whisper-normalizer
A python package for whisper normalizer
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and solves a specific problem (Whisper-compatible text normalization) with low install friction. It is particularly valuable if you work with Indic languages or need to evaluate ASR systems using Whisper's normalization standard.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with four runtime dependencies (more_itertools, regex, indic-numtowords, text2num).
- Repository is active with recent commits and no archived status.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
last release 2026-07-26 (19 days) · last repo commit 2026-08-11 · 79 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 532,870 downloads/mo, #6,144 on PyPI
Alternatives
Verify before relying
pip install whisper_normalizer
from whisper_normalizer.english import EnglishTextNormalizer
normalizer = EnglishTextNormalizer()
result = normalizer("I'm a little teapot, short and stout.")- Whether BasicTextNormalizer's preserve_marks=True parameter is available in version 0.1.15
- Performance characteristics when normalizing large text volumes
- Whether all Indic languages mentioned in the description are fully supported in this version
What it is and what it does
whisper_normalizer is a Python implementation of OpenAI's Whisper text normalization algorithm, designed to standardize text for automatic speech recognition (ASR) systems. It reduces unintentional penalties in metrics like WER and CER by converting text in different styles into a standardized form, separating actual transcription errors from formatting or punctuation differences.
The package provides two main normalizers: EnglishTextNormalizer for English text and BasicTextNormalizer for general use. It extends the original Whisper approach with specialized support for Indic languages (including Malayalam) derived from the indic-nlp-library, addressing issues that arise when applying Whisper's default normalization to low-resource and script-heavy languages. The package depends on more_itertools, regex, indic-numtowords, and text2num.
Use it for
- Normalize transcription output from speech-to-text models before evaluating accuracy metrics
- Preprocess text in Indic languages (Malayalam, Tamil, etc.) while preserving combining marks and diacritics
- Standardize English text for ASR evaluation pipelines compatible with Whisper, MMS, or Conformer models
- Apply consistent text normalization across multilingual speech recognition workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and solves a specific problem (Whisper-compatible text normalization) with low install friction. It is particularly valuable if you work with Indic languages or need to evaluate ASR systems using Whisper's normalization standard.
Install
whisper-normalizer on PyPI
Before you install
Low friction: pure Python wheel with four runtime dependencies (more_itertools, regex, indic-numtowords, text2num). Repository is active with recent commits and no archived status.
License in practice
MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install whisper_normalizer
from whisper_normalizer.english import EnglishTextNormalizer
normalizer = EnglishTextNormalizer()
result = normalizer("I'm a little teapot, short and stout.")Verify before relying
- Whether BasicTextNormalizer's preserve_marks=True parameter is available in version 0.1.15
- Performance characteristics when normalizing large text volumes
- Whether all Indic languages mentioned in the description are fully supported in this version
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesmore_itertoolsregexindic-numtowordstext2num |
| Maintenance | Actively maintained 19 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 532,870 / month, #6,144 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersNatural Language :: EnglishOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: OS IndependentOperating System :: POSIXOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: whisper_normalizer-0.1.15-py3-none-any.whl
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See also bnunicodenormalizer · indic-nlp-library · openai-whisper · wetext · whisper-timestamped · mlx-whisper · faster-whisper · silpa_common · whisperx · charset-normalizer