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arXiv · 2601.18094

OneVoice: One Model, Triple Scenarios-Towards Unified Zero-shot Voice Conversion

Zhichao Wang·Tao Li·Wenshuo Ge·Zihao Cui·Shilei Zhang·Junlan Feng
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Abstract

Recent progress of voice conversion~(VC) has achieved a new milestone in speaker cloning and linguistic preservation. But the field remains fragmented, relying on specialized models for linguistic-preserving, expressive, and singing scenarios. We propose OneVoice, a unified zero-shot framework capable of handling all three scenarios within a single model. OneVoice is built upon a continuous language model trained with VAE-free next-patch diffusion, ensuring high fidelity and efficient sequence modeling. Its core design for unification lies in a Mixture-of-Experts (MoE) designed to explicitly model shared conversion knowledge and scenario-specific expressivity. Expert selection is coordinated by a dual-path routing mechanism, including shared expert isolation and scenario-aware domain expert assignment with global-local cues. For precise conditioning, scenario-specific prosodic features are fused into each layer via a gated mechanism, allowing adaptive usage of prosody information. Furthermore, to enable the core idea and alleviate the imbalanced issue (abundant speech vs. scarce singing), we adopt a two-stage progressive training that includes foundational pre-training and scenario enhancement with LoRA-based domain experts. Experiments show that OneVoice matches or surpasses specialized models across all three scenarios, while verifying flexible control over scenarios and offering a fast decoding version as few as 2 steps. Audio samples are available on demo page.

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Zhichao Wang, Tao Li, Wenshuo Ge, Zihao Cui, Shilei Zhang, Junlan Feng. 2026-05-21. OneVoice: One Model, Triple Scenarios-Towards Unified Zero-shot Voice Conversion. https://arxiv.org/abs/2601.18094

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