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

Amphion

Amphion es un proyecto de audio de código abierto de open-mmlab (open-mmlab/Amphion en GitHub). Tiene 10.305 estrellas en GitHub y 851 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 25 de marzo de 2026.

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

★ 10,3k estrellas⑂ 851 bifurcacionesMITactualizado mar 2026
▦ visual: el README de este proyecto no incluye ninguna captura incrustable,
así que aquí va un marcador. La guía de abajo está completa de todos modos.
// datos del proyecto

Datos del proyecto

Estrellas en GitHub
10.305
Bifurcaciones
851
Licencia
MIT
Última actualización en GitHub
25 de marzo de 2026
Categoría
Audio
Tipo de ficha
Guía de instalación completa
Sitio oficial
Official site
// cómo funciona

Cómo 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).

// características

Por qué gusta 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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// ejecútalo

Ejecútalo tú mismo

  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

Qué puedes crear

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// preguntas frecuentes

Preguntas frecuentes

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.

// sigue explorando

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// alternativas

Alternativas en Audio

Los demás proyectos de Audio con más estrellas del catálogo de LibreAI: la lista realista de alternativas si Amphion no es lo que buscas.

// más en audio

Más en Audio

Proyectos de Audio cercanos en el catálogo, los más próximos en estrellas a Amphion: sigue explorando a ambos lados.

Consigue Amphion

Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.