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About this paper
Pengyu Ren, Wenhao Guan, Kaidi Wang, Peijie Chen, Qingyang Hong, Lin Li
ReFlow-VC introduces a zero-shot voice conversion method utilizing Rectified Flow models and a speaker feature optimization module, achieving high audio fidelity with significantly faster inference speeds. The approach yields superior naturalness and speaker similarity scores compared to diffusion-based models, even with single-step sampling that rivals 30-step diffusion performance.
Key insights
- Rectified Flow models can achieve high-fidelity speech generation with significantly fewer sampling steps than diffusion models, with single-step inference matching the quality of multi-step diffusion approaches.
- Optimizing speaker features by fusing content and pitch information through a dedicated module substantially improves speaker naturalness and similarity in zero-shot voice conversion.
- The conditional rectified flow model demonstrates robustness and numerical stability, performing well even when trained on relatively small datasets and exhibiting consistent performance with recursive applications (2-ReFlow-VC).
Summary by alphaXiv.
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