ESPnet: End-to-End Speech Processing Toolkit
Shinji Watanabe
Takaaki Hori
Shigeki Karita
Tomoki Hayashi
Jiro Nishitoba
Y. Unno
Nelson Yalta
Jahn Heymann
Matthew Wiesner
Nanxin Chen
Adithya Renduchintala
Tsubasa Ochiai

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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