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# 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 argparse
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import yaml
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--cfg_path", type=str, required=True)
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parser.add_argument("--keyword", type=str, required=True)
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args = parser.parse_args()
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# yapf: enable
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def load_stats_file(stats_file):
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values = []
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with open(stats_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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threshold, fa_per_hour, frr = arr
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values.append([float(fa_per_hour), float(frr) * 100])
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values.reverse()
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return np.array(values)
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def plot_det_curve(keywords, stats_file, figure_file, xlim, x_step, ylim,
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y_step):
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plt.figure(dpi=200)
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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plt.rcParams['font.size'] = 12
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for index, keyword in enumerate(keywords):
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values = load_stats_file(stats_file)
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plt.plot(values[:, 0], values[:, 1], label=keyword)
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plt.xlim([0, xlim])
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plt.ylim([0, ylim])
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plt.xticks(range(0, xlim + x_step, x_step))
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plt.yticks(range(0, ylim + y_step, y_step))
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plt.xlabel('False Alarm Per Hour')
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plt.ylabel('False Rejection Rate (\\%)')
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plt.grid(linestyle='--')
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plt.legend(loc='best', fontsize=16)
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plt.savefig(figure_file)
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if __name__ == '__main__':
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args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
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with open(args.cfg_path, 'r') as f:
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config = yaml.safe_load(f)
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scoring_conf = config['scoring']
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img_file = os.path.abspath(scoring_conf['img_file'])
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stats_file = os.path.abspath(scoring_conf['stats_file'])
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keywords = [args.keyword]
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plot_det_curve(keywords, stats_file, img_file, 10, 2, 10, 2)
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print('DET curve image saved to: {}'.format(img_file))
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