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

• URL: http://arxiv.org/abs/2605.30748v2
• Date: Mon, 01 Jun 2026 01:53:31 GMT
• Title: Chatterbox-Flash: Prior-Calibrated Block Diffusion for Streaming Zero-Shot TTS
• Abstract summary: 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.<n>We find that naively transferring mainstream block-diffusion decoding to discrete speech tokens degrades quality.<n>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.
• Score: 2.6809014985508584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/resemble-ai/chatterbox-flash.

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