Source code for nlpaug.augmenter.spectrogram.time_masking

import numpy as np

from nlpaug.augmenter.spectrogram import SpectrogramAugmenter
from nlpaug.util import Action
import nlpaug.model.spectrogram as nms

[docs]class TimeMaskingAug(SpectrogramAugmenter): """ Augmenter that mask spectrogram based on frequency by random values. :param tuple zone: Default value is (0.2, 0.8). Assign a zone for augmentation. By default, no any augmentation will be applied in first 20% and last 20% of whole audio. :param float coverage: Default value is 1 and value should be between 0 and 1. Portion of augmentation. If `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. 42 seconds ((0.8-0.2)*0.7*60) audio will be chosen for augmentation. :param str name: Name of this augmenter >>> import nlpaug.augmenter.spectogram as nas >>> aug = nas.TimeMaskingAug() """ def __init__(self, name='TimeMasking_Aug', zone=(0.2, 0.8), coverage=1., verbose=0, silence=False, stateless=True): super().__init__(action=Action.SUBSTITUTE, zone=zone, coverage=coverage, factor=(1, 1), verbose=verbose, name=name, silence=silence, stateless=stateless) self.model = nms.TimeMasking()
[docs] def substitute(self, data): """ From:, Time masking is applied so that t consecutive time steps [t0, t0 + t) are masked, where t is first chosen from a uniform distribution from 0 to the time mask parameter T, and t0 is chosen from [0, tau - t). """ tau = data.shape[1] t0, time_end = self.get_augment_range_by_coverage(data) t = self.get_random_factor(high=time_end, dtype='int') if not self.stateless: self.tau, self.t, self.t0 = tau, t, t0 return self.model.manipulate(data, t=t, t0=t0)