Translate The Beauty In Songs: Jointly Learning To Align Melody And Translate Lyrics

Abstract

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyrics translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of augmented data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyli2023translate

Related papers

• Songtrans: An Unified Song Transcription And Alignment Method For Lyrics And Notes (2024)0.00
• Songglm: Lyric-to-melody Generation With 2D Alignment Encoding And Multi-task Pre-training (2024)3.58
• Contrastive Learning-based Audio To Lyrics Alignment For Multiple Languages (2023)6.77
• Neural Melody Composition From Lyrics (2018)9.59
• Joint Learning Of Wording And Formatting For Singable Melody-to-lyric Generation (2023)0.00
• A Syllable-structured, Contextually-based Conditionally Generation Of Chinese Lyrics (2019)7.16
• End-to-end Lyrics Alignment For Polyphonic Music Using An Audio-to-character Recognition Model (2019)13.11
• Unsupervised Melody-to-lyric Generation (2023)0.00