EsOff/WeTextProcessing

Text Normalization & Inverse Text Normalization

Text Normalization & Inverse Text Normalization

1. How To Use

1.1 Quick Start:

# install
pip install WeTextProcessing

# tn usage
>>> from tn.chinese.normalizer import Normalizer
>>> normalizer = Normalizer()
>>> normalizer.normalize("2.5平方电线")
# itn usage
>>> from itn.chinese.inverse_normalizer import InverseNormalizer
>>> invnormalizer = InverseNormalizer()
>>> invnormalizer.normalize("二点五平方电线")

1.2 Advanced Usage:

DIY your own rules && Deploy WeTextProcessing with cpp runtime !!

For users who want modifications and adapt tn/itn rules to fix badcase, please try:

git clone https://github.com/wenet-e2e/WeTextProcessing.git
cd WeTextProcessing
# `overwrite_cache` will rebuild all rules according to
#   your modifications on tn/chinese/rules/xx.py (itn/chinese/rules/xx.py).
#   After rebuild, you can find new far files at `$PWD/tn` and `$PWD/itn`.
python normalize.py --text "2.5平方电线" --overwrite_cache
python inverse_normalize.py --text "二点五平方电线" --overwrite_cache

Once you successfully rebuild your rules, you can deploy them either with your installed pypi packages:

# tn usage
>>> from tn.chinese.normalizer import Normalizer
>>> normalizer = Normalizer(cache_dir="PATH_TO_GIT_CLONED_WETEXTPROCESSING/tn")
>>> normalizer.normalize("2.5平方电线")
# itn usage
>>> from itn.chinese.inverse_normalizer import InverseNormalizer
>>> invnormalizer = InverseNormalizer(cache_dir="PATH_TO_GIT_CLONED_WETEXTPROCESSING/itn")
>>> invnormalizer.normalize("二点五平方电线")

Or with cpp runtime:

cmake -B build -S runtime -DCMAKE_BUILD_TYPE=Release
cmake --build build
# tn usage
./build/bin/processor_main --far PATH_TO_GIT_CLONED_WETEXTPROCESSING/tn/zh_tn_normalizer.far --text "2.5平方电线"
# itn usage
./build/bin/processor_main --far PATH_TO_GIT_CLONED_WETEXTPROCESSING/itn/zh_itn_normalizer.far --text "二点五平方电线"

2. TN Pipeline

Please refer to TN.README

3. ITN Pipeline

Please refer to ITN.README

Acknowledge

1. Thank the authors of foundational libraries like OpenFst & Pynini.
2. Thank NeMo team & NeMo open-source community.
3. Thank Zhenxiang Ma, Jiayu Du, and SpeechColab organization.
4. Referred Pynini for reading the FAR, and printing the shortest path of a lattice in the C++ runtime.
5. Referred TN of NeMo for the data to build the tagger graph.
6. Referred ITN of chinese_text_normalization for the data to build the tagger graph.