# Amphion: An Open-Source Audio, Music, and Speech Generation Toolkit
**Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation.** Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development. Amphion offers a unique feature: **visualizations** of classic models or architectures. We believe that these visualizations are beneficial for junior researchers and engineers who wish to gain a better understanding of the model.
**The North-Star objective of Amphion is to offer a platform for studying the conversion of any inputs into audio.** Amphion is designed to support individual generation tasks, including but not limited to,
- **TTS**: Text to Speech (⛳ supported)
- **SVS**: Singing Voice Synthesis (👨💻 developing)
- **VC**: Voice Conversion (👨💻 developing)
- **SVC**: Singing Voice Conversion (⛳ supported)
- **TTA**: Text to Audio (⛳ supported)
- **TTM**: Text to Music (👨💻 developing)
- more…
In addition to the specific generation tasks, Amphion includes several **vocoders** and **evaluation metrics**. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. Moreover, Amphion is dedicated to advancing audio generation in real-world applications, such as building **large-scale datasets** for speech synthesis.
## 🚀 News
- **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [](https://arxiv.org/abs/2407.05361) [](https://huggingface.co/datasets/amphion/Emilia) [](https://emilia-dataset.github.io/Emilia-Demo-Page/) [](preprocessors/Emilia/README.md)
- **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [](egs/tts/VALLE_V2/README.md)
- **2024/03/12**: Amphion now support **NaturalSpeech3 FACodec** and release pretrained checkpoints. [](https://arxiv.org/abs/2403.03100) [](https://huggingface.co/amphion/naturalspeech3_facodec) [](https://huggingface.co/spaces/amphion/naturalspeech3_facodec) [](models/codec/ns3_codec/README.md)
- **2024/02/22**: The first Amphion visualization tool, **SingVisio**, release. [](https://arxiv.org/abs/2402.12660) [](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [](https://github.com/open-mmlab/Amphion/assets/33707885/0a6e39e8-d5f1-4288-b0f8-32da5a2d6e96) [](egs/visualization/SingVisio/README.md)
- **2023/12/18**: Amphion v0.1 release. [](https://arxiv.org/abs/2312.09911) [](https://huggingface.co/amphion) [](https://www.youtube.com/watch?v=1aw0HhcggvQ) [](https://github.com/open-mmlab/Amphion/pull/39)
- **2023/11/28**: Amphion alpha release. [](https://github.com/open-mmlab/Amphion/pull/2)
## ⭐ Key Features
### TTS: Text to Speech
- Amphion achieves state-of-the-art performance compared to existing open-source repositories on text-to-speech (TTS) systems. It supports the following models or architectures:
- [FastSpeech2](https://arxiv.org/abs/2006.04558): A non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks.
- [VITS](https://arxiv.org/abs/2106.06103): An end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning
- [VALL-E](https://arxiv.org/abs/2301.02111): A zero-shot TTS architecture that uses a neural codec language model with discrete codes.
- [NaturalSpeech2](https://arxiv.org/abs/2304.09116): An architecture for TTS that utilizes a latent diffusion model to generate natural-sounding voices.
- [Jets](Jets): An end-to-end TTS model that jointly trains FastSpeech2 and HiFi-GAN with an alignment module.
### SVC: Singing Voice Conversion
- Ampion supports multiple content-based features from various pretrained models, including [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), and [ContentVec](https://github.com/auspicious3000/contentvec). Their specific roles in SVC has been investigated in our NeurIPS 2023 workshop paper. [](https://arxiv.org/abs/2310.11160) [](egs/svc/MultipleContentsSVC)
- Amphion implements several state-of-the-art model architectures, including diffusion-, transformer-, VAE- and flow-based models. The diffusion-based architecture uses [Bidirectional dilated CNN](https://openreview.net/pdf?id=a-xFK8Ymz5J) as a backend and supports several sampling algorithms such as [DDPM](https://arxiv.org/pdf/2006.11239.pdf), [DDIM](https://arxiv.org/pdf/2010.02502.pdf), and [PNDM](https://arxiv.org/pdf/2202.09778.pdf). Additionally, it supports single-step inference based on the [Consistency Model](https://openreview.net/pdf?id=FmqFfMTNnv).
