Simple and Controllable Music Generation
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.
64829a5f709d2cdcac4a8592
Jade Copet
claimed_verified
2023-06-14T09:29:28.726Z
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Jade Copet
user
JadeCopet
64829a5f709d2cdcac4a8593
Felix Kreuk
claimed_verified
2023-06-09T09:31:22.026Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/IOWMd17Iwls0dXsY1OWjK.jpeg
Felix Kreuk
user
felixkreuk
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Itai Gat
claimed_verified
2023-06-09T09:31:18.811Z
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Itai Gat
user
itaigat
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Tal Remez
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David Kant
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Gabriel Synnaeve
claimed_verified
2023-06-09T09:31:28.583Z
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Gabriel Synnaeve
user
gsynnaeve
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Yossi Adi
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Alexandre Défossez
claimed_verified
2023-06-09T15:31:53.230Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1666708948380-noauth.jpeg
Alexandre Défossez
user
adefossez
Simple and Controllable Music Generation
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.
MusicGen, a single-stage transformer language model, generates high-quality music conditioned on text or melodic features using efficient token interleaving patterns, outperforming existing models on text-to-music benchmarks.
Language Model transformer LM token interleaving patterns