Verified project record

open-mmlab/Amphion

Amphion is an open-source toolkit for reproducible research and development in audio, music, and speech generation. It provides a comprehensive platform spanning multiple generation tasks, neural vocoders, evaluation metrics, dataset preprocessing pipelines, and model architecture visualizations.

Project overview

It bundles a broad range of audio generation capabilities with educational model visualization tools, making it particularly valuable for junior researchers and engineers entering audio, music, and speech generation fields.

Project type: Audio & Speech
Use cases: Audio & Speech
Deployment: Refer to project documentation
License: MIT

Best for

Junior researchers and engineers entering audio, music, and speech generation research who need a reproducible, comprehensive toolkit.
Educators seeking interactive visualizations of classic model architectures for instructional purposes.

Key capabilities

Supports individual generation tasks including text-to-speech (TTS), singing voice synthesis (SVS), voice conversion (VC), audio conversion (AC), singing voice conversion (SVC), and text-to-audio (TTA).
Supports various widely-used neural vocoders including GAN-based, Flow-based, Diffusion-based, and Auto-regressive based architectures.
Provides comprehensive objective evaluation of generated audio to help assess model output quality.
Provides visualization tools to interactively illustrate the internal processing mechanism of classic models for educational purposes.
Supports text-to-audio generation using a latent diffusion model.
Unifies data preprocessing for open-source datasets including AudioCaps, LibriTTS, LJSpeech, M4Singer, Opencpop, OpenSinger, SVCC, VCTK, and others.

Limitations and risks

Docker usage requires NVIDIA Driver, NVIDIA Container Toolkit, and CUDA to be installed and configured.
Setting up and using Amphion requires coding proficiency, including familiarity with Python environments and shell scripts.

Getting started

Clone the repository, create a Python 3.9.15 conda environment, activate it, and run the env.sh shell script to install dependencies.
Alternatively, deploy using Docker, which requires NVIDIA Driver, NVIDIA Container Toolkit, and CUDA for GPU support.

Evidence and sources

GitHub project description: 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…
README: Amphion is designed to support individual generation tasks, including but not limited to, - **TTS**: Text to Speech (⛳ supported) - **SVS**: Singing Voice Synthesis (⛳ supported)…
README: Amphion achieves state-of-the-art performance compared to existing open-source repositories on text-to-speech (TTS) systems.
README: 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:
README: Amphion supports the following voice conversion models:

Reviewed at: 2026-07-18T03:20:18Z