On the Fleurs benchmark, the Whisper large-v2 model had a word error rate of 16.0 percent for Arabic.

Checked against its source on October 16, 2026

In the source's own words

“Model Afrikaans Amharic Arabic Assamese Azerbaijani Belarusian Bulgarian Bengali Bosnian Catalan Chinese Czech Welsh Danish ... Whisper large-v2 36.7 140.3 16.0 106.2 23.4 45.4 14.6 104.1 15.7 7.3 14.7 13.3 33.0 13.8”

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.
What it does not show: Read speech in good audio. Real dictation, accents and noise raise error rates. For languages written without spaces the paper reports character-level scoring, and the error rate can exceed 100 percent when the model writes extra text.

Source

Robust Speech Recognition via Large-Scale Weak Supervision (full text)Preprint · Alec Radford et al., OpenAI (arXiv), 2022

Used to calculate

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, the Whisper large-v2 model had a word error rate of 16.0 percent for Arabic.

In the source's own words

Source

Used to calculate

Cited on

On the Fleurs benchmark, the Whisper large-v2 model had a word error rate of 16.0 percent for Arabic.

In the source's own words

Source

Used to calculate

Cited on