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arXiv · 2410.21951

Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

Bohan Li·Hankun Wang·Situo Zhang·Yiwei Guo·Kai Yu
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Abstract

The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems. However, it incurs substantial inference time, particularly due to the challenges in the next-token prediction posed by lengthy sequences of speech tokens. In this work, we introduce VADUSA, one of the first approaches to accelerate auto-regressive TTS through speculative decoding. Our results show that VADUSA not only significantly improves inference speed but also enhances performance by incorporating draft heads to predict future speech content auto-regressively. Furthermore, the inclusion of a tolerance mechanism during sampling accelerates inference without compromising quality. Our approach demonstrates strong generalization across large datasets and various types of speech tokens.

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Bohan Li, Hankun Wang, Situo Zhang, Yiwei Guo, Kai Yu. 2024-10-29. Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding. https://arxiv.org/abs/2410.21951

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