Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning

• URL: http://arxiv.org/abs/2308.11276v1
• Date: Tue, 22 Aug 2023 08:43:33 GMT
• Title: Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
• Authors: Shansong Liu, Atin Sakkeer Hussain, Chenshuo Sun, Ying Shan
• Abstract summary: Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale publicly available music datasets with natural language captions. We propose the Music Understanding LLaMA (MU-LLaMA), capable of answering music-related questions and generating captions for music files. We present a methodology for generating question-answer pairs from existing audio captioning datasets and introduce the MusicQA dataset.
• Score: 37.76488341368786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale publicly available music datasets with natural language captions. To address this, we propose the Music Understanding LLaMA (MU-LLaMA), capable of answering music-related questions and generating captions for music files. Our model utilizes audio representations from a pretrained MERT model to extract music features. However, obtaining a suitable dataset for training the MU-LLaMA model remains challenging, as existing publicly accessible audio question answering datasets lack the necessary depth for open-ended music question answering. To fill this gap, we present a methodology for generating question-answer pairs from existing audio captioning datasets and introduce the MusicQA Dataset designed for answering open-ended music-related questions. The experiments demonstrate that the proposed MU-LLaMA model, trained on our designed MusicQA dataset, achieves outstanding performance in both music question answering and music caption generation across various metrics, outperforming current state-of-the-art (SOTA) models in both fields and offering a promising advancement in the T2M-Gen research field.

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