"""
Augmenter that apply vocal tract length perturbation (VTLP) operation to audio.
"""
from nlpaug.augmenter.audio import AudioAugmenter
import nlpaug.model.audio as nma
from nlpaug.util import Action
[docs]class VtlpAug(AudioAugmenter):
# https://pdfs.semanticscholar.org/3de0/616eb3cd4554fdf9fd65c9c82f2605a17413.pdf
"""
: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 `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
augmented.
:param tuple factor: Input data vocal will be increased (decreased). Augmented value will be picked
within the range of this tuple value. Vocal will be reduced if value is between 0 and 1.
:param int fhi: Boundary frequency. Default value is 4800.
:param str name: Name of this augmenter
>>> import nlpaug.augmenter.audio as naa
>>> aug = naa.VtlpAug()
"""
def __init__(self, sampling_rate, zone=(0.2, 0.8), coverage=0.1, fhi=4800, factor=(0.9, 1.1),
name='Vtlp_Aug', verbose=0, stateless=True):
super().__init__(
action=Action.SUBSTITUTE, zone=zone, coverage=coverage, factor=factor, name=name,
device='cpu', verbose=verbose, stateless=stateless)
self.sampling_rate = sampling_rate
self.fhi = fhi
self.model = nma.Vtlp()
def substitute(self, data):
if self.duration is None:
start_pos, end_pos = self.get_augment_range_by_coverage(data)
else:
start_pos, end_pos = self.get_augment_range_by_duration(data)
warp_factor = self.get_random_factor()
if not self.stateless:
self.start_pos, self.end_pos, self.aug_factor = start_pos, end_pos, warp_factor
return self.model.manipulate(data, start_pos=start_pos, end_pos=end_pos, sampling_rate=self.sampling_rate,
warp_factor=warp_factor)