Fine-tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation

This article demonstrates fine-tuning NVIDIA Parakeet TDT 0.6B V2 speech recognition model on AWS infrastructure for domain-specific applications, using Heidi's healthcare platform as a case study.

• Synthetic data generation pipeline combines LLMs, text-to-speech, and noise augmentation for medical terminology
• Distributed training uses Amazon EC2 p4d.24xlarge instances with NVIDIA A100 GPUs and DeepSpeed optimization
• NVIDIA NeMo framework handles ASR model fine-tuning with Token-and-Duration Transducer architecture
• Docker containers ensure reproducible training and inference environments across infrastructure
• Model deployment on Amazon EKS with Amazon FSx for Lustre provides scalable, low-latency serving
• OpenAI-compatible API wrapper enables standard integration patterns for model access
• AI Gateway and Langfuse provide production-grade security, observability, and request tracing
• KEDA enables pod auto-scaling based on custom metrics like average processing time
• Complete code samples and Kubernetes manifests available in GitHub repository

This end-to-end solution demonstrates building production-ready, domain-adapted ASR systems combining AWS managed services with open-source AI tools for healthcare and other specialized domains.