title: Stable Audio Open
summary: Open generative models are vitally important for the community, allowing for
  fine-tunes and serving as baselines when presenting new models. However, most
  current text-to-audio models are private and not accessible for artists and
  researchers to build upon. Here we describe the architecture and training
  process of a new open-weights text-to-audio model trained with Creative Commons
  data. Our evaluation shows that the model's performance is competitive with the
  state-of-the-art across various metrics. Notably, the reported FDopenl3 results
  (measuring the realism of the generations) showcase its potential for
  high-quality stereo sound synthesis at 44.1kHz.
paper_authors._id: 669dbec5a8b62d05158f7d8b
paper_authors.name: Zach Evans
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paper_authors.fullname: Zach Evans
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paper_authors.user: Fauno15
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paper_authors.name: Julian D. Parker
paper_authors.status: admin_assigned
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paper_authors.fullname: Julian Parker
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paper_authors.name: CJ Carr
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paper_authors.fullname: CJ Carr
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paper_authors.user: cjus13
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paper_authors.name: Zack Zukowski
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paper_authors.fullname: Zack Zukowski
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paper_authors.user: drawordoodle
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paper_authors.name: Josiah Taylor
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paper_authors.fullname: Josiah Taylor
paper_authors.type: user
paper_authors.user: AtOdds
paper_authors._id: 669dbec5a8b62d05158f7d90
paper_authors.name: Jordi Pons
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-22T08:51:32.700Z
paper_authors._id: 6682a312d17f4c29394f6c97
paper_authors.avatarUrl: /avatars/18f943e694ce54c4949d467d73cfba14.svg
paper_authors.fullname: Jordi Pons
paper_authors.type: user
paper_authors.user: jordipons
paper_title: Stable Audio Open
paper_summary: Open generative models are vitally important for the community, allowing for
  fine-tunes and serving as baselines when presenting new models. However, most
  current text-to-audio models are private and not accessible for artists and
  researchers to build upon. Here we describe the architecture and training
  process of a new open-weights text-to-audio model trained with Creative Commons
  data. Our evaluation shows that the model's performance is competitive with the
  state-of-the-art across various metrics. Notably, the reported FDopenl3 results
  (measuring the realism of the generations) showcase its potential for
  high-quality stereo sound synthesis at 44.1kHz.
paper_ai_keywords: text-to-audio models Creative Commons data FDopenl3 stereo sound synthesis