LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling
This work addresses computational bottlenecks for SVC applications, offering a more efficient solution for voice conversion tasks, though it appears incremental as it builds on existing SVC and diffusion model frameworks.
The paper tackles the problem of high computational complexity and audio quality limitations in Singing Voice Conversion (SVC) by proposing LHQ-SVC, a lightweight model that maintains competitive performance while significantly improving processing speed and efficiency across devices.
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