Improve the speed of kaldi.fbank with fused operator (#947)

Co-authored-by: linqj3 <linqj3@lenovo.com>

torchaudio/compliance/kaldi.py

@@ -573,7 +573,7 @@ def fbank(waveform: Tensor,
     # size (m, padded_window_size // 2 + 1, 2)
     fft = torch.rfft(strided_input, 1, normalized=False, onesided=True)
 
-    power_spectrum = fft.pow(2).sum(2).unsqueeze(1)  # size (m, 1, padded_window_size // 2 + 1)
+    power_spectrum = fft.pow(2).sum(2)  # size (m, padded_window_size // 2 + 1)
     if not use_power:
         power_spectrum = power_spectrum.pow(0.5)
 
@@ -582,11 +582,11 @@ def fbank(waveform: Tensor,
                                     low_freq, high_freq, vtln_low, vtln_high, vtln_warp)
     mel_energies = mel_energies.to(device=device, dtype=dtype)
 
-    # pad right column with zeros and add dimension, size (1, num_mel_bins, padded_window_size // 2 + 1)
-    mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0).unsqueeze(0)
+    # pad right column with zeros and add dimension, size (num_mel_bins, padded_window_size // 2 + 1)
+    mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0)
 
     # sum with mel fiterbanks over the power spectrum, size (m, num_mel_bins)
-    mel_energies = (power_spectrum * mel_energies).sum(dim=2)
+    mel_energies = torch.mm(power_spectrum, mel_energies.T)
     if use_log_fbank:
         # avoid log of zero (which should be prevented anyway by dithering)
         mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log()