def utt_scores(scores, scp, utt2label):
"""return predictions and labels per utterance
"""
utt2label = ako.read_key_label(utt2label)
key_list = ako.read_all_key(scp)
preds, labels = [], []
idx = 0
for key in key_list:
preds.append(scores[idx])
idx += 1
labels.append(utt2label[key])
return np.array(preds), np.array(labels)
def utt_scores(scores, scp, utt2label):
"""return predictions and labels per utterance
"""
utt2len = ako.read_key_len(scp)
utt2label = ako.read_key_label(utt2label)
key_list = ako.read_all_key(scp)
preds, labels = [], []
idx = 0
for key in key_list:
frames_per_utt = utt2len[key]
avg_scores = np.average(scores[idx:idx+frames_per_utt])
idx = idx + frames_per_utt
preds.append(avg_scores)
labels.append(utt2label[key])
return np.array(preds), np.array(labels)
def prediction(args, model, device, eval_loader, eval_scp, eval_utt2label,
opti_threshold):
logger.info("Starting evaluation")
utt2len = ako.read_key_len(eval_scp)
utt2label = ako.read_key_label(eval_utt2label)
key_list = ako.read_all_key(eval_scp)
eval_loss, eval_scores, eval_correct = compute_loss(
model, device, eval_loader)
eval_eer = utt_eer(eval_scores, utt2len, utt2label, key_list,
opti_threshold)
logger.info("===> Final predictions done. Here is a snippet")
logger.info(
'===> evalidation set: Average loss: {:.4f}\tEER: {:.4f}\n'.format(
eval_loss, eval_eer))
return eval_loss, eval_eer
def validation(args, model, device, train_loader, val_loader, val_scp,
val_utt2label):
logger.info("Starting Validation")
utt2len = ako.read_key_len(val_scp)
utt2label = ako.read_key_label(val_utt2label)
key_list = ako.read_all_key(val_scp)
train_loss, _, train_correct = compute_loss(model, device, train_loader)
val_loss, val_scores, val_correct = compute_loss(model, device, val_loader)
val_eer, threshold = best_eer(val_scores, utt2len, utt2label, key_list)
logger.info(
'\n===> Training set: Average loss: {:.4f}\tAccuracy: {}/{} ({:.0f}%)\n'
.format(train_loss, train_correct, len(train_loader.dataset),
100. * train_correct / len(train_loader.dataset)))
logger.info(
'===> Validation set: Average loss: {:.4f}\tEER: {:.4f}\tThreshold: {}\n'
.format(val_loss, val_eer, threshold))
return val_loss, val_eer, threshold
def compute_utt_eer(scores, scp, utt2label, threshold):
"""utterance-based eer
"""
utt2len = ako.read_key_len(scp)
utt2label = ako.read_key_label(utt2label)
key_list = ako.read_all_key(scp)
preds, labels = [], []
idx = 0
for key in key_list:
frames_per_utt = utt2len[key]
avg_scores = np.average(scores[idx:idx + frames_per_utt])
idx = idx + frames_per_utt
if avg_scores < threshold:
preds.append(0)
else:
preds.append(1)
labels.append(utt2label[key])
eer = compute_eer(labels, preds)
confuse_mat = compute_confuse(labels, preds)
return eer, confuse_mat