title: Simple and Controllable Music Generation
summary: 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.
paper_authors._id: 64829a5f709d2cdcac4a8592
paper_authors.name: Jade Copet
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paper_authors.fullname: Jade Copet
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paper_authors.user: JadeCopet
paper_authors._id: 64829a5f709d2cdcac4a8593
paper_authors.name: Felix Kreuk
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paper_authors.fullname: Felix Kreuk
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paper_authors.user: felixkreuk
paper_authors._id: 64829a5f709d2cdcac4a8594
paper_authors.name: Itai Gat
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paper_authors.fullname: Itai Gat
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paper_authors.user: itaigat
paper_authors._id: 64829a5f709d2cdcac4a8595
paper_authors.name: Tal Remez
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paper_authors.name: David Kant
paper_authors._id: 64829a5f709d2cdcac4a8597
paper_authors.name: Gabriel Synnaeve
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paper_authors.fullname: Gabriel Synnaeve
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paper_authors.user: gsynnaeve
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paper_authors.name: Yossi Adi
paper_authors._id: 64829a5f709d2cdcac4a8599
paper_authors.name: Alexandre Défossez
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-09T15:31:53.230Z
paper_authors._id: 6357f5f50dce5559211a3503
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1666708948380-noauth.jpeg
paper_authors.fullname: Alexandre Défossez
paper_authors.type: user
paper_authors.user: adefossez
paper_title: Simple and Controllable Music Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Language Model transformer LM token interleaving patterns