parameters: (self, y)
return_statement: return mel_output
docstring: Computes mel-spectrograms from a batch of waves
          PARAMS
          ------
          y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
  
          RETURNS
          -------
          mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
docstring_summary: Computes mel-spectrograms from a batch of waves
          PARAMS
          ------
          y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
docstring_tokens: Computes mel - spectrograms from a batch of waves PARAMS ------ y : Variable ( torch . FloatTensor ) with shape ( B T ) in range [ - 1 1 ]
function: def mel_spectrogram(self, y):
          """Computes mel-spectrograms from a batch of waves
          PARAMS
          ------
          y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
  
          RETURNS
          -------
          mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
          """
          assert(torch.min(y.data) >= -1)
          assert(torch.max(y.data) <= 1)
  
          magnitudes, phases = self.stft_fn.transform(y)
          magnitudes = magnitudes.data
          mel_output = torch.matmul(self.mel_basis, magnitudes)
          mel_output = self.spectral_normalize(mel_output)
          return mel_output
function_tokens: def mel_spectrogram ( self , y ) : assert ( torch . min ( y . data ) >= - 1 ) assert ( torch . max ( y . data ) <= 1 ) magnitudes , phases = self . stft_fn . transform ( y ) magnitudes = magnitudes . data mel_output = torch . matmul ( self . mel_basis , magnitudes ) mel_output = self . spectral_normalize ( mel_output ) return mel_output