Computer Science > Sound
arXiv:2402.16153 (cs)
[Submitted on 25 Feb 2024]
Title:ChatMusician: Understanding and Generating Music Intrinsically with LLM
Authors: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
View a PDF of the paper titled ChatMusician: Understanding and Generating Music Intrinsically with LLM, by Ruibin Yuan and 34 other authors
View PDFHTML (experimental)Abstract: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.
| Comments: | GitHub:this https URL |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2402.16153 [cs.SD] |
| (orarXiv:2402.16153v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2402.16153 arXiv-issued DOI via DataCite |
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled ChatMusician: Understanding and Generating Music Intrinsically with LLM, by Ruibin Yuan and 34 other authors
Current browse context:
cs.SD
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.