30 lines
905 B
Python
30 lines
905 B
Python
import numpy as np
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import torch
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import torch.nn as nn
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class Subspectrogram(object):
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def __init__(self, height, hop_size):
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self.height = height
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self.hop_size = hop_size
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def __call__(self, sample):
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if len(sample.shape) < 3:
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sample = sample.reshape(1, *sample.shape)
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# sample shape: 1 x num_mels x num_frames
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sub_specs = []
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neg = 0 if self.height == self.hop_size else self.hop_size
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for i in range(0, sample.shape[1]-neg, self.hop_size):
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sub_spec = sample[:, i:i+self.height:,]
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sub_specs.append(sub_spec)
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return np.concatenate(sub_specs)
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if __name__ == '__main__':
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import numpy as np
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sub_spec_tnfm = Subspectrogram(20, 20)
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X = np.random.rand(1, 80, 40)
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Y = sub_spec_tnfm(X)
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print(f'\tSub-Spectrogram transformation from shape {X.shape} to {Y.shape}')
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print('\tDone ...') |