Papers › JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
Peike Li, BoYu Chen, Yao Yao, Yikai Wang, Allen Wang, Alex Wang
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 https://jenmusic.ai/audio-demos
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Music Generation | MusicCaps | JEN-1 | FAD | 2.00 | #5 of 21 | Archive leaderboard | report |
| Text-to-Music Generation | MusicCaps | JEN-1 | KL_passt | 1.29 | #5 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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