MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response

• URL: http://arxiv.org/abs/2309.08730v3
• Date: Tue, 2 Apr 2024 13:35:59 GMT
• Title: MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
• Authors: Zihao Deng, Yinghao Ma, Yudong Liu, Rongchen Guo, Ge Zhang, Wenhu Chen, Wenhao Huang, Emmanouil Benetos,
• Abstract summary: MusiLingo is a novel system for music caption generation and music-related query responses. We train it on an extensive music caption dataset and fine-tune it with instructional data. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs.
• Score: 42.73982391253872
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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