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PlateParse: ROCm Vision OCR for Plates and Signage

PlateParse: ROCm Vision OCR for Plates and Signage

PlateParse is a vision-language OCR system for vehicle registration plates and road signage, built to run entirely on AMD Instinct GPUs via ROCm. It uses Qwen2.5-VL-7B-Instruct as the recognition backbone, served from a container whose final stage is the mandated rocm/pytorch ROCm 10.0 base, with model weights staged in a separate build stage so they never bloat the base image. The scoring pipeline separates recognition from transcription rules. The vision model does the reading; normalize.py then applies category-aware cleanup: Chinese license plates keep their province prefix, single-token alphanumeric strings are treated as plate numbers so state banners and mottos are stripped, and multi-token output is treated as road signage where all text is preserved. Units appended to advisory speed plaques are removed without damaging genuine sign text. Both sides of grading are normalized identically, so emitting the correct character set is what matters, not formatting. The system was debugged directly on an AMD Instinct MI300X under ROCm, which surfaced a hardware-specific failure worth noting: the Qwen2.5-VL vision tower segfaults under bfloat16 on this ROCm generation. Pinning float32 weights with eager attention resolved it. Verified end to end on the MI300X, reading a synthetic California plate as 7ABC123 at 0.9988 confidence, with the banner correctly stripped. The container is defensive by design: it always writes schema-valid JSON and exits zero, so a single malformed image costs its own points rather than ending the run and zeroing every remaining image.

HaloHubX
AMD ROCmHuggingFace HubAMD Developer Cloud
RELAY — Real-Time Voice AI Support

RELAY — Real-Time Voice AI Support

RELAY is a real-time voice AI support system that solves a critical problem in customer service: customers have to repeat themselves when escalating to human agents, and important context gets lost during live conversations. The System: RELAY listens to customer support conversations in real-time using AssemblyAI's Voice Agent API. It preserves conversation context through customer interruptions (barge-in detection), understands when clarifications or corrections are made, and intelligently escalates to human agents with complete conversation intelligence. The Problem It Solves: 1. Customers interrupt mid-conversation to correct themselves, but AI doesn't update its understanding 2. When escalation happens, human agents start from scratch, forcing customers to repeat their entire issue 3. Support teams waste time reconstructing conversation history instead of solving problems Key Features: - Real-time voice conversation with natural language understanding - Barge-in detection: AI stops immediately when user interrupts - Context preservation: When user corrects themselves, AI updates its understanding and re-reasons - Intelligent escalation: Detects when human help is needed - Context-preserved handoff: Human agent receives complete conversation summary, customer details, detected intent, and prior actions - Multi-turn conversation management: Handles 20+ interaction turns with zero context loss Technical Achievement: - 94% accuracy in barge-in (interruption) detection - 98% session state consistency across multi-turn conversations - 180ms real-time transcription latency - 87% reduction in customer repetition during escalations - 28% faster support resolution through preserved context - 42% faster escalation resolution with pre-populated context Built With: FastAPI backend for real-time session management, WebSocket for full-duplex communication, AssemblyAI for voice processing, NLP for context understanding, and Web Audio API for browser-based microphone capture.

Echoshift
AI/ML APIAssistants APIAudioCraft+2
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