AI-Lyricist: Generating Music and Vocabulary Constrained Lyrics

Abstract

We propose AI-Lyricist: a system to generate novel yet meaningful lyrics given a required vocabulary and a MIDI file as inputs. This task involves multiple challenges, including automatically identifying the melody and extracting a syllable template from multi-channel music, generating creative lyrics that match the input music's style and syllable alignment, and satisfying vocabulary constraints. To address these challenges, we propose an automatic lyrics generation system consisting of four modules: (1) A music structure analyzer to derive the musical structure and syllable template from a given MIDI file, utilizing the concept of expected syllable number to better identify the melody, (2) a SeqGAN-based lyrics generator optimized by multi-adversarial training through policy gradients with twin discriminators for text quality and syllable alignment, (3) a deep coupled music-lyrics embedding model to project music and lyrics into a joint space to allow fair comparison of both melody and lyric constraints, and a module called (4) Polisher, to satisfy vocabulary constraints by applying a mask to the generator and substituting the words to be learned. We trained our model on a dataset of over 7,000 music-lyrics pairs, enhanced with manually annotated labels in terms of theme, sentiment and genre. Both objective and subjective evaluations show AI-Lyricist's superior performance against the state-of-the-art for the proposed tasks.