← Thursday, October 1, 2026Models & trainingGuide

Fine-tuning Nemotron cuts Saudi Arabic dialect WER from 55.05% to 29.96%

developer.nvidia.com by Elizabeth Goodman 14 min read Editor score 8/10

Fine-tuning with weighted replay, duration bucketing and partial encoder unfreezing reduced Najdi and Hijazi test WER from 55.05% to 29.96%, without degrading other Arabic dialects. The workflow and trade-offs offer a concrete template for adapting multilingual ASR to deployment-specific speech.

What: A NeMo fine-tuning workflow adapts Nemotron 3.5 ASR to Saudi Najdi and Hijazi speech while aiming to retain existing language capabilities.
How: Fine-tuning on 133.7 hours reduced target-split WER from 55.05% to 29.96%; English WER improved from 11.04% to 10.42%.
Why it matters: Dialect-specific adaptation can improve deployed ASR performance without degrading other Arabic dialects, according to the reported evaluation.

The workflow mixes target-dialect speech with FLEURS data, fine-tunes the model, and evaluates results. Sketched from the article's own text.

Read on developer.nvidia.com →

Discussion

Discussion is not switched on yet. Use Note above to keep your own thoughts on this story.

Headline, summary, brief and diagram are written by a model from the source's own text; the link, byline and numbers come from the source. Read the original before relying on a claim.