Wav2vec Base Crema Sentiment Analysis
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Wav2vec Base Crema Sentiment Analysis
Developed by Piyush2512
A speech emotion analysis model fine-tuned based on facebook/wav2vec2-base, achieving 70.87% accuracy on the evaluation set
Downloads 38
Release Time : 4/23/2024
Model Overview
This model is a deep learning model for emotion analysis of speech signals, capable of identifying emotional categories from speech. Based on the wav2vec2 architecture, it is suitable for speech emotion classification tasks.
Model Features
High Top3 Accuracy
Achieves 94.5% Top3 accuracy on the evaluation set, effectively identifying major emotional categories
Based on wav2vec2 Architecture
Utilizes the powerful speech feature extraction capability of wav2vec2 to achieve high-quality emotion analysis
Fine-tuning
After 30 epochs of fine-tuning, the model performance has been significantly improved
Model Capabilities
Speech Emotion Recognition
Emotion Classification
Speech Feature Extraction
Use Cases
Emotion Analysis
Customer Service Call Analysis
Analyze the emotional tendencies in customer calls
Can identify customer satisfaction
Mental Health Assessment
Analyze user emotional states through speech
Assists in mental health monitoring
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