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Open Foundation Models"],"sub_categories":["Windows Manager","\u003cspan id=\"tool\"\u003eLLM (LLM \u0026 Tool)\u003c/span\u003e","Creative Uses of Generative AI Image Synthesis Tools","网络服务_其他","LoRA Adapters and Quantized Models","音频","Music and Audio","音乐声音","Text-to-Speech","3. 音频（音乐、语音生成）"],"readme":"# AudioCraft\n![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)\n![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)\n![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)\n\nAudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code\nfor two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.\n\n\n## Installation\nAudioCraft requires Python 3.9, PyTorch 2.1.0. To install AudioCraft, you can run the following:\n\n```shell\n# Best to make sure you have torch installed first, in particular before installing xformers.\n# Don't run this if you already have PyTorch installed.\npython -m pip install 'torch==2.1.0'\n# You might need the following before trying to install the packages\npython -m pip install setuptools wheel\n# Then proceed to one of the following\npython -m pip install -U audiocraft  # stable release\npython -m pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge\npython -m pip install -e .  # or if you cloned the repo locally (mandatory if you want to train).\npython -m pip install -e '.[wm]'  # if you want to train a watermarking model\n```\n\nWe also recommend having `ffmpeg` installed, either through your system or Anaconda:\n```bash\nsudo apt-get install ffmpeg\n# Or if you are using Anaconda or Miniconda\nconda install \"ffmpeg\u003c5\" -c conda-forge\n```\n\n## Models\n\nAt the moment, AudioCraft contains the training code and inference code for:\n* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model.\n* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model.\n* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec.\n* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion.\n* [MAGNeT](./docs/MAGNET.md): A state-of-the-art non-autoregressive model for text-to-music and text-to-sound.\n* [AudioSeal](./docs/WATERMARKING.md): A state-of-the-art audio watermarking.\n* [MusicGen Style](./docs/MUSICGEN_STYLE.md): A state-of-the-art text-and-style-to-music model.\n* [JASCO](./docs/JASCO.md): \"High quality text-to-music model conditioned on chords, melodies and drum tracks\"\n\n\n## Training code\n\nAudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models.\nFor a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to\nthe [AudioCraft training documentation](./docs/TRAINING.md).\n\nFor reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model\nthat provides pointers to configuration, example grids and model/task-specific information and FAQ.\n\n\n## API documentation\n\nWe provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft.\n\n\n## FAQ\n\n#### Is the training code available?\n\nYes! 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).\n\n#### Where are the models stored?\n\nHugging Face stored the model in a specific location, which can be overridden by setting the `AUDIOCRAFT_CACHE_DIR` environment variable for the AudioCraft models.\nIn 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).\nFinally, 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).\n\n\n## License\n* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).\n* 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).\n\n\n## Citation\n\nFor the general framework of AudioCraft, please cite the following.\n```\n@inproceedings{copet2023simple,\n    title={Simple and Controllable Music Generation},\n    author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},\n    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},\n    year={2023},\n}\n```\n\nWhen referring to a specific model, please cite as mentioned in the model specific README, e.g\n[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2Faudiocraft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffacebookresearch%2Faudiocraft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2Faudiocraft/lists"}