---
language:
  - en
license: other
tags:
  - whisper
  - qwen3
  - ctranslate2
  - automatic-speech-recognition
  - text-generation
  - air-traffic-control
  - atc
  - singapore
  - military
pipeline_tag: automatic-speech-recognition
---

# ASTRA ATC Models

Fine-tuned models for Singapore military air traffic control, built for the [ASTRA](https://github.com/aether-raid) training simulator.

## Pipeline

```
Audio  -->  VAD (Silero)  -->  ASR (Whisper)  -->  Rule Formatter  -->  Display Text
                               "camel climb flight level zero nine zero"
                                                                        "CAMEL climb FL090"
```

The production pipeline uses a **rule-based formatter** (23 deterministic rules, <1ms, 0 VRAM) instead of the LLM. The LLM is retained for reference.

## Models

### [ASR/](./ASR) — Whisper Large v3 (CTranslate2 float16)

Fine-tuned for Singapore military ATC speech. Uses CTranslate2 float16 format for fast inference with [faster-whisper](https://github.com/SYSTRAN/faster-whisper).

| Metric | Value |
|--------|-------|
| WER | **0.66%** |
| Base model | `openai/whisper-large-v3` |
| Size | 2.9 GB |
| Training | Full fine-tune with enhanced VHF radio augmentation |

### [LLM/](./LLM) — Qwen3-1.7B Display Formatter (Legacy)

> **Legacy.** Superseded by a deterministic rule-based formatter. Retained for reference.

Converts normalized ASR output into structured ATC display text.

| Metric | Value |
|--------|-------|
| Exact match | **100%** (161/161) |
| Base model | `unsloth/Qwen3-1.7B` |
| Size | 3.3 GB |

## Architecture

```
Audio --> VAD (Silero) --> ASR (Whisper ct2) --> Post-processing --> Rule Formatter --> Display Text
```

| Component | Technology | Latency | VRAM |
|-----------|-----------|---------|------|
| VAD | Silero VAD (ONNX) | ~50ms | <100 MB |
| ASR | Whisper Large v3 (CTranslate2) | ~500ms-2s | ~2 GB |
| Formatter | 23 deterministic rules | <1ms | 0 MB |

Total VRAM: ~2 GB (ASR only).

## Domain

Singapore military ATC covering:
- **Airbases**: Tengah (WSAT, runway 18/36), Paya Lebar (WSAP, runway 02/20)
- **Aircraft**: F-16C/D, F-15SG, C-130, Hercules
- **Approaches**: ILS, GCA, PAR, TACAN, DVOR/DME, VOR/DME, Visual Straight-in
- **100+ callsigns**: CAMEL, NINJA, BEETLE, TAIPAN, MAVERICK, JAGUAR, LANCER, etc.
- **Categories**: departure, approach, handoff, maneuver, landing, emergency, ground, recovery, pilot reports, military-specific ops

## Training History

### ASR

| Run | WER | Base | Key Change |
|-----|-----|------|------------|
| ct2_run5 | 0.48% | jacktol/whisper-large-v3-finetuned-for-ATC | Initial fine-tune |
| ct2_run6 | 0.40% | jacktol/whisper-large-v3-finetuned-for-ATC | +augmentation, weight decay |
| ct2_run7 | 0.24% | jacktol/whisper-large-v3-finetuned-for-ATC | Frozen encoder, +50 real recordings |
| **ct2_run8** | **0.66%** | openai/whisper-large-v3 | Full retrain from base, enhanced augmentation |

> ct2_run8 trains from the original Whisper base for better generalisation to real-world ATC audio.

### LLM (Legacy)

| Run | Accuracy | Key Change |
|-----|----------|------------|
| llm_run3 | 98.1% (Qwen3-8B) | QLoRA 4-bit, 871 examples |
| llm_run4 | 100% (Qwen3-1.7B) | bf16 LoRA, 1,915 examples with ASR noise augmentation |

## Quick Start

### ASR

```python
from faster_whisper import WhisperModel

model = WhisperModel("./ASR", device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.wav", language="en", beam_size=5)
text = " ".join(seg.text.strip() for seg in segments)
```

### Download

```bash
# Full repo (ASR + LLM)
huggingface-cli download aether-raid/astra-atc-models --local-dir ./models

# ASR only (recommended)
huggingface-cli download aether-raid/astra-atc-models --include "ASR/*" --local-dir ./models

# LLM only (legacy)
huggingface-cli download aether-raid/astra-atc-models --include "LLM/*" --local-dir ./models
```