+
+[Examples](https://suno-ai.notion.site/Bark-Examples-5edae8b02a604b54a42244ba45ebc2e2) โข [Suno Studio Waitlist](https://3os84zs17th.typeform.com/suno-studio) โข [Updates](#-updates) โข [How to Use](#-usage-in-python) โข [Installation](#-installation) โข [FAQ](#-faq)
+
+[//]: (vertical spaces around image) +
+
+
+
+ +Bark is a transformer-based text-to-audio model created by [Suno](https://suno.ai). Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use. + +## โ Disclaimer +Bark was developed for research purposes. It is not a conventional text-to-speech model but instead a fully generative text-to-audio model, which can deviate in unexpected ways from provided prompts. Suno does not take responsibility for any output generated. Use at your own risk, and please act responsibly. + +## ๐ง Demos + +[](https://huggingface.co/spaces/suno/bark) +[](https://replicate.com/suno-ai/bark) +[](https://colab.research.google.com/drive/1eJfA2XUa-mXwdMy7DoYKVYHI1iTd9Vkt?usp=sharing) + +## ๐ Updates + +**2023.05.01** +- ยฉ๏ธ Bark is now licensed under the MIT License, meaning it's now available for commercial use! +- โก 2x speed-up on GPU. 10x speed-up on CPU. We also added an option for a smaller version of Bark, which offers additional speed-up with the trade-off of slightly lower quality. +- ๐ [Long-form generation](notebooks/long_form_generation.ipynb), voice consistency enhancements and other examples are now documented in a new [notebooks](./notebooks) section. +- ๐ฅ We created a [voice prompt library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c). We hope this resource helps you find useful prompts for your use cases! You can also join us on [Discord](https://discord.gg/J2B2vsjKuE), where the community actively shares useful prompts in the **#audio-prompts** channel. +- ๐ฌ Growing community support and access to new features here: + + [](https://discord.gg/J2B2vsjKuE) + +- ๐พ You can now use Bark with GPUs that have low VRAM (<4GB). + +**2023.04.20** +- ๐ถ Bark release! + +## ๐ Usage in Python + +
+
+
+```python
+from bark import SAMPLE_RATE, generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+from IPython.display import Audio
+
+# download and load all models
+preload_models()
+
+# generate audio from text
+text_prompt = """
+ Hello, my name is Suno. And, uh โ and I like pizza. [laughs]
+ But I also have other interests such as playing tic tac toe.
+"""
+audio_array = generate_audio(text_prompt)
+
+# save audio to disk
+write_wav("bark_generation.wav", SAMPLE_RATE, audio_array)
+
+# play text in notebook
+Audio(audio_array, rate=SAMPLE_RATE)
+```
+
+[pizza.webm](https://user-images.githubusercontent.com/5068315/230490503-417e688d-5115-4eee-9550-b46a2b465ee3.webm)
+
+
+
+๐ช Basics
+
+```python
+from bark import SAMPLE_RATE, generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+from IPython.display import Audio
+
+# download and load all models
+preload_models()
+
+# generate audio from text
+text_prompt = """
+ Hello, my name is Suno. And, uh โ and I like pizza. [laughs]
+ But I also have other interests such as playing tic tac toe.
+"""
+audio_array = generate_audio(text_prompt)
+
+# save audio to disk
+write_wav("bark_generation.wav", SAMPLE_RATE, audio_array)
+
+# play text in notebook
+Audio(audio_array, rate=SAMPLE_RATE)
+```
+
+[pizza.webm](https://user-images.githubusercontent.com/5068315/230490503-417e688d-5115-4eee-9550-b46a2b465ee3.webm)
+
+
+
+
+Bark supports various languages out-of-the-box and automatically determines language from input text. When prompted with code-switched text, Bark will attempt to employ the native accent for the respective languages. English quality is best for the time being, and we expect other languages to further improve with scaling. +
+
+ +```python + +text_prompt = """ + ์ถ์์ ๋ด๊ฐ ๊ฐ์ฅ ์ข์ํ๋ ๋ช ์ ์ด๋ค. ๋๋ ๋ฉฐ์น ๋์ ํด์์ ์ทจํ๊ณ ์น๊ตฌ ๋ฐ ๊ฐ์กฑ๊ณผ ์๊ฐ์ ๋ณด๋ผ ์ ์์ต๋๋ค. +""" +audio_array = generate_audio(text_prompt) +``` +[suno_korean.webm](https://user-images.githubusercontent.com/32879321/235313033-dc4477b9-2da0-4b94-9c8b-a8c2d8f5bb5e.webm) + +*Note: since Bark recognizes languages automatically from input text, it is possible to use for example a german history prompt with english text. This usually leads to english audio with a german accent.* + +
+
+๐ Foreign Language
++Bark supports various languages out-of-the-box and automatically determines language from input text. When prompted with code-switched text, Bark will attempt to employ the native accent for the respective languages. English quality is best for the time being, and we expect other languages to further improve with scaling. +
+
+ +```python + +text_prompt = """ + ์ถ์์ ๋ด๊ฐ ๊ฐ์ฅ ์ข์ํ๋ ๋ช ์ ์ด๋ค. ๋๋ ๋ฉฐ์น ๋์ ํด์์ ์ทจํ๊ณ ์น๊ตฌ ๋ฐ ๊ฐ์กฑ๊ณผ ์๊ฐ์ ๋ณด๋ผ ์ ์์ต๋๋ค. +""" +audio_array = generate_audio(text_prompt) +``` +[suno_korean.webm](https://user-images.githubusercontent.com/32879321/235313033-dc4477b9-2da0-4b94-9c8b-a8c2d8f5bb5e.webm) + +*Note: since Bark recognizes languages automatically from input text, it is possible to use for example a german history prompt with english text. This usually leads to english audio with a german accent.* + +
+
+Bark can generate all types of audio, and, in principle, doesn't see a difference between speech and music. Sometimes Bark chooses to generate text as music, but you can help it out by adding music notes around your lyrics.
+
+
+ +```python +text_prompt = """ + โช In the jungle, the mighty jungle, the lion barks tonight โช +""" +audio_array = generate_audio(text_prompt) +``` +[lion.webm](https://user-images.githubusercontent.com/5068315/230684766-97f5ea23-ad99-473c-924b-66b6fab24289.webm) +
+
+๐ถ Music
+Bark can generate all types of audio, and, in principle, doesn't see a difference between speech and music. Sometimes Bark chooses to generate text as music, but you can help it out by adding music notes around your lyrics.
++
+ +```python +text_prompt = """ + โช In the jungle, the mighty jungle, the lion barks tonight โช +""" +audio_array = generate_audio(text_prompt) +``` +[lion.webm](https://user-images.githubusercontent.com/5068315/230684766-97f5ea23-ad99-473c-924b-66b6fab24289.webm) +
+
+
+Bark supports 100+ speaker presets across [supported languages](#supported-languages). You can browse the library of speaker presets [here](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c), or in the [code](bark/assets/prompts). The community also often shares presets in [Discord](https://discord.gg/J2B2vsjKuE).
+
+Bark tries to match the tone, pitch, emotion and prosody of a given preset, but does not currently support custom voice cloning. The model also attempts to preserve music, ambient noise, etc.
+
+
+ +```python +text_prompt = """ + I have a silky smooth voice, and today I will tell you about + the exercise regimen of the common sloth. +""" +audio_array = generate_audio(text_prompt, history_prompt="v2/en_speaker_1") +``` + +[sloth.webm](https://user-images.githubusercontent.com/5068315/230684883-a344c619-a560-4ff5-8b99-b4463a34487b.webm) +
+
+### Generating Longer Audio
+
+By default, `generate_audio` works well with around 13 seconds of spoken text. For an example of how to do long-form generation, see this [example notebook](notebooks/long_form_generation.ipynb).
+
+๐ค Voice Presets
+
+Bark supports 100+ speaker presets across [supported languages](#supported-languages). You can browse the library of speaker presets [here](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c), or in the [code](bark/assets/prompts). The community also often shares presets in [Discord](https://discord.gg/J2B2vsjKuE).
+
+Bark tries to match the tone, pitch, emotion and prosody of a given preset, but does not currently support custom voice cloning. The model also attempts to preserve music, ambient noise, etc.
++
+ +```python +text_prompt = """ + I have a silky smooth voice, and today I will tell you about + the exercise regimen of the common sloth. +""" +audio_array = generate_audio(text_prompt, history_prompt="v2/en_speaker_1") +``` + +[sloth.webm](https://user-images.githubusercontent.com/5068315/230684883-a344c619-a560-4ff5-8b99-b4463a34487b.webm) +
+
+
+
+
+
+## ๐ป Installation
+
+```
+pip install git+https://github.com/suno-ai/bark.git
+```
+
+or
+
+```
+git clone https://github.com/suno-ai/bark
+cd bark && pip install .
+```
+*Note: Do NOT use 'pip install bark'. It installs a different package, which is not managed by Suno.*
+
+
+## ๐ ๏ธ Hardware and Inference Speed
+
+Bark has been tested and works on both CPU and GPU (`pytorch 2.0+`, CUDA 11.7 and CUDA 12.0).
+
+On enterprise GPUs and PyTorch nightly, Bark can generate audio in roughly real-time. On older GPUs, default colab, or CPU, inference time might be significantly slower. For older GPUs or CPU you might want to consider using smaller models. Details can be found in out tutorial sections here.
+
+The full version of Bark requires around 12GB of VRAM to hold everything on GPU at the same time.
+To use a smaller version of the models, which should fit into 8GB VRAM, set the environment flag `SUNO_USE_SMALL_MODELS=True`.
+
+If you don't have hardware available or if you want to play with bigger versions of our models, you can also sign up for early access to our model playground [here](https://3os84zs17th.typeform.com/suno-studio).
+
+## โ๏ธ Details
+
+Bark is fully generative tex-to-audio model devolved for research and demo purposes. It follows a GPT style architecture similar to [AudioLM](https://arxiv.org/abs/2209.03143) and [Vall-E](https://arxiv.org/abs/2301.02111) and a quantized Audio representation from [EnCodec](https://github.com/facebookresearch/encodec). It is not a conventional TTS model, but instead a fully generative text-to-audio model capable of deviating in unexpected ways from any given script. Different to previous approaches, the input text prompt is converted directly to audio without the intermediate use of phonemes. It can therefore generalize to arbitrary instructions beyond speech such as music lyrics, sound effects or other non-speech sounds.
+
+Below is a list of some known non-speech sounds, but we are finding more every day. Please let us know if you find patterns that work particularly well on [Discord](https://discord.gg/J2B2vsjKuE)!
+
+- `[laughter]`
+- `[laughs]`
+- `[sighs]`
+- `[music]`
+- `[gasps]`
+- `[clears throat]`
+- `โ` or `...` for hesitations
+- `โช` for song lyrics
+- CAPITALIZATION for emphasis of a word
+- `[MAN]` and `[WOMAN]` to bias Bark toward male and female speakers, respectively
+
+### Supported Languages
+
+| Language | Status |
+| --- | --- |
+| English (en) | โ
|
+| German (de) | โ
|
+| Spanish (es) | โ
|
+| French (fr) | โ
|
+| Hindi (hi) | โ
|
+| Italian (it) | โ
|
+| Japanese (ja) | โ
|
+| Korean (ko) | โ
|
+| Polish (pl) | โ
|
+| Portuguese (pt) | โ
|
+| Russian (ru) | โ
|
+| Turkish (tr) | โ
|
+| Chinese, simplified (zh) | โ
|
+
+Requests for future language support [here](https://github.com/suno-ai/bark/discussions/111) or in the **#forums** channel on [Discord](https://discord.com/invite/J2B2vsjKuE).
+
+## ๐ Appreciation
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT) for a dead-simple and blazing fast implementation of GPT-style models
+- [EnCodec](https://github.com/facebookresearch/encodec) for a state-of-the-art implementation of a fantastic audio codec
+- [AudioLM](https://github.com/lucidrains/audiolm-pytorch) for related training and inference code
+- [Vall-E](https://arxiv.org/abs/2301.02111), [AudioLM](https://arxiv.org/abs/2209.03143) and many other ground-breaking papers that enabled the development of Bark
+
+## ยฉ License
+
+Bark is licensed under the MIT License.
+
+Please contact us at `bark@suno.ai` to request access to a larger version of the model.
+
+## ๐ฑย Community
+
+- [Twitter](https://twitter.com/OnusFM)
+- [Discord](https://discord.gg/J2B2vsjKuE)
+
+## ๐งย Suno Studio (Early Access)
+
+Weโre developing a playground for our models, including Bark.
+
+If you are interested, you can sign up for early access [here](https://3os84zs17th.typeform.com/suno-studio).
+
+## โ FAQ
+
+#### How do I specify where models are downloaded and cached?
+* Bark uses Hugging Face to download and store models. You can see find more info [here](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhome).
+
+
+#### Bark's generations sometimes differ from my prompts. What's happening?
+* Bark is a GPT-style model. As such, it may take some creative liberties in its generations, resulting in higher-variance model outputs than traditional text-to-speech approaches.
+
+#### What voices are supported by Bark?
+* Bark supports 100+ speaker presets across [supported languages](#supported-languages). You can browse the library of speaker presets [here](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c). The community also shares presets in [Discord](https://discord.gg/J2B2vsjKuE). Bark also supports generating unique random voices that fit the input text. Bark does not currently support custom voice cloning.
+
+#### Why is the output limited to ~13-14 seconds?
+* Bark is a GPT-style model, and its architecture/context window is optimized to output generations with roughly this length.
+
+#### How much VRAM do I need?
+* The full version of Bark requires around 12Gb of memory to hold everything on GPU at the same time. However, even smaller cards down to ~2Gb work with some additional settings. Simply add the following code snippet before your generation:
+
+```python
+import os
+os.environ["SUNO_OFFLOAD_CPU"] = True
+os.environ["SUNO_USE_SMALL_MODELS"] = True
+```
+
+#### My generated audio sounds like a 1980s phone call. What's happening?
+* Bark generates audio from scratch. It is not meant to create only high-fidelity, studio-quality speech. Rather, outputs could be anything from perfect speech to multiple people arguing at a baseball game recorded with bad microphones.
