{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"openWakeWord detects wake words and phrases in audio streams using pre-trained neural network models, enabling voice-activated applications to listen for specific spoken triggers in real-time.","skillfed_tags":["voice-activation","audio-processing","edge-ml"],"use_cases":["Build a voice-activated smart home controller that listens for specific phrases to trigger actions.","Add wake-word detection to a web application using the included streaming audio examples to process microphone input.","Train a custom wake-word model for a specific application using the provided Google Colab notebook and synthetic speech generation.","Reduce false positives in a voice interface by enabling voice activity detection (VAD) alongside wake-word scoring.","Deploy multiple wake-word models simultaneously on edge devices for multi-trigger voice control.","Batch-process large audio file collections to detect wake words using the bulk_predict utility with multiprocessing."],"what_it_does":"openWakeWord is an open-source framework for detecting wake words and phrases in audio streams. It provides pre-trained models for common trigger words that work in real-world noisy environments, and can be extended with custom models trained on synthetic speech. The library processes audio in 80 ms frames and returns confidence scores between 0 and 1, making it suitable for voice-activated applications like smart assistants, IoT devices, and web-based voice interfaces.\n\nThe package depends on onnxruntime and tflite-runtime for inference, plus scipy and scikit-learn for audio processing. It includes optional Speex noise suppression for Linux systems and a built-in Silero voice activity detection model to reduce false positives. The framework is designed to be lightweight enough for resource-constrained devices while remaining simple to integrate into Python applications.","worth_installing":"Yes, if you need open-source wake-word detection with pre-trained models and low install friction. The permissive Apache 2.0 license and active repository are strengths. However, the aging maintenance status and lack of recent updates warrant caution if you require ongoing support or compatibility with the latest Python ecosystem. Suitable for prototyping and production use in voice-activated applications where you can tolerate a slower release cadence."},"id":"openwakeword","links":{"html":"https://skillfed.io/packages/openwakeword","md":"https://skillfed.io/packages/openwakeword.md","pypi":"https://pypi.org/project/openwakeword/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-02-11","license_spdx":null,"license_treatment":"permissive","name":"openwakeword","python_support":"supports_current","summary":"An open-source audio wake word (or phrase) detection framework with a focus on performance and simplicity"},"popularity":{"monthly_downloads":276641,"position":8162,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.0"}