JEN-1: Text-Guided Universal Music Generation with Omnidirectional
  Diffusion Models
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
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Peike Li
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Boyu Chen
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Yao Yao
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2023-08-11T02:11:28.217Z
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Yao
user
YaoYaoEver
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Yikai Wang
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2024-03-22T09:29:48.607Z
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Yikai Wang
user
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Allen Wang
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Alex Wang
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2024-06-20T07:35:14.639Z
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Dr. Alex Wang
user
dralexw
JEN-1: Text-Guided Universal Music Generation with Omnidirectional
  Diffusion Models
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
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.
diffusion model autoregressive non-autoregressive in-context learning text-guided music generation music inpainting continuation text-music alignment music quality