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9c4c1f56",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Ignoring sox: markers 'platform_system == \"Darwin\"' don't match your environment\n",
+ "Ignoring soundfile: markers 'platform_system == \"Windows\"' don't match your environment\n",
+ "Ignoring fairseq: markers 'platform_system == \"Windows\"' don't match your environment\n",
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+ "Collecting frozenlist>=1.1.1\n",
+ " Downloading frozenlist-1.4.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (220 kB)\n",
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+ "Collecting starlette<0.28.0,>=0.27.0\n",
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+ "\u001b[?25hCollecting h11>=0.8\n",
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+ "Requirement already satisfied: jsonschema>=3.0 in /home/jamal/.local/lib/python3.8/site-packages (from altair<6.0,>=4.2.0->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (4.17.3)\n",
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+ "\u001b[?25hCollecting pydantic-core==2.4.0\n",
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+ "\u001b[?25hCollecting annotated-types>=0.4.0\n",
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+ "Requirement already satisfied: cycler>=0.10 in /home/jamal/.local/lib/python3.8/site-packages (from matplotlib~=3.0->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (0.11.0)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /home/jamal/.local/lib/python3.8/site-packages (from matplotlib~=3.0->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (4.38.0)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jamal/.local/lib/python3.8/site-packages (from matplotlib~=3.0->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (1.4.4)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /home/jamal/.local/lib/python3.8/site-packages (from sympy->torch->fairseq->-r old_setup_files/requirements-pip.txt (line 12)) (1.3.0)\n",
+ "Requirement already satisfied: wheel in /usr/lib/python3/dist-packages (from nvidia-curand-cu11==10.2.10.91; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->fairseq->-r old_setup_files/requirements-pip.txt (line 12)) (0.34.2)\n",
+ "Requirement already satisfied: cmake in /home/jamal/.local/lib/python3.8/site-packages (from triton==2.0.0; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->fairseq->-r old_setup_files/requirements-pip.txt (line 12)) (3.27.0)\n",
+ "Requirement already satisfied: lit in /home/jamal/.local/lib/python3.8/site-packages (from triton==2.0.0; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->fairseq->-r old_setup_files/requirements-pip.txt (line 12)) (16.0.6)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.32.0,>=1.31.18->boto3->-r old_setup_files/requirements-pip.txt (line 14)) (1.14.0)\n",
+ "Requirement already satisfied: anyio<5,>=3.4.0 in /home/jamal/.local/lib/python3.8/site-packages (from starlette<0.28.0,>=0.27.0->fastapi->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (3.6.2)\n",
+ "Requirement already satisfied: pkgutil-resolve-name>=1.3.10; python_version < \"3.9\" in /home/jamal/.local/lib/python3.8/site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (1.3.10)\n",
+ "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/lib/python3/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio>=3.34.0->-r old_setup_files/requirements-pip.txt (line 34)) (0.15.5)\n",
+ "Building wheels for collected packages: ffmpeg, encodec, ffmpy, antlr4-python3-runtime\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Building wheel for ffmpeg (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for ffmpeg: filename=ffmpeg-1.4-py3-none-any.whl size=6083 sha256=19514e448b6bdeb0af7ec0711ab1153c6ed7a4c2c94d70be977b7c7fe20bb378\n",
+ " Stored in directory: /home/jamal/.cache/pip/wheels/30/33/46/5ab7eca55b9490dddbf3441c68a29535996270ef1ce8b9b6d7\n",
+ " Building wheel for encodec (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for encodec: filename=encodec-0.1.1-py3-none-any.whl size=45768 sha256=5be14b57922136d74f505c3f00b442410ed01e1df97724788ca60a81ae4c88c3\n",
+ " Stored in directory: /home/jamal/.cache/pip/wheels/83/ca/c5/2770ecff40c79307803c30f8d4c5dcb533722f5f7c049ee9db\n",
+ " Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5580 sha256=30a6f174f3c528a86e4132068eecaac56ddfdca93b4128ed8631c5f17707f7d7\n",
+ " Stored in directory: /home/jamal/.cache/pip/wheels/75/a3/1a/2f3f90b9a4eb0408109ae1b5bae01efbdf8ab4ef98797433e4\n",
+ " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=24d2cd414c2d61b3f9bc6b30748b85ab68aa6d00c4ed7145823ba466469b0796\n",
+ " Stored in directory: /home/jamal/.cache/pip/wheels/c8/d0/ab/d43c02eaddc5b9004db86950802442ad9a26f279c619e28da0\n",
+ "Successfully built ffmpeg encodec ffmpy antlr4-python3-runtime\n",
+ "\u001b[31mERROR: pydantic-core 2.4.0 has requirement typing-extensions!=4.7.0,>=4.6.0, but you'll have typing-extensions 4.5.0 which is incompatible.\u001b[0m\n",
+ "\u001b[31mERROR: pydantic 2.1.1 has requirement typing-extensions>=4.6.1, but you'll have typing-extensions 4.5.0 which is incompatible.\u001b[0m\n",
+ "Installing collected packages: regex, fsspec, huggingface-hub, safetensors, tokenizers, transformers, diffusers, appdirs, ffmpeg-downloader, ffmpeg, ffmpeg-python, sox, omegaconf, cython, lxml, tabulate, portalocker, sacrebleu, torchaudio, antlr4-python3-runtime, hydra-core, bitarray, fairseq, jmespath, botocore, s3transfer, boto3, funcy, pathvalidate, rich-argparse, einops, encodec, pydub, audio2numpy, faiss-cpu, sentencepiece, lion-pytorch, ema-pytorch, vector-quantize-pytorch, local-attention, beartype, accelerate, audiolm-pytorch, universal-startfile, frozenlist, aiosignal, async-timeout, yarl, aiohttp, orjson, python-multipart, pydantic-core, annotated-types, pydantic, starlette, fastapi, h11, uvicorn, aiofiles, toolz, altair, mdit-py-plugins, ffmpy, httpcore, httpx, gradio-client, semantic-version, gradio\n",
+ "Successfully installed accelerate-0.21.0 aiofiles-23.1.0 aiohttp-3.8.5 aiosignal-1.3.1 altair-5.0.1 annotated-types-0.5.0 antlr4-python3-runtime-4.8 appdirs-1.4.4 async-timeout-4.0.2 audio2numpy-0.1.2 audiolm-pytorch-1.2.24 beartype-0.15.0 bitarray-2.8.0 boto3-1.28.18 botocore-1.31.18 cython-3.0.0 diffusers-0.19.3 einops-0.6.1 ema-pytorch-0.2.3 encodec-0.1.1 fairseq-0.12.2 faiss-cpu-1.7.4 fastapi-0.100.1 ffmpeg-1.4 ffmpeg-downloader-0.2.0 ffmpeg-python-0.2.0 ffmpy-0.3.1 frozenlist-1.4.0 fsspec-2023.6.0 funcy-2.0 gradio-3.39.0 gradio-client-0.3.0 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.16.4 hydra-core-1.0.7 jmespath-1.0.1 lion-pytorch-0.1.2 local-attention-1.8.6 lxml-4.9.3 mdit-py-plugins-0.3.3 omegaconf-2.0.6 orjson-3.9.2 pathvalidate-3.1.0 portalocker-2.7.0 pydantic-2.1.1 pydantic-core-2.4.0 pydub-0.25.1 python-multipart-0.0.6 regex-2023.6.3 rich-argparse-1.2.0 s3transfer-0.6.1 sacrebleu-2.3.1 safetensors-0.3.1 semantic-version-2.10.0 sentencepiece-0.1.99 sox-1.4.1 starlette-0.27.0 tabulate-0.9.0 tokenizers-0.13.3 toolz-0.12.0 torchaudio-2.0.2 transformers-4.31.0 universal-startfile-0.2 uvicorn-0.23.2 vector-quantize-pytorch-1.6.30 yarl-1.9.2\n",
+ "Requirement already satisfied: encodec in /home/jamal/.local/lib/python3.8/site-packages (0.1.1)\n",
+ "Requirement already satisfied: rich-argparse in /home/jamal/.local/lib/python3.8/site-packages (1.2.0)\n",
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+ "Requirement already satisfied: pydub in /home/jamal/.local/lib/python3.8/site-packages (0.25.1)\n",
+ "Collecting devtools\n",
+ " Downloading devtools-0.11.0-py3-none-any.whl (19 kB)\n",
+ "Requirement already satisfied: einops in /home/jamal/.local/lib/python3.8/site-packages (from encodec) (0.6.1)\n",
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+ "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /home/jamal/.local/lib/python3.8/site-packages (from librosa) (1.0.2)\n",
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+ "Requirement already satisfied: executing>=1.1.1 in /home/jamal/.local/lib/python3.8/site-packages (from devtools) (1.2.0)\n",
+ "Requirement already satisfied: asttokens<3.0.0,>=2.0.0 in /home/jamal/.local/lib/python3.8/site-packages (from devtools) (2.2.1)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.7.99)\n",
+ "Requirement already satisfied: nvidia-nccl-cu11==2.14.3; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (2.14.3)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.7.99)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.7.91)\n",
+ "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.10.3.66)\n",
+ "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (10.2.10.91)\n",
+ "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (10.9.0.58)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.4.0.1)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (8.5.0.96)\n",
+ "Requirement already satisfied: networkx in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (3.1)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.7.101)\n",
+ "Requirement already satisfied: sympy in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (1.12)\n",
+ "Requirement already satisfied: filelock in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (3.12.2)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (11.7.4.91)\n",
+ "Requirement already satisfied: triton==2.0.0; platform_system == \"Linux\" and platform_machine == \"x86_64\" in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (2.0.0)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: jinja2 in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (3.1.2)\n",
+ "Requirement already satisfied: typing-extensions in /home/jamal/.local/lib/python3.8/site-packages (from torch->encodec) (4.5.0)\n",
+ "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/jamal/.local/lib/python3.8/site-packages (from rich>=11.0.0->rich-argparse) (2.14.0)\n",
+ "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /home/jamal/.local/lib/python3.8/site-packages (from rich>=11.0.0->rich-argparse) (2.2.0)\n",
+ "Requirement already satisfied: importlib-metadata; python_version < \"3.9\" in /home/jamal/.local/lib/python3.8/site-packages (from numba>=0.43.0->librosa) (6.0.0)\n",
+ "Requirement already satisfied: llvmlite<0.41,>=0.40.0dev0 in /home/jamal/.local/lib/python3.8/site-packages (from numba>=0.43.0->librosa) (0.40.1)\n",
+ "Requirement already satisfied: cffi>=1.0 in /home/jamal/.local/lib/python3.8/site-packages (from soundfile>=0.10.2->librosa) (1.15.1)\n",
+ "Requirement already satisfied: platformdirs>=2.5.0 in /home/jamal/.local/lib/python3.8/site-packages (from pooch>=1.0->librosa) (2.6.2)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /home/jamal/.local/lib/python3.8/site-packages (from pooch>=1.0->librosa) (2.28.2)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/jamal/.local/lib/python3.8/site-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (3.2.0)\n",
+ "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from asttokens<3.0.0,>=2.0.0->devtools) (1.14.0)\n",
+ "Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from nvidia-cuda-runtime-cu11==11.7.99; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->encodec) (45.2.0)\n",
+ "Requirement already satisfied: wheel in /usr/lib/python3/dist-packages (from nvidia-cuda-runtime-cu11==11.7.99; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->encodec) (0.34.2)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /home/jamal/.local/lib/python3.8/site-packages (from sympy->torch->encodec) (1.3.0)\n",
+ "Requirement already satisfied: lit in /home/jamal/.local/lib/python3.8/site-packages (from triton==2.0.0; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->encodec) (16.0.6)\n",
+ "Requirement already satisfied: cmake in /home/jamal/.local/lib/python3.8/site-packages (from triton==2.0.0; platform_system == \"Linux\" and platform_machine == \"x86_64\"->torch->encodec) (3.27.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /home/jamal/.local/lib/python3.8/site-packages (from jinja2->torch->encodec) (2.1.2)\n",
+ "Requirement already satisfied: mdurl~=0.1 in /home/jamal/.local/lib/python3.8/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich>=11.0.0->rich-argparse) (0.1.2)\n",
+ "Requirement already satisfied: zipp>=0.5 in /usr/lib/python3/dist-packages (from importlib-metadata; python_version < \"3.9\"->numba>=0.43.0->librosa) (1.0.0)\n",
+ "Requirement already satisfied: pycparser in /home/jamal/.local/lib/python3.8/site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.21)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2019.11.28)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jamal/.local/lib/python3.8/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.26.7)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /home/jamal/.local/lib/python3.8/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.0.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.8)\n",
+ "Installing collected packages: devtools\n",
+ "Successfully installed devtools-0.11.0\n",
+ "fish: Unknown command: python\n",
+ "fish: \n",
+ "python bark_webui.py --share\n",
+ "^\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -r old_setup_files/requirements-pip.txt\n",
+ "!pip install encodec rich-argparse librosa pydub devtools"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e6884c4a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting typing-extensions\n",
+ " Downloading typing_extensions-4.7.1-py3-none-any.whl (33 kB)\n",
+ "\u001b[31mERROR: tensorflow 2.13.0 has requirement numpy<=1.24.3,>=1.22, but you'll have numpy 1.20.3 which is incompatible.\u001b[0m\n",
+ "\u001b[31mERROR: tensorflow 2.13.0 has requirement typing-extensions<4.6.0,>=3.6.6, but you'll have typing-extensions 4.7.1 which is incompatible.\u001b[0m\n",
+ "Installing collected packages: typing-extensions\n",
+ " Attempting uninstall: typing-extensions\n",
+ " Found existing installation: typing-extensions 4.5.0\n",
+ " Uninstalling typing-extensions-4.5.0:\n",
+ " Successfully uninstalled typing-extensions-4.5.0\n",
+ "Successfully installed typing-extensions-4.7.1\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "pip install typing-extensions --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3a2e8312",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Traceback (most recent call last):\r\n",
+ " File \"bark_webui.py\", line 14, in Click to toggle example long-form generations (from the example notebook)
+ +[dialog.webm](https://user-images.githubusercontent.com/2565833/235463539-f57608da-e4cb-4062-8771-148e29512b01.webm) + +[longform_advanced.webm](https://user-images.githubusercontent.com/2565833/235463547-1c0d8744-269b-43fe-9630-897ea5731652.webm) + +[longform_basic.webm](https://user-images.githubusercontent.com/2565833/235463559-87efe9f8-a2db-4d59-b764-57db83f95270.webm) + +![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!git clone https://github.com/JonathanFly/bark.git\n",
+ "%cd bark\n",
+ "!pip install -r old_setup_files/requirements-pip.txt\n",
+ "!pip install encodec rich-argparse librosa pydub devtools\n",
+ "!python bark_webui.py --share"
+ ],
+ "metadata": {
+ "id": "h2hmNnKDlWvM"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/notebooks/Bark_Infinity_Long_Form_Audio_Colab.ipynb b/notebooks/Bark_Infinity_Long_Form_Audio_Colab.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ab4691cbf63d1195861a2d7aa1cf9627dbb7eb9a
--- /dev/null
+++ b/notebooks/Bark_Infinity_Long_Form_Audio_Colab.ipynb
@@ -0,0 +1,492 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#@title Connect and check GPU and runtime\n",
+ "from psutil import virtual_memory\n",
+ "gpu_info = !nvidia-smi\n",
+ "gpu_info = '\\n'.join(gpu_info)\n",
+ "ram_gb = virtual_memory().total / 1e9\n",
+ "if gpu_info.find('failed') >= 0:\n",
+ " print('Not connected to a GPU', end=\"\")\n",
+ "elif gpu_info.find('not found') >= 0:\n",
+ " print('Not connected to a GPU', end=\"\")\n",
+ "else:\n",
+ " print('GPU Connected', end=\"\")\n",
+ "print(', your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n"
+ ],
+ "metadata": {
+ "cellView": "form",
+ "id": "ogUYjFfhcxTG"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HJQ4TI0_Qowr"
+ },
+ "source": [
+ "## Setup Notebook, Install dependencies\n",
+ "Run both cells to install system and needed functions. \n",
+ "_If Colab for some reason crashes re-run cell 0.2 before contining._\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "r8wG_tIaOV0Q",
+ "cellView": "form"
+ },
+ "outputs": [],
+ "source": [
+ "#@title 0.1 - Install system\n",
+ "from IPython.display import clear_output\n",
+ "!git clone https://github.com/JonathanFly/bark.git\n",
+ "%cd bark\n",
+ "!pip install -r old_setup_files/requirements-pip.txt\n",
+ "!pip install encodec rich-argparse\n",
+ "!pip install librosa pydub devtools\n",
+ "\n",
+ "#clear_output()\n",
+ "#print('Cell completed.')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jKTvqvVkOwXM",
+ "cellView": "form"
+ },
+ "outputs": [],
+ "source": [
+ " #@title 0.2 - Setup required functions and helpers\n",
+ "import os\n",
+ "import time\n",
+ "from bark_infinity import config\n",
+ "import numpy as np\n",
+ "\n",
+ "logger = config.logger\n",
+ "logger.setLevel(\"WARNING\")\n",
+ "\n",
+ "from bark_infinity import generation\n",
+ "from bark_infinity import api\n",
+ "\n",
+ "import rich\n",
+ "from rich import print\n",
+ "from rich import pretty\n",
+ "from rich.pretty import pprint\n",
+ "from rich import inspect\n",
+ "\n",
+ "import librosa\n",
+ "from pydub import AudioSegment\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Audio\n",
+ "from io import BytesIO\n",
+ "\n",
+ "# None of this code, just fiddlign with Colab stuff\n",
