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  ESPnet is an end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, and so on.
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  ESPnet uses [pytorch](http://pytorch.org/) as a deep learning engine and also follows [Kaldi](http://kaldi-asr.org/) style data processing, feature extraction/format, and recipes to provide a complete setup for various speech processing experiments.
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- ## Citations
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- ```
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  @inproceedings{watanabe2018espnet,
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  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
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  title={{ESPnet}: End-to-End Speech Processing Toolkit},
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  journal={arXiv preprint arXiv:2111.14706},
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  year={2021}
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  }
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- ```
 
 
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  ESPnet is an end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, and so on.
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  ESPnet uses [pytorch](http://pytorch.org/) as a deep learning engine and also follows [Kaldi](http://kaldi-asr.org/) style data processing, feature extraction/format, and recipes to provide a complete setup for various speech processing experiments.
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+ <details><summary>Citations</summary>
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  @inproceedings{watanabe2018espnet,
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  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
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  title={{ESPnet}: End-to-End Speech Processing Toolkit},
 
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  journal={arXiv preprint arXiv:2111.14706},
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  year={2021}
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  }
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+
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+ </details>