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README.md

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-# 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 trainingcon 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://[email protected]/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.
-
-## 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.
-
-```json
-@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.