Whisper
Whisper is OpenAI's open-source speech recognition model offering state-of-the-art transcription accuracy across 99 languages, available free to run locally or via the OpenAI API.
Whisper is an open-source automatic speech recognition (ASR) system developed and released by OpenAI in September 2022. Trained on 680,000 hours of multilingual and multitask supervised data collected from the internet, Whisper represents a significant leap forward in accessible, high-accuracy speech transcription. The model is released under the MIT license, making it completely free to use, modify, and integrate into commercial and non-commercial applications without restriction.
The architecture behind Whisper is a sequence-to-sequence Transformer model — the same fundamental design that powers large language models — applied to audio. Whisper takes raw audio as input and produces text output directly, handling tasks including transcription, translation, language identification, and voice activity detection within a single unified model. The largest Whisper model, whisper-large-v3, delivers accuracy that surpasses many commercially licensed ASR systems on challenging real-world audio conditions.
One of Whisper's most celebrated strengths is its robustness. Unlike many speech recognition systems that degrade significantly with background noise, accents, non-native speakers, or domain-specific terminology, Whisper maintains strong performance across diverse acoustic conditions. It handles heavily accented speech, technical jargon, multiple speakers in sequence, and audio with moderate background noise far better than earlier generation models. This robustness has made it the preferred foundation for a wide range of transcription services and applications.
Whisper supports transcription and translation across 99 languages, with particularly strong performance in English, Spanish, French, German, Japanese, Chinese, Korean, Portuguese, Russian, and Arabic, among many others. Beyond transcription in the source language, Whisper can translate audio in any supported language directly into English text — a single-step multilingual-to-English pipeline that is valuable for content understanding and accessibility use cases.
The model is freely available on GitHub and can be run locally on any machine with sufficient compute. For high-volume or production use cases, OpenAI provides Whisper as a managed API endpoint priced at $0.006 per minute of audio — one of the most cost-effective commercial transcription options available. Whisper's open-source availability has also made it the backbone for dozens of third-party transcription products, meeting note tools, podcast platforms, and developer tools that build AI voice features on top of its capabilities.
Key Features
- State-of-the-art speech recognition accuracy across 99 languages trained on 680,000 hours of multilingual audio
- Robust performance in challenging conditions including background noise, strong accents, and technical terminology
- Free and open-source under MIT license — run locally with no usage fees or restrictions
- Multiple model sizes (tiny, base, small, medium, large-v3) to balance speed and accuracy for any hardware
- Direct audio-to-English translation for any of the 99 supported languages in a single pipeline step
- Language detection to automatically identify the language being spoken without manual configuration
- Available as a managed API via OpenAI at just $0.006 per minute for high-volume production use
- Powers dozens of third-party apps and services as the backbone transcription engine
- Voice activity detection to identify speech segments and filter silence in audio files
- Handles diverse audio formats and sources including MP3, MP4, WAV, FLAC, and more
Frequently Asked Questions
Is Whisper truly free? What are the costs?
Whisper is completely free to download and run locally under the MIT open-source license. There are no usage fees, no rate limits, and no commercial restrictions when self-hosting. For users who want a managed service without infrastructure overhead, OpenAI offers Whisper as an API at $0.006 per minute of audio — approximately $0.36 per hour of audio — which is among the most affordable transcription API pricing available. The model weights, code, and documentation are all freely available on GitHub.
How do I run Whisper locally?
Running Whisper locally requires Python and pip. Install it with 'pip install openai-whisper', then run transcription from the command line with 'whisper audio.mp3 --model large-v3'. The first run will download the selected model weights automatically. For the large-v3 model, a GPU with at least 10GB of VRAM is recommended for fast inference, though smaller models like 'medium' and 'small' run acceptably on CPUs and less powerful GPUs. The Python API is also available for integration into custom applications.
Which Whisper model size should I use?
Model selection depends on your accuracy requirements and hardware. The 'tiny' and 'base' models are fastest and suitable for English with clean audio on any hardware. The 'small' and 'medium' models offer a good balance of accuracy and speed, working well on modern CPUs. The 'large-v3' model delivers the highest accuracy across all languages and conditions, but requires a capable GPU for reasonable inference speed. For most production use cases requiring high accuracy, large-v3 is recommended, and this is what the OpenAI API uses.
How accurate is Whisper compared to other transcription services?
Whisper large-v3 is competitive with or exceeds the accuracy of many commercial transcription services on diverse audio benchmarks, particularly for non-English languages, accented speech, and noisy audio. It achieves word error rates below 5% on many standard English benchmarks. For specialized domains with very specific vocabulary, fine-tuned models may outperform Whisper, and for certain languages, purpose-built models (such as Vito for Korean) may deliver better accuracy. However, for general-purpose multilingual transcription, Whisper is widely regarded as the best freely available option.
Can Whisper translate audio from other languages into English?
Yes, Whisper supports direct audio-to-English translation as a built-in task. You can pass audio in any of the 99 supported languages and receive an English text output without a separate translation step. This is accomplished by specifying '--task translate' in the CLI or setting the task parameter in the API. Note that Whisper's translation is designed for English as the target language only — for translation into other target languages, you would transcribe first and then use a separate translation model.
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