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
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 302–311
- URL:
- https://www.aclweb.org/anthology/2020.acl-demos.34
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.acl-demos.34.pdf
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