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

arXiv · 2010.13956

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

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Abstract

In this study, we present recent developments on ESPnet: End-to-End Speech Processing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-augmented Transformer. This paper shows the results for a wide range of end-to-end speech processing applications, such as automatic speech recognition (ASR), speech translations (ST), speech separation (SS) and text-to-speech (TTS). Our experiments reveal various training tips and significant performance benefits obtained with the Conformer on different tasks. These results are competitive or even outperform the current state-of-art Transformer models. We are preparing to release all-in-one recipes using open source and publicly available corpora for all the above tasks with pre-trained models. Our aim for this work is to contribute to our research community by reducing the burden of preparing state-of-the-art research environments usually requiring high resources.

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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. 2020-10-26. Recent Developments on ESPnet Toolkit Boosted by Conformer. https://arxiv.org/abs/2010.13956

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