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.
Jukebox
OpenAI · 2020
Synthesizes raw audio songs with singing vocals conditioned on genre, style, and lyrics.
Code is available at GitHub repository. The codebase is not actively maintained anymore, but it is still available for use. It includes the model architecture, training and evaluation code, and utilities for generating music samples.
Training data
~ Partial
Training data is partially described in the preprint article. However, relevant information is missing, such as specific sources, and data itself cannot be accessed. Dataset is not publicly available.
Part of the evaluation procedure is documented in the research paper. However, key information is missing, such as the evaluation dataset, objective evaluation metrics and details on the manual evaluation.
A demo/summary page is available. However, most sonifications of the examples generations are not available anymore. There exist another site with generation examples, but most of them are not available https://jukebox.openai.com/.