Module

Real-Time Audio Transcription with OpenAI Whisper

2 tutorials
intermediate

In this module, you'll gain hands-on experience with OpenAI’s Whisper, a powerful automatic speech recognition (ASR) model. Whisper simplifies the complex task of transcribing audio to text, making it accessible to learners who want to integrate speech recognition into their projects.

The purpose of this module is to guide you step-by-step on how to implement real-time audio transcription using Whisper. By the end of the module, you will have built a complete working app capable of transcribing audio in real time and serving the transcriptions via a web interface.

Imagine you're building an AI-based assistant, a virtual meeting transcription tool, or even an app for accessibility that converts spoken language into text in real-time. Whisper allows you to perform this task effectively, and this module will help you bridge the gap between concept and implementation. Think of it like learning how to "listen" to your users and turn their voices into text—a vital skill in today’s AI-driven world.

Let’s get started!

Helpful prior knowledge

Learning Outcomes

By the end of this module, you will be able to:

  • Understand the fundamentals of OpenAI's Whisper ASR model and how it performs audio transcription.
  • Set up and configure the development environment for working with Whisper.
  • Implement audio transcription for pre-recorded audio files using Whisper.
  • Build a simple real-time transcription interface using Flask, SocketIO and Bootstrap.
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