Release NVIDIA WakeWord Trainer v11

cli/wake_word_sample_trainer

@@ -302,11 +302,11 @@ TRAIN_ARGS=(
   --test_tflite_streaming_quantized 1
   --use_weights best_weights
   mixednet
-  --pointwise_filters "64,64,64,64"
+  --pointwise_filters "128,128,128,128"
   --repeat_in_block "1,1,1,1"
   --mixconv_kernel_sizes "[5], [7,11], [9,15], [23]"
   --residual_connection "0,0,0,0"
-  --first_conv_filters 32
+  --first_conv_filters 64
   --first_conv_kernel_size 5
   --stride 2
 )
@@ -386,6 +386,7 @@ fi
 TRAINING_DONE="false"
 
 echo "🏋️ Starting model training and TFLite export (this is the longest stage)…"
+echo "🧠 Model quality: high_accuracy_plus"
 if run_attempt "Attempt 1/3: GPU training (default runtime profile)" ; then
   echo "✅ Training complete (GPU path)."
   TRAINING_DONE="true"
@@ -495,8 +496,9 @@ from pathlib import Path
 json_path = Path(os.environ["JSON_PATH"])
 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
+probability_cutoff = 0.85
+sliding_window_size = 4
+strict_min_close_miss_threshold = 0.68
 calibration = {}
 
 if calibration_path.exists():
@@ -515,7 +517,10 @@ 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)))
+close_miss_threshold = max(
+    0.01,
+    min(0.99, round(max(strict_min_close_miss_threshold, probability_cutoff - 0.17), 3)),
+)
 
 meta = {
     "type": "micro",