TR2021-073

The 2020 ESPnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans


    •  Watanabe, S., Boyer, F., Chang, X., Guo, P., Hayashi, T., Higuchi, Y., Hori, T., Huang, W.-C., Inaguma, H., Kamo, N., Shigeki, K., Li, C., Shi, J., Subramanian, A.S., Zhang, W., "The 2020 ESPNET Update: New Features, Broadened Applications, Performance Improvements, and Future Plans", IEEE Data Science and Learning Workshop (DSLW), June 2021.
      BibTeX TR2021-073 PDF
      • @inproceedings{Watanabe2021jun,
      • author = {Watanabe, Shinji and Boyer, Florian and Chang, Xuankai and Guo, Pengcheng and Hayashi, Tomoki and Higuchi, Yosuke and Hori, Takaaki and Huang, Wen-Chin and Inaguma, Hirofumi and Kamo, Naoyuki and Shigeki, Karita and Li, Chenda and Shi, Jing and Subramanian, Aswin S and Zhang, Wangyou},
      • title = {The 2020 ESPNET Update: New Features, Broadened Applications, Performance Improvements, and Future Plans},
      • booktitle = {IEEE Data Science and Learning Workshop (DSLW)},
      • year = 2021,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2021-073}
      • }
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  • Research Areas:

    Artificial Intelligence, Machine Learning, Speech & Audio

Abstract:

This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to mainly deal with end-to-end speech recognition experiments based on sequence-to-sequence modeling. The project has grown rapidly and now covers a wide range of speech processing applications. Now ESPnet also includes text to speech (TTS), voice conversation (VC), speech translation (ST), and speech enhancement (SE) with support for beamforming, speech separation, denoising, and dereverberation. All applications are trained in an end-to-end manner, thanks to the generic sequence to sequence modeling properties, and they can be further integrated and jointly optimized. Also, ESPnet provides reproducible all-in-one recipes for these applications with state-of-the-art performance in various benchmarks by incorporating transformer, advanced data augmentation, and conformer. This project aims to provide up-to-date speech processing experience to the community so that researchers in academia and various industry scales can develop their technologies collaboratively.

 

  • Related Publication

  •  Watanabe, S., Boyer, F., Chang, X., Guo, P., Hayashi, T., Higuchi, Y., Hori, T., Huang, W.-C., Inaguma, H., Kamo, N., Shigeki, K., Li, C., Shi, J., Subramanian, A.S., Zhang, W., "The 2020 Espnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans", arXiv, January 2021.
    BibTeX
    • @article{Watanabe2021jan,
    • author = {Watanabe, Shinji and Boyer, Florian and Chang, Xuankai and Guo, Pengcheng and Hayashi, Tomoki and Higuchi, Yosuke and Hori, Takaaki and Huang, Wen-Chin and Inaguma, Hirofumi and Kamo, Naoyuki and Shigeki, Karita and Li, Chenda and Shi, Jing and Subramanian, Aswin S and Zhang, Wangyou},
    • title = {The 2020 Espnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans},
    • journal = {arXiv},
    • year = 2021,
    • month = jan
    • }