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augmentation.py
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augmentation.py
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import string
import torch
from torch import Tensor
from sparse_img_wrap import sparse_image_warp
import random
class ConcatFeature(torch.nn.Module):
def __init__(self,merge_size=3):
super(ConcatFeature, self).__init__()
self.merge_size = merge_size
def forward(self, waveform:Tensor) -> Tensor:
feat, waveform_len = waveform.shape
waveform = waveform.T
if waveform_len % self.merge_size != 0:
pad_wave = torch.zeros((self.merge_size - (waveform_len % self.merge_size), feat))
waveform = torch.cat([waveform, pad_wave], dim=0)
return waveform.reshape(-1, feat*self.merge_size).T
class TimeWrap(torch.nn.Module):
def __init__(self, W=5):
super(TimeWrap, self).__init__()
self.W = W
def forward(self, waveform:Tensor) -> Tensor:
waveform = waveform.T
feat, waveform_len = waveform.shape
device = waveform.device
waveform = waveform.unsqueeze(0)
y = feat//2
horizontal_line_at_ctr = waveform[0][y]
assert len(horizontal_line_at_ctr) == waveform_len
point_to_warp = horizontal_line_at_ctr[random.randrange(self.W, waveform_len - self.W)]
assert isinstance(point_to_warp, torch.Tensor)
# Uniform distribution from (0,W) with chance to be up to W negative
dist_to_warp = random.randrange(-self.W, self.W)
src_pts, dest_pts = (torch.tensor([[[y, point_to_warp]]], device=device),
torch.tensor([[[y, point_to_warp + dist_to_warp]]], device=device))
warped_waveform, dense_flows = sparse_image_warp(waveform, src_pts, dest_pts)
warped_waveform = warped_waveform.squeeze(3).T.squeeze(-1)
return warped_waveform
class TimeMask(torch.nn.Module):
def __init__(self, T=40, num_masks=1, replace_with_zero=False):
super(TimeMask, self).__init__()
'''
uniform distribution from 0 to the time mask parameter T
'''
self.T = T
self.num_masks = num_masks
self.replace_with_zero = replace_with_zero
def forward(self, waveform):
cloned = waveform.clone()
len_spectro = cloned.shape[1]
for i in range(0, self.num_masks):
t = random.randrange(0, self.T)
t_zero = random.randrange(0, len_spectro - t)
# avoids randrange error if values are equal and range is empty
if (t_zero == t_zero + t): return cloned
mask_end = random.randrange(t_zero, t_zero + t)
if (self.replace_with_zero): cloned[:,t_zero:mask_end] = 0
else: cloned[:,t_zero:mask_end] = cloned.mean()
return cloned
class FreqMask(torch.nn.Module):
def __init__(self, F=40, num_masks=1, replace_with_zero=False):
super(FreqMask, self).__init__()
'''
F : frequency mask parameter F,
'''
self.F = F
self.num_masks = num_masks
self.replace_with_zero = replace_with_zero
def forward(self, waveform):
cloned = waveform.clone()
num_mel_channels = cloned.shape[0]
for i in range(0, self.num_masks):
f = random.randrange(0, self.F)
f_zero = random.randrange(0, num_mel_channels - f)
# avoids randrange error if values are equal and range is empty
if (f_zero == f_zero + f): return cloned
mask_end = random.randrange(f_zero, f_zero + f)
if (self.replace_with_zero): cloned[f_zero:mask_end, :] = 0
else: cloned[f_zero:mask_end, :] = cloned.mean()
return cloned
if __name__ == "__main__":
import torchaudio
transforms_piplines = [
torchaudio.transforms.MelSpectrogram(
# n_mfcc=args.audio_feat,
n_fft=512, n_mels=40,
# melkwargs={'n_fft':1024, 'win_length': 1024}
),
TimeWrap(W=5),
TimeMask(T=100),
FreqMask(F=40),
ConcatFeature()
]
transforms = torch.nn.Sequential(*transforms_piplines)
data, sr = torchaudio.load_wav('../common_voice/clips/common_voice_en_19664034.wav')
print(data[0].shape)
data = transforms(data[0])
print(data.shape)