NotaGen
NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms
클래식 음악을 작곡하는 심볼릭 음악 생성 모델
Abstract
We introduce NotaGen, a symbolic music generation model aims to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts pre-training, fine-tuning, and reinforcement learning paradigms (henceforth referred to as the LLM training paradigms). It is pre-trained on 1.6M pieces of music, and then fine-tuned on approximately 9K high-quality classical compositions conditioned on ``period-composer-instrumentation'' prompts. For reinforcement learning, we propose the CLaMP-DPO method, which further enhances generation quality and controllability without requiring human annotations or predefined rewards. Our experiments demonstrate the efficacy of CLaMP-DPO in symbolic music generation models with different architectures and encoding schemes. Furthermore, subjective A/B tests show that NotaGen outperforms baseline models against human compositions, greatly advancing musical aesthetics in symbolic music generation.
Documentations
- https://arxiv.org/abs/2502.18008
- [2502.18008] NotaGen - Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms