Summary of Song Form-aware Full-song Text-to-lyrics Generation with Multi-level Granularity Syllable Count Control, by Yunkee Chae et al.
Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control
by Yunkee Chae, Eunsik Shin, Hwang Suntae, Seungryeol Paik, Kyogu Lee
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for lyrics generation enables multi-level syllable control, allowing for precise phrasing and adherence to song form structures like verses and choruses. This approach generates complete lyrics conditioned on input text and song form, ensuring alignment with specified syllable constraints. The framework’s unique contribution lies in its ability to manage syllables at the word, phrase, line, and paragraph levels, addressing the limitations of conventional line-by-line approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lyrics generation is a challenging task that requires controlling syllables while following specific song structures like verses and choruses. Currently, methods don’t do this well, leading to unnatural phrasing. A new approach solves this by allowing control at multiple levels: word, phrase, line, and paragraph. This helps generate complete lyrics that match the given syllable constraints. |
Keywords
» Artificial intelligence » Alignment
