Whisper Speech-to-Text — OpenAI's Open-Source Model as a Free Transcription Solution
Whisper is a speech recognition model open-sourced by OpenAI under the MIT license in September 2022. Anyone can run it for free on their own computer — no fees
Whisper is a speech recognition model open-sourced by OpenAI under the MIT license in September 2022. Anyone can run it for free on their own computer — no fees, no uploading audio files to the cloud. It was trained on 680,000 hours of multilingual audio, supports recognition and translation across 99 languages, and achieves over 95% accuracy on Chinese (including Traditional Chinese) with clean recordings. For individuals and small teams handling large volumes of meeting recordings, interviews, and course videos, this is currently the lowest-cost transcription solution available. What Whisper Is: Not Just "Another Speech-to-Text Tool" The biggest difference between Whisper and Google voice input or iOS dictation is that it runs offline and the model is downloadable. In "Whisper is trained on 680,000 hours of multilingual and multitask supervised data" (source: OpenAI official blog, September 2022) , OpenAI explains that the model uses an encoder-decoder Transformer architecture: audio is split into 30-second segments, converted into log-Mel spectrograms, fed into the model, and text is output in one pass. Its weights and inference code are fully public in the GitHub repository under the MIT license, meaning commercial use, modification, and redistribution are all unrestricted. This is what determines its practical value: audio that must not leak — law firm interview recordings, medical consultation records, internal corporate meetings — can be processed entirely on your own machine, without passing through any third-party server. The composition of the training data also explains its strengths and weaknesses. "Of the training data, 65% is English audio paired with English text, 18% is non-English audio paired with English text, and 17% is non-English audio paired with text in the same language" (source: OpenAI Whisper paper, 2022) . English is overwhelmingly dominant, so English recognition quality is noticeably better than other languages. Chinese sits in the seco
FAQ
What Whisper Is: Not Just "Another Speech-to-Text Tool"
The biggest difference between Whisper and Google voice input or iOS dictation is that it runs offline and the model is downloadable. In "Whisper is trained on 680,000 hours of multilingual and multitask supervised data" (source: OpenAI official blog, September 2022) , OpenAI explains that the model uses an encoder-decoder Transformer architecture: audio is split into 30-second segments, converted into log-Mel spectrograms, fed into the model, and text is output in one pass. Its weights and infe
How to Choose Among the Model Sizes
Whisper offers six model sizes, with memory requirements and speed differing by more than 10x. Choosing wrong will freeze your computer outright. The official specifications are as follows: tiny : 39 million parameters, needs about 1GB VRAM, roughly 10x the speed of large. Suited to error-tolerant scenarios like live captions and voice commands. base : 74 million parameters, needs about 1GB VRAM, roughly 7x the speed. small : 244 million parameters, needs about 2GB VRAM, roughly 4x the speed. Th
Problems Whisper Does Not Solve
Whisper does not do speaker diarization. The output is one continuous block of text with no "Speaker A" / "Speaker B" labels. Transcripts of multi-person meetings require an additional tool like pyannote.audio to cluster voiceprints, then align that with Whisper's timestamps — a pipeline with a technical barrier far above plain transcription. Timestamp precision is another weak point. Whisper natively outputs segment-level timestamps with errors of up to 1 to 2 seconds, which produces noticeably
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Reviewed and verified by FeiYueh · Last verified 2026-09-02. Independently maintained — not AI-generated boilerplate.
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