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134 lines
4.9 KiB
134 lines
4.9 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from wekws(https://github.com/wenet-e2e/wekws)
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import os
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import paddle
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from tqdm import tqdm
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from yacs.config import CfgNode
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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def load_label_and_score(keyword_index: int,
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ds: paddle.io.Dataset,
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score_file: os.PathLike):
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score_table = {} # {utt_id: scores_over_frames}
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with open(score_file, 'r', encoding='utf8') as fin:
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for line in fin:
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arr = line.strip().split()
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key = arr[0]
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current_keyword = arr[1]
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str_list = arr[2:]
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if int(current_keyword) == keyword_index:
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scores = list(map(float, str_list))
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if key not in score_table:
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score_table.update({key: scores})
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keyword_table = {} # scores of keyword utt_id
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filler_table = {} # scores of non-keyword utt_id
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filler_duration = 0.0
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for key, index, duration in zip(ds.keys, ds.labels, ds.durations):
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assert key in score_table
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if index == keyword_index:
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keyword_table[key] = score_table[key]
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else:
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filler_table[key] = score_table[key]
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filler_duration += duration
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return keyword_table, filler_table, filler_duration
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if __name__ == '__main__':
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parser = default_argument_parser()
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parser.add_argument(
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'--keyword_index', type=int, default=0, help='keyword index')
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parser.add_argument(
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'--step',
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type=float,
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default=0.01,
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help='threshold step of trigger score')
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parser.add_argument(
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'--window_shift',
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type=int,
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default=50,
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help='window_shift is used to skip the frames after triggered')
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parser.add_argument(
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"--score_file",
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type=str,
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required=True,
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help='output file of trigger scores')
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parser.add_argument(
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'--stats_file',
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type=str,
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default='./stats.0.txt',
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help='output file of detection error tradeoff')
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args = parser.parse_args()
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# https://yaml.org/type/float.html
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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# Dataset
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ds_class = dynamic_import(config['dataset'])
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test_ds = ds_class(
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data_dir=config['data_dir'],
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mode='test',
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feat_type=config['feat_type'],
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sample_rate=config['sample_rate'],
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frame_shift=config['frame_shift'],
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frame_length=config['frame_length'],
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n_mels=config['n_mels'], )
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keyword_table, filler_table, filler_duration = load_label_and_score(
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args.keyword_index, test_ds, args.score_file)
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print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
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pbar = tqdm(total=int(1.0 / args.step))
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with open(args.stats_file, 'w', encoding='utf8') as fout:
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keyword_index = args.keyword_index
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threshold = 0.0
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while threshold <= 1.0:
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num_false_reject = 0
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# transverse the all keyword_table
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for key, score_list in keyword_table.items():
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# computer positive test sample, use the max score of list.
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score = max(score_list)
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if float(score) < threshold:
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num_false_reject += 1
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num_false_alarm = 0
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# transverse the all filler_table
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for key, score_list in filler_table.items():
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i = 0
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while i < len(score_list):
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if score_list[i] >= threshold:
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num_false_alarm += 1
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i += args.window_shift
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else:
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i += 1
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if len(keyword_table) != 0:
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false_reject_rate = num_false_reject / len(keyword_table)
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num_false_alarm = max(num_false_alarm, 1e-6)
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if filler_duration != 0:
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false_alarm_per_hour = num_false_alarm / \
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(filler_duration / 3600.0)
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fout.write('{:.6f} {:.6f} {:.6f}\n'.format(
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threshold, false_alarm_per_hour, false_reject_rate))
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threshold += args.step
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pbar.update(1)
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pbar.close()
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print('DET saved to: {}'.format(args.stats_file))
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