ESPnet: End-to-End Speech Processing Toolkit

Shinji Watanabe; Takaaki Hori; Shigeki Karita; Tomoki Hayashi; Jiro Nishitoba; Yuya Unno; Nelson Enrique Yalta Soplin; Jahn Heymann; Matthew Wiesner; Nanxin Chen; Adithya Renduchintala; Tsubasa Ochiai

arXiv · 1804.00015

ESPnet: End-to-End Speech Processing Toolkit

Shinji Watanabe·Takaaki Hori·Shigeki Karita·Tomoki Hayashi·Jiro Nishitoba·Yuya Unno·Nelson Enrique Yalta Soplin·Jahn Heymann·Matthew Wiesner·Nanxin Chen·Adithya Renduchintala·Tsubasa Ochiai

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Abstract

This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts widely-used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the Kaldi ASR toolkit style for data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. This paper explains a major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks.

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Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, Tsubasa Ochiai. 2018-03-30. ESPnet: End-to-End Speech Processing Toolkit. https://arxiv.org/abs/1804.00015

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