Semantic Scholar Open Access 2023 636 sitasi

Simple and Controllable Music Generation

Jade Copet F. Kreuk Itai Gat Tal Remez David Kant +3 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Engineering

Penulis (8)

Jade Copet

F. Kreuk

Itai Gat

Tal Remez

David Kant

Gabriel Synnaeve

Yossi Adi

Alexandre D'efossez

Format Sitasi

APA MLA BibTeX

Copet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G. et al. (2023). Simple and Controllable Music Generation. https://doi.org/10.48550/arXiv.2306.05284

Akses Cepat

Lihat di Sumber doi.org/10.48550/arXiv.2306.05284

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 636×
Sumber Database: Semantic Scholar
DOI: 10.48550/arXiv.2306.05284
Akses: Open Access ✓