Audio
Coqui TTS
Deep learning toolkit for text-to-speech, battle-tested in research and production
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View guide →Open research toolkit for text-to-speech, voice conversion, singing voice and text-to-audio generation.
Amphion is an open-source audio project by open-mmlab (open-mmlab/Amphion on GitHub). It has 10,305 GitHub stars and 851 forks, is released under the MIT license, and was last updated on GitHub on 25 March 2026.
Clone the repo, create the pinned conda environment and run the dependency script - or pull the Docker image instead.
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.
Use Amphion's unified preprocessing for datasets like LibriTTS, LJSpeech or VCTK, or download a pretrained checkpoint from Hugging Face.
Run the model's training or inference recipe and score results with the built-in objective metrics (intelligibility, speaker similarity, FAD and more).
FastSpeech2, VITS, VALL-E, NaturalSpeech2, Jets, the non-autoregressive MaskGCT and the controllable zero-shot Vevo family, with per-model code and checkpoints.
Zero-shot voice conversion (Vevo, FACodec, Noro), accent conversion, and singing voice conversion/synthesis including the unified Vevo2 framework for speech and singing.
A latent-diffusion text-to-audio model, plus neural audio codecs such as DualCodec and FACodec for token-based generation.
GAN vocoders (MelGAN, HiFi-GAN, BigVGAN), flow (WaveGlow), diffusion (DiffWave) and autoregressive (WaveNet, WaveRNN) vocoders under one roof.
F0 and energy modeling metrics, intelligibility (CER/WER), spectrogram distortion (FAD, MCD, PESQ, STOI) and speaker-similarity scoring.
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.
Clone Amphion and enter the project directory.
git clone https://github.com/open-mmlab/Amphion.git
cd AmphionCreate 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 amphionRun the project's environment script to install the required Python packages.
sh env.shAmphion is maintained by the open-mmlab community and contributing research groups, with releases announced through the repository and its Discord.
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.
No. Amphion is a local research toolkit; models train and run on your own hardware, and you will want a GPU for serious training.
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.
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.
Deep learning toolkit for text-to-speech, battle-tested in research and production
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The most-starred other Audio projects in the LibreAI catalogue — the realistic shortlist if Amphion is not the right fit.
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Robust Speech Recognition via Large-Scale Weak Supervision
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
Port of OpenAI's Whisper model in C/C++
Free and open — grab the source, star the repo, and start creating.