Shangda Wu


2025

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CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
Shangda Wu | Yashan Wang | Ruibin Yuan | Guo Zhancheng | Xu Tan | Ge Zhang | Monan Zhou | Jing Chen | Xuefeng Mu | Yuejie Gao | Yuanliang Dong | Jiafeng Liu | Xiaobing Li | Feng Yu | Maosong Sun
Findings of the Association for Computational Linguistics: NAACL 2025

Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems. These limitations reduce their effectiveness in a global, multimodal music environment. To address these issues, we introduce CLaMP 2, a system compatible with 101 languages that supports both ABC notation (a text-based musical notation format) and MIDI (Musical Instrument Digital Interface) for music information retrieval. CLaMP 2, pre-trained on 1.5 million ABC-MIDI-text triplets, includes a multilingual text encoder and a multimodal music encoder aligned via contrastive learning. By leveraging large language models, we obtain refined and consistent multilingual descriptions at scale, significantly reducing textual noise and balancing language distribution. Our experiments show that CLaMP 2 achieves state-of-the-art results in both multilingual semantic search and music classification across modalities, thus establishing a new standard for inclusive and global music information retrieval.

2024

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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 | Liumeng Xue | Ziyang Ma | Qin Liu | Tianyu Zheng | Yizhi Li | Yinghao Ma | Yiming Liang | Xiaowei Chi | Ruibo Liu | Zili Wang | Chenghua Lin | Qifeng Liu | Tao Jiang | Wenhao Huang | Wenhu Chen | Jie Fu | Emmanouil Benetos | Gus Xia | Roger Dannenberg | Wei Xue | Shiyin Kang | Yike Guo
Findings of the Association for Computational Linguistics: ACL 2024

While LLMs demonstrate impressive capabilities in musical knowledge, we find that music reasoning is still an unsolved task.We introduce ChatMusician, an open-source large language model (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 external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score.ChatMusician is capable of composing well-structured, full-length music, condition on texts, chords, melodies, motifs, musical forms, etc.On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 by a noticeable margin. We show that ChatMusician preserves or even surpasses the original LLaMA2 7B’s language abilities by evaluating on MMLU benchmark.Our work reveals that LLMs can be an excellent compressor for music, which can be seen as humanity’s creative language, but there remains significant territory to be conquered.We release our 5B token music-language corpora MusicPiles, the collected MusicTheoryBench, code, model and demo.