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84 lines
2.9 KiB
84 lines
2.9 KiB
3 years ago
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# Copyright (c) 2021 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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import argparse
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import ast
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import os
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from typing import Dict
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import numpy as np
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from paddleaudio.utils import logger
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument('--tagging_file', type=str, required=True, help='')
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parser.add_argument('--top_k', type=int, default=10, help='Get top k predicted results of audioset labels.')
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parser.add_argument('--smooth', type=ast.literal_eval, default=True, help='Set "True" to apply posterior smoothing.')
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parser.add_argument('--smooth_size', type=int, default=5, help='Window size of posterior smoothing.')
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parser.add_argument('--label_file', type=str, default='./assets/audioset_labels.txt', help='File of audioset labels.')
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parser.add_argument('--output_dir', type=str, default='./output_dir', help='Directory to save tagging labels.')
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args = parser.parse_args()
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# yapf: enable
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def smooth(results: np.ndarray, win_size: int):
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"""
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Execute posterior smoothing in-place.
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"""
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for i in range(len(results) - 1, -1, -1):
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if i < win_size - 1:
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left = 0
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else:
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left = i + 1 - win_size
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results[i] = np.sum(results[left:i + 1], axis=0) / (i - left + 1)
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def generate_topk_label(k: int, label_map: Dict, result: np.ndarray):
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"""
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Return top k result.
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"""
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result = np.asarray(result)
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topk_idx = (-result).argsort()[:k]
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ret = ''
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for idx in topk_idx:
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label, score = label_map[idx], result[idx]
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ret += f'{label}: {score}\n'
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return ret
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if __name__ == "__main__":
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label_map = {}
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with open(args.label_file, 'r') as f:
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for i, l in enumerate(f.readlines()):
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label_map[i] = l.strip()
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results = np.load(args.tagging_file, allow_pickle=True)
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times, scores = results['time'], results['scores']
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if args.smooth:
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logger.info('Posterior smoothing...')
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smooth(scores, win_size=args.smooth_size)
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if not os.path.exists(args.output_dir):
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os.makedirs(args.output_dir)
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output_file = os.path.join(
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args.output_dir,
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os.path.basename(args.tagging_file).split('.')[0] + '.txt')
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with open(output_file, 'w') as f:
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for time, score in zip(times, scores):
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f.write(f'{time}\n')
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f.write(generate_topk_label(args.top_k, label_map, score) + '\n')
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logger.info(f'Saved tagging labels to {output_file}')
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