  Transformers have recently dominated the ASR field. Although able to yield
good performance, they involve an autoregressive (AR) decoder to generate
tokens one by one, which is computationally inefficient. To speed up inference,
non-autoregressive (NAR) methods, e.g. single-step NAR, were designed, to
enable parallel generation. However, due to an independence assumption within
the output tokens, performance of single-step NAR is inferior to that of AR
models, especially with a large-scale corpus. There are two challenges to
improving single-step NAR: Firstly to accurately predict the number of output
tokens and extract hidden variables; secondly, to enhance modeling of
interdependence between output tokens. To tackle both challenges, we propose a
fast and accurate parallel transformer, termed Paraformer. This utilizes a
continuous integrate-and-fire based predictor to predict the number of tokens
and generate hidden variables. A glancing language model (GLM) sampler then
generates semantic embeddings to enhance the NAR decoder's ability to model
context interdependence. Finally, we design a strategy to generate negative
samples for minimum word error rate training to further improve performance.
Experiments using the public AISHELL-1, AISHELL-2 benchmark, and an
industrial-level 20,000 hour task demonstrate that the proposed Paraformer can
attain comparable performance to the state-of-the-art AR transformer, with more
than 10x speedup.
