Musilingo: Bridging Music And Text With Pre-trained Language Models For Music Captioning And Query Response

Deng Zihao, Ma Yinghao, Liu Yudong, Guo Rongchen, Zhang Ge, Chen Wenhu, Huang Wenhao, Benetos Emmanouil. 2023

[Paper]    
Applications Multimodal Models

Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT with a frozen LLM, bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q&A datasets, we created the MusicInstruct (MI) dataset from captions in the MusicCaps datasets, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs. Our introduced dataset enables notable advancements beyond previous ones.

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