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58 lines
1.7 KiB
58 lines
1.7 KiB
"""Compute mean and std for feature normalizer, and save to file."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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from data_utils.normalizer import FeatureNormalizer
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from data_utils.augmentor.augmentation import AugmentationPipeline
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from data_utils.featurizer.audio_featurizer import AudioFeaturizer
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parser = argparse.ArgumentParser(
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description='Computing mean and stddev for feature normalizer.')
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parser.add_argument(
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"--manifest_path",
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default='datasets/manifest.train',
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type=str,
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help="Manifest path for computing normalizer's mean and stddev."
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"(default: %(default)s)")
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parser.add_argument(
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"--num_samples",
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default=2000,
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type=int,
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help="Number of samples for computing mean and stddev. "
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"(default: %(default)s)")
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parser.add_argument(
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"--augmentation_config",
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default='{}',
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type=str,
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help="Augmentation configuration in json-format. "
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"(default: %(default)s)")
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parser.add_argument(
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"--output_file",
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default='mean_std.npz',
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type=str,
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help="Filepath to write mean and std to (.npz)."
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"(default: %(default)s)")
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args = parser.parse_args()
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def main():
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augmentation_pipeline = AugmentationPipeline(args.augmentation_config)
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audio_featurizer = AudioFeaturizer()
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def augment_and_featurize(audio_segment):
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augmentation_pipeline.transform_audio(audio_segment)
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return audio_featurizer.featurize(audio_segment)
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normalizer = FeatureNormalizer(
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mean_std_filepath=None,
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manifest_path=args.manifest_path,
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featurize_func=augment_and_featurize,
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num_samples=args.num_samples)
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normalizer.write_to_file(args.output_file)
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if __name__ == '__main__':
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main()
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