks.append(self._apply_slice(waveform, sil_tags[-1][1], total_frames)) + return chunks + + +def main(): + import os.path + from argparse import ArgumentParser + + import librosa + import soundfile + + parser = ArgumentParser() + parser.add_argument('audio', type=str, help='The audio to be sliced') + parser.add_argument('--out', type=str, help='Output directory of the sliced audio clips') + parser.add_argument('--db_thresh', type=float, required=False, default=-40, + help='The dB threshold for silence detection') + parser.add_argument('--min_length', type=int, required=False, default=5000, + help='The minimum milliseconds required for each sliced audio clip') + parser.add_argument('--min_interval', type=int, required=False, default=300, + help='The minimum milliseconds for a silence part to be sliced') + parser.add_argument('--hop_size', type=int, required=False, default=10, + help='Frame length in milliseconds') + parser.add_argument('--max_sil_kept', type=int, required=False, default=500, + help='The maximum silence length kept around the sliced clip, presented in milliseconds') + args = parser.parse_args() + out = args.out + if out is None: + out = os.path.dirname(os.path.abspath(args.audio)) + audio, sr = librosa.load(args.audio, sr=None, mono=False) + slicer = Slicer( + sr=sr, + threshold=args.db_thresh, + min_length=args.min_length, + min_interval=args.min_interval, + hop_size=args.hop_size, + max_sil_kept=args.max_sil_kept + ) + chunks = slicer.slice(audio) + if not os.path.exists(out): + os.makedirs(out) + for i, chunk in enumerate(chunks): + if len(chunk.shape) > 1: + chunk = chunk.T + soundfile.write(os.path.join(out, f'%s_%d.wav' % (os.path.basename(args.audio).rsplit('.', maxsplit=1)[0], i)), chunk, sr) + + +if __name__ == '__main__': + main() diff --git a/onnx_export.py b/onnx_export.py new file mode 100644 index 0000000000000000000000000000000000000000..a70a912cc1b6dd908ff6496bbc6fa8dd576e233b --- /dev/null +++ b/onnx_export.py @@ -0,0 +1,54 @@ +import torch +from onnxexport.model_onnx import SynthesizerTrn +import utils + +def main(NetExport): + path = "SoVits4.0" + if NetExport: + device = torch.device("cpu") + hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json") + SVCVITS = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + **hps.model) + _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None) + _ = SVCVITS.eval().to(device) + for i in SVCVITS.parameters(): + i.requires_grad = False + + n_frame = 10 + test_hidden_unit = torch.rand(1, n_frame, 256) + test_pitch = torch.rand(1, n_frame) + test_mel2ph = torch.arange(0, n_frame, dtype=torch.int64)[None] # torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0) + test_uv = torch.ones(1, n_frame, dtype=torch.float32) + test_noise = torch.randn(1, 192, n_frame) + test_sid = torch.LongTensor([0]) + input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"] + output_names = ["audio", ] + + torch.onnx.export(SVCVITS, + ( + test_hidden_unit.to(device), + test_pitch.to(device), + test_mel2ph.to(device), + test_uv.to(device), + test_noise.to(device), + test_sid.to(device) + ), + f"checkpoints/{path}/model.onnx", + dynamic_axes={ + "c": [0, 1], + "f0": [1], + "mel2ph": [1], + "uv": [1], + "noise": [2], + }, + do_constant_folding=False, + opset_version=16, + verbose=False, + input_names=input_names, + output_names=output_names) + + +if __name__ == '__main__': + main(True) diff --git a/onnx_export_speaker_mix.py b/onnx_export_speaker_mix.py new file mode 100644 index 0000000000000000000000000000000000000000..b1371691fc8b4302dcda36c90214452a470f9228 --- /dev/null +++ b/onnx_export_speaker_mix.py @@ -0,0 +1,67 @@ +import torch +from onnxexport.model_onnx_speaker_mix import SynthesizerTrn +import utils + +def main(HubertExport, NetExport): + path = "SummerPockets" + if NetExport: + device = torch.device("cpu") + hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json") + SVCVITS = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + **hps.model) + _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None) + _ = SVCVITS.eval().to(device) + for i in SVCVITS.parameters(): + i.requires_grad = False + test_hidden_unit = torch.rand(1, 10, SVCVITS.gin_channels) + test_pitch = torch.rand(1, 10) + test_mel2ph = torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0) + test_uv = torch.ones(1, 10, dtype=torch.float32) + test_noise = torch.randn(1, 192, 10) + + export_mix = True + + test_sid = torch.LongTensor([0]) + spk_mix = [] + if export_mix: + n_spk = len(hps.spk) + for i in range(n_spk): + spk_mix.append(1.0/float(n_spk)) + test_sid = torch.tensor(spk_mix) + SVCVITS.export_chara_mix(n_spk) + test_sid = test_sid.unsqueeze(0) + test_sid = test_sid.repeat(10, 1) + + input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"] + output_names = ["audio", ] + SVCVITS.eval() + + torch.onnx.export(SVCVITS, + ( + test_hidden_unit.to(device), + test_pitch.to(device), + test_mel2ph.to(device), + test_uv.to(device), + test_noise.to(device), + test_sid.to(device) + ), + f"checkpoints/{path}/model.onnx", + dynamic_axes={ + "c": [0, 1], + "f0": [1], + "mel2ph": [1], + "uv": [1], + "noise": [2], + "sid":[0] + }, + do_constant_folding=False, + opset_version=16, + verbose=False, + input_names=input_names, + output_names=output_names) + + +if __name__ == '__main__': + main(False, True) diff --git a/onnxexport/__pycache__/model_onnx.cpython-38.pyc b/onnxexport/__pycache__/model_onnx.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..37d45564e7570eda1d76f6277f399b9a9d751a67 Binary files /dev/null and b/onnxexport/__pycache__/model_onnx.cpython-38.pyc differ diff --git a/onnxexport/__pycache__/model_onnx.cpython-39.pyc b/onnxexport/__pycache__/model_onnx.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..af6c0271077c712e597c20e393dd75f272393a48 Binary files /dev/null and b/onnxexport/__pycache__/model_onnx.cpython-39.pyc differ diff --git a/onnxexport/model_onnx.py b/onnxexport/model_onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..e28bae95ec1e53aa05d06fc784ff86d55f228d60 --- /dev/null +++ b/onnxexport/model_onnx.py @@ -0,0 +1,335 @@ +import torch +from torch import nn +from torch.nn import functional as F + +import modules.attentions as attentions +import modules.commons as commons +import modules.modules as modules + +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +import utils +from modules.commons import init_weights, get_padding +from vdecoder.hifigan.models import Generator +from utils import f0_to_coarse + + +class ResidualCouplingBlock(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + n_flows=4, + gin_channels=0): + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for i in range(n_flows): + self.flows.append( + modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=gin_channels, mean_only=True)) + self.flows.append(modules.Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x + + +class Encoder(nn.Module): + def __init__(self, + in_channels, + out_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + + self.pre = nn.Conv1d(in_channels, hidden_channels, 1) + self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, g=None): + # print(x.shape,x_lengths.shape) + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + x = self.pre(x) * x_mask + x = self.enc(x, x_mask, g=g) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class TextEncoder(nn.Module): + def __init__(self, + out_channels, + hidden_channels, + kernel_size, + n_layers, + gin_channels=0, + filter_channels=None, + n_heads=None, + p_dropout=None): + super().__init__() + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.gin_channels = gin_channels + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + self.f0_emb = nn.Embedding(256, hidden_channels) + + self.enc_ = attentions.Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + + def forward(self, x, x_mask, f0=None, z=None): + x = x + self.f0_emb(f0).transpose(1, 2) + x = self.enc_(x * x_mask, x_mask) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + z * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + self.use_spectral_norm = use_spectral_norm + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 16, 15, 1, padding=7)), + norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), + norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class F0Decoder(nn.Module): + def __init__(self, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + spk_channels=0): + super().__init__() + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.spk_channels = spk_channels + + self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1) + self.decoder = attentions.FFT( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1) + self.cond = nn.Conv1d(spk_channels, hidden_channels, 1) + + def forward(self, x, norm_f0, x_mask, spk_emb=None): + x = torch.detach(x) + if spk_emb is not None: + x = x + self.cond(spk_emb) + x += self.f0_prenet(norm_f0) + x = self.prenet(x) * x_mask + x = self.decoder(x * x_mask, x_mask) + x = self.proj(x) * x_mask + return x + + +class SynthesizerTrn(nn.Module): + """ + Synthesizer for Training + """ + + def __init__(self, + spec_channels, + segment_size, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + gin_channels, + ssl_dim, + n_speakers, + sampling_rate=44100, + **kwargs): + super().__init__() + self.spec_channels = spec_channels + self.inter_channels = inter_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.resblock = resblock + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.upsample_rates = upsample_rates + self.upsample_initial_channel = upsample_initial_channel + self.upsample_kernel_sizes = upsample_kernel_sizes + self.segment_size = segment_size + self.gin_channels = gin_channels + self.ssl_dim = ssl_dim + self.emb_g = nn.Embedding(n_speakers, gin_channels) + + self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2) + + self.enc_p = TextEncoder( + inter_channels, + hidden_channels, + filter_channels=filter_channels, + n_heads=n_heads, + n_layers=n_layers, + kernel_size=kernel_size, + p_dropout=p_dropout + ) + hps = { + "sampling_rate": sampling_rate, + "inter_channels": inter_channels, + "resblock": resblock, + "resblock_kernel_sizes": resblock_kernel_sizes, + "resblock_dilation_sizes": resblock_dilation_sizes, + "upsample_rates": upsample_rates, + "upsample_initial_channel": upsample_initial_channel, + "upsample_kernel_sizes": upsample_kernel_sizes, + "gin_channels": gin_channels, + } + self.dec = Generator(h=hps) + self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) + self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) + self.f0_decoder = F0Decoder( + 1, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + spk_channels=gin_channels + ) + self.emb_uv = nn.Embedding(2, hidden_channels) + self.predict_f0 = False + + def forward(self, c, f0, mel2ph, uv, noise=None, g=None): + + decoder_inp = F.pad(c, [0, 0, 1, 0]) + mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]]) + c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H] + + c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) + g = g.unsqueeze(0) + g = self.emb_g(g).transpose(1, 2) + x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) + x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2) + + if self.predict_f0: + lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 + norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False) + pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) + f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1) + + z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise) + z = self.flow(z_p, c_mask, g=g, reverse=True) + o = self.dec(z * c_mask, g=g, f0=f0) + return o diff --git a/onnxexport/model_onnx_speaker_mix.py b/onnxexport/model_onnx_speaker_mix.py new file mode 100644 index 0000000000000000000000000000000000000000..355e590da30a4651925ffb24938b8c2af558c098 --- /dev/null +++ b/onnxexport/model_onnx_speaker_mix.py @@ -0,0 +1,350 @@ +import torch +from torch import nn +from torch.nn import functional as F +import modules.attentions as attentions +import modules.commons as commons +import modules.modules as modules + +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm + +import utils +from modules.commons import init_weights, get_padding +from vdecoder.hifigan.models import Generator +from utils import f0_to_coarse + + +class ResidualCouplingBlock(nn.Module): + def __init__(self, + channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + n_flows=4, + gin_channels=0): + super().__init__() + self.channels = channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.n_flows = n_flows + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for i in range(n_flows): + self.flows.append( + modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, + gin_channels=gin_channels, mean_only=True)) + self.flows.append(modules.Flip()) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + for flow in self.flows: + x, _ = flow(x, x_mask, g=g, reverse=reverse) + else: + for flow in reversed(self.flows): + x = flow(x, x_mask, g=g, reverse=reverse) + return x + + +class Encoder(nn.Module): + def __init__(self, + in_channels, + out_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_layers = n_layers + self.gin_channels = gin_channels + + self.pre = nn.Conv1d(in_channels, hidden_channels, 1) + self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + + def forward(self, x, x_lengths, g=None): + # print(x.shape,x_lengths.shape) + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + x = self.pre(x) * x_mask + x = self.enc(x, x_mask, g=g) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class TextEncoder(nn.Module): + def __init__(self, + out_channels, + hidden_channels, + kernel_size, + n_layers, + gin_channels=0, + filter_channels=None, + n_heads=None, + p_dropout=None): + super().__init__() + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.gin_channels = gin_channels + self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) + self.f0_emb = nn.Embedding(256, hidden_channels) + + self.enc_ = attentions.Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + + def forward(self, x, x_mask, f0=None, z=None): + x = x + self.f0_emb(f0).transpose(1, 2) + x = self.enc_(x * x_mask, x_mask) + stats = self.proj(x) * x_mask + m, logs = torch.split(stats, self.out_channels, dim=1) + z = (m + z * torch.exp(logs)) * x_mask + return z, m, logs, x_mask + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + self.use_spectral_norm = use_spectral_norm + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 16, 15, 1, padding=7)), + norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), + norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, modules.LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class F0Decoder(nn.Module): + def __init__(self, + out_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + spk_channels=0): + super().__init__() + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.spk_channels = spk_channels + + self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1) + self.decoder = attentions.FFT( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout) + self.proj = nn.Conv1d(hidden_channels, out_channels, 1) + self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1) + self.cond = nn.Conv1d(spk_channels, hidden_channels, 1) + + def forward(self, x, norm_f0, x_mask, spk_emb=None): + x = torch.detach(x) + if spk_emb is not None: + x = x + self.cond(spk_emb) + x += self.f0_prenet(norm_f0) + x = self.prenet(x) * x_mask + x = self.decoder(x * x_mask, x_mask) + x = self.proj(x) * x_mask + return x + + +class SynthesizerTrn(nn.Module): + """ + Synthesizer for Training + """ + + def __init__(self, + spec_channels, + segment_size, + inter_channels, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + resblock, + resblock_kernel_sizes, + resblock_dilation_sizes, + upsample_rates, + upsample_initial_channel, + upsample_kernel_sizes, + gin_channels, + ssl_dim, + n_speakers, + sampling_rate=44100, + **kwargs): + super().__init__() + self.spec_channels = spec_channels + self.inter_channels = inter_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.resblock = resblock + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.upsample_rates = upsample_rates + self.upsample_initial_channel = upsample_initial_channel + self.upsample_kernel_sizes = upsample_kernel_sizes + self.segment_size = segment_size + self.gin_channels = gin_channels + self.ssl_dim = ssl_dim + self.emb_g = nn.Embedding(n_speakers, gin_channels) + + self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2) + + self.enc_p = TextEncoder( + inter_channels, + hidden_channels, + filter_channels=filter_channels, + n_heads=n_heads, + n_layers=n_layers, + kernel_size=kernel_size, + p_dropout=p_dropout + ) + hps = { + "sampling_rate": sampling_rate, + "inter_channels": inter_channels, + "resblock": resblock, + "resblock_kernel_sizes": resblock_kernel_sizes, + "resblock_dilation_sizes": resblock_dilation_sizes, + "upsample_rates": upsample_rates, + "upsample_initial_channel": upsample_initial_channel, + "upsample_kernel_sizes": upsample_kernel_sizes, + "gin_channels": gin_channels, + } + self.dec = Generator(h=hps) + self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) + self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) + self.f0_decoder = F0Decoder( + 1, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + spk_channels=gin_channels + ) + self.emb_uv = nn.Embedding(2, hidden_channels) + self.predict_f0 = False + self.speaker_map = [] + self.export_mix = False + + def export_chara_mix(self, n_speakers_mix): + self.speaker_map = torch.zeros((n_speakers_mix, 1, 1, self.gin_channels)) + for i in range(n_speakers_mix): + self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]])) + self.speaker_map = self.speaker_map.unsqueeze(0) + self.export_mix = True + + def forward(self, c, f0, mel2ph, uv, noise=None, g=None, cluster_infer_ratio=0.1): + decoder_inp = F.pad(c, [0, 0, 1, 0]) + mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]]) + c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H] + + c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) + + if self.export_mix: # [N, S] * [S, B, 1, H] + g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] + g = g * self.speaker_map # [N, S, B, 1, H] + g = torch.sum(g, dim=1) # [N, 1, B, 1, H] + g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] + else: + g = g.unsqueeze(0) + g = self.emb_g(g).transpose(1, 2) + + x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) + x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2) + + if self.predict_f0: + lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 + norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False) + pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) + f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1) + + z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise) + z = self.flow(z_p, c_mask, g=g, reverse=True) + o = self.dec(z * c_mask, g=g, f0=f0) + return o diff --git a/pre_trained_model/768l12/D_0.pth b/pre_trained_model/768l12/D_0.pth new file mode 100644 index 0000000000000000000000000000000000000000..3439e9f831191aac36f813f45e5c7d710c6a63fa --- /dev/null +++ b/pre_trained_model/768l12/D_0.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:273a1da965da0f3b51c7f630c3aa1bf0ef4739da4ab367a9f063a6e12058e8ce +size 187027770 diff --git a/pre_trained_model/768l12/G_0.pth b/pre_trained_model/768l12/G_0.pth new file mode 100644 index 0000000000000000000000000000000000000000..17efd226452e5dba872beaf211f7c89d2ad59f85 --- /dev/null +++ b/pre_trained_model/768l12/G_0.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+version https://git-lfs.github.com/spec/v1 +oid sha256:e7bfff64704b51c8f49d23fee8e292a47ac0b4dbf9887ebd5f867abf9353dc33 +size 187027205 diff --git a/pre_trained_model/whisper-ppg/G_0.pth b/pre_trained_model/whisper-ppg/G_0.pth new file mode 100644 index 0000000000000000000000000000000000000000..d64ea346f3ff387ad1787cd832830602f883de43 --- /dev/null +++ b/pre_trained_model/whisper-ppg/G_0.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5ff28db8fa5894fdcb29c8aff760e45c9fd3b88a892391dcf0d0257e80a78b1 +size 237719813 diff --git a/preprocess_flist_config.py b/preprocess_flist_config.py new file mode 100644 index 0000000000000000000000000000000000000000..37cadc9d5414647b9154423ef373669c402cff66 --- /dev/null +++ b/preprocess_flist_config.py @@ -0,0 +1,104 @@ +import os +import argparse +import re + +from tqdm import tqdm +from random import shuffle +import json +import wave + +import diffusion.logger.utils as du + +config_template = json.load(open("configs_template/config_template.json")) + +pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$') + +def get_wav_duration(file_path): + with wave.open(file_path, 'rb') as wav_file: + # 获取音频帧数 + n_frames = wav_file.getnframes() + # 获取采样率 + framerate = wav_file.getframerate() + # 计算时长（秒） + duration = n_frames / float(framerate) + return duration + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list") + parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list") + parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir") + parser.add_argument("--speech_encoder", type=str, default="vec768l12", help="choice a speech encoder|'vec768l12','vec256l9','hubertsoft','whisper-ppg','cnhubertlarge','dphubert','whisper-ppg-large','wavlmbase+'") + parser.add_argument("--vol_aug", action="store_true", help="Whether to use volume embedding and volume augmentation") + args = parser.parse_args() + + train = [] + val = [] + idx = 0 + spk_dict = {} + spk_id = 0 + for speaker in tqdm(os.listdir(args.source_dir)): + spk_dict[speaker] = spk_id + spk_id += 1 + wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))] + new_wavs = [] + for file in wavs: + if not file.endswith("wav"): + continue + if not pattern.match(file): + print(f"warning：文件名{file}中包含非字母数字下划线，可能会导致错误。（也可能不会）") + if get_wav_duration(file) < 0.3: + print("skip too short audio:", file) + continue + new_wavs.append(file) + wavs = new_wavs + shuffle(wavs) + train += wavs[2:] + val += wavs[:2] + + shuffle(train) + shuffle(val) + + print("Writing", args.train_list) + with open(args.train_list, "w") as f: + for fname in tqdm(train): + wavpath = fname + f.write(wavpath + "\n") + + print("Writing", args.val_list) + with open(args.val_list, "w") as f: + for fname in tqdm(val): + wavpath = fname + f.write(wavpath + "\n") + + + d_config_template = du.load_config("configs_template/diffusion_template.yaml") + d_config_template["model"]["n_spk"] = spk_id + d_config_template["data"]["encoder"] = args.speech_encoder + d_config_template["spk"] = spk_dict + + config_template["spk"] = spk_dict + config_template["model"]["n_speakers"] = spk_id + config_template["model"]["speech_encoder"] = args.speech_encoder + + if args.speech_encoder == "vec768l12" or args.speech_encoder == "dphubert" or args.speech_encoder == "wavlmbase+": + config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 768 + d_config_template["data"]["encoder_out_channels"] = 768 + elif args.speech_encoder == "vec256l9" or args.speech_encoder == 'hubertsoft': + config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 256 + d_config_template["data"]["encoder_out_channels"] = 256 + elif args.speech_encoder == "whisper-ppg" or args.speech_encoder == 'cnhubertlarge': + config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 1024 + d_config_template["data"]["encoder_out_channels"] = 1024 + elif args.speech_encoder == "whisper-ppg-large": + config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 1280 + d_config_template["data"]["encoder_out_channels"] = 1280 + + if args.vol_aug: + config_template["train"]["vol_aug"] = config_template["model"]["vol_embedding"] = True + + print("Writing configs/config.json") + with open("configs/config.json", "w") as f: + json.dump(config_template, f, indent=2) + print("Writing configs/diffusion_template.yaml") + du.save_config("configs/diffusion.yaml",d_config_template) diff --git a/preprocess_hubert_f0.py b/preprocess_hubert_f0.py new file mode 100644 index 0000000000000000000000000000000000000000..3871d8d78ff2ca1b847c6b6fa40e0d3d68545007 --- /dev/null +++ b/preprocess_hubert_f0.py @@ -0,0 +1,159 @@ +import math +import multiprocessing +import os +import argparse +from random import shuffle +import random + +import torch +from glob import glob +from tqdm import tqdm +from modules.mel_processing import spectrogram_torch +import json + +import utils +import logging +logging.getLogger("numba").setLevel(logging.WARNING) +logging.getLogger("matplotlib").setLevel(logging.WARNING) + +import diffusion.logger.utils as du +from diffusion.vocoder import Vocoder + +import librosa +import numpy as np + +hps = utils.get_hparams_from_file("configs/config.json") +dconfig = du.load_config("configs/diffusion.yaml") +sampling_rate = hps.data.sampling_rate +hop_length = hps.data.hop_length +speech_encoder = hps["model"]["speech_encoder"] + + +def process_one(filename, hmodel,f0p,diff=False,mel_extractor=None): + # print(filename) + wav, sr = librosa.load(filename, sr=sampling_rate) + audio_norm = torch.FloatTensor(wav) + audio_norm = audio_norm.unsqueeze(0) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + soft_path = filename + ".soft.pt" + if not os.path.exists(soft_path): + wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000) + wav16k = torch.from_numpy(wav16k).to(device) + c = hmodel.encoder(wav16k) + torch.save(c.cpu(), soft_path) + + f0_path = filename + ".f0.npy" + if not os.path.exists(f0_path): + f0_predictor = utils.get_f0_predictor(f0p,sampling_rate=sampling_rate, hop_length=hop_length,device=None,threshold=0.05) + f0,uv = f0_predictor.compute_f0_uv( + wav + ) + np.save(f0_path, np.asanyarray((f0,uv),dtype=object)) + + + spec_path = filename.replace(".wav", ".spec.pt") + if not os.path.exists(spec_path): + # Process spectrogram + # The following code can't be replaced by torch.FloatTensor(wav) + # because load_wav_to_torch return a tensor that need to be normalized + + if sr != hps.data.sampling_rate: + raise ValueError( + "{} SR doesn't match target {} SR".format( + sr, hps.data.sampling_rate + ) + ) + + #audio_norm = audio / hps.data.max_wav_value + + spec = spectrogram_torch( + audio_norm, + hps.data.filter_length, + hps.data.sampling_rate, + hps.data.hop_length, + hps.data.win_length, + center=False, + ) + spec = torch.squeeze(spec, 0) + torch.save(spec, spec_path) + + if diff or hps.model.vol_embedding: + volume_path = filename + ".vol.npy" + volume_extractor = utils.Volume_Extractor(hop_length) + if not os.path.exists(volume_path): + volume = volume_extractor.extract(audio_norm) + np.save(volume_path, volume.to('cpu').numpy()) + + if diff: + mel_path = filename + ".mel.npy" + if not os.path.exists(mel_path) and mel_extractor is not None: + mel_t = mel_extractor.extract(audio_norm.to(device), sampling_rate) + mel = mel_t.squeeze().to('cpu').numpy() + np.save(mel_path, mel) + aug_mel_path = filename + ".aug_mel.npy" + aug_vol_path = filename + ".aug_vol.npy" + max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5 + max_shift = min(1, np.log10(1/max_amp)) + log10_vol_shift = random.uniform(-1, max_shift) + keyshift = random.uniform(-5, 5) + if mel_extractor is not None: + aug_mel_t = mel_extractor.extract(audio_norm * (10 ** log10_vol_shift), sampling_rate, keyshift = keyshift) + aug_mel = aug_mel_t.squeeze().to('cpu').numpy() + aug_vol = volume_extractor.extract(audio_norm * (10 ** log10_vol_shift)) + if not os.path.exists(aug_mel_path): + np.save(aug_mel_path,np.asanyarray((aug_mel,keyshift),dtype=object)) + if not os.path.exists(aug_vol_path): + np.save(aug_vol_path,aug_vol.to('cpu').numpy()) + + +def process_batch(filenames,f0p,diff=False,mel_extractor=None): + print("Loading speech encoder for content...") + device = "cuda" if torch.cuda.is_available() else "cpu" + hmodel = utils.get_speech_encoder(speech_encoder,device=device) + print("Loaded speech encoder.") + for filename in tqdm(filenames): + process_one(filename, hmodel,f0p,diff,mel_extractor) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--in_dir", type=str, default="dataset/44k", help="path to input dir" + ) + parser.add_argument( + '--use_diff',action='store_true', help='Whether to use the diffusion model' + ) + parser.add_argument( + '--f0_predictor', type=str, default="dio", help='Select F0 predictor, can select crepe,pm,dio,harvest, default pm(note: crepe is original F0 using mean filter)' + ) + parser.add_argument( + '--num_processes', type=int, default=1, help='You are advised to set the number of processes to the same as the number of CPU cores' + ) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + args = parser.parse_args() + f0p = args.f0_predictor + print(speech_encoder) + print(f0p) + if args.use_diff: + print("use_diff") + print("Loading Mel Extractor...") + mel_extractor = Vocoder(dconfig.vocoder.type, dconfig.vocoder.ckpt, device = device) + print("Loaded Mel Extractor.") + else: + mel_extractor = None + filenames = glob(f"{args.in_dir}/*/*.wav", recursive=True) # [:10] + shuffle(filenames) + multiprocessing.set_start_method("spawn", force=True) + + num_processes = args.num_processes + chunk_size = int(math.ceil(len(filenames) / num_processes)) + chunks = [ + filenames[i : i + chunk_size] for i in range(0, len(filenames), chunk_size) + ] + print([len(c) for c in chunks]) + processes = [ + multiprocessing.Process(target=process_batch, args=(chunk,f0p,args.use_diff,mel_extractor)) for chunk in chunks + ] + for p in processes: + p.start() diff --git a/pretrain/checkpoint_best_legacy_500.pt b/pretrain/checkpoint_best_legacy_500.pt new file mode 100644 index 0000000000000000000000000000000000000000..9a2f13fb9c7047dff746e2d5d88c0d0a5aecf643 --- /dev/null +++ b/pretrain/checkpoint_best_legacy_500.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60d936ec5a566776fc392e69ad8b630d14eb588111233fe313436e200a7b187b +size 1330114945 diff --git a/pretrain/hubert-soft-0d54a1f4.pt b/pretrain/hubert-soft-0d54a1f4.pt new file mode 100644 index 0000000000000000000000000000000000000000..5ccd36b11dc124c97a0b73fa5f39eed8d1a6f27a --- /dev/null +++ b/pretrain/hubert-soft-0d54a1f4.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e82e7d079df05fe3aa535f6f7d42d309bdae1d2a53324e2b2386c56721f4f649 +size 378435957 diff --git a/pretrain/medium.pt b/pretrain/medium.pt new file mode 100644 index 0000000000000000000000000000000000000000..8aca41c710014a3d39774cd7592fa086177c672f --- /dev/null +++ b/pretrain/medium.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1 +size 1528008539 diff --git a/pretrain/meta.py b/pretrain/meta.py new file mode 100644 index 0000000000000000000000000000000000000000..cc35dd3c0dfe8436e7d635f2db507cedca75ed49 --- /dev/null +++ b/pretrain/meta.py @@ -0,0 +1,31 @@ +def download_dict(): + return { + "vec768l12": { + "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr", + "output": "./pretrain/checkpoint_best_legacy_500.pt" + }, + "vec256l9": { + "url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr", + "output": "./pretrain/checkpoint_best_legacy_500.pt" + }, + "hubertsoft": { + "url": "https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt", + "output": "./pretrain/hubert-soft-0d54a1f4.pt" + }, + "whisper-ppg": { + "url": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "output": "./pretrain/medium.pt" + } + } + + +def get_speech_encoder(config_path="configs/config.json"): + import json + + with open(config_path, "r") as f: + data = f.read() + config = json.loads(data) + speech_encoder = config["model"]["speech_encoder"] + dict = download_dict() + + return dict[speech_encoder]["url"], dict[speech_encoder]["output"] diff --git a/pretrain/nsf_hifigan/NOTICE.txt b/pretrain/nsf_hifigan/NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..228fc663c20c3166dc16dca0b3b94dca38a489b8 --- /dev/null +++ b/pretrain/nsf_hifigan/NOTICE.txt @@ -0,0 +1,74 @@ +--- DiffSinger Community Vocoder --- + +ARCHITECTURE: NSF-HiFiGAN +RELEASE DATE: 2022-12-11 + +HYPER PARAMETERS: + - 44100 sample rate + - 128 mel bins + - 512 hop size + - 2048 window size + - fmin at 40Hz + - fmax at 16000Hz + + +NOTICE: + +All model weights in the [DiffSinger Community Vocoder Project](https://openvpi.github.io/vocoders/), including +model weights in this directory, are provided by the [OpenVPI Team](https://github.com/openvpi/), under the +[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. + + +ACKNOWLEDGEMENTS: + +Training data of this vocoder is provided and permitted by the following organizations, societies and individuals: + +孙飒 https://www.qfssr.cn +赤松_Akamatsu https://www.zhibin.club +乐威 https://www.zhibin.club +伯添 https://space.bilibili.com/24087011 +雲宇光 https://space.bilibili.com/660675050 +橙子言 https://space.bilibili.com/318486464 +人衣大人 https://space.bilibili.com/2270344 +玖蝶 https://space.bilibili.com/676771003 +Yuuko +白夜零BYL https://space.bilibili.com/1605040503 +嗷天 https://space.bilibili.com/5675252 +洛泠羽 https://space.bilibili.com/347373318 +灰条纹的灰猫君 https://space.bilibili.com/2083633 +幽寂 https://space.bilibili.com/478860 +恶魔王女 https://space.bilibili.com/2475098 +AlexYHX 芮晴 +绮萱 https://y.qq.com/n/ryqq/singer/003HjD6H4aZn1K +诗芸 https://y.qq.com/n/ryqq/singer/0005NInj142zm0 +汐蕾 https://y.qq.com/n/ryqq/singer/0023cWMH1Bq1PJ +1262917464 +炜阳 +叶卡yolka +幸の夏 https://space.bilibili.com/1017297686 +暮色未量 https://space.bilibili.com/272904686 +晓寞sama https://space.bilibili.com/3463394 +没头绪的节操君 +串串BunC https://space.bilibili.com/95817834 +落雨 https://space.bilibili.com/1292427 +长尾巴的翎艾 https://space.bilibili.com/1638666 +声闻计划 https://space.bilibili.com/392812269 +唐家大小姐 http://5sing.kugou.com/palmusic/default.html +不伊子 + +Training machines are provided by: + +花儿不哭 https://space.bilibili.com/5760446 + + +TERMS OF REDISTRIBUTIONS: + +1. Do not sell this vocoder, or charge any fees from redistributing it, as prohibited by + the license. +2. Include a copy of the CC BY-NC-SA 4.0 license, or a link referring to it. +3. Include a copy of this notice, or any other notices informing that this vocoder is + provided by the OpenVPI Team, that this vocoder is licensed under CC BY-NC-SA 4.0, and + with a complete acknowledgement list as shown above. +4. If you fine-tuned or modified the weights, leave a notice about what has been changed. +5. (Optional) Leave a link to the official release page of the vocoder, and tell users + that other versions and future updates of this vocoder can be obtained from the website. diff --git a/pretrain/nsf_hifigan/NOTICE.zh-CN.txt b/pretrain/nsf_hifigan/NOTICE.zh-CN.txt new file mode 100644 index 0000000000000000000000000000000000000000..b206a0bd1d3b80feb66c52a7452856021d06805a --- /dev/null +++ b/pretrain/nsf_hifigan/NOTICE.zh-CN.txt @@ -0,0 +1,72 @@ +--- DiffSinger 社区声码器 --- + +架构：NSF-HiFiGAN +发布日期：2022-12-11 + +超参数： + - 44100 sample rate + - 128 mel bins + - 512 hop size + - 2048 window size + - fmin at 40Hz + - fmax at 16000Hz + + +注意事项： + +[DiffSinger 社区声码器企划](https://openvpi.github.io/vocoders/) 中的所有模型权重， +包括此目录下的模型权重，均由 [OpenVPI Team](https://github.com/openvpi/) 提供，并基于 +[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) +进行许可。 + + +致谢： + +此声码器的训练数据由以下组织、社团和个人提供并许可： + +孙飒 https://www.qfssr.cn +赤松_Akamatsu https://www.zhibin.club +乐威 https://www.zhibin.club +伯添 https://space.bilibili.com/24087011 +雲宇光 https://space.bilibili.com/660675050 +橙子言 https://space.bilibili.com/318486464 +人衣大人 https://space.bilibili.com/2270344 +玖蝶 https://space.bilibili.com/676771003 +Yuuko +白夜零BYL https://space.bilibili.com/1605040503 +嗷天 https://space.bilibili.com/5675252 +洛泠羽 https://space.bilibili.com/347373318 +灰条纹的灰猫君 https://space.bilibili.com/2083633 +幽寂 https://space.bilibili.com/478860 +恶魔王女 https://space.bilibili.com/2475098 +AlexYHX 芮晴 +绮萱 https://y.qq.com/n/ryqq/singer/003HjD6H4aZn1K +诗芸 https://y.qq.com/n/ryqq/singer/0005NInj142zm0 +汐蕾 https://y.qq.com/n/ryqq/singer/0023cWMH1Bq1PJ +1262917464 +炜阳 +叶卡yolka +幸の夏 https://space.bilibili.com/1017297686 +暮色未量 https://space.bilibili.com/272904686 +晓寞sama https://space.bilibili.com/3463394 +没头绪的节操君 +串串BunC https://space.bilibili.com/95817834 +落雨 https://space.bilibili.com/1292427 +长尾巴的翎艾 https://space.bilibili.com/1638666 +声闻计划 https://space.bilibili.com/392812269 +唐家大小姐 http://5sing.kugou.com/palmusic/default.html +不伊子 + +训练算力的提供者如下： + +花儿不哭 https://space.bilibili.com/5760446 + + +二次分发条款： + +1. 请勿售卖此声码器或从其二次分发过程中收取任何费用，因为此类行为受到许可证的禁止。 +2. 请在二次分发文件中包含一份 CC BY-NC-SA 4.0 许可证的副本或指向该许可证的链接。 +3. 请在二次分发文件中包含这份声明，或以其他形式声明此声码器由 OpenVPI Team 提供并基于 CC BY-NC-SA 4.0 许可， + 并附带上述完整的致谢名单。 +4. 如果您微调或修改了权重，请留下一份关于其受到了何种修改的说明。 +5.（可选）留下一份指向此声码器的官方发布页面的链接，并告知使用者可从该网站获取此声码器的其他版本和未来的更新。 diff --git a/pretrain/nsf_hifigan/config.json b/pretrain/nsf_hifigan/config.json new file mode 100644 index 0000000000000000000000000000000000000000..79821fb218253a51b8bcbaa2eaed539a79c78d32 --- /dev/null +++ b/pretrain/nsf_hifigan/config.json @@ -0,0 +1,38 @@ +{ + "resblock": "1", + "num_gpus": 4, + "batch_size": 10, + "learning_rate": 0.0002, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + + "upsample_rates": [ 8, 8, 2, 2, 2], + "upsample_kernel_sizes": [16,16, 4, 4, 4], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [3,7,11], + "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], + "discriminator_periods": [3, 5, 7, 11, 17, 23, 37], + + "segment_size": 16384, + "num_mels": 128, + "num_freq": 1025, + "n_fft" : 2048, + "hop_size": 512, + "win_size": 2048, + + "sampling_rate": 44100, + + "fmin": 40, + "fmax": 16000, + "fmax_for_loss": null, + + "num_workers": 16, + + "dist_config": { + "dist_backend": "nccl", + "dist_url": "tcp://localhost:54321", + "world_size": 1 + } +} diff --git a/pretrain/nsf_hifigan/model b/pretrain/nsf_hifigan/model new file mode 100644 index 0000000000000000000000000000000000000000..6ff8d81f7fe19ab507232cdd35667f3ccba9893c --- /dev/null +++ b/pretrain/nsf_hifigan/model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c576b63b7ed952161b70fad34e0562ace502ce689195520d8a2a6c051de29d6 +size 56825430 diff --git a/pretrain/nsf_hifigan/put_nsf_hifigan_ckpt_here b/pretrain/nsf_hifigan/put_nsf_hifigan_ckpt_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pretrain/put_hubert_ckpt_here b/pretrain/put_hubert_ckpt_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d09b883952ffc220c78396ddde41c57b8ca8df4d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,26 @@ +ffmpeg-python +Flask +Flask_Cors +gradio>=3.7.0 +numpy==1.23.5 +pyworld +scipy==1.10.0 +SoundFile==0.12.1 +torch +torchaudio +torchcrepe +tqdm +scikit-maad +praat-parselmouth +onnx +onnxsim +onnxoptimizer +fairseq==0.12.2 +librosa==0.9.1 +tensorboard +tensorboardX +transformers +edge_tts +pyyaml +pynvml +faiss-cpu diff --git a/requirements_onnx_encoder.txt b/requirements_onnx_encoder.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a415361d802c9aaa86e6c2648fc602ea16f2776 --- /dev/null +++ b/requirements_onnx_encoder.txt @@ -0,0 +1,27 @@ +Flask +Flask_Cors +gradio>=3.7.0 +numpy==1.23.0 +pyworld==0.2.5 +scipy==1.10.0 +SoundFile==0.12.1 +torch==1.13.1 +torchaudio==0.13.1 +torchcrepe +tqdm +scikit-maad +praat-parselmouth +onnx +onnxsim +onnxoptimizer +onnxruntime-gpu +librosa==0.9.1 +tensorboard +tensorboardX +edge_tts +pyyaml +pynvml +transformers +ffmpeg-python +ffmpeg +faiss \ No newline at end of file diff --git a/requirements_win.txt b/requirements_win.txt new file mode 100644 index 0000000000000000000000000000000000000000..b7cf7fdf28234141adc4c61da3a787c0620b28f3 --- /dev/null +++ b/requirements_win.txt @@ -0,0 +1,29 @@ +librosa==0.9.1 +fairseq==0.12.2 +ffmpeg-python +Flask==2.1.2 +Flask_Cors==3.0.10 +gradio==3.24.1 +numpy==1.23.5 +playsound==1.3.0 +PyAudio==0.2.12 +pydub==0.25.1 +pyworld==0.3.0 +requests==2.28.1 +scipy==1.7.3 +sounddevice==0.4.5 +SoundFile==0.10.3.post1 +starlette==0.19.1 +tqdm==4.63.0 +torchcrepe==0.0.18 +scikit-maad==1.3.12 +praat-parselmouth==0.4.3 +onnx==1.13.1 +onnxsim==0.4.19 +onnxoptimizer==0.3.10 +tensorboardX==2.6 +transformers==4.28.1 +edge_tts==6.1.3 +pyyaml +pynvml==11.5.0 +faiss-cpu==1.7.3 diff --git a/resample.py b/resample.py new file mode 100644 index 0000000000000000000000000000000000000000..301e2924ec588ec61a55a2a6f2b2f68726dfda5b --- /dev/null +++ b/resample.py @@ -0,0 +1,49 @@ +import os +import argparse +import librosa +import numpy as np +from multiprocessing import Pool, cpu_count +from scipy.io import wavfile +from tqdm import tqdm + + +def process(item): + spkdir, wav_name, args = item + # speaker 's5', 'p280', 'p315' are excluded, + speaker = spkdir.replace("\\", "/").split("/")[-1] + wav_path = os.path.join(args.in_dir, speaker, wav_name) + if os.path.exists(wav_path) and '.wav' in wav_path: + os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True) + wav, sr = librosa.load(wav_path, sr=None) + wav, _ = librosa.effects.trim(wav, top_db=40) + peak = np.abs(wav).max() + if peak > 1.0: + wav = 0.98 * wav / peak + wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2) + if not args.skip_loudnorm: + wav2 /= max(wav2.max(), -wav2.min()) + save_name = wav_name + save_path2 = os.path.join(args.out_dir2, speaker, save_name) + wavfile.write( + save_path2, + args.sr2, + (wav2 * np.iinfo(np.int16).max).astype(np.int16) + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--sr2", type=int, default=44100, help="sampling rate") + parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir") + parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir") + parser.add_argument("--skip_loudnorm", action="store_true", help="Skip loudness matching if you have done it") + args = parser.parse_args() + processs = 30 if cpu_count() > 60 else (cpu_count()-2 if cpu_count() > 4 else 1) + pool = Pool(processes=processs) + + for speaker in os.listdir(args.in_dir): + spk_dir = os.path.join(args.in_dir, speaker) + if os.path.isdir(spk_dir): + print(spk_dir) + for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])): + pass diff --git a/settings.yaml b/settings.yaml new file mode 100644 index 0000000000000000000000000000000000000000..65621c5e90bb65a8d7048a28c63552d74da2c836 --- /dev/null +++ b/settings.yaml @@ -0,0 +1,18 @@ +sovits_params: + log_interval: 200 + eval_interval: 800 + keep_ckpts: 10 + batch_size: 6 + learning_rate: 0.0001 + fp16_run: false + all_in_mem: false +diff_params: + num_workers: 2 + cache_all_data: true + cache_device: cuda + amp_dtype: fp32 + diff_batch_size: 48 + diff_lr: 0.0002 + diff_interval_log: 10 + diff_interval_val: 2000 + diff_force_save: 2000 diff --git a/shadowdiffusion.png b/shadowdiffusion.png new file mode 100644 index 0000000000000000000000000000000000000000..dedec9d787f156ba2d2ca6675cfc6c9d4287fe04 Binary files /dev/null and b/shadowdiffusion.png differ diff --git a/spkmix.py b/spkmix.py new file mode 100644 index 0000000000000000000000000000000000000000..1d266e017859aca3c48727c5acbef9c8da8c1411 --- /dev/null +++ b/spkmix.py @@ -0,0 +1,11 @@ +# 角色混合轨道 编写规则： +# 角色ID : [[起始时间1, 终止时间1, 起始数值1, 起始数值1], [起始时间2, 终止时间2, 起始数值2, 起始数值2]] +# 起始时间和前一个的终止时间必须相同，第一个起始时间必须为0，最后一个终止时间必须为1 （时间的范围为0-1） +# 全部角色必须填写，不使用的角色填[[0., 1., 0., 0.]]即可 +# 融合数值可以随便填，在指定的时间段内从起始数值线性变化为终止数值，内部会自动确保线性组合为1，可以放心使用 + +spk_mix_map = { + 0 : [[0., 0.5, 1, 0.5], [0.5, 1, 0.5, 1]], + 1 : [[0., 0.35, 1, 0.5], [0.35, 0.75, 0.75, 1], [0.75, 1, 0.45, 1]], + 2 : [[0., 0.35, 1, 0.5], [0.35, 0.75, 0.75, 1], [0.75, 1, 0.45, 1]] +} \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..dba77bbb563d2ea12ced5424d4fe9088f9c84a42 --- /dev/null +++ b/train.py @@ -0,0 +1,331 @@ +import logging +import multiprocessing +import time + +logging.getLogger('matplotlib').setLevel(logging.WARNING) +logging.getLogger('numba').setLevel(logging.WARNING) + +import os +import json +import argparse +import itertools +import math +import torch +from torch import nn, optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +import torch.multiprocessing as mp +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.cuda.amp import autocast, GradScaler + +import modules.commons as commons +import utils +from data_utils import TextAudioSpeakerLoader, TextAudioCollate +from models import ( + SynthesizerTrn, + MultiPeriodDiscriminator, +) +from modules.losses import ( + kl_loss, + generator_loss, discriminator_loss, feature_loss +) + +from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch + +torch.backends.cudnn.benchmark = True +global_step = 0 +start_time = time.time() + +# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO' + + +def main(): + """Assume Single Node Multi GPUs Training Only""" + assert torch.cuda.is_available(), "CPU training is not allowed." + hps = utils.get_hparams() + + n_gpus = torch.cuda.device_count() + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = hps.train.port + + mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) + + +def run(rank, n_gpus, hps): + global global_step + if rank == 0: + logger = utils.get_logger(hps.model_dir) + logger.info(hps) + utils.check_git_hash(hps.model_dir) + writer = SummaryWriter(log_dir=hps.model_dir) + writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) + + # for pytorch on win, backend use gloo + dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank) + torch.manual_seed(hps.train.seed) + torch.cuda.set_device(rank) + collate_fn = TextAudioCollate() + all_in_mem = hps.train.all_in_mem # If you have enough memory, turn on this option to avoid disk IO and speed up training. + train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem) + num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count() + if all_in_mem: + num_workers = 0 + train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True, + batch_size=hps.train.batch_size, collate_fn=collate_fn) + if rank == 0: + eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem,vol_aug = False) + eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False, + batch_size=1, pin_memory=False, + drop_last=False, collate_fn=collate_fn) + + net_g = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + **hps.model).cuda(rank) + net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) + optim_g = torch.optim.AdamW( + net_g.parameters(), + hps.train.learning_rate, + betas=hps.train.betas, + eps=hps.train.eps) + optim_d = torch.optim.AdamW( + net_d.parameters(), + hps.train.learning_rate, + betas=hps.train.betas, + eps=hps.train.eps) + net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True) + net_d = DDP(net_d, device_ids=[rank]) + + skip_optimizer = False + try: + _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, + optim_g, skip_optimizer) + _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, + optim_d, skip_optimizer) + epoch_str = max(epoch_str, 1) + name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth") + global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1 + #global_step = (epoch_str - 1) * len(train_loader) + except: + print("load old checkpoint failed...") + epoch_str = 1 + global_step = 0 + if skip_optimizer: + epoch_str = 1 + global_step = 0 + + warmup_epoch = hps.train.warmup_epochs + scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) + scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) + + scaler = GradScaler(enabled=hps.train.fp16_run) + + for epoch in range(epoch_str, hps.train.epochs + 1): + # set up warm-up learning rate + if epoch <= warmup_epoch: + for param_group in optim_g.param_groups: + param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch + for param_group in optim_d.param_groups: + param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch + # training + if rank == 0: + train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, + [train_loader, eval_loader], logger, [writer, writer_eval]) + else: + train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, + [train_loader, None], None, None) + # update learning rate + scheduler_g.step() + scheduler_d.step() + + +def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): + net_g, net_d = nets + optim_g, optim_d = optims + scheduler_g, scheduler_d = schedulers + train_loader, eval_loader = loaders + if writers is not None: + writer, writer_eval = writers + + # train_loader.batch_sampler.set_epoch(epoch) + global global_step + + net_g.train() + net_d.train() + for batch_idx, items in enumerate(train_loader): + c, f0, spec, y, spk, lengths, uv,volume = items + g = spk.cuda(rank, non_blocking=True) + spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True) + c = c.cuda(rank, non_blocking=True) + f0 = f0.cuda(rank, non_blocking=True) + uv = uv.cuda(rank, non_blocking=True) + lengths = lengths.cuda(rank, non_blocking=True) + mel = spec_to_mel_torch( + spec, + hps.data.filter_length, + hps.data.n_mel_channels, + hps.data.sampling_rate, + hps.data.mel_fmin, + hps.data.mel_fmax) + + with autocast(enabled=hps.train.fp16_run): + y_hat, ids_slice, z_mask, \ + (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths, + spec_lengths=lengths,vol = volume) + + y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length) + y_hat_mel = mel_spectrogram_torch( + y_hat.squeeze(1), + hps.data.filter_length, + hps.data.n_mel_channels, + hps.data.sampling_rate, + hps.data.hop_length, + hps.data.win_length, + hps.data.mel_fmin, + hps.data.mel_fmax + ) + y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice + + # Discriminator + y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) + + with autocast(enabled=False): + loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) + loss_disc_all = loss_disc + + optim_d.zero_grad() + scaler.scale(loss_disc_all).backward() + scaler.unscale_(optim_d) + grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) + scaler.step(optim_d) + + with autocast(enabled=hps.train.fp16_run): + # Generator + y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) + with autocast(enabled=False): + loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel + loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl + loss_fm = feature_loss(fmap_r, fmap_g) + loss_gen, losses_gen = generator_loss(y_d_hat_g) + loss_lf0 = F.mse_loss(pred_lf0, lf0) + loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0 + optim_g.zero_grad() + scaler.scale(loss_gen_all).backward() + scaler.unscale_(optim_g) + grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) + scaler.step(optim_g) + scaler.update() + + if rank == 0: + if global_step % hps.train.log_interval == 0: + lr = optim_g.param_groups[0]['lr'] + losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl] + reference_loss=0 + for i in losses: + reference_loss += i + logger.info('Train Epoch: {} [{:.0f}%]'.format( + epoch, + 100. * batch_idx / len(train_loader))) + logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}") + + scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, + "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} + scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl, + "loss/g/lf0": loss_lf0}) + + # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) + # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) + # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) + image_dict = { + "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), + "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), + "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), + "all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(), + pred_lf0[0, 0, :].detach().cpu().numpy()), + "all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(), + norm_lf0[0, 0, :].detach().cpu().numpy()) + } + + utils.summarize( + writer=writer, + global_step=global_step, + images=image_dict, + scalars=scalar_dict + ) + + if global_step % hps.train.eval_interval == 0: + evaluate(hps, net_g, eval_loader, writer_eval) + utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, + os.path.join(hps.model_dir, "G_{}.pth".format(global_step))) + utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, + os.path.join(hps.model_dir, "D_{}.pth".format(global_step))) + keep_ckpts = getattr(hps.train, 'keep_ckpts', 0) + if keep_ckpts > 0: + utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True) + + global_step += 1 + + if rank == 0: + global start_time + now = time.time() + durtaion = format(now - start_time, '.2f') + logger.info(f'====> Epoch: {epoch}, cost {durtaion} s') + start_time = now + + +def evaluate(hps, generator, eval_loader, writer_eval): + generator.eval() + image_dict = {} + audio_dict = {} + with torch.no_grad(): + for batch_idx, items in enumerate(eval_loader): + c, f0, spec, y, spk, _, uv,volume = items + g = spk[:1].cuda(0) + spec, y = spec[:1].cuda(0), y[:1].cuda(0) + c = c[:1].cuda(0) + f0 = f0[:1].cuda(0) + uv= uv[:1].cuda(0) + if volume!=None: + volume = volume[:1].cuda(0) + mel = spec_to_mel_torch( + spec, + hps.data.filter_length, + hps.data.n_mel_channels, + hps.data.sampling_rate, + hps.data.mel_fmin, + hps.data.mel_fmax) + y_hat,_ = generator.module.infer(c, f0, uv, g=g,vol = volume) + + y_hat_mel = mel_spectrogram_torch( + y_hat.squeeze(1).float(), + hps.data.filter_length, + hps.data.n_mel_channels, + hps.data.sampling_rate, + hps.data.hop_length, + hps.data.win_length, + hps.data.mel_fmin, + hps.data.mel_fmax + ) + + audio_dict.update({ + f"gen/audio_{batch_idx}": y_hat[0], + f"gt/audio_{batch_idx}": y[0] + }) + image_dict.update({ + f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()), + "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()) + }) + utils.summarize( + writer=writer_eval, + global_step=global_step, + images=image_dict, + audios=audio_dict, + audio_sampling_rate=hps.data.sampling_rate + ) + generator.train() + + +if __name__ == "__main__": + main() diff --git a/train_diff.py b/train_diff.py new file mode 100644 index 0000000000000000000000000000000000000000..aa24cfbc5a7a09c2fae8e6897b5689bba8cfae00 --- /dev/null +++ b/train_diff.py @@ -0,0 +1,71 @@ +import os +import argparse +import torch +from torch.optim import lr_scheduler +from diffusion.logger import utils +from diffusion.data_loaders import get_data_loaders +from diffusion.solver import train +from diffusion.unit2mel import Unit2Mel +from diffusion.vocoder import Vocoder + + +def parse_args(args=None, namespace=None): + """Parse command-line arguments.""" + parser = argparse.ArgumentParser() + parser.add_argument( + "-c", + "--config", + type=str, + required=True, + help="path to the config file") + return parser.parse_args(args=args, namespace=namespace) + + +if __name__ == '__main__': + # parse commands + cmd = parse_args() + + # load config + args = utils.load_config(cmd.config) + print(' > config:', cmd.config) + print(' > exp:', args.env.expdir) + + # load vocoder + vocoder = Vocoder(args.vocoder.type, args.vocoder.ckpt, device=args.device) + + # load model + model = Unit2Mel( + args.data.encoder_out_channels, + args.model.n_spk, + args.model.use_pitch_aug, + vocoder.dimension, + args.model.n_layers, + args.model.n_chans, + args.model.n_hidden) + + + # load parameters + optimizer = torch.optim.AdamW(model.parameters()) + initial_global_step, model, optimizer = utils.load_model(args.env.expdir, model, optimizer, device=args.device) + for param_group in optimizer.param_groups: + param_group['initial_lr'] = args.train.lr + param_group['lr'] = args.train.lr * (args.train.gamma ** max(((initial_global_step-2)//args.train.decay_step),0) ) + param_group['weight_decay'] = args.train.weight_decay + scheduler = lr_scheduler.StepLR(optimizer, step_size=args.train.decay_step, gamma=args.train.gamma,last_epoch=initial_global_step-2) + + # device + if args.device == 'cuda': + torch.cuda.set_device(args.env.gpu_id) + model.to(args.device) + + for state in optimizer.state.values(): + for k, v in state.items(): + if torch.is_tensor(v): + state[k] = v.to(args.device) + + # datas + loader_train, loader_valid = get_data_loaders(args, whole_audio=False) + + # run + train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_valid) + diff --git a/train_index.py b/train_index.py new file mode 100644 index 0000000000000000000000000000000000000000..a8d8cae451b9c2a18dce3db6e2023bc29d48a021 --- /dev/null +++ b/train_index.py @@ -0,0 +1,30 @@ +import utils +import pickle +import os +import argparse + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--root_dir", type=str, default="dataset/44k", help="path to root dir" + ) + parser.add_argument('-c', '--config', type=str, default="./configs/config.json", + help='JSON file for configuration') + parser.add_argument( + "--output_dir", type=str, default="logs/44k", help="path to output dir" + ) + + args = parser.parse_args() + + hps = utils.get_hparams_from_file(args.config) + spk_dic = hps.spk + result = {} + + for k,v in spk_dic.items(): + print(f"now, index {k} feature...") + index = utils.train_index(k,args.root_dir) + result[v] = index + + with open(os.path.join(args.output_dir,"feature_and_index.pkl"),"wb") as f: + pickle.dump(result,f) \ No newline at end of file diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8f463a7c4011b1eed5f4f200fab1f6b744af60e7 --- /dev/null +++ b/utils.py @@ -0,0 +1,539 @@ +import os +import glob +import re +import sys +import argparse +import logging +import json +import subprocess +import warnings +import random +import functools +import librosa +import numpy as np +from scipy.io.wavfile import read +import torch +from torch.nn import functional as F +from modules.commons import sequence_mask +import faiss +import tqdm + +MATPLOTLIB_FLAG = False + +logging.basicConfig(stream=sys.stdout, level=logging.WARN) +logger = logging + +f0_bin = 256 +f0_max = 1100.0 +f0_min = 50.0 +f0_mel_min = 1127 * np.log(1 + f0_min / 700) +f0_mel_max = 1127 * np.log(1 + f0_max / 700) + +def normalize_f0(f0, x_mask, uv, random_scale=True): + # calculate means based on x_mask + uv_sum = torch.sum(uv, dim=1, keepdim=True) + uv_sum[uv_sum == 0] = 9999 + means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum + + if random_scale: + factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device) + else: + factor = torch.ones(f0.shape[0], 1).to(f0.device) + # normalize f0 based on means and factor + f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1) + if torch.isnan(f0_norm).any(): + exit(0) + return f0_norm * x_mask + +def plot_data_to_numpy(x, y): + global MATPLOTLIB_FLAG + if not MATPLOTLIB_FLAG: + import matplotlib + matplotlib.use("Agg") + MATPLOTLIB_FLAG = True + mpl_logger = logging.getLogger('matplotlib') + mpl_logger.setLevel(logging.WARNING) + import matplotlib.pylab as plt + import numpy as np + + fig, ax = plt.subplots(figsize=(10, 2)) + plt.plot(x) + plt.plot(y) + plt.tight_layout() + + fig.canvas.draw() + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + plt.close() + return data + + +def f0_to_coarse(f0): + is_torch = isinstance(f0, torch.Tensor) + f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) + f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1 + + f0_mel[f0_mel <= 1] = 1 + f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1 + f0_coarse = (f0_mel + 0.5).int() if is_torch else np.rint(f0_mel).astype(np.int) + assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min()) + return f0_coarse + +def get_content(cmodel, y): + with torch.no_grad(): + c = cmodel.extract_features(y.squeeze(1))[0] + c = c.transpose(1, 2) + return c + +def get_f0_predictor(f0_predictor,hop_length,sampling_rate,**kargs): + if f0_predictor == "pm": + from modules.F0Predictor.PMF0Predictor import PMF0Predictor + f0_predictor_object = PMF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate) + elif f0_predictor == "crepe": + from modules.F0Predictor.CrepeF0Predictor import CrepeF0Predictor + f0_predictor_object = CrepeF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate,device=kargs["device"],threshold=kargs["threshold"]) + elif f0_predictor == "harvest": + from modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor + f0_predictor_object = HarvestF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate) + elif f0_predictor == "dio": + from modules.F0Predictor.DioF0Predictor import DioF0Predictor + f0_predictor_object = DioF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate) + else: + raise Exception("Unknown f0 predictor") + return f0_predictor_object + +def get_speech_encoder(speech_encoder,device=None,**kargs): + if speech_encoder == "vec768l12": + from vencoder.ContentVec768L12 import ContentVec768L12 + speech_encoder_object = ContentVec768L12(device = device) + elif speech_encoder == "vec256l9": + from vencoder.ContentVec256L9 import ContentVec256L9 + speech_encoder_object = ContentVec256L9(device = device) + elif speech_encoder == "vec256l9-onnx": + from vencoder.ContentVec256L9_Onnx import ContentVec256L9_Onnx + speech_encoder_object = ContentVec256L9_Onnx(device = device) + elif speech_encoder == "vec256l12-onnx": + from vencoder.ContentVec256L12_Onnx import ContentVec256L12_Onnx + speech_encoder_object = ContentVec256L12_Onnx(device = device) + elif speech_encoder == "vec768l9-onnx": + from vencoder.ContentVec768L9_Onnx import ContentVec768L9_Onnx + speech_encoder_object = ContentVec768L9_Onnx(device = device) + elif speech_encoder == "vec768l12-onnx": + from vencoder.ContentVec768L12_Onnx import ContentVec768L12_Onnx + speech_encoder_object = ContentVec768L12_Onnx(device = device) + elif speech_encoder == "hubertsoft-onnx": + from vencoder.HubertSoft_Onnx import HubertSoft_Onnx + speech_encoder_object = HubertSoft_Onnx(device = device) + elif speech_encoder == "hubertsoft": + from vencoder.HubertSoft import HubertSoft + speech_encoder_object = HubertSoft(device = device) + elif speech_encoder == "whisper-ppg": + from vencoder.WhisperPPG import WhisperPPG + speech_encoder_object = WhisperPPG(device = device) + elif speech_encoder == "cnhubertlarge": + from vencoder.CNHubertLarge import CNHubertLarge + speech_encoder_object = CNHubertLarge(device = device) + elif speech_encoder == "dphubert": + from vencoder.DPHubert import DPHubert + speech_encoder_object = DPHubert(device = device) + elif speech_encoder == "whisper-ppg-large": + from vencoder.WhisperPPGLarge import WhisperPPGLarge + speech_encoder_object = WhisperPPGLarge(device = device) + elif speech_encoder == "wavlmbase+": + from vencoder.WavLMBasePlus import WavLMBasePlus + speech_encoder_object = WavLMBasePlus(device = device) + else: + raise Exception("Unknown speech encoder") + return speech_encoder_object + +def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False): + assert os.path.isfile(checkpoint_path) + checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') + iteration = checkpoint_dict['iteration'] + learning_rate = checkpoint_dict['learning_rate'] + if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None: + optimizer.load_state_dict(checkpoint_dict['optimizer']) + saved_state_dict = checkpoint_dict['model'] + if hasattr(model, 'module'): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + new_state_dict = {} + for k, v in state_dict.items(): + try: + # assert "dec" in k or "disc" in k + # print("load", k) + new_state_dict[k] = saved_state_dict[k] + assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape) + except: + print("error, %s is not in the checkpoint" % k) + logger.info("%s is not in the checkpoint" % k) + new_state_dict[k] = v + if hasattr(model, 'module'): + model.module.load_state_dict(new_state_dict) + else: + model.load_state_dict(new_state_dict) + print("load ") + logger.info("Loaded checkpoint '{}' (iteration {})".format( + checkpoint_path, iteration)) + return model, optimizer, learning_rate, iteration + + +def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): + logger.info("Saving model and optimizer state at iteration {} to {}".format( + iteration, checkpoint_path)) + if hasattr(model, 'module'): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + torch.save({'model': state_dict, + 'iteration': iteration, + 'optimizer': optimizer.state_dict(), + 'learning_rate': learning_rate}, checkpoint_path) + +def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True): + """Freeing up space by deleting saved ckpts + + Arguments: + path_to_models -- Path to the model directory + n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth + sort_by_time -- True -> chronologically delete ckpts + False -> lexicographically delete ckpts + """ + ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))] + name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1))) + time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))) + sort_key = time_key if sort_by_time else name_key + x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key) + to_del = [os.path.join(path_to_models, fn) for fn in + (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])] + del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}") + del_routine = lambda x: [os.remove(x), del_info(x)] + rs = [del_routine(fn) for fn in to_del] + +def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): + for k, v in scalars.items(): + writer.add_scalar(k, v, global_step) + for k, v in histograms.items(): + writer.add_histogram(k, v, global_step) + for k, v in images.items(): + writer.add_image(k, v, global_step, dataformats='HWC') + for k, v in audios.items(): + writer.add_audio(k, v, global_step, audio_sampling_rate) + + +def latest_checkpoint_path(dir_path, regex="G_*.pth"): + f_list = glob.glob(os.path.join(dir_path, regex)) + f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) + x = f_list[-1] + print(x) + return x + + +def plot_spectrogram_to_numpy(spectrogram): + global MATPLOTLIB_FLAG + if not MATPLOTLIB_FLAG: + import matplotlib + matplotlib.use("Agg") + MATPLOTLIB_FLAG = True + mpl_logger = logging.getLogger('matplotlib') + mpl_logger.setLevel(logging.WARNING) + import matplotlib.pylab as plt + import numpy as np + + fig, ax = plt.subplots(figsize=(10,2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + plt.xlabel("Frames") + plt.ylabel("Channels") + plt.tight_layout() + + fig.canvas.draw() + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + plt.close() + return data + + +def plot_alignment_to_numpy(alignment, info=None): + global MATPLOTLIB_FLAG + if not MATPLOTLIB_FLAG: + import matplotlib + matplotlib.use("Agg") + MATPLOTLIB_FLAG = True + mpl_logger = logging.getLogger('matplotlib') + mpl_logger.setLevel(logging.WARNING) + import matplotlib.pylab as plt + import numpy as np + + fig, ax = plt.subplots(figsize=(6, 4)) + im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', + interpolation='none') + fig.colorbar(im, ax=ax) + xlabel = 'Decoder timestep' + if info is not None: + xlabel += '\n\n' + info + plt.xlabel(xlabel) + plt.ylabel('Encoder timestep') + plt.tight_layout() + + fig.canvas.draw() + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + plt.close() + return data + + +def load_wav_to_torch(full_path): + sampling_rate, data = read(full_path) + return torch.FloatTensor(data.astype(np.float32)), sampling_rate + + +def load_filepaths_and_text(filename, split="|"): + with open(filename, encoding='utf-8') as f: + filepaths_and_text = [line.strip().split(split) for line in f] + return filepaths_and_text + + +def get_hparams(init=True): + parser = argparse.ArgumentParser() + parser.add_argument('-c', '--config', type=str, default="./configs/config.json", + help='JSON file for configuration') + parser.add_argument('-m', '--model', type=str, required=True, + help='Model name') + + args = parser.parse_args() + model_dir = os.path.join("./logs", args.model) + + if not os.path.exists(model_dir): + os.makedirs(model_dir) + + config_path = args.config + config_save_path = os.path.join(model_dir, "config.json") + if init: + with open(config_path, "r") as f: + data = f.read() + with open(config_save_path, "w") as f: + f.write(data) + else: + with open(config_save_path, "r") as f: + data = f.read() + config = json.loads(data) + + hparams = HParams(**config) + hparams.model_dir = model_dir + return hparams + + +def get_hparams_from_dir(model_dir): + config_save_path = os.path.join(model_dir, "config.json") + with open(config_save_path, "r") as f: + data = f.read() + config = json.loads(data) + + hparams =HParams(**config) + hparams.model_dir = model_dir + return hparams + + +def get_hparams_from_file(config_path, infer_mode = False): + with open(config_path, "r") as f: + data = f.read() + config = json.loads(data) + hparams =HParams(**config) if not infer_mode else InferHParams(**config) + return hparams + + +def check_git_hash(model_dir): + source_dir = os.path.dirname(os.path.realpath(__file__)) + if not os.path.exists(os.path.join(source_dir, ".git")): + logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( + source_dir + )) + return + + cur_hash = subprocess.getoutput("git rev-parse HEAD") + + path = os.path.join(model_dir, "githash") + if os.path.exists(path): + saved_hash = open(path).read() + if saved_hash != cur_hash: + logger.warn("git hash values are different. {}(saved) != {}(current)".format( + saved_hash[:8], cur_hash[:8])) + else: + open(path, "w").write(cur_hash) + + +def get_logger(model_dir, filename="train.log"): + global logger + logger = logging.getLogger(os.path.basename(model_dir)) + logger.setLevel(logging.DEBUG) + + formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") + if not os.path.exists(model_dir): + os.makedirs(model_dir) + h = logging.FileHandler(os.path.join(model_dir, filename)) + h.setLevel(logging.DEBUG) + h.setFormatter(formatter) + logger.addHandler(h) + return logger + + +def repeat_expand_2d(content, target_len, mode = 'left'): + # content : [h, t] + return repeat_expand_2d_left(content, target_len) if mode == 'left' else repeat_expand_2d_other(content, target_len, mode) + + + +def repeat_expand_2d_left(content, target_len): + # content : [h, t] + + src_len = content.shape[-1] + target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device) + temp = torch.arange(src_len+1) * target_len / src_len + current_pos = 0 + for i in range(target_len): + if i < temp[current_pos+1]: + target[:, i] = content[:, current_pos] + else: + current_pos += 1 + target[:, i] = content[:, current_pos] + + return target + + +# mode : 'nearest'| 'linear'| 'bilinear'| 'bicubic'| 'trilinear'| 'area' +def repeat_expand_2d_other(content, target_len, mode = 'nearest'): + # content : [h, t] + content = content[None,:,:] + target = F.interpolate(content,size=target_len,mode=mode)[0] + return target + + +def mix_model(model_paths,mix_rate,mode): + mix_rate = torch.FloatTensor(mix_rate)/100 + model_tem = torch.load(model_paths[0]) + models = [torch.load(path)["model"] for path in model_paths] + if mode == 0: + mix_rate = F.softmax(mix_rate,dim=0) + for k in model_tem["model"].keys(): + model_tem["model"][k] = torch.zeros_like(model_tem["model"][k]) + for i,model in enumerate(models): + model_tem["model"][k] += model[k]*mix_rate[i] + torch.save(model_tem,os.path.join(os.path.curdir,"output.pth")) + return os.path.join(os.path.curdir,"output.pth") + +def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比 from RVC + # print(data1.max(),data2.max()) + rms1 = librosa.feature.rms( + y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2 + ) # 每半秒一个点 + rms2 = librosa.feature.rms(y=data2.detach().cpu().numpy(), frame_length=sr2 // 2 * 2, hop_length=sr2 // 2) + rms1 = torch.from_numpy(rms1).to(data2.device) + rms1 = F.interpolate( + rms1.unsqueeze(0), size=data2.shape[0], mode="linear" + ).squeeze() + rms2 = torch.from_numpy(rms2).to(data2.device) + rms2 = F.interpolate( + rms2.unsqueeze(0), size=data2.shape[0], mode="linear" + ).squeeze() + rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6) + data2 *= ( + torch.pow(rms1, torch.tensor(1 - rate)) + * torch.pow(rms2, torch.tensor(rate - 1)) + ) + return data2 + +def train_index(spk_name,root_dir = "dataset/44k/"): #from: RVC https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI + print("The feature index is constructing.") + exp_dir = os.path.join(root_dir,spk_name) + listdir_res = [] + for file in os.listdir(exp_dir): + if ".wav.soft.pt" in file: + listdir_res.append(os.path.join(exp_dir,file)) + if len(listdir_res) == 0: + raise Exception("You need to run preprocess_hubert_f0.py!") + npys = [] + for name in sorted(listdir_res): + phone = torch.load(name)[0].transpose(-1,-2).numpy() + npys.append(phone) + big_npy = np.concatenate(npys, 0) + big_npy_idx = np.arange(big_npy.shape[0]) + np.random.shuffle(big_npy_idx) + big_npy = big_npy[big_npy_idx] + n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) + index = faiss.index_factory(big_npy.shape[1] , "IVF%s,Flat" % n_ivf) + index_ivf = faiss.extract_index_ivf(index) # + index_ivf.nprobe = 1 + index.train(big_npy) + batch_size_add = 8192 + for i in range(0, big_npy.shape[0], batch_size_add): + index.add(big_npy[i : i + batch_size_add]) + # faiss.write_index( + # index, + # f"added_{spk_name}.index" + # ) + print("Successfully build index") + return index + + +class HParams(): + def __init__(self, **kwargs): + for k, v in kwargs.items(): + if type(v) == dict: + v = HParams(**v) + self[k] = v + + def keys(self): + return self.