Point Docker trainer at native Tater firmware

cli/wake_word_sample_trainer

@@ -497,6 +497,7 @@ calibration_path = Path(os.environ.get("CALIBRATION_PATH", ""))
 language = (os.environ.get("LANGUAGE", "en") or "en").strip().lower()
 probability_cutoff = 0.97
 sliding_window_size = 5
+calibration = {}
 
 if calibration_path.exists():
     try:
@@ -510,21 +511,60 @@ if calibration_path.exists():
     except Exception as exc:
         print(f"⚠️ Failed to read detector calibration ({exc}); using defaults.")
 
+probability_cutoff = round(probability_cutoff, 3)
+sliding_window_size = max(1, min(10, int(sliding_window_size)))
+selected_metrics = calibration.get("selected_metrics") if isinstance(calibration.get("selected_metrics"), dict) else {}
+evaluation = calibration.get("evaluation") if isinstance(calibration.get("evaluation"), dict) else {}
+close_miss_threshold = max(0.01, min(0.99, round(max(0.01, probability_cutoff - 0.19), 3)))
+
 meta = {
     "type": "micro",
     "wake_word": os.environ["WAKE_WORD_TITLE"],
+    "label": os.environ["WAKE_WORD_TITLE"].replace("_", " ").title(),
     "author": "Tater Totterson",
     "website": "https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git",
     "model": os.environ["TFLITE_FILENAME"],
     "trained_languages": [language],
     "version": 2,
+    "model_format": "tflite_stream_state_internal_quant",
+    "quantization": "int8",
+    "sample_rate": 16000,
     "micro": {
-        "probability_cutoff": round(probability_cutoff, 2),
+        "probability_cutoff": probability_cutoff,
         "sliding_window_size": sliding_window_size,
         "feature_step_size": 10,
         "tensor_arena_size": 30000,
         "minimum_esphome_version": "2024.7.0",
     },
+    "tater_native": {
+        "format_version": 1,
+        "wake_threshold": probability_cutoff,
+        "wake_sliding_window": sliding_window_size,
+        "close_miss_threshold": close_miss_threshold,
+        "frontend": {
+            "name": "tflm_microfrontend",
+            "sample_rate": 16000,
+            "feature_duration_ms": 30,
+            "feature_step_ms": 10,
+            "feature_size": 40,
+            "input_feature_frames": 2,
+            "lower_band_limit": 125.0,
+            "upper_band_limit": 7500.0,
+        },
+        "recommended_for": ["tater-native-satellite", "voice-pe"],
+    },
+    "calibration": {
+        "target_false_accepts_per_hour": calibration.get("target_false_accepts_per_hour"),
+        "selected_false_accepts_per_hour_limit": calibration.get("selected_false_accepts_per_hour_limit"),
+        "recall": selected_metrics.get("recall"),
+        "false_accepts_per_hour": selected_metrics.get("false_accepts_per_hour"),
+        "ambient_hours": selected_metrics.get("ambient_hours"),
+        "positive_dataset": evaluation.get("positive_dataset"),
+        "ambient_dataset": evaluation.get("ambient_dataset"),
+        "positive_tracks": evaluation.get("positive_tracks"),
+        "ambient_tracks": evaluation.get("ambient_tracks"),
+        "generated_at": calibration.get("generated_at"),
+    },
 }
 json_path.write_text(json.dumps(meta, indent=4) + "\n", encoding="utf-8")
 PY