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--- |
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title: MusicGen |
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python_version: '3.9' |
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tags: |
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- music generation |
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- language models |
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- LLMs |
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app_file: app.py |
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emoji: 🎵 |
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colorFrom: white |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 3.34.0 |
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pinned: true |
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suggested_hardware: a10g-large |
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license: cc-by-nc-4.0 |
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duplicated_from: musicgen/MusicGen |
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--- |
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# Audiocraft |
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Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model. |
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## MusicGen |
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Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive |
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Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't not require a self-supervised semantic representation, and it generates |
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all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict |
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them in parallel, thus having only 50 auto-regressive steps per second of audio. |
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Check out our [sample page][musicgen_samples] or test the available demo! |
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<a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> |
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<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/> |
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</a> |
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<br> |
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## Installation |
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Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following: |
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```shell |
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# Best to make sure you have torch installed first, in particular before installing xformers. |
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# Don't run this if you already have PyTorch installed. |
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pip install 'torch>=2.0' |
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# Then proceed to one of the following |
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pip install -U audiocraft # stable release |
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pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge |
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pip install -e . # or if you cloned the repo locally |
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``` |
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## Usage |
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You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally, or use the provided [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing). Finally, a demo is also available on the [`facebook/MusiGen` HugginFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support). |
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## API |
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We provide a simple API and 4 pre-trained models. The pre trained models are: |
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- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small) |
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- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium) |
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- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody) |
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- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large) |
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We observe the best trade-off between quality and compute with the `medium` or `melody` model. |
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In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller |
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GPUs will be able to generate short sequences, or longer sequences with the `small` model. |
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**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`. |
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You can install it with: |
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``` |
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apt get install ffmpeg |
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``` |
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See after a quick example for using the API. |
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```python |
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import torchaudio |
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from audiocraft.models import MusicGen |
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from audiocraft.data.audio import audio_write |
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model = MusicGen.get_pretrained('melody') |
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model.set_generation_params(duration=8) # generate 8 seconds. |
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wav = model.generate_unconditional(4) # generates 4 unconditional audio samples |
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descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] |
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wav = model.generate(descriptions) # generates 3 samples. |
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melody, sr = torchaudio.load('./assets/bach.mp3') |
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# generates using the melody from the given audio and the provided descriptions. |
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wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr) |
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for idx, one_wav in enumerate(wav): |
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# Will save under {idx}.wav, with loudness normalization at -14 db LUFS. |
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audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") |
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``` |
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## Model Card |
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See [the model card page](./MODEL_CARD.md). |
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## FAQ |
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#### Will the training code be released? |
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Yes. We will soon release the training code for MusicGen and EnCodec. |
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## Citation |
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``` |
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@article{copet2023simple, |
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title={Simple and Controllable Music Generation}, |
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author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, |
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year={2023}, |
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journal={arXiv preprint arXiv:2306.05284}, |
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} |
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``` |
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## License |
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* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). |
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* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). |
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[arxiv]: https://arxiv.org/abs/2306.05284 |
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[musicgen_samples]: https://ai.honu.io/papers/musicgen/ |
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