title: JEN-1: Text-Guided Universal Music Generation with Omnidirectional
    Diffusion Models
summary: Music generation has attracted growing interest with the advancement of deep
  generative models. However, generating music conditioned on textual
  descriptions, known as text-to-music, remains challenging due to the complexity
  of musical structures and high sampling rate requirements. Despite the task's
  significance, prevailing generative models exhibit limitations in music
  quality, computational efficiency, and generalization. This paper introduces
  JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a
  diffusion model incorporating both autoregressive and non-autoregressive
  training. Through in-context learning, JEN-1 performs various generation tasks
  including text-guided music generation, music inpainting, and continuation.
  Evaluations demonstrate JEN-1's superior performance over state-of-the-art
  methods in text-music alignment and music quality while maintaining
  computational efficiency. Our demos are available at
  http://futureverse.com/research/jen/demos/jen1
paper_authors._id: 64d44ab6508a6313e33990d7
paper_authors.name: Peike Li
paper_authors._id: 64d44ab6508a6313e33990d8
paper_authors.name: Boyu Chen
paper_authors._id: 64d44ab6508a6313e33990d9
paper_authors.name: Yao Yao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-11T02:11:28.217Z
paper_authors._id: 63bd8d569a15a3e9419516e8
paper_authors.avatarUrl: /avatars/6b3449c34e15c1fa798708c67d36167e.svg
paper_authors.fullname: Yao
paper_authors.type: user
paper_authors.user: YaoYaoEver
paper_authors._id: 64d44ab6508a6313e33990da
paper_authors.name: Yikai Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-22T09:29:48.607Z
paper_authors._id: 64206b35e40f66bcd1e3c985
paper_authors.avatarUrl: /avatars/a8f830d1c47702f642045399e59c9fe8.svg
paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiw
paper_authors._id: 64d44ab6508a6313e33990db
paper_authors.name: Allen Wang
paper_authors._id: 64d44ab6508a6313e33990dc
paper_authors.name: Alex Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-20T07:35:14.639Z
paper_authors._id: 6673cbc4ccd71b5bb7609a62
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/M543BjPm3K4A-OBYOFUEE.png
paper_authors.fullname: Dr. Alex Wang
paper_authors.type: user
paper_authors.user: dralexw
paper_title: JEN-1: Text-Guided Universal Music Generation with Omnidirectional
    Diffusion Models
paper_summary: Music generation has attracted growing interest with the advancement of deep
  generative models. However, generating music conditioned on textual
  descriptions, known as text-to-music, remains challenging due to the complexity
  of musical structures and high sampling rate requirements. Despite the task's
  significance, prevailing generative models exhibit limitations in music
  quality, computational efficiency, and generalization. This paper introduces
  JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a
  diffusion model incorporating both autoregressive and non-autoregressive
  training. Through in-context learning, JEN-1 performs various generation tasks
  including text-guided music generation, music inpainting, and continuation.
  Evaluations demonstrate JEN-1's superior performance over state-of-the-art
  methods in text-music alignment and music quality while maintaining
  computational efficiency. Our demos are available at
  http://futureverse.com/research/jen/demos/jen1
paper_ai_summary: JEN-1, a diffusion model combining autoregressive and non-autoregressive training, achieves superior performance in text-to-music generation, music inpainting, and continuation, while maintaining computational efficiency.
paper_ai_keywords: diffusion model autoregressive non-autoregressive in-context learning text-guided music generation music inpainting continuation text-music alignment music quality