### TTA: Text to Audio
- Amphion supports the TTA with a latent diffusion model. It is designed like [AudioLDM](https://arxiv.org/abs/2301.12503), [Make-an-Audio](https://arxiv.org/abs/2301.12661), and [AUDIT](https://arxiv.org/abs/2304.00830). It is also the official implementation of the text-to-audio generation part of our NeurIPS 2023 paper. [](https://arxiv.org/abs/2304.00830) [](egs/tta/RECIPE.md)
### Vocoder
- Amphion supports various widely-used neural vocoders, including:
- GAN-based vocoders: [MelGAN](https://arxiv.org/abs/1910.06711), [HiFi-GAN](https://arxiv.org/abs/2010.05646), [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts), [BigVGAN](https://arxiv.org/abs/2206.04658), [APNet](https://arxiv.org/abs/2305.07952).
- Flow-based vocoders: [WaveGlow](https://arxiv.org/abs/1811.00002).
- Diffusion-based vocoders: [Diffwave](https://arxiv.org/abs/2009.09761).
- Auto-regressive based vocoders: [WaveNet](https://arxiv.org/abs/1609.03499), [WaveRNN](https://arxiv.org/abs/1802.08435v1).
- Amphion provides the official implementation of [Multi-Scale Constant-Q Transform Discriminator](https://arxiv.org/abs/2311.14957) (our ICASSP 2024 paper). It can be used to enhance any architecture GAN-based vocoders during training, and keep the inference stage (such as memory or speed) unchanged. [](https://arxiv.org/abs/2311.14957) [](egs/vocoder/gan/tfr_enhanced_hifigan)
### Evaluation
Amphion provides a comprehensive objective evaluation of the generated audio. The evaluation metrics contain:
- **F0 Modeling**: F0 Pearson Coefficients, F0 Periodicity Root Mean Square Error, F0 Root Mean Square Error, Voiced/Unvoiced F1 Score, etc.
- **Energy Modeling**: Energy Root Mean Square Error, Energy Pearson Coefficients, etc.
- **Intelligibility**: Character/Word Error Rate, which can be calculated based on [Whisper](https://github.com/openai/whisper) and more.
- **Spectrogram Distortion**: Frechet Audio Distance (FAD), Mel Cepstral Distortion (MCD), Multi-Resolution STFT Distance (MSTFT), Perceptual Evaluation of Speech Quality (PESQ), Short Time Objective Intelligibility (STOI), etc.
- **Speaker Similarity**: Cosine similarity, which can be calculated based on [RawNet3](https://github.com/Jungjee/RawNet), [Resemblyzer](https://github.com/resemble-ai/Resemblyzer), [WeSpeaker](https://github.com/wenet-e2e/wespeaker), [WavLM](https://github.com/microsoft/unilm/tree/master/wavlm) and more.
### Datasets
- Amphion unifies the data preprocess of the open-source datasets including [AudioCaps](https://audiocaps.github.io/), [LibriTTS](https://www.openslr.org/60/), [LJSpeech](https://keithito.com/LJ-Speech-Dataset/), [M4Singer](https://github.com/M4Singer/M4Singer), [Opencpop](https://wenet.org.cn/opencpop/), [OpenSinger](https://github.com/Multi-Singer/Multi-Singer.github.io), [SVCC](http://vc-challenge.org/), [VCTK](https://datashare.ed.ac.uk/handle/10283/3443), and more. The supported dataset list can be seen [here](egs/datasets/README.md) (updating).
- Amphion (exclusively) supports the [**Emilia**](preprocessors/Emilia/README.md) dataset and its preprocessing pipeline **Emilia-Pipe** for in-the-wild speech data!
