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Wav2vec2 Large Nonverbalvocalization Classification
A multilingual nonverbal vocalization classification model based on the wav2vec2 architecture, capable of recognizing 16 common nonverbal sounds
Downloads 568
Release Time : 1/9/2023
Model Overview
This model is specifically designed for classifying nonverbal vocalizations such as coughing, yawning, sighing, etc., suitable for audio analysis scenarios
Model Features
Multilingual support
The model is language-agnostic and can process nonverbal vocalizations in various language environments
Wide range of sound categories
Supports classification of 16 common nonverbal vocalizations, covering various sounds in daily life
Based on wav2vec2 architecture
Utilizes the advanced wav2vec2-large architecture to provide high-quality audio feature extraction and classification capabilities
Model Capabilities
Nonverbal sound recognition
Audio classification
Multilingual audio processing
Use Cases
Health monitoring
Sleep quality analysis
Assess sleep quality by detecting sounds like teeth grinding and yawning
Respiratory health monitoring
Identify sounds like coughing and wheezing to assist in respiratory health assessment
Emotion analysis
Emotional state recognition
Analyze user's emotional state through sounds like sighing and crying
Smart home
Environmental sound recognition
Identify various nonverbal sounds in home environments for smart control
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