@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://aclanthology.org/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 {``}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{\^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.",
}
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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. 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 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. 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.</abstract>
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%0 Conference Proceedings
%T Moûsai: Efficient Text-to-Music Diffusion Models
%A Schneider, Flavio
%A Kamal, Ojasv
%A Jin, Zhijing
%A Schölkopf, Bernhard
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F schneider-etal-2024-mousai
%X 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 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. 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.
%U https://aclanthology.org/2024.acl-long.437
%P 8050-8068
Markdown (Informal)
[Moûsai: Efficient Text-to-Music Diffusion Models](https://aclanthology.org/2024.acl-long.437) (Schneider et al., ACL 2024)
ACL
- Flavio Schneider, Ojasv Kamal, Zhijing Jin, and Bernhard Schölkopf. 2024. Moûsai: Efficient Text-to-Music Diffusion Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8050–8068, Bangkok, Thailand. Association for Computational Linguistics.