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

Amphion

Amphion ist ein Open-Source-Projekt aus der Kategorie Audio von open-mmlab (open-mmlab/Amphion auf GitHub). Es hat 10.305 Sterne auf GitHub und 851 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 25. März 2026 auf GitHub aktualisiert.

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

★ 10,3 Tsd. Sterne⑂ 851 ForksMITaktualisiert Mär 2026
▦ Visual — das README dieses Projekts enthält keinen einbettbaren Screenshot,
darum hier ein Platzhalter. Die Anleitung unten ist trotzdem vollständig.
// projektdaten

Projektdaten

GitHub-Sterne
10.305
Forks
851
Lizenz
MIT
Zuletzt auf GitHub aktualisiert
25. März 2026
Kategorie
Audio
Eintragstyp
Vollständige Installationsanleitung
Offizielle Website
Official site
// so funktioniert es

So funktioniert es

  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).

// funktionen

Warum es so beliebt ist

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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// selbst ausführen

Führe es selbst aus

  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
// anwendungsfälle

Was du damit bauen kannst

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// häufige fragen

Häufige Fragen

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.

// weiter entdecken

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// alternativen

Alternativen im Bereich Audio

Die anderen Projekte der Kategorie Audio mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls Amphion nicht das Richtige ist.

// mehr in audio

Mehr in Audio

Verwandte Projekte der Kategorie Audio im Katalog, die Amphion bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.

Hol dir Amphion

Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.