MusGO Framework: Assessing Openness in Music-Generative AI
This website builds on the paper MusGO: A Community-Driven Framework for Assessing Openness in Music-Generative AI, authored by Roser Batlle-Roca, Laura Ibáñez-Martínez, Xavier Serra,
Emilia Gómez, and Martín Rocamora, and published in the Proceedings of the 26th International Society for Music Information Retrieval Conference (ISMIR 2025).
It serves not only as a companion to the publication, but also as a living resource, which is continuously updated and shaped by contributions from the community.
Full-song lyrics-to-music foundation model employing parallel token modeling (mixed semantic tokens and dual-track acoustic tokens) and multi-preference alignment.
Application types:Lyrics-to-songText-to-musicStyle transfer
LeVo is trained on 2 million music tracks (~110,000 hours), including part of DISCO-10M, Millong Song Dataset and some copyrighted in-house data. No further information is provided, and the training data is not publicly available.
Evaluation protocols are thoroughly detailed, including objective metrics and a 20-expert subjective evaluation (see Appendix D for futher details). However, dataset for evaluation is not fully specified nor publicly available.