__dict__.keys() + + def items(self): + return self.__dict__.items() + + def values(self): + return self.__dict__.values() + + def __len__(self): + return len(self.__dict__) + + def __getitem__(self, key): + return getattr(self, key) + + def __setitem__(self, key, value): + return setattr(self, key, value) + + def __contains__(self, key): + return key in self.__dict__ + + def __repr__(self): + return self.__dict__.__repr__() + + def get(self,index): + return self.__dict__.get(index) + + +class InferHParams(HParams): + def __init__(self, **kwargs): + for k, v in kwargs.items(): + if type(v) == dict: + v = InferHParams(**v) + self[k] = v + + def __getattr__(self,index): + return self.get(index) + + +class Volume_Extractor: + def __init__(self, hop_size = 512): + self.hop_size = hop_size + + def extract(self, audio): # audio: 2d tensor array + if not isinstance(audio,torch.Tensor): + audio = torch.Tensor(audio) + n_frames = int(audio.size(-1) // self.hop_size) + audio2 = audio ** 2 + audio2 = torch.nn.functional.pad(audio2, (int(self.hop_size // 2), int((self.hop_size + 1) // 2)), mode = 'reflect') + volume = 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b/vdecoder/hifigan/env.py @@ -0,0 +1,15 @@ +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/vdecoder/hifigan/models.py b/vdecoder/hifigan/models.py new file mode 100644 index 0000000000000000000000000000000000000000..2c868f3deb3a14ee3b819b1b070d3123736337ef --- /dev/null +++ b/vdecoder/hifigan/models.py @@ -0,0 +1,503 @@ +import os +import json +from .env import AttrDict +import numpy as np +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from .utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +def load_model(model_path, device='cuda'): + config_file = os.path.join(os.path.split(model_path)[0], 'config.json') + with open(config_file) as f: + data = f.read() + + global h + json_config = json.loads(data) + h = AttrDict(json_config) + + generator = Generator(h).to(device) + + cp_dict = torch.load(model_path) + generator.load_state_dict(cp_dict['generator']) + generator.eval() + generator.remove_weight_norm() + del cp_dict + return generator, h + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +def padDiff(x): + return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ + device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + # for normal case + + # To prevent torch.cumsum numerical overflow, + # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. + # Buffer tmp_over_one_idx indicates the time step to add -1. + # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi + tmp_over_one = torch.cumsum(rad_values, 1) % 1 + tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 + cumsum_shift = torch.zeros_like(rad_values) + cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + + sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) + * 2 * np.pi) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + with torch.no_grad(): + f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, + device=f0.device) + # fundamental component + fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) + + # generate sine waveforms + sine_waves = self._f02sine(fn) * self.sine_amp + + # generate uv signal + # uv = torch.ones(f0.shape) + # uv = uv * (f0 > self.voiced_threshold) + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + + self.num_kernels = len(h["resblock_kernel_sizes"]) + self.num_upsamples = len(h["upsample_rates"]) + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"])) + self.m_source = SourceModuleHnNSF( + sampling_rate=h["sampling_rate"], + harmonic_num=8) + self.noise_convs = nn.ModuleList() + self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3)) + resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2 + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])): + c_cur = h["upsample_initial_channel"] // (2 ** (i + 1)) + self.ups.append(weight_norm( + ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)), + k, u, padding=(k - u +1 ) // 2))) + if i + 1 < len(h["upsample_rates"]): # + stride_f0 = np.prod(h["upsample_rates"][i + 1:]) + self.noise_convs.append(Conv1d( + 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2)) + else: + self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h["upsample_initial_channel"] // (2 ** (i + 1)) + for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1) + + def forward(self, x, f0, g=None): + # print(1,x.shape,f0.shape,f0[:, None].shape) + f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + # print(2,f0.shape) + har_source, noi_source, uv = self.m_source(f0) + har_source = har_source.transpose(1, 2) + x = self.conv_pre(x) + x = x + self.cond(g) + # print(124,x.shape,har_source.shape) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + # print(3,x.shape) + x = self.ups[i](x) + x_source = self.noise_convs[i](har_source) + # print(4,x_source.shape,har_source.shape,x.shape) + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, periods=None): + super(MultiPeriodDiscriminator, self).__init__() + self.periods = periods if periods is not None else [2, 3, 5, 7, 11] + self.discriminators = nn.ModuleList() + for period in self.periods: + self.discriminators.append(DiscriminatorP(period)) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ]) + self.meanpools = nn.ModuleList([ + AvgPool1d(4, 2, padding=2), + AvgPool1d(4, 2, padding=2) + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/vdecoder/hifigan/nvSTFT.py b/vdecoder/hifigan/nvSTFT.py new file mode 100644 index 0000000000000000000000000000000000000000..88597d62a505715091f9ba62d38bf0a85a31b95a --- /dev/null +++ b/vdecoder/hifigan/nvSTFT.py @@ -0,0 +1,111 @@ +import math +import os +os.environ["LRU_CACHE_CAPACITY"] = "3" +import random +import torch +import torch.utils.data +import numpy as np +import librosa +from librosa.util import normalize +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read +import soundfile as sf + +def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False): + sampling_rate = None + try: + data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile. + except Exception as ex: + print(f"'{full_path}' failed to load.\nException:") + print(ex) + if return_empty_on_exception: + return [], sampling_rate or target_sr or 32000 + else: + raise Exception(ex) + + if len(data.shape) > 1: + data = data[:, 0] + assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension) + + if np.issubdtype(data.dtype, np.integer): # if audio data is type int + max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX + else: # if audio data is type fp32 + max_mag = max(np.amax(data), -np.amin(data)) + max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32 + + data = torch.FloatTensor(data.astype(np.float32))/max_mag + + if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except + return [], sampling_rate or target_sr or 32000 + if target_sr is not None and sampling_rate != target_sr: + data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr)) + sampling_rate = target_sr + + return data, sampling_rate + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + +class STFT(): + def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5): + self.target_sr = sr + + self.n_mels = n_mels + self.n_fft = n_fft + self.win_size = win_size + self.hop_length = hop_length + self.fmin = fmin + self.fmax = fmax + self.clip_val = clip_val + self.mel_basis = {} + self.hann_window = {} + + def get_mel(self, y, center=False): + sampling_rate = self.target_sr + n_mels = self.n_mels + n_fft = self.n_fft + win_size = self.win_size + hop_length = self.hop_length + fmin = self.fmin + fmax = self.fmax + clip_val = self.clip_val + + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + if fmax not in self.mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) + self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) + self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)], + center=center, pad_mode='reflect', normalized=False, onesided=True) + # print(111,spec) + spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) + # print(222,spec) + spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec) + # print(333,spec) + spec = dynamic_range_compression_torch(spec, clip_val=clip_val) + # print(444,spec) + return spec + + def __call__(self, audiopath): + audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr) + spect = self.get_mel(audio.unsqueeze(0)).squeeze(0) + return spect + +stft = STFT() diff --git a/vdecoder/hifigan/utils.py b/vdecoder/hifigan/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9c93c996d3cc73c30d71c1fc47056e4230f35c0f --- /dev/null +++ b/vdecoder/hifigan/utils.py @@ -0,0 +1,68 @@ +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm +# matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def del_old_checkpoints(cp_dir, prefix, n_models=2): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) # get checkpoint paths + cp_list = sorted(cp_list)# sort by iter + if len(cp_list) > n_models: # if more than n_models models are found + for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models + open(cp, 'w').close()# empty file contents + os.unlink(cp)# delete file (move to trash when using Colab) + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] + diff --git a/vdecoder/hifiganwithsnake/alias/__init__.py b/vdecoder/hifiganwithsnake/alias/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a2318b63198250856809c0cb46210a4147b829bc --- /dev/null +++ b/vdecoder/hifiganwithsnake/alias/__init__.py @@ -0,0 +1,6 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +from .filter import * +from .resample import * +from .act import * \ No newline at end of file diff --git a/vdecoder/hifiganwithsnake/alias/act.py b/vdecoder/hifiganwithsnake/alias/act.py new file mode 100644 index 0000000000000000000000000000000000000000..308344fb6ccbc39317c584a3ee1fb2f29084678e --- /dev/null +++ b/vdecoder/hifiganwithsnake/alias/act.py @@ -0,0 +1,129 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch import sin, pow +from torch.nn import Parameter +from .resample import UpSample1d, DownSample1d + + +class Activation1d(nn.Module): + def __init__(self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + # x: [B,C,T] + def forward(self, x): + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + + return x + + +class SnakeBeta(nn.Module): + ''' + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + ''' + super(SnakeBeta, self).__init__() + self.in_features = in_features + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = Parameter(torch.zeros(in_features) * alpha) + self.beta = Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = Parameter(torch.ones(in_features) * alpha) + self.beta = Parameter(torch.ones(in_features) * alpha) + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta = x + 1/b * sin^2 (xa) + ''' + alpha = self.alpha.unsqueeze( + 0).unsqueeze(-1) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + return x + + +class Mish(nn.Module): + """ + Mish activation function is proposed in "Mish: A Self + Regularized Non-Monotonic Neural Activation Function" + paper, https://arxiv.org/abs/1908.08681. + """ + + def __init__(self): + super().__init__() + + def forward(self, x): + return x * torch.tanh(F.softplus(x)) + + +class SnakeAlias(nn.Module): + def __init__(self, + channels, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = SnakeBeta(channels, alpha_logscale=True) + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + # x: [B,C,T] + def forward(self, x): + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + + return x \ No newline at end of file diff --git a/vdecoder/hifiganwithsnake/alias/filter.py b/vdecoder/hifiganwithsnake/alias/filter.py new file mode 100644 index 0000000000000000000000000000000000000000..7ad6ea87c1f10ddd94c544037791d7a4634d5ae1 --- /dev/null +++ b/vdecoder/hifiganwithsnake/alias/filter.py @@ -0,0 +1,95 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +if 'sinc' in dir(torch): + sinc = torch.sinc +else: + # This code is adopted from adefossez's julius.core.sinc under the MIT License + # https://adefossez.github.io/julius/julius/core.html + # LICENSE is in incl_licenses directory. + def sinc(x: torch.Tensor): + """ + Implementation of sinc, i.e. sin(pi * x) / (pi * x) + __Warning__: Different to julius.sinc, the input is multiplied by `pi`! + """ + return torch.where(x == 0, + torch.tensor(1., device=x.device, dtype=x.dtype), + torch.sin(math.pi * x) / math.pi / x) + + +# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License +# https://adefossez.github.io/julius/julius/lowpass.html +# LICENSE is in incl_licenses directory. +def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size] + even = (kernel_size % 2 == 0) + half_size = kernel_size // 2 + + #For kaiser window + delta_f = 4 * half_width + A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + if A > 50.: + beta = 0.1102 * (A - 8.7) + elif A >= 21.: + beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.) + else: + beta = 0. + window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) + + # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio + if even: + time = (torch.arange(-half_size, half_size) + 0.5) + else: + time = torch.arange(kernel_size) - half_size + if cutoff == 0: + filter_ = torch.zeros_like(time) + else: + filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) + # Normalize filter to have sum = 1, otherwise we will have a small leakage + # of the constant component in the input signal. + filter_ /= filter_.sum() + filter = filter_.view(1, 1, kernel_size) + + return filter + + +class LowPassFilter1d(nn.Module): + def __init__(self, + cutoff=0.5, + half_width=0.6, + stride: int = 1, + padding: bool = True, + padding_mode: str = 'replicate', + kernel_size: int = 12): + # kernel_size should be even number for stylegan3 setup, + # in this implementation, odd number is also possible. + super().__init__() + if cutoff < -0.: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + self.kernel_size = kernel_size + self.even = (kernel_size % 2 == 0) + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = stride + self.padding = padding + self.padding_mode = padding_mode + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter) + + #input [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + if self.padding: + x = F.pad(x, (self.pad_left, self.pad_right), + mode=self.padding_mode) + out = F.conv1d(x, self.filter.expand(C, -1, -1), + stride=self.stride, groups=C) + + return out \ No newline at end of file diff --git a/vdecoder/hifiganwithsnake/alias/resample.py b/vdecoder/hifiganwithsnake/alias/resample.py new file mode 100644 index 0000000000000000000000000000000000000000..750e6c3402cc5ac939c4b9d075246562e0e1d1a7 --- /dev/null +++ b/vdecoder/hifiganwithsnake/alias/resample.py @@ -0,0 +1,49 @@ +# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 +# LICENSE is in incl_licenses directory. + +import torch.nn as nn +from torch.nn import functional as F +from .filter import LowPassFilter1d +from .filter import kaiser_sinc_filter1d + + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, + half_width=0.6 / ratio, + kernel_size=self.kernel_size) + self.register_buffer("filter", filter) + + # x: [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + x = F.pad(x, (self.pad, self.pad), mode='replicate') + x = self.ratio * F.conv_transpose1d( + x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) + x = x[..., self.pad_left:-self.pad_right] + + return x + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio, + half_width=0.6 / ratio, + stride=ratio, + kernel_size=self.kernel_size) + + def forward(self, x): + xx = self.lowpass(x) + + return xx \ No newline at end of file diff --git a/vdecoder/hifiganwithsnake/env.py b/vdecoder/hifiganwithsnake/env.py new file mode 100644 index 0000000000000000000000000000000000000000..2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056 --- /dev/null +++ b/vdecoder/hifiganwithsnake/env.py @@ -0,0 +1,15 @@ +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/vdecoder/hifiganwithsnake/models.py b/vdecoder/hifiganwithsnake/models.py new file mode 100644 index 0000000000000000000000000000000000000000..64f0e4dc985afd7993f78bb1b9743139990fa4d1 --- /dev/null +++ b/vdecoder/hifiganwithsnake/models.py @@ -0,0 +1,518 @@ +import os +import json +from .env import AttrDict +import numpy as np +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from .utils import init_weights, get_padding +from vdecoder.hifiganwithsnake.alias.act import SnakeAlias + +LRELU_SLOPE = 0.1 + + +def load_model(model_path, device='cuda'): + config_file = os.path.join(os.path.split(model_path)[0], 'config.json') + with open(config_file) as f: + data = f.read() + + global h + json_config = json.loads(data) + h = AttrDict(json_config) + + generator = Generator(h).to(device) + + cp_dict = torch.load(model_path) + generator.load_state_dict(cp_dict['generator']) + generator.eval() + generator.remove_weight_norm() + del cp_dict + return generator, h + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + self.num_layers = len(self.convs1) + len(self.convs2) + self.activations = nn.ModuleList([ + SnakeAlias(channels) for _ in range(self.num_layers) + ]) + + def forward(self, x): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): + xt = a1(x) + xt = c1(xt) + xt = a2(xt) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + self.num_layers = len(self.convs) + self.activations = nn.ModuleList([ + SnakeAlias(channels) for _ in range(self.num_layers) + ]) + + def forward(self, x): + for c,a in zip(self.convs, self.activations): + xt = a(x) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +def padDiff(x): + return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ + device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: + # for normal case + + # To prevent torch.cumsum numerical overflow, + # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. + # Buffer tmp_over_one_idx indicates the time step to add -1. + # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi + tmp_over_one = torch.cumsum(rad_values, 1) % 1 + tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 + cumsum_shift = torch.zeros_like(rad_values) + cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + + sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) + * 2 * np.pi) + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + with torch.no_grad(): + f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, + device=f0.device) + # fundamental component + fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) + + # generate sine waveforms + sine_waves = self._f02sine(fn) * self.sine_amp + + # generate uv signal + # uv = torch.ones(f0.shape) + # uv = uv * (f0 > self.voiced_threshold) + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + + self.num_kernels = len(h["resblock_kernel_sizes"]) + self.num_upsamples = len(h["upsample_rates"]) + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"])) + self.m_source = SourceModuleHnNSF( + sampling_rate=h["sampling_rate"], + harmonic_num=8) + self.noise_convs = nn.ModuleList() + self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3)) + resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2 + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])): + c_cur = h["upsample_initial_channel"] // (2 ** (i + 1)) + self.ups.append(weight_norm( + ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)), + k, u, padding=(k - u + 1) // 2))) + if i + 1 < len(h["upsample_rates"]): # + stride_f0 = np.prod(h["upsample_rates"][i + 1:]) + self.noise_convs.append(Conv1d( + 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+ 1) // 2)) + else: + self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) + self.resblocks = nn.ModuleList() + self.snakes = nn.ModuleList() + for i in range(len(self.ups)): + ch = h["upsample_initial_channel"] // (2 ** (i + 1)) + self.snakes.append(SnakeAlias(h["upsample_initial_channel"] // (2 ** (i)))) + for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.snake_post = SnakeAlias(ch) + self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1) + + def forward(self, x, f0, g=None): + # print(1,x.shape,f0.shape,f0[:, None].shape) + f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + # print(2,f0.shape) + har_source, noi_source, uv = self.m_source(f0) + har_source = har_source.transpose(1, 2) + x = self.conv_pre(x) + x = x + self.cond(g) + # print(124,x.shape,har_source.shape) + for i in range(self.num_upsamples): + x = self.snakes[i](x) + # print(3,x.shape) + x = self.ups[i](x) + x_source = self.noise_convs[i](har_source) + # print(4,x_source.shape,har_source.shape,x.shape) + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = self.snake_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, periods=None): + super(MultiPeriodDiscriminator, self).__init__() + self.periods = periods if periods is not None else [2, 3, 5, 7, 11] + self.discriminators = nn.ModuleList() + for period in self.periods: + self.discriminators.append(DiscriminatorP(period)) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ]) + self.meanpools = nn.ModuleList([ + AvgPool1d(4, 2, padding=2), + AvgPool1d(4, 2, padding=2) + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/vdecoder/hifiganwithsnake/nvSTFT.py b/vdecoder/hifiganwithsnake/nvSTFT.py new file mode 100644 index 0000000000000000000000000000000000000000..88597d62a505715091f9ba62d38bf0a85a31b95a --- /dev/null +++ b/vdecoder/hifiganwithsnake/nvSTFT.py @@ -0,0 +1,111 @@ +import math +import os +os.environ["LRU_CACHE_CAPACITY"] = "3" +import random +import torch +import torch.utils.data +import numpy as np +import librosa +from librosa.util import normalize +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read +import soundfile as sf + +def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False): + sampling_rate = None + try: + data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile. + except Exception as ex: + print(f"'{full_path}' failed to load.\nException:") + print(ex) + if return_empty_on_exception: + return [], sampling_rate or target_sr or 32000 + else: + raise Exception(ex) + + if len(data.shape) > 1: + data = data[:, 0] + assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension) + + if np.issubdtype(data.dtype, np.integer): # if audio data is type int + max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX + else: # if audio data is type fp32 + max_mag = max(np.amax(data), -np.amin(data)) + max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32 + + data = torch.FloatTensor(data.astype(np.float32))/max_mag + + if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except + return [], sampling_rate or target_sr or 32000 + if target_sr is not None and sampling_rate != target_sr: + data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr)) + sampling_rate = target_sr + + return data, sampling_rate + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + +class STFT(): + def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5): + self.target_sr = sr + + self.n_mels = n_mels + self.n_fft = n_fft + self.win_size = win_size + self.hop_length = hop_length + self.fmin = fmin + self.fmax = fmax + self.clip_val = clip_val + self.mel_basis = {} + self.hann_window = {} + + def get_mel(self, y, center=False): + sampling_rate = self.target_sr + n_mels = self.n_mels + n_fft = self.n_fft + win_size = self.win_size + hop_length = self.hop_length + fmin = self.fmin + fmax = self.fmax + clip_val = self.clip_val + + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + if fmax not in self.mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) + self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) + self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)], + center=center, pad_mode='reflect', normalized=False, onesided=True) + # print(111,spec) + spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) + # print(222,spec) + spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec) + # print(333,spec) + spec = dynamic_range_compression_torch(spec, clip_val=clip_val) + # print(444,spec) + return spec + + def __call__(self, audiopath): + audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr) + spect = self.get_mel(audio.unsqueeze(0)).squeeze(0) + return spect + +stft = STFT() diff --git a/vdecoder/hifiganwithsnake/utils.py b/vdecoder/hifiganwithsnake/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9c93c996d3cc73c30d71c1fc47056e4230f35c0f --- /dev/null +++ b/vdecoder/hifiganwithsnake/utils.py @@ -0,0 +1,68 @@ +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm +# matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def del_old_checkpoints(cp_dir, prefix, n_models=2): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) # get checkpoint paths + cp_list = sorted(cp_list)# sort by iter + if len(cp_list) > n_models: # if more than n_models models are found + for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models + open(cp, 'w').close()# empty file contents + os.unlink(cp)# delete file (move to trash when using Colab) + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] + diff --git a/vdecoder/nsf_hifigan/__pycache__/env.cpython-38.pyc b/vdecoder/nsf_hifigan/__pycache__/env.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a1004b3654b4596ea8030b2deced7977c319016 Binary files /dev/null and b/vdecoder/nsf_hifigan/__pycache__/env.cpython-38.pyc differ diff --git a/vdecoder/nsf_hifigan/__pycache__/env.cpython-39.pyc b/vdecoder/nsf_hifigan/__pycache__/env.cpython-39.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..03c0dd3da0363044790f4274fdb2458c9c4ba9f1 Binary files /dev/null and b/vdecoder/nsf_hifigan/__pycache__/utils.cpython-39.pyc differ diff --git a/vdecoder/nsf_hifigan/env.py b/vdecoder/nsf_hifigan/env.py new file mode 100644 index 0000000000000000000000000000000000000000..2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056 --- /dev/null +++ b/vdecoder/nsf_hifigan/env.py @@ -0,0 +1,15 @@ +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/vdecoder/nsf_hifigan/models.py b/vdecoder/nsf_hifigan/models.py new file mode 100644 index 0000000000000000000000000000000000000000..c2c889ec2fbd215702298ba2b7c411c6f5630d80 --- /dev/null +++ b/vdecoder/nsf_hifigan/models.py @@ -0,0 +1,439 @@ +import os +import json +from .env import AttrDict +import numpy as np +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from .utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +def load_model(model_path, device='cuda'): + h = load_config(model_path) + + generator = Generator(h).to(device) + + cp_dict = torch.load(model_path, map_location=device) + generator.load_state_dict(cp_dict['generator']) + generator.eval() + generator.remove_weight_norm() + del cp_dict + return generator, h + +def load_config(model_path): + config_file = os.path.join(os.path.split(model_path)[0], 'config.json') + with open(config_file) as f: + data = f.read() + + json_config = json.loads(data) + h = AttrDict(json_config) + return h + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + + def _f02uv(self, f0): + # generate uv signal + uv = torch.ones_like(f0) + uv = uv * (f0 > self.voiced_threshold) + return uv + + @torch.no_grad() + def forward(self, f0, upp): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + f0 = f0.unsqueeze(-1) + fn = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1))) + rad_values = (fn / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 + rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + is_half = rad_values.dtype is not torch.float32 + tmp_over_one = torch.cumsum(rad_values.double(), 1) # % 1 #####%1意味着后面的cumsum无法再优化 + if is_half: + tmp_over_one = tmp_over_one.half() + else: + tmp_over_one = tmp_over_one.float() + tmp_over_one *= upp + tmp_over_one = F.interpolate( + tmp_over_one.transpose(2, 1), scale_factor=upp, + mode='linear', align_corners=True + ).transpose(2, 1) + rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1) + tmp_over_one %= 1 + tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 + cumsum_shift = torch.zeros_like(rad_values) + cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + rad_values = rad_values.double() + cumsum_shift = cumsum_shift.double() + sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi) + if is_half: + sine_waves = sine_waves.half() + else: + sine_waves = sine_waves.float() + sine_waves = sine_waves * self.sine_amp + uv = self._f02uv(f0) + uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1) + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x, upp): + sine_wavs, uv, _ = self.l_sin_gen(x, upp) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + return sine_merge + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.m_source = SourceModuleHnNSF( + sampling_rate=h.sampling_rate, + harmonic_num=8 + ) + self.noise_convs = nn.ModuleList() + self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == '1' else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + c_cur = h.upsample_initial_channel // (2 ** (i + 1)) + self.ups.append(weight_norm( + ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)), + k, u, padding=(k - u) // 2))) + if i + 1 < len(h.upsample_rates): # + stride_f0 = int(np.prod(h.upsample_rates[i + 1:])) + self.noise_convs.append(Conv1d( + 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2)) + else: + self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) + self.resblocks = nn.ModuleList() + ch = h.upsample_initial_channel + for i in range(len(self.ups)): + ch //= 2 + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.upp = int(np.prod(h.upsample_rates)) + + def forward(self, x, f0): + har_source = self.m_source(f0, self.upp).transpose(1, 2) + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + x_source = self.noise_convs[i](har_source) + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self, periods=None): + super(MultiPeriodDiscriminator, self).__init__() + self.periods = periods if periods is not None else [2, 3, 5, 7, 11] + self.discriminators = nn.ModuleList() + for period in self.periods: + self.discriminators.append(DiscriminatorP(period)) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ]) + self.meanpools = nn.ModuleList([ + AvgPool1d(4, 2, padding=2), + AvgPool1d(4, 2, padding=2) + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/vdecoder/nsf_hifigan/nvSTFT.py b/vdecoder/nsf_hifigan/nvSTFT.py new file mode 100644 index 0000000000000000000000000000000000000000..62bd5a008f81929054f036c81955d5d73377f772 --- /dev/null +++ b/vdecoder/nsf_hifigan/nvSTFT.py @@ -0,0 +1,134 @@ +import math +import os +os.environ["LRU_CACHE_CAPACITY"] = "3" +import random +import torch +import torch.utils.data +import numpy as np +import librosa +from librosa.util import normalize +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read +import soundfile as sf +import torch.nn.functional as F + +def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False): + sampling_rate = None + try: + data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile. + except Exception as ex: + print(f"'{full_path}' failed to load.