Interrogation Transcription System for Law Enforcement

Overview

Automated real-time transcription of interviews to generate official protocols in a secure environment. On-premise (air-gapped) deployment ensuring maximum security and data privacy. Core Model: Python, OpenAI Whisper, Pyannote, Docker, on-premise deployment Orchestration: Custom system for real-time processing (voice detection + chunking + transcription). Supports up to 10 simultaneous sessions. Fine-tuning Pipeline: Created a pipeline for periodic model updates using client-provided datasets (edited transcripts). Focused on adapting to (local dialect) and low-quality audio. Metrics: Used WER (Word Error Rate) and CER (Character Error Rate) to validate model performance. Deployment: On-premise (Air-gapped). All components are deployed locally to ensure maximum security and data privacy.

Achievements

• A production-ready system (active for 3+ years) that generates real-time protocols from microphone input, resilient to background noise and street recordings. • Supports up to 10 simultaneous transcription sessions (with different numbers of users per session). • Reduced Whisper latency from ~4s (Medium model) to 1.05s (Turbo) while maintaining high accuracy in noisy environments. • Production quality thresholds: WER < 7%, CER < 7% with automated re-training when exceeded. • Successfully adapted recognition for local dialect and low-quality audio sources.

Responsibilities

• Designed and built custom real-time processing system: voice detection (Pyannote) + chunking + transcription pipeline.
• Implemented batch optimization, buffer tuning, and custom VAD logic for real-time Whisper-based recognition.
• Created fine-tuning pipeline for periodic model updates using client-provided datasets (edited transcripts).
• Managed model evolution: OpenAI Whisper Medium → Large → Turbo.
• Built automated QA pipeline using WER/CER metrics with auto-retraining triggers.
• Deployed all components locally on air-gapped infrastructure (via RAY.IO, Docker, vLLM).