  Voice conversion is a method that allows for the transformation of speaking
style while maintaining the integrity of linguistic information. There are many
researchers using deep generative models for voice conversion tasks. Generative
Adversarial Networks (GANs) can quickly generate high-quality samples, but the
generated samples lack diversity. The samples generated by the Denoising
Diffusion Probabilistic Models (DDPMs) are better than GANs in terms of mode
coverage and sample diversity. But the DDPMs have high computational costs and
the inference speed is slower than GANs. In order to make GANs and DDPMs more
practical we proposes DiffGAN-VC, a variant of GANs and DDPMS, to achieve
non-parallel many-to-many voice conversion (VC). We use large steps to achieve
denoising, and also introduce a multimodal conditional GANs to model the
denoising diffusion generative adversarial network. According to both objective
and subjective evaluation experiments, DiffGAN-VC has been shown to achieve
high voice quality on non-parallel data sets. Compared with the CycleGAN-VC
method, DiffGAN-VC achieves speaker similarity, naturalness and higher sound
quality.
