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AI-Powered Assistant

Overview

An advanced AI-powered assistant leveraging computer vision and speech processing techniques. This assistant integrates various artificial intelligence and machine learning functionalities, including:

• Text-To-Speech (TTS)

• Speech Recognition

• Wake-Up Word Functionality

• Real-time Face Detection

• Face Recognition

• Emotion Analysis

Features

Real-time Face Detection: Detects human faces using OpenCV and deep learning models.

Face Recognition: Identifies known individuals using pre-trained models.

Emotion Analysis: Analyzes facial expressions to detect emotions.

Speech Recognition: Converts spoken language into text for interaction.

Text-To-Speech (TTS): Converts text responses into human-like speech.

Wake-Up Word: Listens for a specific word to activate the assistant.

Technologies Used

• Python

• OpenCV (for computer vision tasks)

• Dlib (for face recognition)

• TensorFlow/Keras (for deep learning-based emotion analysis)

• Google Speech Recognition API (for speech-to-text conversion)

• pyttsx3 (for offline text-to-speech conversion)

• pyaudio (for capturing and processing audio)

Installation

• Clone the repository:

git clone https://github.com/KartikBansal2004/AI-Assistant.git cd AI-Powered-Assistant

• Install dependencies:

pip install -r requirements.txt

• Run the assistant:

python assistant.py

Usage

• Speak the wake-up word to activate the assistant.

• Interact using voice commands.

• The assistant will recognize faces and analyze emotions in real-time.

Future Enhancements

• Integration with smart home devices.

• Multilingual support.

• Advanced natural language processing (NLP) capabilities.

Contributing

Feel free to contribute by submitting issues, feature requests, or pull requests.

Developed with ❤️ using Python and AI techniques.

About

It is an AI-Powered Assistant based on Computer vision Techniques. This Assistant contains Text-To-Speech, Speech Recognition, Wake-Up Word Functionality, Real-time Face Detection, Face Recognition And Emotion Analysis using Python, AI and Machine Learning Techniques

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