Skip to main content

WeTextProcessing Runtime

Project description

WeTextProcessing Runtime

PyPI License

Python runtime for WeTextProcessing (does not depend on Pynini).

WeTextProcessing is a text processing library that provides text normalization (TN) and inverse text normalization (ITN) capabilities for Chinese, English and Japanese text. It uses Finite State Transducers (FSTs) for efficient text processing.

Features

  • Text Normalization (TN) for Chinese, English and Japanese
  • Inverse Text Normalization (ITN) for Chinese and Japanese
  • Traditional to Simplified Chinese conversion
  • Full-width to Half-width character conversion
  • Interjection removal
  • Punctuation removal
  • Out-of-vocabulary (OOV) word tagging
  • Erhua removal (for Chinese)
  • 0-to-9 conversion (for Chinese and Japanese ITN)

Installation

pip install wetext

Usage

Python API

Text Normalization (TN)

from wetext import Normalizer

# Chinese TN with erhua removal
normalizer = Normalizer(lang="zh", operator="tn", remove_erhua=True)
result = normalizer.normalize("你好 WeTextProcessing 1.0,全新版本儿,简直666")
print(result)  # 你好 WeTextProcessing 一点零,全新版本,简直六六六

# English TN
normalizer = Normalizer(lang="en", operator="tn")
result = normalizer.normalize("Today is August 8, 2024.")
print(result)  # Today is the eighth of august , twenty twenty four.

Inverse Text Normalization (ITN)

from wetext import Normalizer

# Chinese ITN
normalizer = Normalizer(lang="zh", operator="itn", enable_0_to_9=False)
result = normalizer.normalize("你好 WeTextProcessing 一点零,全新版本儿,简直六六六,九和六")
print(result)  # 你好 WeTextProcessing 1.0,全新版本儿,简直666,九和六

Command Line Interface

# Basic usage
wetext "你好 WeTextProcessing 1.0,全新版本儿,简直666"

# With options
wetext --lang zh --operator tn --remove-erhua "你好 WeTextProcessing 1.0,全新版本儿,简直666"

# Convert traditional to simplified Chinese
wetext --traditional-to-simple "你好,這是測試。"

# Remove punctuations
wetext --remove-puncts "你好,這是測試。"

API Reference

Normalizer Class

Normalizer(
    lang: Literal["auto", "en", "zh", "ja"] = "auto",
    operator: Literal["tn", "itn"] = "tn",
    traditional_to_simple: bool = False,
    full_to_half: bool = False,
    remove_interjections: bool = False,
    remove_puncts: bool = False,
    tag_oov: bool = False,
    enable_0_to_9: bool = False,
    remove_erhua: bool = False,
)

Parameters

  • lang: The language of the text. Can be "auto", "en", "zh" or "ja". Default is "auto".
  • operator: The operator to use. Can be "tn" (text normalization) or "itn" (inverse text normalization). Default is "tn".
  • traditional_to_simple: Whether to convert traditional Chinese to simplified Chinese. Default is False.
  • full_to_half: Whether to convert full-width characters to half-width characters. Default is False.
  • remove_interjections: Whether to remove interjections. Default is False.
  • remove_puncts: Whether to remove punctuation. Default is False.
  • tag_oov: Whether to tag out-of-vocabulary words. Default is False.
  • enable_0_to_9: Whether to enable 0-to-9 conversion for ITN. Default is False.
  • remove_erhua: Whether to remove erhua for TN. Default is False.

Methods

  • normalize(text: str, lang: Optional[Literal["auto", "en", "zh", "ja"]] = None) -> str: Normalize the text.

CLI Options

  • --lang, -l: Set the language. Choices are "auto", "en", "zh", "ja". Default is "auto".
  • --operator, -o: Set the operator. Choices are "tn", "itn". Default is "tn".
  • --traditional-to-simple: Convert traditional Chinese to simplified Chinese.
  • --full-to-half: Convert full-width characters to half-width characters.
  • --remove-interjections: Remove interjections.
  • --remove-puncts: Remove punctuation.
  • --tag-oov: Tag out-of-vocabulary words.
  • --enable-0-to-9: Enable 0-to-9 conversion.
  • --remove-erhua: Remove erhua.

License

Apache License 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

wetext-0.1.3.tar.gz (1.8 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

wetext-0.1.3-py3-none-any.whl (1.9 MB view details)

Uploaded Python 3

File details

Details for the file wetext-0.1.3.tar.gz.

File metadata

  • Download URL: wetext-0.1.3.tar.gz
  • Upload date:
  • Size: 1.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for wetext-0.1.3.tar.gz
Algorithm Hash digest
SHA256 1b76a7241ee1426a8e12f3494c38472fc7d987cb1ea0a296ff52b5be21a76687
MD5 a2ec9c9f3403aa26b59b345493b66b9b
BLAKE2b-256 194aef76ac98ff040f7466c745f65698bf53eaa90510d2394b6b1f3bc188ef50

See more details on using hashes here.

File details

Details for the file wetext-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: wetext-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for wetext-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 24a689ae49fffc42e3bf42063fee2f51802a4fd4b4d25d8cfb6e612f19244c25
MD5 5f8bf840d4df3177da63eb78310dc8c9
BLAKE2b-256 c964fb27a2e559d80dead89db8a9ad4d0ca574f28c19167e7c0323d2ad3d8a56

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page