用轻量模型实现音乐片段智能补全，支持风格迁移与可控生成。
Adaptable Symbolic Music Infilling with MIDI-RWKV
- 基于RWKV-7架构设计小模型，支持边缘设备实时协作创作。
- 仅需少量样本即可通过状态微调实现音乐风格适配。
- 适用于作曲家在现有作品中高效补全多轨长段落音乐。
现有自动音乐生成多为端到端系统，生成完整乐曲或续写片段，难以支持作曲家迭代。本文聚焦计算机辅助作曲，提出可适应风格、支持多轨、长上下文且可控的符号化音乐补全任务。我们提出MIDI-RWKV，一个基于RWKV-7线性架构的小型基础模型，可在边缘设备上实现高效且连贯的音乐协同创作。同时证明，通过微调其初始状态，可在极低样本条件下实现有效风格适配。我们在多个定量与定性指标上评估MIDI-RWKV及其状态微调方法，结果优于现有模型，并公开模型权重与代码于https://github.com/christianazinn/MIDI-RWKV。
原文摘要 · Abstract (English)
Existing work in automatic music generation has mostly focused on end-to-end systems that generate either entire compositions or continuations of pieces, which are difficult for composers to iterate on. The area of computer-assisted composition, where generative models integrate into existing creative workflows, remains comparatively underexplored. In this study, we address the tasks of model style adaptation and multi-track, long-context, and controllable symbolic music infilling to enhance the process of computer-assisted composition. We present MIDI-RWKV, a small foundation 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 style adaptation in the very-low-sample regime. We evaluate MIDI-RWKV and its state tuning on several quantitative and qualitative metrics with respect to existing models, and release model weights and code at https://github.com/christianazinn/MIDI-RWKV.
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