{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Evaluates and analyzes speaker diarization systems by computing metrics, diagnostics, and error analysis for reproducible assessment of who-spoke-when predictions.","skillfed_tags":["speaker-diarization","evaluation-metrics","audio-processing"],"use_cases":["Compute diarization error rate (DER) and other standard metrics to benchmark a speaker diarization model against a reference annotation.","Analyze where a diarization system fails\u2014false alarms, missed speech, speaker confusion\u2014to guide model improvements.","Generate reproducible evaluation reports for research papers or system documentation.","Compare multiple diarization hypotheses against the same reference to rank or select the best performer.","Integrate metric computation into a diarization pipeline's validation or test harness."],"what_it_does":"pyannote.metrics is a toolkit for evaluating speaker diarization systems\u2014the task of determining who spoke when in an audio recording. It computes standard metrics (like diarization error rate), provides diagnostic breakdowns of errors, and supports reproducible evaluation workflows. The package wraps numpy, pandas, scikit-learn, and scipy to handle metric computation and analysis, and integrates with pyannote-core and pyannote-database for data representation.\n\nIt is designed for researchers and engineers building or benchmarking speaker diarization pipelines. Rather than a diarization system itself, it is the measurement and analysis layer: you provide reference (ground truth) and hypothesis (predicted) speaker segmentations, and the toolkit computes how well they match, where errors occur, and why. The package has been actively maintained since 2014 and is widely used in the speech processing community.","worth_installing":"Yes, if you are building or evaluating speaker diarization systems. The package is actively maintained, has low install friction, and is a standard tool in the diarization research community. However, verify the license terms first, as the metadata does not declare one explicitly. Not relevant for applications outside speaker diarization."},"id":"pyannote-metrics","links":{"html":"https://skillfed.io/packages/pyannote-metrics","md":"https://skillfed.io/packages/pyannote-metrics.md","pypi":"https://pypi.org/project/pyannote-metrics/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-06","license_spdx":null,"license_treatment":"unclear","name":"pyannote-metrics","python_support":"supports_current","summary":"A toolkit for reproducible evaluation, diagnostic, and error analysis of speaker diarization systems"},"popularity":{"monthly_downloads":2892700,"position":2837,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"4.1"}