Moûsai: Efficient Text-to-Music Diffusion Models
Primary Area: generative models
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Keywords: Music Generation, Stable Diffusion
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Abstract: Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another “language” of communication – music. In our work, we bridge text and music via a text-to-music generation model that is highly efficient, expressive, and can handle long-term structure. Specifically, we develop Moûsai, a cascading two-stage latent diffusion model that can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions. Moreover, our model features high efficiency, which enables real-time inference on a single consumer GPU with a reasonable speed. Through experiments and property analyses, we show our model’s competence over a variety of criteria compared with existing music generation models.
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Submission Number: 4101
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