facebookresearch / audiocraft

# AudioCraft
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:
 [code] 
We also recommend having `ffmpeg` installed, either through your system or Anaconda:
 [code] 
## 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 a model that relies on Demucs (e.g. `musicgen-melody`) and want to change the download location for Demucs, refer to the Torch Hub documentation.
## License
* The code in this repository is released under the MIT license as found in the LICENSE file.
* The models weights in this repository are released under the CC-BY-NC 4.0 license as found in the LICENSE_weights file.
## Citation
For the general framework of AudioCraft, please cite the following.
 [code] 
When referring to a specific model, please cite as mentioned in the model specific README, e.g
./docs/MUSICGEN.md, ./docs/AUDIOGEN.md, etc.