Open-source wakeword detection library for voice-enabled apps
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This project provides an open-source framework for wake word detection, enabling developers to build voice-enabled applications. It offers pre-trained models for common English phrases, focusing on performance and ease of use for real-world applications.
How It Works
The framework utilizes a three-component architecture: an ONNX-based melspectrogram pre-processing function, a shared feature extraction backbone (re-implemented from a Google TFHub module) that generates speech embeddings, and a classification model (e.g., fully-connected or RNN) for wake word detection. This modular design allows for efficient processing and easier modification, with models processing audio in 80ms frames.
Quick Start & Requirements
pip install openwakeword
sudo apt-get install libspeexdsp-dev
for optional Speex noise suppression.openwakeword.utils.download_models()
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Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
The project is English-only due to reliance on English TTS models for training data. It is not recommended for highly constrained edge devices or microcontrollers, with alternatives like microWakeWord suggested for such use cases. Commercial use of pre-trained models is restricted by the CC-BY-NC-SA 4.0 license.
1 year ago
Inactive