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

Amphion

Amphion est un projet de audio open source, créé par open-mmlab (open-mmlab/Amphion sur GitHub). Il compte 10 305 étoiles sur GitHub et 851 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 25 mars 2026.

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

★ 10,3 k étoiles⑂ 851 forksMITmis à jour mars 2026
▦ visuel — le README de ce projet ne contient aucune capture intégrable,
voici donc un emplacement réservé. Le guide ci-dessous est complet malgré tout.
// détails du projet

Détails du projet

Étoiles GitHub
10 305
Forks
851
Licence
MIT
Dernière mise à jour sur GitHub
25 mars 2026
Catégorie
Audio
Type de fiche
Guide d'installation complet
Site officiel
Official site
// fonctionnement

Comment ça marche

  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).

// fonctionnalités

Pourquoi on l'adore

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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// lancez-le

Lancez-le vous-même

  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
// cas d'usage

Ce que vous pouvez créer

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// faq

Questions fréquentes

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.

// continuez l'exploration

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// alternatives

Alternatives en Audio

Les autres projets de la catégorie Audio avec le plus d'étoiles du catalogue LibreAI — la sélection réaliste d'alternatives si Amphion ne vous convient pas.

// plus en audio

Plus en Audio

Projets de la catégorie Audio proches dans le catalogue, les plus proches en étoiles de Amphion — continuez à explorer de part et d'autre.

Obtenir Amphion

Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.