+ "# Just to save Colab with outputs and float32 wavs are GIGANTO\n",
+ "# actually this doesn't work, the iPython widget converts it back to float32? or I messed up\n",
+ "\n",
+ "def display_audio_int16_but(audio_arr_segments, file_name, sample_rate=generation.SAMPLE_RATE, width='200px'):\n",
+ " file_name_label = widgets.Label(value=f\"Playing: {file_name}\")\n",
+ " file_name_label.layout.width = width\n",
+ " audio_data_int16 = audio_arr_segments\n",
+ " if isinstance(audio_data_int16, list):\n",
+ " audio_data_int16 = np.concatenate(audio_data_int16)\n",
+ "\n",
+ " #audio_data_int16 = np.int16(audio_data_int16 * np.iinfo(np.int16).max)\n",
+ "\n",
+ "\n",
+ " audio_widget = Audio(audio_data_int16, rate=sample_rate)\n",
+ " display(file_name_label, audio_widget)\n",
+ "\n",
+ "\n",
+ "def on_button_click(button):\n",
+ " audio_data, sample_rate = librosa.load(button.wav_path, sr=None)\n",
+ " file_name = os.path.basename(button.wav_path)\n",
+ " display_audio_int16_but(audio_data,file_name, sample_rate)\n",
+ "\n",
+ "def display_wav_files(directory, matchType=\".wav\"):\n",
+ " subdirs, wav_files = [], []\n",
+ "\n",
+ " for item in os.listdir(directory):\n",
+ " item_path = os.path.join(directory, item)\n",
+ "\n",
+ " if os.path.isfile(item_path) and item_path.endswith(matchType):\n",
+ " wav_files.append(item_path)\n",
+ " elif os.path.isdir(item_path):\n",
+ " subdirs.append(item_path)\n",
+ "\n",
+ " wav_files.sort(key=lambda x: os.path.basename(x))\n",
+ "\n",
+ " for wav_file in wav_files:\n",
+ "\n",
+ " filename = os.path.basename(wav_file)\n",
+ " print(f\" {filename}\")\n",
+ " display( Audio(filename=wav_file, rate=generation.SAMPLE_RATE) )\n",
+ " #button = widgets.Button(description=f\"Play {filename}\")\n",
+ " #button.wav_path = wav_file\n",
+ " #button.on_click(on_button_click)\n",
+ " #display(button)\n",
+ "\n",
+ " for subdir in sorted(subdirs):\n",
+ " print(f\"<{subdir}>\")\n",
+ " display_wav_files(subdir, matchType)\n",
+ "\n",
+ "def display_mp4_files(directory):\n",
+ " return display_wav_files(directory, '.mp4')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1.0 - Gradio App"
+ ],
+ "metadata": {
+ "id": "VbIE0Bv8jxtN"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#@markdown Run the WebUI with all features.\n", + "#@markdown When loaded click the second link to launch WebUI in another window.\n", + "!python bark_webui.py --share" + ], + "metadata": { + "cellView": "form", + "id": "BQfEqnxMpUk1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OTRtNy1xT1sI" + }, + "source": [ + "## 2.0 - Manual generation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LKLe_gYkQ59l" + }, + "source": [ + "### 2.1 - Choose Bark Models and set Text and Other Generation Options\n", + "\n", + "Required for 3.0 and 4.0" + ] + }, + { + "cell_type": "code", + "source": [ + "# Time to complete cell: ca. 3min\n", + "generation.OFFLOAD_CPU = False # On your home system set to True probably, but Colab GPU should have plenty of memory for all three models\n", + "generation.preload_models() # Optional, will lazy load if not preloaded. First time run in New Colab has to download models" + ], + "metadata": { + "id": "QLa2jPOUjSyd" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nTzF9iamO1Tm" + }, + "outputs": [], + "source": [ + "text = \"\"\"\n", + "Hey, have you heard about this new text-to-audio model called \"Bark\"?\n", + "It's like rain on your wedding day. It's a free ride when you've already paid. It's the good advice that you just didn't take.\n", + "And who would've thought? It figures.\n", + "\n", + "Well, life has a funny way of sneaking up on you. When you think everything's okay and everything's going right.\n", + "And life has a funny way of helping you out. When you think everything's gone wrong.\n", + "And everything blows up in your face.\n", + "\n", + "It's a traffic jam when you're already late. A \"No smoking\" sign on your cigarette break.\n", + "It's like ten thousand spoons when all you need is a knife. It's meeting the man of my dreams.\n", + "And then meeting his beautiful wife.\n", + "\n", + "And isn't it ironic? Don't you think? A little too ironic.\n", + "And yeah, I really do think.\n", + "\"\"\"\n", + "\n", + "# For split set split_character_goal_length and split_character_max_length\n", + "kwargs = {}\n", + "\n", + "kwargs = config.load_all_defaults()\n", + "kwargs['text_prompt'] = text\n", + "kwargs['hoarder_mode'] = True\n", + "kwargs[\"output_dir\"] = 'bark_samples'\n", + "kwargs[\"history_prompt\"] = None\n", + "# kwargs[\"single_starting_seed\"] = None #\n", + "# If you set seed you might want manually call generation.set_seed(-1) after to disable deterministic generation settings\n", + "# I'm not cleaning up after this paramater at the moment and I'm not sure on other side effects\n", + "kwargs[\"stable_mode_interval\"] = 1 # 0 for continous, 2,3,4 for mixed\n", + "kwargs[\"split_character_goal_length\"] = 90\n", + "kwargs[\"split_character_max_length\"] = 130\n", + "# kwargs[\"output_iterations\"] = 1\n", + "kwargs[\"add_silence_between_segments\"] = .025 # See: https://github.com/suno-ai/bark/blob/main/notebooks/long_form_generation.ipynb but not great for songs or stable_mode_interval 0\n", + "kwargs[\"semantic_min_eos_p\"] = 0.05 # 0.20 is default, lower means more likely to stotp\n", + "\n", + "\n", + "# not sure on overall effect so far from these, but for example:\n", + "kwargs[\"semantic_top_k\"] = 50\n", + "kwargs[\"semantic_top_p\"] = 0.95" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.0 First Attempt" + ], + "metadata": { + "id": "uuqkchecXnbm" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### 3.2 Before we run, let's double check out settings" + ], + "metadata": { + "id": "6ANm93mHZIa6" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6SPmYLSoQBBp" + }, + "outputs": [], + "source": [ + "kwargs[\"dry_run\"] = True # Check how the text is being split, don't actually run the model.\n", + "full_generation_segments, audio_arr_segments, final_filename_will_be, _ = api.generate_audio_long(**kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IEFQgcemX7Ih" + }, + "outputs": [], + "source": [ + "# that's the output we expect to see, we didn't generate audio yet\n", + "# these text segments look a little small small so let's try this instead\n", + "kwargs[\"split_character_goal_length\"] = 110\n", + "kwargs[\"split_character_max_length\"] = 175\n", + "\n", + "full_generation_segments, audio_arr_segments, final_filename_will_be, _ = api.generate_audio_long(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### 3.2 Run Bark" + ], + "metadata": { + "id": "iPwaLVKCZNaN" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gnvv0zEZY7vP" + }, + "outputs": [], + "source": [ + "# These segement sizes look better so now so set dry_run to False to run for real\n", + "# Because we set hoarder_mode we can see the wav files for each segment in the Colab File Manager\n", + "\n", + "kwargs[\"dry_run\"] = False\n", + "full_generation_segments, audio_arr_segments, final_filename_will_be, _ = api.generate_audio_long(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### 3.3 Save and list files + playbutton" + ], + "metadata": { + "id": "pGeaoAZ6Y9yX" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Qqyw-Uk1axiC" + }, + "outputs": [], + "source": [ + "print(f\"Final audiofile: {final_filename_will_be}\")\n", + "# (we see many wav because we set hoarder_mode, but one file will be the final product\n", + "# set hoarder_mode=False if you just want the file wav and aren't in explore mode\n", + "\n", + "# or play here\n", + "Audio(np.concatenate(audio_arr_segments), rate=generation.SAMPLE_RATE)\n" + ] + }, + { + "cell_type": "code", + "source": [ + "# because we set hoarder mode we also saved each segement as its own seperate sample with wav\n", + "\n", + "!find \"bark_samples/\" -name \"*.npz\"\n", + "\n", + "display_mp4_files(\"bark_samples/\")" + ], + "metadata": { + "id": "yqORA8ajXMrk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 4.0 Second Attempt. Can we do better?" + ], + "metadata": { + "id": "psbR-0mxW4Dn" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### 4.1 settings" + ], + "metadata": { + "id": "g8vPUGahb0Ar" + } + }, + { + "cell_type": "code", + "source": [ + "# we used stable_mode_interval = 1, so the history_prompt does not evolve between segments\n", + "# even still the voices that are saved for each segment are one-generation different than the original history prompt\n", + "# this means they are a *little* bit different, and we may prefer one of them over the original\n", + "# for example maybe segment 2 was a little more clear, or had a particular emotion, we could use that segment's version as the speaker\n", + "# in the particular run I'm doing now, that segment ended with a little bit an interesting accent. I'm curious if I can bring that out more.\n", + "\n", + "# (should probably rename the file to something sensible though)\n", + "\n", + "kwargs[\"history_prompt\"] = \"/content/bark/bark_samples/Hey_have_you_he-23-0714-0743-27-SPK-random/002_Its_the_good_ad-23-0714-0744-51-SPK-random.mp4.npz\"" + ], + "metadata": { + "id": "WQvyiM2sW5xC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### 4.2 generate" + ], + "metadata": { + "id": "t95ZGI6kcNBq" + } + }, + { + "cell_type": "code", + "source": [ + "kwargs[\"text_prompt\"] = f\"I'm speaker number two. I'm the best speaker. Also I'm a free spirit. Let me evolve my voice with every step. Here's my version.\"\n", + "kwargs[\"text_prompt\"] += text\n", + "kwargs[\"stable_mode_interval\"] = 0\n", + "kwargs[\"output_dir\"] = \"speaker_2_test\"\n", + "kwargs[\"add_silence_between_segments\"] = 0.0 # No silence, fully merge clips\n", + "\n", + "kwargs[\"semantic_min_eos_p\"] = 0.20 # Back to default, let Bark umm and ahh a bit\n", + "full_generation_segments, audio_arr_segments, final_filename_will_be, _ = api.generate_audio_long(**kwargs)\n" + ], + "metadata": { + "id": "btp4V86BfAT1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### 4.3 Save and list files + playbutton" + ], + "metadata": { + "id": "GrXY5zfscCwi" + } + }, + { + "cell_type": "code", + "source": [ + "print(f\"Final audiofile: {final_filename_will_be}\")\n", + "Audio(np.concatenate(audio_arr_segments), rate=generation.SAMPLE_RATE)" + ], + "metadata": { + "id": "9fthW9oGw-5g" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# this clip probably got really weird after a full segments, fully feedbacking into itself. So kwargs[\"stable_mode_interval\"] = 3 might be a good compromise\n", + "\n", + "display_mp4_files(\"speaker_2_test\")" + ], + "metadata": { + "id": "Rlv4CQrfthY8" + }, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "HJQ4TI0_Qowr", + "FHlxNCt3QwIr", + "uuqkchecXnbm" + ], + "provenance": [], + "machine_shape": "hm", + "gpuType": "T4", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/Bark_Testing.ipynb b/notebooks/Bark_Testing.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d25d4428347e352d9ab6af7c79561733fae13b2b --- /dev/null +++ b/notebooks/Bark_Testing.ipynb @@ -0,0 +1,8826 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "HJQ4TI0_Qowr" + }, + "source": [ + "## Setup Notebook, Install\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r8wG_tIaOV0Q", + "outputId": "6ea4ba04-3578-41e7-f9b5-4a44ca654452" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: destination path 'bark' already exists and is not an empty directory.\n", + "/home/jon/mamba_projects/bark_postfixes/bark/bark\n", + "\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements-pip.txt'\u001b[0m\u001b[31m\n", + "\u001b[0mRequirement already satisfied: encodec in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (0.1.1)\n", + "Requirement already satisfied: rich-argparse in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (1.1.0)\n", + "Requirement already satisfied: torch in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from encodec) (2.0.0)\n", + "Requirement already satisfied: numpy in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from encodec) (1.24.3)\n", + "Requirement already satisfied: einops in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from encodec) (0.6.1)\n", + "Requirement already satisfied: torchaudio in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from encodec) (2.0.0)\n", + "Requirement already satisfied: rich>=11.0.0 in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from rich-argparse) (12.5.1)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from rich>=11.0.0->rich-argparse) (2.11.2)\n", + "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from rich>=11.0.0->rich-argparse) (0.9.1)\n", + "Requirement already satisfied: filelock in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from torch->encodec) (3.9.0)\n", + "Requirement already satisfied: typing-extensions in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from torch->encodec) (4.5.0)\n", + "Requirement already satisfied: sympy in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from torch->encodec) (1.11.1)\n", + "Requirement already satisfied: networkx in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from torch->encodec) (2.8.4)\n", + "Requirement already satisfied: jinja2 in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from torch->encodec) (3.1.2)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages (from jinja2->torch->encodec) (2.1.1)\n", + "Requirement already satisfied: mpmath>=0.19 in /home/jon/mambaforge/envs/bark-infinity-oneclick/lib/python3.10/site-packages/mpmath-1.2.1-py3.10.egg (from sympy->torch->encodec) (1.2.1)\n", + "^C\n", + "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!git clone https://github.com/JonathanFly/bark.git\n", + "%cd bark\n", + "!pip install -r requirements-pip.txt\n", + "!pip install encodec rich-argparse\n", + "!pip install librosa pydub" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "FHlxNCt3QwIr" + }, + "source": [ + "## Run Once Per Notebook Restart (if files still exist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jKTvqvVkOwXM" + }, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "from bark_infinity import config\n", + "import numpy as np\n", + "\n", + "logger = config.logger\n", + "logger.setLevel(\"WARNING\")\n", + "\n", + "from bark_infinity import generation\n", + "from bark_infinity import api\n", + "\n", + "import rich\n", + "from rich import print\n", + "from rich import pretty\n", + "from rich.pretty import pprint\n", + "from rich import inspect\n", + "\n", + "from pydub import AudioSegment\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, Audio\n", + "from io import BytesIO\n", + "\n", + "# None of this code, just fiddlign with Colab stuff\n", + "# Just to save Colab with outputs and float32 wavs are GIGANTO\n", + "# actually this doesn't work, the iPython widget converts it back to float32? or I messed up\n", + "\n", + "def display_audio_int16_but(audio_arr_segments, file_name, sample_rate=generation.SAMPLE_RATE, width='200px'):\n", + " file_name_label = widgets.Label(value=f\"Playing: {file_name}\")\n", + " file_name_label.layout.width = width\n", + " audio_data_int16 = audio_arr_segments\n", + " if isinstance(audio_data_int16, list):\n", + " audio_data_int16 = np.concatenate(audio_data_int16) \n", + "\n", + " #audio_data_int16 = np.int16(audio_data_int16 * np.iinfo(np.int16).max)\n", + "\n", + "\n", + " audio_widget = Audio(audio_data_int16, rate=sample_rate)\n", + " display(file_name_label, audio_widget)\n", + " \n", + "\n", + "def on_button_click(button):\n", + " audio_data, sample_rate = librosa.load(button.wav_path, sr=None)\n", + " file_name = os.path.basename(button.wav_path)\n", + " display_audio_int16_but(audio_data,file_name, sample_rate)\n", + "\n", + "\n", + "def display_wav_files(directory):\n", + " subdirs, wav_files = [], []\n", + " \n", + " for item in os.listdir(directory):\n", + " item_path = os.path.join(directory, item)\n", + " \n", + " if os.path.isfile(item_path) and item_path.endswith('.wav'):\n", + " wav_files.append(item_path)\n", + " elif os.path.isdir(item_path):\n", + " subdirs.append(item_path)\n", + "\n", + " wav_files.sort(key=lambda x: os.path.basename(x))\n", + "\n", + " for wav_file in wav_files:\n", + "\n", + " filename = os.path.basename(wav_file)\n", + " print(f\" {filename}\")\n", + " button = widgets.Button(description=f\"Play {filename}\")\n", + " button.wav_path = wav_file \n", + " button.on_click(on_button_click)\n", + " display(button)\n", + "\n", + " for subdir in sorted(subdirs):\n", + " print(f\"<{subdir}>\")\n", + " display_wav_files(subdir)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def call_with_non_none_params(func, **kwargs):\n", + " non_none_params = {key: value for key, value in kwargs.items() if value is not None}\n", + " return func(**non_none_params)\n", + "\n", + "global save_semantic_tokens\n", + "def generate_audio_barki(\n", + " text: str,\n", + " **kwargs,\n", + "):\n", + " \"\"\"Generate audio array from input text.