## Inverse Text Normalization: Weighted Finite State Transducer

#### Model Overview

This model is an OpenFST finite state archive (.far) for use within the opensource Sparrowhawk normalization engine [2].  This model has been created for English (en-US) text.

#### Model Architecture

This model uses weighted finite-state transducer (WFST) grammars that map strings in spoken form to strings in written form.

#### Training

The model was constructed using Nemo toolkit as outlined in [intro](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_normalization/intro.html) and [Text processing](https://github.com/NVIDIA/NeMo/tree/main/tutorials/text_processing)

#### How to Use this Model

To use this model , we can use Riva Skills Quick start guide , it is a starting point to try out Riva models . Information regarding Quick start guide can be found : [here](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart).

#### Input

This model is part of a pipeline for speech to text and accepts text as input.

#### Output

The output is written form text.

#### References

[1] [NeMo Inverse Text Normalization: From Development To Production](https://arxiv.org/abs/2104.05055)

[2] [Google Sparrowhawk] (https://github.com/google/sparrowhawk)

#### License

By downloading and using the models and resources packaged with Riva Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/license)

Riva ASR English Inverse Normalization Grammar

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