CLC number: TN912.3
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-01-09
Cited: 0
Clicked: 2935
Citations: Bibtex RefMan EndNote GB/T7714
Wei ZHAO, Li XU. Efficient decoding self-attention for end-to-end speech synthesis[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(7): 1127-1138.
@article{title="Efficient decoding self-attention for end-to-end speech synthesis",
author="Wei ZHAO, Li XU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="7",
pages="1127-1138",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100501"
}
%0 Journal Article
%T Efficient decoding self-attention for end-to-end speech synthesis
%A Wei ZHAO
%A Li XU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 7
%P 1127-1138
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100501
TY - JOUR
T1 - Efficient decoding self-attention for end-to-end speech synthesis
A1 - Wei ZHAO
A1 - Li XU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 7
SP - 1127
EP - 1138
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100501
Abstract: self-attention has been innovatively applied to text-to-speech (TTS) because of its parallel structure and superior strength in modeling sequential data. However, when used in end-to-end speech synthesis with an autoregressive decoding scheme, its inference speed becomes relatively low due to the quadratic complexity in sequence length. This problem becomes particularly severe on devices without graphics processing units (GPUs). To alleviate the dilemma, we propose an efficient decoding self-attention (EDSA) module as an alternative. Combined with a dynamic programming decoding procedure, TTS model inference can be effectively accelerated to have a linear computation complexity. We conduct studies on Mandarin and English datasets and find that our proposed model with EDSA can achieve 720% and 50% higher inference speed on the central processing unit (CPU) and GPU respectively, with almost the same performance. Thus, this method may make the deployment of such models easier when there are limited GPU resources. In addition, our model may perform better than the baseline Transformer TTS on out-of-domain utterances.
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