W

Wav2vec2 Ser English Finetuned

Developed by dihuzz
This model is fine-tuned based on the Wav2Vec2 architecture, specifically designed to recognize six emotional states (sadness, anger, disgust, fear, happiness, neutral) in English speech, with an accuracy of 92.42%.
Downloads 68
Release Time : 4/11/2025

Model Overview

A fine-tuned Wav2Vec2 model for English speech emotion recognition tasks, capable of accurately classifying six basic emotions.

Model Features

High accuracy
Achieves 92.42% accuracy on the test set with a loss value of only 0.219
Multi-emotion recognition
Can recognize six basic emotions: sadness, anger, disgust, fear, happiness, and neutral
Based on Wav2Vec2 architecture
Utilizes the powerful feature extraction capabilities of Wav2Vec2 for speech emotion classification
Lightweight inference
Suitable for real-time applications and can run efficiently on standard GPUs

Model Capabilities

English speech emotion classification
Real-time emotion analysis
Speech emotion recognition

Use Cases

Mental health
Mental state monitoring
Analyze users' emotional states through speech for mental health applications
Can automatically detect changes in users' emotions
Customer service
Customer service quality assessment
Analyze emotional states in customer service calls
Helps improve service quality
Human-computer interaction
Emotional voice assistants
Enable voice assistants to understand user emotions and respond accordingly
Enhances user experience
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase