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title: "AI Tube Engine MusicGen" | |
python_version: "3.9" | |
tags: | |
- "music generation" | |
- "language models" | |
- "LLMs" | |
app_file: "demos/musicgen_app.py" | |
emoji: 🎵 | |
colorFrom: gray | |
colorTo: blue | |
sdk: gradio | |
sdk_version: 3.34.0 | |
pinned: false | |
license: "cc-by-nc-4.0" | |
# AudioCraft | |
![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg) | |
![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg) | |
![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg) | |
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 2.0.0. 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. | |
pip install 'torch>=2.0' | |
# Then proceed to one of the following | |
pip install -U audiocraft # stable release | |
pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge | |
pip install -e . # or if you cloned the repo locally (mandatory if you want to train). | |
``` | |
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](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model. | |
* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model. | |
* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec. | |
* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion. | |
## 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](./docs/TRAINING.md). | |
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](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft. | |
## FAQ | |
#### Is the training code available? | |
Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md) and [Multi Band Diffusion](./docs/MBD.md). | |
#### Where are the models stored? | |
Hugging Face stored the model in a specific location, which can be overriden 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](https://huggingface.co/docs/transformers/installation#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](https://pytorch.org/docs/stable/hub.html#where-are-my-downloaded-models-saved). | |
## License | |
* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). | |
* The models weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). | |
## Citation | |
For the general framework of AudioCraft, please cite the following. | |
``` | |
@article{copet2023simple, | |
title={Simple and Controllable Music Generation}, | |
author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, | |
year={2023}, | |
journal={arXiv preprint arXiv:2306.05284}, | |
} | |
``` | |
When referring to a specific model, please cite as mentioned in the model specific README, e.g | |
[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc. | |