# Audiocraft
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 EnCodec tokenizer 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!
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/