{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Scores automatic speech recognition or transcription output against a reference text, computing WER and CER with detailed alignment reports and diagnostic tools.","skillfed_tags":["asr-evaluation","nlp-metrics","speech-recognition"],"use_cases":["Compute aggregate WER/CER metrics on ASR output files to track system performance over time","Generate detailed per-utterance alignment reports to diagnose specific transcription errors and patterns","Compare multiple ASR systems or hypotheses against the same reference to rank model quality","Analyze out-of-vocabulary word recognition rates using OOV-CER for targeted model improvement","Extract per-speaker or per-group error metrics from mixed-speaker transcriptions for speaker-specific analysis","Produce JSON summaries of top errors for automated reporting and integration into evaluation pipelines"],"what_it_does":"texterrors is a command-line tool and Python library for evaluating automatic speech recognition and transcription systems. It computes standard metrics like word error rate (WER) and character error rate (CER), then provides detailed aligned error reports showing exactly where and how transcriptions diverged from reference text. The tool supports multiple input formats (ark-like, CTM), can group metrics by speaker or other categories, and offers specialized analysis modes like out-of-vocabulary word tracking and oracle WER selection across multiple hypotheses.\n\nThe package depends on Levenshtein for edit-distance alignment, numpy for numerical operations, typer for CLI scaffolding, loguru for logging, regex for pattern matching, and termcolor for colored terminal output. It ships prebuilt wheels for Python 3.10, 3.11, and 3.12 across macOS, Linux, and Windows, making installation straightforward on common platforms. Character-aware alignment is available as an option to produce more intuitive word alignments when words are similar, though it can increase reported WER by exposing insertions and deletions that token-only alignment would hide.","worth_installing":"Yes. texterrors is actively maintained, has no known vulnerabilities, carries a permissive license, and directly solves a common problem in speech recognition evaluation. Medium install friction is acceptable given the prebuilt wheels and straightforward dependency set. Install it if you regularly evaluate ASR or transcription systems."},"id":"texterrors","links":{"html":"https://skillfed.io/packages/texterrors","md":"https://skillfed.io/packages/texterrors.md","pypi":"https://pypi.org/project/texterrors/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-09","license_spdx":null,"license_treatment":"permissive","name":"texterrors","python_support":"supports_current","summary":"For WER"},"popularity":{"monthly_downloads":875922,"position":4832,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.8"}