\nException:") + print(ex) + if return_empty_on_exception: + return [], sampling_rate or target_sr or 48000 + else: + raise Exception(ex) + + if len(data.shape) > 1: + data = data[:, 0] + assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension) + + if np.issubdtype(data.dtype, np.integer): # if audio data is type int + max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX + else: # if audio data is type fp32 + max_mag = max(np.amax(data), -np.amin(data)) + max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32 + + data = torch.FloatTensor(data.astype(np.float32))/max_mag + + if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except + return [], sampling_rate or target_sr or 48000 + if target_sr is not None and sampling_rate != target_sr: + data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr)) + sampling_rate = target_sr + + return data, sampling_rate + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + +class STFT(): + def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5): + self.target_sr = sr + + self.n_mels = n_mels + self.n_fft = n_fft + self.win_size = win_size + self.hop_length = hop_length + self.fmin = fmin + self.fmax = fmax + self.clip_val = clip_val + self.mel_basis = {} + self.hann_window = {} + + def get_mel(self, y, keyshift=0, speed=1, center=False): + sampling_rate = self.target_sr + n_mels = self.n_mels + n_fft = self.n_fft + win_size = self.win_size + hop_length = self.hop_length + fmin = self.fmin + fmax = self.fmax + clip_val = self.clip_val + + factor = 2 ** (keyshift / 12) + n_fft_new = int(np.round(n_fft * factor)) + win_size_new = int(np.round(win_size * factor)) + hop_length_new = int(np.round(hop_length * speed)) + + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + mel_basis_key = str(fmax)+'_'+str(y.device) + if mel_basis_key not in self.mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) + self.mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device) + + keyshift_key = str(keyshift)+'_'+str(y.device) + if keyshift_key not in self.hann_window: + self.hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device) + + pad_left = (win_size_new - hop_length_new) //2 + pad_right = max((win_size_new- hop_length_new + 1) //2, win_size_new - y.size(-1) - pad_left) + if pad_right < y.size(-1): + mode = 'reflect' + else: + mode = 'constant' + y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode = mode) + y = y.squeeze(1) + + spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=self.hann_window[keyshift_key], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + # print(111,spec) + spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) + if keyshift != 0: + size = n_fft // 2 + 1 + resize = spec.size(1) + if resize < size: + spec = F.pad(spec, (0, 0, 0, size-resize)) + spec = spec[:, :size, :] * win_size / win_size_new + + # print(222,spec) + spec = torch.matmul(self.mel_basis[mel_basis_key], spec) + # print(333,spec) + spec = dynamic_range_compression_torch(spec, clip_val=clip_val) + # print(444,spec) + return spec + + def __call__(self, audiopath): + audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr) + spect = self.get_mel(audio.unsqueeze(0)).squeeze(0) + return spect + +stft = STFT() diff --git a/vdecoder/nsf_hifigan/utils.py b/vdecoder/nsf_hifigan/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..84bff024f4d2e2de194b2a88ee7bbe5f0d33f67c --- /dev/null +++ b/vdecoder/nsf_hifigan/utils.py @@ -0,0 +1,68 @@ +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def del_old_checkpoints(cp_dir, prefix, n_models=2): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) # get checkpoint paths + cp_list = sorted(cp_list)# sort by iter + if len(cp_list) > n_models: # if more than n_models models are found + for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models + open(cp, 'w').close()# empty file contents + os.unlink(cp)# delete file (move to trash when using Colab) + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + '????????') + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] + diff --git a/vencoder/CNHubertLarge.py b/vencoder/CNHubertLarge.py new file mode 100644 index 0000000000000000000000000000000000000000..9db93781c36884c4096fa6fa5a12a95d385e80b8 --- /dev/null +++ b/vencoder/CNHubertLarge.py @@ -0,0 +1,33 @@ +from vencoder.encoder import SpeechEncoder +import torch +from fairseq import checkpoint_utils + +class CNHubertLarge(SpeechEncoder): + def __init__(self,vec_path = "pretrain/chinese-hubert-large-fairseq-ckpt.pt",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 1024 + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [vec_path], + suffix="", + ) + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + self.model = models[0].to(self.dev) + self.model.eval() + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + padding_mask = torch.BoolTensor(feats.shape).fill_(False) + inputs = { + "source": feats.to(wav.device), + "padding_mask": padding_mask.to(wav.device) + } + with torch.no_grad(): + logits = self.model.extract_features(**inputs) + return logits[0].transpose(1, 2) \ No newline at end of file diff --git a/vencoder/ContentVec256L12_Onnx.py b/vencoder/ContentVec256L12_Onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..9ad5085e02654fd1fcfbdad7d476bfa9b763d2c6 --- /dev/null +++ b/vencoder/ContentVec256L12_Onnx.py @@ -0,0 +1,28 @@ +from vencoder.encoder import SpeechEncoder +import onnxruntime +import torch + +class ContentVec256L12_Onnx(SpeechEncoder): + def __init__(self,vec_path = "pretrain/vec-256-layer-12.onnx",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 256 + if device is None: + self.dev = torch.device("cpu") + else: + self.dev = torch.device(device) + if device == 'cpu' or device == torch.device("cpu") or device is None: + providers = ['CPUExecutionProvider'] + elif device == 'cuda' or device == torch.device("cuda"): + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + self.model = onnxruntime.InferenceSession(vec_path, providers=providers) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + feats = feats.unsqueeze(0).cpu().detach().numpy() + onnx_input = {self.model.get_inputs()[0].name: feats} + logits = self.model.run(None, onnx_input) + return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) diff --git a/vencoder/ContentVec256L9.py b/vencoder/ContentVec256L9.py new file mode 100644 index 0000000000000000000000000000000000000000..b0089c789cd87cfd3b1badb2fc45cb1b88041eab --- /dev/null +++ b/vencoder/ContentVec256L9.py @@ -0,0 +1,35 @@ +from vencoder.encoder import SpeechEncoder +import torch +from fairseq import checkpoint_utils + +class ContentVec256L9(SpeechEncoder): + def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None): + print("load model(s) from {}".format(vec_path)) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [vec_path], + suffix="", + ) + self.hidden_dim = 256 + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + self.model = models[0].to(self.dev) + self.model.eval() + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + padding_mask = torch.BoolTensor(feats.shape).fill_(False) + inputs = { + "source": feats.to(wav.device), + "padding_mask": padding_mask.to(wav.device), + "output_layer": 9, # layer 9 + } + with torch.no_grad(): + logits = self.model.extract_features(**inputs) + feats = self.model.final_proj(logits[0]) + return feats.transpose(1, 2) diff --git a/vencoder/ContentVec256L9_Onnx.py b/vencoder/ContentVec256L9_Onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..fae2b928252801795b038f51451b234e007f6f03 --- /dev/null +++ b/vencoder/ContentVec256L9_Onnx.py @@ -0,0 +1,28 @@ +from vencoder.encoder import SpeechEncoder +import onnxruntime +import torch + +class ContentVec256L9_Onnx(SpeechEncoder): + def __init__(self,vec_path = "pretrain/vec-256-layer-9.onnx",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 256 + if device is None: + self.dev = torch.device("cpu") + else: + self.dev = torch.device(device) + if device == 'cpu' or device == torch.device("cpu") or device is None: + providers = ['CPUExecutionProvider'] + elif device == 'cuda' or device == torch.device("cuda"): + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + self.model = onnxruntime.InferenceSession(vec_path, providers=providers) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + feats = feats.unsqueeze(0).cpu().detach().numpy() + onnx_input = {self.model.get_inputs()[0].name: feats} + logits = self.model.run(None, onnx_input) + return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/vencoder/ContentVec768L12.py b/vencoder/ContentVec768L12.py new file mode 100644 index 0000000000000000000000000000000000000000..0d1591c8843b920d5685e822354e8e6adc9a9e19 --- /dev/null +++ b/vencoder/ContentVec768L12.py @@ -0,0 +1,34 @@ +from vencoder.encoder import SpeechEncoder +import torch +from fairseq import checkpoint_utils + +class ContentVec768L12(SpeechEncoder): + def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 768 + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [vec_path], + suffix="", + ) + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + self.model = models[0].to(self.dev) + self.model.eval() + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + padding_mask = torch.BoolTensor(feats.shape).fill_(False) + inputs = { + "source": feats.to(wav.device), + "padding_mask": padding_mask.to(wav.device), + "output_layer": 12, # layer 12 + } + with torch.no_grad(): + logits = self.model.extract_features(**inputs) + return logits[0].transpose(1, 2) \ No newline at end of file diff --git a/vencoder/ContentVec768L12_Onnx.py b/vencoder/ContentVec768L12_Onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..8dde0f173ed60169282128cc51eb1c200c5d82c5 --- /dev/null +++ b/vencoder/ContentVec768L12_Onnx.py @@ -0,0 +1,28 @@ +from vencoder.encoder import SpeechEncoder +import onnxruntime +import torch + +class ContentVec768L12_Onnx(SpeechEncoder): + def __init__(self,vec_path = "pretrain/vec-768-layer-12.onnx",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 768 + if device is None: + self.dev = torch.device("cpu") + else: + self.dev = torch.device(device) + if device == 'cpu' or device == torch.device("cpu") or device is None: + providers = ['CPUExecutionProvider'] + elif device == 'cuda' or device == torch.device("cuda"): + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + self.model = onnxruntime.InferenceSession(vec_path, providers=providers) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + feats = feats.unsqueeze(0).cpu().detach().numpy() + onnx_input = {self.model.get_inputs()[0].name: feats} + logits = self.model.run(None, onnx_input) + return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/vencoder/ContentVec768L9_Onnx.py b/vencoder/ContentVec768L9_Onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..7cdac4cd93478d3ddddb4b76dd9d9ccc5d1af2d4 --- /dev/null +++ b/vencoder/ContentVec768L9_Onnx.py @@ -0,0 +1,28 @@ +from vencoder.encoder import SpeechEncoder +import onnxruntime +import torch + +class ContentVec768L9_Onnx(SpeechEncoder): + def __init__(self,vec_path = "pretrain/vec-768-layer-9.onnx",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 768 + if device is None: + self.dev = torch.device("cpu") + else: + self.dev = torch.device(device) + if device == 'cpu' or device == torch.device("cpu") or device is None: + providers = ['CPUExecutionProvider'] + elif device == 'cuda' or device == torch.device("cuda"): + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + self.model = onnxruntime.InferenceSession(vec_path, providers=providers) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + feats = feats.unsqueeze(0).cpu().detach().numpy() + onnx_input = {self.model.get_inputs()[0].name: feats} + logits = self.model.run(None, onnx_input) + return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/vencoder/DPHubert.py b/vencoder/DPHubert.py new file mode 100644 index 0000000000000000000000000000000000000000..95b98b8b2e08e76139ce652bbbdb60dc42248a19 --- /dev/null +++ b/vencoder/DPHubert.py @@ -0,0 +1,26 @@ +from vencoder.encoder import SpeechEncoder +import torch +from vencoder.dphubert.model import wav2vec2_model + +class DPHubert(SpeechEncoder): + def __init__(self,vec_path = "pretrain/DPHuBERT-sp0.75.pth",device=None): + print("load model(s) from {}".format(vec_path)) + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + ckpt = torch.load(vec_path) + self.hidden_dim = 768 + self.model = wav2vec2_model(**ckpt["config"]).to(self.dev) + self.model.load_state_dict(ckpt["state_dict"], strict=False) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats[None,:] + with torch.no_grad(): + with torch.inference_mode(): + units = self.model(feats)[0] + return units.transpose(1,2) diff --git a/vencoder/HubertSoft.py b/vencoder/HubertSoft.py new file mode 100644 index 0000000000000000000000000000000000000000..c7155e9edd8b3d898643f59111cd0c7a83067749 --- /dev/null +++ b/vencoder/HubertSoft.py @@ -0,0 +1,24 @@ +from vencoder.encoder import SpeechEncoder +import torch +from vencoder.hubert import hubert_model +class HubertSoft(SpeechEncoder): + def __init__(self,vec_path = "pretrain/hubert-soft-0d54a1f4.pt",device=None): + print("load model(s) from {}".format(vec_path)) + hubert_soft = hubert_model.hubert_soft(vec_path) + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + self.hidden_dim = 256 + self.model = hubert_soft.to(self.dev) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats[None,None,:] + with torch.no_grad(): + with torch.inference_mode(): + units = self.model.units(feats) + return units.transpose(1,2) diff --git a/vencoder/HubertSoft_Onnx.py b/vencoder/HubertSoft_Onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..06f10a4ca79c429ed59ab9743578128e8db506cc --- /dev/null +++ b/vencoder/HubertSoft_Onnx.py @@ -0,0 +1,28 @@ +from vencoder.encoder import SpeechEncoder +import onnxruntime +import torch + +class HubertSoft_Onnx(SpeechEncoder): + def __init__(self,vec_path = "pretrain/hubert-soft.onnx",device=None): + print("load model(s) from {}".format(vec_path)) + self.hidden_dim = 256 + if device is None: + self.dev = torch.device("cpu") + else: + self.dev = torch.device(device) + if device == 'cpu' or device == torch.device("cpu") or device is None: + providers = ['CPUExecutionProvider'] + elif device == 'cuda' or device == torch.device("cuda"): + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + self.model = onnxruntime.InferenceSession(vec_path, providers=providers) + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + feats = feats.unsqueeze(0).cpu().detach().numpy() + onnx_input = {self.model.get_inputs()[0].name: feats} + logits = self.model.run(None, onnx_input) + return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/vencoder/WavLMBasePlus.py b/vencoder/WavLMBasePlus.py new file mode 100644 index 0000000000000000000000000000000000000000..b105dbc610b85232ed8e3f6986dfce626cf54b3c --- /dev/null +++ b/vencoder/WavLMBasePlus.py @@ -0,0 +1,29 @@ +from vencoder.encoder import SpeechEncoder +import torch +from vencoder.wavlm.WavLM import WavLM, WavLMConfig + +class WavLMBasePlus(SpeechEncoder): + def __init__(self,vec_path = "pretrain/WavLM-Base+.pt",device=None): + print("load model(s) from {}".format(vec_path)) + checkpoint = torch.load(vec_path) + self.cfg = WavLMConfig(checkpoint['cfg']) + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + self.hidden_dim = self.cfg.encoder_embed_dim + self.model = WavLM(self.cfg) + self.model.load_state_dict(checkpoint['model']) + self.model.to(self.dev).eval() + + def encoder(self, wav): + feats = wav + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + if self.cfg.normalize: + feats = torch.nn.functional.layer_norm(feats , feats.shape) + with torch.no_grad(): + with torch.inference_mode(): + units = self.model.extract_features(feats[None,:])[0] + return units.transpose(1,2) diff --git a/vencoder/WhisperPPG.py b/vencoder/WhisperPPG.py new file mode 100644 index 0000000000000000000000000000000000000000..aa988b0a6d05696ea519d1652e5801302ba8a6c6 --- /dev/null +++ b/vencoder/WhisperPPG.py @@ -0,0 +1,30 @@ +from vencoder.encoder import SpeechEncoder +import torch + +from vencoder.whisper.model import Whisper, ModelDimensions +from vencoder.whisper.audio import pad_or_trim, log_mel_spectrogram + + +class WhisperPPG(SpeechEncoder): + def __init__(self,vec_path = "pretrain/medium.pt",device=None): + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + checkpoint = torch.load(vec_path, map_location=device) + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + self.hidden_dim = dims + self.model = model.to(self.dev) + + def encoder(self, wav): + audio = wav + audln = audio.shape[0] + ppgln = audln // 320 + audio = pad_or_trim(audio) + mel = log_mel_spectrogram(audio).to(self.dev) + with torch.no_grad(): + ppg = self.model.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy() + ppg = torch.FloatTensor(ppg[:ppgln,]).to(self.dev) + return ppg[None,:,:].transpose(1, 2) diff --git a/vencoder/WhisperPPGLarge.py b/vencoder/WhisperPPGLarge.py new file mode 100644 index 0000000000000000000000000000000000000000..cab1ca646a1559c2a05b24ec38474408f27b3f08 --- /dev/null +++ b/vencoder/WhisperPPGLarge.py @@ -0,0 +1,30 @@ +from vencoder.encoder import SpeechEncoder +import torch + +from vencoder.whisper.model import Whisper, ModelDimensions +from vencoder.whisper.audio import pad_or_trim, log_mel_spectrogram + + +class WhisperPPGLarge(SpeechEncoder): + def __init__(self,vec_path = "pretrain/large-v2.pt",device=None): + if device is None: + self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + self.dev = torch.device(device) + checkpoint = torch.load(vec_path, map_location=device) + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + self.hidden_dim = dims + self.model = model.to(self.dev) + + def encoder(self, wav): + audio = wav + audln = audio.shape[0] + ppgln = audln // 320 + audio = pad_or_trim(audio) + mel = log_mel_spectrogram(audio).to(self.dev) + with torch.no_grad(): + ppg = self.model.encoder(mel.unsqueeze(0)).squeeze().data.cpu().float().numpy() + ppg = torch.FloatTensor(ppg[:ppgln,]).to(self.dev) + return ppg[None,:,:].transpose(1, 2) diff --git a/vencoder/__init__.py b/vencoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vencoder/__pycache__/ContentVec256L9.cpython-38.pyc b/vencoder/__pycache__/ContentVec256L9.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1437a986561e931d4077b36efb11525801da77ed Binary files /dev/null and b/vencoder/__pycache__/ContentVec256L9.cpython-38.pyc differ diff --git a/vencoder/__pycache__/ContentVec768L12.cpython-38.pyc b/vencoder/__pycache__/ContentVec768L12.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..649f847cef3f3206a6576955f13ecf6a996aa801 Binary files /dev/null and b/vencoder/__pycache__/ContentVec768L12.cpython-38.pyc differ diff --git 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b/vencoder/dphubert/components.py @@ -0,0 +1,1410 @@ +"""Building blocks for speech SSL models supporting pruning. + +Originally from: +https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py + +""" + +from collections import defaultdict +from typing import List, Optional, Tuple +import math + +import torch +from torch import nn, Tensor +from torch.nn import Module, Parameter + +from .hardconcrete import HardConcrete +from .pruning_utils import ( + prune_linear_layer, + prune_conv1d_layer, + prune_layer_norm, +) + + +def _init_transformer_params(module): + """ + Initialize the weights of Transformer module in Wav2Vec2/HuBERT. + + If the module is ``nn.Linear``, normalize the weight with mean 0 and standard deviation 0.02. + If ``bias`` is set to ``True`` in the module, set ``bias`` to 0. + + If the module is ``nn.Embedding``, normalize the weight with mean 0 and standard deviation 0.02. + If ``padding_idx`` is not None, set the weight of padding to 0. + + Note: + Ths method corresponds to + `init_bert_params + `__ + in the original ``fairseq`` implementation. + """ + + def normal_(data): + data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device)) + + if isinstance(module, nn.Linear): + normal_(module.weight.data) + if module.bias is not None: + module.bias.data.zero_() + if isinstance(module, nn.Embedding): + normal_(module.weight.data) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +class LayerNorm(nn.LayerNorm): + """Layer norm with transpose""" + + def forward(self, input: Tensor) -> Tensor: + x = input.transpose(-2, -1) + x = nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + x = x.transpose(-2, -1) + return x + + +class ConvLayerBlock(Module): + """Convolution unit of FeatureExtractor""" + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int, + bias: bool, + layer_norm: Optional[Module], + prune_conv_channels: bool = False, + ): + super().__init__() + self.kernel_size = kernel_size + self.stride = stride + self.layer_norm = layer_norm + self.conv = nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + bias=bias, + ) + + if prune_conv_channels: + self.hard_concrete = HardConcrete(n_in=out_channels, init_mean=0.01) + else: + self.hard_concrete = None + + def forward( + self, + x: Tensor, + length: Optional[Tensor], + ) -> Tuple[Tensor, Optional[Tensor]]: + """ + Args: + x (Tensor): Shape: ``[batch, in_channels, in_frame]``. + length (Tensor or None, optional): Shape ``[batch, ]``. + Returns: + Tensor: Shape ``[batch, out_channels, out_frames]``. + Optional[Tensor]: Shape ``[batch, ]``. + """ + x = self.conv(x) + if self.layer_norm is not None: + x = self.layer_norm(x) + x = nn.functional.gelu(x) + + if self.hard_concrete is not None: + channel_mask = self.hard_concrete() # hard concrete mask, (out_channels,) + x = x * channel_mask.unsqueeze(-1) + + if length is not None: + length = torch.div(length - self.kernel_size, self.stride, rounding_mode="floor") + 1 + # When input length is 0, the resulting length can be negative. So fix it here. + length = torch.max(torch.zeros_like(length), length) + return x, length + + def get_num_params_and_out_channels(self, in_channels): + if self.hard_concrete is not None: + out_channels = self.hard_concrete.l0_norm() + else: + out_channels = self.conv.out_channels + + num_params = in_channels * out_channels * self.kernel_size + if self.conv.bias is not None: + num_params += out_channels + if self.layer_norm is not None: + num_params += out_channels * 2 + + return num_params, out_channels + + +class FeatureExtractor(Module): + """Extract features from audio + + Args: + conv_layers (nn.ModuleList): + convolution layers + """ + + def __init__( + self, + conv_layers: nn.ModuleList, + ): + super().__init__() + self.conv_layers = conv_layers + + # NOTE: a dummy weight used to save the soft mask of the last conv layer + self.dummy_weight = nn.Parameter( + torch.ones(conv_layers[-1].conv.out_channels, dtype=torch.float32), + requires_grad=False + ) + + def forward( + self, + x: Tensor, + length: Optional[Tensor], + ) -> Tuple[Tensor, Optional[Tensor]]: + """ + Args: + x (Tensor): + Input Tensor representing a batch of audio, + shape: ``[batch, time]``. + length (Tensor or None, optional): + Valid length of each input sample. shape: ``[batch, ]``. + + Returns: + Tensor: + The resulting feature, shape: ``[batch, frame, feature]`` + Optional[Tensor]: + Valid length of each output sample. shape: ``[batch, ]``. + """ + if x.ndim != 2: + raise ValueError("Expected the input Tensor to be 2D (batch, time), " "but received {list(x.shape)}") + + x = x.unsqueeze(1) # (batch, channel==1, frame) + for layer in self.conv_layers: + x, length = layer(x, length) # (batch, feature, frame) + x = x.transpose(1, 2) # (batch, frame, feature) + x = x * self.dummy_weight + return x, length + + def get_num_params_and_final_out_channels(self): + in_channels = 1 + num_params = 0 + for layer in self.conv_layers: + layer_params, in_channels = layer.get_num_params_and_out_channels(in_channels) + num_params += layer_params + + num_params += in_channels # dummy weight + + return num_params, in_channels + + def prune(self): + """"Prune conv layers and dummy weight based on hardconcrete parameters. + This is an in-place operation. + """ + new_config = [] # [(output_channel, kernel_size, stride), ...] + for idx, layer in enumerate(self.conv_layers): + if layer.hard_concrete is not None: + assert not layer.hard_concrete.training + mask = layer.hard_concrete() # (out_features,) + index = mask.nonzero().squeeze(-1) # 2D -> 1D + assert len(index) > 0, f"Conv channels pruned to zero at index {idx}" + new_config.append( + (len(index), layer.kernel_size, layer.stride) + ) + + # prune the current layer + prune_conv1d_layer(layer.conv, index, "output") + if layer.layer_norm is not None: + prune_layer_norm(layer.layer_norm, index) + + # prune the next layer + if idx == len(self.conv_layers) - 1: + self.dummy_weight.data *= mask + self.dummy_weight = nn.Parameter( + self.dummy_weight.index_select(0, index).clone().detach(), requires_grad=False + ) + else: + self.conv_layers[idx+1].conv.weight.data *= mask.unsqueeze(-1) + prune_conv1d_layer(self.conv_layers[idx+1].conv, index, dim="input") + + layer.hard_concrete = None + else: + new_config.append( + (layer.conv.out_channels, layer.kernel_size, layer.stride) + ) + index = torch.arange(layer.conv.out_channels, dtype=torch.long) + + return new_config, index + + +class FeatureProjection(Module): + """Layer that connects FeatureExtractor and Encoder + + Projects features to encoder dimension. + + Args: + in_features (int): Input feature dim. + out_features (int): Output feature dim. + dropout (float): Dropout probability. + """ + + def __init__( + self, + in_features: int, + out_features: int, + dropout: float, + ): + super().__init__() + self.layer_norm = nn.LayerNorm(in_features) + self.projection = nn.Linear( + in_features, + out_features, + ) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + """ + Args: + x (Tensor): + Feature Tensor. shape: ``[batch, frame, in_feature]`` + Returns: + Tensor: Projected features. ``[batch, frame, out_feature]``. + """ + x = self.layer_norm(x) + x = self.projection(x) + x = self.dropout(x) + return x + + def get_num_params(self, in_features): + return in_features * 2 + (in_features + 1) * self.projection.out_features + + +class ConvolutionalPositionalEmbedding(Module): + """Positional embedding which is placed at the beginning of Transformer. + + Args: + embed_dim (int): Feature dimension of the input Tensor. + kernel_size (int): The number of frames to be use. + groups (int): The number of groups in feature dimensions. + """ + + def __init__( + self, + embed_dim: int, + kernel_size: int, + groups: int, + ): + super().__init__() + self.embed_dim = embed_dim + self.kernel_size = kernel_size + self.conv = nn.Conv1d( + in_channels=embed_dim, + out_channels=embed_dim, + kernel_size=kernel_size, + padding=kernel_size // 2, + groups=groups, + ) + + self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) + self.num_remove: int = 1 if kernel_size % 2 == 0 else 0 + + def __prepare_scriptable__(self): + for hook in self.conv._forward_pre_hooks.values(): + # The hook we want to remove is an instance of WeightNorm class, so + # normally we would do `if isinstance(...)` but this class is not accessible + # because of shadowing, so we check the module name directly. + # https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3 + if hook.__module__ == "torch.nn.utils.weight_norm" and hook.__class__.__name__ == "WeightNorm": + torch.nn.utils.remove_weight_norm(self.conv) + return self + + def forward(self, x): + """ + Args: + x (Tensor): shape ``[batch, frame, feature]``. + + Returns: + Tensor: The resulting feature. Shape ``[batch, frame, feature]``. + """ + x = x.transpose(-2, -1) + x = self.conv(x) + if self.num_remove > 0: + x = x[..., : -self.num_remove] + x = torch.nn.functional.gelu(x) + x = x.transpose(-2, -1) + return x + + +class SelfAttention(Module): + """Multihead Self Attention module + + Args: + embed_dim (int): Total dimension of the model. + num_heads (int): The number of heads. + dropout (float, optional): + Dropout probability on attn_output_weights. Default: ``0.0`` + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + head_dim: int, + dropout: float = 0.0, + prune_heads: bool = False, # whether to prune attention heads + prune_layer: bool = False, # whether to prune entire attention layers + ): + super().__init__() + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = head_dim + self.dropout = torch.nn.Dropout(dropout) + + self.scaling = self.head_dim**-0.5 + + self.k_proj = nn.Linear(embed_dim, num_heads * head_dim, bias=True) + self.v_proj = nn.Linear(embed_dim, num_heads * head_dim, bias=True) + self.q_proj = nn.Linear(embed_dim, num_heads * head_dim, bias=True) + self.out_proj = nn.Linear(num_heads * head_dim, embed_dim, bias=True) + + if prune_heads: + self.hard_concrete_for_heads = HardConcrete(n_in=num_heads, init_mean=0.01) + else: + self.hard_concrete_for_heads = None + + if prune_layer: + self.hard_concrete_for_layer = HardConcrete(n_in=1, init_mean=0.01) + else: + self.hard_concrete_for_layer = None + + def forward( + self, + x: Tensor, + attention_mask: Optional[Tensor] = None, + position_bias: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + """ + Args: + x (Tensor): shape: ``[batch_size, sequence_length, embed_dim]``. + attention_mask (Tensor or ``None``, optional): + shape: ``[batch_size, 1, sequence_length, sequence_length]`` + position_bias: Not used. Only for the compatibility with :py:class:`WavLMSelfAttention`. + key_padding_mask (Tensor or ``None``): Not used. Only for the compatibility with + :py:class:`WavLMSelfAttention`. + Returns: + (Tensor, ``None``): The resulting attention output and ``None`` (necessary for compatibility + with :py:class:`WavLMSelAttention`). + Attention output shape: ``[batch, sequence_length, embed_dim]``. + """ + if x.ndim != 3 or x.shape[2] != self.embed_dim: + raise ValueError( + f"The expected input shape is (batch, sequence, embed_dim=={self.embed_dim}). " f"Found {x.shape}." + ) + batch_size, length, embed_dim = x.size() + + shape = (batch_size, length, self.num_heads, self.head_dim) + q = self.q_proj(x).view(*shape).transpose(2, 1) # B, nH, L, Hd + k = self.k_proj(x).view(*shape).permute(0, 2, 3, 1) # B, nH, Hd, L + v = self.v_proj(x).view(*shape).transpose(2, 1) # B, nH, L, Hd + + # scale down q to avoid value overflow. + weights = (self.scaling * q) @ k # B, nH, L, L + if attention_mask is not None: + weights += attention_mask + # subtracting a constant value from the tensor won't change the output of softmax. + # apply the subtraction to avoid value overflow in torch.nn.functional.softmax. + # for more details, please see Equation 7 in https://arxiv.org/abs/2112.08778 + weights = weights - weights.max(dim=-1, keepdim=True)[0] + + weights = torch.nn.functional.softmax(weights, dim=-1) + weights = self.dropout(weights) + + output = weights @ v # B, nH, L, Hd + + if self.hard_concrete_for_heads is not None: + head_mask = self.hard_concrete_for_heads() # (nH,) + output = output * head_mask.unsqueeze(-1).unsqueeze(-1) + + output = output.transpose(2, 1).reshape(batch_size, length, self.num_heads * self.head_dim) + + output = self.out_proj(output) + + if self.hard_concrete_for_layer is not None: + layer_mask = self.hard_concrete_for_layer() # (1,) + output = output * layer_mask + + return output, None # Necessary for compatibility with WavLMSelAttention + + def get_num_params(self): + if self.hard_concrete_for_heads is not None: + num_heads = self.hard_concrete_for_heads.l0_norm() + else: + num_heads = self.num_heads + num_params = (self.embed_dim + 1) * num_heads * self.head_dim * 3 \ + + (num_heads * self.head_dim + 1) * self.embed_dim + + if self.hard_concrete_for_layer is not None: + num_params *= self.hard_concrete_for_layer.l0_norm() + + return num_params + + def prune(self): + new_config = { + "use_attention": True, + "num_heads": self.num_heads, + } + if self.hard_concrete_for_layer is not None: + assert not self.hard_concrete_for_layer.training + layer_mask = self.hard_concrete_for_layer() # (1,) + self.out_proj.weight.data *= layer_mask + self.out_proj.bias.data *= layer_mask + if layer_mask == 0: + new_config["use_attention"] = False + self.hard_concrete_for_layer = None + + if self.hard_concrete_for_heads is not None: + assert not self.hard_concrete_for_heads.training + head_mask = self.hard_concrete_for_heads() # (num_heads,) + new_config["num_heads"] = len(head_mask.nonzero()) + if new_config["num_heads"] == 0: + new_config["use_attention"] = False + else: + full_mask = head_mask.repeat_interleave(self.head_dim) + full_index = full_mask.nonzero().squeeze(-1) # 1D + + prune_linear_layer(self.k_proj, full_index, "output") + prune_linear_layer(self.v_proj, full_index, "output") + prune_linear_layer(self.q_proj, full_index, "output") + + self.out_proj.weight.data *= full_mask + prune_linear_layer(self.out_proj, full_index, "input") + self.hard_concrete_for_heads = None + + return new_config + + +class WavLMSelfAttention(SelfAttention): + """Multi-headed self-attention for WavLM model :cite:`chen2022wavlm`. + + Args: + embed_dim (int): Total dimension of the model. + num_heads (int): The number of heads. + dropout (float, optional): Dropout probability on attn_output_weights. (Default: to ``0.0``) + bias (bool, optional): If ``True``, add bias to input / output projection layers. (Default: ``True``) + has_relative_attention_bias (bool, optional): If ``True``, apply relative position embedding. + Necessary in the first encoder layer, but not in the subsequent ones. (Default: ``False``) + num_buckets (int, optional): Number of buckets for relative position embedding. (Default: ``32``) + max_distance (int, optional): Naximum distance for relative position embedding. (Default: ``128``) + gru_rel_pos (bool, optional): If ``True``, apply gated relative position embedding. (Default: ``False``) + """ + + def __init__( + self, + embed_dim: int, + total_num_heads: int, + remaining_heads: Optional[List[int]] = None, + dropout: float = 0.0, + bias: bool = True, + has_relative_attention_bias: bool = False, + num_buckets: int = 32, + max_distance: int = 128, + gru_rel_pos: bool = True, + prune_heads: bool = False, + prune_layer: bool = False, + ): + self.total_num_heads = total_num_heads + if remaining_heads is None: + self.remaining_heads = list(range(total_num_heads)) + else: + self.remaining_heads = remaining_heads # list of indices + + self.head_dim = embed_dim // total_num_heads + + super().__init__(embed_dim, len(self.remaining_heads), self.head_dim, dropout, prune_heads, prune_layer) + + self.has_relative_attention_bias = has_relative_attention_bias + self.num_buckets = num_buckets + self.max_distance = max_distance + + if has_relative_attention_bias: + self.rel_attn_embed = nn.Embedding(num_buckets, total_num_heads) + else: + self.rel_attn_embed = None + + # override linear layers to customize bias + self.k_proj = nn.Linear(embed_dim, len(self.remaining_heads) * self.head_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, len(self.remaining_heads) * self.head_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, len(self.remaining_heads) * self.head_dim, bias=bias) + self.out_proj = nn.Linear(len(self.remaining_heads) * self.head_dim, embed_dim, bias=bias) + + self.gru_rel_pos = gru_rel_pos + if self.gru_rel_pos: + self.gru_rel_pos_linear = nn.Linear(self.head_dim, 8) + self.gru_rel_pos_const = nn.Parameter(torch.ones(1, total_num_heads, 1, 1)) + self.has_position_bias = True + + def compute_bias(self, query_length: int, key_length: int) -> Tensor: + """Compute relative position embeddings for WavLM model. + Args: + query_length (int): Query position can take values between 0 and ``query_length - 1``. + key_length (int): Key position can take values between 0 and ``key_length - 1``. + Returns: + Tensor of shape `(num_heads, query_length, key_length)`, relative positions embeddings + """ + context_position = torch.arange(query_length, dtype=torch.long)[:, None] + memory_position = torch.arange(key_length, dtype=torch.long)[None, :] + relative_position = memory_position - context_position # Shape (query_length, key_length) + relative_position_bucket = self._relative_positions_bucket(relative_position, bidirectional=True) + relative_position_bucket = relative_position_bucket.to(self.rel_attn_embed.weight.device) + values = self.rel_attn_embed(relative_position_bucket) # Shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]) + return values + + def _relative_positions_bucket(self, relative_positions: Tensor, bidirectional: bool = True): + """Compute relative position buckets for WavLM model. Computation similar to formula (5) in WavLM + paper :cite:`chen2022wavlm`. + Args: + relative_positions (Tensor): Relative offsets between query and key positions, + of shape ``(query_length, key_length)``. + bidirectional (bool): If ``True``, values will be filled both above and below the diagonal in the resulting + matrix. If ``False``, the elements above the diagonal (i.e. with negative relative offsets) will be set + to zero. (Default ``True``) + Returns: + Tensor of shape ``(query_length, key_length)`` filled bucketed values of with relative positions. + """ + num_buckets = self.num_buckets + max_distance = self.max_distance + # Shape (query_length, key_length) + relative_buckets = torch.zeros_like(relative_positions, dtype=torch.long) + + if bidirectional: + num_buckets = num_buckets // 2 + relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets + relative_positions = torch.abs(relative_positions) + else: + relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions)) + + max_exact = num_buckets // 2 + is_small = relative_positions < max_exact + + relative_postion_if_large = max_exact + ( + torch.log(relative_positions.