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| title: MusicGen | |
| python_version: '3.9' | |
| tags: | |
| - music generation | |
| - language models | |
| - LLMs | |
| app_file: app.py | |
| emoji: 🎵 | |
| colorFrom: white | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 3.34.0 | |
| pinned: true | |
| license: cc-by-nc-4.0 | |
| duplicated_from: facebook/MusicGen | |
| # 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. | |
| ## MusicGen | |
| Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive | |
| 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 require a self-supervised semantic representation, and it generates | |
| all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict | |
| them in parallel, thus having only 50 auto-regressive steps per second of audio. | |
| Check out our [sample page][musicgen_samples] or test the available demo! | |
| <a target="_blank" href="https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing"> | |
| <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> | |
| </a> | |
| <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> | |
| <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/> | |
| </a> | |
| <br> | |
| We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data. | |
| ## Installation | |
| 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: | |
| ```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 | |
| ``` | |
| ## Usage | |
| We offer a number of way to interact with MusicGen: | |
| 1. A demo is also available on the [`facebook/MusicGen` HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support). | |
| 2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing). | |
| 3. You can use the gradio demo locally by running `python app.py`. | |
| 4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU). | |
| 5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly | |
| updated with contributions from @camenduru and the community. | |
| ## API | |
| We provide a simple API and 4 pre-trained models. The pre trained models are: | |
| - `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small) | |
| - `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium) | |
| - `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody) | |
| - `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large) | |
| We observe the best trade-off between quality and compute with the `medium` or `melody` model. | |
| In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller | |
| GPUs will be able to generate short sequences, or longer sequences with the `small` model. | |
| **Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`. | |
| You can install it with: | |
| ``` | |
| apt-get install ffmpeg | |
| ``` | |
| See after a quick example for using the API. | |
| ```python | |
| import torchaudio | |
| from audiocraft.models import MusicGen | |
| from audiocraft.data.audio import audio_write | |
| model = MusicGen.get_pretrained('melody') | |
| model.set_generation_params(duration=8) # generate 8 seconds. | |
| wav = model.generate_unconditional(4) # generates 4 unconditional audio samples | |
| descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] | |
| wav = model.generate(descriptions) # generates 3 samples. | |
| melody, sr = torchaudio.load('./assets/bach.mp3') | |
| # generates using the melody from the given audio and the provided descriptions. | |
| wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr) | |
| for idx, one_wav in enumerate(wav): | |
| # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. | |
| audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True) | |
| ``` | |
| ## Model Card | |
| See [the model card page](./MODEL_CARD.md). | |
| ## FAQ | |
| #### Will the training code be released? | |
| Yes. We will soon release the training code for MusicGen and EnCodec. | |
| #### I need help on Windows | |
| @FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4) | |
| #### I need help for running the demo on Colab | |
| Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo). | |
| ## Citation | |
| ``` | |
| @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}, | |
| } | |
| ``` | |
| ## License | |
| * The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). | |
| * The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). | |
| [arxiv]: https://arxiv.org/abs/2306.05284 | |
| [musicgen_samples]: https://ai.honu.io/papers/musicgen/ | |