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Ser Model

Developed by aherzberg
A fine-tuned speech emotion recognition model based on facebook/wav2vec2-base, achieving 84.71% accuracy on the evaluation set
Audio Classification
Transformers
Open Source License:Apache-2.0#Speech Emotion Recognition#High Accuracy#Fine-tuned wav2vec2
Downloads 30
Release Time : 2/26/2023

Model Overview

This model is a Speech Emotion Recognition (SER) model based on the wav2vec2 architecture, obtained by fine-tuning facebook/wav2vec2-base, primarily used for recognizing emotional states from speech

Model Features

High Accuracy
Achieves 84.71% classification accuracy on the evaluation set
Based on wav2vec2 Architecture
Leverages the powerful speech feature extraction capabilities of wav2vec2
End-to-End Training
Learns emotional features directly from raw speech signals

Model Capabilities

Speech Emotion Recognition
Speech Feature Extraction
Emotion Classification

Use Cases

Human-Computer Interaction
Smart Customer Service Sentiment Analysis
Analyzes emotional states in customer speech to improve service quality
Mental Health
Emotional State Monitoring
Tracks user emotional changes through speech analysis
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