docstr: denormalize values
docstr_tok: ["denormalize","values"]
code: def denormalize(self, S):
      """denormalize values"""
      # pylint: disable=no-else-return
      S_denorm = S.copy()
      if self.signal_norm:
          # mean-var scaling
          if hasattr(self, "mel_scaler"):
              if S_denorm.shape[0] == self.num_mels:
                  return self.mel_scaler.inverse_transform(S_denorm.T).T
              elif S_denorm.shape[0] == self.fft_size / 2:
                  return self.linear_scaler.inverse_transform(
                      S_denorm.T
                  ).T
              else:
                  raise RuntimeError(
                      " [!] Mean-Var stats does not match the given feature dimensions."
                  )
          if self.symmetric_norm:
              if self.clip_norm:
                  S_denorm = np.clip(
                      S_denorm, -self.max_norm, self.max_norm
                  )  # pylint: disable=invalid-unary-operand-type
              S_denorm = (
                  (S_denorm + self.max_norm)
                  * -self.min_level_db
                  / (2 * self.max_norm)
              ) + self.min_level_db
              return S_denorm + self.ref_level_db
          else:
              if self.clip_norm:
                  S_denorm = np.clip(S_denorm, 0, self.max_norm)
              S_denorm = (
                  S_denorm * -self.min_level_db / self.max_norm
              ) + self.min_level_db
              return S_denorm + self.ref_level_db
      else:
          return S_denorm
  
imports: import librosa
  import soundfile
  import numpy
  import scipy.io.wavfile
  import scipy.signal
  from TTS.tts.utils.data import StandardScaler
label_desc: Use image node_id 5 for calling the AudioProcessor obj's underlying member method code with example usage: obj.denormalize(S) and returns: S_denorm, unknown, unknown, self, self