  In this paper, we propose an open source, production first, and production
ready speech recognition toolkit called WeNet in which a new two-pass approach
is implemented to unify streaming and non-streaming end-to-end (E2E) speech
recognition in a single model. The main motivation of WeNet is to close the gap
between the research and the production of E2E speechrecognition models. WeNet
provides an efficient way to ship ASR applications in several real-world
scenarios, which is the main difference and advantage to other open source E2E
speech recognition toolkits. In our toolkit, a new two-pass method is
implemented. Our method propose a dynamic chunk-based attention strategy of the
the transformer layers to allow arbitrary right context length modifies in
hybrid CTC/attention architecture. The inference latency could be easily
controlled by only changing the chunk size. The CTC hypotheses are then
rescored by the attention decoder to get the final result. Our experiments on
the AISHELL-1 dataset using WeNet show that, our model achieves 5.03\% relative
character error rate (CER) reduction in non-streaming ASR compared to a
standard non-streaming transformer. After model quantification, our model
perform reasonable RTF and latency.
