ap = AudioProcessor(data_dirs=data_dirs,
wanted_words=classes,
silence_percentage=13.0,
unknown_percentage=60.0,
validation_percentage=10.0,
testing_percentage=10.0,
model_settings=model_settings,
output_representation=output_representation)
train_gen = data_gen(ap, sess, batch_size=batch_size, mode='training')
val_gen = data_gen(ap, sess, batch_size=batch_size, mode='validation')
model = speech_model(
'conv_1d_time_stacked',
model_settings['fingerprint_size'] if output_representation != 'raw'
else model_settings['desired_samples'],
# noqa
num_classes=model_settings['label_count'],
**model_settings)
# embed()
checkpoints_path = os.path.join('checkpoints', 'conv_bite_new')
if not os.path.exists(checkpoints_path):
os.makedirs(checkpoints_path)
callbacks = [
# ConfusionMatrixCallback(
# val_gen,
# ap.set_size('validation') // batch_size,
# wanted_words=prepare_words_list(get_classes(wanted_only=True)),
# all_words=prepare_words_list(classes),
ap = AudioProcessor(data_dirs=data_dirs,
wanted_words=classes,
silence_percentage=13.0,
unknown_percentage=60.0,
validation_percentage=10.0,
testing_percentage=0.0,
model_settings=model_settings,
output_representation=output_representation)
train_gen = data_gen(ap, sess, batch_size=batch_size, mode='training')
data = next(train_gen)
print(data[0].shape)
val_gen = data_gen(ap, sess, batch_size=batch_size, mode='validation')
model = speech_model(model_settings['desired_samples'],
num_classes=model_settings['label_count'],
**model_settings)
# embed()
checkpoint_path = 'ml/checkpoints/spectrogram_model/' + \
datetime.now().strftime("%Y%m%d-%H%M%S") + "/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
callbacks = [
ConfusionMatrixCallback(val_gen,
ap.set_size('validation') // batch_size,
wanted_words=prepare_words_list(
get_classes(wanted_only=True)),
all_words=prepare_words_list(classes),
label2int=ap.word_to_index),
tf.keras.callbacks.ReduceLROnPlateau(
monitor='val_categorical_accuracy',
ap = AudioProcessor(
data_dirs=data_dirs,
wanted_words=classes,
silence_percentage=13.0,
unknown_percentage=60.0,
validation_percentage=10.0,
testing_percentage=0.0,
model_settings=model_settings,
output_representation=output_representation)
train_gen = data_gen(ap, sess, batch_size=batch_size, mode='training')
val_gen = data_gen(ap, sess, batch_size=batch_size, mode='validation')
model = speech_model(
'conv_1d_time_stacked',
model_settings['fingerprint_size']
if output_representation != 'raw' else model_settings['desired_samples'],
# noqa
num_classes=model_settings['label_count'],
**model_settings)
# embed()
checkpoints_path = os.path.join('checkpoints', 'conv_1d_time_stacked_model')
if not os.path.exists(checkpoints_path):
os.makedirs(checkpoints_path)
callbacks = [
ConfusionMatrixCallback(
val_gen,
ap.set_size('validation') // batch_size,
wanted_words=prepare_words_list(get_classes(wanted_only=True)),
all_words=prepare_words_list(classes),
import tensorflow as tf
import argparse
from model import speech_model
parser = argparse.ArgumentParser(description='set input arguments')
parser.add_argument(
'-checkpoint_dir',
action='store',
type=str,
default=16000,
help='model weights checkpoint path')
args = parser.parse_args()
model = speech_model(16000,
num_classes=7)
# check the latest directory and pass it
checkpoint_path = args.checkpoint_dir + 'cp.ckpt'
model.load_weights(checkpoint_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tf_lite_model = converter.convert()
open("ml/models/tflite_model.tflite", 'wb').write(tf_lite_model)