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Fix train.py CLI defaults, optional Piper, and validation batch size #351

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Fix train.py CLI defaults, optional Piper, and validation batch size #351

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17 changes: 9 additions & 8 deletions openwakeword/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -606,44 +606,45 @@ def convert_onnx_to_tflite(onnx_model_path, output_path):
"--generate_clips",
help="Execute the synthetic data generation process",
action="store_true",
default="False",
default=False,
required=False
)
parser.add_argument(
"--augment_clips",
help="Execute the synthetic data augmentation process",
action="store_true",
default="False",
default=False,
required=False
)
parser.add_argument(
"--overwrite",
help="Overwrite existing openwakeword features when the --augment_clips flag is used",
action="store_true",
default="False",
default=False,
required=False
)
parser.add_argument(
"--train_model",
help="Execute the model training process",
action="store_true",
default="False",
default=False,
required=False
)
parser.add_argument(
"--convert_to_tflite",
help="Convert the trained ONNX model to TFLite format",
action="store_true",
default="False",
default=False,
required=False
)

args = parser.parse_args()
config = yaml.load(open(args.training_config, 'r').read(), yaml.Loader)

# imports Piper for synthetic sample generation
sys.path.insert(0, os.path.abspath(config["piper_sample_generator_path"]))
from generate_samples import generate_samples
if config.get("piper_sample_generator_path"):
sys.path.insert(0, os.path.abspath(config["piper_sample_generator_path"]))
from generate_samples import generate_samples

# Define output locations
config["output_dir"] = os.path.abspath(config["output_dir"])
Expand Down Expand Up @@ -876,7 +877,7 @@ def __iter__(self):
X_val_fp_labels = np.zeros(X_val_fp.shape[0]).astype(np.float32)
X_val_fp = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(torch.from_numpy(X_val_fp), torch.from_numpy(X_val_fp_labels)),
batch_size=len(X_val_fp_labels)
batch_size=min(256, len(X_val_fp_labels))
)

X_val_pos = np.load(os.path.join(feature_save_dir, "positive_features_test.npy"))
Expand Down