Live Transcription With Python and Flask

In this tutorial, we learn how to create a live voice-to-text transcription application using Python and Flask 2.0 with the help of Deepgram's AI speech recognition technology. The project involves setting up a virtual environment, installing necessary dependencies like Flask, Deepgram SDK, dotenv library, and aiohttp-wsgi. We then create a basic Flask application that renders an HTML page for live transcription. Next, we add the Deepgram API key to our project by creating a .env file and storing it in the GitHub repository's .gitignore file. The application uses JavaScript to access microphone data from the browser and establishes WebSocket connections between the Python server holding the Flask application and the client (browser). We also create a WebSocket connection between our central Flask server and Deepgram for real-time transcription. Finally, we run the application and start getting real-time transcriptions on the localhost. The complete code for this project is available on Github with instructions to run it.

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