Emotion Recognition

In my project, I've developed a real-time multilingual emotion recognition system. It listens to spoken language, accurately detects the language used, and then provides insights into the emotions expressed.

The tool includes advanced features like language identification and sentiment analysis. I've focused on practical applications to ensure a user-friendly experience for understanding emotional content in speech.

The purpose of my project is to provide a meaningful interpretation of audio recordings and calls. My goal was to create a cutting-edge real-time multilingual emotion recognition system. This innovative system will allow users to comprehend the emotional nuances embedded within spoken language. With its ability to decode emotional context, this tool will prove invaluable for a wide range of applications.

Developing a real-time multilingual emotion recognition system presented both technical and practical accomplishments. The ability to automatically detect the emotional tone and tenor behind spoken words has powerful implications. From gauging audience reactions to live events to monitoring customer service calls, this technology offers diverse applications.

Furthermore, this project presents an opportunity to delve into the intricacies of human-machine interaction, offering potential applications in diverse fields including market research, mental health support, and beyond.

-Programming Language: Python

-Automatic Speech Recognition (ASR) and Language Detection: Whisper from OpenAI

-Pre-trained Model: Utilized a pre-trained model from the Hugging Face Model Hub.

- User Interface: Integrated Gradio for the user interface and interaction.