class_docstr: Some of the audio processing funtions using Torch for faster batch processing.
  
  Args:
  
      n_fft (int):
          FFT window size for STFT.
  
      hop_length (int):
          number of frames between STFT columns.
  
      win_length (int, optional):
          STFT window length.
  
      pad_wav (bool, optional):
          If True pad the audio with (n_fft - hop_length) / 2). Defaults to False.
  
      window (str, optional):
          The name of a function to create a window tensor that is applied/multiplied to each frame/window. Defaults to "hann_window"
  
      sample_rate (int, optional):
          target audio sampling rate. Defaults to None.
  
      mel_fmin (int, optional):
          minimum filter frequency for computing melspectrograms. Defaults to None.
  
      mel_fmax (int, optional):
          maximum filter frequency for computing melspectrograms. Defaults to None.
  
      n_mels (int, optional):
          number of melspectrogram dimensions. Defaults to None.
  
      use_mel (bool, optional):
          If True compute the melspectrograms otherwise. Defaults to False.
  
      do_amp_to_db_linear (bool, optional):
          enable/disable amplitude to dB conversion of linear spectrograms. Defaults to False.
  
      spec_gain (float, optional):
          gain applied when converting amplitude to DB. Defaults to 1.0.
  
      power (float, optional):
          Exponent for the magnitude spectrogram, e.g., 1 for energy, 2 for power, etc.  Defaults to None.
  
      use_htk (bool, optional):
          Use HTK formula in mel filter instead of Slaney.
  
      mel_norm (None, 'slaney', or number, optional):
          If 'slaney', divide the triangular mel weights by the width of the mel band
          (area normalization).
  
          If numeric, use `librosa.util.normalize` to normalize each filter by to unit l_p norm.
          See `librosa.util.normalize` for a full description of supported norm values
          (including `+-np.inf`).
  
          Otherwise, leave all the triangles aiming for a peak value of 1.0. Defaults to "slaney".
code: def _db_to_amp(x, spec_gain=1.0):
      return torch.exp(x) / spec_gain
  
file_path: TTS/TTS/utils/audio/torch_transforms.py
imports: import librosa
  import torch
  from torch import nn
label_desc: Use image node_id 5 for calling the TorchSTFT obj's underlying member method code with example usage: obj._db_to_amp(x, spec_gain) and returns: unknown