\n", + "\n", + " Args:\n", + " text: text to be turned into audio\n", + " history_prompt: history choice for audio cloning\n", + " text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)\n", + " waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)\n", + " silent: disable progress bar\n", + " output_full: return full generation to be used as a history prompt\n", + "\n", + "\n", + " Returns:\n", + " numpy audio array at sample frequency 24khz\n", + " \"\"\"\n", + " #logger.debug(locals())\n", + " #print(\"before load all defaults\")\n", + " kwargs = config.load_all_defaults(**kwargs)\n", + "\n", + " #logger.debug(locals())\n", + " #print(\"after load all defaults\")\n", + " history_prompt = kwargs.get(\"history_prompt\", None)\n", + " text_temp = kwargs.get(\"text_temp\", None)\n", + " waveform_temp = kwargs.get(\"waveform_temp\", None)\n", + " silent = kwargs.get(\"silent\", None)\n", + " output_full = kwargs.get(\"output_full\", None)\n", + "\n", + " global gradio_try_to_cancel\n", + " global done_cancelling\n", + "\n", + " seed = kwargs.get(\"seed\",None)\n", + " if seed is not None:\n", + " generation.set_seed(seed)\n", + "\n", + " ## TODO seperate stage seeds\n", + "\n", + " ## Semantic Options\n", + " semantic_temp = text_temp\n", + " if kwargs.get(\"semantic_temp\", None):\n", + " semantic_temp = kwargs.get(\"semantic_temp\")\n", + "\n", + " semantic_seed = kwargs.get(\"semantic_seed\",None)\n", + " if semantic_seed is not None:\n", + " generation.set_seed(semantic_seed)\n", + "\n", + "\n", + " if api.gradio_try_to_cancel:\n", + " done_cancelling = True\n", + " return None, None\n", + " semantic_tokens = call_with_non_none_params(\n", + " api.generate_text_semantic,\n", + " text=text,\n", + " history_prompt=history_prompt,\n", + " temp=semantic_temp,\n", + " top_k=kwargs.get(\"semantic_top_k\", None),\n", + " top_p=kwargs.get(\"semantic_top_p\", None),\n", + " silent=silent,\n", + " min_eos_p = kwargs.get(\"semantic_min_eos_p\", None),\n", + " max_gen_duration_s = kwargs.get(\"semantic_max_gen_duration_s\", None),\n", + " allow_early_stop = kwargs.get(\"semantic_allow_early_stop\", True),\n", + " use_kv_caching=kwargs.get(\"semantic_use_kv_caching\", True),\n", + " )\n", + " \n", + " inspect(semantic_tokens)\n", + " if api.gradio_try_to_cancel:\n", + " done_cancelling = True\n", + " return None, None\n", + "\n", + " ## Coarse Options\n", + " coarse_temp = waveform_temp\n", + " if kwargs.get(\"coarse_temp\", None):\n", + " coarse_temp = kwargs.get(\"coarse_temp\")\n", + "\n", + " coarse_seed = kwargs.get(\"coarse_seed\",None)\n", + " if coarse_seed is not None:\n", + " generation.set_seed(coarse_seed)\n", + " \n", + " \n", + " if api.gradio_try_to_cancel:\n", + " done_cancelling = True\n", + " return None, None\n", + " \n", + " coarse_tokens = call_with_non_none_params(\n", + " generation.generate_coarse,\n", + " x_semantic=semantic_tokens,\n", + " history_prompt=history_prompt,\n", + " temp=coarse_temp,\n", + " top_k=kwargs.get(\"coarse_top_k\", None),\n", + " top_p=kwargs.get(\"coarse_top_p\", None),\n", + " silent=silent,\n", + " max_coarse_history=kwargs.get(\"coarse_max_coarse_history\", None),\n", + " sliding_window_len=kwargs.get(\"coarse_sliding_window_len\", None),\n", + " use_kv_caching=kwargs.get(\"coarse_kv_caching\", True),\n", + " )\n", + "\n", + " fine_temp = kwargs.get(\"fine_temp\", 0.5)\n", + "\n", + " fine_seed = kwargs.get(\"fine_seed\",None)\n", + " if fine_seed is not None:\n", + " generation.set_seed(fine_seed)\n", + "\n", + " if api.gradio_try_to_cancel:\n", + " done_cancelling = True\n", + " return None, None\n", + " fine_tokens = call_with_non_none_params(\n", + " api.generate_fine,\n", + " x_coarse_gen=coarse_tokens,\n", + " history_prompt=history_prompt,\n", + " temp=fine_temp,\n", + " silent=silent,\n", + " )\n", + "\n", + " # do we ever care about setting this seed? Probably not? You can always just decode it again\n", + "\n", + " if api.gradio_try_to_cancel:\n", + " done_cancelling = True\n", + " return None, None\n", + " audio_arr = api.codec_decode(fine_tokens)\n", + " full_generation = {\n", + " \"semantic_prompt\": semantic_tokens,\n", + " \"coarse_prompt\": coarse_tokens,\n", + " \"fine_prompt\": fine_tokens,\n", + " }\n", + "\n", + " if api.gradio_try_to_cancel:\n", + " done_cancelling = True\n", + " return None, None\n", + " \n", + " hoarder_mode = kwargs.get(\"hoarder_mode\", None)\n", + " total_segments = kwargs.get(\"total_segments\", 1)\n", + " if hoarder_mode and (total_segments > 1):\n", + " kwargs[\"text\"] = text\n", + " api.write_one_segment(audio_arr, full_generation, **kwargs)\n", + "\n", + " if output_full:\n", + " return full_generation, audio_arr\n", + " \n", + " return audio_arr\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jon/mamba_projects/bark_postfixes/bark\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\u001b[1;36m0.7\u001b[0m, \u001b[32m'top_k'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;35mNone\u001b[0m, \u001b[32m'top_p'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'max_coarse_history'\u001b[0m: \u001b[1;36m630\u001b[0m, \u001b[32m'sliding_window_len'\u001b[0m: \u001b[1;36m60\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'use_kv_caching'\u001b[0m: \u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 17/17 [00:11<00:00, 1.48it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
generate_fine {'x_coarse_gen': array([[ 855, 855, 1019, ..., 408, 408, 408], generation.py:626\n", + " [ 424, 937, 913, ..., 518, 424, 518]]), 'history_prompt': None, 'temp': 0.5, \n", + "'silent': False} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m855\u001b[0m, \u001b[1;36m855\u001b[0m, \u001b[1;36m1019\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=739196;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=453353;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m \u001b[1;36m424\u001b[0m, \u001b[1;36m937\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m518\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m518\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 1/1 [00:04<00:00, 4.22s/it]\n" + ] + } + ], + "source": [ + "text = \"\"\"With me, brave the tumultuous seas, claim treasures untold, and send foes to their watery \n", + "doom\"\"\"\n", + "logger.setLevel(\"DEBUG\")\n", + "kwargs[\"semantic_min_eos_p\"] = 0.2 \n", + "kwargs[\"text_prompt\"] = text\n", + "generation.set_seed(37)\n", + "kwargs['output_full'] = True\n", + "kwargs['output_dir'] = \"bark_test\"\n", + "kwargs[\"history_prompt\"] = None\n", + "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n", + "barkifull, audio_arr_segments_barki = api.generate_audio_barki(text, **kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
337\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1;36m337\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "print(len(api.our_semantic_tokens))\n",
+ "import copy\n",
+ "\n",
+ "tokens = copy.deepcopy(api.our_semantic_tokens)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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OWNDATA : False โ\n", + "โ WRITEABLE : True โ\n", + "โ ALIGNED : True โ\n", + "โ WRITEBACKIFCOPY : False โ\n", + "โ โ\n", + "โ flat = <numpy.flatiter object at 0x7a33500> โ\n", + "โ imag = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, โ\n", + "โ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) โ\n", + "โ itemsize = 8 โ\n", + "โ nbytes = 3432 โ\n", + "โ ndim = 1 โ\n", + "โ real = array([ 131, 10, 10, 266, 266, 266, 206, 206, 206, 206, 206, โ\n", + "โ 65, 206, 65, 344, 65, 344, 147, 147, 147, 147, 1271, โ\n", + "โ 1489, 7907, 5274, 8871, 579, 107, 107, 5727, 1620, 41, 41, โ\n", + "โ 41, 5119, 6557, 6557, 3963, 3963, 3767, 3767, 26, 26, 26, โ\n", + "โ 26, 26, 26, 894, 736, 894, 10, 230, 230, 230, 10, โ\n", + "โ 206, 6032, 206, 6032, 147, 206, 206, 206, 206, 10, 206, โ\n", + "โ 206, 3252, 2966, 147, 147, 1278, 147, 147, 147, 147, 147, โ\n", + "โ 991, 991, 402, 10, 41, 245, 8969, 4860, 6323, 4099, 4868, โ\n", + "โ 2244, 2244, 250, 9768, 1307, 4388, 9351, 7895, 178, 166, 1620, โ\n", + "โ 1573, 6274, 7095, 7784, 210, 50, 10, 5, 2837, 3368, 3415, โ\n", + "โ 41, 41, 41, 3135, 2065, 3240, 4579, 9792, 3400, 210, 10, โ\n", + "โ 232, 401, 401, 9502, 2402, 2312, 2312, 3664, 1859, 5422, 1796, โ\n", + "โ 3917, 880, 59, 59, 28, 28, 28, 28, 28, 28, 28, โ\n", + "โ 1133, 3195, 6155, 6155, 3182, 8522, 8522, 3767, 3767, 44, 44, โ\n", + "โ 4775, 44, 44, 9839, 8142, 3753, 5986, 1540, 59, 59, 28, โ\n", + "โ 107, 589, 589, 10, 10, 27, 399, 287, 1460, 927, 9763, โ\n", + "โ 4600, 6185, 6185, 7304, 3301, 4149, 5293, 5293, 5293, 911, 1544, โ\n", + "โ 41, 41, 489, 850, 547, 210, 10, 5, 265, 401, 2739, โ\n", + "โ 2773, 2848, 3840, 3890, 3890, 1450, 9403, 4146, 4146, 190, 190, โ\n", + "โ 2280, 4394, 6022, 6022, 1517, 153, 71, 92, 92, 107, 1835, โ\n", + "โ 3454, 3454, 3700, 1948, 1948, 444, 1315, 171, 171, 171, 489, โ\n", + "โ 489, 489, 210, 10, 5, 5, 9367, 9367, 8498, 2745, 2745, โ\n", + "โ 6696, 2779, 2779, 2779, 2779, 9454, 9454, 9454, 9454, 8417, 7006, โ\n", + "โ 6672, 8270, 6018, 6018, 6139, 6139, 227, 6047, 441, 5647, 10, โ\n", + "โ 10, 1096, 1096, 10, 10, 266, 206, 206, 206, 206, 206, โ\n", + "โ 206, 10, 206, 206, 206, 206, 65, 65, 344, 147, 147, โ\n", + "โ 57, 41, 57, 604, 5851, 8604, 5673, 4119, 171, 41, 171, โ\n", + "โ 489, 59, 28, 28, 28, 28, 28, 10, 28, 6801, 9235, โ\n", + "โ 3198, 1858, 29, 3793, 1315, 171, 171, 171, 171, 171, 489, โ\n", + "โ 181, 181, 28, 28, 10, 337, 8963, 6412, 6412, 1001, 1001, โ\n", + "โ 4816, 5301, 6303, 5301, 1667, 8525, 8525, 7365, 7365, 1647, 1647, โ\n", + "โ 92, 59, 31, 31, 67, 10, 5, 8911, 5853, 5563, 5563, โ\n", + "โ 521, 41, 483, 9968, 5839, 3177, 1493, 1536, 393, 429, 429, โ\n", + "โ 41, 38, 4967, 774, 774, 6247, 6247, 6247, 9804, 620, 118, โ\n", + "โ 10, 27, 455, 9192, 8099, 8099, 389, 389, 9944, 7069, 2853, โ\n", + "โ 2853, 5640, 26, 122, 50, 210, 41, 10, 41, 8136, 4448, โ\n", + "โ 9329, 9329, 4236, 4236, 4236, 4236, 9104, 9104, 407, 407, 407, โ\n", + "โ 407, 6258, 6258, 8685, 6258, 1385, 5340, 1980, 6047, 1980, 4838, โ\n", + "โ 10, 1811, 278, 1811, 278, 3587, 278, 848, 174, 10, 8555]) โ\n", + "โ shape = (429,) โ\n", + "โ size = 429 โ\n", + "โ strides = (8,) โ\n", + "โ T = array([ 131, 10, 10, 266, 266, 266, 206, 206, 206, 206, 206, โ\n", + "โ 65, 206, 65, 344, 65, 344, 147, 147, 147, 147, 1271, โ\n", + "โ 1489, 7907, 5274, 8871, 579, 107, 107, 5727, 1620, 41, 41, โ\n", + "โ 41, 5119, 6557, 6557, 3963, 3963, 3767, 3767, 26, 26, 26, โ\n", + "โ 26, 26, 26, 894, 736, 894, 10, 230, 230, 230, 10, โ\n", + "โ 206, 6032, 206, 6032, 147, 206, 206, 206, 206, 10, 206, โ\n", + "โ 206, 3252, 2966, 147, 147, 1278, 147, 147, 147, 147, 147, โ\n", + "โ 991, 991, 402, 10, 41, 245, 8969, 4860, 6323, 4099, 4868, โ\n", + "โ 2244, 2244, 250, 9768, 1307, 4388, 9351, 7895, 178, 166, 1620, โ\n", + "โ 1573, 6274, 7095, 7784, 210, 50, 10, 5, 2837, 3368, 3415, โ\n", + "โ 41, 41, 41, 3135, 2065, 3240, 4579, 9792, 3400, 210, 10, โ\n", + "โ 232, 401, 401, 9502, 2402, 2312, 2312, 3664, 1859, 5422, 1796, โ\n", + "โ 3917, 880, 59, 59, 28, 28, 28, 28, 28, 28, 28, โ\n", + "โ 1133, 3195, 6155, 6155, 3182, 8522, 8522, 3767, 3767, 44, 44, โ\n", + "โ 4775, 44, 44, 9839, 8142, 3753, 5986, 1540, 59, 59, 28, โ\n", + "โ 107, 589, 589, 10, 10, 27, 399, 287, 1460, 927, 9763, โ\n", + "โ 4600, 6185, 6185, 7304, 3301, 4149, 5293, 5293, 5293, 911, 1544, โ\n", + "โ 41, 41, 489, 850, 547, 210, 10, 5, 265, 401, 2739, โ\n", + "โ 2773, 2848, 3840, 3890, 3890, 1450, 9403, 4146, 4146, 190, 190, โ\n", + "โ 2280, 4394, 6022, 6022, 1517, 153, 71, 92, 92, 107, 1835, โ\n", + "โ 3454, 3454, 3700, 1948, 1948, 444, 1315, 171, 171, 171, 489, โ\n", + "โ 489, 489, 210, 10, 5, 5, 9367, 9367, 8498, 2745, 2745, โ\n", + "โ 6696, 2779, 2779, 2779, 2779, 9454, 9454, 9454, 9454, 8417, 7006, โ\n", + "โ 6672, 8270, 6018, 6018, 6139, 6139, 227, 6047, 441, 5647, 10, โ\n", + "โ 10, 1096, 1096, 10, 10, 266, 206, 206, 206, 206, 206, โ\n", + "โ 206, 10, 206, 206, 206, 206, 65, 65, 344, 147, 147, โ\n", + "โ 57, 41, 57, 604, 5851, 8604, 5673, 4119, 171, 41, 171, โ\n", + "โ 489, 59, 28, 28, 28, 28, 28, 10, 28, 6801, 9235, โ\n", + "โ 3198, 1858, 29, 3793, 1315, 171, 171, 171, 171, 171, 489, โ\n", + "โ 181, 181, 28, 28, 10, 337, 8963, 6412, 6412, 1001, 1001, โ\n", + "โ 4816, 5301, 6303, 5301, 1667, 8525, 8525, 7365, 7365, 1647, 1647, โ\n", + "โ 92, 59, 31, 31, 67, 10, 5, 8911, 5853, 5563, 5563, โ\n", + "โ 521, 41, 483, 9968, 5839, 3177, 1493, 1536, 393, 429, 429, โ\n", + "โ 41, 38, 4967, 774, 774, 6247, 6247, 6247, 9804, 620, 118, โ\n", + "โ 10, 27, 455, 9192, 8099, 8099, 389, 389, 9944, 7069, 2853, โ\n", + "โ 2853, 5640, 26, 122, 50, 210, 41, 10, 41, 8136, 4448, โ\n", + "โ 9329, 9329, 4236, 4236, 4236, 4236, 9104, 9104, 407, 407, 407, โ\n", + "โ 407, 6258, 6258, 8685, 6258, 1385, 5340, 1980, 6047, 1980, 4838, โ\n", + "โ 10, 1811, 278, 1811, 278, 3587, 278, 848, 174, 10, 8555]) โ\n", + "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n", + "\n" + ], + "text/plain": [ + "\u001b[34mโญโ\u001b[0m\u001b[34mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ \u001b[0m\u001b[1;34m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'numpy.ndarray'\u001b[0m\u001b[1;34m>\u001b[0m\u001b[34m โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[34mโโฎ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;35mndarray\u001b[0m\u001b[1;36m(\u001b[0m\u001b[36mshape, \u001b[0m\u001b[33mdtype\u001b[0m\u001b[36m=\u001b[0m\u001b[35mfloat\u001b[0m\u001b[36m, \u001b[0m\u001b[33mbuffer\u001b[0m\u001b[36m=\u001b[0m\u001b[3;35mNone\u001b[0m\u001b[36m, \u001b[0m\u001b[33moffset\u001b[0m\u001b[36m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[36m,\u001b[0m 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"\u001b[34mโ\u001b[0m \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m65\u001b[0m, \u001b[1;36m65\u001b[0m, \u001b[1;36m344\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m41\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m604\u001b[0m, \u001b[1;36m5851\u001b[0m, \u001b[1;36m8604\u001b[0m, \u001b[1;36m5673\u001b[0m, \u001b[1;36m4119\u001b[0m, \u001b[1;36m171\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m41\u001b[0m, \u001b[1;36m171\u001b[0m, \u001b[1;36m489\u001b[0m, \u001b[1;36m59\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m28\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m6801\u001b[0m, \u001b[1;36m9235\u001b[0m, \u001b[1;36m3198\u001b[0m, \u001b[1;36m1858\u001b[0m, \u001b[1;36m29\u001b[0m, 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\u001b[1;36m4236\u001b[0m, \u001b[1;36m9104\u001b[0m, \u001b[1;36m9104\u001b[0m, \u001b[1;36m407\u001b[0m, \u001b[1;36m407\u001b[0m, \u001b[1;36m407\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m407\u001b[0m, \u001b[1;36m6258\u001b[0m, \u001b[1;36m6258\u001b[0m, \u001b[1;36m8685\u001b[0m, \u001b[1;36m6258\u001b[0m, \u001b[1;36m1385\u001b[0m, \u001b[1;36m5340\u001b[0m, \u001b[1;36m1980\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m6047\u001b[0m, \u001b[1;36m1980\u001b[0m, \u001b[1;36m4838\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m1811\u001b[0m, \u001b[1;36m278\u001b[0m, \u001b[1;36m1811\u001b[0m, \u001b[1;36m278\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m3587\u001b[0m, \u001b[1;36m278\u001b[0m, \u001b[1;36m848\u001b[0m, \u001b[1;36m174\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m8555\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mctypes\u001b[0m = \u001b[1m<\u001b[0m\u001b[1;95mnumpy.core._internal._ctypes\u001b[0m\u001b[39m object at \u001b[0m\u001b[1;36m0x7f63a46c7340\u001b[0m\u001b[1m>\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mdata\u001b[0m = \u001b[1m<\u001b[0m\u001b[1;95mmemory\u001b[0m\u001b[39m at \u001b[0m\u001b[1;36m0x7f62b7223ac0\u001b[0m\u001b[1m>\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mdtype\u001b[0m = \u001b[1;35mdtype\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'int64'\u001b[0m\u001b[1m)\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mflags\u001b[0m = C_CONTIGUOUS : \u001b[3;92mTrue\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m F_CONTIGUOUS : \u001b[3;92mTrue\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m OWNDATA : \u001b[3;91mFalse\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m WRITEABLE : \u001b[3;92mTrue\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m ALIGNED : \u001b[3;92mTrue\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m WRITEBACKIFCOPY : \u001b[3;91mFalse\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mflat\u001b[0m = \u001b[1m<\u001b[0m\u001b[1;95mnumpy.flatiter\u001b[0m\u001b[39m object at \u001b[0m\u001b[1;36m0x7a33500\u001b[0m\u001b[1m>\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mimag\u001b[0m = \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m0\u001b[0m, 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\u001b[3;33mitemsize\u001b[0m = \u001b[1;36m8\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mnbytes\u001b[0m = \u001b[1;36m3432\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mndim\u001b[0m = \u001b[1;36m1\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[3;33mreal\u001b[0m = \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m131\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m266\u001b[0m, \u001b[1;36m266\u001b[0m, \u001b[1;36m266\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[1;36m65\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m65\u001b[0m, \u001b[1;36m344\u001b[0m, \u001b[1;36m65\u001b[0m, \u001b[1;36m344\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m147\u001b[0m, 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semantic_prompt 429\n",
+ "\n"
+ ],
+ "text/plain": [
+ "semantic_prompt \u001b[1;36m429\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "coarse_prompt 2\n",
+ "\n"
+ ],
+ "text/plain": [
+ "coarse_prompt \u001b[1;36m2\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "fine_prompt 8\n",
+ "\n"
+ ],
+ "text/plain": [
+ "fine_prompt \u001b[1;36m8\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for x in barkifull.keys():\n",
+ " print(x, len(barkifull[x]))\n",
+ "\n",
+ "api.save_as_prompt(\"custom_prompts/seed37_bark_piece.npz\", barkifull)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logger.setLevel(\"DEBUG\")\n",
+ "generation.set_seed(37)\n",
+ "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n",
+ "\n",
+ "text = \"\"\"With me, brave the tumultuous seas, claim treasures untold, and send foes to their watery \n",
