{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic/2"}],"enrichment":{"capability":"Computes edit distance, alignment, and word error rate (WER) between sequences using Kaldi's original algorithms, with support for compound word matching and statistical confidence intervals.","skillfed_tags":["speech-recognition","nlp-metrics","sequence-alignment"],"use_cases":["Measure WER for speech recognition systems with guaranteed Kaldi compatibility.","Align reference and hypothesis sequences to identify specific error types (insertions, deletions, substitutions).","Compute corpus-level error statistics across batches of transcription pairs.","Extract 95% confidence intervals for WER using bootstrapping to assess statistical significance.","Handle compound word inconsistencies (e.g., 'white paper' vs 'whitepaper') at zero cost in error calculations."],"what_it_does":"kaldialign wraps Kaldi's edit-distance and alignment algorithms directly into Python via pybind11, ensuring bit-for-bit compatibility with Kaldi's scoring. It exposes functions for computing edit distance (with insertion, deletion, substitution counts), sequence alignment, corpus-level error rates, and WER confidence intervals via bootstrapping. The package solves the problem of inconsistent Levenshtein implementations across tools by using Kaldi's proven code unchanged.\n\nThe library is designed for speech recognition and NLP workflows where WER measurement must match Kaldi's results exactly. It supports optional SCLITE-style weighting (insertion/deletion cost 3, substitution cost 4), compound word matching to handle transcription inconsistencies, and statistical confidence interval estimation. No external runtime dependencies are required; the package is self-contained after installation.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, supports current Python versions (3.10\u20133.14), and solves a specific, high-value problem: exact Kaldi-compatible WER scoring. Medium install friction is acceptable given prebuilt wheels for all major platforms. Use it if you need reproducible speech recognition metrics that match Kaldi exactly."},"id":"kaldialign","links":{"html":"https://skillfed.io/packages/kaldialign","md":"https://skillfed.io/packages/kaldialign.md","pypi":"https://pypi.org/project/kaldialign/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-15","license_spdx":null,"license_treatment":"permissive","name":"kaldialign","python_support":"supports_current","summary":"Kaldi alignment methods wrapped into Python"},"popularity":{"monthly_downloads":318565,"position":7651,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.0"}