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import numpy as np
np.random.seed(1234)
import torch
from torchaudio.transforms import MelSpectrogram, Spectrogram, MelScale
class MelSpectrogramFixed(torch.nn.Module):
"""In order to remove padding of torchaudio package + add log scale."""
def __init__(self, **kwargs):
super(MelSpectrogramFixed, self).__init__()
self.torchaudio_backend = MelSpectrogram(**kwargs)
def forward(self, x):
outputs = torch.log(self.torchaudio_backend(x) + 0.001)
return outputs[..., :-1]
class SpectrogramFixed(torch.nn.Module):
"""In order to remove padding of torchaudio package + add log10 scale."""
def __init__(self, **kwargs):
super(SpectrogramFixed, self).__init__()
self.torchaudio_backend = Spectrogram(**kwargs)
def forward(self, x):
outputs = self.torchaudio_backend(x)
return outputs[..., :-1]
class MelfilterFixed(torch.nn.Module):
"""In order to remove padding of torchaudio package + add log10 scale."""
def __init__(self, **kwargs):
super(MelfilterFixed, self).__init__()
self.torchaudio_backend = MelScale(**kwargs)
def forward(self, x):
outputs = torch.log(self.torchaudio_backend(x) + 0.001)
return outputs |