{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"A self-contained voice activity detector that processes 10ms chunks of 16-bit mono PCM audio at 16kHz and returns a speech probability score to classify audio as speech or silence.","skillfed_tags":["audio-processing","machine-learning","edge-computing"],"use_cases":["Real-time speech detection in voice assistant applications to trigger wake-word detection only when speech is present.","Audio preprocessing in transcription pipelines to skip silence and reduce processing of non-speech segments.","Endpoint detection in voice recording applications to automatically stop recording when silence is detected.","Edge device deployment where a lightweight, offline VAD is needed without cloud dependencies.","Audio stream filtering in IoT devices or embedded systems with limited computational resources."],"what_it_does":"pymicro-vad is a lightweight, self-contained voice activity detector built on machine learning architecture from microWakeWord. It takes small chunks of audio (10ms at a time) and returns a probability score indicating whether that chunk contains speech or silence. The detector is designed to run locally without external dependencies or network calls, making it suitable for embedded systems, edge devices, or applications where low latency and offline operation are important.\n\nYou feed it raw 16-bit mono PCM audio at 16kHz sample rate, 160 samples per call (exactly 10ms), and it returns a speech probability. Scores below zero indicate the detector needs more audio to make a decision, scores above your chosen threshold indicate speech, and scores in between indicate silence. The package ships with pre-compiled wheels for common Linux platforms, though building from source requires python3-dev and build-essential.","worth_installing":"Yes, if you need a lightweight, offline voice activity detector for real-time audio processing. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and provides pre-built wheels for common platforms. The main constraint is the strict audio format requirement (10ms chunks of 16-bit mono PCM at 16kHz), which you must handle yourself."},"id":"pymicro-vad","links":{"html":"https://skillfed.io/packages/pymicro-vad","md":"https://skillfed.io/packages/pymicro-vad.md","pypi":"https://pypi.org/project/pymicro-vad/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-29","license_spdx":null,"license_treatment":"permissive","name":"pymicro-vad","python_support":"supports_current","summary":"Self-contained voice activity detector"},"popularity":{"monthly_downloads":119537,"position":12068,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.0"}