Fine-Tuning NVIDIA Nemotron for Saudi Arabic Dialects, with a Path to Other Languages | NVIDIA Technical Blog

This post details a specific workflow for fine-tuning NVIDIA Nemotron 3.5 ASR for Saudi Arabic dialects. It introduces a practical recipe involving minimal data curation, weighted replay mixing with FLEURS data, duration-based bucketing, and partial encoder unfreezing. The post quantifies the reduction in Word Error Rate (WER) achieved for Saudi dialects and demonstrates that English performance is maintained or slightly improved. It also discusses the trade-offs between unfreezing all encoder layers versus a partial unfreeze for different resource constraints. Furthermore, it touches upon extending the workflow to include speaker diarization and provides links to reproducible notebooks and agent skills.