On the Fleurs benchmark, the Whisper large-v2 model had a word error rate of 21.5 percent for Hindi.

Checked against its source on October 16, 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.
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

On the Fleurs benchmark, the Whisper large-v2 model had a word error rate of 21.5 percent for Hindi.

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 21.5 percent for Hindi.

In the source's own words

Source

Used to calculate

Cited on