taco/microWakeWord-Trainer-Nvidia-Docker
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Release NVIDIA WakeWord Trainer v11

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MasterPhooey
2026-07-12 12:02:36 -05:00
parent a1b22200e0
commit 3d341d0617
6 changed files with 108 additions and 3110 deletions
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6
cli/calibrate_detector.py
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@@ -18,14 +18,14 @@ from microwakeword.data import FeatureHandler
from microwakeword.inference import Model
DEFAULT_WINDOW_SIZES = [3, 4, 5, 6, 7]
DEFAULT_TARGET_FAPH = float(os.environ.get("MWW_CALIBRATION_TARGET_FAPH", "1.0"))
DEFAULT_WINDOW_SIZES = [4, 5, 6, 7]
DEFAULT_TARGET_FAPH = float(os.environ.get("MWW_CALIBRATION_TARGET_FAPH", "0.25"))
DEFAULT_COOLDOWN_SLICES = int(os.environ.get("MWW_CALIBRATION_COOLDOWN_SLICES", "25"))
DEFAULT_POSITIVE_SKIP_SLICES = int(
os.environ.get("MWW_CALIBRATION_POSITIVE_SKIP_SLICES", "25")
)
DEFAULT_CUTOFF_STEP = float(os.environ.get("MWW_CALIBRATION_CUTOFF_STEP", "0.01"))
DEFAULT_CUTOFF_MIN = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MIN", "0.00"))
DEFAULT_CUTOFF_MIN = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MIN", "0.85"))
DEFAULT_CUTOFF_MAX = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MAX", "1.00"))

15
cli/wake_word_sample_trainer
View File

@@ -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",