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

TL;DR

LHQ-SVC introduces a lightweight diffusion-based singing voice conversion model that achieves high quality output with reduced computational requirements, suitable for CPU deployment.

Contribution

The paper presents LHQ-SVC, a novel, efficient SVC model that balances high audio quality with low resource consumption, advancing practical applications.

Findings

Maintains competitive voice conversion quality.

Significantly improves processing speed and efficiency.

Optimized for CPU execution with parallel computing.

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