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arXiv · 2211.13939

Efficient Incremental Text-to-Speech on GPUs

Muyang Du·Chuan Liu·Jiaxing Qi·Junjie Lai
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Abstract

Incremental text-to-speech, also known as streaming TTS, has been increasingly applied to online speech applications that require ultra-low response latency to provide an optimal user experience. However, most of the existing speech synthesis pipelines deployed on GPU are still non-incremental, which uncovers limitations in high-concurrency scenarios, especially when the pipeline is built with end-to-end neural network models. To address this issue, we present a highly efficient approach to perform real-time incremental TTS on GPUs with Instant Request Pooling and Module-wise Dynamic Batching. Experimental results demonstrate that the proposed method is capable of producing high-quality speech with a first-chunk latency lower than 80ms under 100 QPS on a single NVIDIA A10 GPU and significantly outperforms the non-incremental twin in both concurrency and latency. Our work reveals the effectiveness of high-performance incremental TTS on GPUs.

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BibTeXRIS

Muyang Du, Chuan Liu, Jiaxing Qi, Junjie Lai. 2022-11-25. Efficient Incremental Text-to-Speech on GPUs. https://arxiv.org/abs/2211.13939

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