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107 lines
3.9 KiB
107 lines
3.9 KiB
# Copyright (c) 2023 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 paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline
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from paddlespeech.s2t.frontend.featurizer.audio_featurizer import AudioFeaturizer
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from paddlespeech.s2t.frontend.normalizer import FeatureNormalizer
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from paddlespeech.utils.argparse import add_arguments
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from paddlespeech.utils.argparse import print_arguments
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def compute_cmvn(manifest_path="data/librispeech/manifest.train",
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output_path="data/librispeech/mean_std.npz",
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num_samples=2000,
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num_workers=0,
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spectrum_type="linear",
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feat_dim=13,
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delta_delta=False,
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stride_ms=10,
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window_ms=20,
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sample_rate=16000,
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use_dB_normalization=True,
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target_dB=-20):
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augmentation_pipeline = AugmentationPipeline('{}')
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audio_featurizer = AudioFeaturizer(
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spectrum_type=spectrum_type,
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feat_dim=feat_dim,
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delta_delta=delta_delta,
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stride_ms=float(stride_ms),
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window_ms=float(window_ms),
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n_fft=None,
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max_freq=None,
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target_sample_rate=sample_rate,
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use_dB_normalization=use_dB_normalization,
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target_dB=target_dB,
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dither=0.0)
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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=manifest_path,
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featurize_func=augment_and_featurize,
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num_samples=num_samples,
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num_workers=num_workers)
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normalizer.write_to_file(output_path)
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def define_argparse():
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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('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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add_arg('num_samples', int, 2000, "# of samples to for statistics.")
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add_arg('num_workers',
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default=0,
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type=int,
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help='num of subprocess workers for processing')
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add_arg('spectrum_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc, fbank.",
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choices=['linear', 'mfcc', 'fbank'])
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add_arg('feat_dim', int, 13, "Audio feature dim.")
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add_arg('delta_delta', bool, False, "Audio feature with delta delta.")
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add_arg('stride_ms', int, 10, "stride length in ms.")
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add_arg('window_ms', int, 20, "stride length in ms.")
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add_arg('sample_rate', int, 16000, "target sample rate.")
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add_arg('use_dB_normalization', bool, True, "do dB normalization.")
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add_arg('target_dB', int, -20, "target dB.")
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# yapf: disable
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args = parser.parse_args()
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return args
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def main():
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args = define_argparse()
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print_arguments(args, globals())
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compute_cmvn(**vars(args))
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if __name__ == '__main__':
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main()
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