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_postion_if_large = torch.min( + relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large) + return relative_buckets + + def forward( + self, + query: Tensor, + attention_mask: Optional[Tensor] = None, + position_bias: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + """ + Args: + query (Tensor): Input of shape ``(batch_size, src_len, embed_dim)``. + key_padding_mask (Tensor or None, optional): Mask to exclude keys that are pads, of shape + `(batch, src_len)`, where padding elements are indicated by 1s. (Default: ``None``) + attn_mask: Needs to be ``None``. The argument exists for compatibility with + ``EncoderLayer``. (Default: ``None``) + position_bias (Tensor or None, optional): Position bias of shape + ``(batch_size * num_heads, src_len, src_len)``. When used inside WavLM model encoder, will be + generated in the first layer and then passed from each encoder layer to the next one. + (Default: ``None``) + Returns: + attn_output (Tensor): Attention output of shape ``(batch_size, src_len, embed_dim)``. + position_bias (Tensor or None): Position bias of shape ``(batch_size * num_heads, src_len, src_len)``. + """ + bsz, seq_len, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert key_padding_mask is None + + # only for the first layer + if self.rel_attn_embed is not None and position_bias is None: + position_bias = self.compute_bias(seq_len, seq_len) + position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.total_num_heads, seq_len, seq_len) + + attn_mask_rel_pos: Optional[Tensor] = None + if position_bias is not None: + attn_mask_rel_pos = position_bias + if self.gru_rel_pos: # Apply gating on relative position bias + query_layer = query.view(bsz, seq_len, self.total_num_heads, -1) + query_layer = query_layer.permute(0, 2, 1, 3) + + gate_a, gate_b = torch.sigmoid( + self.gru_rel_pos_linear(query_layer).view(bsz, self.total_num_heads, seq_len, 2, 4).sum(-1, keepdim=False) + ).chunk(2, dim=-1) + gate_a_1 = gate_a * (gate_b * self.gru_rel_pos_const - 1.0) + 2.0 + attn_mask_rel_pos = gate_a_1.view(bsz * self.total_num_heads, -1, 1) * position_bias + + attn_mask_rel_pos = attn_mask_rel_pos.view((-1, seq_len, seq_len)) + attn_mask_rel_pos = attn_mask_rel_pos.reshape(bsz, self.total_num_heads, seq_len, seq_len)[:, self.remaining_heads, :, :] + + attn_mask = attn_mask_rel_pos + if attention_mask is not None: + attn_mask = attn_mask + attention_mask + if key_padding_mask is not None: + attn_mask = attn_mask.masked_fill( + key_padding_mask.reshape(bsz, 1, 1, seq_len), + float("-inf") + ) + attn_output, _ = super().forward(query, attention_mask=attn_mask) + + return attn_output, position_bias + + def prune(self): + new_config = { + "use_attention": True, + "remaining_heads": self.remaining_heads, + } + if self.hard_concrete_for_layer is not None: + assert not self.hard_concrete_for_layer.training + layer_mask = self.hard_concrete_for_layer() # (1,) + self.out_proj.weight.data *= layer_mask + self.out_proj.bias.data *= layer_mask + if layer_mask == 0: + new_config["use_attention"] = False + self.hard_concrete_for_layer = None + + if self.hard_concrete_for_heads is not None: + assert not self.hard_concrete_for_heads.training + head_mask = self.hard_concrete_for_heads() # (num_heads,) + new_config["remaining_heads"] = head_mask.nonzero().squeeze(-1).tolist() + if len(new_config["remaining_heads"]) == 0: + new_config["use_attention"] = False + else: + full_mask = head_mask.repeat_interleave(self.head_dim) + full_index = full_mask.nonzero().squeeze(-1) # 1D + + prune_linear_layer(self.k_proj, full_index, "output") + prune_linear_layer(self.v_proj, full_index, "output") + prune_linear_layer(self.q_proj, full_index, "output") + + self.out_proj.weight.data *= full_mask + prune_linear_layer(self.out_proj, full_index, "input") + self.hard_concrete_for_heads = None + + return new_config + + +class FeedForward(Module): + """Layer that follows attention layer in encoder layer.""" + + def __init__( + self, + io_features: int, + intermediate_features: int, + intermediate_dropout: float, + output_dropout: float, + prune_intermediate: bool = False, + prune_layer: bool = False, + ): + super().__init__() + self.intermediate_dense = nn.Linear(io_features, intermediate_features) + self.intermediate_dropout = nn.Dropout(intermediate_dropout) + self.output_dense = nn.Linear(intermediate_features, io_features) + self.output_dropout = nn.Dropout(output_dropout) + + if prune_intermediate: + self.hard_concrete_for_intermediate = HardConcrete( + n_in=intermediate_features, init_mean=0.5 + ) + else: + self.hard_concrete_for_intermediate = None + + if prune_layer: + self.hard_concrete_for_layer = HardConcrete(n_in=1, init_mean=0.01) + else: + self.hard_concrete_for_layer = None + + def forward(self, x): + """ + Args: + x (Tensor): shape: `(batch, sequence_length, io_features)` + Returns: + x (Tensor): shape: `(batch, sequence_length, io_features)` + """ + x = self.intermediate_dense(x) + x = torch.nn.functional.gelu(x) + x = self.intermediate_dropout(x) + + if self.hard_concrete_for_intermediate is not None: + intermediate_mask = self.hard_concrete_for_intermediate() # (intermediate_features,) + x = x * intermediate_mask + + x = self.output_dense(x) + x = self.output_dropout(x) + + if self.hard_concrete_for_layer is not None: + layer_mask = self.hard_concrete_for_layer() # (1,) + x = x * layer_mask + + return x + + def get_num_params(self): + io_features = self.intermediate_dense.in_features + if self.hard_concrete_for_intermediate is not None: + intermediate_features = self.hard_concrete_for_intermediate.l0_norm() + else: + intermediate_features = self.intermediate_dense.out_features + num_params = (io_features + 1) * intermediate_features + (intermediate_features + 1) * io_features + + if self.hard_concrete_for_layer is not None: + num_params *= self.hard_concrete_for_layer.l0_norm() + + return num_params + + def prune(self): + new_config = { + "use_feed_forward": True, + "ff_interm_features": self.intermediate_dense.out_features + } + if self.hard_concrete_for_layer is not None: + assert not self.hard_concrete_for_layer.training + layer_mask = self.hard_concrete_for_layer() + self.output_dense.weight.data *= layer_mask + self.output_dense.bias.data *= layer_mask + if layer_mask == 0: + new_config["use_feed_forward"] = False + self.hard_concrete_for_layer = None + + if self.hard_concrete_for_intermediate is not None: + assert not self.hard_concrete_for_intermediate.training + interm_mask = self.hard_concrete_for_intermediate() + interm_index = interm_mask.nonzero().squeeze(-1) # NOTE: must specify dim=-1 + new_config["ff_interm_features"] = len(interm_index) + if new_config["ff_interm_features"] == 0: + new_config["use_feed_forward"] = False + else: + prune_linear_layer(self.intermediate_dense, interm_index, "output") + + self.output_dense.weight.data *= interm_mask + prune_linear_layer(self.output_dense, interm_index, "input") + self.hard_concrete_for_intermediate = None + + return new_config + + +class EncoderLayer(Module): + """A layer unit in encoder. Combines multihead self attention and feed forward.""" + + def __init__( + self, + attention: Optional[Module], # can be None if the entire layer is pruned + dropout: float, + layer_norm_first: bool, + feed_forward: Optional[Module], # can be None if the entire layer is pruned + embed_dim: int, + ): + super().__init__() + self.attention = attention + self.dropout = nn.Dropout(dropout) + self.layer_norm = nn.LayerNorm(embed_dim) + self.layer_norm_first = layer_norm_first + self.feed_forward = feed_forward + self.final_layer_norm = nn.LayerNorm(embed_dim) + self.embed_dim = embed_dim + + def forward( + self, + x: Tensor, + attention_mask: Optional[Tensor] = None, + position_bias: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + """ + Args: + x (Tensor): Input of shape ``(batch, sequence_length, embed_dim)``. + attention_mask (Tensor or ``None``, optional): attention mask + of shape ``(batch, 1, sequence_length, sequence_length)``. (Default: ``None``) + position_bias (Tensor or ``None``, optional): position bias of shape + ``(batch_size * num_heads, src_len, src_len)``. + Only necessary for WavLM model, ``None`` otherwise. (Default: ``None``) + key_padding_mask (Tensor or ``None``, optional): key padding mask of shape ``(batch_size, src_len)``. + Only used for WavLM model, ignored otherwise. (Default: ``None``) + Returns: + (x, position_bias): Shapes are the same as in the input. Position bias is only relevant for WaLM model, + ``None`` otherwise. + """ + if self.attention is not None: + residual = x + + if self.layer_norm_first: + x = self.layer_norm(x) + + x, position_bias = self.attention( + x, attention_mask=attention_mask, position_bias=position_bias, key_padding_mask=key_padding_mask + ) + + x = self.dropout(x) + x = residual + x + + if self.layer_norm_first: + if self.feed_forward is not None: + x = x + self.feed_forward(self.final_layer_norm(x)) + else: + # NOTE: for post norm, the layer norms should always be applied even if the layers are pruned. + x = self.layer_norm(x) + if self.feed_forward is not None: + x = x + self.feed_forward(x) + x = self.final_layer_norm(x) + return x, position_bias + + def get_num_params(self): + num_params = self.embed_dim * 2 * 2 # two layer norms + if self.attention is not None: + num_params += self.attention.get_num_params() + if self.feed_forward is not None: + num_params += self.feed_forward.get_num_params() + return num_params + + +class Transformer(Module): + def __init__( + self, + pos_conv_embed: Module, + dropout: float, + layers: Module, + layer_norm_first: bool, + layer_drop: float, + ): + super().__init__() + self.pos_conv_embed = pos_conv_embed + self.layer_norm = nn.LayerNorm(pos_conv_embed.embed_dim) + self.layer_norm_first = layer_norm_first + self.layer_drop = layer_drop + self.dropout = nn.Dropout(dropout) + self.layers = layers + + def _preprocess(self, x: Tensor): + x = x + self.pos_conv_embed(x) + + if self.layer_norm_first: + x = self.layer_norm(x) + + x = self.dropout(x) + return x + + def forward( + self, + x: Tensor, + attention_mask: Optional[Tensor] = None, + position_bias: Optional[Tensor] = None, + ) -> Tensor: + x = self._preprocess(x) + for layer in self.layers: + if not (self.training and torch.rand(1).item() <= self.layer_drop): + x, position_bias = layer(x, attention_mask, position_bias=position_bias) + + if not self.layer_norm_first: + x = self.layer_norm(x) + return x + + def get_intermediate_outputs( + self, + x: Tensor, + attention_mask: Optional[Tensor] = None, + num_layers: Optional[int] = None, + position_bias: Optional[Tensor] = None, + ) -> List[Tensor]: + if num_layers is not None: + if not 0 < num_layers <= len(self.layers): + raise ValueError(f"`num_layers` must be between [1, {len(self.layers)}]") + + ret: List[Tensor] = [] + x = self._preprocess(x) + for layer in self.layers: + x, position_bias = layer(x, attention_mask, position_bias=position_bias) + ret.append(x) + if num_layers is not None and len(ret) >= num_layers: + return ret + return ret + + def get_num_params(self): + # pos_conv_embed and layer_norm + num_params = sum(p.numel() for p in self.pos_conv_embed.parameters()) + self.pos_conv_embed.embed_dim * 2 + for layer in self.layers: + num_params += layer.get_num_params() + return num_params + + def prune(self): + new_config = defaultdict(list) + for layer in self.layers: + attention_config = layer.attention.prune() + new_config["use_attention"].append(attention_config["use_attention"]) + if "remaining_heads" in attention_config: + new_config["remaining_heads"].append(attention_config["remaining_heads"]) + else: + new_config["num_heads"].append(attention_config["num_heads"]) + + if not attention_config["use_attention"]: + layer.attention = None + + ff_config = layer.feed_forward.prune() + new_config["use_feed_forward"].append(ff_config["use_feed_forward"]) + new_config["ff_interm_features"].append(ff_config["ff_interm_features"]) + if not ff_config["use_feed_forward"]: + layer.feed_forward = None + + return new_config + + +class Encoder(Module): + def __init__( + self, + feature_projection: Module, + transformer: Module, + ): + super().__init__() + self.feature_projection = feature_projection + self.transformer = transformer + + def _preprocess( + self, + features: Tensor, + lengths: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + x = self.feature_projection(features) + + mask: Optional[Tensor] = None + if lengths is not None: + batch_size, max_len, _ = x.shape + # create mask for padded elements and zero-out them + mask = torch.arange(max_len, device=lengths.device).expand(batch_size, max_len) >= lengths[:, None] + x[mask] = 0.0 + # extend the mask to attention shape and set weight + mask = -10000.0 * mask[:, None, None, :].to(dtype=features.dtype) + mask = mask.expand(batch_size, 1, max_len, max_len) + return x, mask + + def forward( + self, + features: Tensor, + lengths: Optional[Tensor] = None, + ) -> Tensor: + x, mask = self._preprocess(features, lengths) + x = self.transformer(x, attention_mask=mask) + return x + + def extract_features( + self, + features: Tensor, + lengths: Optional[Tensor] = None, + num_layers: Optional[int] = None, + ) -> List[Tensor]: + x, masks = self._preprocess(features, lengths) + interm = self.transformer.get_intermediate_outputs(x, attention_mask=masks, num_layers=num_layers) + return [x] + interm + + def get_num_params(self, in_features): + """Calculate the current model size.""" + feature_projection_size = self.feature_projection.get_num_params(in_features) + transformer_size = self.transformer.get_num_params() + return feature_projection_size + transformer_size + + def prune(self, conv_out_index): + """In-place pruning of submodules.""" + prune_layer_norm(self.feature_projection.layer_norm, conv_out_index) + prune_linear_layer(self.feature_projection.projection, conv_out_index, "input") + transformer_config = self.transformer.prune() + return transformer_config + + +################################################################################ +def _get_feature_extractor( + norm_mode: str, + shapes: List[Tuple[int, int, int]], + bias: bool, + prune_conv_channels: bool = False, +) -> FeatureExtractor: + """ + Args: + norm_mode (str): + Either "group_norm" or "layer_norm". + If "group_norm", then a single normalization is applied + in the first convolution block. Otherwise, all the convolution + blocks will have layer normalization. + This option corresponds to "extractor_mode" from fairseq. + Expected values are "group_norm" for Base arch, and + "layer_norm" for Large arch. + shapes (list of tuple of int): + Configuration of convolution layers. List of convolution configuration, + i.e. ``[(output_channel, kernel_size, stride), ...]`` + This option corresponds to "conv_feature_layers" from fairseq. + Expected values are + ``[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2`` + for all the architectures. + bias (bool): + Whether to include bias term to each convolution operation. + This option corresponds to "conv_bias" from fairseq. + Expected values are False for Base arch, and True for Large arch. + + See Also: + * Original implementation + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L666-L733 + * "extractor_mode" + - Def and base: + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L38-L45 + - Large: + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L52 + * "conv_feature_layers" + - Def, base and large: + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L94-L100 + * "conv_bias" + - Def and base: + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L101-L103 + - Large: + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L61 + """ + if norm_mode not in ["group_norm", "layer_norm"]: + raise ValueError("Invalid norm mode") + blocks = [] + in_channels = 1 + for i, (out_channels, kernel_size, stride) in enumerate(shapes): + normalization = None + if norm_mode == "group_norm" and i == 0: + normalization = nn.GroupNorm( + num_groups=out_channels, + num_channels=out_channels, + affine=True, + ) + elif norm_mode == "layer_norm": + normalization = LayerNorm( + normalized_shape=out_channels, + elementwise_affine=True, + ) + blocks.append( + ConvLayerBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + bias=bias, + layer_norm=normalization, + prune_conv_channels=prune_conv_channels, + ) + ) + in_channels = out_channels + return FeatureExtractor(nn.ModuleList(blocks)) + + +def _get_encoder( + in_features: int, + embed_dim: int, + dropout_input: float, + pos_conv_kernel: int, + pos_conv_groups: int, + num_layers: int, + use_attention: List[bool], + use_feed_forward: List[bool], + num_heads: List[int], + head_dim: int, + attention_dropout: float, + ff_interm_features: List[int], + ff_interm_dropout: float, + dropout: float, + layer_norm_first: bool, + layer_drop: float, + prune_attention_heads: bool = False, + prune_attention_layer: bool = False, + prune_feed_forward_intermediate: bool = False, + prune_feed_forward_layer: bool = False, +) -> Encoder: + """ + Args: + in_features (int): The number of input features. + embed_dim (int): + The dimension of embedding. + This option corresponds to "encoder_embed_dim" from fairseq. + Expected values are 768 for Base arch, and 1024 for Large arch. + dropout_input (float): + The dropout probability applied after the input feature is projected + to ``embed_dim``. + This option corresponds to "dropout_input" from fairseq. + Expected values are 0.1 for both Base and Large arch. + pos_conv_kernel (int): + The kernel size of convolutional positional embeddings. + This option corresponds to "conv_pos" from fairseq. + Expected values are 128 for both Base and Large arch. + pos_conv_groups (int): + The number of groups of convolutional positional embeddings. + This option corresponds to "conv_pos_groups" from fairseq. + Expected values are 16 for both Base and Large arch. + num_layers (int): + The number of self attention layers in transformer block. + This option corresponds to "encoder_layers" from fairseq. + Expected values are 12 for Base and 24 for Large arch. + num_heads (int): + The number of heads in self attention layers. + This option corresponds to "encoder_attention_heads" from fairseq. + Expected values are 12 for Base and 16 for Large arch. + attention_dropout (float): + The dropout probability applied after softmax in self-attention layer. + This option corresponds to "attention_dropout" from fairseq. + Expected values are 0.1 for Base and 0.0 for Large arch. + ff_interm_features (int): + The dimension of hidden features in feed forward layer. + This option corresponds to "encoder_ffn_embed_dim" from fairseq. + Expected values are 3072 for Base and 4096 for Large arch. + ff_interm_dropout (float): + The dropout probability applied in feedforward layer. + This option correspinds to "activation_dropout" from fairseq. + Expected values are 0.1 for both Base and Large arch. + dropout (float): + The dropout probability applied at the end of feed forward layer. + This option corresponds to "dropout" from fairseq. + Expected values are 0.1 for Base and 0.0 for Large arch. + layer_norm_first (bool): + Control the order of layer norm in transformer layer and each encoder layer. + If True, in transformer layer, layer norm is applied before features are fed + to encoder layers. In encoder layer, two layer norms are applied before and after + self attention. + If False, in transformer layer, layer norm is applied after features are fed + to encoder layers. In encoder layer, two layer norms are applied after self + attention, before and after feed forward. + This option corresponds to "layer_norm_first" from fairseq. + Expected values are False for Base and True for Large arch. + layer_drop (float): + Probability to drop each encoder layer during training. + This option corresponds to "layerdrop" from fairseq. + Expected values are 0.1 for both Base and Large arch. + + See Also: + * "encoder_embed_dim" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L49-L51 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L64 + * "dropout_input" + - Def, base and large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L75-L78 + * "conv_pos" + - Def, base and large + NOTE: The description is wrong. + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L204-L207 + - Usage + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L756 + * "conv_pos_groups" + - Def, base and large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L208-L211 + * "encoder_layers" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L46-L48 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L63 + * "encoder_attention_heads" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L55-L57 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L66 + * "attention_dropout" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L66-L68 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L60 + * "encoder_ffn_embed_dim" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L52-L54 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L65 + * "activation_dropout" + - Def + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L69-L71 + - Base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/base_960h.yaml#L55 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/vox_960h.yaml#L55 + * "dropout" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L63-L65 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L59 + * "layer_norm_first" + - Def and base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L91-L93 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L53 + * "layerdrop" + - Def + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L72-L74 + - Base + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/base_960h.yaml#L54 + - Large + https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/vox_960h.yaml#L54 + """ + feature_projection = FeatureProjection(in_features, embed_dim, dropout_input) + pos_conv = ConvolutionalPositionalEmbedding(embed_dim, pos_conv_kernel, pos_conv_groups) + + # Original impl + # https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L768-L782 + encoder_layers = nn.ModuleList() + for idx in range(num_layers): + if use_attention[idx]: + attention = SelfAttention( + embed_dim=embed_dim, + num_heads=num_heads[idx], + head_dim=head_dim, + dropout=attention_dropout, + prune_heads=prune_attention_heads, + prune_layer=prune_attention_layer, + ) + else: + attention = None + if use_feed_forward[idx]: + feed_forward = FeedForward( + io_features=embed_dim, + intermediate_features=ff_interm_features[idx], + intermediate_dropout=ff_interm_dropout, + output_dropout=dropout, + prune_intermediate=prune_feed_forward_intermediate, + prune_layer=prune_feed_forward_layer, + ) + else: + feed_forward = None + encoder_layers.append( + EncoderLayer( + attention=attention, + dropout=dropout, + layer_norm_first=layer_norm_first, + feed_forward=feed_forward, + embed_dim=embed_dim, + ) + ) + transformer = Transformer( + pos_conv_embed=pos_conv, + dropout=dropout, + layers=encoder_layers, + layer_norm_first=not layer_norm_first, + layer_drop=layer_drop, + ) + return Encoder(feature_projection, transformer) + + +def _get_wavlm_encoder( + in_features: int, + embed_dim: int, + dropout_input: float, + pos_conv_kernel: int, + pos_conv_groups: int, + num_layers: int, + use_attention: List[bool], + use_feed_forward: List[bool], + total_num_heads: List[int], + remaining_heads: List[List[int]], + num_buckets: int, + max_distance: int, + attention_dropout: float, + ff_interm_features: List[int], + ff_interm_dropout: float, + dropout: float, + layer_norm_first: bool, + layer_drop: float, + prune_attention_heads: bool = False, + prune_attention_layer: bool = False, + prune_feed_forward_intermediate: bool = False, + prune_feed_forward_layer: bool = False, +) -> Encoder: + """ + Construct encoder for WavLM model :cite:`chen2022wavlm`. The structure of the encoder and most of the argments are + the same as in :py:func:`_get_encoder` so refer there for documentation. The only difference from Wav2Vec2 encoder + is usage of `WavLMSelfAttention` instead of `SelfAttention` and two additional parameters: `num_buckets` and + `max_distance`. + Args: + in_features (int): See :py:func:`_get_encoder`. + embed_dim (int): See :py:func:`_get_encoder`. + dropout_input (float): See :py:func:`_get_encoder`. + pos_conv_kernel (int): See :py:func:`_get_encoder`. + pos_conv_groups (int): See :py:func:`_get_encoder`. + num_layers (int): See :py:func:`_get_encoder`. + num_heads (int): See :py:func:`_get_encoder`. + num_buckets (int): Number of buckets for relative position embedding. + max_distance (int): Maximum distance for relative position embedding. + attention_dropout (float): See :py:func:`_get_encoder`. + ff_interm_features (int): See :py:func:`_get_encoder`. + ff_interm_dropout (float): See :py:func:`_get_encoder`. + dropout (float): See :py:func:`_get_encoder`. + layer_norm_first (bool): See :py:func:`_get_encoder`. + layer_drop (float): See :py:func:`_get_encoder`. + + """ + feature_projection = FeatureProjection(in_features, embed_dim, dropout_input) + pos_conv = ConvolutionalPositionalEmbedding(embed_dim, pos_conv_kernel, pos_conv_groups) + + # Original impl + # https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L768-L782 + encoder_layers = nn.ModuleList() + for i in range(num_layers): + if use_attention[i]: + attention = WavLMSelfAttention( + embed_dim=embed_dim, + total_num_heads=total_num_heads[i], + remaining_heads=remaining_heads[i], + dropout=attention_dropout, + has_relative_attention_bias=(i == 0), # Position embedding is only necessary in the first layer. + num_buckets=num_buckets, + max_distance=max_distance, + prune_heads=prune_attention_heads, + prune_layer=prune_attention_layer, + ) + else: + attention = None + if use_feed_forward[i]: + feed_forward = FeedForward( + io_features=embed_dim, + intermediate_features=ff_interm_features[i], + intermediate_dropout=ff_interm_dropout, + output_dropout=dropout, + prune_intermediate=prune_feed_forward_intermediate, + prune_layer=prune_feed_forward_layer, + ) + else: + feed_forward = None + encoder_layers.append( + EncoderLayer( + attention=attention, + dropout=dropout, + layer_norm_first=layer_norm_first, + feed_forward=feed_forward, + embed_dim=embed_dim, + ) + ) + transformer = Transformer( + pos_conv_embed=pos_conv, + dropout=dropout, + layers=encoder_layers, + layer_norm_first=not layer_norm_first, + layer_drop=layer_drop, + ) + return Encoder(feature_projection, transformer) + + +def _get_padding_mask(input: Tensor, lengths: Tensor) -> Tensor: + """Generate the padding mask given the padded input and the lengths Tensors. + Args: + input (Tensor): The padded Tensor of dimension `[batch, max_len, frequency]`. + lengths (Tensor): The lengths Tensor of dimension `[batch,]`. + + Returns: + (Tensor): The padding mask. + """ + batch_size, max_len, _ = input.shape + mask = torch.arange(max_len, device=lengths.device).expand(batch_size, max_len) >= lengths[:, None] + return mask + + +class GradMultiply(torch.autograd.Function): + @staticmethod + def forward(ctx, x, scale): + ctx.scale = scale + res = x.new(x) + return res + + @staticmethod + def backward(ctx, grad): + return grad * ctx.scale, None diff --git a/vencoder/dphubert/hardconcrete.py b/vencoder/dphubert/hardconcrete.py new file mode 100644 index 0000000000000000000000000000000000000000..468a30d1eccdf20ee7493e71792c46e48449c4e3 --- /dev/null +++ b/vencoder/dphubert/hardconcrete.py @@ -0,0 +1,122 @@ +"""Implementation of the hard Concrete distribution. + +Originally from: +https://github.com/asappresearch/flop/blob/master/flop/hardconcrete.py + +""" + +import math + +import torch +import torch.nn as nn + + +class HardConcrete(nn.Module): + """A HarcConcrete module. + Use this module to create a mask of size N, which you can + then use to perform L0 regularization. + + To obtain a mask, simply run a forward pass through the module + with no input data. The mask is sampled in training mode, and + fixed during evaluation mode, e.g.: + + >>> module = HardConcrete(n_in=100) + >>> mask = module() + >>> norm = module.l0_norm() + """ + + def __init__( + self, + n_in: int, + init_mean: float = 0.5, + init_std: float = 0.01, + temperature: float = 2/3, # from CoFi + stretch: float = 0.1, + eps: float = 1e-6 + ) -> None: + """Initialize the HardConcrete module. + Parameters + ---------- + n_in : int + The number of hard concrete variables in this mask. + init_mean : float, optional + Initial drop rate for hard concrete parameter, + by default 0.5., + init_std: float, optional + Used to initialize the hard concrete parameters, + by default 0.01. + temperature : float, optional + Temperature used to control the sharpness of the + distribution, by default 1.0 + stretch : float, optional + Stretch the sampled value from [0, 1] to the interval + [-stretch, 1 + stretch], by default 0.1. + """ + super().__init__() + + self.n_in = n_in + self.limit_l = -stretch + self.limit_r = 1.0 + stretch + self.log_alpha = nn.Parameter(torch.zeros(n_in)) + self.beta = temperature + self.init_mean = init_mean + self.init_std = init_std + self.bias = -self.beta * math.log(-self.limit_l / self.limit_r) + + self.eps = eps + self.compiled_mask = None + self.reset_parameters() + + def reset_parameters(self): + """Reset the parameters of this module.""" + self.compiled_mask = None + mean = math.log(1 - self.init_mean) - math.log(self.init_mean) + self.log_alpha.data.normal_(mean, self.init_std) + + def l0_norm(self) -> torch.Tensor: + """Compute the expected L0 norm of this mask. + Returns + ------- + torch.Tensor + The expected L0 norm. + """ + return (self.log_alpha + self.bias).sigmoid().sum() + + def forward(self) -> torch.Tensor: + """Sample a hard concrete mask. + Returns + ------- + torch.Tensor + The sampled binary mask + """ + if self.training: + # Reset the compiled mask + self.compiled_mask = None + # Sample mask dynamically + u = self.log_alpha.new(self.n_in).uniform_(self.eps, 1 - self.eps) + s = torch.sigmoid((torch.log(u / (1 - u)) + self.log_alpha) / self.beta) + s = s * (self.limit_r - self.limit_l) + self.limit_l + mask = s.clamp(min=0., max=1.) + + else: + # Compile new mask if not cached + if self.compiled_mask is None: + # Get expected sparsity + expected_num_zeros = self.n_in - self.l0_norm().item() + num_zeros = round(expected_num_zeros) + # Approximate expected value of each mask variable z; + # We use an empirically validated magic number 0.8 + soft_mask = torch.sigmoid(self.log_alpha / self.beta * 0.8) + # Prune small values to set to 0 + _, indices = torch.topk(soft_mask, k=num_zeros, largest=False) + soft_mask[indices] = 0. + self.compiled_mask = soft_mask + mask = self.compiled_mask + + return mask + + def extra_repr(self) -> str: + return str(self.n_in) + + def __repr__(self) -> str: + return "{}({})".format(self.__class__.__name__, self.extra_repr()) diff --git a/vencoder/dphubert/model.py b/vencoder/dphubert/model.py new file mode 100644 index 0000000000000000000000000000000000000000..348ede2c3edc3e5588ee75760085dee9eafd9d68 --- /dev/null +++ b/vencoder/dphubert/model.py @@ -0,0 +1,966 @@ +"""Speech SSL models supporting pruning. + +Originally from: +https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/model.py + +""" + +import math +from typing import List, Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.nn import Module + +from . import components + + +class Wav2Vec2Model(Module): + """Acoustic model used in *wav2vec 2.0* :cite:`baevski2020wav2vec`. + + Note: + To build the model, please use one of the factory functions. + :py:func:`wav2vec2_model`, :py:func:`wav2vec2_base`, :py:func:`wav2vec2_large`, + :py:func:`wav2vec2_large_lv60k`, :py:func:`hubert_base`, :py:func:`hubert_large`, + and :py:func:`hubert_xlarge`. + + See Also: + * :class:`torchaudio.pipelines.Wav2Vec2Bundle`: Pretrained models (without fine-tuning) + * :class:`torchaudio.pipelines.Wav2Vec2ASRBundle`: ASR pipelines with pretrained models. + + Args: + feature_extractor (torch.nn.Module): + Feature extractor that extracts feature vectors from raw audio Tensor. + + encoder (torch.nn.Module): + Encoder that converts the audio features into the sequence of probability + distribution (in negative log-likelihood) over labels. + + aux (torch.nn.Module or None, optional): + Auxiliary module. If provided, the output from encoder is passed to this module. + """ # noqa: E501 + + def __init__( + self, + normalize_waveform: bool, + feature_extractor: Module, + encoder: Module, + aux: Optional[Module] = None, + ): + super().