Source code for

    Augmenter that apply mask normalization to audio.

import random

from import AudioAugmenter
import as nma
from nlpaug.util import Action, WarningMessage

[docs]class NormalizeAug(AudioAugmenter): """ :param str method: It supports 'minmax', 'max' and 'standard'. For 'minmax', data will be substracted by min value in data and dividing by range of max value and min value. For 'max', data will be divided by max value only. For 'standard', data will be substracted by mean value and dividing by value of standard deviation. If 'random' is used, method will be picked randomly in each augment. :param tuple zone: Assign a zone for augmentation. Default value is (0.2, 0.8) which means that no any augmentation will be applied in first 20% and last 20% of whole audio. :param float coverage: Portion of augmentation. Value should be between 0 and 1. If `0.1` is assigned, augment operation will be applied to target audio segment. For example, the audio duration is 60 seconds while zone and coverage are (0.2, 0.8) and 0.7 respectively. 25.2 seconds ((0.8-0.2)*0.7*60) audio will be augmented. :param str name: Name of this augmenter >>> import as naa >>> aug = naa.NormalizeAug() """ def __init__(self, method='max', zone=(0.2, 0.8), coverage=0.3, name='Normalize_Aug', verbose=0, stateless=True): super().__init__( action=Action.SUBSTITUTE, zone=zone, coverage=coverage, name=name, device='cpu', verbose=verbose, stateless=stateless) self.model = nma.Normalization() self.method = method self.validate() def random_method(self): return self.sample(self.model.get_support_methods(), 1)[0] def substitute(self, data): start_pos, end_pos = self.get_augment_range_by_coverage(data) method = self.random_method() if self.method == 'random' else self.method if not self.stateless: self.start_pos = start_pos self.end_pos = end_pos self.run_method = method return self.model.manipulate(data, method=method, start_pos=start_pos, end_pos=end_pos) def validate(self): if self.method not in ['random'] + self.model.get_support_methods(): raise ValueError('{} does not support yet. You may pick one of {}'.format( self.method, ['random'] + self.model.get_support_methods())) return True