Chatterbox-Flash: Prior-Calibrated Block Diffusion for Streaming Zero-Shot TTS
Learn how Chatterbox-Flash enables parallel token generation for zero-shot text-to-speech while retaining block-by-block streaming, and how to apply prior-calibrated block diffusion for improved quality
- Fine-tune a pretrained autoregressive TTS decoder into a block-diffusion decoder using Chatterbox-Flash
- Apply prior-calibrated block diffusion to mitigate long-tail token distribution biases
- Implement parallel token generation within each block while retaining block-by-block streaming
- Evaluate the quality of the generated speech using objective metrics
- Compare the performance of Chatterbox-Flash with other zero-shot TTS models
This research benefits AI engineers and researchers working on text-to-speech systems, particularly those interested in zero-shot learning and block diffusion decoding. The findings can be applied to improve the quality and efficiency of TTS models.
💡 Prior-calibrated block diffusion can improve the quality of zero-shot text-to-speech models by mitigating biases in parallel position selection
🔊 Introducing Chatterbox-Flash: a zero-shot TTS model that enables parallel token generation while retaining block-by-block streaming! 🚀 #TTS #ZeroShotLearning
Key Takeaways
Learn how Chatterbox-Flash enables parallel token generation for zero-shot text-to-speech while retaining block-by-block streaming, and how to apply prior-calibrated block diffusion for improved quality
Full Article
Abstract:
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 h
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