The 2020 ESPnet update: new features, broadened applications, performance improvements, and future plans
This update provides a comprehensive, reproducible, and state-of-the-art toolkit for researchers and developers in academia and industry working on various speech processing tasks.
This paper details the 2020 updates to ESPnet, an end-to-end speech processing toolkit. It now supports a wider range of applications including text-to-speech, voice conversion, speech translation, and speech enhancement, and incorporates advanced models like Transformer and Conformer to achieve state-of-the-art performance across various benchmarks.
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.