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dscripka avatar

dscripka/openWakeWord

0
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2,428 stars·297 forks·Jupyter Notebook·Apache-2.0·18 views

OpenWakeWord

OpenWakeWord is a real-time audio processing framework that listens to microphone streams to identify spoken wake phrases and trigger voice-activated actions. It analyzes incoming audio feeds continuously, scoring confidence levels against predefined trigger phrases to initiate downstream application events.

The system incorporates background noise suppression and voice activity analysis to maintain reliable detection accuracy and minimize accidental triggers in noisy environments. Users can adjust numerical probability thresholds to balance responsiveness and false-positive rates for specific operational conditions.

Additionally, the project functions as a training toolkit for generating synthetic speech data and creating custom target phrase detectors and user-specific speaker verification models with minimal manual data collection. It includes evaluation tools to measure false-reject and false-accept rates using realistic environmental audio corpora and noise mixing.

Features

  • Wake Word Detection - Listens to microphone streams in real time to identify specific spoken phrases and trigger automated actions.
  • Wake Word Detection Models - Generates synthetic speech data and trains custom target phrase detectors with minimal manual data collection.
  • Speech Model Training - Serves as a training toolkit for generating synthetic speech data and training custom wake word and speaker verification models.
  • Verifications - Creates user-specific speaker verification models to restrict wake word activations to authorized voices and ignore strangers.
  • Speaker-Verified Detections - Creates user-specific speaker verification models to restrict wake word activations to authorized voices.
  • Audio Trigger Detection - Analyzes incoming microphone feeds in real time to identify predefined spoken trigger phrases.
  • Voice Activity Detection - Incorporates voice activity analysis to ignore non-speech sounds and prevent accidental triggers.
  • Audio Noise Suppression - Applies real-time noise suppression algorithms to incoming audio streams to minimize false activations in noisy environments.

Star history

Star history chart for dscripka/openwakewordStar history chart for dscripka/openwakeword

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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    Snowboy is a deep neural network-based wake word detection engine that runs entirely on-device, processing live audio to recognize a spoken trigger phrase without sending any data to the cloud. The project provides both pre-trained universal models for common wake words like "Alexa" or "Snowboy" and the ability to detect a custom wake word defined by the user. The engine is designed to be lightweight enough for embedded devices, using less than 10% CPU on a single-core 700MHz ARM processor such as a Raspberry Pi. Users can train a personalized wake-word model by submitting voice samples throu

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Frequently asked questions

What does dscripka/openwakeword do?

OpenWakeWord is a real-time audio processing framework that listens to microphone streams to identify spoken wake phrases and trigger voice-activated actions. It analyzes incoming audio feeds continuously, scoring confidence levels against predefined trigger phrases to initiate downstream application events.

What are the main features of dscripka/openwakeword?

The main features of dscripka/openwakeword are: Wake Word Detection, Wake Word Detection Models, Speech Model Training, Verifications, Speaker-Verified Detections, Audio Trigger Detection, Voice Activity Detection, Audio Noise Suppression.

What are some open-source alternatives to dscripka/openwakeword?

Open-source alternatives to dscripka/openwakeword include: picovoice/porcupine — Porcupine is an on-device wake word detection engine that listens for a specific spoken phrase in real-time audio and… livekit/livekit — LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with… k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device… wzpan/wukong-robot — wukong-robot is an open-source, Chinese-language voice assistant platform that integrates ChatGPT for multi-turn… kitt-ai/snowboy — Snowboy is a deep neural network-based wake word detection engine that runs entirely on-device, processing live audio… mycroftai/mycroft-core — Mycroft Core is an open-source voice assistant platform that processes spoken commands and runs modular skills for…

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