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PaddleSpeech/tools/compute_mean_std.py

52 lines
1.7 KiB

"""Compute mean and std for feature normalizer, and save to file."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import functools
import _init_paths
from data_utils.normalizer import FeatureNormalizer
from data_utils.augmentor.augmentation import AugmentationPipeline
from data_utils.featurizer.audio_featurizer import AudioFeaturizer
from utils.utility import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('num_samples', int, 2000, "# of samples to for statistics.")
add_arg('specgram_type', str,
'linear',
"Audio feature type. Options: linear, mfcc.",
choices=['linear', 'mfcc'])
add_arg('manifest_path', str,
'datasets/manifest.train',
"Filepath of manifest to compute normalizer's mean and stddev.")
add_arg('output_path', str,
'mean_std.npz',
"Filepath of write mean and stddev to (.npz).")
# yapf: disable
args = parser.parse_args()
def main():
print_arguments(args)
augmentation_pipeline = AugmentationPipeline('{}')
audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
def augment_and_featurize(audio_segment):
augmentation_pipeline.transform_audio(audio_segment)
return audio_featurizer.featurize(audio_segment)
normalizer = FeatureNormalizer(
mean_std_filepath=None,
manifest_path=args.manifest_path,
featurize_func=augment_and_featurize,
num_samples=args.num_samples)
normalizer.write_to_file(args.output_path)
if __name__ == '__main__':
main()