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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+LLM/tokenizer.json filter=lfs diff=lfs merge=lfs -text

ASR/README.md

@@ -0,0 +1,97 @@
+---
+language:
+  - en
+license: other
+tags:
+  - whisper
+  - ctranslate2
+  - automatic-speech-recognition
+  - air-traffic-control
+  - atc
+  - singapore
+  - military
+  - faster-whisper
+base_model: jacktol/whisper-large-v3-finetuned-for-ATC
+pipeline_tag: automatic-speech-recognition
+metrics:
+  - wer
+model-index:
+  - name: whisper-large-v3-atc-singapore
+    results:
+      - task:
+          type: automatic-speech-recognition
+        metrics:
+          - name: WER
+            type: wer
+            value: 0.24
+---
+
+# Whisper Large v3 — Singapore Military ATC (CTranslate2 float16)
+
+Fine-tuned Whisper Large v3 for Singapore Air Force air traffic control speech recognition.
+
+## Performance
+
+| Run | WER | Data | Key Change |
+|-----|-----|------|------------|
+| ct2_run5 | 0.48% | 6,680 synthetic | Baseline fine-tune |
+| ct2_run6 | 0.40% | 6,680 synthetic | +augmentation, weight decay |
+| **ct2_run7** | **0.24%** | 6,730 (synthetic + real) | +50 real recordings, frozen encoder |
+
+## Model Details
+
+| Key | Value |
+|-----|-------|
+| Base model | `jacktol/whisper-large-v3-finetuned-for-ATC` |
+| Format | CTranslate2 float16 |
+| Size | 2.9 GB |
+| Best WER | 0.24% (epoch 1) |
+| Domain | Singapore military ATC (Tengah WSAT, Paya Lebar WSAP) |
+
+## Training
+
+- **Continued training** from ct2_run6 best checkpoint (WER 0.40%)
+- **Encoder frozen** — only decoder fine-tuned to preserve acoustic features
+- Learning rate: 2e-6 (4x lower than run6)
+- Optimizer: AdamW 8-bit
+- Effective batch size: 16
+- Mixed precision: fp16
+- Early stopping: patience 2
+
+### Dataset
+
+- 6,680 synthetic entries (1,670 phrases x 4 TTS voice variants)
+- 50 real human recordings (20x oversampled = 1,000 effective entries)
+- Total: 6,730 entries
+
+### Augmentation
+
+Gaussian noise, time stretch, band-pass filter (300-3400 Hz VHF simulation), random clip, MP3 compression, SpecAugment, random silence padding.
+
+## Usage
+
+```python
+from faster_whisper import WhisperModel
+
+model = WhisperModel("path/to/ASR", device="cuda", compute_type="float16")
+segments, info = model.transcribe(
+    "audio.wav",
+    language="en",
+    beam_size=5,
+    hotwords="tengah paya lebar tacan sinjon pandan tuas murai seletar sembawang",
+)
+text = " ".join(seg.text.strip() for seg in segments)
+# "camel cleared i l s approach runway three six"
+```
+
+## Output Format
+
+The model outputs **normalized spoken text** (lowercase, fully expanded):
+
+| Input audio says | Model outputs |
+|-----------------|---------------|
+| "CAMEL climb flight level zero nine zero" | `camel climb flight level zero nine zero` |
+| "Contact Tengah Approach one three zero decimal zero" | `contact tengah approach one three zero decimal zero` |
+| "Squawk seven seven zero zero" | `squawk seven seven zero zero` |
+
+Use the companion LLM formatter to convert to display text (e.g., `CAMEL climb FL090`).

ASR/config.json

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+}

ASR/model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0be75c051de8f101137150567f68e66f79ed2f37f7fa3bd925576f74ff01fb3
+size 3087284237

ASR/preprocessor_config.json

@@ -0,0 +1,15 @@
+{
+  "chunk_length": 30,
+  "dither": 0.0,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

LLM/README.md

@@ -0,0 +1,94 @@
+---
+language:
+  - en
+license: other
+tags:
+  - qwen3
+  - text-generation
+  - text2text-generation
+  - air-traffic-control
+  - atc
+  - singapore
+  - military
+  - lora
+  - unsloth
+base_model: unsloth/Qwen3-1.7B
+---
+
+# Qwen3-1.7B — ATC Display Text Formatter
+
+Fine-tuned Qwen3-1.7B that converts normalized ASR output into structured ATC display text. Designed to work downstream of the companion Whisper ASR model.
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Exact match accuracy | **100.0%** (161/161) |
+| Avg character edit distance | 0.0 |
+| Best eval loss | 0.0005 |
+
+## Model Details
+
+| Key | Value |
+|-----|-------|
+| Base model | `unsloth/Qwen3-1.7B` |
+| Method | bf16 LoRA (rank 16, alpha 32) |
+| Merged size | 3.3 GB |
+| Train examples | 1,915 |
+| Eval examples | 161 |
+| Thinking mode | Disabled |
+
+## Training
+
+- Framework: Unsloth + SFTTrainer (trl)
+- Optimizer: AdamW 8-bit
+- Learning rate: 1.2e-4
+- Effective batch size: 16
+- Precision: bf16
+- Packing: enabled
+- Train on responses only: yes
+- Converged at step 380 (epoch 3.2)
+
+### Dataset
+
+1,670 unique ATC phrases from `axite.json`, stratified 90/10 split by category. Includes ASR noise augmentation (simulated ASR errors) for robustness.
+
+## What It Does
+
+Converts normalized spoken text (ASR output) into structured display text:
+
+| Input (normalized) | Output (display) |
+|-------------------|-----------------|
+| `camel climb flight level zero nine zero` | `CAMEL climb FL090` |
+| `contact tengah approach one three zero decimal zero` | `contact Tengah Approach 130.0` |
+| `squawk seven seven zero zero` | `squawk 7700` |
+| `request clearance, ninja two f sixteens for western coast departure for i l s.` | `Request clearance, NINJA 2xF16 for Western Coast Departure for ILS.` |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("path/to/LLM", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("path/to/LLM")
+
+messages = [
+    {"role": "system", "content": "Convert the following air traffic control transcript into structured display text."},
+    {"role": "user", "content": "camel climb flight level zero nine zero"},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.3, top_p=0.9, top_k=30)
+result = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+# "CAMEL climb FL090"
+```
+
+## Inference Settings
+
+| Parameter | Value |
+|-----------|-------|
+| Temperature | 0.3 |
+| Top-p | 0.9 |
+| Top-k | 30 |
+| Max new tokens | 128 |
+| Thinking | Disabled (`enable_thinking=False`) |

