LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling

Yubo Huang, Xin Lai, Muyang Ye, Anran Zhu, Zixi Wang +4 more
9/13/2024
cs.SDcs.AIeess.AS

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Abstract

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer's voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and computational complexity. In this paper, we propose LHQ-SVC, a lightweight, CPU-compatible model based on the SVC framework and diffusion model, designed to reduce model size and computational demand without sacrificing performance. We incorporate features to improve inference quality, and optimize for CPU execution by using performance tuning tools and parallel computing frameworks. Our experiments demonstrate that LHQ-SVC maintains competitive performance, with significant improvements in processing speed and efficiency across different devices. The results suggest that LHQ-SVC can meet

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Cite this paper

@article{huang2024lhqsvc,
  title  = {LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling},
  author = {Yubo Huang and Xin Lai and Muyang Ye and Anran Zhu and Zixi Wang and Jingzehua Xu and Shuai Zhang and Zhiyuan Zhou and Weijie Niu},
  year   = {2024},
  eprint = {2409.08583},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.08583v2}
}

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