ChatMusician: Understanding and Generating Music Intrinsically with LLM
Ruibin Yuan Hanfeng Lin Yi Wang Zeyue Tian Shangda Wu Tianhao Shen Ge Zhang Yuhang Wu Cong Liu Ziya Zhou Ziyang Ma Liumeng Xue Ziyu Wang Qin Liu Tianyu Zheng Yizhi Li Yinghao Ma Yiming Liang Xiaowei Chi Ruibo Liu Zili Wang Pengfei Li Jingcheng Wu Chenghua Lin Qifeng Liu Tao Jiang Wenhao Huang Wenhu Chen Emmanouil Benetos Jie Fu Gus Xia Roger Dannenberg Wei Xue Shiyin Kang Yike Guo
While Large Language Models (LLMs) demonstrate impressive capabilities in
text generation, we find that their ability has yet to be generalized to music,
humanity's creative language. We introduce ChatMusician, an open-source LLM
that integrates intrinsic musical abilities. It is based on continual
pre-training and finetuning LLaMA2 on a text-compatible music representation,
ABC notation, and the music is treated as a second language. ChatMusician can
understand and generate music with a pure text tokenizer without any external
multi-modal neural structures or tokenizers. Interestingly, endowing musical
abilities does not harm language abilities, even achieving a slightly higher
MMLU score. Our model is capable of composing well-structured, full-length
music, conditioned on texts, chords, melodies, motifs, musical forms, etc,
surpassing GPT-4 baseline. On our meticulously curated college-level music
understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and
GPT-3.5 on zero-shot setting by a noticeable margin. Our work reveals that LLMs
can be an excellent compressor for music, but there remains significant
territory to be conquered. We release our 4B token music-language corpora
MusicPile, the collected MusicTheoryBench, code, model and demo in GitHub.