SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition

• URL: http://arxiv.org/abs/2402.17645v2
• Date: Fri, 30 May 2025 22:33:14 GMT
• Title: SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition
• Authors: Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang,
• Abstract summary: SongComposer is a music-specialized large language model (LLM)<n>It integrates the capability of simultaneously composing melodies into LLMs by leveraging three key innovations.<n>It outperforms advanced LLMs in tasks such as lyric-to-melody generation, melody-to-lyric generation, song continuation, and text-to-song creation.<n>We will release SongCompose, a large-scale dataset for training, containing paired lyrics and melodies in Chinese and English.
• Score: 82.38021790213752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating lyrics and melodies for the vocal track in a symbolic format, known as song composition, demands expert musical knowledge of melody, an advanced understanding of lyrics, and precise alignment between them. Despite achievements in sub-tasks such as lyric generation, lyric-to-melody, and melody-to-lyric, etc, a unified model for song composition has not yet been achieved. In this paper, we introduce SongComposer, a pioneering step towards a unified song composition model that can readily create symbolic lyrics and melodies following instructions. SongComposer is a music-specialized large language model (LLM) that, for the first time, integrates the capability of simultaneously composing lyrics and melodies into LLMs by leveraging three key innovations: 1) a flexible tuple format for word-level alignment of lyrics and melodies, 2) an extended tokenizer vocabulary for song notes, with scalar initialization based on musical knowledge to capture rhythm, and 3) a multi-stage pipeline that captures musical structure, starting with motif-level melody patterns and progressing to phrase-level structure for improved coherence. Extensive experiments demonstrate that SongComposer outperforms advanced LLMs, including GPT-4, in tasks such as lyric-to-melody generation, melody-to-lyric generation, song continuation, and text-to-song creation. Moreover, we will release SongCompose, a large-scale dataset for training, containing paired lyrics and melodies in Chinese and English.

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