gladiaio/normalization

A lightweight library for normalizing speech transcripts before computing WER

43
/ 100
Emerging

This tool helps speech-to-text (STT) professionals accurately evaluate their systems. It takes raw text from both the original spoken content and the STT system's output, standardizes their formatting, and then produces clean, comparable text ready for calculating Word Error Rate (WER). This ensures that only genuine recognition errors, not formatting differences, impact performance scores. Anyone evaluating or comparing speech recognition technologies, such as data scientists, AI researchers, or product managers, would find this useful.

Use this if you need to reliably measure the accuracy of speech-to-text systems by normalizing transcripts to a consistent format before computing Word Error Rate.

Not ideal if you need to analyze the raw, unformatted output of a speech-to-text system, including punctuation, capitalization, and numbers in their original forms.

speech-to-text ASR-evaluation transcription-quality natural-language-processing AI-benchmarking
No Package No Dependents
Maintenance 13 / 25
Adoption 5 / 25
Maturity 11 / 25
Community 14 / 25

How are scores calculated?

Stars

10

Forks

3

Language

Python

License

MIT

Last pushed

Mar 23, 2026

Commits (30d)

0

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