Simple and Controllable Music Generation
Authors: name: Jade Copet, name: Felix Kreuk, name: Itai Gat, name: Tal Remez, name: David Kant, name: Gabriel Synnaeve, name: Yossi Adi, name: Alexandre Défossez

We tackle the task of conditional music generation. We introduce MusicGen, a
single Language Model (LM) that operates over several streams of compressed
discrete music representation, i.e., tokens. Unlike prior work, MusicGen is
comprised of a single-stage transformer LM together with efficient token
interleaving patterns, which eliminates the need for cascading several models,
e.g., hierarchically or upsampling. Following this approach, we demonstrate how
MusicGen can generate high-quality samples, while being conditioned on textual
description or melodic features, allowing better controls over the generated
output. We conduct extensive empirical evaluation, considering both automatic
and human studies, showing the proposed approach is superior to the evaluated
baselines on a standard text-to-music benchmark. Through ablation studies, we
shed light over the importance of each of the components comprising MusicGen.
Music samples, code, and models are available at
https://github.com/facebookresearch/audiocraft.