SongGLM: Lyric-to-Melody Generation with 2D Alignment Encoding and Multi-Task Pre-Training

• URL: http://arxiv.org/abs/2412.18107v1
• Date: Tue, 24 Dec 2024 02:30:07 GMT
• Title: SongGLM: Lyric-to-Melody Generation with 2D Alignment Encoding and Multi-Task Pre-Training
• Authors: Jiaxing Yu, Xinda Wu, Yunfei Xu, Tieyao Zhang, Songruoyao Wu, Le Ma, Kejun Zhang,
• Abstract summary: SongGLM is a lyric-to-melody generation system that leverages 2D alignment encoding and multi-task pre-training. We construct a large-scale lyric-melody paired dataset comprising over 200,000 English song pieces for pre-training and fine-tuning.
• Score: 7.3026780262967685
• License:
• Abstract: Lyric-to-melody generation aims to automatically create melodies based on given lyrics, requiring the capture of complex and subtle correlations between them. However, previous works usually suffer from two main challenges: 1) lyric-melody alignment modeling, which is often simplified to one-syllable/word-to-one-note alignment, while others have the problem of low alignment accuracy; 2) lyric-melody harmony modeling, which usually relies heavily on intermediates or strict rules, limiting model's capabilities and generative diversity. In this paper, we propose SongGLM, a lyric-to-melody generation system that leverages 2D alignment encoding and multi-task pre-training based on the General Language Model (GLM) to guarantee the alignment and harmony between lyrics and melodies. Specifically, 1) we introduce a unified symbolic song representation for lyrics and melodies with word-level and phrase-level (2D) alignment encoding to capture the lyric-melody alignment; 2) we design a multi-task pre-training framework with hierarchical blank infilling objectives (n-gram, phrase, and long span), and incorporate lyric-melody relationships into the extraction of harmonized n-grams to ensure the lyric-melody harmony. We also construct a large-scale lyric-melody paired dataset comprising over 200,000 English song pieces for pre-training and fine-tuning. The objective and subjective results indicate that SongGLM can generate melodies from lyrics with significant improvements in both alignment and harmony, outperforming all the previous baseline methods.

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