def test_multiple_mlf_files():
os.chdir(data_path)
feature_dim = 33
num_classes = 132
context = 2
test_mlf_path = "../../../../Tests/EndToEndTests/Speech/Data/glob_00001.mlf"
features_file = "glob_0000.scp"
label_files = ["glob_0000.mlf", test_mlf_path]
label_mapping_file = "state.list"
fd = HTKFeatureDeserializer(
StreamDefs(amazing_features=StreamDef(
shape=feature_dim, context=(context, context), scp=features_file)))
ld = HTKMLFDeserializer(
label_mapping_file,
StreamDefs(
awesome_labels=StreamDef(shape=num_classes, mlf=label_files)))
# Make sure we can read at least one minibatch.
mbsource = MinibatchSource([fd, ld])
mbsource.next_minibatch(1)
os.chdir(abs_path)
def test_htk_deserializers():
mbsize = 640
epoch_size = 1000 * mbsize
lr = [0.001]
feature_dim = 33
num_classes = 132
context = 2
os.chdir(data_path)
features_file = "glob_0000.scp"
labels_file = "glob_0000.mlf"
label_mapping_file = "state.list"
fd = HTKFeatureDeserializer(
StreamDefs(amazing_features=StreamDef(
shape=feature_dim, context=(context, context), scp=features_file)))
ld = HTKMLFDeserializer(
label_mapping_file,
StreamDefs(
awesome_labels=StreamDef(shape=num_classes, mlf=labels_file)))
reader = MinibatchSource([fd, ld])
features = C.input_variable(((2 * context + 1) * feature_dim))
labels = C.input_variable((num_classes))
model = Sequential(
[For(range(3), lambda: Recurrence(LSTM(256))),
Dense(num_classes)])
z = model(features)
ce = C.cross_entropy_with_softmax(z, labels)
errs = C.classification_error(z, labels)
learner = C.adam_sgd(z.parameters,
lr=C.learning_rate_schedule(lr, C.UnitType.sample,
epoch_size),
momentum=C.momentum_as_time_constant_schedule(1000),
low_memory=True,
gradient_clipping_threshold_per_sample=15,
gradient_clipping_with_truncation=True)
trainer = C.Trainer(z, (ce, errs), learner)
input_map = {
features: reader.streams.amazing_features,
labels: reader.streams.awesome_labels
}
pp = C.ProgressPrinter(freq=0)
# just run and verify it doesn't crash
for i in range(3):
mb_data = reader.next_minibatch(mbsize, input_map=input_map)
trainer.train_minibatch(mb_data)
pp.update_with_trainer(trainer, with_metric=True)
assert True
os.chdir(abs_path)
def create_mb_source(features_file, labels_file, label_mapping_filem, total_number_of_samples):
for file_name in [features_file, labels_file, label_mapping_file]:
if not os.path.exists(file_name):
raise RuntimeError("File '%s' does not exist. Please check that datadir argument is set correctly." % (file_name))
fd = HTKFeatureDeserializer(StreamDefs(
amazing_features = StreamDef(shape=feature_dim, context=(context,context), scp=features_file)))
ld = HTKMLFDeserializer(label_mapping_file, StreamDefs(
awesome_labels = StreamDef(shape=num_classes, mlf=labels_file)))
# Enabling BPTT with truncated_length > 0
return MinibatchSource([fd,ld], truncation_length=250, epoch_size=total_number_of_samples)