WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Binbin Zhang, Di Wu, Zhendong Peng, Xingchen Song, Zhuoyuan Yao, Hang Lv, Lei Xie, Chao Yang, Fuping Pan, Jianwei Niu
Abstract
Recently, we made available WeNet [1], a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address the streaming and non-streaming decoding modes in a single model.To further improve ASR performance and facilitate various production requirements, in this paper, we present WeNet 2.0 with four important updates.(1) We propose U2++, a unified two-pass framework with bidirectional attention decoders, which includes the future contextual information by a right-toleft attention decoder to improve the representative ability of the shared encoder and the performance during the rescoring stage.(2) We introduce an n-gram based language model and a WFSTbased decoder into WeNet 2.0, promoting the use of rich text data in production scenarios.(3) We design a unified contextual biasing framework, which leverages user-specific context (e.g., contact lists) to provide rapid adaptation ability for production and improves ASR accuracy in both with-LM and without-LM scenarios.(4) We design a unified IO to support large-scale data for effective model training.In summary, the brand-new WeNet 2.0 achieves up to 10% relative recognition performance improvement over the original WeNet on various corpora and makes available several important production-oriented features.