Whisper

Overview

Whisper is an open-source automatic speech recognition (ASR) system developed by OpenAI. It is trained on a large dataset of diverse audio, enabling robust transcription and translation across many languages. Whisper offers multiple model sizes for different accuracy and speed trade-offs.

Category: Audio & VoicePricing: FreeRating: ★ 4.6Best for: Transcription, speech-to-text, multilingual ASR

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Key Features

  • Open-source
  • Multilingual support
  • Multiple model sizes
  • Robust to background noise
  • Transcription and translation

Pros

  • Free and open-source
  • High accuracy across languages
  • Available in different sizes for flexibility

Cons

  • Requires significant computational resources for larger models
  • May have latency on CPU-only systems

Who Is This Tool Best For?

Transcription, speech-to-text, multilingual ASR

What readers ask us about Whisper

What is Whisper?

An open-source speech recognition model from OpenAI. It transcribes and translates audio across many languages, and because the weights are published it can be run locally or through hosted services.

Who is it for?

Developers building transcription into an application, researchers working with multilingual audio, and anyone who needs speech-to-text without sending recordings to a third party.

Is Whisper free?

Yes — the model is open source and it is scored 4.6 here. Free refers to the weights: you still supply compute, and running the larger models without a GPU is slow.

How accurate is it?

Variable. Clean single-speaker audio is strong; overlapping speech, heavy accents, crosstalk and noisy recordings degrade it, and it can produce fluent output for audio it did not hear correctly.

Use cases

Transcribing interviews and meetings

Batch processing recordings into text is the core use, and running it locally keeps sensitive audio on your own machine.

Building speech features into an app

Because the model is open, it can be embedded in a product without a per-minute transcription bill.

Working across languages

Transcription and translation across many languages in one model simplifies multilingual pipelines considerably.

Pricing

Whisper is a subscription product. Rated 4.6; see the vendor link on ToolFlect. There is no licence fee, so the cost is hardware and time: the largest models are materially slower without a GPU, and long recordings take as long as they take. Hosted services wrap the same model at a per-minute rate if you would rather not run it. Pricing is the OpenAI page’s to publish.

Alternatives

  • AssemblyAI — A hosted API with speaker diarisation and higher accuracy on messy audio.
  • Deepgram — Hosted transcription built for low-latency streaming use.
  • Trint — An editor-first service aimed at journalists and researchers.

Look them up in our AI tools directory.

What to pin down beforehand

Treat transcripts as drafts

Check names, numbers and anything quoted. Fluent output can still be wrong, and errors are hardest to spot where the text reads well.

Decide where audio may go

Recordings are sensitive personal data. Running the model locally is the difference between processing audio and disclosing it.

Test on your worst audio

A model that performs on a studio recording may struggle with your actual material. Evaluate on the hard files before committing.