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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 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.
Projects with overlapping indexed features 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…
Porcupine is an on-device wake word detection engine that listens for a specific spoken phrase in real-time audio and triggers actions, all processed locally without any cloud connectivity. It includes a custom wake word model creator that generates production-ready models from just a few spoken examples in seconds, requiring no training data. Beyond wake word detection, Porcupine also provides on-device speech recognition for real-time transcription with custom vocabulary, an on-device audio content searcher that indexes and finds spoken phrases in audio files or streams, and a lightweight vo
LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections. The platform distinguishes itself through it
Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web
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