  Automatic Speech Recognition (ASR) systems typically yield output in lexical
form. However, humans prefer a written form output. To bridge this gap, ASR
systems usually employ Inverse Text Normalization (ITN).
  In previous works, Weighted Finite State Transducers (WFST) have been
employed to do ITN. WFSTs are nicely suited to this task but their size and
run-time costs can make deployment on embedded applications challenging.
  In this paper, we describe the development of an on-device ITN system that is
streaming, lightweight & accurate. At the core of our system is a streaming
transformer tagger, that tags lexical tokens from ASR. The tag informs which
ITN category might be applied, if at all. Following that, we apply an
ITN-category-specific WFST, only on the tagged text, to reliably perform the
ITN conversion. We show that the proposed ITN solution performs equivalent to
strong baselines, while being significantly smaller in size and retaining
customization capabilities.
