{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"RealtimeSTT converts speech from microphone or external audio streams into text using voice activity detection and pluggable transcription engines like faster-whisper, with optional wake-word activation.","skillfed_tags":["speech-recognition","audio-processing","voice-activity-detection"],"use_cases":["Build a voice assistant that listens for a wake word, then transcribes commands in real-time with minimal latency.","Create a dictation tool that continuously records speech and processes transcripts asynchronously via callbacks.","Stream audio from a websocket or external source and transcribe it without touching the local microphone.","Prototype a speech-to-text pipeline where you can swap transcription engines (faster-whisper, Kroko, Silero) without changing application code.","Detect ambient noise levels and voice activity to trigger recording or filtering in audio processing pipelines."],"what_it_does":"RealtimeSTT is a Python library that captures audio from a microphone or external stream, detects when speech is happening using voice activity detection (VAD), and transcribes it to text. It's designed for applications like voice assistants, dictation tools, and streaming servers that need to turn speech into text with minimal code. The library supports multiple transcription backends (faster-whisper, OpenAI Whisper, Silero, and others) via optional extras, so you install only what you need. It includes WebRTC VAD by default and can optionally use Silero VAD for better accuracy, plus optional wake-word detection through Porcupine or OpenWakeWord.\n\nThe core package is lightweight, but it depends on PyAudio, torch, torchaudio, scipy, websockets, and other audio libraries. Installation requires system-level PortAudio headers on Linux and macOS. Python 3.11 or newer is required. The package uses multiprocessing for model work, which means you must guard your main code with `if __name__ == \"__main__\":` on Windows. You can feed audio from files, streams, or websockets by setting `use_microphone=False` and calling `feed_audio()` with PCM chunks.","worth_installing":"Yes. RealtimeSTT is actively maintained, has no known vulnerabilities, and offers a clean API for real-time speech-to-text with flexible backend selection. The permissive MIT license and low install friction (pure wheel, optional extras) make it low-risk. The main gotchas are the system PortAudio dependency and Python 3.11+ requirement; if your environment meets those, it's a solid choice for local speech recognition without cloud APIs."},"id":"realtimestt","links":{"html":"https://skillfed.io/packages/realtimestt","md":"https://skillfed.io/packages/realtimestt.md","pypi":"https://pypi.org/project/realtimestt/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-31","license_spdx":null,"license_treatment":"permissive","name":"realtimestt","python_support":"supports_current","summary":"A fast Voice Activity Detection and Transcription System"},"popularity":{"monthly_downloads":111392,"position":12417,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.2"}