MuPT: A Generative Symbolic Music Pretrained Transformer
<|reference_start|>MuPT: A Generative Symbolic Music Pretrained Transformer: In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our findings suggest that LLMs are inherently more compatible with ABC Notation, which aligns more closely with their design and strengths, thereby enhancing the model's performance in musical composition. To address the challenges associated with misaligned measures from different tracks during generation, we propose the development of a Synchronized Multi-Track ABC Notation (SMT-ABC Notation), which aims to preserve coherence across multiple musical tracks. Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set. Furthermore, we explore the implications of the Symbolic Music Scaling Law (SMS Law) on model performance. The results indicate a promising direction for future research in music generation, offering extensive resources for community-led research through our open-source contributions.<|reference_end|>
@article{qu2024mupt:,
    title={MuPT: A Generative Symbolic Music Pretrained Transformer},
    author={Xingwei Qu, Yuelin Bai, Yinghao Ma, Ziya Zhou, Ka Man Lo, Jiaheng Liu,
  Ruibin Yuan, Lejun Min, Xueling Liu, Tianyu Zhang, Xinrun Du, Shuyue Guo,
  Yiming Liang, Yizhi Li, Shangda Wu, Junting Zhou, Tianyu Zheng, Ziyang Ma,
  Fengze Han, Wei Xue, Gus Xia, Emmanouil Benetos, Xiang Yue, Chenghua Lin, Xu
  Tan, Stephen W. Huang, Jie Fu, and Ge Zhang},
    journal={arXiv preprint arXiv:2404.06393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06393},
    primaryClass={cs.SD cs.AI eess.AS}
}