JARVIS

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  This is Jarvis TEXT TO SPEECH

Inspiration

   The growing need for hands-free interaction and smart data logging inspired us to create a voice-based system that understands and stores spoken input instantly.

What it does

    The system captures voice input, converts it to text using Python and LiveKit

How we built it

   We used Python for backend logic, integrated LiveKit for real-time voice processing, applied speech-to-text models for transcription, 

Challenges we ran into

   We faced issues with accurate speech recognition, background noise filtering, and integrating real-time voice input 

Accomplishments that we're proud of

   Successfully built a real-time voice recognition system

What we learned

   Real-time voice processing using LiveKit
   Working with speech-to-text ML models
   Handling API integrations and database connectivity
   Overcoming real-world noise and voice variations

What's next for JARVIS

   Add multilingual voice support
    Implement voice-based commands and actions
    Deploy as a web or mobile assistant app
    Improve accuracy using custom-trained ML models

Built With

• google
• livekit
• python
• vscode