Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics

• URL: http://arxiv.org/abs/2303.15705v1
• Date: Tue, 28 Mar 2023 03:17:59 GMT
• Title: Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
• Authors: Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, Zhi Yu
• Abstract summary: We propose Lyrics-Melody Translation with Adaptive Grouping (LTAG) as a holistic solution to automatic song translation. 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. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.
• Score: 38.35809268026605
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SongComposer: A Large Language Model for Lyric and Melody Composition in Song Generation [88.33522730306674]
  SongComposer could understand and generate melodies and lyrics in symbolic song representations. We resort to symbolic song representation, the mature and efficient way humans designed for music. With extensive experiments, SongComposer demonstrates superior performance in lyric-to-melody generation, melody-to-lyric generation, song continuation, and text-to-song creation.
  arXiv  Detail & Related papers  (2024-02-27T16:15:28Z)
• A Computational Evaluation Framework for Singable Lyric Translation [17.492053233802135]
  We present a computational framework for the quantitative evaluation of singable lyric translation. We measure syllable count distance, phoneme repetition similarity, musical structure distance, and semantic similarity. Our framework seamlessly integrates musical, linguistic, and cultural dimensions of lyrics.
  arXiv  Detail & Related papers  (2023-08-26T00:27:08Z)
• LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT [48.28624219567131]
  We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method. We use Whisper, a weakly supervised robust speech recognition model, and GPT-4, today's most performant chat-based large language model. Our experiments show that LyricWhiz significantly reduces Word Error Rate compared to existing methods in English.
  arXiv  Detail & Related papers  (2023-06-29T17:01:51Z)
• Unsupervised Melody-to-Lyric Generation [91.29447272400826]
  We propose a method for generating high-quality lyrics without training on any aligned melody-lyric data. We leverage the segmentation and rhythm alignment between melody and lyrics to compile the given melody into decoding constraints. Our model can generate high-quality lyrics that are more on-topic, singable, intelligible, and coherent than strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T17:20:25Z)
• Unsupervised Melody-Guided Lyrics Generation [84.22469652275714]
  We propose to generate pleasantly listenable lyrics without training on melody-lyric aligned data. We leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
  arXiv  Detail & Related papers  (2023-05-12T20:57:20Z)
• SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint [54.012194728496155]
  SongMASS is proposed to overcome the challenges of lyric-to-melody generation and melody-to-lyric generation. It leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling. We show that SongMASS generates lyric and melody with significantly better quality than the baseline method.
  arXiv  Detail & Related papers  (2020-12-09T16:56:59Z)
• Speech-to-Singing Conversion in an Encoder-Decoder Framework [38.111942306157545]
  We take a learning based approach to the problem of converting spoken lines into sung ones. We learn encodings that enable us to synthesize singing that preserves the linguistic content and timbre of the speaker.
  arXiv  Detail & Related papers  (2020-02-16T15:33:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.