INCREDIBLE Fast AI Real Time Speech to Text Transcribtion - Build From Scratch

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AI Summary

Tutorial Summary: Building a Real-Time Transcription AI Co-Pilot

• Introduction
  • Overview of using open-source language models for real-time transcription of meetings and communications.
  • Mention of OpenAI’s Whisper for high-quality speech recognition but with slow processing.
• Project Goal
  • Create a real-time transcription system using an alternative, faster version of Whisper.
  • Develop a web server to record directly in the browser, segment audio, and transcribe in near real-time.
• Steps to Build the Transcription System
  1. Explore Replicate
     • Sign up for free with GitHub.
     • Access various open-source language models.
  2. Set Up Development Environment
     • Install Visual Studio Code and Python.
     • Create a virtual environment and a new file app.py.
  3. Connect to Fast Whisper on Replicate
     • Follow instructions for Python integration.
     • Install and import Replicate, paste example code.
     • Set authentication token from Replicate.
  4. AWS Configuration
     • Use Boto3 library to interact with AWS.
     • Create a new S3 bucket, set permissions, and upload sound files.
     • Create restricted AWS user and generate access keys.
  5. Build Web Interface for Recording
     • Install Flask and set up server.
     • Create index.html with recording button and transcript display.
     • Write JavaScript to handle recording and backend communication.
     • Serve assets from a static folder.
  6. Backend Processing
     • Add route to handle audio data.
     • Extract audio, write to temp file, and upload to S3.
     • Return transcript in JSON format.
• Testing and Conclusion
  • Test the system with a sample recording.
  • Celebrate successful real-time transcription.
  • Encourage likes and subscriptions for support.
• Final Notes
  • Source code access provided.
  • Reminder to handle AWS credentials securely.