| --- |
| 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 |
| 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? |
|
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| Yes. We will soon release the training code for MusicGen and EnCodec. |
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|
| #### I need help on Windows |
|
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| @FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4) |
|
|
| #### I need help for running the demo on Colab |
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| Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo). |
|
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|
|
| ## 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/ |
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|
