  Voice conversion is an increasingly popular technology, and the growing
number of real-time applications requires models with streaming conversion
capabilities. Unlike typical (non-streaming) voice conversion, which can
leverage the entire utterance as full context, streaming voice conversion faces
significant challenges due to the missing future information, resulting in
degraded intelligibility, speaker similarity, and sound quality. To address
this challenge, we propose DualVC, a dual-mode neural voice conversion approach
that supports both streaming and non-streaming modes using jointly trained
separate network parameters. Furthermore, we propose intra-model knowledge
distillation and hybrid predictive coding (HPC) to enhance the performance of
streaming conversion. Additionally, we incorporate data augmentation to train a
noise-robust autoregressive decoder, improving the model's performance on
long-form speech conversion. Experimental results demonstrate that the proposed
model outperforms the baseline models in the context of streaming voice
conversion, while maintaining comparable performance to the non-streaming
topline system that leverages the complete context, albeit with a latency of
only 252.8 ms.