__init__() + self.normalize_waveform = normalize_waveform + self.feature_extractor = feature_extractor + self.encoder = encoder + self.aux = aux + + @torch.jit.export + def extract_features( + self, + waveforms: Tensor, + lengths: Optional[Tensor] = None, + num_layers: Optional[int] = None, + ) -> Tuple[List[Tensor], Optional[Tensor]]: + """Extract feature vectors from raw waveforms + + This returns the list of outputs from the intermediate layers of + transformer block in encoder. + + Args: + waveforms (Tensor): Audio tensor of shape `(batch, frames)`. + lengths (Tensor or None, optional): + Indicates the valid length of each audio in the batch. + Shape: `(batch, )`. + When the ``waveforms`` contains audios with different durations, + by providing ``lengths`` argument, the model will compute + the corresponding valid output lengths and apply proper mask in + transformer attention layer. + If ``None``, it is assumed that the entire audio waveform + length is valid. + num_layers (int or None, optional): + If given, limit the number of intermediate layers to go through. + Providing `1` will stop the computation after going through one + intermediate layers. If not given, the outputs from all the + intermediate layers are returned. + + Returns: + (List[Tensor], Optional[Tensor]): + List of Tensors + Features from requested layers. + Each Tensor is of shape: `(batch, time frame, feature dimension)` + Tensor or None + If ``lengths`` argument was provided, a Tensor of shape `(batch, )` + is returned. + It indicates the valid length in time axis of each feature Tensor. + """ + if self.normalize_waveform: + if lengths is not None: + waveforms = [ + F.layer_norm(wave[:length], (length,)) for wave, length in zip(waveforms, lengths) + ] + waveforms = torch.nn.utils.rnn.pad_sequence(waveforms, batch_first=True) + else: + waveforms = F.layer_norm(waveforms, waveforms.shape[-1:]) + + x, lengths = self.feature_extractor(waveforms, lengths) + x = self.encoder.extract_features(x, lengths, num_layers) # (num_layers+1,), including the input + return x, lengths + + def get_num_params(self): + """Calculate the current size.""" + feature_extractor_size, encoder_in_features = self.feature_extractor.get_num_params_and_final_out_channels() + encoder_size = self.encoder.get_num_params(encoder_in_features) + return feature_extractor_size + encoder_size + + def prune(self): + self.eval() # must be in eval mode + conv_config, conv_out_index = self.feature_extractor.prune() # [(output_channel, kernel_size, stride), ...] + transformer_config = self.encoder.prune(conv_out_index) # NOTE: this is a defaultdict(list) + use_attention = transformer_config["use_attention"] + use_feed_forward = transformer_config["use_feed_forward"] + num_heads = transformer_config["num_heads"] # can be [] + remaining_heads = transformer_config["remaining_heads"] # can be [] + ff_interm_features = transformer_config["ff_interm_features"] + + return conv_config, use_attention, use_feed_forward, num_heads, remaining_heads, ff_interm_features + + def forward( + self, + waveforms: Tensor, + lengths: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Compute the sequence of probability distribution over labels. + + Args: + waveforms (Tensor): Audio tensor of shape `(batch, frames)`. + lengths (Tensor or None, optional): + Indicates the valid length of each audio in the batch. + Shape: `(batch, )`. + When the ``waveforms`` contains audios with different durations, + by providing ``lengths`` argument, the model will compute + the corresponding valid output lengths and apply proper mask in + transformer attention layer. + If ``None``, it is assumed that all the audio in ``waveforms`` + have valid length. Default: ``None``. + + Returns: + (Tensor, Optional[Tensor]): + Tensor + The sequences of probability distribution (in logit) over labels. + Shape: `(batch, frames, num labels)`. + Tensor or None + If ``lengths`` argument was provided, a Tensor of shape `(batch, )` + is returned. + It indicates the valid length in time axis of the output Tensor. + """ + if self.normalize_waveform: + if lengths is not None: + waveforms = [ + F.layer_norm(wave[:length], (length,)) for wave, length in zip(waveforms, lengths) + ] + waveforms = torch.nn.utils.rnn.pad_sequence(waveforms, batch_first=True) + else: + waveforms = F.layer_norm(waveforms, waveforms.shape[-1:]) + + x, lengths = self.feature_extractor(waveforms, lengths) + x = self.encoder(x, lengths) + if self.aux is not None: + x = self.aux(x) + return x, lengths + + +def wav2vec2_model(**configs) -> Wav2Vec2Model: + """Wraps the original wav2vec2_model and wavlm_model.""" + + if "encoder_remaining_heads" in configs: + return wavlm_model(**configs) + + return wav2vec2_model_original(**configs) + + +def wav2vec2_model_original( + extractor_mode: str, + extractor_conv_layer_config: Optional[List[Tuple[int, int, int]]], + extractor_conv_bias: bool, + encoder_embed_dim: int, + encoder_projection_dropout: float, + encoder_pos_conv_kernel: int, + encoder_pos_conv_groups: int, + encoder_num_layers: int, + encoder_use_attention: List[bool], + encoder_use_feed_forward: List[bool], + encoder_num_heads: List[int], + encoder_head_dim: int, + encoder_attention_dropout: float, + encoder_ff_interm_features: List[int], + encoder_ff_interm_dropout: float, + encoder_dropout: float, + encoder_layer_norm_first: bool, + encoder_layer_drop: float, + aux_num_out: Optional[int], + normalize_waveform: bool, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds custom :class:`~torchaudio.models.Wav2Vec2Model`. + + Note: + The "feature extractor" below corresponds to + `ConvFeatureExtractionModel `__ + in the original ``fairseq`` implementation. + This is referred as "(convolutional) feature encoder" in the *wav2vec 2.0* + :cite:`baevski2020wav2vec` paper. + + The "encoder" below corresponds to `TransformerEncoder `__, + and this is referred as "Transformer" in the paper. + + Args: + extractor_mode (str): Operation mode of feature extractor. + Valid values are ``"group_norm"`` or ``"layer_norm"``. + If ``"group_norm"``, then a single normalization is applied + in the first convolution block. Otherwise, all the convolution + blocks will have layer normalization. + + This option corresponds to ``extractor_mode`` from ``fairseq``. + extractor_conv_layer_config (list of integer tuples or None): + Configuration of convolution layers in feature extractor. + List of convolution configuration, + i.e. ``[(output_channel, kernel_size, stride), ...]`` + + If ``None`` is provided, then the following default value is used. + + .. code-block:: python + + [ + (512, 10, 5), + (512, 3, 2), + (512, 3, 2), + (512, 3, 2), + (512, 3, 2), + (512, 2, 2), + (512, 2, 2), + ] + + This option corresponds to ``conv_feature_layers`` from ``fairseq``. + + extractor_conv_bias (bool): + Whether to include bias term to each convolution operation. + + This option corresponds to ``conv_bias`` from ``fairseq``. + + encoder_embed_dim (int): + The dimension of embedding in encoder. + + This option corresponds to ``encoder_embed_dim`` from ``fairseq``. + + encoder_projection_dropout (float): + The dropout probability applied after the input feature is projected + to ``encoder_embed_dim``. + + This option corresponds to ``dropout_input`` from ``fairseq``. + + encoder_pos_conv_kernel (int): + The kernel size of convolutional positional embeddings. + + This option corresponds to ``conv_pos`` from ``fairseq``. + + encoder_pos_conv_groups (int): + The number of groups of convolutional positional embeddings. + + This option corresponds to ``conv_pos_groups`` from ``fairseq``. + + encoder_num_layers (int): + The number of self attention layers in transformer block. + + This option corresponds to ``encoder_layers`` from ``fairseq``. + + encoder_num_heads (int): + The number of heads in self attention layers. + + This option corresponds to ``encoder_attention_heads`` from ``fairseq``. + + encoder_attention_dropout (float): + The dropout probability applied after softmax in self-attention layer. + + This option corresponds to ``attention_dropout`` from ``fairseq``. + + encoder_ff_interm_features (int): + The dimension of hidden features in feed forward layer. + + This option corresponds to ``encoder_ffn_embed_dim`` from ``fairseq``. + + encoder_ff_interm_dropout (float): + The dropout probability applied in feedforward layer. + + This option correspinds to ``activation_dropout`` from ``fairseq``. + + encoder_dropout (float): + The dropout probability applied at the end of feed forward layer. + + This option corresponds to ``dropout`` from ``fairseq``. + + encoder_layer_norm_first (bool): + Control the order of layer norm in transformer layer and each encoder layer. + If True, in transformer layer, layer norm is applied before features are fed + to encoder layers. In encoder layer, two layer norms are applied before and after + self attention. + If False, in transformer layer, layer norm is applied after features are fed + to encoder layers. In encoder layer, two layer norms are applied after self + attention, before and after feed forward. + + This option corresponds to ``layer_norm_first`` from ``fairseq``. + + encoder_layer_drop (float): + Probability to drop each encoder layer during training. + + This option corresponds to ``layerdrop`` from ``fairseq``. + + aux_num_out (int or None): + When provided, attach an extra linear layer on top of encoder, which can be + used for fine-tuning. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + if extractor_conv_layer_config is None: + extractor_conv_layer_config = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2 + + feature_extractor = components._get_feature_extractor( + extractor_mode, extractor_conv_layer_config, extractor_conv_bias, + prune_conv_channels=extractor_prune_conv_channels, + ) + encoder = components._get_encoder( + in_features=extractor_conv_layer_config[-1][0], + embed_dim=encoder_embed_dim, + dropout_input=encoder_projection_dropout, + pos_conv_kernel=encoder_pos_conv_kernel, + pos_conv_groups=encoder_pos_conv_groups, + num_layers=encoder_num_layers, + use_attention=encoder_use_attention, + use_feed_forward=encoder_use_feed_forward, + num_heads=encoder_num_heads, + head_dim=encoder_head_dim, + attention_dropout=encoder_attention_dropout, + ff_interm_features=encoder_ff_interm_features, + ff_interm_dropout=encoder_ff_interm_dropout, + dropout=encoder_dropout, + layer_norm_first=encoder_layer_norm_first, + layer_drop=encoder_layer_drop, + prune_attention_heads=encoder_prune_attention_heads, + prune_attention_layer=encoder_prune_attention_layer, + prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + aux = None + if aux_num_out is not None: + aux = torch.nn.Linear(in_features=encoder_embed_dim, out_features=aux_num_out) + return Wav2Vec2Model(normalize_waveform, feature_extractor, encoder, aux) + + +def wav2vec2_base( + encoder_projection_dropout: float = 0.1, + encoder_attention_dropout: float = 0.1, + encoder_ff_interm_dropout: float = 0.1, + encoder_dropout: float = 0.1, + encoder_layer_drop: float = 0.1, + aux_num_out: Optional[int] = None, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds "base" :class:`~torchaudio.models.Wav2Vec2Model` from *wav2vec 2.0* :cite:`baevski2020wav2vec` + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int or None, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + return wav2vec2_model( + extractor_mode="group_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=768, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=12, + encoder_num_heads=12, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=3072, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=False, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + extractor_prune_conv_channels=extractor_prune_conv_channels, + encoder_prune_attention_heads=encoder_prune_attention_heads, + encoder_prune_attention_layer=encoder_prune_attention_layer, + encoder_prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + encoder_prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + + +def wav2vec2_large( + encoder_projection_dropout: float = 0.1, + encoder_attention_dropout: float = 0.1, + encoder_ff_interm_dropout: float = 0.1, + encoder_dropout: float = 0.1, + encoder_layer_drop: float = 0.1, + aux_num_out: Optional[int] = None, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds "large" :class:`~torchaudio.models.Wav2Vec2Model` from *wav2vec 2.0* :cite:`baevski2020wav2vec` + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int or None, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + return wav2vec2_model( + extractor_mode="group_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=1024, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=24, + encoder_num_heads=16, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=4096, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=False, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + extractor_prune_conv_channels=extractor_prune_conv_channels, + encoder_prune_attention_heads=encoder_prune_attention_heads, + encoder_prune_attention_layer=encoder_prune_attention_layer, + encoder_prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + encoder_prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + + +def wav2vec2_large_lv60k( + encoder_projection_dropout: float = 0.1, + encoder_attention_dropout: float = 0.0, + encoder_ff_interm_dropout: float = 0.1, + encoder_dropout: float = 0.0, + encoder_layer_drop: float = 0.1, + aux_num_out: Optional[int] = None, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds "large lv-60k" :class:`~torchaudio.models.Wav2Vec2Model` from *wav2vec 2.0* :cite:`baevski2020wav2vec` + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int or None, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + return wav2vec2_model( + extractor_mode="layer_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=True, + encoder_embed_dim=1024, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=24, + encoder_num_heads=16, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=4096, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=True, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + extractor_prune_conv_channels=extractor_prune_conv_channels, + encoder_prune_attention_heads=encoder_prune_attention_heads, + encoder_prune_attention_layer=encoder_prune_attention_layer, + encoder_prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + encoder_prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + + +def hubert_base( + encoder_projection_dropout: float = 0.1, + encoder_attention_dropout: float = 0.1, + encoder_ff_interm_dropout: float = 0.0, + encoder_dropout: float = 0.1, + encoder_layer_drop: float = 0.05, + aux_num_out: Optional[int] = None, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds "base" :class:`HuBERT ` from *HuBERT* :cite:`hsu2021hubert` + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int or None, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + return wav2vec2_model( + extractor_mode="group_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=768, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=12, + encoder_use_attention=[True] * 12, + encoder_use_feed_forward=[True] * 12, + encoder_num_heads=[12] * 12, + encoder_head_dim=64, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=[3072] * 12, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=False, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + extractor_prune_conv_channels=extractor_prune_conv_channels, + encoder_prune_attention_heads=encoder_prune_attention_heads, + encoder_prune_attention_layer=encoder_prune_attention_layer, + encoder_prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + encoder_prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + + +def hubert_large( + encoder_projection_dropout: float = 0.0, + encoder_attention_dropout: float = 0.0, + encoder_ff_interm_dropout: float = 0.0, + encoder_dropout: float = 0.0, + encoder_layer_drop: float = 0.0, + aux_num_out: Optional[int] = None, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds "large" :class:`HuBERT ` from *HuBERT* :cite:`hsu2021hubert` + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int or None, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + return wav2vec2_model( + extractor_mode="layer_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=1024, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=24, + encoder_num_heads=16, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=4096, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=True, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + extractor_prune_conv_channels=extractor_prune_conv_channels, + encoder_prune_attention_heads=encoder_prune_attention_heads, + encoder_prune_attention_layer=encoder_prune_attention_layer, + encoder_prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + encoder_prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + + +def hubert_xlarge( + encoder_projection_dropout: float = 0.0, + encoder_attention_dropout: float = 0.0, + encoder_ff_interm_dropout: float = 0.0, + encoder_dropout: float = 0.0, + encoder_layer_drop: float = 0.0, + aux_num_out: Optional[int] = None, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds "extra large" :class:`HuBERT ` from *HuBERT* :cite:`hsu2021hubert` + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int or None, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ # noqa: E501 + return wav2vec2_model( + extractor_mode="layer_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=1280, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=48, + encoder_num_heads=16, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=5120, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=True, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + extractor_prune_conv_channels=extractor_prune_conv_channels, + encoder_prune_attention_heads=encoder_prune_attention_heads, + encoder_prune_attention_layer=encoder_prune_attention_layer, + encoder_prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + encoder_prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + + +def _init_hubert_pretrain_model(module): + if isinstance(module, components.LayerNorm): + torch.nn.init.kaiming_normal_(module.conv.weight) + elif isinstance(module, components.ConvolutionalPositionalEmbedding): + # normalize the weight to normal distribution. + std = math.sqrt(4.0 / (module.embed_dim * module.kernel_size)) + torch.nn.init.normal_(module.conv.weight, mean=0.0, std=std) + torch.nn.init.constant_(module.conv.bias, 0.0) + elif isinstance(module, components.SelfAttention): + # normalize the query, key, value, and out_proj parameters in self attention module. + torch.nn.init.xavier_uniform_(module.k_proj.weight, gain=1 / math.sqrt(2)) + torch.nn.init.xavier_uniform_(module.v_proj.weight, gain=1 / math.sqrt(2)) + torch.nn.init.xavier_uniform_(module.q_proj.weight, gain=1 / math.sqrt(2)) + torch.nn.init.xavier_uniform_(module.out_proj.weight) + torch.nn.init.constant_(module.out_proj.bias, 0.0) + elif isinstance(module, components.Transformer): + module.apply(components._init_transformer_params) + else: + pass + + +def wavlm_model( + extractor_mode: str, + extractor_conv_layer_config: Optional[List[Tuple[int, int, int]]], + extractor_conv_bias: bool, + encoder_embed_dim: int, + encoder_projection_dropout: float, + encoder_pos_conv_kernel: int, + encoder_pos_conv_groups: int, + encoder_num_layers: int, + encoder_use_attention: List[bool], + encoder_use_feed_forward: List[bool], + encoder_total_num_heads: List[int], + encoder_remaining_heads: List[List[int]], + encoder_num_buckets: int, + encoder_max_distance: int, + encoder_attention_dropout: float, + encoder_ff_interm_features: List[int], + encoder_ff_interm_dropout: float, + encoder_dropout: float, + encoder_layer_norm_first: bool, + encoder_layer_drop: float, + aux_num_out: Optional[int], + normalize_waveform: bool, + extractor_prune_conv_channels: bool = False, + encoder_prune_attention_heads: bool = False, + encoder_prune_attention_layer: bool = False, + encoder_prune_feed_forward_intermediate: bool = False, + encoder_prune_feed_forward_layer: bool = False, +) -> Wav2Vec2Model: + """Builds custom WaveLM model :cite:`chen2022wavlm`. The architecture is compatible + with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output object is + :class:`~torchaudio.models.Wav2Vec2Model`. Most of the arguments have the same meaning + as in :py:func:`wav2vec2_model` so please refer there for documentation. + + Args: + extractor_mode (str): Operation mode of feature extractor. + See :py:func:`wav2vec2_model`. + + extractor_conv_layer_config (list of integer tuples or None): + See :py:func:`wav2vec2_model`. + + extractor_conv_bias (bool): + See :py:func:`wav2vec2_model`. + + encoder_embed_dim (int): + See :py:func:`wav2vec2_model`. + + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + + encoder_pos_conv_kernel (int): + See :py:func:`wav2vec2_model`. + + encoder_pos_conv_groups (int): + See :py:func:`wav2vec2_model`. + + encoder_num_layers (int): + See :py:func:`wav2vec2_model`. + + encoder_num_heads (int): + See :py:func:`wav2vec2_model`. + + encoder_num_buckets (int): + Number of buckets for relative position embedding. + encoder_max_distance (int): + Maximum distance for relative position embedding. + + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + + encoder_ff_interm_features (int): + See :py:func:`wav2vec2_model`. + + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + + encoder_layer_norm_first (bool): + See :py:func:`wav2vec2_model`. + + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + + aux_num_out (int or None): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ + if extractor_conv_layer_config is None: + extractor_conv_layer_config = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2 + + feature_extractor = components._get_feature_extractor( + extractor_mode, extractor_conv_layer_config, extractor_conv_bias, + prune_conv_channels=extractor_prune_conv_channels, + ) + encoder = components._get_wavlm_encoder( + in_features=extractor_conv_layer_config[-1][0], + embed_dim=encoder_embed_dim, + dropout_input=encoder_projection_dropout, + pos_conv_kernel=encoder_pos_conv_kernel, + pos_conv_groups=encoder_pos_conv_groups, + num_layers=encoder_num_layers, + use_attention=encoder_use_attention, + use_feed_forward=encoder_use_feed_forward, + total_num_heads=encoder_total_num_heads, + remaining_heads=encoder_remaining_heads, + num_buckets=encoder_num_buckets, + max_distance=encoder_max_distance, + attention_dropout=encoder_attention_dropout, + ff_interm_features=encoder_ff_interm_features, + ff_interm_dropout=encoder_ff_interm_dropout, + dropout=encoder_dropout, + layer_norm_first=encoder_layer_norm_first, + layer_drop=encoder_layer_drop, + prune_attention_heads=encoder_prune_attention_heads, + prune_attention_layer=encoder_prune_attention_layer, + prune_feed_forward_intermediate=encoder_prune_feed_forward_intermediate, + prune_feed_forward_layer=encoder_prune_feed_forward_layer, + ) + aux = None + if aux_num_out is not None: + aux = torch.nn.Linear(in_features=encoder_embed_dim, out_features=aux_num_out) + return Wav2Vec2Model(normalize_waveform, feature_extractor, encoder, aux) + + +def wavlm_base( + encoder_projection_dropout: float = 0.1, + encoder_attention_dropout: float = 0.1, + encoder_ff_interm_dropout: float = 0.1, + encoder_dropout: float = 0.1, + encoder_layer_drop: float = 0.1, + aux_num_out: Optional[int] = None, +) -> Wav2Vec2Model: + """Builds "base" WaveLM model :cite:`chen2022wavlm`. The architecture is compatible + with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is + :class:`~torchaudio.models.Wav2Vec2Model`. + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ + return wavlm_model( + extractor_mode="group_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=768, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=12, + encoder_num_heads=12, + encoder_num_buckets=320, + encoder_max_distance=800, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=3072, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=False, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + ) + + +def wavlm_large( + encoder_projection_dropout: float = 0.1, + encoder_attention_dropout: float = 0.1, + encoder_ff_interm_dropout: float = 0.0, + encoder_dropout: float = 0.1, + encoder_layer_drop: float = 0.1, + aux_num_out: Optional[int] = None, +) -> Wav2Vec2Model: + """Builds "large" WaveLM model :cite:`chen2022wavlm`. The architecture is compatible + with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is + :class:`~torchaudio.models.Wav2Vec2Model`. + + Args: + encoder_projection_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_attention_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_ff_interm_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_dropout (float): + See :py:func:`wav2vec2_model`. + encoder_layer_drop (float): + See :py:func:`wav2vec2_model`. + aux_num_out (int, optional): + See :py:func:`wav2vec2_model`. + + Returns: + Wav2Vec2Model: + The resulting model. + """ + return wavlm_model( + extractor_mode="layer_norm", + extractor_conv_layer_config=None, + extractor_conv_bias=False, + encoder_embed_dim=1024, + encoder_projection_dropout=encoder_projection_dropout, + encoder_pos_conv_kernel=128, + encoder_pos_conv_groups=16, + encoder_num_layers=24, + encoder_num_heads=16, + encoder_num_buckets=320, + encoder_max_distance=800, + encoder_attention_dropout=encoder_attention_dropout, + encoder_ff_interm_features=4096, + encoder_ff_interm_dropout=encoder_ff_interm_dropout, + encoder_dropout=encoder_dropout, + encoder_layer_norm_first=True, + encoder_layer_drop=encoder_layer_drop, + aux_num_out=aux_num_out, + ) diff --git a/vencoder/dphubert/pruning_utils.py b/vencoder/dphubert/pruning_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ac185980c2c3da716bf3ce402a541ffe70776acf --- /dev/null +++ b/vencoder/dphubert/pruning_utils.py @@ -0,0 +1,51 @@ +"""Utility functions for pruning.""" + +from typing import Union + +import torch +import torch.nn as nn + + +def prune_linear_layer(layer: nn.Linear, index: torch.LongTensor, dim: str): + "Prune linear layer in place." + # NOTE: weight: (out_features, in_features), bias: (out_features,) + if dim == "input": + dim = 1 + layer.in_features = len(index) + elif dim == "output": + dim = 0 + layer.out_features = len(index) + else: + raise ValueError + + layer.weight = nn.Parameter(layer.weight.index_select(dim, index).clone().detach()) + if layer.bias is not None and dim == 0: + layer.bias = nn.Parameter(layer.bias.index_select(0, index).clone().detach()) + + +def prune_conv1d_layer(layer: nn.Conv1d, index: torch.LongTensor, dim: str): + """Prune conv1d in place.""" + # NOTE: weight: (out_channels, in_channels, kernel_size), bias: (out_channels,) + if dim == "input": + dim = 1 + layer.in_channels = len(index) + elif dim == "output": + dim = 0 + layer.out_channels = len(index) + else: + raise ValueError + + layer.weight = nn.Parameter(layer.weight.index_select(dim, index).clone().detach()) + if layer.bias is not None and dim == 0: + layer.bias = nn.Parameter(layer.bias.index_select(0, index).clone().detach()) + + +def prune_layer_norm(layernorm: Union[nn.LayerNorm, nn.GroupNorm], index: torch.LongTensor): + """Prune layer norm or group norm in place.""" + layernorm.weight = nn.Parameter(layernorm.weight.index_select(0, index).clone().detach()) + layernorm.bias = nn.Parameter(layernorm.bias.index_select(0, index).clone().detach()) + if isinstance(layernorm, nn.LayerNorm): + layernorm.normalized_shape = (len(index),) + elif isinstance(layernorm, nn.GroupNorm): + layernorm.num_groups = len(index) + layernorm.num_channels = len(index) diff --git a/vencoder/dphubert/utils/__init__.py b/vencoder/dphubert/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vencoder/dphubert/utils/import_huggingface_wavlm.py b/vencoder/dphubert/utils/import_huggingface_wavlm.py new file mode 100644 index 0000000000000000000000000000000000000000..1a2ea31c14df5450298ddc5e1f56c98769144828 --- /dev/null +++ b/vencoder/dphubert/utils/import_huggingface_wavlm.py @@ -0,0 +1,129 @@ +"""Import Hugging Face transformers's wav2vec2.0 pretrained weights to torchaudios's format. + +Originally from: +https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/utils/import_huggingface.py + +""" + +import logging +from typing import Any, Dict + +from torch.nn import Module + +from ..model import wav2vec2_model, Wav2Vec2Model, wavlm_model + +_LG = logging.getLogger(__name__) + + +def _get_config(cfg): + config = { + "extractor_mode": f"{cfg.feat_extract_norm}_norm", + "extractor_conv_layer_config": list(zip(cfg.conv_dim, cfg.conv_kernel, cfg.conv_stride)), + "extractor_conv_bias": cfg.conv_bias, + "encoder_embed_dim": cfg.hidden_size, + "encoder_projection_dropout": cfg.feat_proj_dropout, + "encoder_pos_conv_kernel": cfg.num_conv_pos_embeddings, + "encoder_pos_conv_groups": cfg.num_conv_pos_embedding_groups, + "encoder_num_layers": cfg.num_hidden_layers, + "encoder_num_heads": cfg.num_attention_heads, + "encoder_attention_dropout": cfg.attention_dropout, + "encoder_ff_interm_features": cfg.intermediate_size, + "encoder_ff_interm_dropout": cfg.activation_dropout, + "encoder_dropout": cfg.hidden_dropout, + "encoder_layer_norm_first": cfg.do_stable_layer_norm, + "encoder_layer_drop": cfg.layerdrop, + } + return config + + +def _get_config_wavlm(cfg): + config = { + "extractor_mode": f"{cfg.feat_extract_norm}_norm", + "extractor_conv_layer_config": list(zip(cfg.conv_dim, cfg.conv_kernel, cfg.conv_stride)), + "extractor_conv_bias": cfg.conv_bias, + "encoder_embed_dim": cfg.hidden_size, + "encoder_projection_dropout": cfg.feat_proj_dropout, + "encoder_pos_conv_kernel": cfg.num_conv_pos_embeddings, + "encoder_pos_conv_groups": cfg.num_conv_pos_embedding_groups, + "encoder_num_layers": cfg.num_hidden_layers, + "encoder_use_attention": [True] * cfg.num_hidden_layers, + "encoder_use_feed_forward": [True] * cfg.num_hidden_layers, + "encoder_total_num_heads": [cfg.num_attention_heads for _ in range(cfg.num_hidden_layers)], + "encoder_remaining_heads": [list(range(cfg.num_attention_heads)) for _ in range(cfg.num_hidden_layers)], + "encoder_num_buckets": cfg.num_buckets, + "encoder_max_distance": cfg.max_bucket_distance, + "encoder_attention_dropout": cfg.attention_dropout, + "encoder_ff_interm_features": [cfg.intermediate_size for _ in range(cfg.num_hidden_layers)], + "encoder_ff_interm_dropout": cfg.activation_dropout, + "encoder_dropout": cfg.hidden_dropout, + "encoder_layer_norm_first": cfg.do_stable_layer_norm, + "encoder_layer_drop": cfg.layerdrop, + "normalize_waveform": cfg.feat_extract_norm == "layer", + } + return config + + +def _build(config, original): + is_for_ctc = original.__class__.__name__ in ["Wav2Vec2ForCTC", "WavLMForCTC"] + if is_for_ctc: + aux_num_out = original.config.vocab_size + wav2vec2 = original.wav2vec2 + else: + _LG.warning( + "The model is not an instance of Wav2Vec2ForCTC or WavLMForCTC. " '"lm_head" module is not imported.' + ) + aux_num_out = None + wav2vec2 = original + is_wavlm = original.__class__.__name__ in ["WavLMModel", "WavLMForCTC"] + if is_wavlm: + imported = wavlm_model(**config, aux_num_out=aux_num_out) + else: + imported = wav2vec2_model(**config, aux_num_out=aux_num_out) + print(imported.feature_extractor.load_state_dict(wav2vec2.feature_extractor.state_dict(), strict=False)) + print(imported.encoder.feature_projection.load_state_dict(wav2vec2.feature_projection.state_dict(), strict=False)) + encoder_state_dict = wav2vec2.encoder.state_dict() + if is_wavlm: # Rename paramaters of linear transformations for compatibility with the HF model + transform_wavlm_encoder_state(encoder_state_dict, config["encoder_num_layers"]) + print(imported.encoder.transformer.load_state_dict(encoder_state_dict, strict=False)) + if is_for_ctc: + imported.aux.load_state_dict(original.lm_head.state_dict()) + return imported + + +def transform_wavlm_encoder_state(state: Dict[str, Any], encoder_num_layers: int): + """Converts WavLM encoder state from HuggingFace format. In particular, concatenates linear projection weights and + biases to align with the structure of ``torch.nn.MultiheadAttention``. + """ + pass + + +def import_huggingface_model(original: Module) -> Wav2Vec2Model: + """Builds :class:`Wav2Vec2Model` from the corresponding model object of + `Transformers `_. + + Args: + original (torch.nn.Module): An instance of ``Wav2Vec2ForCTC`` from ``transformers``. + + Returns: + Wav2Vec2Model: Imported model. + + Example + >>> from torchaudio.models.wav2vec2.utils import import_huggingface_model + >>> + >>> original = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + >>> model = import_huggingface_model(original) + >>> + >>> waveforms, _ = torchaudio.load("audio.wav") + >>> logits, _ = model(waveforms) + """ + _LG.info("Importing model.") + _LG.info("Loading model configuration.") + is_wavlm = original.__class__.__name__ in ["WavLMModel", "WavLMForCTC"] + if is_wavlm: + config = _get_config_wavlm(original.config) + else: + config = _get_config(original.config) + _LG.debug(" - config: %s", config) + _LG.info("Building model.") + imported = _build(config, original) + return imported diff --git a/vencoder/encoder.py b/vencoder/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf5678533cf16f2e81248535d35e4c3c1c5799a --- /dev/null +++ b/vencoder/encoder.py @@ -0,0 +1,12 @@ +class SpeechEncoder(object): + def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None): + self.model = None #This is Model + self.hidden_dim = 768 + pass + + def encoder(self,wav): + ''' + input: wav:[batchsize,signal_length] + output: embedding:[batchsize,hidden_dim,wav_frame] + ''' + pass \ No newline at end of file diff --git a/vencoder/hubert/__init__.py b/vencoder/hubert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vencoder/hubert/hubert_model.py b/vencoder/hubert/hubert_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7fb642d89b07ca60792debab18e3454f52d8f357 --- /dev/null +++ b/vencoder/hubert/hubert_model.py @@ -0,0 +1,222 @@ +import copy +import random +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as t_func +from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present + + +class Hubert(nn.Module): + def __init__(self, num_label_embeddings: int = 100, mask: bool = True): + super().__init__() + self._mask = mask + self.feature_extractor = FeatureExtractor() + self.feature_projection = FeatureProjection() + self.positional_embedding = PositionalConvEmbedding() + self.norm = nn.LayerNorm(768) + self.dropout = nn.Dropout(0.1) + self.encoder = TransformerEncoder( + nn.TransformerEncoderLayer( + 768, 12, 3072, activation="gelu", batch_first=True + ), + 12, + ) + self.proj = nn.Linear(768, 256) + + self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_()) + self.label_embedding = nn.Embedding(num_label_embeddings, 256) + + def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + mask = None + if self.training and self._mask: + mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2) + x[mask] = self.masked_spec_embed.to(x.dtype) + return x, mask + + def encode( + self, x: torch.Tensor, layer: Optional[int] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + x = self.feature_extractor(x) + x = self.feature_projection(x.transpose(1, 2)) + x, mask = self.mask(x) + x = x + self.positional_embedding(x) + x = self.dropout(self.norm(x)) + x = self.encoder(x, output_layer=layer) + return x, mask + + def logits(self, x: torch.Tensor) -> torch.Tensor: + logits = torch.cosine_similarity( + x.unsqueeze(2), + self.label_embedding.weight.unsqueeze(0).unsqueeze(0), + dim=-1, + ) + return logits / 0.1 + + def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + x, mask = self.encode(x) + x = self.proj(x) + logits = self.logits(x) + return logits, mask + + +class HubertSoft(Hubert): + def __init__(self): + super().__init__() + + @torch.inference_mode() + def units(self, wav: torch.Tensor) -> torch.Tensor: + wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2)) + x, _ = self.encode(wav) + return self.proj(x) + + +class FeatureExtractor(nn.Module): + def __init__(self): + super().__init__() + self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False) + self.norm0 = nn.GroupNorm(512, 512) + self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False) + self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = t_func.gelu(self.norm0(self.conv0(x))) + x = t_func.gelu(self.conv1(x)) + x = t_func.gelu(self.conv2(x)) + x = t_func.gelu(self.conv3(x)) + x = t_func.gelu(self.conv4(x)) + x = t_func.gelu(self.conv5(x)) + x = t_func.gelu(self.conv6(x)) + return x + + +class FeatureProjection(nn.Module): + def __init__(self): + super().