+ "doom\"\"\"\n",
+ "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n",
+ "\n",
+ "\n",
+ "fullgen, gen_audio_orig_segs = api.generate_audio(text, output_full=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ "set_seed Enabling deterministic algorithms generation.py:929\n", + "\n" + ], + "text/plain": [ + "set_seed Enabling deterministic algorithms \u001b]8;id=368965;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=553125;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#929\u001b\\\u001b[2m929\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
set_seed Set seed to 37 generation.py:944\n", + "\n" + ], + "text/plain": [ + "set_seed Set seed to \u001b[1;36m37\u001b[0m \u001b]8;id=647827;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=697756;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#944\u001b\\\u001b[2m944\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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'stable_mode_interval': 1, 'single_starting_seed': None, \n", + "'split_character_goal_length': 145, 'split_character_max_length': 190, 'split_character_jitter': 0, \n", + "'add_silence_between_segments': 0.0, 'split_each_text_prompt_by': None, \n", + "'split_each_text_prompt_by_value': None, 'extra_confused_travolta_mode': None, \n", + "'semantic_history_starting_weight': 1.0, 'semantic_history_future_weight': 1.0, \n", + "'semantic_prev_segment_weight': 0.5, 'coarse_history_starting_weight': 1.0, \n", + "'coarse_history_future_weight': 0.5, 'coarse_prev_segment_weight': 0.5, 'fine_history_starting_weight': \n", + "1.0, 'fine_history_future_weight': 0.0, 'fine_prev_segment_weight': 0.0, \n", + "'custom_audio_processing_function': None, 'use_smaller_models': False, 'semantic_temp': 0.7, \n", + "'semantic_top_k': None, 'semantic_top_p': None, 'semantic_min_eos_p': 2, 'semantic_max_gen_duration_s': \n", + "None, 'semantic_allow_early_stop': True, 'semantic_use_kv_caching': True, 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() Segment Breakdown \n", + "โโโโโณโโโโโโโโณโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n", + "โ # โ Words โ Time Est โ Splitting long text aiming for 145 chars max 190 โ\n", + "โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ\n", + "โ 1 โ 16 โ 6.40 s โ With me, brave the tumultuous seas, claim treasures untold, and send foes to their โ\n", + "โ โ โ 94 chars โ watery doom โ\n", + "โโโโโดโโโโโโโโดโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n", + "\n" + ], + "text/plain": [ + "\u001b[3m () Segment Breakdown \u001b[0m\n", + "โโโโโณโโโโโโโโณโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n", + "โ\u001b[1m \u001b[0m\u001b[1m#\u001b[0m\u001b[1m \u001b[0mโ\u001b[1m \u001b[0m\u001b[1mWords\u001b[0m\u001b[1m \u001b[0mโ\u001b[1m \u001b[0m\u001b[1mTime Est\u001b[0m\u001b[1m \u001b[0mโ\u001b[1m \u001b[0m\u001b[1mSplitting long text aiming for 145 chars max 190 \u001b[0m\u001b[1m \u001b[0mโ\n", + "โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ\n", + "โ\u001b[35m \u001b[0m\u001b[35m1\u001b[0m\u001b[35m \u001b[0mโ\u001b[32m \u001b[0m\u001b[32m16 \u001b[0m\u001b[32m \u001b[0mโ\u001b[32m \u001b[0m\u001b[32m6.40 s \u001b[0m\u001b[32m \u001b[0mโ With me, brave the tumultuous seas, claim treasures untold, and send foes to their โ\n", + "โ\u001b[35m \u001b[0mโ\u001b[32m \u001b[0mโ\u001b[32m \u001b[0m\u001b[32m94 chars\u001b[0m\u001b[32m \u001b[0mโ watery doom โ\n", + "โโโโโดโโโโโโโโดโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
write_seg_npz .npz saved to long/With_me_brave_t-SPK-hark_woman_3.wav_initial_prompt.npz api.py:316\n", + "\n" + ], + "text/plain": [ + "write_seg_npz .npz saved to long/With_me_brave_t-SPK-hark_woman_3.wav_initial_prompt.npz \u001b]8;id=827442;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=113095;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#316\u001b\\\u001b[2m316\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "segment_text: With me, brave the tumultuous seas, claim treasures untold, and send foes to their watery doom\n" + ] + }, + { + "data": { + "text/html": [ + "
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\u001b[1;36m59\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m107\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m107\u001b[0m, \u001b[1;36m385\u001b[0m, \u001b[1;36m2465\u001b[0m, \u001b[1;36m230\u001b[0m, \u001b[1;36m131\u001b[0m, \u001b[1;36m230\u001b[0m, \u001b[1;36m2443\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m193\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m206\u001b[0m, \u001b[1;36m517\u001b[0m, \u001b[1;36m147\u001b[0m, 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\u001b[1;36m5\u001b[0m, \u001b[1;36m5\u001b[0m, \u001b[1;36m2837\u001b[0m, \u001b[1;36m5853\u001b[0m, \u001b[1;36m5563\u001b[0m, \u001b[1;36m5819\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m402\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m6821\u001b[0m, \u001b[1;36m3973\u001b[0m, \u001b[1;36m5265\u001b[0m, \u001b[1;36m1282\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m27\u001b[0m, \u001b[1;36m429\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m6945\u001b[0m, \u001b[1;36m8234\u001b[0m, \u001b[1;36m5838\u001b[0m, \u001b[1;36m5451\u001b[0m, \u001b[1;36m3139\u001b[0m, \u001b[1;36m8512\u001b[0m, \u001b[1;36m5611\u001b[0m, \u001b[1;36m8092\u001b[0m, \u001b[1;36m8092\u001b[0m, \u001b[1;36m8695\u001b[0m, \u001b[1;36m8688\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m3694\u001b[0m, \u001b[1;36m6069\u001b[0m, \u001b[1;36m288\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m4331\u001b[0m, \u001b[1;36m4418\u001b[0m, \u001b[1;36m7265\u001b[0m, \u001b[1;36m7265\u001b[0m, \u001b[1;36m1262\u001b[0m, \u001b[1;36m26\u001b[0m, \u001b[1;36m122\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m50\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m5\u001b[0m, \u001b[1;36m652\u001b[0m, \u001b[1;36m8921\u001b[0m, \u001b[1;36m8921\u001b[0m, \u001b[1;36m9245\u001b[0m, \u001b[1;36m8141\u001b[0m, \u001b[1;36m8141\u001b[0m, \u001b[1;36m6997\u001b[0m, \u001b[1;36m6997\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m3\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m27\u001b[0m, \u001b[1;36m27\u001b[0m, \u001b[1;36m232\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m403\u001b[0m, \u001b[1;36m403\u001b[0m, \u001b[1;36m9563\u001b[0m, \u001b[1;36m9563\u001b[0m, \u001b[1;36m481\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m481\u001b[0m, \u001b[1;36m481\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m107\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m107\u001b[0m, \u001b[1;36m385\u001b[0m, \u001b[1;36m9355\u001b[0m, \u001b[1;36m2672\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m131\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m266\u001b[0m, \u001b[1;36m1373\u001b[0m, \u001b[1;36m1572\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m7567\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m2966\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m2009\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m2009\u001b[0m, \u001b[1;36m8735\u001b[0m, \u001b[1;36m8735\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m5128\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m9817\u001b[0m, \u001b[1;36m9817\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'coarse_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[33m...\u001b[0m, \u001b[1;36m479\u001b[0m, \u001b[1;36m850\u001b[0m, \u001b[1;36m865\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m363\u001b[0m, \u001b[1;36m363\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m430\u001b[0m, \u001b[1;36m132\u001b[0m, \u001b[1;36m648\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'fine_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m479\u001b[0m, \u001b[1;36m850\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[1;36m865\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m \u001b[1;36m363\u001b[0m, \u001b[1;36m363\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m430\u001b[0m, \u001b[1;36m132\u001b[0m, \u001b[1;36m648\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m1000\u001b[0m, \u001b[1;36m36\u001b[0m, \u001b[1;36m786\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m127\u001b[0m, \u001b[1;36m62\u001b[0m, \u001b[1;36m910\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[33m...\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m1011\u001b[0m, \u001b[1;36m939\u001b[0m, 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\u001b[1;36m385\u001b[0m, \u001b[1;36m9355\u001b[0m, \u001b[1;36m2672\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m131\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m266\u001b[0m, \u001b[1;36m1373\u001b[0m, \u001b[1;36m1572\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m7567\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m2966\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m2009\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m2009\u001b[0m, \u001b[1;36m8735\u001b[0m, \u001b[1;36m8735\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m5128\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m9817\u001b[0m, \u001b[1;36m9817\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'coarse_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[33m...\u001b[0m, \u001b[1;36m479\u001b[0m, \u001b[1;36m850\u001b[0m, \u001b[1;36m865\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m363\u001b[0m, \u001b[1;36m363\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m430\u001b[0m, \u001b[1;36m132\u001b[0m, \u001b[1;36m648\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'fine_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m479\u001b[0m, \u001b[1;36m850\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[1;36m865\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m \u001b[1;36m363\u001b[0m, \u001b[1;36m363\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m430\u001b[0m, \u001b[1;36m132\u001b[0m, \u001b[1;36m648\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m1000\u001b[0m, \u001b[1;36m36\u001b[0m, \u001b[1;36m786\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m127\u001b[0m, \u001b[1;36m62\u001b[0m, \u001b[1;36m910\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[33m...\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m1011\u001b[0m, \u001b[1;36m939\u001b[0m, \u001b[1;36m939\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m521\u001b[0m, \u001b[1;36m760\u001b[0m, \u001b[1;36m632\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m \u001b[1;36m562\u001b[0m, \u001b[1;36m782\u001b[0m, \u001b[1;36m748\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m413\u001b[0m, \u001b[1;36m709\u001b[0m, \u001b[1;36m528\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m \u001b[1;36m948\u001b[0m, \u001b[1;36m518\u001b[0m, \u001b[1;36m975\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m285\u001b[0m, \u001b[1;36m214\u001b[0m, \u001b[1;36m42\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m, \u001b[33mdtype\u001b[0m=\u001b[35mint32\u001b[0m\u001b[1m)\u001b[0m\u001b[1m}\u001b[0m, \u001b[32m'prompt_file'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'split_input_into_separate_prompts_by'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'split_input_into_separate_prompts_by_value'\u001b[0m: 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\u001b[3;91mFalse\u001b[0m, \u001b[32m'GLOBAL_ENABLE_MPS'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'USE_SMALL_MODELS'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'OFFLOAD_CPU'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'text_temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'waveform_temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'confused_travolta_mode'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;91mFalse\u001b[0m, \u001b[32m'seed'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'stable_mode_interval'\u001b[0m: \u001b[1;36m1\u001b[0m, \u001b[32m'single_starting_seed'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'split_character_goal_length'\u001b[0m: \u001b[1;36m145\u001b[0m, \u001b[32m'split_character_max_length'\u001b[0m: \u001b[1;36m190\u001b[0m, \u001b[32m'split_character_jitter'\u001b[0m: \u001b[1;36m0\u001b[0m, \u001b[2m \u001b[0m\n", + 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generate_coarse {'x_semantic': array([3589, 17, 298, 9182, 6948, 6948, 92, 28, 148, 234, generation.py:460\n", + "3825, \n", + " 5947, 171, 41, 171, 181, 59, 28, 28, 7313, 41, 1298, \n", + " 8407, 9054, 9054, 1896, 1896, 7433, 4869, 7858, 5566, 812, 23, \n", + " 171, 492, 492, 28, 28, 107, 8218, 1879, 1879, 2359, 8141, \n", + " 8141, 6997, 6997, 6997, 2199, 620, 59, 59, 28, 28, 28, \n", + " 28, 28, 28, 107, 385, 2954, 9381, 6319, 230, 10, 8849, \n", + " 7010, 7010, 441, 441, 5128, 41, 43, 448, 8747, 8747, 9224, \n", + " 9224, 6819, 6819, 2313, 41, 27, 474, 2928, 8060, 7443, 50, \n", + " 10, 27, 5248, 9005, 3675, 3675, 5932, 5932, 9768, 9768, 9945, \n", + " 26, 26, 26, 288, 262, 131, 10, 27, 282, 385, 385, \n", + " 1863, 2465, 2465, 230, 206, 230, 230, 230, 206, 206, 3317, \n", + " 206, 3317, 3317, 206, 206, 206, 206, 4653, 5196, 206, 206, \n", + " 206, 206, 5196, 206, 206, 65, 822, 822, 206, 1278, 1278, \n", + " 147, 147, 8395, 8395, 383, 5064, 10, 5, 651, 1169, 3504, \n", + " 3504, 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write_seg_npz .npz saved to long/With_me_brave_t-SPK-hark_woman_3.wav api.py:316\n", + "\n" + ], + "text/plain": [ + "write_seg_npz .npz saved to long/With_me_brave_t-SPK-hark_woman_3.wav \u001b]8;id=9210;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=7946;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#316\u001b\\\u001b[2m316\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to long/With_me_brave_t-SPK-hark_woman_3.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to long/With_me_brave_t-SPK-hark_woman_3.wav \u001b]8;id=71943;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=741508;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved to long/With_me_brave_t-SPK-hark_woman_3.wav\n" + ] + } + ], + "source": [ + "kwargs['output_dir'] = \"long\"\n", + "generation.set_seed(37)\n", + "full_generation_segments, audio_arr_segments, final_filename_will_be = api.generate_audio_long(**kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "
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Run inspect(inspect) for options. โ\n", + "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n", + "\n" + ], + "text/plain": [ + "\u001b[34mโญโ\u001b[0m\u001b[34mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ \u001b[0m\u001b[1;34m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'list'\u001b[0m\u001b[1;34m>\u001b[0m\u001b[34m โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[34mโโฎ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[36mBuilt-in mutable sequence.\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[32mโญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[32mโ\u001b[0m \u001b[1m[\u001b[0m \u001b[32mโ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[32mโ\u001b[0m \u001b[2;32mโ \u001b[0m\u001b[1m{\u001b[0m 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set_seed Enabling deterministic algorithms generation.py:929\n", + "\n" + ], + "text/plain": [ + "set_seed Enabling deterministic algorithms \u001b]8;id=679719;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=739196;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#929\u001b\\\u001b[2m929\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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generate_audio_barki {'text': 'With me, brave the tumultuous seas, claim treasures untold, and send foes api.py:362\n", + "to their watery \\ndoom', 'kwargs': {'text_prompt': 'With me, brave the tumultuous seas, claim treasures \n", + "untold, and send foes to their watery \\ndoom', 'list_speakers': None, 'dry_run': False, \n", + "'text_splits_only': False, 'history_prompt': None, 'prompt_file': None, \n", + "'split_input_into_separate_prompts_by': None, 'split_input_into_separate_prompts_by_value': None, \n", + "'always_save_speaker': True, 'output_iterations': 1, 'output_filename': None, 'output_dir': 'seed37', \n", + "'hoarder_mode': True, 'extra_stats': False, 'text_use_gpu': True, 'text_use_small': False, \n", + "'coarse_use_gpu': True, 'coarse_use_small': False, 'fine_use_gpu': True, 'fine_use_small': False, \n", + "'codec_use_gpu': True, 'force_reload': False, 'GLOBAL_ENABLE_MPS': None, 'USE_SMALL_MODELS': None, \n", + "'OFFLOAD_CPU': None, 'text_temp': 0.7, 'waveform_temp': 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generate_text_semantic {'text': 'With me, brave the tumultuous seas, claim treasures untold, and generation.py:302\n", + "send foes to their watery \\ndoom', 'history_prompt': None, 'temp': 0.7, 'top_k': None, 'top_p': \n", + "None, 'silent': False, 'min_eos_p': 0.2, 'max_gen_duration_s': None, 'allow_early_stop': True, \n", + "'use_kv_caching': True} \n", + "\n" + ], + "text/plain": [ + "generate_text_semantic \u001b[1m{\u001b[0m\u001b[32m'text'\u001b[0m: \u001b[32m'With me, brave the tumultuous seas, claim treasures untold, and \u001b[0m \u001b]8;id=295619;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=970216;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#302\u001b\\\u001b[2m302\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[32msend foes to their watery \\ndoom'\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'top_k'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'top_p'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'min_eos_p'\u001b[0m: \u001b[1;36m0.2\u001b[0m, \u001b[32m'max_gen_duration_s'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'allow_early_stop'\u001b[0m: \u001b[3;92mTrue\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'use_kv_caching'\u001b[0m: \u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 100/100 [00:05<00:00, 17.48it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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generate_fine {'x_coarse_gen': array([[876, 738, 855, ..., 408, 408, 738], generation.py:626\n", + " [700, 841, 544, ..., 424, 424, 544]]), 'history_prompt': None, 'temp': 0.5, 'silent': \n", + "False} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m876\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m855\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m738\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=433174;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=78803;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m\u001b[1;36m700\u001b[0m, \u001b[1;36m841\u001b[0m, \u001b[1;36m544\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m544\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 1/1 [00:04<00:00, 4.00s/it]\n" + ] + } + ], + "source": [ + "text = \"\"\"With me, brave the tumultuous seas, claim treasures untold, and send foes to their watery \n", + "doom\"\"\"\n", + "kwargs[\"semantic_min_eos_p\"] = 0.2 \n", + "kwargs[\"text_prompt\"] = text\n", + "generation.set_seed(37)\n", + "kwargs['output_full'] = True\n", + "kwargs['output_dir'] = \"seed37\"\n", + "kwargs[\"history_prompt\"] = None\n", + "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n", + "barkifull, audio_arr_segments_barki = api.generate_audio_barki(text, **kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "coarse_prompt 2\n",
