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README.en.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 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://git@github.com/facebookresearch/audiocraft#egg=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](./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.
+* [MAGNeT](./docs/MAGNET.md): A state-of-the-art non-autoregressive model for text-to-music and text-to-sound.
+* [AudioSeal](./docs/WATERMARKING.md): A state-of-the-art audio watermarking.
+* [MusicGen Style](./docs/MUSICGEN_STYLE.md): A state-of-the-art text-and-style-to-music model.
+* [JASCO](./docs/JASCO.md): "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](./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),[Multi Band Diffusion](./docs/MBD.md) and [JASCO](./docs/JASCO.md).
+
+#### 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](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.
+```
+@inproceedings{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},
+    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+    year={2023},
+}
+```
+
+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.