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52 lines
1.7 KiB
52 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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import functools
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import _init_paths
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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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from utils.utility import add_arguments, print_arguments
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('num_samples', int, 2000, "# of samples to for statistics.")
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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add_arg('manifest_path', str,
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'data/librispeech/manifest.train',
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"Filepath of manifest to compute normalizer's mean and stddev.")
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add_arg('output_path', str,
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'data/librispeech/mean_std.npz',
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"Filepath of write mean and stddev to (.npz).")
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# yapf: disable
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args = parser.parse_args()
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def main():
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print_arguments(args)
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augmentation_pipeline = AugmentationPipeline('{}')
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audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
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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_path)
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if __name__ == '__main__':
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main()
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