Parth Lad

End-to-end ASR pipelines with WER benchmarking across six platforms including AssemblyAI, Sarvam, Azure, and Meta Omnilingual. Custom XTTS V2 fine-tuning and OmniVoice voice cloning deployed for Indic languages and enterprise BFSI use cases.

Production pipelines covering segmentation, format conversion, denoising, speaker diarization, and DNSMOS-based quality scoring at scale. Handles both single-speaker TTS corpora and two-person conversational audio including podcasts and phone call recordings.

Automotive-grade wake word systems for enterprise clients covering the full pipeline from data collection through signal processing to production deployment. Optimised for low-latency, always-on detection in resource-constrained edge environments.

Studio-grade recording setups with HITL annotation — automated pipelines handle first-pass processing while human reviewers validate uncertain outputs before data enters training. Covers single-speaker TTS corpora, conversational datasets, and automated quality frameworks.

Object detection and tracking with YOLO-based models for real-time and batch inference workloads. Includes frame extraction pipelines, annotation workflow management, and dataset curation for consistent model training.

Linux-native development with Docker containerisation, Azure GPU VMs for model training, and RunPod for speech processing pipelines including OmniVoice voice cloning. AWS and Cloudflare R2 for scalable audio storage across production workloads.