FPUTS: Fully Parallel UFANS-Based End-to-End Text-to-Speech System
arXiv:1812.05710 [cs, eess, stat], vol.
Statistics - Machine Learning,Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Sound,Electrical Engineering and Systems Science - Audio and Speech Processing
- Dabiao Ma
- Zhiba Su
- Wenxuan Wang
- Yuhao Lu
A Text-to-speech (TTS) system that can generate high quality audios with small time latency and fewer errors is required for industrial applications and services. In this paper, we propose a new non-autoregressive, fully parallel end-to-end TTS system. It utilizes the new attention structure and the recently proposed convolutional structure, UFANS. Different to RNN, UFANS can capture long term information in a fully parallel manner. Compared with the most popular end-to-end text-to-speech systems, our system can generate equal or better quality audios with fewer errors and reach at least 10 times speed up of inference.