Slight Insight

AI is no longer a buzzword reserved for tech giants, it’s becoming part of everyday tools. One exciting example is Whisper AI, an open-source speech recognition model developed by OpenAI. Whisper can take your audio recordings and turn them into text with impressive accuracy. Whether you’re a student transcribing lectures, a content creator turning podcasts into blogs, or just curious about AI, running Whisper on your own machine unlocks a world of possibilities.

Here’s today’s slight insight on Running Whisper AI on your own machine.

System Requirements & Prerequisites

Before we jump into installation, let’s make sure your system is ready. Whisper is lightweight compared to some AI models, but smooth performance still depends on your setup.

Requirement	Recommended Spec
Operating System	Windows 10/11, macOS, or Linux
Processor (CPU)	Quad-core or better
Memory (RAM)	At least 8GB (16GB+ preferred)
GPU (optional)	NVIDIA GPU with CUDA for faster processing
Python Version	3.8 or later

Installing Whisper AI and Its Components

To run Whisper, you’ll need to install a few things.

Step	Tool	Purpose
1	Python	Base programming language
2	PyTorch	Machine learning framework used by Whisper
3	Chocolatey	Package manager (Windows) for easier installs
4	FFmpeg	Handles audio/video formats
5	Whisper AI	The speech recognition model itself

Install Python

Install PyTorch

Now we can install the PyTorch library, which is the machine-learning framework that powers Whisper’s neural networks. PyTorch requires specific configuration based on your system, so we’ll use their interactive installation guide.

Go to the PyTorch website and copy the install command matching your setup (CPU or GPU).

• Command:
  • pip3 install torch torchvision –index-url https://download.pytorch.org/whl/cu126

Install Package Manager

Before we can install FFmpeg, Windows users need a package manager.

Visit chocolatey.org Click the “Install” tab in the top right corner Choose “Individual” use Open PowerShell as Administrator:

• Type “PowerShell” in your computer’s search bar
• Select “Windows PowerShell”
• Click “Run as Administrator”
• Copy the installation command from Chocolatey’s website Paste into PowerShell and press Enter
  • Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString(‘https://community.chocolatey.org/install.ps1’))

Install FFmpeg

This library converts between different audio formats, processes audio streams, and handles the technical aspects of reading your media files so Whisper can focus on transcription.

• on Ubuntu or Debian
  • sudo apt update && sudo apt install ffmpeg
• on Arch Linux
  • sudo pacman -S ffmpeg
• on MacOS using Homebrew (https://brew.sh/)
  • brew install ffmpeg
• on Windows using Chocolatey (https://chocolatey.org/)
  • choco install ffmpeg
• on Windows using Scoop (https://scoop.sh/)
  • scoop install ffmpeg

Install Whisper AI

You’re now ready to install Whisper itself! This final step brings everything together.

pip install -U openai-whisper

Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:

pip install git+https://github.com/openai/whisper.git

Whisper comes with several model sizes, each offering different trade-offs between accuracy and speed:

Size	Parameters	English-only model	Multilingual model	Required VRAM	Relative speed
tiny	39 M	tiny.en	tiny	~1 GB	~10x
base	74 M	base.en	base	~1 GB	~7x
small	244 M	small.en	small	~2 GB	~4x
medium	769 M	medium.en	medium	~5 GB	~2x
large	1550 M	N/A	large	~10 GB	1x
turbo	809 M	N/A	turbo	~6 GB	~8x

The first time you run Whisper, it will automatically download your chosen model. This process might take several minutes depending on your internet connection, but subsequent uses will be much faster.

Using Whisper AI

Now comes the exciting part, using Whisper to transcribe your audio files. The beauty of Whisper lies in its simplicity; you can start with basic commands and gradually explore more sophisticated features as your needs grow.

Commandline usage

whisper audio.mp3 –model small

• audio.mp3 → your input file.
• --model small → choose the model size (tiny, base, small, medium, large).

Python usage

Transcription can also be performed within Python

import whisper

model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")
print(result["text"])

Whisper supports multiple output formats, each serving different purposes:

• TXT: Plain text transcription
• VTT: Video subtitles with timestamps
• SRT: Standard subtitle format
• JSON: Detailed output with confidence scores and timing

Conclusion

Setting up Whisper AI may sound technical at first, but it’s just a matter of stacking the right tools: Python, PyTorch, Chocolatey, FFmpeg, and Whisper itself. Once installed, you have a powerful transcription tool running locally on your computer. Whisper puts you in control of your data while giving you enterprise-grade AI right at home.

Whether you’re a student, content creator, or developer, Whisper can save hours of manual transcription and unlock new productivity workflows.

Frequently Asked Questions