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 also 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.
Here is the Amphion v0.1 demo, whose voice, audio effects, and singing voice are generated by our models. Just enjoy it!
Amphion-Demo-EN.mp4
- 2023/12/18: Amphion v0.1 release.
- 2023/11/28: Amphion alpha release.
- Amphion achieves state-of-the-art performance when compared with existing open-source repositories on text-to-speech (TTS) systems. It supports the following models or architectures:
- FastSpeech2: A non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks.
- VITS: An end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning
- Vall-E: A zero-shot TTS architecture that uses a neural codec language model with discrete codes.
- NaturalSpeech2: An architecture for TTS that utilizes a latent diffusion model to generate natural-sounding voices.
- Ampion supports multiple content-based features from various pretrained models, including WeNet, Whisper, and ContentVec. Their specific roles in SVC has been investigated in our NeurIPS 2023 workshop paper.
- 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 as a backend and supports several sampling algorithms such as DDPM, DDIM, and PNDM. Additionally, it supports single-step inference based on the Consistency Model.
- Amphion supports the TTA with a latent diffusion model. It is designed like AudioLDM, Make-an-Audio, and AUDIT. It is also the official implementation of the text-to-audio generation part of our NeurIPS 2023 paper.
- Amphion supports various widely-used neural vocoders, including:
- Amphion provides the official implementation of Multi-Scale Constant-Q Transform Discriminator (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.
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 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, Resemblyzer, WeSpeaker, WavLM and more.
Amphion unifies the data preprocess of the open-source datasets including AudioCaps, LibriTTS, LJSpeech, M4Singer, Opencpop, OpenSinger, SVCC, VCTK, and more. The supported dataset list can be seen here (updating).
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.shWe detail the instructions of different tasks in the following recipes:
We appreciate all contributions to improve Amphion. Please refer to CONTRIBUTING.md for the contributing guideline.
- ming024's FastSpeech2 and jaywalnut310's VITS for model architecture code.
- lifeiteng's VALL-E for training pipeline and model architecture design.
- WeNet, Whisper, ContentVec, and RawNet3 for pretrained models and inference code.
- HiFi-GAN for GAN-based Vocoder's architecture design and training strategy.
- Encodec for well-organized GAN Discriminator's architecture and basic blocks.
- Latent Diffusion for model architecture design.
- TensorFlowTTS for preparing the MFA tools.
Amphion is under the MIT License. It is free for both research and commercial use cases.
@article{zhang2023amphion,
title={Amphion: An Open-Source Audio, Music and Speech Generation Toolkit},
author={Xueyao Zhang and Liumeng Xue and Yuancheng Wang and Yicheng Gu and Xi Chen and Zihao Fang and Haopeng Chen and Lexiao Zou and Chaoren Wang and Jun Han and Kai Chen and Haizhou Li and Zhizheng Wu},
journal={arXiv},
year={2023},
volume={abs/2312.09911}
}