base on Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning. # AudioCraft    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://[email protected]/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. ## 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. ", Assign "at most 3 tags" to the expected json: {"id":"2456","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"