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Explainer6 min read

What Is OpenAI Whisper and How Does It Transcribe Audio?

A plain-language explanation of OpenAI’s Whisper speech recognition model — how it works, its accuracy, supported languages, and practical applications.

If you’ve used any modern transcription tool in the last few years, chances are it’s powered by Whisper — or something derived from it. OpenAI’s Whisper is a speech recognition model that transformed the accuracy and accessibility of automatic transcription. Here’s what it is, how it works, and why it matters.

What Is Whisper?

Whisper is an automatic speech recognition (ASR) model released by OpenAI in September 2022. Unlike earlier speech recognition systems that were trained on small, curated datasets, Whisper was trained on 680,000 hours of multilingual audio data collected from the internet.

This massive training dataset is what gives Whisper its key advantage: it handles accents, background noise, technical jargon, and code-switching (mixing languages mid-sentence) far better than previous models. It supports transcription in 99+ languages and can translate non-English speech to English.

How Does Whisper Work?

At a high level, Whisper uses an encoder-decoder transformer architecture — the same type of neural network behind GPT and other large language models, but optimized for audio input.

The process works in three stages:

  1. Audio preprocessing: The audio is converted to a mel spectrogram — a visual representation of sound frequencies over time. Think of it as a heatmap of your audio, where the x-axis is time and the y-axis is frequency.
  2. Encoding: The spectrogram is fed through the encoder, which learns to understand patterns in the audio — phonemes, words, pauses, speaker characteristics, and background noise.
  3. Decoding: The decoder generates text token by token, predicting the most likely next word given the audio context and the text generated so far. It also predicts timestamps, language tags, and special tokens like punctuation.

What makes Whisper special is that it was trained as a multitask model. The same model can transcribe (audio → text in the same language), translate (audio → English text), detect the language being spoken, and identify timestamps — all without needing separate systems for each task.

How Accurate Is Whisper?

On standard English benchmarks, Whisper achieves a word error rate (WER) of approximately 5-6% — comparable to professional human transcriptionists. For context, a 5% WER means roughly 1 word in 20 is wrong.

Accuracy varies by language, audio quality, and speaking style:

  • Clear English audio: 3-5% WER (near-perfect)
  • Accented English: 5-10% WER (still very usable)
  • Common European languages: 5-10% WER
  • Less-resourced languages: 10-20% WER (depends on training data availability)
  • Noisy environments: 10-15% WER (better than most alternatives)

Since the original release, OpenAI has continued improving the model. The GPT-4o-based transcription engine (used by tools like Verbato) further improves accuracy, especially on noisy audio and non-English languages.

What Languages Does Whisper Support?

Whisper supports transcription in 99+ languages. The strongest performance is in English, Spanish, French, German, Portuguese, Italian, Dutch, Polish, and Japanese. Performance in lower-resource languages (e.g., Swahili, Yoruba, Mongolian) is improving with each model update but may have higher error rates.

Whisper can also auto-detect the language being spoken, which is useful when you’re not sure what language a recording is in — or when speakers switch languages mid-conversation.

Open Source vs API

Whisper is available in two forms:

  • Open source: The model weights are freely available on GitHub. Anyone can download and run Whisper locally on their own hardware. This requires a GPU for reasonable speed and technical knowledge to set up.
  • API: OpenAI offers Whisper through their API, where you send audio and receive text. This is what most consumer tools (including Verbato) use — the API handles the infrastructure so you don’t need your own GPU.

Running Whisper locally gives you maximum privacy (audio never leaves your machine), but the API is far more convenient and handles edge cases like very long files automatically.

How Verbato Uses Whisper

Verbato is built on OpenAI’s transcription API, which uses the latest Whisper-derived models including GPT-4o transcription. On top of the raw transcription, Verbato adds:

  • Speaker diarization — identifying who said what in multi-speaker recordings
  • Intelligent chunking — splitting long files for optimal processing and reassembling results seamlessly
  • Multiple export formats — TXT, SRT, VTT, JSON, DOCX, and PDF from a single transcription
  • Multi-channel intake — upload via web, WhatsApp, Telegram, or URL
  • Click-to-listen — click any sentence to jump to that moment in the original audio

The combination of Whisper’s accuracy with these additional features makes transcription practical for everyday use — not just technically possible.

Try It Yourself

Want to see how Whisper-powered transcription works? Try Verbato free — 3 transcriptions per day, no credit card required. Or read our guide on how to transcribe audio to text for a complete walkthrough.