Metadata-Version: 2.1 Name: audiocraft Version: 1.4.0a1 Summary: Audio generation research library for PyTorch Home-page: https://github.com/facebookresearch/audiocraft Author: FAIR Speech & Audio Author-email: defossez@meta.com, jadecopet@meta.com License: MIT License Classifier: License :: OSI Approved :: MIT License Classifier: Topic :: Multimedia :: Sound/Audio Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Requires-Python: >=3.8.0 Description-Content-Type: text/markdown License-File: LICENSE License-File: LICENSE_weights Requires-Dist: av==11.0.0 Requires-Dist: einops Requires-Dist: flashy>=0.0.1 Requires-Dist: hydra-core>=1.1 Requires-Dist: hydra_colorlog Requires-Dist: julius Requires-Dist: num2words Requires-Dist: numpy<2.0.0 Requires-Dist: sentencepiece Requires-Dist: spacy>=3.6.1 Requires-Dist: torch==2.1.0 Requires-Dist: torchaudio<2.1.2,>=2.0.0 Requires-Dist: huggingface_hub Requires-Dist: tqdm Requires-Dist: transformers>=4.31.0 Requires-Dist: xformers<0.0.23 Requires-Dist: demucs Requires-Dist: librosa Requires-Dist: soundfile Requires-Dist: gradio Requires-Dist: torchmetrics Requires-Dist: encodec Requires-Dist: protobuf Requires-Dist: torchvision==0.16.0 Requires-Dist: torchtext==0.16.0 Requires-Dist: pesq Requires-Dist: pystoi Provides-Extra: dev Requires-Dist: coverage; extra == "dev" Requires-Dist: flake8; extra == "dev" Requires-Dist: mypy; extra == "dev" Requires-Dist: pdoc3; extra == "dev" Requires-Dist: pytest; extra == "dev" Provides-Extra: wm Requires-Dist: audioseal; extra == "wm" # 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. ## 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 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.