Intelligent Air Traffic Control Voice & Situational Awareness Platform
SKYPARSE is an AI system that transcribes live ATC (Air Traffic Control) radio communications, extracts structured intent—callsigns, instructions, and parameters—and maps them to real-time aircraft positions using ADS-B surveillance data. It serves as the foundational intelligence layer for air traffic control AI, starting with voice comprehension and expanding into decision support, compliance monitoring, and safety analytics.
┌──────────────────────────────────────────────────────────────┐
│ SKYPARSE │
│ │
│ ┌──────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ Audio │──▶│ ASR Engine │──▶│ Intent Parser │ │
│ │ Ingestion│ │ (Whisper │ │ (NLP / LLM) │ │
│ │ │ │ fine-tuned)│ │ │ │
│ └──────────┘ └──────────────┘ └──────────┬───────────┘ │
│ │ │
│ ┌──────────┐ ┌──────────▼───────────┐ │
│ │ ADS-B │──────────────────────▶│ Fusion Engine │ │
│ │ Feed │ │ (Match intent │ │
│ │ │ │ to aircraft) │ │
│ └──────────┘ └──────────┬───────────┘ │
│ │ │
│ ┌──────────▼───────────┐ │
│ │ Visualization │ │
│ │ (Web Dashboard) │ │
│ └──────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
Data flow: LiveATC audio → noise reduction & VAD segmentation → Whisper ASR transcription → LLM-based intent extraction → callsign-to-aircraft fusion with ADS-B → real-time dashboard visualization.
# 1. Clone and configure
cd skyparse
cp .env.example .env
# Edit .env with your API keys (ANTHROPIC_API_KEY, etc.)
# 2. Launch all services
docker-compose up --build
# 3. Access
# API: http://localhost:8000
# Dashboard: http://localhost:3000# 1. Create a virtual environment
python -m venv venv
source venv/bin/activate # or `venv\Scripts\activate` on Windows
# 2. Install dependencies
pip install -e ".[dev]"
# 3. Start PostgreSQL + TimescaleDB (requires Docker)
docker-compose up -d db
# 4. Configure environment
cp .env.example .env
# Edit .env with your credentials
# 5. Run the API server
uvicorn api.main:app --reload --host 0.0.0.0 --port 8000| Layer | Technology |
|---|---|
| ASR Model | Whisper large-v3 + LoRA fine-tuning (PyTorch, HF PEFT) |
| Intent Parser | Claude API (prototype) → fine-tuned Llama/Mistral (prod) |
| Audio Pipeline | librosa, soundfile, noisereduce, Silero VAD |
| ADS-B Data | OpenSky Network API |
| Backend | FastAPI + WebSocket |
| Frontend | React + TypeScript + Mapbox GL JS |
| Database | PostgreSQL 16 + TimescaleDB |
| Deployment | Docker, AWS (ECS/EKS) |
| ML Training | PyTorch + Hugging Face Transformers + W&B |
skyparse/
├── pyproject.toml # Python project configuration
├── Dockerfile # API service container
├── docker-compose.yml # Full-stack orchestration
├── .env.example # Environment variable template
│
├── data/
│ ├── collectors/ # LiveATC downloader, OpenSky ADS-B collector
│ ├── processors/ # Audio preprocessing, transcript labeling, ADS-B processing
│ └── schemas/ # Intent taxonomy, Pydantic models
│
├── models/
│ ├── asr/ # Whisper fine-tuning, evaluation, inference
│ ├── intent/ # LLM parser, local parser, callsign resolver
│ └── fusion/ # Temporal alignment, compliance tracking
│
├── api/
│ ├── main.py # FastAPI application entry point
│ ├── routes/ # HTTP & WebSocket endpoints
│ └── services/ # Pipeline orchestration, database ops
│
├── frontend/ # React + TypeScript dashboard
│ └── src/
│ ├── components/ # RadarView, TranscriptFeed, InstructionTimeline
│ ├── hooks/ # useWebSocket, useAircraftState
│ └── utils/ # Aviation math, Mapbox layers
│
├── tests/ # pytest test suite + fixtures
├── exploration/ # Jupyter-style exploration scripts
├── scripts/ # Dev setup, data download, pipeline runners
└── docs/ # Architecture, ATC phraseology, deployment guides
MIT