Image by Author   #  Introduction   Transcribing audio into text is a common need for developers, whether you're building a voice-to-text app, analysing meeting recordings, or adding captions to videos. Doing it locally (on your own machine) protects privacy and avoids recurring cloud costs. In this article, you will learn how to set up a fast, local transcription system using Whisper and its optimised version called Faster-Whisper . We will cover audio preprocessing like converting MP3 to WAV, write a Python script, and discuss running on both CPUs and GPUs. #  What Is Whisper? And Why Use a Local Variant?   OpenAI's Whisper is an automatic speech recognition (ASR) model. It's trained on a large amount of multilingual audio and performs well even with background noise or different accents. However, the original Whisper can be slow on a CPU and uses significant memory. That's where optimised variants come in to help. whisper.cpp is written in C++ with no heavy dependencies.…