Chatterbox-Flash: Prior-Calibrated Block Diffusion for Streaming Zero-Shot TTS

Abstract:We present Chatterbox-Flash, a zero-shot text-to-speech model obtained by fine-tuning a pretrained autoregressive TTS decoder into a block-diffusion decoder, enabling parallel token generation within each block while retaining block-by-block streaming. We find that naively transferring mainstream block-diffusion decoding to discrete speech tokens degrades quality, as a long-tail token distribution biases parallel position selection toward a few high-frequency tokens. To mitigate this without architectural modification, we introduce two inference-time techniques: prior-calibrated scoring, which subtracts the block-level marginal token distribution, and an early-decoding schedule, which adaptively terminates iteration based on calibrated confidence. On standard zero-shot TTS benchmarks, Chatterbox-Flash attains high-fidelity synthesis comparable to strong autoregressive and non-autoregressive baselines, while supporting streaming inference with time-to-first-packet on par with streaming AR systems and substantially lower real-time factor. Code and audio samples are available at this https URL.

Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)

arXiv:2605.30748 [cs.SD]

(or arXiv:2605.30748v3 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2605.30748 Focus to learn more arXiv-issued DOI via DataCite

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