__init__() + self.norm = nn.LayerNorm(512) + self.projection = nn.Linear(512, 768) + self.dropout = nn.Dropout(0.1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.norm(x) + x = self.projection(x) + x = self.dropout(x) + return x + + +class PositionalConvEmbedding(nn.Module): + def __init__(self): + super().__init__() + self.conv = nn.Conv1d( + 768, + 768, + kernel_size=128, + padding=128 // 2, + groups=16, + ) + self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv(x.transpose(1, 2)) + x = t_func.gelu(x[:, :, :-1]) + return x.transpose(1, 2) + + +class TransformerEncoder(nn.Module): + def __init__( + self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int + ) -> None: + super(TransformerEncoder, self).__init__() + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for _ in range(num_layers)] + ) + self.num_layers = num_layers + + def forward( + self, + src: torch.Tensor, + mask: torch.Tensor = None, + src_key_padding_mask: torch.Tensor = None, + output_layer: Optional[int] = None, + ) -> torch.Tensor: + output = src + for layer in self.layers[:output_layer]: + output = layer( + output, src_mask=mask, src_key_padding_mask=src_key_padding_mask + ) + return output + + +def _compute_mask( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + device: torch.device, + min_masks: int = 0, +) -> torch.Tensor: + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`" + ) + + # compute number of masked spans in batch + num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random()) + num_masked_spans = max(num_masked_spans, min_masks) + + # make sure num masked indices <= sequence_length + if num_masked_spans * mask_length > sequence_length: + num_masked_spans = sequence_length // mask_length + + # SpecAugment mask to fill + mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool) + + # uniform distribution to sample from, make sure that offset samples are < sequence_length + uniform_dist = torch.ones( + (batch_size, sequence_length - (mask_length - 1)), device=device + ) + + # get random indices to mask + mask_indices = torch.multinomial(uniform_dist, num_masked_spans) + + # expand masked indices to masked spans + mask_indices = ( + mask_indices.unsqueeze(dim=-1) + .expand((batch_size, num_masked_spans, mask_length)) + .reshape(batch_size, num_masked_spans * mask_length) + ) + offsets = ( + torch.arange(mask_length, device=device)[None, None, :] + .expand((batch_size, num_masked_spans, mask_length)) + .reshape(batch_size, num_masked_spans * mask_length) + ) + mask_idxs = mask_indices + offsets + + # scatter indices to mask + mask = mask.scatter(1, mask_idxs, True) + + return mask + + +def hubert_soft( + path: str, +) -> HubertSoft: + r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`. + Args: + path (str): path of a pretrained model + """ + hubert = HubertSoft() + checkpoint = torch.load(path) + consume_prefix_in_state_dict_if_present(checkpoint, "module.") + hubert.load_state_dict(checkpoint) + hubert.eval() + return hubert diff --git a/vencoder/hubert/hubert_model_onnx.py b/vencoder/hubert/hubert_model_onnx.py new file mode 100644 index 0000000000000000000000000000000000000000..d18f3c2a0fc29592a573a9780308d38f059640b9 --- /dev/null +++ b/vencoder/hubert/hubert_model_onnx.py @@ -0,0 +1,217 @@ +import copy +import random +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as t_func +from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present + + +class Hubert(nn.Module): + def __init__(self, num_label_embeddings: int = 100, mask: bool = True): + super().__init__() + self._mask = mask + self.feature_extractor = FeatureExtractor() + self.feature_projection = FeatureProjection() + self.positional_embedding = PositionalConvEmbedding() + self.norm = nn.LayerNorm(768) + self.dropout = nn.Dropout(0.1) + self.encoder = TransformerEncoder( + nn.TransformerEncoderLayer( + 768, 12, 3072, activation="gelu", batch_first=True + ), + 12, + ) + self.proj = nn.Linear(768, 256) + + self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_()) + self.label_embedding = nn.Embedding(num_label_embeddings, 256) + + def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + mask = None + if self.training and self._mask: + mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2) + x[mask] = self.masked_spec_embed.to(x.dtype) + return x, mask + + def encode( + self, x: torch.Tensor, layer: Optional[int] = None + ) -> Tuple[torch.Tensor, torch.Tensor]: + x = self.feature_extractor(x) + x = self.feature_projection(x.transpose(1, 2)) + x, mask = self.mask(x) + x = x + self.positional_embedding(x) + x = self.dropout(self.norm(x)) + x = self.encoder(x, output_layer=layer) + return x, mask + + def logits(self, x: torch.Tensor) -> torch.Tensor: + logits = torch.cosine_similarity( + x.unsqueeze(2), + self.label_embedding.weight.unsqueeze(0).unsqueeze(0), + dim=-1, + ) + return logits / 0.1 + + +class HubertSoft(Hubert): + def __init__(self): + super().__init__() + + def units(self, wav: torch.Tensor) -> torch.Tensor: + wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2)) + x, _ = self.encode(wav) + return self.proj(x) + + def forward(self, x): + return self.units(x) + +class FeatureExtractor(nn.Module): + def __init__(self): + super().__init__() + self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False) + self.norm0 = nn.GroupNorm(512, 512) + self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False) + self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False) + self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = t_func.gelu(self.norm0(self.conv0(x))) + x = t_func.gelu(self.conv1(x)) + x = t_func.gelu(self.conv2(x)) + x = t_func.gelu(self.conv3(x)) + x = t_func.gelu(self.conv4(x)) + x = t_func.gelu(self.conv5(x)) + x = t_func.gelu(self.conv6(x)) + return x + + +class FeatureProjection(nn.Module): + def __init__(self): + super().__init__() + self.norm = nn.LayerNorm(512) + self.projection = nn.Linear(512, 768) + self.dropout = nn.Dropout(0.1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.norm(x) + x = self.projection(x) + x = self.dropout(x) + return x + + +class PositionalConvEmbedding(nn.Module): + def __init__(self): + super().__init__() + self.conv = nn.Conv1d( + 768, + 768, + kernel_size=128, + padding=128 // 2, + groups=16, + ) + self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv(x.transpose(1, 2)) + x = t_func.gelu(x[:, :, :-1]) + return x.transpose(1, 2) + + +class TransformerEncoder(nn.Module): + def __init__( + self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int + ) -> None: + super(TransformerEncoder, self).__init__() + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for _ in range(num_layers)] + ) + self.num_layers = num_layers + + def forward( + self, + src: torch.Tensor, + mask: torch.Tensor = None, + src_key_padding_mask: torch.Tensor = None, + output_layer: Optional[int] = None, + ) -> torch.Tensor: + output = src + for layer in self.layers[:output_layer]: + output = layer( + output, src_mask=mask, src_key_padding_mask=src_key_padding_mask + ) + return output + + +def _compute_mask( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + device: torch.device, + min_masks: int = 0, +) -> torch.Tensor: + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`" + ) + + # compute number of masked spans in batch + num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random()) + num_masked_spans = max(num_masked_spans, min_masks) + + # make sure num masked indices <= sequence_length + if num_masked_spans * mask_length > sequence_length: + num_masked_spans = sequence_length // mask_length + + # SpecAugment mask to fill + mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool) + + # uniform distribution to sample from, make sure that offset samples are < sequence_length + uniform_dist = torch.ones( + (batch_size, sequence_length - (mask_length - 1)), device=device + ) + + # get random indices to mask + mask_indices = torch.multinomial(uniform_dist, num_masked_spans) + + # expand masked indices to masked spans + mask_indices = ( + mask_indices.unsqueeze(dim=-1) + .expand((batch_size, num_masked_spans, mask_length)) + .reshape(batch_size, num_masked_spans * mask_length) + ) + offsets = ( + torch.arange(mask_length, device=device)[None, None, :] + .expand((batch_size, num_masked_spans, mask_length)) + .reshape(batch_size, num_masked_spans * mask_length) + ) + mask_idxs = mask_indices + offsets + + # scatter indices to mask + mask = mask.scatter(1, mask_idxs, True) + + return mask + + +def hubert_soft( + path: str, +) -> HubertSoft: + r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`. + Args: + path (str): path of a pretrained model + """ + hubert = HubertSoft() + checkpoint = torch.load(path) + consume_prefix_in_state_dict_if_present(checkpoint, "module.") + hubert.load_state_dict(checkpoint) + hubert.eval() + return hubert diff --git a/vencoder/wavlm/WavLM.py b/vencoder/wavlm/WavLM.py new file mode 100644 index 0000000000000000000000000000000000000000..777befb7865c298fad94bd48003bd071feef7064 --- /dev/null +++ b/vencoder/wavlm/WavLM.py @@ -0,0 +1,743 @@ +# -------------------------------------------------------- +# WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf) +# Github source: https://github.com/microsoft/unilm/tree/master/wavlm +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Based on fairseq code bases +# https://github.com/pytorch/fairseq +# -------------------------------------------------------- + +import math +import logging +from typing import List, Optional, Tuple + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import LayerNorm +from vencoder.wavlm.modules import ( + Fp32GroupNorm, + Fp32LayerNorm, + GradMultiply, + MultiheadAttention, + SamePad, + init_bert_params, + get_activation_fn, + TransposeLast, + GLU_Linear, +) + +logger = logging.getLogger(__name__) + + +def compute_mask_indices( + shape: Tuple[int, int], + padding_mask: Optional[torch.Tensor], + mask_prob: float, + mask_length: int, + mask_type: str = "static", + mask_other: float = 0.0, + min_masks: int = 0, + no_overlap: bool = False, + min_space: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape + + Args: + shape: the the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_type: how to compute mask lengths + static = fixed size + uniform = sample from uniform distribution [mask_other, mask_length*2] + normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element + poisson = sample from possion distribution with lambda = mask length + min_masks: minimum number of masked spans + no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping + min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans + """ + + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + # add a random number for probabilistic rounding + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + + all_num_mask = max(min_masks, all_num_mask) + + mask_idcs = [] + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + if mask_type == "static": + lengths = np.full(num_mask, mask_length) + elif mask_type == "uniform": + lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask) + elif mask_type == "normal": + lengths = np.random.normal(mask_length, mask_other, size=num_mask) + lengths = [max(1, int(round(x))) for x in lengths] + elif mask_type == "poisson": + lengths = np.random.poisson(mask_length, size=num_mask) + lengths = [int(round(x)) for x in lengths] + else: + raise Exception("unknown mask selection " + mask_type) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + if no_overlap: + mask_idc = [] + + def arrange(s, e, length, keep_length): + span_start = np.random.randint(s, e - length) + mask_idc.extend(span_start + i for i in range(length)) + + new_parts = [] + if span_start - s - min_space >= keep_length: + new_parts.append((s, span_start - min_space + 1)) + if e - span_start - keep_length - min_space > keep_length: + new_parts.append((span_start + length + min_space, e)) + return new_parts + + parts = [(0, sz)] + min_length = min(lengths) + for length in sorted(lengths, reverse=True): + lens = np.fromiter( + (e - s if e - s >= length + min_space else 0 for s, e in parts), + np.int, + ) + l_sum = np.sum(lens) + if l_sum == 0: + break + probs = lens / np.sum(lens) + c = np.random.choice(len(parts), p=probs) + s, e = parts.pop(c) + parts.extend(arrange(s, e, length, min_length)) + mask_idc = np.asarray(mask_idc) + else: + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ] + ) + + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + mask[i, mask_idc] = True + + return mask + + +class WavLMConfig: + def __init__(self, cfg=None): + self.extractor_mode: str = "default" # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True) + self.encoder_layers: int = 12 # num encoder layers in the transformer + + self.encoder_embed_dim: int = 768 # encoder embedding dimension + self.encoder_ffn_embed_dim: int = 3072 # encoder embedding dimension for FFN + self.encoder_attention_heads: int = 12 # num encoder attention heads + self.activation_fn: str = "gelu" # activation function to use + + self.layer_norm_first: bool = False # apply layernorm first in the transformer + self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2" # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...] + self.conv_bias: bool = False # include bias in conv encoder + self.feature_grad_mult: float = 1.0 # multiply feature extractor var grads by this + + self.normalize: bool = False # normalize input to have 0 mean and unit variance during training + + # dropouts + self.dropout: float = 0.1 # dropout probability for the transformer + self.attention_dropout: float = 0.1 # dropout probability for attention weights + self.activation_dropout: float = 0.0 # dropout probability after activation in FFN + self.encoder_layerdrop: float = 0.0 # probability of dropping a tarnsformer layer + self.dropout_input: float = 0.0 # dropout to apply to the input (after feat extr) + self.dropout_features: float = 0.0 # dropout to apply to the features (after feat extr) + + # masking + self.mask_length: int = 10 # mask length + self.mask_prob: float = 0.65 # probability of replacing a token with mask + self.mask_selection: str = "static" # how to choose mask length + self.mask_other: float = 0 # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh + self.no_mask_overlap: bool = False # whether to allow masks to overlap + self.mask_min_space: int = 1 # min space between spans (if no overlap is enabled) + + # channel masking + self.mask_channel_length: int = 10 # length of the mask for features (channels) + self.mask_channel_prob: float = 0.0 # probability of replacing a feature with 0 + self.mask_channel_selection: str = "static" # how to choose mask length for channel masking + self.mask_channel_other: float = 0 # secondary mask argument (used for more complex distributions), see help in compute_mask_indices + self.no_mask_channel_overlap: bool = False # whether to allow channel masks to overlap + self.mask_channel_min_space: int = 1 # min space between spans (if no overlap is enabled) + + # positional embeddings + self.conv_pos: int = 128 # number of filters for convolutional positional embeddings + self.conv_pos_groups: int = 16 # number of groups for convolutional positional embedding + + # relative position embedding + self.relative_position_embedding: bool = False # apply relative position embedding + self.num_buckets: int = 320 # number of buckets for relative position embedding + self.max_distance: int = 1280 # maximum distance for relative position embedding + self.gru_rel_pos: bool = False # apply gated relative position embedding + + if cfg is not None: + self.update(cfg) + + def update(self, cfg: dict): + self.__dict__.update(cfg) + + +class WavLM(nn.Module): + def __init__( + self, + cfg: WavLMConfig, + ) -> None: + super().__init__() + logger.info(f"WavLM Config: {cfg.__dict__}") + + self.cfg = cfg + feature_enc_layers = eval(cfg.conv_feature_layers) + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim + else None + ) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.encoder = TransformerEncoder(cfg) + self.layer_norm = LayerNorm(self.embed) + + def apply_mask(self, x, padding_mask): + B, T, C = x.shape + if self.mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def forward_padding_mask( + self, features: torch.Tensor, padding_mask: torch.Tensor, + ) -> torch.Tensor: + extra = padding_mask.size(1) % features.size(1) + if extra > 0: + padding_mask = padding_mask[:, :-extra] + padding_mask = padding_mask.view( + padding_mask.size(0), features.size(1), -1 + ) + padding_mask = padding_mask.all(-1) + return padding_mask + + def extract_features( + self, + source: torch.Tensor, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = False, + ret_conv: bool = False, + output_layer: Optional[int] = None, + ret_layer_results: bool = False, + ): + + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + + features = features.transpose(1, 2) + features = self.layer_norm(features) + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + + if mask: + x, mask_indices = self.apply_mask( + features, padding_mask + ) + else: + x = features + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + x, layer_results = self.encoder( + x, + padding_mask=padding_mask, + layer=None if output_layer is None else output_layer - 1 + ) + + res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results} + + feature = res["features"] if ret_conv else res["x"] + if ret_layer_results: + feature = (feature, res["layer_results"]) + return feature, res["padding_mask"] + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers: List[Tuple[int, int, int]], + dropout: float = 0.0, + mode: str = "default", + conv_bias: bool = False, + conv_type: str = "default" + ): + super().__init__() + + assert mode in {"default", "layer_norm"} + + def block( + n_in, + n_out, + k, + stride, + is_layer_norm=False, + is_group_norm=False, + conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert ( + is_layer_norm and is_group_norm + ) == False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=True), + TransposeLast(), + ), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + Fp32GroupNorm(dim, dim, affine=True), + nn.GELU(), + ) + else: + return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) + + self.conv_type = conv_type + if self.conv_type == "default": + in_d = 1 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + (dim, k, stride) = cl + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=mode == "layer_norm", + is_group_norm=mode == "default" and i == 0, + conv_bias=conv_bias, + ) + ) + in_d = dim + elif self.conv_type == "conv2d": + in_d = 1 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3 + (dim, k, stride) = cl + + self.conv_layers.append( + torch.nn.Conv2d(in_d, dim, k, stride) + ) + self.conv_layers.append(torch.nn.ReLU()) + in_d = dim + elif self.conv_type == "custom": + in_d = 1 + idim = 80 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3 + (dim, k, stride) = cl + self.conv_layers.append( + torch.nn.Conv2d(in_d, dim, k, stride, padding=1) + ) + self.conv_layers.append( + torch.nn.LayerNorm([dim, idim]) + ) + self.conv_layers.append(torch.nn.ReLU()) + in_d = dim + if (i + 1) % 2 == 0: + self.conv_layers.append( + torch.nn.MaxPool2d(2, stride=2, ceil_mode=True) + ) + idim = int(math.ceil(idim / 2)) + else: + pass + + def forward(self, x, mask=None): + + # BxT -> BxCxT + x = x.unsqueeze(1) + if self.conv_type == "custom": + for conv in self.conv_layers: + if isinstance(conv, nn.LayerNorm): + x = x.transpose(1, 2) + x = conv(x).transpose(1, 2) + else: + x = conv(x) + x = x.transpose(2, 3).contiguous() + x = x.view(x.size(0), -1, x.size(-1)) + else: + for conv in self.conv_layers: + x = conv(x) + if self.conv_type == "conv2d": + b, c, t, f = x.size() + x = x.transpose(2, 3).contiguous().view(b, c * f, t) + return x + + +class TransformerEncoder(nn.Module): + def __init__(self, args): + super().__init__() + + self.dropout = args.dropout + self.embedding_dim = args.encoder_embed_dim + + self.pos_conv = nn.Conv1d( + self.embedding_dim, + self.embedding_dim, + kernel_size=args.conv_pos, + padding=args.conv_pos // 2, + groups=args.conv_pos_groups, + ) + dropout = 0 + std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim)) + nn.init.normal_(self.pos_conv.weight, mean=0, std=std) + nn.init.constant_(self.pos_conv.bias, 0) + + self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2) + self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU()) + + if hasattr(args, "relative_position_embedding"): + self.relative_position_embedding = args.relative_position_embedding + self.num_buckets = args.num_buckets + self.max_distance = args.max_distance + else: + self.relative_position_embedding = False + self.num_buckets = 0 + self.max_distance = 0 + + self.layers = nn.ModuleList( + [ + TransformerSentenceEncoderLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=args.encoder_ffn_embed_dim, + num_attention_heads=args.encoder_attention_heads, + dropout=self.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + activation_fn=args.activation_fn, + layer_norm_first=args.layer_norm_first, + has_relative_attention_bias=(self.relative_position_embedding and i == 0), + num_buckets=self.num_buckets, + max_distance=self.max_distance, + gru_rel_pos=args.gru_rel_pos, + ) + for i in range(args.encoder_layers) + ] + ) + + self.layer_norm_first = args.layer_norm_first + self.layer_norm = LayerNorm(self.embedding_dim) + self.layerdrop = args.encoder_layerdrop + + self.apply(init_bert_params) + + def forward(self, x, padding_mask=None, streaming_mask=None, layer=None): + x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer) + + if self.layer_norm_first and layer is None: + x = self.layer_norm(x) + + return x, layer_results + + def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None): + + if padding_mask is not None: + x[padding_mask] = 0 + + x_conv = self.pos_conv(x.transpose(1, 2)) + x_conv = x_conv.transpose(1, 2) + x = x + x_conv + + if not self.layer_norm_first: + x = self.layer_norm(x) + + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + layer_results = [] + z = None + if tgt_layer is not None: + layer_results.append((x, z)) + r = None + pos_bias = None + for i, layer in enumerate(self.layers): + dropout_probability = np.random.random() + if not self.training or (dropout_probability > self.layerdrop): + x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, + self_attn_mask=streaming_mask, pos_bias=pos_bias) + if tgt_layer is not None: + layer_results.append((x, z)) + if i == tgt_layer: + r = x + break + + if r is not None: + x = r + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, layer_results + + +class TransformerSentenceEncoderLayer(nn.Module): + """ + Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__( + self, + embedding_dim: float = 768, + ffn_embedding_dim: float = 3072, + num_attention_heads: float = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + layer_norm_first: bool = False, + has_relative_attention_bias: bool = False, + num_buckets: int = 0, + max_distance: int = 0, + rescale_init: bool = False, + gru_rel_pos: bool = False, + ) -> None: + + super().__init__() + # Initialize parameters + self.embedding_dim = embedding_dim + self.dropout = dropout + self.activation_dropout = activation_dropout + + # Initialize blocks + self.activation_name = activation_fn + self.activation_fn = get_activation_fn(activation_fn) + self.self_attn = MultiheadAttention( + self.embedding_dim, + num_attention_heads, + dropout=attention_dropout, + self_attention=True, + has_relative_attention_bias=has_relative_attention_bias, + num_buckets=num_buckets, + max_distance=max_distance, + rescale_init=rescale_init, + gru_rel_pos=gru_rel_pos, + ) + + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(self.activation_dropout) + self.dropout3 = nn.Dropout(dropout) + + self.layer_norm_first = layer_norm_first + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = LayerNorm(self.embedding_dim) + + if self.activation_name == "glu": + self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish") + else: + self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) + self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = LayerNorm(self.embedding_dim) + + def forward( + self, + x: torch.Tensor, + self_attn_mask: torch.Tensor = None, + self_attn_padding_mask: torch.Tensor = None, + need_weights: bool = False, + pos_bias=None + ): + """ + LayerNorm is applied either before or after the self-attention/ffn + modules similar to the original Transformer imlementation. + """ + residual = x + + if self.layer_norm_first: + x = self.self_attn_layer_norm(x) + x, attn, pos_bias = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + need_weights=False, + attn_mask=self_attn_mask, + position_bias=pos_bias + ) + x = self.dropout1(x) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + if self.activation_name == "glu": + x = self.fc1(x) + else: + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + x = self.dropout3(x) + x = residual + x + else: + x, attn, pos_bias = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + need_weights=need_weights, + attn_mask=self_attn_mask, + position_bias=pos_bias + ) + + x = self.dropout1(x) + x = residual + x + + x = self.self_attn_layer_norm(x) + + residual = x + if self.activation_name == "glu": + x = self.fc1(x) + else: + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + x = self.dropout3(x) + x = residual + x + x = self.final_layer_norm(x) + + return x, attn, pos_bias + diff --git a/vencoder/wavlm/modules.py b/vencoder/wavlm/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..1dcfc6f061cc189ca51fc90107116f38e2e48daf --- /dev/null +++ b/vencoder/wavlm/modules.py @@ -0,0 +1,827 @@ +# -------------------------------------------------------- +# WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf) +# Github source: https://github.com/microsoft/unilm/tree/master/wavlm +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Based on fairseq code bases +# https://github.com/pytorch/fairseq +# -------------------------------------------------------- + +import math +import warnings +from typing import Dict, Optional, Tuple +import torch +from torch import Tensor, nn +from torch.nn import Parameter +import torch.nn.functional as F + + +class TransposeLast(nn.Module): + def __init__(self, deconstruct_idx=None): + super().__init__() + self.deconstruct_idx = deconstruct_idx + + def forward(self, x): + if self.deconstruct_idx is not None: + x = x[self.deconstruct_idx] + return x.transpose(-2, -1) + + +class Fp32LayerNorm(nn.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.layer_norm( + input.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class Fp32GroupNorm(nn.GroupNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.group_norm( + input.float(), + self.num_groups, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class GradMultiply(torch.autograd.Function): + @staticmethod + def forward(ctx, x, scale): + ctx.scale = scale + res = x.new(x) + return res + + @staticmethod + def backward(ctx, grad): + return grad * ctx.scale, None + + +class SamePad(nn.Module): + def __init__(self, kernel_size, causal=False): + super().__init__() + if causal: + self.remove = kernel_size - 1 + else: + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + if self.remove > 0: + x = x[:, :, : -self.remove] + return x + + +class Swish(nn.Module): + """Swish function + """ + + def __init__(self): + """Construct an MultiHeadedAttention object.""" + super(Swish, self).__init__() + self.act = torch.nn.Sigmoid() + + def forward(self, x): + return x * self.act(x) + + +class GLU_Linear(nn.Module): + def __init__(self, input_dim, output_dim, glu_type="sigmoid", bias_in_glu=True): + super(GLU_Linear, self).__init__() + + self.glu_type = glu_type + self.output_dim = output_dim + + if glu_type == "sigmoid": + self.glu_act = torch.nn.Sigmoid() + elif glu_type == "swish": + self.glu_act = Swish() + elif glu_type == "relu": + self.glu_act = torch.nn.ReLU() + elif glu_type == "gelu": + self.glu_act = torch.nn.GELU() + + if bias_in_glu: + self.linear = nn.Linear(input_dim, output_dim * 2, True) + else: + self.linear = nn.Linear(input_dim, output_dim * 2, False) + + def forward(self, x): + # to be consistent with GLU_Linear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case + x = self.linear(x) + + if self.glu_type == "bilinear": + x = (x[:, :, 0:self.output_dim] * x[:, :, self.output_dim:self.output_dim * 2]) + else: + x = (x[:, :, 0:self.output_dim] * self.glu_act(x[:, :, self.output_dim:self.output_dim * 2])) + + return x + + +def gelu_accurate(x): + if not hasattr(gelu_accurate, "_a"): + gelu_accurate._a = math.sqrt(2 / math.pi) + return ( + 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3)))) + ) + + +def gelu(x: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.gelu(x.float()).type_as(x) + + +def get_activation_fn(activation: str): + """Returns the activation function corresponding to `activation`""" + + if activation == "relu": + return F.relu + elif activation == "gelu": + return gelu + elif activation == "gelu_fast": + warnings.warn( + "--activation-fn=gelu_fast has been renamed to gelu_accurate" + ) + return gelu_accurate + elif activation == "gelu_accurate": + return gelu_accurate + elif activation == "tanh": + return torch.tanh + elif activation == "linear": + return lambda x: x + elif activation == "glu": + return lambda x: x + else: + raise RuntimeError("--activation-fn {} not supported".format(activation)) + + +def init_bert_params(module): + """ + Initialize the weights specific to the BERT Model. + This overrides the default initializations depending on the specified arguments. + 1. If normal_init_linear_weights is set then weights of linear + layer will be initialized using the normal distribution and + bais will be set to the specified value. + 2. If normal_init_embed_weights is set then weights of embedding + layer will be initialized using the normal distribution. + 3. If normal_init_proj_weights is set then weights of + in_project_weight for MultiHeadAttention initialized using + the normal distribution (to be validated). + """ + + def normal_(data): + # with FSDP, module params will be on CUDA, so we cast them back to CPU + # so that the RNG is consistent with and without FSDP + data.copy_( + data.cpu().normal_(mean=0.0, std=0.02).to(data.device) + ) + + if isinstance(module, nn.Linear): + normal_(module.weight.data) + if module.bias is not None: + module.bias.data.zero_() + if isinstance(module, nn.Embedding): + normal_(module.weight.data) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + if isinstance(module, MultiheadAttention): + normal_(module.q_proj.weight.data) + normal_(module.k_proj.weight.data) + normal_(module.v_proj.weight.data) + + +def quant_noise(module, p, block_size): + """ + Wraps modules and applies quantization noise to the weights for + subsequent quantization with Iterative Product Quantization as + described in "Training with Quantization Noise for Extreme Model Compression" + + Args: + - module: nn.Module + - p: amount of Quantization Noise + - block_size: size of the blocks for subsequent quantization with iPQ + + Remarks: + - Module weights must have the right sizes wrt the block size + - Only Linear, Embedding and Conv2d modules are supported for the moment + - For more detail on how to quantize by blocks with convolutional weights, + see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks" + - We implement the simplest form of noise here as stated in the paper + which consists in randomly dropping blocks + """ + + # if no quantization noise, don't register hook + if p <= 0: + return module + + # supported modules + assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d)) + + # test whether module.weight has the right sizes wrt block_size + is_conv = module.weight.ndim == 4 + + # 2D matrix + if not is_conv: + assert ( + module.weight.size(1) % block_size == 0 + ), "Input features must be a multiple of block sizes" + + # 4D matrix + else: + # 1x1 convolutions + if module.kernel_size == (1, 1): + assert ( + module.in_channels % block_size == 0 + ), "Input channels must be a multiple of block sizes" + # regular convolutions + else: + k = module.kernel_size[0] * module.kernel_size[1] + assert k % block_size == 0, "Kernel size must be a multiple of block size" + + def _forward_pre_hook(mod, input): + # no noise for evaluation + if mod.training: + if not is_conv: + # gather weight and sizes + weight = mod.weight + in_features = weight.size(1) + out_features = weight.size(0) + + # split weight matrix into blocks and randomly drop selected blocks + mask = torch.zeros( + in_features // block_size * out_features, device=weight.device + ) + mask.bernoulli_(p) + mask = mask.repeat_interleave(block_size, -1).view(-1, in_features) + + else: + # gather weight and sizes + weight = mod.weight + in_channels = mod.in_channels + out_channels = mod.out_channels + + # split weight matrix into blocks and randomly drop selected blocks + if mod.kernel_size == (1, 1): + mask = torch.zeros( + int(in_channels // block_size * out_channels), + device=weight.device, + ) + mask.bernoulli_(p) + mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels) + else: + mask = torch.zeros( + weight.size(0), weight.size(1), device=weight.device + ) + mask.bernoulli_(p) + mask = ( + mask.unsqueeze(2) + .unsqueeze(3) + .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1]) + ) + + # scale weights and apply mask + mask = mask.to( + torch.bool + ) # x.bool() is not currently supported in TorchScript + s = 1 / (1 - p) + mod.weight.data = s * weight.masked_fill(mask, 0) + + module.register_forward_pre_hook(_forward_pre_hook) + return module + + +class MultiheadAttention(nn.Module): + """Multi-headed attention. + + See "Attention Is All You Need" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + has_relative_attention_bias=False, + num_buckets=32, + max_distance=128, + gru_rel_pos=False, + rescale_init=False, + ): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout_module = nn.Dropout(dropout) + + self.has_relative_attention_bias = has_relative_attention_bias + self.num_buckets = num_buckets + self.max_distance = max_distance + if self.has_relative_attention_bias: + self.relative_attention_bias = nn.Embedding(num_buckets, num_heads) + + self.head_dim = embed_dim // num_heads + self.q_head_dim = self.head_dim + self.k_head_dim = self.head_dim + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim ** -0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, ( + "Self-attention requires query, key and " "value to be of the same size" + ) + + k_bias = True + if rescale_init: + k_bias = False + + k_embed_dim = embed_dim + q_embed_dim = embed_dim + + self.k_proj = quant_noise( + nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size + ) + + self.out_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.gru_rel_pos = gru_rel_pos + if self.gru_rel_pos: + self.grep_linear = nn.Linear(self.q_head_dim, 8) + self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1)) + + self.reset_parameters() + + def reset_parameters(self): + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) + else: + nn.init.xavier_uniform_(self.k_proj.weight) + nn.init.xavier_uniform_(self.v_proj.weight) + nn.init.xavier_uniform_(self.q_proj.weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.out_proj.bias is not None: + nn.init.constant_(self.out_proj.bias, 0.0) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + if self.has_relative_attention_bias: + nn.init.xavier_normal_(self.relative_attention_bias.weight) + + def _relative_positions_bucket(self, relative_positions, bidirectional=True): + num_buckets = self.num_buckets + max_distance = self.max_distance + relative_buckets = 0 + + if bidirectional: + num_buckets = num_buckets // 2 + relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets + relative_positions = torch.abs(relative_positions) + else: + relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions)) + + max_exact = num_buckets // 2 + is_small = relative_positions < max_exact + + relative_postion_if_large = max_exact + ( + torch.log(relative_positions.