ESPnet-ST: All-in-One Speech Translation Toolkit

Hirofumi Inaguma, Shun Kiyono, Kevin Duh, Shigeki Karita, Nelson Yalta, Tomoki Hayashi, Shinji Watanabe


Abstract
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at https://github.com/espnet/espnet.
Anthology ID:
2020.acl-demos.34
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2020
Address:
Online
Editors:
Asli Celikyilmaz, Tsung-Hsien Wen
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
302–311
Language:
URL:
https://aclanthology.org/2020.acl-demos.34
DOI:
10.18653/v1/2020.acl-demos.34
Bibkey:
Cite (ACL):
Hirofumi Inaguma, Shun Kiyono, Kevin Duh, Shigeki Karita, Nelson Yalta, Tomoki Hayashi, and Shinji Watanabe. 2020. ESPnet-ST: All-in-One Speech Translation Toolkit. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 302–311, Online. Association for Computational Linguistics.
Cite (Informal):
ESPnet-ST: All-in-One Speech Translation Toolkit (Inaguma et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-demos.34.pdf
Video:
 http://slideslive.com/38928603
Code
 espnet/espnet
Data
How2MuST-C