title: Fast Timing-Conditioned Latent Audio Diffusion
summary: Generating long-form 44.1kHz stereo audio from text prompts can be
  computationally demanding. Further, most previous works do not tackle that
  music and sound effects naturally vary in their duration. Our research focuses
  on the efficient generation of long-form, variable-length stereo music and
  sounds at 44.1kHz using text prompts with a generative model. Stable Audio is
  based on latent diffusion, with its latent defined by a fully-convolutional
  variational autoencoder. It is conditioned on text prompts as well as timing
  embeddings, allowing for fine control over both the content and length of the
  generated music and sounds. Stable Audio is capable of rendering stereo signals
  of up to 95 sec at 44.1kHz in 8 sec on an A100 GPU. Despite its compute
  efficiency and fast inference, it is one of the best in two public
  text-to-music and -audio benchmarks and, differently from state-of-the-art
  models, can generate music with structure and stereo sounds.
paper_authors._id: 65c463ca6b793334efab148c
paper_authors.name: Zach Evans
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-02-08T18:21:16.847Z
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paper_authors.fullname: Zach Evans
paper_authors.type: user
paper_authors.user: Fauno15
paper_authors._id: 65c463ca6b793334efab148d
paper_authors.name: CJ Carr
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-08T11:51:24.248Z
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paper_authors.fullname: CJ Carr
paper_authors.type: user
paper_authors.user: cjus13
paper_authors._id: 65c463ca6b793334efab148e
paper_authors.name: Josiah Taylor
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-08T11:51:33.200Z
paper_authors._id: 642c16a0c3694d2b74558f14
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paper_authors.fullname: Josiah Taylor
paper_authors.type: user
paper_authors.user: AtOdds
paper_authors._id: 65c463ca6b793334efab148f
paper_authors.name: Scott H. Hawley
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-08T11:51:43.230Z
paper_authors._id: 608ce15acbb3288dd7faf8e7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/608ce15acbb3288dd7faf8e7/8blAfMJirdA_UwjbargH-.jpeg
paper_authors.fullname: Scott Hawley
paper_authors.type: user
paper_authors.user: drscotthawley
paper_authors._id: 65c463ca6b793334efab1490
paper_authors.name: Jordi Pons
paper_title: Fast Timing-Conditioned Latent Audio Diffusion
paper_summary: Generating long-form 44.1kHz stereo audio from text prompts can be
  computationally demanding. Further, most previous works do not tackle that
  music and sound effects naturally vary in their duration. Our research focuses
  on the efficient generation of long-form, variable-length stereo music and
  sounds at 44.1kHz using text prompts with a generative model. Stable Audio is
  based on latent diffusion, with its latent defined by a fully-convolutional
  variational autoencoder. It is conditioned on text prompts as well as timing
  embeddings, allowing for fine control over both the content and length of the
  generated music and sounds. Stable Audio is capable of rendering stereo signals
  of up to 95 sec at 44.1kHz in 8 sec on an A100 GPU. Despite its compute
  efficiency and fast inference, it is one of the best in two public
  text-to-music and -audio benchmarks and, differently from state-of-the-art
  models, can generate music with structure and stereo sounds.
paper_ai_keywords: latent diffusion variational autoencoder fully-convolutional text prompts timing embeddings stereo signals text-to-music text-to-audio A100 GPU