whatmodel.sh / stt / Whisper base.en

Whisper base.en

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Whisper base.en is an open-weights speech-to-text model from OpenAI, released September 2022. Its published size is 0.074B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact checkpoint openai/whisper-base.en (openai-whisper name 'base.en'). Params from the Whisper README size table. Licence as declared on the HF card.

35.4±2.8#142 of 162 speech-to-text modelsfrom 6 benchmark results across 4 sources

Score within the Open ASR English short-form WER (March 2026) comparison set only.

$ how it compares

The speech-to-text models ranked either side of Whisper base.en, out of 176 with a score, then more from OpenAI.

#modelscorewhereRAMreleased
154Whisper small OpenAI39±2local~1.1GB2022-09
155Moonshine Base Useful Sensors (Moonshine AI)38±3local~0.8GB2024-10
156Whisper base.en OpenAI35±3local~0.7GB2022-09
157SpeechBrain wav2vec2 CTC (LibriSpeech) SpeechBrain35±4local~2GB2022-06
158Vosk small en-us 0.15 Alpha Cephei (Vosk)31±6local~0.3GB2020-12
more from OpenAI
13GPT Transcribe OpenAI64±4api $0.0045/audio min2026-07
48GPT-Realtime-Whisper OpenAI59±3api $0.017/audio min2026-05
61Whisper large-v3-turbo OpenAI57±1api $0.0005/audio min +local~2.4GB2024-10

see all speech-to-text models · find the ones that fit your machine

$ what it's good at

other tests5 not in a task · 97% of its score
testresultvs the others in its rundifficultycountswhy
LibriSpeech test-clean WER +14.3% ✎▁···▁▁▃·▄▄██ beats 6/56avg 2.3%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 36%blunt · several runs
Open ASR English short-form WER (March 2026)10.3% ✎▁▁▁▃··▁▁▃██▆ beats 19/86avg 8.2%▇▇▇▇▇▇▇▇▇▇▇ 27%blunt
FLEURS English test WER7.6% ✎▁··▁▁▁▁▃▁▃█▁ beats 10/56avg 5.8%▇▇▇▇▇ 14%blunt
Picovoice STT benchmark · English streaming 4-set mean WER19.8% ✎█·█···▄▄▄··▄ beats 2/7avg 8.3%▇▇▇▇▇ 13%typical
Picovoice STT benchmark · English batch 4-set mean WER9.5% ✎▄···▄···▄··█ beats 1/4avg 5.7%▇▇▇ 7.7%blunt

$ where to run it

Other ways to run it

version

  1. Run it as published · needs ~0.68 GB · from github.com/openai (official)
    uvx --from openai-whisper whisper audio.mp3 --model base.en

Official weights and documentation: huggingface.co/openai ↗

$ how the score was worked out · transcribe.cpp model catalog… 48%, Open ASR Leaderboard 27%, Picovoice speech-to-text-benc… 21% and 1 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
transcribe.cpp model catalog · LibriSpeech test-clean WER of validated full-precision ports2▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48%MIT
Open ASR Leaderboard1▇▇▇▇▇▇▇▇ 27%Apache-2.0
Picovoice speech-to-text-benchmark · English batch-engine WER (README)2▇▇▇▇▇▇ 21%Apache-2.0
Speech Robust Bench (arXiv:2403.07937v3) · clean-speech WER of 25+ open ASR models1▇ 2%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 35.4

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json