Installing Whisper: A Step-by-Step Setup Guide

Installing Whisper: Step-by-Step Guide

Whisper is one of the most powerful tools for automatic speech recognition. Developed by OpenAI, it’s free to use, supports multiple languages, and works impressively well even with noisy recordings. But how do you actually install it?

In this guide, you’ll get clear, easy-to-follow instructions on how to install Whisper on Windows, macOS, or Linux — no programming experience required.

Preparation: What You Need Before Installing Whisper

Before installing Whisper, a bit of setup is needed. Don’t worry — no coding skills required, just a bit of patience.

Install Python

Whisper runs on Python. Recommended versions: 3.8 to 3.11.

• Download it from python.org
• On Windows, make sure to check “Add Python to PATH” during installation
• Verify installation:

bash

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python --version


Set Up a Virtual Environment (Recommended)

A virtual environment keeps the Whisper setup isolated and clean.

bash

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# Windows or macOS/Linux
python -m venv whisper-env

# Activate the environment
# macOS/Linux
source whisper-env/bin/activate

# Windows
whisper-env\Scripts\activate.bat


Install FFmpeg

Whisper relies on FFmpeg to handle audio formats.

• Windows (using Chocolatey):

bash

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choco install ffmpeg


• macOS (using Homebrew):

bash

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brew install ffmpeg


• Linux (Debian/Ubuntu):

bash

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sudo apt update && sudo apt install ffmpeg


Verify installation:

bash

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ffmpeg -version


Installing Whisper: Step by Step

Step 1: Install Whisper via pip

With Python and FFmpeg set up, install Whisper:

bash

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pip install -U openai-whisper


If you run into errors (e.g., related to tiktoken or Rust), try:

bash

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pip install --upgrade pip


You may also need the Rust compiler (rustup.rs) if dependencies fail to compile.

Step 2: Run a Test Transcription

Place an audio file (e.g., example.mp3) in your working directory. Then create a Python script:

python

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import whisper

model = whisper.load_model("small")
result = model.transcribe("example.mp3")
print(result["text"])


Run it:

bash

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python transcribe.py


The model will download automatically on first use.

Installing Whisper by Operating System

Windows

1. Install Python and FFmpeg

Ensure they’re in the PATH variable so you can run python and ffmpeg globally.

2. Activate Virtual Environment

bash

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python -m venv whisper-env
whisper-env\Scripts\activate.bat


3. Install Whisper and PyTorch (Optional)

For GPU acceleration:

bash

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pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118
pip install -U openai-whisper


4. Test Transcription

Use the script above. If errors occur, check audio format, file integrity, or try a simpler file.

macOS

1. Install Homebrew, Python, FFmpeg

bash

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brew install python@3.11
brew install ffmpeg


2. Create Virtual Environment

bash

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python3 -m venv whisper-env
source whisper-env/bin/activate


3. Install Whisper

bash

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pip install -U openai-whisper


4. Test Transcription

Same process as Windows. On M1/M2 Macs, PyTorch supports Metal acceleration (MPS), which improves speed, especially with larger models.

Linux (Debian/Ubuntu)

1. Install FFmpeg and Python

bash

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sudo apt update
sudo apt install ffmpeg python3 python3-pip python3-venv


2. Create and Activate Virtual Environment

bash

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python3 -m venv whisper-env
source whisper-env/bin/activate


3. Install Whisper

bash

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pip install -U openai-whisper


4. Optional: PyTorch with CUDA for NVIDIA GPUs

bash

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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118


5. Run Test Script

Use the same method as above.

Whisper Model Sizes and Memory Requirements

Whisper downloads the model when first used. Choose based on your needs:

• tiny: Fast, low accuracy (~75 MB)
• base: Basic balance (~142 MB)
• small: Solid for general use (~244 MB)
• medium: High quality (~769 MB)
• large: Maximum accuracy (~1.55 GB)

Models are stored in:
~/.cache/whisper

Common Errors and How to Fix Them

FFmpeg Not Found

Check if it’s in your PATH:

bash

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ffmpeg -version


“No module named 'whisper'”

Make sure your virtual environment is active before running scripts.

CUDA Not Recognized

Install PyTorch with the correct CUDA version for your system (CU118, CU121, etc.). You’ll find the correct option on PyTorch’s Get Started page.

Transcription Fails

Verify the audio file is supported and unencrypted. Formats like MP3, WAV, FLAC, M4A, OGG, and AAC usually work. Avoid DRM or variable bitrate issues.

Conclusion: Install Whisper or Choose an Alternative?

With just a few setup steps, Whisper can run on any modern computer. It’s a robust tool for transcription, podcasts, research, or content creation — fully offline and free.

If you'd prefer a no-setup experience, tools like Sally offer Whisper's capabilities with added AI summaries, CRM integration, and a plug-and-play UI.

Voice recognition has never been this accessible. Want to save time? Try Sally for free today.