File size: 1,673 Bytes
c22b544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import torch
import torch.nn as nn
class FeatureExtractor(nn.Module):
def __init__(self, input_ch=2, n_fft=1024, hop_length=512):
super(FeatureExtractor, self).__init__()
self.input_ch = input_ch
self.n_fft = n_fft
self.hop_length = hop_length
self.register_buffer('window', torch.hann_window(n_fft))
def forward(self, x):
"""
x: (batch, channels=2, time)
returns: (batch, channels=2, time_frames, freq_bins)
"""
batch_size, channels, time_len = x.shape
assert channels == self.input_ch, "Input must have 2 channels!"
# (batch, channels, time) -> (batch * channels, time)
x = x.view(batch_size * channels, time_len)
stft_output = torch.stft(
x, n_fft=self.n_fft, hop_length=self.hop_length,
window=self.window, return_complex=True
) # (batch, channels, freq_bins, time_frames)
stft_output = stft_output.view(batch_size, channels, *stft_output.shape[-2:]) # (batch, channels, freq_bins, time_frames)
# Separate magnitude and phase
magnitude = torch.abs(stft_output) # (batch, channels, freq_bins, time_frames)
phase = torch.angle(stft_output) # (batch, channels, freq_bins, time_frames)
# Permute to (batch, time_frames, freq_bins, channels)
magnitude = magnitude.permute(0, 3, 2, 1) # (batch, time_frames, freq_bins, channels)
phase = phase.permute(0, 3, 2, 1)
# Concatenate magnitude and phase along channel axis
features = torch.cat([magnitude, phase], dim=-1) # (batch, time_frames, freq_bins, 2*channels)
return features
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