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Wav2vec2 Base Ft Keyword Spotting Int8
A speech keyword detection model based on the wav2vec2 architecture, optimized with OpenVINO quantization
Downloads 14
Release Time : 3/2/2022
Model Overview
This model is a speech processing model based on the wav2vec2 architecture, specifically designed for keyword detection tasks. After Optimum OpenVINO quantization, the model improves inference efficiency while maintaining high accuracy.
Model Features
Efficient Quantization
Quantized with Optimum OpenVINO, significantly improving inference efficiency with only a 2.74% drop in accuracy
High Accuracy
Achieves a baseline accuracy of 0.9828 on the evaluation set, maintaining a high accuracy of 0.9553 after quantization
Lightweight
Based on the wav2vec2-base architecture, relatively lightweight and suitable for edge device deployment (inferred)
Model Capabilities
Speech keyword recognition
Real-time audio processing
Edge device deployment
Use Cases
Smart Home
Wake Word Detection
Used to detect device wake words such as 'Hey Siri', 'OK Google', etc.
High accuracy ensures reliable device response
Industrial Applications
Voice Command Recognition
Recognizes specific voice commands in noisy industrial environments
Quantized model is suitable for deployment on edge devices
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