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Creating a pop song melody according to pre-written lyrics is a typicalpractice for composers. A computational model of how lyrics are set as melodiesis important for automatic composition systems, but an end-to-endlyric-to-melody model would require enormous amounts of paired training data.To mitigate the data constraints, we adopt a two-stage approach, dividing thetask into lyric-to-rhythm and rhythm-to-melody modules. However, thelyric-to-rhythm task is still challenging due to its multimodality. In thispaper, we propose a novel lyric-to-rhythm framework that includespart-of-speech tags to achieve better text setting, and a Transformerarchitecture designed to model long-term syllable-to-note associations. For therhythm-to-melody task, we adapt a proven chord-conditioned melody Transformer,which has achieved state-of-the-art results. Experiments for Chineselyric-to-melody generation show that the proposed framework is able to modelkey characteristics of rhythm and pitch distributions in the dataset, and in asubjective evaluation, the melodies generated by our system were rated assimilar to or better than those of a state-of-the-art alternative.

Modeling the Rhythm from Lyrics for Melody Generation of Pop Song