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This commit is contained in:
blessyyyu 2022-03-23 17:53:48 +08:00
parent 1ebc3bff88
commit b2130d7458
3 changed files with 39 additions and 60 deletions
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10
examples/hi_xiaowen/s0/run.sh
View File

@ -21,7 +21,7 @@ score_checkpoint=$dir/avg_${num_average}.pt
download_dir=./data/local # your data dir
. tools/parse_options.sh || exit 1;
window_shift=50
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "Download and extracte all datasets"
@ -100,7 +100,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
--test_data data/test/data.list \
--batch_size 256 \
--checkpoint $score_checkpoint \
--score_file_dir $result_dir \
--score_file $result_dir/score_longwav.txt \
--num_keywords $num_keywords \
--num_workers 8
@ -108,7 +108,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
python kws/bin/compute_det_longwav.py \
--keyword $keyword \
--test_data data/test/data.list \
--score_file $result_dir/score_longwav.${keyword}.txt \
--window_shift $window_shift \
--score_file $result_dir/score_longwav.txt \
--stats_file $result_dir/stats_longwav.${keyword}.txt
done
fi
@ -158,5 +159,4 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
--config $dir/config.yaml \
--jit_model $dir/$jit_model \
--onnx_model $dir/$onnx_model
fi
fi

31
kws/bin/compute_det_longwav.py
View File

@ -15,24 +15,20 @@
import argparse
import json
from collections import defaultdict
def load_label_and_score(keyword, label_file, score_file):
# utt_id : score list
score_table = {}
score_table = defaultdict(list)
with open(score_file, 'r', encoding='utf8') as fin:
for line in fin:
arr = line.strip().split()
# key = utt_id
key = arr[0]
# scores is a list
str_list = arr[1:]
scores = list(map(float, str_list))
score_table[key] = scores
score_table[key].append(scores)
keyword_table = {}
filler_table = {}
filler_duration = 0.0
# label_file = data.list
with open(label_file, 'r', encoding='utf8') as fin:
for line in fin:
obj = json.loads(line.strip())
@ -40,49 +36,48 @@ def load_label_and_score(keyword, label_file, score_file):
assert 'txt' in obj
assert 'duration' in obj
key = obj['key']
# txt is label
index = obj['txt']
duration = obj['duration']
assert key in score_table
# txt == keyword , correct
if index == keyword:
keyword_table[key] = score_table[key]
else:
# false
filler_table[key] = score_table[key]
filler_duration += duration
return keyword_table, filler_table, filler_duration
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='compute det curve')
parser.add_argument('--test_data', required=True, help='label file')
parser.add_argument('--keyword', type=int, default=0, help='score file')
parser.add_argument('--score_file', required=True, help='score file')
parser.add_argument('--step', type=float, default=0.01, help='score file')
parser.add_argument('--window_shift', type=int, default=50,
help='window_shift is used to skip the frames after triggered')
parser.add_argument('--stats_file',
required=True,
help='false reject/alarm stats file')
args = parser.parse_args()
# 'window_shift' is used to skip the frames after triggered
window_shift = 50
window_shift = args.window_shift
keyword_table, filler_table, filler_duration = load_label_and_score(
args.keyword, args.test_data, args.score_file)
print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
with open(args.stats_file, 'w', encoding='utf8') as fout:
keyword_index = int(args.stats_file.split('/')[-1].split('.')[1])
threshold = 0.0
while threshold <= 1.0:
num_false_reject = 0
# transverse the all keyword_table
for key, score_list in keyword_table.items():
for key, scores_list in keyword_table.items():
# computer positive test sample, use the max score of list.
score = max(score_list)
score = max(scores_list[keyword_index])
if float(score) < threshold:
num_false_reject += 1
num_false_alarm = 0
# transverse the all filler_table
for key, score_list in filler_table.items():
for key, scores_list in filler_table.items():
i = 0
score_list = scores_list[keyword_index]
while i < len(score_list):
if score_list[i] >= threshold:
num_false_alarm += 1
@ -97,4 +92,4 @@ if __name__ == '__main__':
fout.write('{:.6f} {:.6f} {:.6f}\n'.format(threshold,
false_alarm_per_hour,
false_reject_rate))
threshold += args.step
threshold += args.step

58
kws/bin/score_longwav.py
View File

@ -55,12 +55,12 @@ def get_args():
default=100,
type=int,
help='prefetch number')
parser.add_argument('--score_file_dir',
parser.add_argument('--score_file',
required=True,
help='output score file')
parser.add_argument('--num_keywords',
required=True,
help='the number of keywords')
help='the number of keywords')
parser.add_argument('--jit_model',
action='store_true',
default=False,
@ -106,42 +106,26 @@ def main():
device = torch.device('cuda' if use_cuda else 'cpu')
model = model.to(device)
model.eval()
# add to write different keyword score file
num_keywords = int(args.num_keywords)
score_file_list = []
dir_abs_path = os.path.abspath(args.score_file_dir)
for i in range(num_keywords):
temp_list = ['score_longwav', 'txt']
temp_list.insert(1, str(i))
suffix = '.'.join(temp_list)
# print('suffix = ', suffix)
score_abs_path = os.path.join(dir_abs_path, suffix)
score_file_list.append(score_abs_path)
for abs_path in score_file_list:
with torch.no_grad(), open(abs_path, 'w', encoding='utf8') as fout:
keyword_label = abs_path.split('/')[-1].split('.')[1]
# print('keyword_label = ', keyword_label)
for batch_idx, batch in enumerate(test_data_loader):
keys, feats, target, lengths = batch
feats = feats.to(device)
lengths = lengths.to(device)
# mask = padding_mask(lengths).unsqueeze(2)
logits = model(feats)
# mask对应的true的部分用0填充
# Getting every frames desn't need to mask
# logits = logits.masked_fill(mask, 0.0)
logits = logits.cpu()
for i in range(len(keys)):
key = keys[i]
score = logits[i][:lengths[i]]
score = score[:, int(keyword_label)]
# keep 2 significant digits
score = ' '.join([str("%.2g" % x) for x in score.tolist()])
fout.write('{} {}\n'.format(key, score))
if batch_idx % 10 == 0:
print('Progress batch {}'.format(batch_idx))
sys.stdout.flush()
score_abs_path = os.path.abspath(args.score_file)
num_keywords = int(args.num_keywords)
with torch.no_grad(), open(score_abs_path, 'w', encoding='utf8') as fout:
for batch_idx, batch in enumerate(test_data_loader):
keys, feats, target, lengths = batch
feats = feats.to(device)
lengths = lengths.to(device)
logits = model(feats)
logits = logits.cpu()
for i in range(len(keys)):
key = keys[i]
score = logits[i][:lengths[i]]
for keyword_i in range(num_keywords):
keyword_scores = score[:, keyword_i]
score_frames = ' '.join(['{:.3g}'.format(x) for x in keyword_scores.tolist()])
fout.write('{} {}\n'.format(key, score_frames))
if batch_idx % 10 == 0:
print('Progress batch {}'.format(batch_idx))
sys.stdout.flush()
if __name__ == '__main__':