PaddleSpeech/paddlespeech/kws/exps/mdtc/compute_det.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from wekws(https://github.com/wenet-e2e/wekws)
import argparse
import os

import paddle
import yaml
from tqdm import tqdm

from paddlespeech.s2t.utils.dynamic_import import dynamic_import

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--cfg_path", type=str, required=True)
parser.add_argument('--keyword_index', type=int, default=0, help='keyword index')
parser.add_argument('--step', type=float, default=0.01, help='threshold step of trigger score')
parser.add_argument('--window_shift', type=int, default=50, help='window_shift is used to skip the frames after triggered')
args = parser.parse_args()
# yapf: enable


def load_label_and_score(keyword_index: int,
                         ds: paddle.io.Dataset,
                         score_file: os.PathLike):
    score_table = {}  # {utt_id: scores_over_frames}
    with open(score_file, 'r', encoding='utf8') as fin:
        for line in fin:
            arr = line.strip().split()
            key = arr[0]
            current_keyword = arr[1]
            str_list = arr[2:]
            if int(current_keyword) == keyword_index:
                scores = list(map(float, str_list))
                if key not in score_table:
                    score_table.update({key: scores})
    keyword_table = {}  # scores of keyword utt_id
    filler_table = {}  # scores of non-keyword utt_id
    filler_duration = 0.0

    for key, index, duration in zip(ds.keys, ds.labels, ds.durations):
        assert key in score_table
        if index == keyword_index:
            keyword_table[key] = score_table[key]
        else:
            filler_table[key] = score_table[key]
            filler_duration += duration

    return keyword_table, filler_table, filler_duration


if __name__ == '__main__':
    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
    with open(args.cfg_path, 'r') as f:
        config = yaml.safe_load(f)

    data_conf = config['data']
    feat_conf = config['feature']
    scoring_conf = config['scoring']

    # Dataset
    ds_class = dynamic_import(data_conf['dataset'])
    test_ds = ds_class(data_dir=data_conf['data_dir'], mode='test', **feat_conf)

    score_file = os.path.abspath(scoring_conf['score_file'])
    stats_file = os.path.abspath(scoring_conf['stats_file'])

    keyword_table, filler_table, filler_duration = load_label_and_score(
        args.keyword, test_ds, score_file)
    print('Filler total duration Hours: {}'.format(filler_duration / 3600.0))
    pbar = tqdm(total=int(1.0 / args.step))
    with open(stats_file, 'w', encoding='utf8') as fout:
        keyword_index = args.keyword_index
        threshold = 0.0
        while threshold <= 1.0:
            num_false_reject = 0
            # transverse the all keyword_table
            for key, score_list in keyword_table.items():
                # computer positive test sample, use the max score of list.
                score = max(score_list)
                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():
                i = 0
                while i < len(score_list):
                    if score_list[i] >= threshold:
                        num_false_alarm += 1
                        i += args.window_shift
                    else:
                        i += 1
            if len(keyword_table) != 0:
                false_reject_rate = num_false_reject / len(keyword_table)
            num_false_alarm = max(num_false_alarm, 1e-6)
            if filler_duration != 0:
                false_alarm_per_hour = num_false_alarm / \
                    (filler_duration / 3600.0)
            fout.write('{:.6f} {:.6f} {:.6f}\n'.format(
                threshold, false_alarm_per_hour, false_reject_rate))
            threshold += args.step
            pbar.update(1)

    pbar.close()
    print('DET saved to: {}'.format(stats_file))