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.
HeartMuLa
HeartMuLa Teams · 2026
An LLM-based song generation model capable of synthesizing high-fidelity music under rich, user-controllable conditions. HeartMuLa supports multi-conditional music creation with inputs including textual style descriptions, lyrics, and reference audio.
Application types:Text-to-songLyrics-to-songStyle transfer
Technical report indicates that HeartMuLa was trainied on ~100,000 hours of high-quality music and describes automated filtering pipelines. However, there is no information about the specific music used.
All components of the codebase are properly documented in the GitHub repository, including installation requirements, examples of scripts usage and configuration files.
A technical report is available on arXiv, but the paper is not yet peer-reviewed. The report provides a detailed description of the model architecture, training procedure, evaluation and inference.
According to the GitHub repository, there is a model demo available in Hugging Face Spaces and in Model Scope. However, neither of the two are currently operational.