def on_epoch_end(self, epoch, logs={}):
"""
Method used at the end of each epoch
to calculate WER from validation data
and save model and logs at checkpoints
"""
wer = calc_wer(self.test_func, self.validation_gen)
print " - average WER: ", wer[1], "\n"
self.values.append([logs.get('loss'), logs.get('val_loss'), wer[1]])
if ((epoch + 1) % self.checkpoint) == 0:
if self.path_to_save:
model_to_save = Model(self.model.inputs, self.model.outputs)
model_to_save.save(self.path_to_save)
self.save_log()
def on_train_end(self, logs={}):
"""
Method used by fit_generator() at the end of training
to calculate the test WER and output prediction samples
"""
try:
test_wer = calc_wer(self.test_func, self.test_gen)
print "\n - Training ended, test wer: ", test_wer[1], " -"
except (Exception, StandardError) as e:
template = "An exception of type {0} occurred. Arguments:\n{1!r}"
message = template.format(type(e).__name__, e.args)
print message
# Print a sample of predictions, for visualisation
print "\nPrediction samples:\n"
predictions = predict_on_batch(self.validation_gen, self.test_func, 6)
for i in predictions:
print "Original: ", i[0]
print "Predicted: ", i[1], "\n"
self.save_log()
def main(args):
try:
if not args.model_load:
raise ValueError("Error within model arguments")
audio_dir = args.audio_dir
print("\nReading test data: ")
_, df = combine_all_wavs_and_trans_from_csvs(audio_dir)
batch_size = args.batch_size
batch_index = args.batch_index
mfcc_features = args.mfccs
n_mels = args.mels
# Sampling rate of data in khz (LibriSpeech is 16khz)
frequency = 16
# Training data_params:
model_load = args.model_load
load_multi = args.load_multi
# Sets the full dataset in audio_dir to be available through data_generator
# The data_generator doesn't actually load the audio files until they are requested through __get_item__()
epoch_length = 0
# Load trained model
# When loading custom objects, Keras needs to know where to find them.
# The CTC lambda is a dummy function
custom_objects = {
'clipped_relu': models.clipped_relu,
'<lambda>': lambda y_true, y_pred: y_pred
}
# When loading a parallel model saved *while* running on GPU, use load_multi
if load_multi:
model = models.load_model(model_load,
custom_objects=custom_objects)
model = model.layers[-2]
print("\nLoaded existing model: ", model_load)
# Load single GPU/CPU model or model saved *after* finished training
else:
model = models.load_model(model_load,
custom_objects=custom_objects)
print("\nLoaded existing model: ", model_load)
# Dummy loss-function to compile model, actual CTC loss-function defined as a lambda layer in model
loss = {'ctc': lambda y_true, y_pred: y_pred}
model.compile(loss=loss, optimizer='Adam')
feature_shape = model.input_shape[0][2]
# Model feature type
if not args.feature_type:
if feature_shape == 26:
feature_type = 'mfcc'
else:
feature_type = 'spectrogram'
else:
feature_type = args.feature_type
print("Feature type: ", feature_type)
# Data generation parameters
data_params = {
'feature_type': feature_type,
'batch_size': batch_size,
'frame_length': 20 * frequency,
'hop_length': 10 * frequency,
'mfcc_features': mfcc_features,
'n_mels': n_mels,
'epoch_length': epoch_length,
'shuffle': False
}
# Data generators for training, validation and testing data
data_generator = DataGenerator(df, **data_params)
# Print(model summary)
model.summary()
# Creates a test function that takes preprocessed sound input and outputs predictions
# Used to calculate WER while training the network
input_data = model.get_layer('the_input').input
y_pred = model.get_layer('ctc').input[0]
test_func = K.function([input_data], [y_pred])
if args.calc_wer:
print("\n - Calculation WER on ", audio_dir)
wer = calc_wer(test_func, data_generator)
print("Average WER: ", wer[1])
predictions = predict_on_batch(data_generator, test_func, batch_index)
print("\n - Predictions from batch index: ", batch_index, "\nFrom: ",
audio_dir, "\n")
for i in predictions:
print("Original: ", i[0])
print("Predicted: ", i[1], "\n")
except (Exception, BaseException, GeneratorExit, SystemExit) as e:
template = "An exception of type {0} occurred. Arguments:\n{1!r}"
message = template.format(type(e).__name__, e.args)
print("e.args: ", e.args)
print(message)
finally:
# Clear memory
K.clear_session()
def main(args):
try:
if not args.model_load:
raise ValueError()
audio_dir = args.audio_dir
print("\nReading test data: ")
_, df = combine_all_wavs_and_trans_from_csvs(audio_dir)
batch_size = args.batch_size
batch_index = args.batch_index
mfcc_features = args.mfccs
n_mels = args.mels
frequency = 22 # Sampling rate of data in khz (heroico is 22khz)
# Training data_params:
model_load = args.model_load
epoch_length = 0
# Load trained model
custom_objects = {
'clipped_relu': models.clipped_relu,
'<lambda>': lambda y_true, y_pred: y_pred
}
# Load single GPU/CPU model or model saved *after* finished training
model = mo.load_model(model_load, custom_objects=custom_objects)
print("\nLoaded existing model: ", model_load)
# Dummy loss-function to compile model, actual CTC loss-function defined as a lambda layer in model
loss = {'ctc': lambda y_true, y_pred: y_pred}
model.compile(loss=loss, optimizer='Adam')
feature_shape = model.input_shape[0][2]
# Model feature type
if not args.feature_type:
if feature_shape == 26:
feature_type = 'mfcc'
else:
feature_type = 'spectrogram'
else:
feature_type = args.feature_type
print("Feature type: ", feature_type)
# Data generation parameters
data_params = {
'feature_type': feature_type,
'batch_size': batch_size,
'frame_length': 20 * frequency,
'hop_length': 10 * frequency,
'mfcc_features': mfcc_features,
'n_mels': n_mels,
'epoch_length': epoch_length,
'shuffle': False
}
# Data generators for training, validation and testing data
data_generator = DataGenerator(df, **data_params)
# Print model summary
model.summary()
# Creates a test function that takes preprocessed sound input and outputs predictions
# Used to calculate WER while training the network
input_data = model.get_layer('the_input').input
y_pred = model.get_layer('ctc').input[0]
test_func = K.function([input_data], [y_pred])
if args.calc_wer:
print("\n - Calculation WER on ", audio_dir)
wer = calc_wer(test_func, data_generator)
print("Average WER: ", wer[1])
predictions = predict_samples(data_generator, test_func)
# predictions = predict_on_batch(data_generator, test_func, batch_index)
# print ("\n - Predictions from batch index: ", batch_index, "\nFrom: ", audio_dir, "\n")
for i in predictions:
print("Original: ", i[0])
print("Predicted: ", i[1], "\n")
except (Exception, GeneratorExit, SystemExit) as e:
raise e
# template = "An exception of type {0} occurred. Arguments:\n{1!r}"
# message = template.format(type(e).__name__, e.args)
# print ("e.args: ", e.args)
# print (message)
finally:
# Clear memory
K.clear_session()