The NeMo Text Processing module provides support for both Text Normalization (TN) and Inverse Text Normalization (ITN) in order to aid upstream and downstream text processing. The included tutorials are intended to help you quickly become familiar with the interface of the module, as well as guiding you in creating and deploying your own grammars for individual text processing needs.

If you wish to learn more about how to use NeMo's for Text Normalization tasks (e.g. conversion of symbolic strings to verbal form - such as 15 -> "fifteen"), please see the Text Normalization tutorial.

If you wish to learn more about Inverse Text Normalization - the inverse task of converting from verbalized strings to symbolic written form, as may be encountered in downstream ASR - consult the Inverse Text Normalization tutorial.

For those curious about constructing grammars tailored to specific languages and use cases, you may be interested in working through the WFST Tutorial, which goes through NeMo's Normalization process in detail.

As NeMo Text Processing utilizes Weighted Finite State Transducer (WFST) graphs to construct its grammars, a working knowledge of Finite State Automata (FSA) and/or regular languages is suggested. Further, we recommend becoming functionally familiar with the pynini library - which functions as the backend for graph construction - and Sparrowhawk - which NeMo utilizes for grammar deployment.