LibreAI / Áudio / Amphion
// audio · open-mmlab/Amphion

Amphion

Amphion é um projeto de áudio de código aberto de open-mmlab (open-mmlab/Amphion no GitHub). Tem 10.305 estrelas no GitHub e 851 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 25 de março de 2026.

Open research toolkit for text-to-speech, voice conversion, singing voice and text-to-audio generation.

★ 10,3 mil estrelas⑂ 851 forksMITatualizado mar 2026
▦ visual: o README deste projeto não inclui nenhuma captura incorporável,
então aqui vai um marcador. O guia abaixo está completo mesmo assim.
// dados do projeto

Dados do projeto

Repositório
open-mmlab/Amphion
Estrelas no GitHub
10.305
Forks
851
Licença
MIT
Última atualização no GitHub
25 de março de 2026
Categoria
Áudio
Tipo de ficha
Guia de instalação completo
Site oficial
Official site
// como funciona

Como funciona

  1. Set up the environment

    Clone the repo, create the pinned conda environment and run the dependency script - or pull the Docker image instead.

  2. Pick a task and model

    Choose your generation task (TTS, voice conversion, singing, text-to-audio) and a model line, such as MaskGCT or Vevo for zero-shot speech, from the per-task READMEs.

  3. Prepare data or grab a checkpoint

    Use Amphion's unified preprocessing for datasets like LibriTTS, LJSpeech or VCTK, or download a pretrained checkpoint from Hugging Face.

  4. Train or generate, then evaluate

    Run the model's training or inference recipe and score results with the built-in objective metrics (intelligibility, speaker similarity, FAD and more).

// recursos

Por que as pessoas gostam tanto

Many TTS architectures

FastSpeech2, VITS, VALL-E, NaturalSpeech2, Jets, the non-autoregressive MaskGCT and the controllable zero-shot Vevo family, with per-model code and checkpoints.

Voice, accent and singing conversion

Zero-shot voice conversion (Vevo, FACodec, Noro), accent conversion, and singing voice conversion/synthesis including the unified Vevo2 framework for speech and singing.

Text-to-audio and neural codecs

A latent-diffusion text-to-audio model, plus neural audio codecs such as DualCodec and FACodec for token-based generation.

Vocoder zoo

GAN vocoders (MelGAN, HiFi-GAN, BigVGAN), flow (WaveGlow), diffusion (DiffWave) and autoregressive (WaveNet, WaveRNN) vocoders under one roof.

Objective evaluation suite

F0 and energy modeling metrics, intelligibility (CER/WER), spectrogram distortion (FAD, MCD, PESQ, STOI) and speaker-similarity scoring.

Emilia dataset and visualizations

The 100k+ hour Emilia in-the-wild speech dataset with its Emilia-Pipe preprocessing pipeline, and interactive visualizations such as SingVisio that explain classic model internals.

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// execute você mesmo

Execute você mesmo

  1. Clone the repository

    Clone Amphion and enter the project directory.

    git clone https://github.com/open-mmlab/Amphion.git
    cd Amphion
  2. Create the conda environment

    Create and activate the Python environment the README specifies (a Docker image is the documented alternative).

    conda create --name amphion python=3.9.15
    conda activate amphion
  3. Install dependencies

    Run the project's environment script to install the required Python packages.

    sh env.sh
// casos de uso

O que você pode criar

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// perguntas frequentes

Perguntas frequentes

Who maintains Amphion?

Amphion is maintained by the open-mmlab community and contributing research groups, with releases announced through the repository and its Discord.

Is Amphion free to use?

The toolkit is MIT-licensed. Individual datasets and some pretrained assets keep their own licenses - for example the Emilia dataset is CC BY-NC 4.0 - so check the asset you use.

Does it need a paid API key?

No. Amphion is a local research toolkit; models train and run on your own hardware, and you will want a GPU for serious training.

How does Amphion compare with Coqui TTS or ESPnet?

Those focus mainly on speech synthesis pipelines; Amphion covers a wider research surface - TTS, voice and singing conversion, text-to-audio, codecs, vocoders, metrics and datasets - with an emphasis on reproducing papers.

How do I get started?

Clone the repo, run the conda setup and env.sh, then follow the per-task README for a model like MaskGCT with its pretrained checkpoint.

// continue explorando

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// alternativas

Alternativas em Áudio

Os outros projetos de Áudio com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se Amphion não for o ideal para você.

// mais em audio

Mais em Áudio

Projetos de Áudio próximos no catálogo, os mais próximos em estrelas de Amphion: continue explorando dos dois lados dele.

Baixe o Amphion

Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.