+ "\n"
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+ "metadata": {},
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#inspect(barkifull)\n",
+ "\n",
+ "for x in barkifull.keys():\n",
+ " print(f\"{x} {len(barkifull[x])}\")\n",
+ "\n",
+ "api.save_as_prompt(\"custom_speakers/seed37_barki.npz\",barkifull)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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generate_fine {'x_coarse_gen': array([[1019, 1019, 1019, ..., 432, 432, 724], generation.py:626\n", + " [ 829, 841, 928, ..., 894, 894, 765]]), 'history_prompt': None, 'temp': 0.5, \n", + "'silent': True} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m1019\u001b[0m, \u001b[1;36m1019\u001b[0m, \u001b[1;36m1019\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m432\u001b[0m, \u001b[1;36m432\u001b[0m, \u001b[1;36m724\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=295619;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=970216;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m \u001b[1;36m829\u001b[0m, \u001b[1;36m841\u001b[0m, \u001b[1;36m928\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m894\u001b[0m, \u001b[1;36m894\u001b[0m, \u001b[1;36m765\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'silent'\u001b[0m: \u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "generation.set_seed(37)\n", + "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n", + "\n", + "\n", + "\n", + "#fullgen, gen_audio_orig_segs = api.generate_audio(text, output_full=True, **kwargs)\n", + "\n", + "\n", + "fullgen, gen_audio_orig_segs = api.generate_audio(text, output_full=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "
k: semantic_prompt\n", + "\n" + ], + "text/plain": [ + "k: semantic_prompt\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
len: 390\n",
+ "\n"
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+ "len: \u001b[1;36m390\u001b[0m\n"
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "k: coarse_prompt\n", + "\n" + ], + "text/plain": [ + "k: coarse_prompt\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
len: 2\n",
+ "\n"
+ ],
+ "text/plain": [
+ "len: \u001b[1;36m2\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "k: fine_prompt\n", + "\n" + ], + "text/plain": [ + "k: fine_prompt\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+ "\n"
+ ],
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+ "len: \u001b[1;36m8\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for k in fullgen:\n",
+ " print(f\"k: {k}\")\n",
+ " print(f\"len: {len(fullgen[k])}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rendering samples for speakers in: custom_speakers\n",
+ " Rendering audio for custom_speakers/hark_woman.npz to custom_speakers/hark_woman_1.wav\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "write_seg_wav .wav saved to custom_speakers/hark_woman_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/hark_woman_1.wav \u001b]8;id=552246;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=649487;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rendering audio for custom_speakers/hark1.npz to custom_speakers/hark1_1.wav\n" + ] + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to custom_speakers/hark1_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/hark1_1.wav \u001b]8;id=9210;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=7946;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rendering audio for custom_speakers/en_fiery.npz to custom_speakers/en_fiery_1.wav\n" + ] + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav \u001b]8;id=71943;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=741508;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "api.render_npz_samples(\"custom_speakers\", start_from=\"semantic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "
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321, 41, 483, 6731, 6731, \n", + " 6295, 6295, 5806, 578, 4, 4, 4, 1602, 660, 230, 10, \n", + " 56, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 147, 193, 2009, 2009, 2659, 206, 147, 2009, 2403, 147, 2009]), 'history_prompt': \n", + "None, 'temp': 0.7, 'top_k': None, 'top_p': None, 'silent': False, 'max_coarse_history': 630, \n", + "'sliding_window_len': 60, 'use_kv_caching': False} \n", + "\n" + ], + "text/plain": [ + "generate_coarse \u001b[1m{\u001b[0m\u001b[32m'x_semantic'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m147\u001b[0m, \u001b[1;36m6242\u001b[0m, \u001b[1;36m302\u001b[0m, \u001b[1;36m1106\u001b[0m, \u001b[1;36m401\u001b[0m, \u001b[1;36m2739\u001b[0m, \u001b[1;36m9511\u001b[0m, \u001b[1;36m9924\u001b[0m, \u001b[1;36m9924\u001b[0m, \u001b[1;36m9924\u001b[0m, 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\u001b[1;36m8851\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m27\u001b[0m, \u001b[1;36m1041\u001b[0m, \u001b[1;36m59\u001b[0m, \u001b[1;36m28\u001b[0m, \u001b[1;36m107\u001b[0m, \u001b[1;36m5558\u001b[0m, \u001b[1;36m957\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m4304\u001b[0m, \u001b[1;36m4304\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m836\u001b[0m, \u001b[1;36m9925\u001b[0m, \u001b[1;36m50\u001b[0m, \u001b[1;36m100\u001b[0m, \u001b[1;36m3208\u001b[0m, \u001b[1;36m27\u001b[0m, \u001b[1;36m3845\u001b[0m, \u001b[1;36m5660\u001b[0m, \u001b[1;36m2921\u001b[0m, \u001b[1;36m298\u001b[0m, \u001b[1;36m17\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m2113\u001b[0m, \u001b[1;36m3745\u001b[0m, \u001b[1;36m5218\u001b[0m, \u001b[1;36m117\u001b[0m, \u001b[1;36m107\u001b[0m, \u001b[1;36m8745\u001b[0m, \u001b[1;36m321\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m483\u001b[0m, \u001b[1;36m6731\u001b[0m, \u001b[1;36m6731\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m6295\u001b[0m, \u001b[1;36m6295\u001b[0m, \u001b[1;36m5806\u001b[0m, \u001b[1;36m578\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m1602\u001b[0m, \u001b[1;36m660\u001b[0m, \u001b[1;36m230\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m56\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m147\u001b[0m, \u001b[1;36m193\u001b[0m, \u001b[1;36m2009\u001b[0m, \u001b[1;36m2009\u001b[0m, \u001b[1;36m2659\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m2009\u001b[0m, \u001b[1;36m2403\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m2009\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'top_k'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'top_p'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'max_coarse_history'\u001b[0m: \u001b[1;36m630\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'sliding_window_len'\u001b[0m: \u001b[1;36m60\u001b[0m, \u001b[32m'use_kv_caching'\u001b[0m: \u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 35/35 [00:31<00:00, 1.09it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
generate_fine {'x_coarse_gen': array([[408, 408, 408, ..., 408, 408, 62], generation.py:626\n", + " [424, 424, 424, ..., 518, 518, 424]]), 'history_prompt': None, 'temp': 0.5, 'silent': \n", + "True} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m62\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=368965;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=553125;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m\u001b[1;36m424\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m518\u001b[0m, \u001b[1;36m518\u001b[0m, \u001b[1;36m424\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rendering audio for custom_speakers/en_fiery.npz to custom_speakers/en_fiery_1.wav\n" + ] + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav \u001b]8;id=763391;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14160;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "api.render_npz_samples(\"custom_speakers\", start_from=\"semantic_prompt\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Audio(audio_arr_segments_barki, rate=generation.SAMPLE_RATE) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generation.preload_models()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"\"\"\n", + "Hark! I, the phantom visage of Edward Teach, rise from the \n", + "abyss, forever bound to the briny depths. With me, brave the tumultuous seas, claim treasures untold,\n", + "and send foes to their watery doom.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "history_prompt = np.load(\"pirates/base/pirate.npz\")\n", + "from rich import inspect" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for key in history_prompt.keys():\n", + " length = len(history_prompt[key])\n", + " print(f\"key: {key}, length: {length}\")\n", + " inspect(history_prompt[key], title=f\"{key} ({length})\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)\n", + "\n", + "semantic_prompt = history_prompt[\"semantic_prompt\"]\n", + "midpoint = len(semantic_prompt) // 2\n", + "new_semantic_first_half = semantic_prompt[:midpoint].astype(np.int32)\n", + "\n", + "\n", + "Instead I would like new_semantic to be half the size of x_semantic_history, just the last half of the space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " if length > 0:\n", + " for sub_key in history_prompt[key].keys():\n", + " print(f\" {sub_key}={history_prompt[key][sub_key]}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "gen_minor_variants = 20\n", + "import random\n", + "\n", + "npz_file = \"pirate.npz\"\n", + "npz_directory = \"pirates/base\"\n", + "npz_filepath = \"pirates/base/pirate.npz\"\n", + "\n", + "semantic_prompt = history_prompt[\"semantic_prompt\"]\n", + "original_semantic_prompt = semantic_prompt.copy()\n", + "starting_point = 128\n", + "ending_point = len(semantic_prompt) - starting_point\n", + "\n", + "\n", + "\n", + "points = np.linspace(starting_point, ending_point, gen_minor_variants)\n", + " \n", + "for starting_point in points:\n", + " starting_point = int(starting_point)\n", + " print(starting_point)\n", + "\n", + " new_semantic_from_beginning = original_semantic_prompt[:starting_point].astype(np.int32)\n", + " new_semantic_from_ending = original_semantic_prompt[starting_point:].astype(np.int32)\n", + "\n", + " for semantic_prompt in [new_semantic_from_beginning, new_semantic_from_ending]:\n", + " \n", + " print(f\"len(semantic_prompt): {len(semantic_prompt)}\")\n", + " print(f\"starting_point: {starting_point}, ending_poinst: {ending_point}\") \n", + "\n", + " temp_coarse = random.uniform(0.5, 0.9)\n", + " top_k_coarse = None if random.random() < 1/3 else random.randint(50, 100)\n", + " top_p_coarse = None if random.random() < 1/3 else random.uniform(0.8, 0.95)\n", + "\n", + " max_coarse_history_options = [630, random.randint(500, 630), random.randint(60, 500)]\n", + " max_coarse_history = random.choice(max_coarse_history_options)\n", + "\n", + " coarse_tokens = generation.generate_coarse(semantic_prompt, temp=temp_coarse, top_k=top_k_coarse, top_p=top_p_coarse, max_coarse_history=max_coarse_history)\n", + "\n", + " temp_fine = random.uniform(0.3, 0.7)\n", + " fine_tokens = generation.generate_fine(coarse_tokens, temp=temp_fine)\n", + "\n", + " history_prompt_render_variant = {\"semantic_prompt\": semantic_prompt, \"coarse_prompt\": coarse_tokens, \"fine_prompt\": fine_tokens}\n", + "\n", + " try:\n", + " audio_arr = generation.codec_decode(fine_tokens)\n", + " base_output_filename = os.path.splitext(npz_file)[0] + f\"_var_{i}.wav\"\n", + " output_filepath = os.path.join(npz_directory, base_output_filename)\n", + " output_filepath = api.generate_unique_filepath(output_filepath)\n", + " print(f\" Rendering minor variant voice audio for {npz_filepath} to {output_filepath}\")\n", + " api.write_seg_wav(output_filepath, audio_arr)\n", + "\n", + " api.write_seg_npz(output_filepath, history_prompt_render_variant)\n", + " except:\n", + " print(f\"
[\n", + " 'How many fucken morons can wave his arms and keep people in tempo? ',\n", + " \"I was there to push people beyond what's expected of them. \",\n", + " 'I believe that is an absolute necessity. ',\n", + " \"Otherwise we're depriving the world of the next Louis Armstrong, or the next Charlie Parker. \",\n", + " 'Have I told you that story about how Charlie Parker became Charlie Parker? ',\n", + " \"Parker's a young kid, pretty good on the Sax, gets up to play at a cutting session, and well, he fucks it \n", + "up. \",\n", + " 'And Jones nearly decapitates him for it, throws a cymbal at his head. '\n", + "]\n", + "\n" + ], + "text/plain": [ + "\u001b[1m[\u001b[0m\n", + " \u001b[32m'How many fucken morons can wave his arms and keep people in tempo? '\u001b[0m,\n", + " \u001b[32m\"I was there to push people beyond what's expected of them. \"\u001b[0m,\n", + " \u001b[32m'I believe that is an absolute necessity. '\u001b[0m,\n", + " \u001b[32m\"Otherwise we're depriving the world of the next Louis Armstrong, or the next Charlie Parker. \"\u001b[0m,\n", + " \u001b[32m'Have I told you that story about how Charlie Parker became Charlie Parker? '\u001b[0m,\n", + " \u001b[32m\"Parker's a young kid, pretty good on the Sax, gets up to play at a cutting session, and well, he fucks it \u001b[0m\n", + "\u001b[32mup. \"\u001b[0m,\n", + " \u001b[32m'And Jones nearly decapitates him for it, throws a cymbal at his head. '\u001b[0m\n", + "\u001b[1m]\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(split_by_sentence(text))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(list(text))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(split_by_words(text))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['How many fucken morons can wave his arms and ke', 'ep pe', 'ople in te', 'mpo? \\nI was there to push pe', \"ople beyond what's e\", \"xpected of them. \\nI believe that is an absolute necessity. \\nOtherwise we're d\", \"epriving the world of the next Louis Armstrong, \\nor the next Charlie Parker. \\nHave I told you that story about how Charlie Parker became Charlie Parker?\\nParker's a young kid, pretty good on the Sax, \\ngets\", 'up to play at a cutting session, \\nand well, he fucks it', 'up. \\nAnd Jones nearly dec', 'apitates him for it, throws a cymbal at his head.']" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "split_text(text, split_type=\"regex\", split_type_string=f\"[\\w]p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
[]\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pprint(split_text(text, split_type=\"sentence\", split_type_quantity=2, split_type_value_type=\"line\"))\n",
+ "\n",
+ "process_text_by\n",
+ "# process by words/lines split into chunks of size X words/lines"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text = \"\"\"\n",
+ "It's like rain on your wedding day.\n",
+ "It's a free ride when you've already paid.\n",
+ "It's the good advice that you just didn't take.\n",
+ "And who would've thought? It figures.\n",
+ "It's like rain on your wedding day.\n",
+ "It's a free ride when you've already paid.\n",
+ "It's the good advice that you just didn't take.\n",
+ "And who would've thought? It figures.\n",
+ "\"\"\"\n",
+ "\n",
+ "result = split_text(text, split_type=\"line\", split_type_quantity=3)\n",
+ "print(result)\n",
+ "\n",
+ "result = split_text(text, split_type=\"word\", split_type_quantity=4)\n",
+ "print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = split_text(text, split_type=\"string\", split_type_quantity=23, split_type_string = \"the\")\n",
+ "print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(split_by_string(\"The dog went to the dog store and ate\", split_type=\"string\", split_type_quantity=23, split_type_string=\"dog\"))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "split_text(text, split_type=\"line\", split_type_quantity=3, split_type_value_type=\"word\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [
+ "HJQ4TI0_Qowr",
+ "FHlxNCt3QwIr",
+ "uuqkchecXnbm"
+ ],
+ "provenance": []
+ },
+ "gpuClass": "standard",
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
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diff --git a/notebooks/Bark_base_test.ipynb b/notebooks/Bark_base_test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8dd449abf23de23f30b3722a065b1060b8aab46a
--- /dev/null
