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

arXiv: https://arxiv.org/abs/2412.18107 · HTML: https://mlsanity.com/paper/2412.18107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [MMM : Exploring Conditional Multi-Track Music Generation with the Transformer](https://mlsanity.com/paper/2008.06048/) (2020)
- [MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation](https://mlsanity.com/paper/2309.10738/) (2023)
- [SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition](https://mlsanity.com/paper/2402.17645/) (2024)
- [Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation](https://mlsanity.com/paper/2208.05697/) (2022)
- [WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning](https://mlsanity.com/paper/2301.04488/) (2023)
- [MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation](https://mlsanity.com/paper/2407.03188/) (2024)

## Built on by (most cited first, 1 of 1)

- [MusiChat: Vibe Composing for Music Creation](https://mlsanity.com/paper/2607.24873/) (2026)

## Abstract (arXiv)

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.