AI Speech-to-Text Web App: Purpose and Prerequisites

In this guide we explore the purpose and prerequisites for building an AI-powered speech-to-text web application that runs locally on your computer. The project uses open source and AI technology to create a practical tool that transcribes spoken audio.

Project Purpose

The goal of the project is to create a web application that:

Records audio from your computer's microphone
Processes the audio with OpenAI's Whisper model running locally
Returns accurate text transcriptions in real time
Runs entirely on your local machine (no data sent to external servers)
Has a simple and user-friendly interface

This application lets you convert speech to text without cloud-based services, which ensures your data remains private and the tool works even without internet connection.

The Model: Whisper

For this project we use Whisper, an open automatic speech recognition (ASR) system developed by OpenAI. Key features:

Open source and freely available
Trained on 680,000 hours of multilingual data for multiple tasks
Supports transcription in multiple languages
Can translate speech to English
Runs efficiently on standard consumer hardware

We use a smaller version of the model that balances accuracy and performance so it works well on standard hardware.

Technical Prerequisites

Knowledge Requirements

Basic to intermediate knowledge of Python
Familiarity with web concepts (HTML, CSS, JavaScript)
Understanding of Python virtual environments
Basic command line skills

Hardware Requirements

Computer with at least 8 GB RAM (16 GB recommended)
At least 2 GB free disk space
A working microphone
Windows, macOS or Linux

Software Requirements

Python 3.8 or later
Git (for fetching project files)
Internet connection (only for initial installation)

Development Tools

We will use the following technologies and libraries:

Python - main programming language
Flask - lightweight web framework for Python
Whisper - OpenAI's speech recognition model
PyAudio - for recording audio from the microphone
AJAX/JavaScript - makes the web interface interactive
Bootstrap - styling for the web interface

Prior Knowledge and Time Commitment

This project suits intermediate-level developers who have some experience with web development. We provide detailed instructions, but a foundation in Python makes the process smoother.

Expected time commitment:

Installation and setup: 30-60 minutes
Development: 2-3 hours
Testing and refinement: 1 hour

When you're done you will have a working speech-to-text application that runs entirely locally and provides accurate transcriptions without sending your data to third parties.

In the next guide we set up the development environment and install all necessary dependencies to get started.