Controllable Syllable-Level Lyrics Generation From Melody With Prior Attention

Abstract

Melody-to-lyrics generation, which is based on syllable-level generation, is an intriguing and challenging topic in the interdisciplinary field of music, multimedia, and machine learning. Many previous research projects generate word-level lyrics sequences due to the lack of alignments between syllables and musical notes. Moreover, controllable lyrics generation from melody is also less explored but important for facilitating humans to generate diverse desired lyrics. In this work, we propose a controllable melody-to-lyrics model that is able to generate syllable-level lyrics with user-desired rhythm. An explicit n-gram (EXPLING) loss is proposed to train the Transformer-based model to capture the sequence dependency and alignment relationship between melody and lyrics and predict the lyrics sequences at the syllable level. A prior attention mechanism is proposed to enhance the controllability and diversity of lyrics generation. Experiments and evaluation metrics verified that our proposed model has the ability to generate higher-quality lyrics than previous methods and the feasibility of interacting with users for controllable and diverse lyrics generation. We believe this work provides valuable insights into human-centered AI research in music generation tasks. The source codes for this work will be made publicly available for further reference and exploration.