### Visualization
Amphion provides visualization tools to interactively illustrate the internal processing mechanism of classic models. This provides an invaluable resource for educational purposes and for facilitating understandable research.
Currently, Amphion supports [SingVisio](egs/visualization/SingVisio/README.md), a visualization tool of the diffusion model for singing voice conversion. [](https://arxiv.org/abs/2402.12660) [](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [](https://github.com/open-mmlab/Amphion/assets/33707885/0a6e39e8-d5f1-4288-b0f8-32da5a2d6e96)
## 📀 Installation
Amphion can be installed through either Setup Installer or Docker Image.
### Setup Installer
```bash
git clone https://github.com/open-mmlab/Amphion.git
cd Amphion
# Install Python Environment
conda create --name amphion python=3.9.15
conda activate amphion
# Install Python Packages Dependencies
sh env.sh
```
### Docker Image
1. Install [Docker](https://docs.docker.com/get-docker/), [NVIDIA Driver](https://www.nvidia.com/download/index.aspx), [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html), and [CUDA](https://developer.nvidia.com/cuda-downloads).
2. Run the following commands:
```bash
git clone https://github.com/open-mmlab/Amphion.git
cd Amphion
docker pull realamphion/amphion
docker run --runtime=nvidia --gpus all -it -v .:/app realamphion/amphion
```
Mount dataset by argument `-v` is necessary when using Docker. Please refer to [Mount dataset in Docker container](egs/datasets/docker.md) and [Docker Docs](https://docs.docker.com/engine/reference/commandline/container_run/#volume) for more details.
## 🐍 Usage in Python
We detail the instructions of different tasks in the following recipes:
- [Text to Speech (TTS)](egs/tts/README.md)
- [Singing Voice Conversion (SVC)](egs/svc/README.md)
- [Text to Audio (TTA)](egs/tta/README.md)
- [Vocoder](egs/vocoder/README.md)
- [Evaluation](egs/metrics/README.md)
- [Visualization](egs/visualization/README.md)
## 👨💻 Contributing
We appreciate all contributions to improve Amphion. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
## 🙏 Acknowledgement
- [ming024's FastSpeech2](https://github.com/ming024/FastSpeech2) and [jaywalnut310's VITS](https://github.com/jaywalnut310/vits) for model architecture code.
- [lifeiteng's VALL-E](https://github.com/lifeiteng/vall-e) for training pipeline and model architecture design.
- [SpeechTokenizer](https://github.com/ZhangXInFD/SpeechTokenizer) for semantic-distilled tokenizer design.
- [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), [ContentVec](https://github.com/auspicious3000/contentvec), and [RawNet3](https://github.com/Jungjee/RawNet) for pretrained models and inference code.
- [HiFi-GAN](https://github.com/jik876/hifi-gan) for GAN-based Vocoder's architecture design and training strategy.
- [Encodec](https://github.com/facebookresearch/encodec) for well-organized GAN Discriminator's architecture and basic blocks.
- [Latent Diffusion](https://github.com/CompVis/latent-diffusion) for model architecture design.
- [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS) for preparing the MFA tools.
## ©️ License
Amphion is under the [MIT License](LICENSE). It is free for both research and commercial use cases.
## 📚 Citations
```bibtex
@article{zhang2023amphion,
title={Amphion: An Open-Source Audio, Music and Speech Generation Toolkit},
author={Xueyao Zhang and Liumeng Xue and Yicheng Gu and Yuancheng Wang and Haorui He and Chaoren Wang and Xi Chen and Zihao Fang and Haopeng Chen and Junan Zhang and Tze Ying Tang and Lexiao Zou and Mingxuan Wang and Jun Han and Kai Chen and Haizhou Li and Zhizheng Wu},
journal={arXiv},
year={2024},
volume={abs/2312.09911}
}
```
