diff --git a/TTS/vocoder/layers/losses.py b/TTS/vocoder/layers/losses.py
index 740b2a73..fab70594 100644
--- a/TTS/vocoder/layers/losses.py
+++ b/TTS/vocoder/layers/losses.py
@@ -4,23 +4,24 @@ from torch import nn
 from torch.nn import functional as F
 
 
-class TorchSTFT(nn.Module):
+class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
     """TODO: Merge this with audio.py"""
     def __init__(self,
                  n_fft,
                  hop_length,
                  win_length,
+                 pad_wav=False,
                  window='hann_window',
                  sample_rate=None,
                  mel_fmin=0,
                  mel_fmax=None,
                  n_mels=80,
                  use_mel=False):
-        """ Torch based STFT operation """
         super(TorchSTFT, self).__init__()
         self.n_fft = n_fft
         self.hop_length = hop_length
         self.win_length = win_length
+        self.pad_wav = pad_wav
         self.sample_rate = sample_rate
         self.mel_fmin = mel_fmin
         self.mel_fmax = mel_fmax
@@ -33,8 +34,22 @@ class TorchSTFT(nn.Module):
             self._build_mel_basis()
 
     def __call__(self, x):
-        padding = int((self.n_fft - self.hop_length) / 2)
-        x = torch.nn.functional.pad(x, (padding, padding), mode='reflect')
+        """Compute spectrogram frames by torch based stft.
+
+        Args:
+            x (Tensor): input waveform
+
+        Returns:
+            Tensor: spectrogram frames.
+
+        Shapes:
+            x: [B x T] or [B x 1 x T]
+        """
+        if x.ndim == 2:
+            x = x.unsqueeze(1)
+        if self.pad_wav:
+            padding = int((self.n_fft - self.hop_length) / 2)
+            x = torch.nn.functional.pad(x, (padding, padding), mode='reflect')
         # B x D x T x 2
         o = torch.stft(
             x.squeeze(1),