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Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning
Created by
Haebom
Author
Yixiao Zhang, Yukara Ikemiya, Woosung Choi, Naoki Murata, Marco A. Mart inez-Ram irez, Liwei Lin, Gus Xia, Wei-Hsiang Liao, Yuki Mitsufuji, Simon Dixon
Outline
This paper presents Instruct-MusicGen, a novel method to fine-tune the existing MusicGen model to improve the efficiency of text-based music editing. Existing text-to-music editing methods have the resource-intensive problem of having to learn a model for a specific task from scratch, and the problem of inaccurate audio reconstruction that occurs in the process of predicting music using a large language model. Instruct-MusicGen adds a text fusion module and an audio fusion module to process text commands and audio inputs simultaneously and generate the desired edited music. Despite adding only 8% of new parameters to the existing MusicGen model and training only 5,000 steps, it outperforms existing methods and achieves performance similar to models specialized for a specific task. This contributes to improving the efficiency of text-to-music editing and expanding the applicability of music language models in dynamic music production environments.
Takeaways, Limitations
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Takeaways:
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Solving resource-intensive problems of existing text-to-music editing models: Achieving high performance with significantly fewer resources (parameters and training steps) than existing models.
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Improve the efficiency of text-based music editing: perform various editing operations (add, remove, split, etc.) efficiently.
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Extending the scope of applications of music language models: Increasing their usability in dynamic music production environments.
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Achieve performance similar to models specialized for specific tasks.
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Limitations:
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In this paper, specific Limitations is not explicitly mentioned. Additional experiments and analyses may reveal situations where performance degradation may occur, or limitations on the type and complexity of editable music.
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Since it is based on the MusicGen model, the __T31_____ of the MusicGen model itself may also affect Instruct-MusicGen.
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5000 steps is a relatively small number of training steps, but it may not be enough in certain situations.
