JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
Peike Li, Boyu Chen, Yao Yao, Yikai Wang, Allen Wang, Alex Wang
Abstract
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 ability. 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 in an end-to-end manner, enabling up to 48kHz high-fidelity stereo music generation. Through multi-task 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 demo pages are available at https://jenmusic.ai/audio-demos