This repository is under development, please refer to https://github.com/NVIDIA/NeMo/tree/main/nemo_text_processing for full functionality. See documentation for details.
nemo-text-processing is a Python package for text normalization and inverse text normalization.
NeMo-text-processing (text normalization and inverse text normalization).
| Google Collab Notebook | Description |
|---|---|
| Text_(Inverse)_Normalization.ipynb | Quick-start guide |
| WFST_Tutorial | In-depth tutorial on grammar customization |
If you have a question which is not answered in the Github discussions, encounter a bug or have a feature request, please create a Github issue. We also welcome you to directly open a pull request to fix a bug or add a feature.
We recommend setting up a fresh Conda environment to install NeMo-text-processing.
conda create --name nemo_tn python==3.8
conda activate nemo_tn(Optional) To use hybrid text normalization install PyTorch using their configurator.
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
NOTE: The command used to install PyTorch may depend on your system.
Use this installation mode if you want the latest released version.
pip install nemo_text_processing
Use this installation mode if you want the a version from particular GitHub branch (e.g main).
pip install Cython
python -m pip install git+https://github.com/NVIDIA/NeMo-text-processing.git@{BRANCH}#egg=nemo_text_processing
Use this installation mode if you are contributing to NeMo-text-processing.
git clone https://github.com/NVIDIA/NeMo-text-processing
cd NeMo-text-processing
./reinstall.sh
NOTE: If you only want the toolkit without additional conda-based dependencies, you may replace reinstall.sh with pip install -e . with the NeMo-text-processing root directory as your current working director.
We welcome community contributions! Please refer to the CONTRIBUTING.md for guidelines.
@inproceedings{zhang21ja_interspeech,
author={Yang Zhang and Evelina Bakhturina and Boris Ginsburg},
title={{NeMo (Inverse) Text Normalization: From Development to Production}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={4857--4859}
}
@inproceedings{bakhturina22_interspeech,
author={Evelina Bakhturina and Yang Zhang and Boris Ginsburg},
title={{Shallow Fusion of Weighted Finite-State Transducer and Language Model for
Text Normalization}},
year=2022,
booktitle={Proc. Interspeech 2022}
}
NeMo-text-processing is under Apache 2.0 license.