Metadata-Version: 2.1
Name: audiocraft
Version: 1.0.0
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
Description: 
        # 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.
        
Platform: UNKNOWN
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
Provides-Extra: dev