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_postion_if_large = torch.min( + relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large) + return relative_buckets + + def compute_bias(self, query_length, key_length): + context_position = torch.arange(query_length, dtype=torch.long)[:, None] + memory_position = torch.arange(key_length, dtype=torch.long)[None, :] + relative_position = memory_position - context_position + relative_position_bucket = self._relative_positions_bucket( + relative_position, + bidirectional=True + ) + relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device) + values = self.relative_attention_bias(relative_position_bucket) + values = values.permute([2, 0, 1]) + return values + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + position_bias: Optional[Tensor] = None + ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + is_tpu = query.device.type == "xla" + + tgt_len, bsz, embed_dim = query.size() + src_len = tgt_len + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + if key is not None: + src_len, key_bsz, _ = key.size() + if not torch.jit.is_scripting(): + assert key_bsz == bsz + assert value is not None + assert src_len, bsz == value.shape[:2] + + if self.has_relative_attention_bias and position_bias is None: + position_bias = self.compute_bias(tgt_len, src_len) + position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, src_len) + + if ( + not is_tpu # don't use PyTorch version on TPUs + and incremental_state is None + and not static_kv + # A workaround for quantization to work. Otherwise JIT compilation + # treats bias in linear module as method. + and not torch.jit.is_scripting() + and self.q_head_dim == self.head_dim + ): + assert key is not None and value is not None + assert attn_mask is None + + attn_mask_rel_pos = None + if position_bias is not None: + attn_mask_rel_pos = position_bias + if self.gru_rel_pos: + query_layer = query.transpose(0, 1) + new_x_shape = query_layer.size()[:-1] + (self.num_heads, -1) + query_layer = query_layer.view(*new_x_shape) + query_layer = query_layer.permute(0, 2, 1, 3) + _B, _H, _L, __ = query_layer.size() + + gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view( + _B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1) + gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0 + attn_mask_rel_pos = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias + + attn_mask_rel_pos = attn_mask_rel_pos.view((-1, tgt_len, tgt_len)) + k_proj_bias = self.k_proj.bias + if k_proj_bias is None: + k_proj_bias = torch.zeros_like(self.q_proj.bias) + + x, attn = F.multi_head_attention_forward( + query, + key, + value, + self.embed_dim, + self.num_heads, + torch.empty([0]), + torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), + self.bias_k, + self.bias_v, + self.add_zero_attn, + self.dropout_module.p, + self.out_proj.weight, + self.out_proj.bias, + self.training, + # self.training or self.dropout_module.apply_during_inference, + key_padding_mask, + need_weights, + attn_mask_rel_pos, + use_separate_proj_weight=True, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + ) + return x, attn, position_bias + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.num_heads, self.q_head_dim) + .transpose(0, 1) + ) + if k is not None: + k = ( + k.contiguous() + .view(-1, bsz * self.num_heads, self.k_head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + src_len = k.size(1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + assert k.size(1) == src_len + + # This is part of a workaround to get around fork/join parallelism + # not supporting Optional types. + if key_padding_mask is not None and key_padding_mask.dim() == 0: + key_padding_mask = None + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + torch.zeros(key_padding_mask.size(0), 1).type_as( + key_padding_mask + ), + ], + dim=1, + ) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + attn_weights += attn_mask + + if key_padding_mask is not None: + # don't attend to padding symbols + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + if not is_tpu: + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + else: + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if before_softmax: + return attn_weights, v, position_bias + + if position_bias is not None: + if self.gru_rel_pos == 1: + query_layer = q.view(bsz, self.num_heads, tgt_len, self.q_head_dim) + _B, _H, _L, __ = query_layer.size() + gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view( + _B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1) + gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0 + position_bias = gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias + + position_bias = position_bias.view(attn_weights.size()) + + attn_weights = attn_weights + position_bias + + attn_weights_float = F.softmax( + attn_weights, dim=-1 + ) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = self.dropout_module(attn_weights) + + assert v is not None + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights, position_bias + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + if src_len > prev_key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask.float() + elif key_padding_mask is not None: + if src_len > key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = key_padding_mask.float() + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) + + def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): + return attn_weights diff --git a/vencoder/whisper/__init__.py b/vencoder/whisper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vencoder/whisper/audio.py b/vencoder/whisper/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..3bdb70ba9357e95ff05853dcc06437c3401ef3be --- /dev/null +++ b/vencoder/whisper/audio.py @@ -0,0 +1,125 @@ +import os +from functools import lru_cache +from typing import Union + +import ffmpeg +import numpy as np +import torch +import torch.nn.functional as F + +from .utils import exact_div + +from librosa.filters import mel as librosa_mel_fn + +# hard-coded audio hyperparameters +SAMPLE_RATE = 16000 +N_FFT = 400 +N_MELS = 80 +HOP_LENGTH = 160 +CHUNK_LENGTH = 30 +N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk +N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input + + +def load_audio(file: str, sr: int = SAMPLE_RATE): + """ + Open an audio file and read as mono waveform, resampling as necessary + + Parameters + ---------- + file: str + The audio file to open + + sr: int + The sample rate to resample the audio if necessary + + Returns + ------- + A NumPy array containing the audio waveform, in float32 dtype. + """ + try: + # This launches a subprocess to decode audio while down-mixing and resampling as necessary. + # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. + out, _ = ( + ffmpeg.input(file, threads=0) + .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr) + .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) + ) + except ffmpeg.Error as e: + raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e + + return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 + + +def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1): + """ + Pad or trim the audio array to N_SAMPLES, as expected by the encoder. + """ + if torch.is_tensor(array): + if array.shape[axis] > length: + array = array.index_select(dim=axis, index=torch.arange(length, device=array.device)) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + ) + """ + assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device) + + +def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/vencoder/whisper/decoding.py b/vencoder/whisper/decoding.py new file mode 100644 index 0000000000000000000000000000000000000000..603546d4c9ff67514d2567576935b974fe373bef --- /dev/null +++ b/vencoder/whisper/decoding.py @@ -0,0 +1,712 @@ +from dataclasses import dataclass, field +from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from .audio import CHUNK_LENGTH +from .tokenizer import Tokenizer, get_tokenizer +from .utils import compression_ratio + +if TYPE_CHECKING: + from .model import Whisper + + +@torch.no_grad() +def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer(model.is_multilingual) + if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence: + raise ValueError(f"This model doesn't have language tokens so it can't perform lang id") + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" + language: Optional[str] = None # language that the audio is in; uses detected language if None + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[int] = None # number of independent samples to collect, when t > 0 + beam_size: Optional[int] = None # number of beams in beam search, when t == 0 + patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424) + + # options for ranking generations (either beams or best-of-N samples) + length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm + + # prompt, prefix, and token suppression + prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context + prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context + suppress_blank: bool = True # this will suppress blank outputs + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + for module, tensor in self.kv_cache.items(): + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = tensor[source_indices].detach() + + +class SequenceRanker: + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + temperature = self.temperature + if temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if len(sequences) < self.beam_size: # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + seq = [t for t in tokens[k, self.sample_begin :].tolist()] + last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + if tokens.shape[1] == self.sample_begin: + # suppress generating non-timestamp tokens at the beginning + logits[:, : self.tokenizer.timestamp_begin] = -np.inf + + # apply the `max_initial_timestamp` option + if self.max_initial_timestamp_index is not None: + last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision) + self.logit_filters.append( + ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + prefix = self.options.prefix + prompt = self.options.prompt + + if prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt + ) + tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state): + # encoded audio features are given; skip audio encoding + print("encoded audio features are given; skip audio encoding") + audio_features = mel + else: + print(mel.shape) + print("===============================") + audio_features = self.model.encoder(mel) + + if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): + return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}") + + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + for i in range(self.sample_len): + logits = self.inference.logits(tokens, audio_features) + + if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult(audio_features=features, language=language, language_probs=probs) + for features, language, probs in zip(audio_features, languages, language_probs) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] + + fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) + ] + + +@torch.no_grad() +def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + result = DecodingTask(model, options).run(mel) + + if single: + result = result[0] + + return result diff --git a/vencoder/whisper/model.py b/vencoder/whisper/model.py new file mode 100644 index 0000000000000000000000000000000000000000..cb3781c17a1e78a33bf62246e5134e8512206d0d --- /dev/null +++ b/vencoder/whisper/model.py @@ -0,0 +1,269 @@ +from dataclasses import dataclass +from typing import Dict +from typing import Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch import nn + +from .decoding import detect_language as detect_language_function, decode as decode_function + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None or self.key not in kv_cache: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache[self.key] + v = kv_cache[self.value] + + wv, qk = self.qkv_attention(q, k, v, mask) + return self.out(wv), qk + + def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + qk = qk.float() + + w = F.softmax(qk, dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] + x = x + self.mlp(self.mlp_ln(x)) + return x + + +class AudioEncoder(nn.Module): + def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + len_x = x.shape[1] + len_e = self.positional_embedding.shape[0] + assert len_x <= len_e, "incorrect audio shape" + pos_e = self.positional_embedding[:len_x, :] + x = (x + pos_e).to(x.dtype) + + for block in self.blocks: + x = block(x) + + x = self.ln_post(x) + return x + + +class TextDecoder(nn.Module): + def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + the encoded audio features to be attended on + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] + x = x.to(xa.dtype) + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + + x = self.ln(x) + logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() + + return logits + + +class Whisper(nn.Module): + def __init__(self, dims: ModelDimensions): + super().__init__() + self.dims = dims + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder(tokens, audio_features) + + def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab == 51865 + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: + cache[module] = output # save as-is, for the first token or cross attention + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + decode = decode_function diff --git a/vencoder/whisper/tokenizer.py b/vencoder/whisper/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..a27cb359ee891590d3f793624f9f8ec768a26cc3 --- /dev/null +++ b/vencoder/whisper/tokenizer.py @@ -0,0 +1,331 @@ +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import GPT2TokenizerFast + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", +} + + +@dataclass(frozen=True) +class Tokenizer: + """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" + + tokenizer: "GPT2TokenizerFast" + language: Optional[str] + sot_sequence: Tuple[int] + + def encode(self, text, **kwargs): + return self.tokenizer.encode(text, **kwargs) + + def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): + return self.tokenizer.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, tokens) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + outputs = [[]] + for token in tokens: + if token >= self.timestamp_begin: + timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" + outputs.append(timestamp) + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] + return "".join(outputs) + + @property + @lru_cache() + def eot(self) -> int: + return self.tokenizer.eos_token_id + + @property + @lru_cache() + def sot(self) -> int: + return self._get_single_token_id("<|startoftranscript|>") + + @property + @lru_cache() + def sot_lm(self) -> int: + return self._get_single_token_id("<|startoflm|>") + + @property + @lru_cache() + def sot_prev(self) -> int: + return self._get_single_token_id("<|startofprev|>") + + @property + @lru_cache() + def no_speech(self) -> int: + return self._get_single_token_id("<|nospeech|>") + + @property + @lru_cache() + def no_timestamps(self) -> int: + return self._get_single_token_id("<|notimestamps|>") + + @property + @lru_cache() + def timestamp_begin(self) -> int: + return self.tokenizer.all_special_ids[-1] + 1 + + @property + @lru_cache() + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError(f"This tokenizer does not have language token configured") + + additional_tokens = dict( + zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ) + ) + candidate = f"<|{self.language}|>" + if candidate in additional_tokens: + return additional_tokens[candidate] + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @property + @lru_cache() + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @property + @lru_cache() + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @property + @lru_cache() + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @property + @lru_cache() + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") + symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def _get_single_token_id(self, text) -> int: + tokens = self.tokenizer.encode(text) + assert len(tokens) == 1, f"{text} is not encoded as a single token" + return tokens[0] + + +@lru_cache(maxsize=None) +def build_tokenizer(name: str = "gpt2"): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + path = os.path.join(os.path.dirname(__file__), "assets", name) + tokenizer = GPT2TokenizerFast.from_pretrained(path) + + specials = [ + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + ] + + tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) + return tokenizer + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + task: Optional[str] = None, # Literal["transcribe", "translate", None] + language: Optional[str] = None, +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + tokenizer_name = "multilingual" + task = task or "transcribe" + language = language or "en" + else: + tokenizer_name = "gpt2" + task = None + language = None + + tokenizer = build_tokenizer(name=tokenizer_name) + all_special_ids: List[int] = tokenizer.all_special_ids + sot: int = all_special_ids[1] + translate: int = all_special_ids[-6] + transcribe: int = all_special_ids[-5] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if language is not None: + sot_sequence.append(sot + 1 + langs.index(language)) + if task is not None: + sot_sequence.append(transcribe if task == "transcribe" else translate) + + return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) diff --git a/vencoder/whisper/utils.py b/vencoder/whisper/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5dacc173c40bcd6e999d728862e29a968000b12e --- /dev/null +++ b/vencoder/whisper/utils.py @@ -0,0 +1,163 @@ +import json +import os +import sys +import zlib +from typing import Callable, TextIO + +system_encoding = sys.getdefaultencoding() + +if system_encoding != "utf-8": + def make_safe(string): + # replaces any character not representable using the system default encoding with an '?', + # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729). + return string.encode(system_encoding, errors="replace").decode(system_encoding) +else: + def make_safe(string): + # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding + return string + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + text_bytes = text.encode("utf-8") + return len(text_bytes) / len(zlib.compress(text_bytes)) + + +def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + + +class ResultWriter: + extension: str + + def __init__(self, output_dir: str): + self.output_dir = output_dir + + def __call__(self, result: dict, audio_path: str): + audio_basename = os.path.basename(audio_path) + output_path = os.path.join(self.output_dir, audio_basename + "." + self.extension) + + with open(output_path, "w", encoding="utf-8") as f: + self.write_result(result, file=f) + + def write_result(self, result: dict, file: TextIO): + raise NotImplementedError + + +class WriteTXT(ResultWriter): + extension: str = "txt" + + def write_result(self, result: dict, file: TextIO): + for segment in result["segments"]: + print(segment['text'].strip(), file=file, flush=True) + + +class WriteVTT(ResultWriter): + extension: str = "vtt" + + def write_result(self, result: dict, file: TextIO): + print("WEBVTT\n", file=file) + for segment in result["segments"]: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +class WriteSRT(ResultWriter): + extension: str = "srt" + + def write_result(self, result: dict, file: TextIO): + for i, segment in enumerate(result["segments"], start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +class WriteTSV(ResultWriter): + """ + Write a transcript to a file in TSV (tab-separated values) format containing lines like: + \t\t + + Using integer milliseconds as start and end times means there's no chance of interference from + an environment setting a language encoding that causes the decimal in a floating point number + to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++. + """ + extension: str = "tsv" + + def write_result(self, result: dict, file: TextIO): + print("start", "end", "text", sep="\t", file=file) + for segment in result["segments"]: + print(round(1000 * segment['start']), file=file, end="\t") + print(round(1000 * segment['end']), file=file, end="\t") + print(segment['text'].strip().replace("\t", " "), file=file, flush=True) + + +class WriteJSON(ResultWriter): + extension: str = "json" + + def write_result(self, result: dict, file: TextIO): + json.dump(result, file) + + +def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]: + writers = { + "txt": WriteTXT, + "vtt": WriteVTT, + "srt": WriteSRT, + "tsv": WriteTSV, + "json": WriteJSON, + } + + if output_format == "all": + all_writers = [writer(output_dir) for writer in writers.values()] + + def write_all(result: dict, file: TextIO): + for writer in all_writers: + writer(result, file) + + return write_all + + return writers[output_format](output_dir) + diff --git a/wav_upload.py b/wav_upload.py new file mode 100644 index 0000000000000000000000000000000000000000..cac679de78634e638e9a998615406b1c36374fb5 --- /dev/null +++ b/wav_upload.py @@ -0,0 +1,23 @@ +from google.colab import files +import shutil +import os +import argparse +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--type", type=str, required=True, help="type of file to upload") + args = parser.parse_args() + file_type = args.type + + basepath = os.getcwd() + uploaded = files.upload() # 上传文件 + assert(file_type in ['zip', 'audio']) + if file_type == "zip": + upload_path = "./upload/" + for filename in uploaded.keys(): + #将上传的文件移动到指定的位置上 + shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, "userzip.zip")) + elif file_type == "audio": + upload_path = "./raw/" + for filename in uploaded.keys(): + #将上传的文件移动到指定的位置上 + shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename)) \ No newline at end of file diff --git a/webUI.py b/webUI.py new file mode 100644 index 0000000000000000000000000000000000000000..17e39b21fa24d7ec9867b693723b7b087840a9b4 --- /dev/null +++ b/webUI.py @@ -0,0 +1,379 @@ +import io +import os + +# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") +import gradio as gr +import gradio.processing_utils as gr_pu +import librosa +import numpy as np +import soundfile +from inference.infer_tool import Svc +import logging +import re +import json + +import subprocess +import edge_tts +import asyncio +from scipy.io import wavfile +import librosa +import torch +import time +import traceback +from itertools import chain +from utils import mix_model +from compress_model import removeOptimizer + +logging.getLogger('numba').setLevel(logging.WARNING) +logging.getLogger('markdown_it').setLevel(logging.WARNING) +logging.getLogger('urllib3').setLevel(logging.WARNING) +logging.getLogger('matplotlib').setLevel(logging.WARNING) +logging.getLogger('multipart').setLevel(logging.WARNING) + +model = None +spk = None +debug = False + +cuda = {} +if torch.cuda.is_available(): + for i in range(torch.cuda.device_count()): + device_name = torch.cuda.get_device_properties(i).name + cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}" + +def upload_mix_append_file(files,sfiles): + try: + if(sfiles == None): + file_paths = [file.name for file in files] + else: + file_paths = [file.name for file in chain(files,sfiles)] + p = {file:100 for file in file_paths} + return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2)) + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def mix_submit_click(js,mode): + try: + assert js.lstrip()!="" + modes = {"凸组合":0, "线性组合":1} + mode = modes[mode] + data = json.loads(js) + data = list(data.items()) + model_path,mix_rate = zip(*data) + path = mix_model(model_path,mix_rate,mode) + return f"成功，文件被保存在了{path}" + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def updata_mix_info(files): + try: + if files == None : return mix_model_output1.update(value="") + p = {file.name:100 for file in files} + return mix_model_output1.update(value=json.dumps(p,indent=2)) + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix): + global model + try: + device = cuda[device] if "CUDA" in device else device + cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster" + fr = ".pkl" in cluster_filepath[1] + #model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance) + model = Svc(model_path.name, + config_path.name, + device=device if device != "Auto" else None, + cluster_model_path = cluster_model_path.name if cluster_model_path is not None else "", + nsf_hifigan_enhance=enhance, + diffusion_model_path = diff_model_path.name if diff_model_path is not None else "", + diffusion_config_path = diff_config_path.name if diff_config_path is not None else "", + shallow_diffusion = True if diff_model_path is not None else False, + only_diffusion = only_diffusion, + spk_mix_enable = use_spk_mix, + feature_retrieval = fr + ) + spks = list(model.spk2id.keys()) + device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev) + msg = f"成功加载模型到设备{device_name}上\n" + if cluster_model_path is None: + msg += "未加载聚类模型或特征检索模型\n" + elif fr: + msg += f"特征检索模型{cluster_filepath[1]}加载成功\n" + else: + msg += f"聚类模型{cluster_filepath[1]}加载成功\n" + if diff_model_path is None: + msg += "未加载扩散模型\n" + else: + msg += f"扩散模型{diff_model_path.name}加载成功\n" + msg += "当前模型的可用音色：\n" + for i in spks: + msg += i + " " + return sid.update(choices = spks,value=spks[0]), msg + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + + +def modelUnload(): + global model + if model is None: + return sid.update(choices = [],value=""),"没有模型需要卸载!" + else: + model.unload_model() + model = None + torch.cuda.empty_cache() + return sid.update(choices = [],value=""),"模型卸载完毕!" + +def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment): + global model + try: + if input_audio is None: + return "You need to upload an audio", None + if model is None: + return "You need to upload an model", None + print(input_audio) + sampling_rate, audio = input_audio + print(audio.shape,sampling_rate) + audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) + print(audio.dtype) + if len(audio.shape) > 1: + audio = librosa.to_mono(audio.transpose(1, 0)) + temp_path = "temp.wav" + soundfile.write(temp_path, audio, sampling_rate, format="wav") + _audio = model.slice_inference( + temp_path, + sid, + vc_transform, + slice_db, + cluster_ratio, + auto_f0, + noise_scale, + pad_seconds, + cl_num, + lg_num, + lgr_num, + f0_predictor, + enhancer_adaptive_key, + cr_threshold, + k_step, + use_spk_mix, + second_encoding, + loudness_envelope_adjustment + ) + model.clear_empty() + os.remove(temp_path) + #构建保存文件的路径，并保存到results文件夹内 + timestamp = str(int(time.time())) + if not os.path.exists("results"): + os.makedirs("results") + output_file = os.path.join("results", sid + "_" + timestamp + ".wav") + soundfile.write(output_file, _audio, model.target_sample, format="wav") + return "Success", output_file + except Exception as e: + if debug: traceback.print_exc() + raise gr.Error(e) + +def tts_func(_text,_rate,_voice): + #使用edge-tts把文字转成音频 + # voice = "zh-CN-XiaoyiNeural"#女性，较高音 + # voice = "zh-CN-YunxiNeural"#男性 + voice = "zh-CN-YunxiNeural"#男性 + if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural" + output_file = _text[0:10]+".wav" + # communicate = edge_tts.Communicate(_text, voice) + # await communicate.save(output_file) + if _rate>=0: + ratestr="+{:.0%}".format(_rate) + elif _rate<0: + ratestr="{:.0%}".format(_rate)#减号自带 + + p=subprocess.Popen("edge-tts "+ + " --text "+_text+ + " --write-media "+output_file+ + " --voice "+voice+ + " --rate="+ratestr + ,shell=True, + stdout=subprocess.PIPE, + stdin=subprocess.PIPE) + p.wait() + return output_file + +def text_clear(text): + return re.sub(r"[\n\,\(\) ]", "", text) + +def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold): + #使用edge-tts把文字转成音频 + text2tts=text_clear(text2tts) + output_file=tts_func(text2tts,tts_rate,tts_voice) + + #调整采样率 + sr2=44100 + wav, sr = librosa.load(output_file) + wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2) + save_path2= text2tts[0:10]+"_44k"+".wav" + wavfile.write(save_path2,sr2, + (wav2 * np.iinfo(np.int16).max).astype(np.int16) + ) + + #读取音频 + sample_rate, data=gr_pu.audio_from_file(save_path2) + vc_input=(sample_rate, data) + + a,b=vc_fn(sid, vc_input, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold) + os.remove(output_file) + os.remove(save_path2) + return a,b + +def model_compression(_model): + if _model == "": + return "请先选择要压缩的模型" + else: + model_path = os.path.split(_model.name) + filename, extension = os.path.splitext(model_path[1]) + output_model_name = f"{filename}_compressed{extension}" + output_path = os.path.join(os.getcwd(), output_model_name) + removeOptimizer(_model.name, output_path) + return f"模型已成功被保存在了{output_path}" + +def debug_change(): + global debug + debug = debug_button.value + +with gr.Blocks( + theme=gr.themes.Base( + primary_hue = gr.themes.colors.green, + font=["Source Sans Pro", "Arial", "sans-serif"], + font_mono=['JetBrains mono', "Consolas", 'Courier New'] + ), +) as app: + with gr.Tabs(): + with gr.TabItem("推理"): + gr.Markdown(value=""" + So-vits-svc 4.0 推理 webui + """) + with gr.Row(variant="panel"): + with gr.Column(): + gr.Markdown(value=""" + 模型设置 + """) + with gr.Row(): + model_path = gr.File(label="选择模型文件") + config_path = gr.File(label="选择配置文件") + with gr.Row(): + diff_model_path = gr.File(label="选择扩散模型文件") + diff_config_path = gr.File(label="选择扩散模型配置文件") + cluster_model_path = gr.File(label="选择聚类模型或特征检索文件（没有可以不选）") + device = gr.Dropdown(label="推理设备，默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"cpu"], value="Auto") + enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False) + only_diffusion = gr.Checkbox(label="是否使用全扩散推理，开启后将不使用So-VITS模型，仅使用扩散模型进行完整扩散推理，默认关闭", value=False) + with gr.Column(): + gr.Markdown(value=""" + 左侧文件全部选择完毕后(全部文件模块显示download)，点击“加载模型”进行解析： + """) + model_load_button = gr.Button(value="加载模型", variant="primary") + model_unload_button = gr.Button(value="卸载模型", variant="primary") + sid = gr.Dropdown(label="音色（说话人）") + sid_output = gr.Textbox(label="Output Message") + + + with gr.Row(variant="panel"): + with gr.Column(): + gr.Markdown(value=""" + 推理设置 + """) + auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）", value=False) + f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)", choices=["pm","dio","harvest","crepe"], value="pm") + vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0) + cluster_ratio = gr.Number(label="聚类模型/特征检索混合比例，0-1之间，0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0) + slice_db = gr.Number(label="切片阈值", value=-40) + noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4) + k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000) + with gr.Column(): + pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5) + cl_num = gr.Number(label="音频自动切片，0为不切片，单位为秒(s)", value=0) + lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0) + lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75) + enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0) + cr_threshold = gr.Number(label="F0过滤阈值，只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05) + loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络", value = 0) + second_encoding = gr.Checkbox(label = "二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，效果时好时差，默认关闭", value=False) + use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False) + with gr.Tabs(): + with gr.TabItem("音频转音频"): + vc_input3 = gr.Audio(label="选择音频") + vc_submit = gr.Button("音频转换", variant="primary") + with gr.TabItem("文字转音频"): + text2tts=gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪") + tts_rate = gr.Number(label="tts语速", value=0) + tts_voice = gr.Radio(label="性别",choices=["男","女"], value="男") + vc_submit2 = gr.Button("文字转换", variant="primary") + with gr.Row(): + with gr.Column(): + vc_output1 = gr.Textbox(label="Output Message") + with gr.Column(): + vc_output2 = gr.Audio(label="Output Audio", interactive=False) + + with gr.TabItem("小工具/实验室特性"): + gr.Markdown(value=""" + So-vits-svc 4.0 小工具/实验室特性 + """) + with gr.Tabs(): + with gr.TabItem("静态声线融合"): + gr.Markdown(value=""" + 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线 + 注意： + 1.该功能仅支持单说话人的模型 + 2.如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音 + 3.保证所有待混合模型的config.json中的model字段是相同的 + 4.输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用 + 5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传 + 6.混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果 + 7.混合完毕后，文件将会保存在项目根目录中，文件名为output.pth + 8.凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会 + + """) + mix_model_path = gr.Files(label="选择需要混合模型文件") + mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple") + mix_model_output1 = gr.Textbox( + label="混合比例调整，单位/%", + interactive = True + ) + mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式",value="凸组合",interactive = True) + mix_submit = gr.Button("声线融合启动", variant="primary") + mix_model_output2 = gr.Textbox( + label="Output Message" + ) + mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1]) + mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1]) + mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2]) + + with gr.TabItem("模型压缩工具"): + gr.Markdown(value=""" + 该工具可以实现对模型的体积压缩，在**不影响模型推理功能**的情况下，将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。 + **注意：压缩后的模型将无法继续训练，请在确认封炉后再压缩。** + """) + model_to_compress = gr.File(label="模型上传") + compress_model_btn = gr.Button("压缩模型", variant="primary") + compress_model_output = gr.Textbox(label="输出信息", value="") + + compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output]) + + + with gr.Tabs(): + with gr.Row(variant="panel"): + with gr.Column(): + gr.Markdown(value=""" + WebUI设置 + """) + debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug) + vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2]) + vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2]) + debug_button.change(debug_change,[],[]) + model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix],[sid,sid_output]) + model_unload_button.click(modelUnload,[],[sid,sid_output]) + app.launch() + + +