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ESPnet: End-to-End Speech Processing Toolkit

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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.

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

Related benchmarks

TaskDatasetResultRank
Automatic Speech RecognitionLibriSpeech (test-other)
WER5.3
966
Automatic Speech RecognitionLibriSpeech clean (test)
WER2
833
Automatic Speech RecognitionLibriSpeech (dev-other)
WER5.2
411
Automatic Speech RecognitionLibriSpeech (dev-clean)
WER (%)1.9
319
Speech RecognitionWSJ (92-eval)
WER8.9
131
Automatic Speech RecognitionAISHELL-1 (test)
CER4.5
71
Speech RecognitionWSJ nov93 (dev)
WER12.4
52
Automatic Speech RecognitionWenetSpeech Meeting (test)
CER15.9
45
Automatic Speech RecognitionGigaSpeech (test)
WER10.5
40
Automatic Speech RecognitionAISHELL-1 (dev)
CER4.2
34
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