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Wav2vec2 Audio Emotion Classification
A fine-tuned audio emotion classification model based on facebook/wav2vec2-base, achieving 73.98% accuracy on the evaluation set
Downloads 77
Release Time : 10/30/2023
Model Overview
This model is used for audio emotion classification tasks and can recognize emotional features in speech
Model Features
High Accuracy
Achieves 73.98% classification accuracy on the evaluation set
Based on wav2vec2
Fine-tuned from the powerful wav2vec2-base model with excellent audio feature extraction capabilities
Lightweight
Based on the base version, relatively lightweight and efficient
Model Capabilities
Audio Emotion Classification
Speech Feature Extraction
Use Cases
Emotion Analysis
Customer Service Call Analysis
Analyze customer emotions in service calls
Identify customer satisfaction levels
Mental Health Assessment
Evaluate the speaker's mental state through voice analysis
Assist in mental health diagnosis
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