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MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
- Source :
- 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics
- Publication Year :
- 2023
-
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.
Details
- Database :
- arXiv
- Journal :
- 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics
- Publication Type :
- Report
- Accession number :
- edsarx.2309.08730
- Document Type :
- Working Paper