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This emotion recognition app analyzes text, facial expressions, and speech to detect emotions. Designed for self-awareness and mental well-being, it provides personalized insights and recommendations.
This API utilizes a pre-trained model for emotion recognition from audio files. It accepts audio files as input, processes them using the pre-trained model, and returns the predicted emotion along with the confidence score. The API leverages the FastAPI framework for easy development and deployment.
This repository provides the codes for MMA-DFER: multimodal (audiovisual) emotion recognition method. This is an official implementation for the paper MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild.
🚀 Pre-process, annotate, evaluate, and train your Affect Computing (e.g., Multimodal Emotion Recognition, Sentiment Analysis) datasets ALL within MER-Factory! (LangGraph Based Agent Workflow)
The repo contains an audio emotion detection model, facial emotion detection model, and a model that combines both these models to predict emotions from a video