Recent Developments on Espnet Toolkit Boosted By Conformer
Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li
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.
BibTeX
@inproceedings{icassp2021_recentdevelopmen,
title = {Recent Developments on Espnet Toolkit Boosted By Conformer},
author = {Pengcheng Guo and Florian Boyer and Xuankai Chang and Tomoki Hayashi and Yosuke Higuchi and Hirofumi Inaguma and Naoyuki Kamo and Chenda Li and Daniel Garcia-Romero and Jiatong Shi and Jing Shi and Shinji Watanabe and Kun Wei and Wangyou Zhang and Yuekai Zhang},
booktitle = {ICASSP 2021},
year = {2021}
}