audiocraft 1.3.0


Audio generation research library for PyTorch

The Audiocraft library is a powerful tool for audio processing and generation using deep learning. Developed by Facebook Research, it includes the Encodec audio compressor tokenizer and MusicGen, a language model for music generation that can be controlled with text and melody. This makes it a valuable resource for developers and researchers interested in music generation and audio processing. The library is built on PyTorch and requires a GPU for operation.

Stars: 20248, Watchers: 20248, Forks: 2033, Open Issues: 284

The facebookresearch/audiocraft repo was created 1 years ago and the last code push was Yesterday.
The project is extremely popular with a mindblowing 20248 github stars!

How to Install audiocraft

You can install audiocraft using pip

pip install audiocraft

or add it to a project with poetry

poetry add audiocraft

Package Details

FAIR Speech & Audio
MIT License
GitHub Repo:


  • Multimedia/Sound/Audio
  • Scientific/Engineering/Artificial Intelligence
No  audiocraft  pypi packages just yet.


A list of common audiocraft errors.

Code Examples

Here are some audiocraft code examples and snippets.

GitHub Issues

The audiocraft package has 284 open issues on GitHub

  • Audio Embeddings
  • Can't find Seed?
  • FFMPEG not found?
  • Version conflicts with hydra-core
  • –listen - how to make it work?
  • Is it possible to use multiple GPU for single generate?
  • Release Date of Traning Code
  • Can anyone explain how the top_p and top_k parameter affects the output of the model?
  • Update Readme: Add links to Replicate deployment of MusicGen
  • how to train and how much data to invest so that there is no noise?
  • Non-Usable download from Gradio?
  • Any chance for adjust the sample and bit rate of WAVs up?
  • help by install
  • How to download file using api
  • HuggingFace

See more issues on GitHub

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