LLM/added_tokens.json

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+  "<|box_end|>": 151649,
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+}

LLM/chat_template.jinja

@@ -0,0 +1,98 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

LLM/config.json

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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
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+    "attention_dropout": 0.0,
+    "torch_dtype": "bfloat16",
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+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 6144,
+    "layer_types": [
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+    "max_position_embeddings": 40960,
+    "max_window_layers": 28,
+    "model_type": "qwen3",
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+    "num_hidden_layers": 28,
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+    "rms_norm_eps": 1e-06,
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+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.3.4",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

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LLM/special_tokens_map.json

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+}

LLM/tokenizer.json

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LLM/tokenizer_config.json

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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151669": {
+      "content": "<|PAD_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 40960,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- set message = messages[index] %}\n    {%- set current_content = message.content if message.content is not none else '' %}\n    {%- set tool_start = '<tool_response>' %}\n    {%- set tool_start_length = tool_start|length %}\n    {%- set start_of_message = current_content[:tool_start_length] %}\n    {%- set tool_end = '</tool_response>' %}\n    {%- set tool_end_length = tool_end|length %}\n    {%- set start_pos = (current_content|length) - tool_end_length %}\n    {%- if start_pos < 0 %}\n        {%- set start_pos = 0 %}\n    {%- endif %}\n    {%- set end_of_message = current_content[start_pos:] %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+}

README.md

@@ -0,0 +1,143 @@
+---
+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 ASR and LLM models for Singapore military air traffic control, built for the [ASTRA](https://github.com/aether-raid) training simulator. The two models work as a pipeline:
+
+```
+Audio  -->  ASR (Whisper)  -->  normalized text  -->  LLM (Qwen3)  -->  display text
+            "camel climb flight level zero nine zero"     "CAMEL climb FL090"
+```
+
+## 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 | **0.24%** |
+| Base model | `jacktol/whisper-large-v3-finetuned-for-ATC` |
+| Size | 2.9 GB |
+| Training data | 6,730 entries (6,680 synthetic + 50 real recordings) |
+
+### [LLM/](./LLM) — Qwen3-1.7B Display Formatter
+
+Converts normalized ASR output into structured ATC display text (uppercases callsigns, contracts flight levels, formats frequencies, etc.).
+
+| Metric | Value |
+|--------|-------|
+| Exact match | **100%** (161/161) |
+| Base model | `unsloth/Qwen3-1.7B` |
+| Size | 3.3 GB |
+| Training data | 1,915 examples |
+
+## Pipeline Architecture
+
+In production, the models are chained with **confidence-based routing**:
+
+- **ASR confidence >= 90%** — rule-based formatter (23 deterministic rules, <1ms, 0 VRAM)
+- **ASR confidence < 90%** — LLM formatter (handles noisy/ambiguous ASR output better)
+
+```
+Audio --> VAD (Silero) --> ASR (Whisper ct2) --> Post-processing
+                                                    |
+                                          confidence >= 0.90?
+                                          /                \
+                                        yes                no
+                                         |                  |
+                                   Rule formatter      LLM formatter
+                                         |                  |
+                                         \                 /
+                                          --> Display text
+```
+
+| State | VRAM |
+|-------|------|
+| ASR only (startup) | ~2 GB |
+| ASR + LLM (after first low-confidence call) | ~5.5 GB |
+
+## 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
+- **Approaches**: ILS, GCA, PAR, TACAN, DVOR/DME, Visual Straight-in
+- **60 callsigns**: CAMEL, NINJA, BEETLE, TAIPAN, HONDA, etc.
+- **Categories**: departure, approach, handoff, maneuver, landing, emergency, ground, recovery, pilot reports, military-specific ops
+
+## Training History
+
+### ASR
+
+| Run | WER | Key Change |
+|-----|-----|------------|
+| ct2_run5 | 0.48% | Initial fine-tune, pitch shift augmentation |
+| ct2_run6 | 0.40% | Removed pitch shift, added BPF/silence padding, weight decay |
+| **ct2_run7** | **0.24%** | Continued training, frozen encoder, +50 real recordings |
+
+### LLM
+
+| 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)
+```
+
+### LLM
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("./LLM", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("./LLM")
+
+messages = [
+    {"role": "system", "content": "Convert the following air traffic control transcript into structured display text."},
+    {"role": "user", "content": "camel climb flight level zero nine zero"},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.3, top_p=0.9, top_k=30)
+result = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+```
+
+## Download
+
+```bash
+# Full repo
+huggingface-cli download aether-raid/astra-atc-models --local-dir ./models
+
+# ASR only
+huggingface-cli download aether-raid/astra-atc-models --include "ASR/*" --local-dir ./models
+
+# LLM only
+huggingface-cli download aether-raid/astra-atc-models --include "LLM/*" --local-dir ./models
+```