# Speech Emotion Analysis

A speech emotion recognition system fine-tuned on Wav2Vec2 Large XLSR-53 model, capable of identifying 7 common emotions

A German emotion classification model fine-tuned on openai/whisper-tiny, achieving 91.59% accuracy on the Emo-DB dataset

This model is used to recognize six basic emotions from English audio: anger, disgust, fear, happiness, sadness, and surprise, trained on the RAVDESS dataset.

A speech emotion analysis model fine-tuned based on facebook/wav2vec2-base, achieving 70.87% accuracy on the evaluation set

Urdu emotion recognition model fine-tuned on Whisper-medium, achieving 91.67% accuracy on the evaluation set

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, used to predict the speaker's emotions in audio samples.

Speech emotion recognition using fine-tuned wav2vec2 model, trained on IEMOCAP dataset