  In this paper, we present a new open source, production first and production
ready end-to-end (E2E) speech recognition toolkit named WeNet. The main
motivation of WeNet is to close the gap between the research and the production
of E2E speech recognition 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. This paper
introduces WeNet from three aspects, including model architecture, framework
design and performance metrics. Our experiments on AISHELL-1 using WeNet, not
only give a promising character error rate (CER) on a unified streaming and
non-streaming two pass (U2) E2E model but also show reasonable RTF and latency,
both of these aspects are favored for production adoption. The toolkit is
publicly available at https://github.com/mobvoi/wenet.
