1. MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
- Author
-
Deng, Zihao, Ma, Yinghao, Liu, Yudong, Guo, Rongchen, Zhang, Ge, Chen, Wenhu, Huang, Wenhao, and Benetos, Emmanouil
- Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Multimedia ,Computer Science - Sound - 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.
- Published
- 2023