Faster-Whisper on CPU with Python | Fast Offline Transcription Tutorial

Published 14/06/2026

Description

In this video, I show how to use Faster-Whisper in Python to transcribe audio and video locally on a CPU. If you want offline speech-to-text without uploading confidential files to an online service, this tutorial walks through the full process: installing Faster-Whisper with pip, downloading models from Hugging Face, running local transcription, and testing performance on a normal PC. 0:00 - Introduction 0:08 - Description 2:15 - GitHub Base 2:38 - Python PIP Install 3:16 - Model Retrieval 6:21 - Simple Run 8:51 - Real World Test 10:22 - Model Performance 12:39 - Multilingual Model 15:43 - Switching to GPU ? 16:48 - Conclusion I also compare different Faster-Whisper model sizes on CPU, including tiny, base, small, medium, and large, so you can see the tradeoff between speed and transcription quality. You’ll see real-world results, timing comparisons, and why smaller models are often enough for fast transcription while larger models may improve recognition of technical terms. On top of that, I test multilingual audio transcription and show one important limitation: Faster-Whisper can handle multiple languages, but mixed-language speech in the same recording is still tricky. I also briefly explain what changes when moving from CPU to GPU, and why CUDA dependencies can make GPU setup more complex. If you're working with Python, local AI, private audio processing, or offline transcription workflows, this video is a practical introduction to Faster-Whisper and CTranslate2. Topics covered: Faster-Whisper installation in Python CPU-based transcription Offline speech-to-text Hugging Face model download Faster-Whisper model comparison Real transcription benchmarks Multilingual transcription test CPU vs GPU considerations If you enjoy practical AI and Python tutorials, feel free to like, subscribe, and leave a comment with your results or your favorite Faster-Whisper model. #FasterWhisper #PythonTutorial #SpeechToText GitHub : https://