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A newer version of the Gradio SDK is available:
4.42.0
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
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. 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, 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 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:
# 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:
- A demo is also available on the
facebook/MusicGen
HuggingFace Space (huge thanks to all the HF team for their support). - You can run the Gradio demo in Colab: colab notebook.
- You can use the gradio demo locally by running
python app.py
. - You can play with MusicGen by running the jupyter notebook at
demo.ipynb
locally (if you have a GPU). - Finally, checkout @camenduru Colab page 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 - 🤗 Hubmedium
: 1.5B model, text to music only - 🤗 Hubmelody
: 1.5B model, text to music and text+melody to music - 🤗 Hublarge
: 3.3B model, text to music only - 🤗 Hub
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 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.
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.
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
I need help for running the demo on Colab
Check @camenduru tutorial on Youtube.
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.
- The weights in this repository are released under the CC-BY-NC 4.0 license as found in the LICENSE_weights file.