Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/wenet-e2e/WeTextProcessing
Text Normalization & Inverse Text Normalization
https://github.com/wenet-e2e/WeTextProcessing
normalization production-ready text-processing
Last synced: 24 days ago
JSON representation
Text Normalization & Inverse Text Normalization
- Host: GitHub
- URL: https://github.com/wenet-e2e/WeTextProcessing
- Owner: wenet-e2e
- License: apache-2.0
- Created: 2022-08-23T14:33:26.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-11-11T05:41:57.000Z (about 1 month ago)
- Last Synced: 2024-11-20T23:34:11.344Z (about 1 month ago)
- Topics: normalization, production-ready, text-processing
- Language: Python
- Homepage:
- Size: 957 KB
- Stars: 481
- Watchers: 10
- Forks: 69
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - wenet-e2e/WeTextProcessing
README
## Text Normalization & Inverse Text Normalization
### 0. Brief Introduction
```diff
- **Must Read Doc** (In Chinese): https://mp.weixin.qq.com/s/q_11lck78qcjylHCi6wVsQ
```
[WeTextProcessing: Production First & Production Ready Text Processing Toolkit](https://mp.weixin.qq.com/s/q_11lck78qcjylHCi6wVsQ)
#### 0.1 Text Normalization
#### 0.2 Inverse Text Normalization
### 1. How To Use
#### 1.1 Quick Start:
```bash
# install
pip install WeTextProcessing
```
Command-usage:
```bash
wetn --text "2.5平方电线"
weitn --text "二点五平方电线"
```
Python usage:
```py
from itn.chinese.inverse_normalizer import InverseNormalizer
from tn.chinese.normalizer import Normalizer as ZhNormalizer
from tn.english.normalizer import Normalizer as EnNormalizer
# NOTE(xcsong): 和默认参数不一致时，必须重新构图，要重新构图请务必指定 `overwrite_cache=True`
# When the parameters differ from the defaults, it is mandatory to re-compose. To re-compose, please ensure you specify `overwrite_cache=True`.
zh_tn_text = "你好 WeTextProcessing 1.0，船新版本儿，船新体验儿，简直666，9和10"
zh_itn_text = "你好 WeTextProcessing 一点零，船新版本儿，船新体验儿，简直六六六，九和六"
en_tn_text = "Hello WeTextProcessing 1.0, life is short, just use wetext, 666, 9 and 10"
zh_tn_model = ZhNormalizer(remove_erhua=True, overwrite_cache=True)
zh_itn_model = InverseNormalizer(enable_0_to_9=False, overwrite_cache=True)
en_tn_model = EnNormalizer(overwrite_cache=True)
print("中文 TN (去除儿化音，重新在线构图):\n\t{} => {}".format(zh_tn_text, zh_tn_model.normalize(zh_tn_text)))
print("中文ITN (小于10的单独数字不转换，重新在线构图):\n\t{} => {}".format(zh_itn_text, zh_itn_model.normalize(zh_itn_text)))
print("英文 TN (暂时还没有可控的选项，后面会加...):\n\t{} => {}\n".format(en_tn_text, en_tn_model.normalize(en_tn_text)))
zh_tn_model = ZhNormalizer(overwrite_cache=False)
zh_itn_model = InverseNormalizer(overwrite_cache=False)
en_tn_model = EnNormalizer(overwrite_cache=False)
print("中文 TN (复用之前编译好的图):\n\t{} => {}".format(zh_tn_text, zh_tn_model.normalize(zh_tn_text)))
print("中文ITN (复用之前编译好的图):\n\t{} => {}".format(zh_itn_text, zh_itn_model.normalize(zh_itn_text)))
print("英文 TN (复用之前编译好的图):\n\t{} => {}\n".format(en_tn_text, en_tn_model.normalize(en_tn_text)))
zh_tn_model = ZhNormalizer(remove_erhua=False, overwrite_cache=True)
zh_itn_model = InverseNormalizer(enable_0_to_9=True, overwrite_cache=True)
print("中文 TN (不去除儿化音，重新在线构图):\n\t{} => {}".format(zh_tn_text, zh_tn_model.normalize(zh_tn_text)))
print("中文ITN (小于10的单独数字也进行转换，重新在线构图):\n\t{} => {}\n".format(zh_itn_text, zh_itn_model.normalize(zh_itn_text)))
```
#### 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:
``` bash
git clone https://github.com/wenet-e2e/WeTextProcessing.git
cd WeTextProcessing
pip install -r requirements.txt
pre-commit install # for clean and tidy code
# `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 -m tn --text "2.5平方电线" --overwrite_cache
python -m itn --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:
```py
# 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:
```bash
cmake -B build -S runtime -DCMAKE_BUILD_TYPE=Release
cmake --build build
# tn usage
cache_dir=PATH_TO_GIT_CLONED_WETEXTPROCESSING/tn
./build/processor_main --tagger $cache_dir/zh_tn_tagger.fst --verbalizer $cache_dir/zh_tn_verbalizer.fst --text "2.5平方电线"
# itn usage
cache_dir=PATH_TO_GIT_CLONED_WETEXTPROCESSING/itn
./build/processor_main --tagger $cache_dir/zh_itn_tagger.fst --verbalizer $cache_dir/zh_itn_verbalizer.fst --text "二点五平方电线"
```
### 2. TN Pipeline
Please refer to [TN.README](tn/README.md)
### 3. ITN Pipeline
Please refer to [ITN.README](itn/README.md)
## Discussion & Communication
For Chinese users, you can aslo scan the QR code on the left to follow our offical account of WeNet.
We created a WeChat group for better discussion and quicker response.
Please scan the personal QR code on the right, and the guy is responsible for inviting you to the chat group.
| 
| 
|
| ---- | ---- |
Or you can directly discuss on [Github Issues](https://github.com/wenet-e2e/WeTextProcessing/issues).
## Acknowledge
1. Thank the authors of foundational libraries like [OpenFst](https://www.openfst.org/twiki/bin/view/FST/WebHome) & [Pynini](https://www.openfst.org/twiki/bin/view/GRM/Pynini).
3. Thank [NeMo](https://github.com/NVIDIA/NeMo) team & NeMo open-source community.
2. Thank [Zhenxiang Ma](https://github.com/mzxcpp), [Jiayu Du](https://github.com/dophist), and [SpeechColab](https://github.com/SpeechColab) organization.
3. Referred [Pynini](https://github.com/kylebgorman/pynini) for reading the FAR, and printing the shortest path of a lattice in the C++ runtime.
4. Referred [TN of NeMo](https://github.com/NVIDIA/NeMo/tree/main/nemo_text_processing/text_normalization/zh) for the data to build the tagger graph.
5. Referred [ITN of chinese_text_normalization](https://github.com/speechio/chinese_text_normalization/tree/master/thrax/src/cn) for the data to build the tagger graph.