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OpenAI Whisper

by OpenAI

Free Audio

★★★★½ 4.6 (4,200 reviews)

Whisper is OpenAI's open-source speech recognition model. Highly accurate across 50+ languages, it can be run locally at no cost, making it the top choice for developers needing reliable transcription without ongoing API costs.

How to Get the Most Out of OpenAI Whisper

Transcribe Audio Files Locally in Batch with No Per-Minute Cost

Install Whisper locally with pip and run it on a folder of audio files: whisper *.mp3 --model medium --language English --output_format srt. It processes all files sequentially, producing SRT subtitle files, TXT transcripts, and JSON word-level timestamps with no API calls and no per-minute cost. For podcasters, journalists, or researchers with large audio archives, local Whisper is the most cost-effective transcription solution available.

Integrate into a Python Pipeline for Automated Transcription Workflows

Use the Whisper Python library to build automated workflows: transcribe recordings as they're added to a folder, extract timestamps and send key quotes to a database, or trigger downstream processes (summarization, translation, CRM logging) when transcription completes. Because Whisper is open-source, it integrates cleanly into any Python stack without API authentication, rate limits, or usage restrictions.

Transcribe Multilingual Audio with Automatic Language Detection

Run Whisper without specifying a language (--language auto) and it detects the spoken language from the first 30 seconds, then transcribes accordingly. For organizations handling multilingual customer calls or content across 50+ supported languages, this automatic detection removes the need to manually route audio to language-specific transcription services. The large model handles code-switching between languages mid-sentence better than most commercial transcription APIs.

Our Take

Whisper is the best choice for developers who need reliable transcription integrated into custom workflows without ongoing API costs — the local deployment model is a significant advantage for high-volume or privacy-sensitive use cases. Accuracy on clean audio is excellent; accuracy on noisy recordings or heavy accents requires the larger models which are slower. For non-technical users who want a polished interface and automatic meeting joining, Otter.ai or Fireflies are far easier to use. Whisper's value is entirely for developers building transcription into products or workflows rather than for consumer-facing transcription tasks.

Frequently Asked Questions

What is OpenAI Whisper and is it free?+

Whisper is an open-source speech recognition model developed by OpenAI that converts audio to text with high accuracy across 50+ languages. The model itself is free to download and run locally with no usage fees. OpenAI also offers Whisper as a paid API at $0.006 per minute of audio for those who prefer cloud processing without local setup.

How accurate is Whisper transcription?+

Whisper's accuracy is excellent for clear audio in English and major European languages — typically 90-95% word error rate for high-quality recordings. Accuracy decreases for heavy accents, technical jargon, low-quality audio, and less common languages. Whisper's large model is significantly more accurate than the small model but requires more compute time and resources.

How do I run Whisper locally?+

Install Python, then run: pip install openai-whisper. Transcribe a file with: whisper audio.mp3 --model medium. Model options range from tiny (fastest, least accurate) to large (slowest, most accurate). The medium model provides a good balance of speed and accuracy for most use cases. A GPU significantly speeds up processing; CPU is possible but slow for long files.

What is Whisper used for in production applications?+

Whisper is widely used in production for podcast transcription pipelines, meeting transcription tools, accessibility features, subtitle generation, voice-command applications, and any workflow requiring reliable batch audio-to-text conversion. Its open-source nature and no-usage-fee model makes it economical for high-volume transcription compared to pay-per-minute cloud APIs.

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Opens official website · Free · 2022 · Millions (via API) users

Pros & Cons

✓ Pros

Completely free to self-host
Supports 50+ languages
Excellent accuracy
Multiple model sizes available
No usage limits when local

✗ Cons

Requires technical setup for local use
No UI — developer tool
GPU recommended for speed
No real-time transcription

Pricing

Free Plan

Yes — fully free and open source

Paid Plan

OpenAI API: $0.006/minute

Who Is It For?

✓ Best For

Developers, batch transcription, multilingual audio

✗ Not Ideal For

Non-technical users, real-time transcription