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.

MeLoDy

ByteDance · 2023

LM-guided diffusion model that generates music audio samples.

Application types: Text-to-music

Architecture:Diffusion

Essential categories

Category	Status	Notes
Source code	✘ Closed	No source code.
Training data	✘ Closed	Not provided and only briefly described.
Model weights	✘ Closed	Not provided.
Code documentation	✘ Closed	No source code.
Training procedure	~ Partial	Training procedure partially described.
Evaluation procedure	~ Partial	Missing information about data and specific implementations.
Research paper	✔︎ Open	Accepted at NeurIPS 2023.
Licensing	✘ Closed	Not available.

Desirable categories

Category	Status	Notes
Model card	∅ Not included	Not provided.
Datasheet	∅ Not included	Not provided.
Package	∅ Not included	Not provided.
User-oriented application	∅ Not included	Not provided.
Supplementary material page	⭐ Included	A complementary web page with sound examples and demonstration of the model's capabilities is available.

Raw YAML file with complete evaluation.