{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/2"}],"enrichment":{"capability":"Defines and manages reproducible experimental protocols for multimedia databases (audio, video, text), organizing resources with metadata and train/dev/test splits for machine learning workflows.","skillfed_tags":["speaker-diarization","multimedia-datasets","protocol-framework"],"use_cases":["Organize speaker diarization datasets with train/dev/test splits and associated RTTM speaker annotations.","Load speaker verification protocols with metadata from multiple file formats (RTTM, CTM, UEM) automatically selected by suffix.","Define reproducible experimental protocols for audio segmentation tasks with lazy-loaded metadata and resource URIs.","Build custom data loaders for proprietary audio or video metadata formats and register them with the protocol system.","Iterate over multimedia resources with on-the-fly preprocessing and augmentation without modifying the underlying dataset files."],"what_it_does":"pyannote-database is a framework for defining and iterating over multimedia datasets with reproducible experimental protocols. It models resources (audio files, video files, images, etc.) as protocol files with URIs and associated metadata, then organizes them into train, development, and test subsets via YAML configuration. The package handles lazy loading and caching of metadata through pluggable data loaders (built-in support for RTTM, UEM, and CTM formats) and allows on-the-fly augmentation via preprocessors.\n\nTypically used in speech processing and speaker analysis workflows, it abstracts away the boilerplate of managing dataset splits and file paths. You define your protocol once in YAML, load it into the registry, and iterate over resources in Python\u2014each resource is a dict-like object with keys populated from metadata files and custom loaders. The package depends on pandas, pyannote-core, and pyyaml, and requires Python 3.10 or later.","worth_installing":"Yes, if you are working with speaker diarization, speaker verification, or other multimedia ML tasks and need a standardized way to define and iterate over dataset splits. The low install friction and stable dependency set make it straightforward to adopt. However, verify the license terms first (they are not declared in the package metadata), and be aware that maintenance is aging\u2014expect no rapid updates but likely sufficient stability for established workflows."},"id":"pyannote-database","links":{"html":"https://skillfed.io/packages/pyannote-database","md":"https://skillfed.io/packages/pyannote-database.md","pypi":"https://pypi.org/project/pyannote-database/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-12-07","license_spdx":null,"license_treatment":"unclear","name":"pyannote-database","python_support":"supports_current","summary":"Interface to multimedia databases and experimental protocols"},"popularity":{"monthly_downloads":2966223,"position":2805,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"6.1.1"}