arXiv:2605.30748v1 Announce Type: cross 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 inferenc
The continuous drive for more efficient and rapid AI inference, particularly in real-time applications like text-to-speech, necessitates innovations in model architecture and decoding strategy.
This development allows for faster, more natural-sounding zero-shot text-to-speech generation in streaming contexts, significantly improving user experience and expanding potential application areas for AI audio.
Streaming text-to-speech can now achieve higher quality and lower latency for unseen voices, making real-time voice cloning and translation more practical and pervasive.
- · AI voice platforms
- · Customer service industries
- · Content creators
- · Assistive technology developers
- · Platforms with high-latency TTS
- · Services relying on pre-recorded audio
More applications will integrate real-time, high-quality, zero-shot text-to-speech functionality.
The proliferation of realistic AI-generated voices will intensify debates around voice authenticity and deepfakes.
This could accelerate the development of personalized voice interfaces as a primary mode of human-computer interaction, surpassing visual interfaces in certain contexts.
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Read at arXiv cs.AI