Whisper is an open-source speech recognition model released by OpenAI in 2022. It was trained on 680,000 hours of multilingual audio and can transcribe speech with near-human accuracy across 99 languages.

Is OpenAI's Whisper free to use?

Yes. OpenAI released Whisper under the MIT license, so it's free to use in any application, including commercial ones. Apps like LexaWrite use it to provide on-device transcription at no cost.

Can Whisper run offline without internet?

Yes. Whisper is a local model that runs entirely on your device. Apps like LexaWrite use whisper.cpp to run it on macOS without sending any audio to a server.

What languages does Whisper support?

Whisper supports 99 languages including English, Spanish, French, German, Japanese, Chinese, Turkish, and many more. It also auto-detects the language being spoken.

How accurate is Whisper AI?

Whisper achieves word error rates comparable to professional human transcription on many benchmarks. The larger the model (e.g., large-v3), the higher the accuracy, at the cost of more RAM and processing time.

What Is Whisper AI? How OpenAI's Speech Recognition Works (2026)

If you’ve looked at any voice dictation app in the past two years, you’ve seen “powered by Whisper” or “uses OpenAI’s Whisper.” But what actually is Whisper, and why does it matter?

Here’s the explanation without the jargon.

Whisper in One Sentence

Whisper is a free, open-source speech recognition model made by OpenAI that converts spoken audio into written text - and it works on your computer without needing the internet.

Why Whisper Changed Everything

Before Whisper (released September 2022), high-quality speech-to-text required either:

Expensive software - Dragon NaturallySpeaking cost $150-$500 and was discontinued for Mac
Cloud APIs - Google, Amazon, and Microsoft offered speech-to-text, but your audio had to be sent to their servers
Apple/Google built-in - Free but significantly less accurate, especially for accents and specialized vocabulary

Whisper changed the equation by being:

Free and open-source - anyone can use it, no license fees
Highly accurate - trained on 680,000 hours of audio, competitive with the best cloud services
Multilingual - supports 99 languages with automatic detection
Runnable locally - works on your own computer, no internet required

This combination didn’t exist before. Free + accurate + private + multilingual had never been available in a single package.

How Whisper Works (Simplified)

The Training

OpenAI collected 680,000 hours of audio from the internet - podcasts, YouTube videos, audiobooks, and other sources - along with their corresponding text transcriptions. They used this massive dataset to train a neural network to predict what text corresponds to what audio.

The key insight: by training on such a diverse dataset (multiple languages, accents, recording conditions, background noise levels), the model learned to handle real-world speech, not just clean studio recordings.

The Architecture

Whisper uses a “transformer” architecture - the same type of AI model behind ChatGPT and other language models. It processes audio in 30-second chunks:

Audio in → raw audio is converted to a spectrogram (a visual representation of sound frequencies over time)
Encoding → the encoder analyzes the spectrogram to understand the speech patterns
Decoding → the decoder converts those patterns into text tokens (words and punctuation)
Text out → the tokens are assembled into readable text

The model also predicts the language being spoken, so it can auto-detect whether you’re speaking English, Spanish, Japanese, or any of the other 99 supported languages.

Model Sizes

Whisper comes in several sizes, each trading speed for accuracy:

Model	Parameters	Size	Relative Speed	Relative Accuracy
Tiny	39M	75 MB	Fastest	Good
Base	74M	142 MB	Fast	Better
Small	244M	466 MB	Moderate	Great
Medium	769M	1.5 GB	Slower	Excellent
Large	1.5B	3 GB	Slowest	Best

Larger models are more accurate but take longer to process. On modern Apple Silicon Macs, even the Large model runs in near-real-time thanks to Metal GPU acceleration.

Detailed model comparison with benchmarks →

Whisper vs. Other Speech Recognition

Whisper vs. Apple Dictation

Apple’s built-in dictation uses Apple’s own speech model. It’s decent for standard English but falls short on:

Accent handling (Whisper is trained on more diverse speech)
Specialized vocabulary (no custom dictionary in Apple Dictation)
Language support (Apple supports ~60 languages vs Whisper’s 99)
Time limits (Apple has timeout restrictions)

Full comparison →

Whisper vs. Google Speech-to-Text

Google’s cloud speech API is accurate but:

Requires internet (audio sent to Google servers)
Costs money at scale ($0.006 per 15 seconds)
Raises privacy concerns (Google processes your audio)

Whisper matches or exceeds Google’s accuracy for most languages while running locally and for free.

Whisper vs. Dragon NaturallySpeaking

Dragon was the gold standard for decades but:

Discontinued for Mac (2018)
Expensive ($150-$500)
Required extensive voice training per user
Windows-only for consumer version

Whisper is more accurate out of the box, free, and works on Mac. Dragon migration guide →

whisper.cpp: Whisper on Your Mac

OpenAI released Whisper as a Python package, which works but isn’t optimized for consumer hardware. Enter whisper.cpp - a C/C++ reimplementation by Georgi Gerganov that:

Runs natively on Apple Silicon using Metal GPU acceleration
Is 2-5x faster than the original Python implementation
Uses less memory
Can be embedded in native Mac apps

This is what apps like LexaWrite use under the hood. The chain is: OpenAI creates the model → whisper.cpp makes it fast on Mac hardware → apps like LexaWrite wrap it in a user-friendly interface.

Technical deep dive into whisper.cpp →

Privacy: Why Local Matters

When you use a cloud-based speech service (Google, Amazon, Otter.ai), here’s what happens:

Your voice is recorded
The recording is sent over the internet to a server
A company’s servers process the audio
The text is sent back to you
The company may store the recording

When you use Whisper locally:

Your voice is recorded
Your Mac processes the audio
Text appears
The audio can be deleted immediately

No internet. No servers. No third party. Your voice data stays on your device.

This matters most for:

Lawyers - attorney-client privilege requires confidentiality
Healthcare - patient information (HIPAA concerns)
Business - trade secrets and sensitive communications
Everyone - voice is biometric data, as unique as a fingerprint

Why your voice should never leave your Mac →

How to Use Whisper

Option 1: Through a Mac App (Easiest)

Apps like LexaWrite, Superwhisper, and MacWhisper package Whisper in a polished interface. You install the app, download a model, and start dictating. No technical setup required.

For real-time dictation: LexaWrite (free)
For file transcription: MacWhisper

Option 2: Command Line (Technical)

Install whisper.cpp via Homebrew:

brew install whisper-cpp

Then transcribe an audio file:

whisper-cpp --model base.en --file recording.wav

Option 3: Python (Developer)

pip install openai-whisper

import whisper
model = whisper.load_model("base")
result = model.transcribe("recording.mp3")
print(result["text"])

The Future of Whisper

OpenAI released Whisper v2 (Large-v2) and v3 (Large-v3) with improved accuracy, especially for non-English languages. The model continues to improve, and because it’s open-source, these improvements flow directly to every app built on top of it.

The trajectory is clear: speech recognition is getting better, faster, and more private. Within a few years, typing may become a secondary input method for most people - reserved for code, formatting, and short edits while voice handles the heavy lifting.

Experience Whisper-powered dictation on your Mac. Try LexaWrite free →