Summary Picovoice Porcupine offers highly accurate, lightweight, on-device wake word detection using deep neural networks. It enables developers to build always-listening voice applications across diverse platforms, from microcontrollers to web browsers, providing a compact and efficient solution for voice command interfaces.

How It Works Porcupine utilizes deep neural networks trained on real-world data for high accuracy. Its design prioritizes compactness and computational efficiency, ideal for resource-constrained environments like IoT. The engine scales to detect multiple wake words concurrently without increasing runtime overhead.

Quick Start & Requirements Installation varies by platform, with SDKs available via package managers (pip, npm/yarn, dotnet) and direct integration for C, Java, Flutter, React Native, Android, and iOS. All integrations require an ACCESS_KEY from the Picovoice Console. Demos typically need a microphone. Platform-specific SDKs may have additional environment setup needs. Links to demos and documentation are embedded within the repository structure.

Highlighted Details

• Broad Platform Support: Operates on microcontrollers (Arm Cortex-M, STM32, Arduino), SBCs (Raspberry Pi), mobile (Android, iOS), desktop (Linux, macOS, Windows), and web browsers (WebAssembly).
• Performance: Claims 11.0x higher accuracy and 6.5x faster speed than alternatives on Raspberry Pi 3.
• Custom Wake Words: Enables self-service training of custom wake word models via the Picovoice Console.