def sample(config, params, load_path, part):
data = Data(**config['data'])
recognizer = create_model(config, data, load_path)
dataset = data.get_dataset(part, add_sources=('uttids',))
stream = data.get_stream(part, batches=False, shuffle=False,
add_sources=('uttids',))
it = stream.get_epoch_iterator(as_dict=True)
print_to = sys.stdout
for number, data in enumerate(it):
uttids = data.pop('uttids', None)
print("Utterance {} ({})".format(number, uttids),
file=print_to)
raw_groundtruth = data.pop('labels')
groundtruth_text = dataset.pretty_print(raw_groundtruth, data)
print("Groundtruth:", groundtruth_text, file=print_to)
sample = recognizer.sample(data)[:, 0]
recognized_text = dataset.pretty_print(sample, data)
print("Recognized:", recognized_text, file=print_to)
def sample(config, params, load_path, part):
data = Data(**config['data'])
recognizer = create_model(config, data, load_path)
dataset = data.get_dataset(part, add_sources=('uttids', ))
stream = data.get_stream(part,
batches=False,
shuffle=False,
add_sources=('uttids', ))
it = stream.get_epoch_iterator(as_dict=True)
print_to = sys.stdout
for number, data in enumerate(it):
uttids = data.pop('uttids', None)
print("Utterance {} ({})".format(number, uttids), file=print_to)
raw_groundtruth = data.pop('labels')
groundtruth_text = dataset.pretty_print(raw_groundtruth, data)
print("Groundtruth:", groundtruth_text, file=print_to)
sample = recognizer.sample(data)[:, 0]
recognized_text = dataset.pretty_print(sample, data)
print("Recognized:", recognized_text, file=print_to)