+++ b/notebooks/Bark_base_test.ipynb
@@ -0,0 +1,7478 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HJQ4TI0_Qowr"
+ },
+ "source": [
+ "## Setup Notebook, Install\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "r8wG_tIaOV0Q",
+ "outputId": "6ea4ba04-3578-41e7-f9b5-4a44ca654452"
+ },
+ "outputs": [],
+ "source": [
+ "!git clone https://github.com/JonathanFly/bark.git\n",
+ "%cd bark\n",
+ "!pip install -r requirements-pip.txt\n",
+ "!pip install encodec rich-argparse\n",
+ "!pip install librosa pydub"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FHlxNCt3QwIr"
+ },
+ "source": [
+ "## Run Once Per Notebook Restart (if files still exist)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jKTvqvVkOwXM"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import time\n",
+ "from bark_infinity import config\n",
+ "import numpy as np\n",
+ "\n",
+ "logger = config.logger\n",
+ "logger.setLevel(\"WARNING\")\n",
+ "\n",
+ "from bark_infinity import generation\n",
+ "from bark_infinity import api\n",
+ "\n",
+ "import rich\n",
+ "from rich import print\n",
+ "from rich import pretty\n",
+ "from rich.pretty import pprint\n",
+ "from rich import inspect\n",
+ "\n",
+ "from pydub import AudioSegment\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Audio\n",
+ "from io import BytesIO\n",
+ "\n",
+ "# None of this code, just fiddlign with Colab stuff\n",
+ "# Just to save Colab with outputs and float32 wavs are GIGANTO\n",
+ "# actually this doesn't work, the iPython widget converts it back to float32? or I messed up\n",
+ "\n",
+ "def display_audio_int16_but(audio_arr_segments, file_name, sample_rate=generation.SAMPLE_RATE, width='200px'):\n",
+ " file_name_label = widgets.Label(value=f\"Playing: {file_name}\")\n",
+ " file_name_label.layout.width = width\n",
+ " audio_data_int16 = audio_arr_segments\n",
+ " if isinstance(audio_data_int16, list):\n",
+ " audio_data_int16 = np.concatenate(audio_data_int16) \n",
+ "\n",
+ " #audio_data_int16 = np.int16(audio_data_int16 * np.iinfo(np.int16).max)\n",
+ "\n",
+ "\n",
+ " audio_widget = Audio(audio_data_int16, rate=sample_rate)\n",
+ " display(file_name_label, audio_widget)\n",
+ " \n",
+ "\n",
+ "def on_button_click(button):\n",
+ " audio_data, sample_rate = librosa.load(button.wav_path, sr=None)\n",
+ " file_name = os.path.basename(button.wav_path)\n",
+ " display_audio_int16_but(audio_data,file_name, sample_rate)\n",
+ "\n",
+ "\n",
+ "def display_wav_files(directory):\n",
+ " subdirs, wav_files = [], []\n",
+ " \n",
+ " for item in os.listdir(directory):\n",
+ " item_path = os.path.join(directory, item)\n",
+ " \n",
+ " if os.path.isfile(item_path) and item_path.endswith('.wav'):\n",
+ " wav_files.append(item_path)\n",
+ " elif os.path.isdir(item_path):\n",
+ " subdirs.append(item_path)\n",
+ "\n",
+ " wav_files.sort(key=lambda x: os.path.basename(x))\n",
+ "\n",
+ " for wav_file in wav_files:\n",
+ "\n",
+ " filename = os.path.basename(wav_file)\n",
+ " print(f\" {filename}\")\n",
+ " button = widgets.Button(description=f\"Play {filename}\")\n",
+ " button.wav_path = wav_file \n",
+ " button.on_click(on_button_click)\n",
+ " display(button)\n",
+ "\n",
+ " for subdir in sorted(subdirs):\n",
+ " print(f\"<{subdir}>\")\n",
+ " display_wav_files(subdir)\n",
+ "\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OTRtNy1xT1sI"
+ },
+ "source": [
+ "## Generate\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LKLe_gYkQ59l"
+ },
+ "source": [
+ "### Choose Bark Models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 354,
+ "referenced_widgets": [
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+ "897fde99f5d248dcbe917779b5cc2526",
+ "218c5f11039f43bb8496a6e21efcee91",
+ "36247efa11dc494c8efc0afafaee5e7b",
+ "bd8681508bcb4d7799036010537633ff",
+ "dba563834f79488ab1516806c5df19b5",
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+ ]
+ },
+ "id": "QLa2jPOUjSyd",
+ "outputId": "50727264-7675-4ac5-b539-735f31fb4a11"
+ },
+ "outputs": [],
+ "source": [
+ "generation.OFFLOAD_CPU = True # On your home system set to True probably, but Colab GPU should have plenty of memory for all three models\n",
+ "generation.preload_models() # Optional, will lazy load if not preloaded. First time run in New Colab has to download models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "text = \"\"\"\n",
+ "Hark! I, the phantom visage of Edward Teach, rise from the \n",
+ "abyss, forever bound to the briny depths. With me, brave the tumultuous seas, claim treasures untold,\n",
+ "and send foes to their watery doom.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import time\n",
+ "from bark_infinity import config\n",
+ "import numpy as np\n",
+ "\n",
+ "logger = config.logger\n",
+ "logger.setLevel(\"WARNING\")\n",
+ "\n",
+ "from bark_infinity import generation\n",
+ "from bark_infinity import api\n",
+ "\n",
+ "import rich\n",
+ "from rich import print\n",
+ "from rich import pretty\n",
+ "from rich.pretty import pprint\n",
+ "from rich import inspect\n",
+ "\n",
+ "\n",
+ "from pydub import AudioSegment\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Audio\n",
+ "from io import BytesIO\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "\n",
+ "# For split set split_character_goal_length and split_character_max_length\n",
+ "kwargs = {}\n",
+ "\n",
+ "kwargs = config.load_all_defaults()\n",
+ "#kwargs['text_prompt'] = text\n",
+ "kwargs['hoarder_mode'] = True\n",
+ "kwargs[\"output_dir\"] = 'bark_samples'\n",
+ "kwargs[\"history_prompt\"] = None\n",
+ "# kwargs[\"single_starting_seed\"] = None # \n",
+ "# If you set seed you might want manually call generation.set_seed(-1) after to disable deterministic generation settings \n",
+ "# I'm not cleaning up after this paramater at the moment and I'm not sure on other side effects\n",
+ "kwargs[\"stable_mode_interval\"] = 1 # 0 for continous, 2,3,4 for mixed\n",
+ "kwargs[\"split_character_goal_length\"] = 145\n",
+ "kwargs[\"split_character_max_length\"] = 190\n",
+ "# kwargs[\"output_iterations\"] = 1\n",
+ "kwargs[\"add_silence_between_segments\"] = 0.0 # See: https://github.com/suno-ai/bark/blob/main/notebooks/long_form_generation.ipynb but not great for songs or stable_mode_interval 0\n",
+ "kwargs[\"semantic_min_eos_p\"] = 0.2 # 0.20 is default, lower means more likely to stotp\n",
+ "\n",
+ "\n",
+ "# not sure on overall effect so far from these, but for example:\n",
+ "kwargs[\"semantic_top_k\"] = None \n",
+ "kwargs[\"semantic_top_p\"] = None\n",
+ "kwargs[\"coarse_top_k\"] = None\n",
+ "kwargs[\"coarse_top_p\"] = None\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "from rich import prompt, print,inspect\n",
+ "pprint(kwargs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kwargs['history_prompt'] = \"custom_speakers/hark_woman.npz\"\n",
+ "text = \"\"\"With me, brave the tumultuous seas, claim treasures untold, and send foes to their watery \n",
+ "doom\"\"\"\n",
+ "kwargs['text_prompt'] = text\n",
+ "kwargs['single_starting_seed'] = None\n",
+ "generation.set_seed(37)\n",
+ "logger.setLevel(\"DEBUG\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "api.gradio_try_to_cancel = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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() Segment Breakdown \n", + "โโโโโณโโโโโโโโณโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n", + "โ # โ Words โ Time Est โ Splitting long text aiming for 145 chars max 190 โ\n", + "โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ\n", + "โ 1 โ 16 โ 6.40 s โ With me, brave the tumultuous seas, claim treasures untold, and send foes to their โ\n", + "โ โ โ 94 chars โ watery doom โ\n", + "โโโโโดโโโโโโโโดโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n", + "\n" + ], + "text/plain": [ + "\u001b[3m () Segment Breakdown \u001b[0m\n", + "โโโโโณโโโโโโโโณโโโโโโโโโโโณโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n", + "โ\u001b[1m \u001b[0m\u001b[1m#\u001b[0m\u001b[1m \u001b[0mโ\u001b[1m \u001b[0m\u001b[1mWords\u001b[0m\u001b[1m \u001b[0mโ\u001b[1m \u001b[0m\u001b[1mTime Est\u001b[0m\u001b[1m \u001b[0mโ\u001b[1m \u001b[0m\u001b[1mSplitting long text aiming for 145 chars max 190 \u001b[0m\u001b[1m \u001b[0mโ\n", + "โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ\n", + "โ\u001b[35m \u001b[0m\u001b[35m1\u001b[0m\u001b[35m \u001b[0mโ\u001b[32m \u001b[0m\u001b[32m16 \u001b[0m\u001b[32m \u001b[0mโ\u001b[32m \u001b[0m\u001b[32m6.40 s \u001b[0m\u001b[32m \u001b[0mโ With me, brave the tumultuous seas, claim treasures untold, and send foes to their โ\n", + "โ\u001b[35m \u001b[0mโ\u001b[32m \u001b[0mโ\u001b[32m \u001b[0m\u001b[32m94 chars\u001b[0m\u001b[32m \u001b[0mโ watery doom โ\n", + "โโโโโดโโโโโโโโดโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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generate_audio_barki {'text': 'With me, brave the tumultuous seas, claim treasures untold, and send foes api.py:362\n", + "to their watery doom', 'kwargs': {'text_prompt': 'With me, brave the tumultuous seas, claim treasures \n", + "untold, and send foes to their watery doom', 'list_speakers': None, 'dry_run': False, \n", + "'text_splits_only': False, 'history_prompt': {'semantic_prompt': array([ 131, 10, 27, 282, 395, \n", + "7686, 131, 131, 266, 1373, 459, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 5196, 91, 7567, 206, 3252, 822, 7782, 7782, \n", + " 602, 5064, 302, 1075, 1556, 1556, 1556, 1556, 6122, 2673, 6437, \n", + " 6437, 6437, 6437, 6105, 4382, 7378, 1471, 1471, 1471, 9935, 9935, \n", + " 5771, 5771, 5771, 178, 27, 10, 27, 27, 3971, 3971, 1233, \n", + " 1206, 206, 230, 230, 230, 1613, 1613, 1613, 1613, 1613, 1613, \n", + " 2009, 2009, 2009, 1380, 6206, 6206, 147, 5064, 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4929, 429, 41, 41, 7168, 5451, 9742, \n", + " 8512, 3427, 5651, 8308, 8308, 8308, 8308, 2665, 2665, 2665, 7038, \n", + " 2323, 4155, 4155, 5012, 5012, 5012, 5012, 5986, 2585, 59, 28, \n", + " 107, 4063, 9608, 417, 9725, 9725, 6445, 2428, 41, 41, 130, \n", + " 41, 1461, 7101, 4640, 2655, 4800, 6358, 808, 808, 41, 41, \n", + " 10, 41, 5991, 1831, 1831, 1831, 621, 621, 5865, 2453, 529, \n", + " 59, 59, 28, 28, 28, 28, 28, 28, 28, 28, 107, \n", + " 107, 385, 2465, 230, 131, 230, 2443, 206, 206, 206, 206, \n", + " 206, 206, 206, 206, 206, 206, 206, 3252, 206, 193, 206, \n", + " 206, 517, 147, 822, 206, 206, 206, 1278, 2305, 302, 5064, \n", + " 1075, 181, 107, 181, 2595, 4051, 7439, 7698, 7698, 389, 7194, \n", + " 1463, 3840, 3890, 969, 3, 41, 1468, 6772, 3281, 1009, 1536, \n", + " 393, 41, 10, 27, 9057, 5665, 4704, 4704, 5764, 5764, 7111, \n", + " 8557, 8557, 8557, 7252, 7252, 2582, 704, 3038, 407, 3038, 3038, \n", + " 3734, 657, 4, 4, 255, 255, 255, 99, 583, 395, 395, \n", + " 4565, 4565, 3889, 1037, 131, 5, 5, 2837, 5853, 5563, 5819, \n", + " 402, 41, 6821, 3973, 5265, 1282, 41, 41, 10, 27, 429, \n", + " 6945, 8234, 5838, 5451, 3139, 8512, 5611, 8092, 8092, 8695, 8688, \n", + " 3694, 6069, 288, 41, 4331, 4418, 7265, 7265, 1262, 26, 122, \n", + " 50, 10, 5, 652, 8921, 8921, 9245, 8141, 8141, 6997, 6997, \n", + " 3, 3, 27, 27, 232, 10, 403, 403, 9563, 9563, 481, \n", + " 481, 481, 28, 107, 28, 28, 28, 107, 385, 9355, 2672, \n", + " 131, 206, 266, 1373, 1572, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 3252, 3252, 206, \n", + " 206, 206, 7567, 3252, 2966, 1278, 206, 1278, 1278, 1278, 2009, \n", + " 2009, 8735, 8735, 147, 5128, 57, 57, 9817, 9817]), 'coarse_prompt': array([[738, 738, 738, \n", + "..., 479, 850, 865], \n", + " [363, 363, 913, ..., 430, 132, 648]]), 'fine_prompt': array([[ 738, 738, 738, ..., 479, 850, \n", + "865], \n", + " [ 363, 363, 913, ..., 430, 132, 648], \n", + " [1000, 36, 786, ..., 127, 62, 910], \n", + " ..., \n", + " [1011, 939, 939, ..., 521, 760, 632], \n", + " [ 562, 782, 748, ..., 413, 709, 528], \n", + " [ 948, 518, 975, ..., 285, 214, 42]], dtype=int32)}, 'prompt_file': None, \n", + "'split_input_into_separate_prompts_by': None, 'split_input_into_separate_prompts_by_value': None, \n", + "'always_save_speaker': True, 'output_iterations': 1, 'output_filename': None, 'output_dir': 'long', \n", + "'hoarder_mode': True, 'extra_stats': False, 'text_use_gpu': True, 'text_use_small': False, \n", + "'coarse_use_gpu': True, 'coarse_use_small': False, 'fine_use_gpu': True, 'fine_use_small': False, \n", + "'codec_use_gpu': True, 'force_reload': False, 'GLOBAL_ENABLE_MPS': None, 'USE_SMALL_MODELS': None, \n", + "'OFFLOAD_CPU': None, 'text_temp': 0.7, 'waveform_temp': 0.7, 'confused_travolta_mode': None, 'silent': \n", + "False, 'seed': None, 'stable_mode_interval': 1, 'single_starting_seed': None, \n", + "'split_character_goal_length': 145, 'split_character_max_length': 190, 'split_character_jitter': 0, \n", + "'add_silence_between_segments': 0.0, 'split_each_text_prompt_by': None, \n", + "'split_each_text_prompt_by_value': None, 'extra_confused_travolta_mode': None, \n", + "'semantic_history_starting_weight': 1.0, 'semantic_history_future_weight': 1.0, \n", + "'semantic_prev_segment_weight': 0.5, 'coarse_history_starting_weight': 1.0, \n", + "'coarse_history_future_weight': 0.5, 'coarse_prev_segment_weight': 0.5, 'fine_history_starting_weight': \n", + "1.0, 'fine_history_future_weight': 0.0, 'fine_prev_segment_weight': 0.0, \n", + "'custom_audio_processing_function': None, 'use_smaller_models': False, 'semantic_temp': 0.7, \n", + "'semantic_top_k': None, 'semantic_top_p': None, 'semantic_min_eos_p': 2, 'semantic_max_gen_duration_s': \n", + "None, 'semantic_allow_early_stop': True, 'semantic_use_kv_caching': True, 'semantic_seed': None, \n", + "'semantic_history_oversize_limit': None, 'coarse_temp': 0.7, 'coarse_top_k': None, 'coarse_top_p': None, \n", + "'coarse_max_coarse_history': 630, 'coarse_sliding_window_len': 60, 'coarse_kv_caching': True, \n", + "'coarse_seed': None, 'coarse_history_time_alignment_hack': -2, 'fine_temp': 0.5, 'fine_seed': None, \n", + "'render_npz_samples': False, 'loglevel': 'WARNING', 'history_prompt_string': \n", + "'custom_speakers/hark_woman.npz', 'segment_number': 1, 'output_full': True, 'total_segments': 1}} \n", + "\n" + ], + "text/plain": [ + "generate_audio_barki \u001b[1m{\u001b[0m\u001b[32m'text'\u001b[0m: \u001b[32m'With me, brave the tumultuous seas, claim treasures untold, and send foes\u001b[0m \u001b]8;id=295619;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=970216;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#362\u001b\\\u001b[2m362\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[32mto their watery doom'\u001b[0m, \u001b[32m'kwargs'\u001b[0m: 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generate_audio_barki {'text': 'With me, brave the tumultuous seas, claim treasures untold, and send foes api.py:366\n", + "to their watery doom', 'kwargs': {'text_prompt': 'With me, brave the tumultuous seas, claim treasures \n", + "untold, and send foes to their watery doom', 'list_speakers': None, 'dry_run': False, \n", + "'text_splits_only': False, 'history_prompt': {'semantic_prompt': array([ 131, 10, 27, 282, 395, \n", + "7686, 131, 131, 266, 1373, 459, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 5196, 91, 7567, 206, 3252, 822, 7782, 7782, \n", + " 602, 5064, 302, 1075, 1556, 1556, 1556, 1556, 6122, 2673, 6437, \n", + " 6437, 6437, 6437, 6105, 4382, 7378, 1471, 1471, 1471, 9935, 9935, \n", + " 5771, 5771, 5771, 178, 27, 10, 27, 27, 3971, 3971, 1233, \n", + " 1206, 206, 230, 230, 230, 1613, 1613, 1613, 1613, 1613, 1613, \n", + " 2009, 2009, 2009, 1380, 6206, 6206, 147, 5064, 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"'top_p': None, 'silent': False, 'min_eos_p': 2, 'max_gen_duration_s': None, 'allow_early_stop': \n", + "True, 'use_kv_caching': True} \n", + "\n" + ], + "text/plain": [ + "generate_text_semantic \u001b[1m{\u001b[0m\u001b[32m'text'\u001b[0m: \u001b[32m'With me, brave the tumultuous seas, claim treasures untold, and \u001b[0m \u001b]8;id=433174;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=78803;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#302\u001b\\\u001b[2m302\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[32msend foes to their watery doom'\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'semantic_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m131\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m27\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[1;36m282\u001b[0m, 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\u001b[1;36m206\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m7567\u001b[0m, \u001b[1;36m3252\u001b[0m, \u001b[1;36m2966\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m206\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m1278\u001b[0m, \u001b[1;36m2009\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1;36m2009\u001b[0m, \u001b[1;36m8735\u001b[0m, \u001b[1;36m8735\u001b[0m, \u001b[1;36m147\u001b[0m, \u001b[1;36m5128\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m57\u001b[0m, \u001b[1;36m9817\u001b[0m, \u001b[1;36m9817\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'coarse_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[1;36m738\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m479\u001b[0m, \u001b[1;36m850\u001b[0m, \u001b[1;36m865\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m363\u001b[0m, \u001b[1;36m363\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m430\u001b[0m, \u001b[1;36m132\u001b[0m, \u001b[1;36m648\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'fine_prompt'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m479\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[1;36m850\u001b[0m, \u001b[1;36m865\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m \u001b[1;36m363\u001b[0m, \u001b[1;36m363\u001b[0m, \u001b[1;36m913\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m430\u001b[0m, \u001b[1;36m132\u001b[0m, \u001b[1;36m648\u001b[0m\u001b[1m]\u001b[0m, \u001b[2m \u001b[0m\n", + " \u001b[1m[\u001b[0m\u001b[1;36m1000\u001b[0m, \u001b[1;36m36\u001b[0m, \u001b[1;36m786\u001b[0m, \u001b[33m...\u001b[0m, 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\u001b[32m'temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'top_k'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'top_p'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'min_eos_p'\u001b[0m: \u001b[1;36m2\u001b[0m, \u001b[32m'max_gen_duration_s'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'allow_early_stop'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;92mTrue\u001b[0m, \u001b[32m'use_kv_caching'\u001b[0m: \u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 100/100 [00:05<00:00, 17.65it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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generate_fine {'x_coarse_gen': array([[958, 967, 432, ..., 160, 160, 160], generation.py:626\n", + " [394, 510, 129, ..., 993, 646, 993]]), 'history_prompt': {'semantic_prompt': array([ 131, \n", + "10, 27, 282, 395, 7686, 131, 131, 266, 1373, 459, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 5196, 91, 7567, 206, 3252, 822, 7782, 7782, \n", + " 602, 5064, 302, 1075, 1556, 1556, 1556, 1556, 6122, 2673, 6437, \n", + " 6437, 6437, 6437, 6105, 4382, 7378, 1471, 1471, 1471, 9935, 9935, \n", + " 5771, 5771, 5771, 178, 27, 10, 27, 27, 3971, 3971, 1233, \n", + " 1206, 206, 230, 230, 230, 1613, 1613, 1613, 1613, 1613, 1613, \n", + " 2009, 2009, 2009, 1380, 6206, 6206, 147, 5064, 302, 302, 7644, \n", + " 9877, 5214, 3427, 3427, 3427, 3427, 133, 8308, 133, 8308, 133, \n", + " 2665, 2665, 7532, 2665, 7532, 4155, 990, 163, 990, 990, 990, \n", + " 9682, 9682, 2292, 2292, 8844, 1573, 402, 483, 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write_seg_wav .wav saved to long/With_me_brave_t-SPK-hark_woman_3.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to long/With_me_brave_t-SPK-hark_woman_3.wav \u001b]8;id=71943;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=741508;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved to long/With_me_brave_t-SPK-hark_woman_3.wav\n" + ] + } + ], + "source": [ + "kwargs['output_dir'] = \"long\"\n", + "generation.set_seed(37)\n", + "full_generation_segments, audio_arr_segments, final_filename_will_be = api.generate_audio_long(**kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "
