README

AudioCraft
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AudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code
for two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.


 Installation
AudioCraft requires Python 3.9, PyTorch 2.1.0. To install AudioCraft, you can run the following:

shell
 Best to make sure you have torch installed first, in particular before installing xformers.
 Don't run this if you already have PyTorch installed.
python -m pip install 'torch==2.1.0'
 You might need the following before trying to install the packages
python -m pip install setuptools wheel
 Then proceed to one of the following
python -m pip install -U audiocraft   stable release
python -m pip install -U git+https://[email protected]/facebookresearch/audiocraftegg=audiocraft   bleeding edge
python -m pip install -e .   or if you cloned the repo locally (mandatory if you want to train).
python -m pip install -e '.[wm]'   if you want to train a watermarking model


We also recommend having ffmpeg installed, either through your system or Anaconda:
bash
sudo apt-get install ffmpeg
 Or if you are using Anaconda or Miniconda
conda install "ffmpeg<5" -c conda-forge


 Models

At the moment, AudioCraft contains the training code and inference code for:
 MusicGen: A state-of-the-art controllable text-to-music model.
 AudioGen: A state-of-the-art text-to-sound model.
 EnCodec: A state-of-the-art high fidelity neural audio codec.
 Multi Band Diffusion: An EnCodec compatible decoder using diffusion.
 MAGNeT: A state-of-the-art non-autoregressive model for text-to-music and text-to-sound.
 AudioSeal: A state-of-the-art audio watermarking.
 MusicGen Style: A state-of-the-art text-and-style-to-music model.
 JASCO: "High quality text-to-music model conditioned on chords, melodies and drum tracks"


 Training code

AudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models.
For a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to
the AudioCraft training documentation.

For reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model
that provides pointers to configuration, example grids and model/task-specific information and FAQ.


 API documentation

We provide some API documentation for AudioCraft.


 FAQ

 Is the training code available?

Yes! We provide the training code for EnCodec, MusicGen,Multi Band Diffusion and JASCO.

 Where are the models stored?

Hugging Face stored the model in a specific location, which can be overridden by setting the AUDIOCRAFT_CACHE_DIR environment variable for the AudioCraft models.
In order to change the cache location of the other Hugging Face models, please check out the Hugging Face Transformers documentation for the cache setup.
Finally, if you use