LOAF-M2L: Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation

• URL: http://arxiv.org/abs/2307.02146v2
• Date: Fri, 19 Jul 2024 04:38:57 GMT
• Title: LOAF-M2L: Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation
• Authors: Longshen Ou, Xichu Ma, Ye Wang,
• Abstract summary: This paper bridges the singability gap with a novel approach to generating singable lyrics by jointly Learning wOrding And during Melody-to-Lyric training. After general-domain pretraining, our proposed model acquires length awareness first from a large text-only lyric corpus. Then, we introduce a new objective informed by musicological research on the relationship between melody and lyrics during melody-to-lyric training, which enables the model to learn the fine-grained format requirements of the melody.
• Score: 7.102743887290909
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite previous efforts in melody-to-lyric generation research, there is still a significant compatibility gap between generated lyrics and melodies, negatively impacting the singability of the outputs. This paper bridges the singability gap with a novel approach to generating singable lyrics by jointly Learning wOrding And Formatting during Melody-to-Lyric training. After general-domain pretraining, our proposed model acquires length awareness first from a large text-only lyric corpus. Then, we introduce a new objective informed by musicological research on the relationship between melody and lyrics during melody-to-lyric training, which enables the model to learn the fine-grained format requirements of the melody. Our model achieves 3.75% and 21.44% absolute accuracy gains in the outputs' number-of-line and syllable-per-line requirements compared to naive fine-tuning, without sacrificing text fluency. Furthermore, our model demonstrates a 63.92% and 74.18% relative improvement of music-lyric compatibility and overall quality in the subjective evaluation, compared to the state-of-the-art melody-to-lyric generation model, highlighting the significance of formatting learning.

Related papers

• Melody Is All You Need For Music Generation [10.366088659024685]
  We present the Melody Guided Music Generation (MG2) model, a novel approach using melody to guide the text-to-music generation. The proposed MG2 model surpasses current open-source text-to-music generation models, utilizing fewer than 1/3 of the parameters and less than 1/200 of the training data.
  arXiv  Detail & Related papers  (2024-09-30T11:13:35Z)
• REFFLY: Melody-Constrained Lyrics Editing Model [50.03960548399128]
  We introduce REFFLY, the first revision framework designed to edit arbitrary forms of plain text draft into high-quality, full-fledged song lyrics. Our approach ensures that the generated lyrics retain the original meaning of the draft, align with the melody, and adhere to the desired song structures.
  arXiv  Detail & Related papers  (2024-08-30T23:22:34Z)
• Controllable Lyrics-to-Melody Generation [14.15838552524433]
  We propose a controllable lyrics-to-melody generation network, ConL2M, which is able to generate realistic melodies from lyrics in user-desired musical style. Our work contains three main novelties: 1) To model the dependencies of music attributes cross multiple sequences, inter-branch memory fusion (Memofu) is proposed to enable information flow between multi-branch stacked LSTM architecture; 2) Reference style embedding (RSE) is proposed to improve the quality of generation as well as control the musical style of generated melodies; 3) Sequence-level statistical loss (SeqLoss) is proposed to help the model learn sequence-level
  arXiv  Detail & Related papers  (2023-06-05T06:14:08Z)
• 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)
• Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation [158.54649047794794]
  Re-creation of Creations (ROC) is a new paradigm for lyric-to-melody generation. ROC achieves good lyric-melody feature alignment in lyric-to-melody generation.
  arXiv  Detail & Related papers  (2022-08-11T08:44:47Z)
• 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)
• Melody-Conditioned Lyrics Generation with SeqGANs [81.2302502902865]
  We propose an end-to-end melody-conditioned lyrics generation system based on Sequence Generative Adversarial Networks (SeqGAN) We show that the input conditions have no negative impact on the evaluation metrics while enabling the network to produce more meaningful results.
  arXiv  Detail & Related papers  (2020-10-28T02:35:40Z)

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.