{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"JiWER computes speech recognition evaluation metrics (WER, MER, WIL, WIP, CER) by calculating minimum-edit distance between reference and hypothesis text using RapidFuzz for speed.","skillfed_tags":["speech-recognition","evaluation-metrics","nlp"],"use_cases":["Compute WER to benchmark an ASR model against a test set of reference transcriptions","Evaluate whether a speech model hallucinates on silent audio by testing empty reference-hypothesis pairs","Compare multiple ASR systems using MER, WIL, and WIP metrics alongside WER","Integrate ASR evaluation into a CI/CD pipeline to track model quality across releases","Analyze character-level errors (CER) to diagnose spelling or phonetic issues in ASR output"],"what_it_does":"JiWER is a Python package for evaluating automatic speech recognition (ASR) systems by computing standard error metrics. It calculates word error rate (WER), match error rate (MER), word information lost (WIL), word information preserved (WIP), and character error rate (CER) by computing the minimum-edit distance between reference transcripts and ASR hypothesis outputs. The package delegates the computationally expensive edit-distance calculation to RapidFuzz, which uses C++ under the hood, making it faster than pure Python implementations.\n\nThe package is designed for researchers and engineers validating ASR model quality. It handles edge cases like empty references (treating them as correct silence predictions) and supports batch processing of multiple reference-hypothesis pairs. With low install friction, no security vulnerabilities, and a permissive Apache 2.0 license, it integrates easily into evaluation pipelines.","worth_installing":"Yes, for ASR evaluation workflows. The package is stable, has no vulnerabilities, and offers the standard metrics researchers need. The 421-day gap since last release is a minor concern for maintenance, but the code is straightforward and the dependency on RapidFuzz is solid. Install if you need to score speech recognition output; skip if you need active development or additional metrics beyond the five supported."},"id":"jiwer","links":{"html":"https://skillfed.io/packages/jiwer","md":"https://skillfed.io/packages/jiwer.md","pypi":"https://pypi.org/project/jiwer/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-06-19","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"jiwer","python_support":"supports_current","summary":"Evaluate your speech-to-text system with similarity measures such as word error rate (WER)"},"popularity":{"monthly_downloads":4676271,"position":2257,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"4.0.0"}