Wake Word Detection

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This skill provides expert guidance for building offline, low-latency wake word detection systems, specifically optimized for JARVIS-style voice assistants. It emphasizes a privacy-first approach by processing audio locally and minimizing data retention to ensure user security. By following test-driven development (TDD) principles and implementing efficiency patterns like VAD preprocessing and model quantization, the skill enables developers to maintain highly accurate keyword spotting while keeping resource consumption under 5% CPU usage and 100MB of memory.

Características Principales

• Performance optimization patterns including VAD preprocessing and ONNX model quantization
• Advanced false positive reduction techniques using detection history and confirmation windows
• 4 GitHub stars
• Privacy-centric audio processing with local-only execution and minimal buffering
• Test-driven development (TDD) workflow for verifying detection accuracy and resource usage
• Efficient audio management via pre-allocated numpy buffers and memory-mapped models

Casos de Uso

• Developing an offline voice activation phrase for a private home assistant
• Integrating always-listening capabilities into resource-constrained IoT devices
• Building secure voice-controlled desktop applications that prioritize user privacy