The 2020 Espnet Update: New Features, Broadened Applications, Performance Improvements, And Future Plans
2020 · Shinji Watanabe, Florian Boyer, Xuankai Chang, et al.
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 sp
Authors
(none)
Tags
Stats
Code
Related papers
- Espnet: End-to-end Speech Processing Toolkit (2018)22.17
- Espnet-tts: Unified, Reproducible, And Integratable Open Source End-to-end Text-to-speech Toolkit (2019)23.32
- Espnet-se: End-to-end Speech Enhancement And Separation Toolkit Designed For Asr Integration (2020)13.55
- Recent Developments On Espnet Toolkit Boosted By Conformer (2020)0.00
- Espnet-se++: Speech Enhancement For Robust Speech Recognition, Translation, And Understanding (2022)18.72
- Espnet-codec: Comprehensive Training And Evaluation Of Neural Codecs For Audio, Music, And Speech (2024)9.03
- User-friendly Automatic Transcription Of Low-resource Languages: Plugging Espnet Into Elpis (2020)2.26
- Espnet-spk: Full Pipeline Speaker Embedding Toolkit With Reproducible Recipes, Self-supervised Front-ends, And Off-the-shelf Models (2024)0.00