MuPT: A Generative Symbolic Music Pretrained Transformer
Xingwei Qu yuelin bai Yinghao Ma Ziya Zhou Ka Man Lo Jiaheng Liu Ruibin Yuan Lejun Min Xueling Liu Tianyu Zhang Xeron Du Shuyue Guo Yiming Liang Yizhi LI Shangda Wu Junting Zhou Tianyu Zheng Ziyang Ma Fengze Han Wei Xue et al. (8 additional authors not shown)
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 $\underline{S}$ynchronized $\underline{M}$ulti-$\underline{T}$rack ABC Notation ($\textbf{SMT-ABC Notation}$), 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 $\underline{S}$ymbolic $\underline{M}$usic $\underline{S}$caling Law ($\textbf{SMS Law}$) on model performance. The results indicate a promising research direction in music generation, offering extensive resources for further research through our open-source contributions.
Pretrained Transformer, SMT-ABC Notation, SMS Law