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arXiv · 2004.10234

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

Hirofumi Inaguma·Shun Kiyono·Kevin Duh·Shigeki Karita·Nelson Enrique Yalta Soplin·Tomoki Hayashi·Shinji Watanabe
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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.

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Hirofumi Inaguma, Shun Kiyono, Kevin Duh, Shigeki Karita, Nelson Enrique Yalta Soplin, Tomoki Hayashi, Shinji Watanabe. 2020-04-21. ESPnet-ST: All-in-One Speech Translation Toolkit. https://arxiv.org/abs/2004.10234

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