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 __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,
      do_amp_to_db=False,
      spec_gain=1.0,
      power=None,
      use_htk=False,
      mel_norm="slaney",
      normalized=False,
  ):
      super().__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
      self.n_mels = n_mels
      self.use_mel = use_mel
      self.do_amp_to_db = do_amp_to_db
      self.spec_gain = spec_gain
      self.power = power
      self.use_htk = use_htk
      self.mel_norm = mel_norm
      self.window = nn.Parameter(
          getattr(torch, window)(win_length), requires_grad=False
      )
      self.mel_basis = None
      self.normalized = normalized
      if use_mel:
          self._build_mel_basis()
  
file_path: TTS/TTS/utils/audio/torch_transforms.py
imports: import librosa
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new TorchSTFT object from inherited base classes: nn with example: obj = TorchSTFT(n_fft, hop_length, win_length, pad_wav, window, sample_rate, mel_fmin, mel_fmax, n_mels, use_mel, do_amp_to_db, spec_gain, power, use_htk, mel_norm, normalized)