  Recent language model (LM) advancements have showcased impressive zero-shot
voice conversion (VC) performance. However, existing LM-based VC models usually
apply offline conversion from source semantics to acoustic features, demanding
the complete source speech and limiting their deployment to real-time
applications. In this paper, we introduce StreamVoice, a novel streaming
LM-based model for zero-shot VC, facilitating real-time conversion given
arbitrary speaker prompts and source speech. Specifically, to enable streaming
capability, StreamVoice employs a fully causal context-aware LM with a
temporal-independent acoustic predictor, while alternately processing semantic
and acoustic features at each time step of autoregression which eliminates the
dependence on complete source speech. To address the potential performance
degradation from the incomplete context in streaming processing, we enhance the
context-awareness of the LM through two strategies: 1) teacher-guided context
foresight, using a teacher model to summarize the present and future semantic
context during training to guide the model's forecasting for missing context;
2) semantic masking strategy, promoting acoustic prediction from preceding
corrupted semantic and acoustic input, enhancing context-learning ability.
Notably, StreamVoice is the first LM-based streaming zero-shot VC model without
any future look-ahead. Experiments demonstrate StreamVoice's streaming
conversion capability while achieving zero-shot performance comparable to
non-streaming VC systems.
