[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72671":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":24,"topics":25,"createdAt":9,"pushedAt":9,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":15,"starSnapshotCount":15,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},72671,"audiocraft","facebookresearch\u002Faudiocraft","facebookresearch","Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor \u002F tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.",null,"Jupyter Notebook",23366,2637,214,362,0,19,36,96,57,117.6,"MIT License",false,"main",true,[],"2026-06-12 04:01:06","# AudioCraft\n![docs badge](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Faudiocraft\u002Fworkflows\u002Faudiocraft_docs\u002Fbadge.svg)\n![linter badge](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Faudiocraft\u002Fworkflows\u002Faudiocraft_linter\u002Fbadge.svg)\n![tests badge](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Faudiocraft\u002Fworkflows\u002Faudiocraft_tests\u002Fbadge.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:\u002F\u002Fgit@github.com\u002Ffacebookresearch\u002Faudiocraft#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](.\u002Fdocs\u002FMUSICGEN.md): A state-of-the-art controllable text-to-music model.\n* [AudioGen](.\u002Fdocs\u002FAUDIOGEN.md): A state-of-the-art text-to-sound model.\n* [EnCodec](.\u002Fdocs\u002FENCODEC.md): A state-of-the-art high fidelity neural audio codec.\n* [Multi Band Diffusion](.\u002Fdocs\u002FMBD.md): An EnCodec compatible decoder using diffusion.\n* [MAGNeT](.\u002Fdocs\u002FMAGNET.md): A state-of-the-art non-autoregressive model for text-to-music and text-to-sound.\n* [AudioSeal](.\u002Fdocs\u002FWATERMARKING.md): A state-of-the-art audio watermarking.\n* [MusicGen Style](.\u002Fdocs\u002FMUSICGEN_STYLE.md): A state-of-the-art text-and-style-to-music model.\n* [JASCO](.\u002Fdocs\u002FJASCO.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](.\u002Fdocs\u002FTRAINING.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\u002Ftask-specific information and FAQ.\n\n\n## API documentation\n\nWe provide some [API documentation](https:\u002F\u002Ffacebookresearch.github.io\u002Faudiocraft\u002Fapi_docs\u002Faudiocraft\u002Findex.html) for AudioCraft.\n\n\n## FAQ\n\n#### Is the training code available?\n\nYes! We provide the training code for [EnCodec](.\u002Fdocs\u002FENCODEC.md), [MusicGen](.\u002Fdocs\u002FMUSICGEN.md),[Multi Band Diffusion](.\u002Fdocs\u002FMBD.md) and [JASCO](.\u002Fdocs\u002FJASCO.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:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Finstallation#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:\u002F\u002Fpytorch.org\u002Fdocs\u002Fstable\u002Fhub.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[.\u002Fdocs\u002FMUSICGEN.md](.\u002Fdocs\u002FMUSICGEN.md), [.\u002Fdocs\u002FAUDIOGEN.md](.\u002Fdocs\u002FAUDIOGEN.md), etc.\n","Audiocraft 是一个基于深度学习的音频处理和生成库，它包含了先进的EnCodec音频压缩\u002F编码器以及MusicGen音乐生成模型，后者能够通过文本和旋律条件控制生成音乐。该项目利用PyTorch框架开发，提供了包括AudioGen（文本到声音）、MusicGen（文本到音乐）在内的多个前沿音频生成模型，支持从训练到推理全流程。Audiocraft适合于需要高质量音频内容生成的研究人员或开发者使用场景，比如音乐创作辅助、声音效果设计等领域。此外，项目还引入了音频水印技术AudioSeal，增强了内容保护能力。",2,"2026-06-11 03:43:05","high_star"]