Fact · Languages and accents
4.2percent word error rate
On the Fleurs benchmark, the Whisper large-v2 model had a word error rate of 4.2 percent for English.
Checked against its source on October 17, 2026
In the source's own words
“Model German Greek English Spanish Estonian Persian Finnish Tagalog French Galician Gujarati Hausa Hebrew Hindi ... Whisper large-v2 4.5 12.5 4.2 3.0 21.9 32.9 9.7 13.8 8.3 15.4 102.7 88.9 27.1 21.5”
Robust Speech Recognition via Large-Scale Weak Supervision (full text), Alec Radford et al., OpenAI (arXiv), 2022, Table 13 (Fleurs).
Open the original (DOI 10.48550/arXiv.2212.04356)- What it measures
- Fleurs is a read-speech benchmark in about 100 languages. The figure is the error rate of the largest Whisper model in the table, run without fine-tuning, in the paper's own evaluation. It is shown beside the Hindi figure for comparison.
- What it does not show
- Read speech in good audio. Real dictation, accents and noise raise error rates for every language. The benchmark does not test legal or exam passages.
Source
Used to calculate
- On the Fleurs benchmark, Whisper large-v2's word error rate in English was about 1.4 times its rate in Spanish.
- On the Fleurs benchmark, Whisper large-v2's word error rate for Arabic was about 3.8 times its rate for English.
- On the Fleurs benchmark, Whisper large-v2's word error rate for Persian was about 7.8 times its rate for English.
- On the FLEURS benchmark, Whisper large-v2's word error rate in Hindi was about 5.1 times its rate in English.
- On the FLEURS benchmark, Whisper large-v2's word error rate in Hindi was about 5.1 times its English rate.
- On the Fleurs benchmark, the Whisper large-v2 word error rate in Korean was about 3.4 times its rate in English.
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