extended-wer-normalizer 0.2.1

jiwer-compatible WER normalizer with number, email, URL, filler, and symbol normalization for voice AI evaluation

extended-wer-normalizer

jiwer-compatible text normalizer for Word Error Rate (WER) evaluation in voice AI.

Extends jiwer's built-in transforms with normalizations that matter for real-world ASR evaluation: phone numbers, emails, URLs, currency, percentages, ordinals, filler words, and stuttering.

Installation

pip install extended-wer-normalizer

Quick start

from extended_wer_normalizer import normalize_for_wer

normalize_for_wer("Call 0176 or email [e-mail], it costs $5.99")
# → "call 0 1 7 6 or email info at example dot com it costs five dollars ninety nine cents"

normalize_for_wer("Um, 1st place goes to Dr. Smith with 50% accuracy")
# → "first place goes to doctor smith with fifty percent accuracy"

jiwer integration

Every normalization is a jiwer.AbstractTransform subclass — compose them freely:

import jiwer
from extended_wer_normalizer.transforms import NormalizeEmails, ExpandDigitRuns

pipeline = jiwer.Compose([
    NormalizeEmails(),
    ExpandDigitRuns(),
    jiwer.ToLowerCase(),
    jiwer.RemovePunctuation(),
    jiwer.ReduceToListOfListOfWords(),
])

wer = jiwer.wer("info at example dot com", "[e-mail]", hypothesis_transform=pipeline)

Use the pre-built pipeline directly with jiwer.wer:

import jiwer
from extended_wer_normalizer import english_wer_pipeline

wer = jiwer.wer(reference, hypothesis, reference_transform=english_wer_pipeline, hypothesis_transform=english_wer_pipeline)

Available transforms

Transform	Example
ExpandDigitRuns	"0176" → "0 1 7 6"
DigitWordsToChars	"zero one seven" → "0 1 7"
WhisperEnglishNormalize	lowercase, punctuation, contractions, compound numbers
WhisperBasicNormalize	language-agnostic basic normalization
FinalDigitWordCleanup	residual digit-word sweep after compound resolution
NormalizeEmails	"[e-mail]" → "user at example dot com"
NormalizeURLs	"https://example.com/path" → "example dot com"
NormalizeCurrency	"$5.99" → "five dollars ninety nine cents"
NormalizePercentages	"50%" → "fifty percent"
NormalizeOrdinals	"1st" → "first", "15th" → "fifteenth"
ExpandAbbreviations	"Dr." → "doctor", "vs." → "versus"
NormalizeSymbols	"cats & dogs" → "cats and dogs"
RemoveFillerWords	removes um, uh, hmm, er, ah, …
CollapseRepetitions	"I I I think" → "I think"

Pipeline design

The English pipeline applies transforms in an order that ensures idempotence and correct interaction with Whisper's EnglishTextNormalizer:

1. Pre-Whisper: email, URL, symbol, abbreviation (patterns Whisper would mangle)
2. Digit preparation: expand digit runs, convert digit words
3. Core: WhisperEnglishNormalize (lowercase, punctuation, contractions, compound numbers → digits)
4. Post-Whisper: digit run expansion, digit word cleanup, currency, percentage, ordinal (patterns Whisper preserves or compacts)
5. Cleanup: filler words, repetition collapse

Non-English

For non-English text, pass language to get Whisper's BasicTextNormalizer:

normalize_for_wer("Das kostet fünf Euro", language="de")