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on
T4
Running
on
T4
| 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 |