ESPnet swMATH ID: 32059 Software Authors: 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 Description: ESPnet: End-to-End Speech Processing Toolkit. 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. Homepage: https://espnet.github.io/espnet/ Dependencies: Python Keywords: Audio Processing; Speech Processing; arXiv_eess.AS; Sound; arXiv_cs.SD Related Software: Kaldi; PyTorch; SentencePiece; Conformer; LibriSpeech; Python; wav2vec; SpeechBrain; PyTorch-Kaldi; NeMo; fairseq; Espresso; Asteroid; TensorFlow; SpecAugment; Transformers; Athena; PIKA; SPGISpeech; GigaSpeech Cited in: 0 Publications Standard Articles 2 Publications describing the Software Year Recent Developments on ESPnet Toolkit Boosted by Conformer Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, Yuekai Zhang 2020 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 2018