# dscripka/openwakeword

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

## Links

- GitHub: https://github.com/dscripka/openWakeWord
- awesome-repositories: https://awesome-repositories.com/repository/dscripka-openwakeword.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Wake Word Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/conversational-voice-interaction/voice-agents/voice-activity-detection/wake-word-detection.md) — Listens to microphone streams in real time to identify specific spoken phrases and trigger automated actions. ([source](https://github.com/dscripka/openwakeword#readme))
- [Wake Word Detection Models](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/wake-word-detection-models.md) — Generates synthetic speech data and trains custom target phrase detectors with minimal manual data collection. ([source](https://github.com/dscripka/openwakeword#readme))
- [Speech Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/model-training-frameworks/speech-model-training.md) — Serves as a training toolkit for generating synthetic speech data and training custom wake word and speaker verification models.
- [Verifications](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/conversational-voice-interaction/voice-agents/voice-activity-detection/verifications.md) — Creates user-specific speaker verification models to restrict wake word activations to authorized voices and ignore strangers.
- [Speaker-Verified Detections](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/conversational-voice-interaction/voice-agents/voice-activity-detection/wake-word-detection/speaker-verified-detections.md) — Creates user-specific speaker verification models to restrict wake word activations to authorized voices. ([source](https://github.com/dscripka/openwakeword#readme))
- [Audio Trigger Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/audio-trigger-detection.md) — Analyzes incoming microphone feeds in real time to identify predefined spoken trigger phrases. ([source](https://github.com/dscripka/openwakeword#readme))
- [Voice Activity Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/detection-error-handling/voice-activity-detection.md) — Incorporates voice activity analysis to ignore non-speech sounds and prevent accidental triggers. ([source](https://github.com/dscripka/openwakeword#readme))

### Part of an Awesome List

- [Audio Noise Suppression](https://awesome-repositories.com/f/awesome-lists/ai/recurrent-neural-networks/audio-noise-suppression.md) — Applies real-time noise suppression algorithms to incoming audio streams to minimize false activations in noisy environments. ([source](https://github.com/dscripka/openwakeword#readme))