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61 lines
2.2 KiB
61 lines
2.2 KiB
# 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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"""Compute mean and std for feature normalizer, and save to file."""
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import argparse
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import functools
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from deepspeech.frontend.normalizer import FeatureNormalizer
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from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
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from deepspeech.frontend.featurizer.audio_featurizer import AudioFeaturizer
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from deepspeech.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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