Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV

Christian Zhou-Zheng, Philippe Pasquier

Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV: 2 upvotes on Hugging Face Daily Papers, #37 of 40 papers on 2025-06-17. Day-by-day upvote history.

Existing work in automatic music generation has primarily focused on end-to-end systems that produce complete compositions or continuations. However, because musical composition is typically an iterative process, such systems make it difficult to engage in the back-and-forth between human and machine that is essential to computer-assisted creativity. In this study, we address the task of personalizable, multi-track, long-context, and controllable symbolic music infilling to enhance the process of computer-assisted composition. We present MIDI-RWKV, a novel model based on the RWKV-7 linear architecture, to enable efficient and coherent musical cocreation on edge devices. We also demonstrate that MIDI-RWKV admits an effective method of finetuning its initial state for personalization in the very-low-sample regime. We evaluate MIDI-RWKV and its state tuning on several quantitative and qualitative metrics, and release model weights and code at https://github.com/christianazinn/MIDI-RWKV.

Paper page on Hugging Face · arXiv

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