def plot_probs(model, dataset, eval_batch_size, save_path=None):
"""Plot CTC posteriors.
Args:
model: model to evaluate
dataset: An instance of a `Dataset` class
eval_batch_size (int): the batch size when evaluating the model
save_path (string): path to save figures of CTC posteriors
"""
# Clean directory
if save_path is not None and isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
for batch, is_new_epoch in dataset:
# Get CTC probs
probs = model.posteriors(batch['xs'], batch['x_lens'], temperature=1)
# NOTE: probs: '[B, T, num_classes]'
# Visualize
for b in range(len(batch['xs'])):
plot_ctc_probs(
probs[b, : batch['x_lens'][b], :],
frame_num=batch['x_lens'][b],
num_stack=dataset.num_stack,
spectrogram=batch['xs'][b, :, :40],
save_path=join(save_path, batch['input_names'][b] + '.png'),
figsize=(14, 7))
if is_new_epoch:
break
def plot(model, dataset, eval_batch_size=None, save_path=None,
space_index=None):
"""
Args:
model: the model to evaluate
dataset: An instance of a `Dataset` class
eval_batch_size (int, optional): the batch size when evaluating the model
save_path (string): path to save figures of CTC posteriors
space_index (int, optional):
"""
# Set batch size in the evaluation
if eval_batch_size is not None:
dataset.batch_size = eval_batch_size
# Clean directory
if isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
vocab_file_path = '../metrics/vocab_files/' + \
dataset.label_type + '_' + dataset.data_size + '.txt'
if dataset.label_type == 'character':
map_fn = Idx2char(vocab_file_path)
elif dataset.label_type == 'character_capital_divide':
map_fn = Idx2char(vocab_file_path, capital_divide=True)
else:
map_fn = Idx2word(vocab_file_path)
for batch, is_new_epoch in dataset:
# Get CTC probs
probs = model.posteriors(batch['xs'], batch['x_lens'], temperature=1)
# NOTE: probs: '[B, T, num_classes]'
# Decode
best_hyps _ = model.decode(batch['xs'], batch['x_lens'], beam_width=1)
# Visualize
for b in range(len(batch['xs'])):
# Convert from list of index to string
str_pred = map_fn(best_hyps[b])
speaker, book = batch['input_names'][b].split('-')[:2]
plot_ctc_probs(
probs[b, :batch['x_lens'][b], :],
frame_num=batch['x_lens'][b],
num_stack=dataset.num_stack,
space_index=space_index,
str_pred=str_pred,
save_path=mkdir_join(save_path, speaker, book, batch['input_names'][b] + '.png'))
if is_new_epoch:
break
def main():
args = parser.parse_args()
# Load a config file (.yml)
params = load_config(join(args.model_path, 'config.yml'), is_eval=True)
# Load dataset
dataset = Dataset(data_save_path=args.data_save_path,
backend=params['backend'],
input_freq=params['input_freq'],
use_delta=params['use_delta'],
use_double_delta=params['use_double_delta'],
data_type='test',
label_type=params['label_type'],
batch_size=args.eval_batch_size,
splice=params['splice'],
num_stack=params['num_stack'],
num_skip=params['num_skip'],
sort_utt=True,
reverse=True,
tool=params['tool'])
params['num_classes'] = dataset.num_classes
# Load model
model = load(model_type=params['model_type'],
params=params,
backend=params['backend'])
# Restore the saved parameters
model.load_checkpoint(save_path=args.model_path, epoch=args.epoch)
# GPU setting
model.set_cuda(deterministic=False, benchmark=True)
save_path = mkdir_join(args.model_path, 'ctc_probs')
######################################################################
# Clean directory
if save_path is not None and isdir(save_path):
shutil.rmtree(save_path)
mkdir(save_path)
for batch, is_new_epoch in dataset:
# Get CTC probs
probs, x_lens, _ = model.posteriors(batch['xs'],
batch['x_lens'],
temperature=1)
# NOTE: probs: '[B, T, num_classes]'
# Visualize
for b in range(len(batch['xs'])):
plot_ctc_probs(probs[b, :x_lens[b], :],
frame_num=x_lens[b],
num_stack=dataset.num_stack,
spectrogram=batch['xs'][b, :, :40],
save_path=join(save_path,
batch['input_names'][b] + '.png'),
figsize=(14, 7))
if is_new_epoch:
break