Efficient Incremental Text-to-Speech on GPUs

• URL: http://arxiv.org/abs/2211.13939v1
• Date: Fri, 25 Nov 2022 07:43:45 GMT
• Title: Efficient Incremental Text-to-Speech on GPUs
• Authors: Muyang Du, Chuan Liu, Jiaxing Qi, Junjie Lai
• Abstract summary: We present a highly efficient approach to perform real-time incremental TTS on GPUs with Instant Request Pooling and Module-wise Dynamic. 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.
• Score: 1.35346836945515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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