  We introduce HybridVC, a voice conversion (VC) framework built upon a
pre-trained conditional variational autoencoder (CVAE) that combines the
strengths of a latent model with contrastive learning. HybridVC supports text
and audio prompts, enabling more flexible voice style conversion. HybridVC
models a latent distribution conditioned on speaker embeddings acquired by a
pretrained speaker encoder and optimises style text embeddings to align with
the speaker style information through contrastive learning in parallel.
Therefore, HybridVC can be efficiently trained under limited computational
resources. Our experiments demonstrate HybridVC's superior training efficiency
and its capability for advanced multi-modal voice style conversion. This
underscores its potential for widespread applications such as user-defined
personalised voice in various social media platforms. A comprehensive ablation
study further validates the effectiveness of our method.
