Audiocraft

Audiocraft is a PyTorch library made by Meta 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.

Audiocraft infinity

Adds generation of songs with a length of over 30 seconds.

Adds the ability to continue songs.

Disables (hopefully) the gradio analytics.

Installation

Audiocraft requires Python 3.9, PyTorch 2.0.0. 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

python app.py

API

Meta provides a simple API and 4 pre-trained models. The pre trained models are:

• small: 300M model, text to music only - 🤗 Hub
• medium: 1.5B model, text to music only - 🤗 Hub
• melody: 1.5B model, text to music and text+melody to music - 🤗 Hub
• large: 3.3B model, text to music only - 🤗 Hub

Needs a GPU! I recommend 12GB of VRAM for the large 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")

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 AGPLv3 license as found in the LICENSE file.

Copyright notice

Copyright (c) Meta Platforms, Inc. and affiliates.