一种端到端语音合成中的高效解码自注意力网络

赵伟; 许力

doi:10.1631/FITEE.2100501

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一种端到端语音合成中的高效解码自注意力网络

常规文章 | Updated：2022-09-08

- 一种端到端语音合成中的高效解码自注意力网络
- Efficient decoding self-attention for end-to-end speech synthesis
- 信息与电子工程前沿（英文） 2022年23卷第7期页码：1127-1138
- Affiliations：
  
  1.College of Electrical Engineering, Zhejiang University, Hangzhou310027, China
  2.Institute of Robotics, Zhejiang University, Yuyao315400, China
- Author bio：
  
  E-mail: weizhao_ee@zju.edu.cn;
  ‡Corresponding authors
- Funds：
  
  National Key Research and Development Program of China(2019YFB1312603);Robotics Institute of Zhejiang University, China(K11801)
- DOI：10.1631/FITEE.2100501
  中图分类号： TN912.3
- 收稿：2021-10-21，
  
  录用：2022-01-09，
  
  网络出版：2022-05-31，
  
  纸质出版：2022-07-23
- Accepted：
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赵伟, 许力. 一种端到端语音合成中的高效解码自注意力网络[J]. 信息与电子工程前沿（英文）, 2022,23(7):1127-1138.

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.
赵伟, 许力. 一种端到端语音合成中的高效解码自注意力网络[J]. 信息与电子工程前沿（英文）, 2022,23(7):1127-1138. DOI： 10.1631/FITEE.2100501.

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. DOI： 10.1631/FITEE.2100501.

摘要

自注意力网络由于其并行结构和强大的序列建模能力，被广泛应用于语音合成（TTS）领域。然而，当使用自回归解码方法进行端到端语音合成时，由于序列长度的二次复杂性，其推理速度相对较慢。当部署设备未配备图形处理器（GPU）时，该效率问题更加严重。为解决该问题，提出一种高效解码自注意力网络（EDSA）作为替代。通过一个动态规划解码过程，有效加速TTS模型推理，使其具有线性计算复杂度。基于普通话和英文数据集的实验结果表明，所提EDSA模型在中央处理器（CPU）和GPU上的推理速度分别提高720%和50%，而性能几乎相同。因此，在GPU资源有限的情况下，该方法可使此类模型的部署更加容易。此外，所提模型在域外语言处理上可能比基线Transformer TTS性能更好。

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

and

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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references

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