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Run inspect(inspect) for options. โ\n", + "โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ\n", + "\n" + ], + "text/plain": [ + "\u001b[34mโญโ\u001b[0m\u001b[34mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ \u001b[0m\u001b[1;34m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m\u001b[32m'list'\u001b[0m\u001b[1;34m>\u001b[0m\u001b[34m โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m\u001b[34mโโฎ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[36mBuilt-in mutable sequence.\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[32mโญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[32mโ\u001b[0m \u001b[1m[\u001b[0m \u001b[32mโ\u001b[0m \u001b[34mโ\u001b[0m\n", + "\u001b[34mโ\u001b[0m \u001b[32mโ\u001b[0m \u001b[2;32mโ \u001b[0m\u001b[1m{\u001b[0m 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generate_text_semantic {'text': 'With me, brave the tumultuous seas, claim treasures untold, and generation.py:302\n", + "send foes to their watery \\ndoom', 'history_prompt': None, 'temp': 0.7, 'top_k': None, 'top_p': \n", + "None, 'silent': False, 'min_eos_p': 0.2, 'max_gen_duration_s': None, 'allow_early_stop': True, \n", + "'use_kv_caching': True} \n", + "\n" + ], + "text/plain": [ + "generate_text_semantic \u001b[1m{\u001b[0m\u001b[32m'text'\u001b[0m: \u001b[32m'With me, brave the tumultuous seas, claim treasures untold, and \u001b[0m \u001b]8;id=295619;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=970216;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#302\u001b\\\u001b[2m302\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[32msend foes to their watery \\ndoom'\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'top_k'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'top_p'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'min_eos_p'\u001b[0m: \u001b[1;36m0.2\u001b[0m, \u001b[32m'max_gen_duration_s'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'allow_early_stop'\u001b[0m: \u001b[3;92mTrue\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'use_kv_caching'\u001b[0m: \u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 100/100 [00:05<00:00, 17.48it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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generate_fine {'x_coarse_gen': array([[876, 738, 855, ..., 408, 408, 738], generation.py:626\n", + " [700, 841, 544, ..., 424, 424, 544]]), 'history_prompt': None, 'temp': 0.5, 'silent': \n", + "False} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m876\u001b[0m, \u001b[1;36m738\u001b[0m, \u001b[1;36m855\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m738\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=433174;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=78803;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m\u001b[1;36m700\u001b[0m, \u001b[1;36m841\u001b[0m, \u001b[1;36m544\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m544\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 1/1 [00:04<00:00, 4.00s/it]\n" + ] + } + ], + "source": [ + "text = \"\"\"With me, brave the tumultuous seas, claim treasures untold, and send foes to their watery \n", + "doom\"\"\"\n", + "kwargs[\"semantic_min_eos_p\"] = 0.2 \n", + "kwargs[\"text_prompt\"] = text\n", + "generation.set_seed(37)\n", + "kwargs['output_full'] = True\n", + "kwargs['output_dir'] = \"seed37\"\n", + "kwargs[\"history_prompt\"] = None\n", + "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n", + "barkifull, audio_arr_segments_barki = api.generate_audio_barki(text, **kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
semantic_prompt 429\n",
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+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ "coarse_prompt 2\n",
+ "\n"
+ ],
+ "text/plain": [
+ "coarse_prompt \u001b[1;36m2\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
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+ "fine_prompt 8\n",
+ "\n"
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#inspect(barkifull)\n",
+ "\n",
+ "for x in barkifull.keys():\n",
+ " print(f\"{x} {len(barkifull[x])}\")\n",
+ "\n",
+ "api.save_as_prompt(\"custom_speakers/seed37_barki.npz\",barkifull)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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generate_fine {'x_coarse_gen': array([[1019, 1019, 1019, ..., 432, 432, 724], generation.py:626\n", + " [ 829, 841, 928, ..., 894, 894, 765]]), 'history_prompt': None, 'temp': 0.5, \n", + "'silent': True} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m1019\u001b[0m, \u001b[1;36m1019\u001b[0m, \u001b[1;36m1019\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m432\u001b[0m, \u001b[1;36m432\u001b[0m, \u001b[1;36m724\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=295619;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=970216;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m \u001b[1;36m829\u001b[0m, \u001b[1;36m841\u001b[0m, \u001b[1;36m928\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m894\u001b[0m, \u001b[1;36m894\u001b[0m, \u001b[1;36m765\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'silent'\u001b[0m: \u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "generation.set_seed(37)\n", + "#kwargs['history_prompt'] = 'custom_speakers/en_fiery.npz'\n", + "\n", + "\n", + "\n", + "#fullgen, gen_audio_orig_segs = api.generate_audio(text, output_full=True, **kwargs)\n", + "\n", + "\n", + "fullgen, gen_audio_orig_segs = api.generate_audio(text, output_full=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "
k: semantic_prompt\n", + "\n" + ], + "text/plain": [ + "k: semantic_prompt\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
len: 390\n",
+ "\n"
+ ],
+ "text/plain": [
+ "len: \u001b[1;36m390\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "k: coarse_prompt\n", + "\n" + ], + "text/plain": [ + "k: coarse_prompt\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
len: 2\n",
+ "\n"
+ ],
+ "text/plain": [
+ "len: \u001b[1;36m2\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "k: fine_prompt\n", + "\n" + ], + "text/plain": [ + "k: fine_prompt\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
len: 8\n",
+ "\n"
+ ],
+ "text/plain": [
+ "len: \u001b[1;36m8\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for k in fullgen:\n",
+ " print(f\"k: {k}\")\n",
+ " print(f\"len: {len(fullgen[k])}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Rendering samples for speakers in: custom_speakers\n",
+ " Rendering audio for custom_speakers/hark_woman.npz to custom_speakers/hark_woman_1.wav\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "write_seg_wav .wav saved to custom_speakers/hark_woman_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/hark_woman_1.wav \u001b]8;id=552246;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=649487;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rendering audio for custom_speakers/hark1.npz to custom_speakers/hark1_1.wav\n" + ] + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to custom_speakers/hark1_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/hark1_1.wav \u001b]8;id=9210;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=7946;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rendering audio for custom_speakers/en_fiery.npz to custom_speakers/en_fiery_1.wav\n" + ] + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav \u001b]8;id=71943;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=741508;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "api.render_npz_samples(\"custom_speakers\", start_from=\"semantic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "
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28, 28, 28, 107, 107, 223, 385, 138, 10, 206, 266, \n", + " 459, 3316, 1444, 206, 206, 206, 206, 206, 206, 206, 206, \n", + " 206, 206, 206, 206, 206, 206, 206, 3252, 91, 206, 206, \n", + " 206, 91, 206, 65, 206, 206, 65, 65, 65, 65, 65, \n", + " 65, 65, 147, 3888, 57, 181, 41, 2595, 6195, 6195, 6925, \n", + " 316, 292, 389, 7454, 1463, 3840, 3890, 9835, 2519, 3, 41, \n", + " 2515, 1335, 1335, 117, 41, 41, 41, 10, 41, 5665, 1680, \n", + " 5764, 5764, 3305, 7111, 8557, 8557, 7252, 2582, 704, 407, 407, \n", + " 407, 627, 41, 4, 255, 255, 255, 583, 583, 4565, 4565, \n", + " 3889, 8595, 8595, 10, 5, 5, 5853, 5563, 5819, 245, 41, \n", + " 6821, 3973, 8035, 50, 41, 41, 10, 41, 429, 9110, 3139, \n", + " 9742, 8512, 5611, 8092, 8092, 8695, 3088, 288, 41, 41, 4331, \n", + " 4418, 7179, 526, 288, 50, 50, 10, 5, 652, 9028, 8921, \n", + " 9245, 8141, 8141, 922, 620, 118, 232, 100, 100, 100, 131, \n", + " 345, 4443, 740, 59, 28, 28, 28, 28, 28, 107, 223, \n", + " 107, 577, 577, 2767, 733, 215, 215, 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\u001b[2m \u001b[0m\n", + "\u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.7\u001b[0m, \u001b[32m'top_k'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'top_p'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'max_coarse_history'\u001b[0m: \u001b[1;36m630\u001b[0m, \u001b[2m \u001b[0m\n", + "\u001b[32m'sliding_window_len'\u001b[0m: \u001b[1;36m60\u001b[0m, \u001b[32m'use_kv_caching'\u001b[0m: \u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โโโโโโโโโโ| 35/35 [00:31<00:00, 1.09it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
generate_fine {'x_coarse_gen': array([[408, 408, 408, ..., 408, 408, 62], generation.py:626\n", + " [424, 424, 424, ..., 518, 518, 424]]), 'history_prompt': None, 'temp': 0.5, 'silent': \n", + "True} \n", + "\n" + ], + "text/plain": [ + "generate_fine \u001b[1m{\u001b[0m\u001b[32m'x_coarse_gen'\u001b[0m: \u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m408\u001b[0m, \u001b[1;36m62\u001b[0m\u001b[1m]\u001b[0m, \u001b]8;id=368965;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py\u001b\\\u001b[2mgeneration.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=553125;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/generation.py#626\u001b\\\u001b[2m626\u001b[0m\u001b]8;;\u001b\\\n", + " \u001b[1m[\u001b[0m\u001b[1;36m424\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[1;36m424\u001b[0m, \u001b[33m...\u001b[0m, \u001b[1;36m518\u001b[0m, \u001b[1;36m518\u001b[0m, \u001b[1;36m424\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'history_prompt'\u001b[0m: \u001b[3;35mNone\u001b[0m, \u001b[32m'temp'\u001b[0m: \u001b[1;36m0.5\u001b[0m, \u001b[32m'silent'\u001b[0m: \u001b[2m \u001b[0m\n", + "\u001b[3;92mTrue\u001b[0m\u001b[1m}\u001b[0m \u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Rendering audio for custom_speakers/en_fiery.npz to custom_speakers/en_fiery_1.wav\n" + ] + }, + { + "data": { + "text/html": [ + "
write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav api.py:325\n", + "\n" + ], + "text/plain": [ + "write_seg_wav .wav saved to custom_speakers/en_fiery_1.wav \u001b]8;id=763391;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py\u001b\\\u001b[2mapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14160;file:///home/jon/mamba_projects/bark_postfixes/bark/bark_infinity/api.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "api.render_npz_samples(\"custom_speakers\", start_from=\"semantic_prompt\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Audio(audio_arr_segments_barki, rate=generation.SAMPLE_RATE) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generation.preload_models()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"\"\"\n", + "Hark! I, the phantom visage of Edward Teach, rise from the \n", + "abyss, forever bound to the briny depths. With me, brave the tumultuous seas, claim treasures untold,\n", + "and send foes to their watery doom.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "history_prompt = np.load(\"pirates/base/pirate.npz\")\n", + "from rich import inspect" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for key in history_prompt.keys():\n", + " length = len(history_prompt[key])\n", + " print(f\"key: {key}, length: {length}\")\n", + " inspect(history_prompt[key], title=f\"{key} ({length})\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)\n", + "\n", + "semantic_prompt = history_prompt[\"semantic_prompt\"]\n", + "midpoint = len(semantic_prompt) // 2\n", + "new_semantic_first_half = semantic_prompt[:midpoint].astype(np.int32)\n", + "\n", + "\n", + "Instead I would like new_semantic to be half the size of x_semantic_history, just the last half of the space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " if length > 0:\n", + " for sub_key in history_prompt[key].keys():\n", + " print(f\" {sub_key}={history_prompt[key][sub_key]}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "gen_minor_variants = 20\n", + "import random\n", + "\n", + "npz_file = \"pirate.npz\"\n", + "npz_directory = \"pirates/base\"\n", + "npz_filepath = \"pirates/base/pirate.npz\"\n", + "\n", + "semantic_prompt = history_prompt[\"semantic_prompt\"]\n", + "original_semantic_prompt = semantic_prompt.copy()\n", + "starting_point = 128\n", + "ending_point = len(semantic_prompt) - starting_point\n", + "\n", + "\n", + "\n", + "points = np.linspace(starting_point, ending_point, gen_minor_variants)\n", + " \n", + "for starting_point in points:\n", + " starting_point = int(starting_point)\n", + " print(starting_point)\n", + "\n", + " new_semantic_from_beginning = original_semantic_prompt[:starting_point].astype(np.int32)\n", + " new_semantic_from_ending = original_semantic_prompt[starting_point:].astype(np.int32)\n", + "\n", + " for semantic_prompt in [new_semantic_from_beginning, new_semantic_from_ending]:\n", + " \n", + " print(f\"len(semantic_prompt): {len(semantic_prompt)}\")\n", + " print(f\"starting_point: {starting_point}, ending_poinst: {ending_point}\") \n", + "\n", + " temp_coarse = random.uniform(0.5, 0.9)\n", + " top_k_coarse = None if random.random() < 1/3 else random.randint(50, 100)\n", + " top_p_coarse = None if random.random() < 1/3 else random.uniform(0.8, 0.95)\n", + "\n", + " max_coarse_history_options = [630, random.randint(500, 630), random.randint(60, 500)]\n", + " max_coarse_history = random.choice(max_coarse_history_options)\n", + "\n", + " coarse_tokens = generation.generate_coarse(semantic_prompt, temp=temp_coarse, top_k=top_k_coarse, top_p=top_p_coarse, max_coarse_history=max_coarse_history)\n", + "\n", + " temp_fine = random.uniform(0.3, 0.7)\n", + " fine_tokens = generation.generate_fine(coarse_tokens, temp=temp_fine)\n", + "\n", + " history_prompt_render_variant = {\"semantic_prompt\": semantic_prompt, \"coarse_prompt\": coarse_tokens, \"fine_prompt\": fine_tokens}\n", + "\n", + " try:\n", + " audio_arr = generation.codec_decode(fine_tokens)\n", + " base_output_filename = os.path.splitext(npz_file)[0] + f\"_var_{i}.wav\"\n", + " output_filepath = os.path.join(npz_directory, base_output_filename)\n", + " output_filepath = api.generate_unique_filepath(output_filepath)\n", + " print(f\" Rendering minor variant voice audio for {npz_filepath} to {output_filepath}\")\n", + " api.write_seg_wav(output_filepath, audio_arr)\n", + "\n", + " api.write_seg_npz(output_filepath, history_prompt_render_variant)\n", + " except:\n", + " print(f\"
| Path | Old value | New value |
|---|---|---|
| {path} | {old_value} | {new_value} |
| No changes | ||