implemented torchaudio based log Mel fb features

* used torchaudio as much as possible
* could not use tochaudios MelSpectrogram directly since it has fixed
  padding
   - the computation here is exactly the same appart from the padding

neural_networks.py

@@ -651,21 +651,38 @@ def forward(self, x):
 class logMelFb(nn.Module):
     def __init__(self, options,inp_dim):
         super(logMelFb, self).__init__()
+        import torchaudio
+        self._sample_rate = int(options['logmelfb_nr_sample_rate'])
         self._nr_of_filters = int(options['logmelfb_nr_filt'])
         self._stft_window_size = int(options['logmelfb_stft_window_size'])
         self._stft_window_shift = int(options['logmelfb_stft_window_shift'])
         self._use_cuda = strtobool(options['use_cuda'])
         self.out_dim = self._nr_of_filters
+        self._mspec = torchaudio.transforms.MelSpectrogram(
+            sr=self._sample_rate,
+            n_fft=self._stft_window_size,
+            ws=self._stft_window_size,
+            hop=self._stft_window_shift,
+            n_mels=self._nr_of_filters,
+        )
 
     def forward(self, x):
-        nr_of_frames = int((x.shape[0] - self._stft_window_size) / float(self._stft_window_shift) + 1)
-        out = torch.zeros(nr_of_frames, x.shape[1], self.out_dim).contiguous()
-        for frame in range(nr_of_frames):
-            frame_start = frame * self._stft_window_shift
-            frame_end = frame_start + self._stft_window_size
-            out[frame, :, :] = x[frame_start:frame_start + self.out_dim, :, :].squeeze(-1).transpose(0, 1)
+        assert x.shape[-1] == 1, 'Multi channel time signal processing not suppored yet'
+        x_reshape_for_stft = torch.squeeze(x, -1).transpose(0, 1)
         if self._use_cuda:
-            out=out.cuda()
+            window = self._mspec.window(self._stft_window_size).cuda()
+        else:
+            window = self._mspec.window(self._stft_window_size)
+        x_stft = torch.stft(
+            x_reshape_for_stft, 
+            self._stft_window_size, 
+            hop_length = self._stft_window_shift, 
+            center = False, 
+            window = window,
+        )
+        x_power_stft = x_stft.pow(2).sum(-1)
+        x_power_stft_reshape_for_filterbank_mult = x_power_stft.transpose(1, 2)
+        out = self._mspec.fm(x_power_stft_reshape_for_filterbank_mult).transpose(0, 1)
         return out
 
 class liGRU(nn.Module):