  Although voice conversion (VC) systems have shown a remarkable ability to
transfer voice style, existing methods still have an inaccurate pitch and low
speaker adaptation quality. To address these challenges, we introduce
Diff-HierVC, a hierarchical VC system based on two diffusion models. We first
introduce DiffPitch, which can effectively generate F0 with the target voice
style. Subsequently, the generated F0 is fed to DiffVoice to convert the speech
with a target voice style. Furthermore, using the source-filter encoder, we
disentangle the speech and use the converted Mel-spectrogram as a data-driven
prior in DiffVoice to improve the voice style transfer capacity. Finally, by
using the masked prior in diffusion models, our model can improve the speaker
adaptation quality. Experimental results verify the superiority of our model in
pitch generation and voice style transfer performance, and our model also
achieves a CER of 0.83% and EER of 3.29% in zero-shot VC scenarios.
