# High-precision emotion recognition

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%.

A Wav2Vec2-fine-tuned English speech emotion recognition model capable of identifying six emotional states

A facial emotion recognition model fine-tuned based on Google's ViT-base model, achieving 91% accuracy on the test set.

A Chinese speech emotion classification model fine-tuned on the CASIA dataset using Tencent Game Partner's pre-trained Chinese HuBERT model, supporting 6 emotion categories.