Automatic Calibration

cli/wake_word_sample_trainer

@@ -317,6 +317,7 @@ fi
 
 TRAINING_DONE="false"
 
+echo "🏋️ Starting model training and TFLite export (this is the longest stage)…"
 if run_attempt "Attempt 1/3: GPU training (default runtime profile)" ; then
   echo "✅ Training complete (GPU path)."
   TRAINING_DONE="true"
@@ -386,12 +387,24 @@ if [ "${TRAINING_DONE}" != "true" ]; then
 fi
 
 source_path="${WORK_DIR}/trained_models/wakeword/tflite_stream_state_internal_quant/stream_state_internal_quant.tflite"
+calibration_path="${WORK_DIR}/trained_models/wakeword/tflite_stream_state_internal_quant/detection_calibration.json"
 
 if [ ! -f "${source_path}" ] ; then
     echo "Output model not found! Training didn't complete successfully.  See ${TRAIN_LOG}"
     exit 1
 fi
 
+echo "🎯 Calibrating detector settings for on-device use…"
+if "${PYTHON_BIN:-python}" "${PROGDIR}/calibrate_detector.py" \
+    --training-config "${WORK_DIR}/trained_models/wakeword/training_config.yaml" \
+    --model "${source_path}" \
+    --output "${calibration_path}"; then
+    echo "✅ Detector calibration complete."
+else
+    echo "⚠️  Detector calibration failed; packaging with default detector settings."
+    rm -f "${calibration_path}" || :
+fi
+
 cp "${WORK_DIR}/trained_models/wakeword/model_summary.txt" "${OUTPUT_DIR}/logs/" || :
 cp -a "${WORK_DIR}/trained_models/wakeword/logs/train" "${OUTPUT_DIR}/logs/" || :
 cp -a "${WORK_DIR}/trained_models/wakeword/logs/validation" "${OUTPUT_DIR}/logs/" || :
@@ -404,24 +417,49 @@ tflite_path="${OUTPUT_DIR}/${tflite_filename}"
 cp "${source_path}" "${tflite_path}"
 
 json_path="${OUTPUT_DIR}/${wake_word_filename}.json"
-cat <<-EOF > "${json_path}"
-{
+export WAKE_WORD_TITLE LANGUAGE JSON_PATH="${json_path}" TFLITE_FILENAME="${tflite_filename}" CALIBRATION_PATH="${calibration_path}"
+echo "📦 Packaging final model artifacts…"
+"${PYTHON_BIN:-python}" - <<'PY'
+import json
+import os
+from pathlib import Path
+
+json_path = Path(os.environ["JSON_PATH"])
+calibration_path = Path(os.environ.get("CALIBRATION_PATH", ""))
+language = (os.environ.get("LANGUAGE", "en") or "en").strip().lower()
+probability_cutoff = 0.97
+sliding_window_size = 5
+
+if calibration_path.exists():
+    try:
+        calibration = json.loads(calibration_path.read_text(encoding="utf-8"))
+        probability_cutoff = float(calibration.get("probability_cutoff", probability_cutoff))
+        sliding_window_size = int(calibration.get("sliding_window_size", sliding_window_size))
+        print(
+            f"🎯 Using calibrated detector settings: "
+            f"cutoff={probability_cutoff:.2f}, window={sliding_window_size}"
+        )
+    except Exception as exc:
+        print(f"⚠️ Failed to read detector calibration ({exc}); using defaults.")
+
+meta = {
     "type": "micro",
-    "wake_word": "${WAKE_WORD_TITLE}",
+    "wake_word": os.environ["WAKE_WORD_TITLE"],
     "author": "Tater Totterson",
     "website": "https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git",
-    "model": "${tflite_filename}",
-    "trained_languages": ["en"],
+    "model": os.environ["TFLITE_FILENAME"],
+    "trained_languages": [language],
     "version": 2,
     "micro": {
-        "probability_cutoff": 0.97,
-        "sliding_window_size": 5,
+        "probability_cutoff": round(probability_cutoff, 2),
+        "sliding_window_size": sliding_window_size,
         "feature_step_size": 10,
         "tensor_arena_size": 30000,
-        "minimum_esphome_version": "2024.7.0"
-    }
+        "minimum_esphome_version": "2024.7.0",
+    },
 }
-EOF
+json_path.write_text(json.dumps(meta, indent=4) + "\n", encoding="utf-8")
+PY
 
 echo "Name:     ${WAKE_WORD_TITLE}"
 echo "Model:    ${tflite_path}"