@inproceedings{schneider-etal-2024-mousai,
    title = "Mo{\^u}sai: Efficient Text-to-Music Diffusion Models",
    author = {Schneider, Flavio  and
      Kamal, Ojasv  and
      Jin, Zhijing  and
      Sch{\"o}lkopf, Bernhard},
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.acl-long.437/",
    doi = "10.18653/v1/2024.acl-long.437",
    pages = "8050--8068",
    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 {\textquotedblleft}language{\textquotedblright} 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{\^u}sai, 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. Music samples for all models: https://bit.ly/audio-diffusion."
}