facebookresearch--audiocraft
JadeCopet
85 行
4.0 KiB
Markdown
85 行
4.0 KiB
Markdown
# AudioCraft
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AudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code
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for two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.
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## Installation
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AudioCraft requires Python 3.9, PyTorch 2.0.0. To install AudioCraft, you can run the following:
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```shell
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# Best to make sure you have torch installed first, in particular before installing xformers.
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# Don't run this if you already have PyTorch installed.
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pip install 'torch>=2.0'
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# Then proceed to one of the following
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pip install -U audiocraft # stable release
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pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge
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pip install -e . # or if you cloned the repo locally (mandatory if you want to train).
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```
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We also recommend having `ffmpeg` installed, either through your system or Anaconda:
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```bash
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sudo apt-get install ffmpeg
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# Or if you are using Anaconda or Miniconda
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conda install "ffmpeg<5" -c conda-forge
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```
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## Models
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At the moment, AudioCraft contains the training code and inference code for:
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* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model.
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* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model.
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* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec.
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* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion.
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## Training code
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AudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models.
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For a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to
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the [AudioCraft training documentation](./docs/TRAINING.md).
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For reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model
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that provides pointers to configuration, example grids and model/task-specific information and FAQ.
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## API documentation
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We provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft.
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## FAQ
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#### Is the training code available?
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Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md) and [Multi Band Diffusion](./docs/MBD.md).
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#### Where are the models stored?
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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.
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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).
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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).
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## License
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* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
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* 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).
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## Citation
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For the general framework of AudioCraft, please cite the following.
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```
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@article{copet2023simple,
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title={Simple and Controllable Music Generation},
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author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
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year={2023},
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journal={arXiv preprint arXiv:2306.05284},
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}
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```
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When referring to a specific model, please cite as mentioned in the model specific README, e.g
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[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc.
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