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arXiv · 2412.06660

MuMu-LLaMA: Multi-modal Music Understanding and Generation via Large Language Models

Shansong Liu·Atin Sakkeer Hussain·Qilong Wu·Chenshuo Sun·Ying Shan
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Abstract

Research on large language models has advanced significantly across text, speech, images, and videos. However, multi-modal music understanding and generation remain underexplored due to the lack of well-annotated datasets. To address this, we introduce a dataset with 167.69 hours of multi-modal data, including text, images, videos, and music annotations. Based on this dataset, we propose MuMu-LLaMA, a model that leverages pre-trained encoders for music, images, and videos. For music generation, we integrate AudioLDM 2 and MusicGen. Our evaluation across four tasks--music understanding, text-to-music generation, prompt-based music editing, and multi-modal music generation--demonstrates that MuMu-LLaMA outperforms state-of-the-art models, showing its potential for multi-modal music applications.

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Shansong Liu, Atin Sakkeer Hussain, Qilong Wu, Chenshuo Sun, Ying Shan. 2024-12-09. MuMu-LLaMA: Multi-modal Music Understanding and Generation via Large Language Models. https://arxiv.org/abs/2412.06660

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