If you have used any AI transcription tool in the last two years, there is a good chance it was secretly powered by OpenAI Whisper. From consumer apps to enterprise software, Whisper has become the default engine behind AI transcription globally. But most people using these tools have no idea what Whisper actually is, how accurate it really is, or what its limitations are.
Here is everything you need to know.
OpenAI Whisper is an automatic speech recognition (ASR) system released by OpenAI in September 2022. Unlike previous speech recognition models that were trained on carefully curated, clean audio datasets, Whisper was trained on 680,000 hours of audio scraped from the internet — including noisy, accented, multilingual, and low-quality audio.
This massive, diverse training dataset is what makes Whisper different. It has heard virtually every type of accent, audio quality, and language combination that exists on the internet, which is why it generalises so well to real-world audio.
OpenAI released Whisper as open-source software, which means any developer can use it freely, and many transcription services — including Bolo Aur Likho — are built on top of it.
OpenAI published benchmarks showing Whisper achieves a Word Error Rate (WER) of around 3-5% on standard English audio — meaning roughly 95-97% of words are transcribed correctly. In practice, with real-world audio conditions:
For context, human transcriptionists typically achieve 99%+ accuracy on clear audio, dropping to 95-97% on difficult audio. Whisper is approaching human-level accuracy for good audio quality.
Whisper supports 99 languages out of the box. Here are the languages with the best accuracy (lowest Word Error Rate):
Whisper also handles code-switching — audio that mixes two languages (like Hinglish or Spanglish) — better than any previous ASR model.
This is an important question many users do not ask. Here is how it works:
When you use Bolo Aur Likho, your audio is sent to OpenAI's API for processing. According to OpenAI's privacy policy, API data is not used to train their models (this is different from ChatGPT, which can use conversations for training). Your audio is processed and the result is returned — OpenAI does not retain it.
On the Bolo Aur Likho side, audio files are deleted immediately after processing. No recordings are stored on our servers at any point.
For highly sensitive audio (legal proceedings, medical consultations, confidential business discussions), you should review the full privacy policies of any tool you use, and consider whether running Whisper locally — on your own hardware — is more appropriate.
💡 Running Whisper locally is possible using the open-source code at github.com/openai/whisper. It requires Python and a decent GPU, but gives you complete privacy since audio never leaves your machine.
You do not need to install anything or write any code to use Whisper. Bolo Aur Likho makes the full Whisper model available for free with a simple upload interface:
No sign up, no credit card, no installation. The same model that powers $17/month subscriptions, available completely free.
Whisper represents a genuine step-change in speech recognition technology. Understanding what it is and how it works helps you use it more effectively and set appropriate expectations for accuracy in different audio conditions.
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