Mo\^usai: Text-to-Music Generation with Long-Context Latent Diffusion

• URL: http://arxiv.org/abs/2301.11757v3
• Date: Mon, 23 Oct 2023 20:47:30 GMT
• Title: Mo\^usai: Text-to-Music Generation with Long-Context Latent Diffusion
• Authors: Flavio Schneider, Ojasv Kamal, Zhijing Jin, Bernhard Sch\"olkopf
• Abstract summary: 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 Mousai, a cascading two-stage latent diffusion model that can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions.
• Score: 27.567536688166776
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Music, much like text, can convey emotions, stories, and ideas, and has its own unique structure and syntax. 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 Mo\^usai, 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. Lastly, to promote the open-source culture, we provide a collection of open-source libraries with the hope of facilitating future work in the field. We open-source the following: Codes: https://github.com/archinetai/audio-diffusion-pytorch; music samples for this paper: http://bit.ly/44ozWDH; all music samples for all models: https://bit.ly/audio-diffusion.

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