Python guide shows how to evaluate caption files on four quality metrics beyond WER

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A technical tutorial published on DEV Community outlines how to build a caption quality-assurance harness in Python that scores WebVTT files across four dimensions: word error rate, missed entities, timing, and reading rate. The guide argues that relying solely on WER is misleading, since a caption file can register 96% accuracy and still be practically unusable. It uses three open-source libraries — jiwer, whisper_normalizer, and webvtt-py — and includes working code for loading cues, normalising text before scoring, and calculating character-per-second readability. The tutorial also flags a breaking API change in recent jiwer versions, where the commonly cited jiwer.compute_measures() function has been replaced by jiwer.process_words(). Developers are advised to pin library versions and integrate the harness into CI pipelines to catch regressions on every model or vendor change.

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