  Inverse text normalization (ITN) converts spoken-domain automatic speech
recognition (ASR) output into written-domain text to improve the readability of
the ASR output. Many state-of-the-art ITN systems use hand-written weighted
finite-state transducer(WFST) grammars since this task has extremely low
tolerance to unrecoverable errors. We introduce an open-source Python
WFST-based library for ITN which enables a seamless path from development to
production. We describe the specification of ITN grammar rules for English, but
the library can be adapted for other languages. It can also be used for
written-to-spoken text normalization. We evaluate the NeMo ITN library using a
modified version of the Google Text normalization dataset.
