text: # -*- coding: utf-8 -*-
  
  import math
  import warnings
  from typing import Callable, Optional
  
  import torch
  from torch import Tensor
  from torchaudio import functional as F
  
  from .functional.functional import (
      _get_sinc_resample_kernel,
      _apply_sinc_resample_kernel,
  )
  
  __all__ = [
      'Spectrogram',
      'GriffinLim',
      'AmplitudeToDB',
      'MelScale',
      'InverseMelScale',
      'MelSpectrogram',
      'MFCC',
      'LFCC',
      'MuLawEncoding',
      'MuLawDecoding',
      'Resample',
      'ComplexNorm',
      'TimeStretch',
      'Fade',
      'FrequencyMasking',
      'TimeMasking',
      'SlidingWindowCmn',
      'Vad',
      'SpectralCentroid',
      'Vol',
      'ComputeDeltas',
      'PitchShift',
  ]
  
  
  class Spectrogram(torch.nn.Module):
      r"""Create a spectrogram from a audio signal.
  
      Args:
          n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
          win_length (int or None, optional): Window size. (Default: ``n_fft``)
          hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
          pad (int, optional): Two sided padding of signal. (Default: ``0``)
          window_fn (Callable[..., Tensor], optional): A function to create a window tensor
              that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
          power (float or None, optional): Exponent for the magnitude spectrogram,
              (must be > 0) e.g., 1 for energy, 2 for power, etc.
              If None, then the complex spectrum is returned instead. (Default: ``2``)
          normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``)
          wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
          center (bool, optional): whether to pad :attr:`waveform` on both sides so
              that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
              (Default: ``True``)
          pad_mode (string, optional): controls the padding method used when
              :attr:`center` is ``True``. (Default: ``"reflect"``)
          onesided (bool, optional): controls whether to return half of results to
              avoid redundancy (Default: ``True``)
          return_complex (bool, optional):
              Indicates whether the resulting complex-valued Tensor should be represented with
              native complex dtype, such as `torch.cfloat` and `torch.cdouble`, or real dtype
              mimicking complex value with an extra dimension for real and imaginary parts.
              (See also ``torch.view_as_real``.)
              This argument is only effective when ``power=None``. It is ignored for
              cases where ``power`` is a number as in those cases, the returned tensor is
              power spectrogram, which is a real-valued tensor.
  
      Example
          >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
          >>> transform = torchaudio.transforms.Spectrogram(n_fft=800)
          >>> spectrogram = transform(waveform)
  
      """
      __constants__ = ['n_fft', 'win_length', 'hop_length', 'pad', 'power', 'normalized']
  
      def __init__(self,
                   n_fft: int = 400,
                   win_length: Optional[int] = None,
                   hop_length: Optional[int] = None,
                   pad: int = 0,
                   window_fn: Callable[..., Tensor] = torch.hann_window,
                   power: Optional[float] = 2.,
                   normalized: bool = False,
                   wkwargs: Optional[dict] = None,
                   center: bool = True,
                   pad_mode: str = "reflect",
                   onesided: bool = True,
                   return_complex: bool = True) -> None:
          super(Spectrogram, self).__init__()
          self.n_fft = n_fft
          # number of FFT bins. the returned STFT result will have n_fft // 2 + 1
          # number of frequencies due to onesided=True in torch.stft
          self.win_length = win_length if win_length is not None else n_fft
          self.hop_length = hop_length if hop_length is not None else self.win_length // 2
          window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
          self.register_buffer('window', window)
          self.pad = pad
          self.power = power
          self.normalized = normalized
          self.center = center
          self.pad_mode = pad_mode
          self.onesided = onesided
          self.return_complex = return_complex
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Dimension (..., freq, time), where freq is
              ``n_fft // 2 + 1`` where ``n_fft`` is the number of
              Fourier bins, and time is the number of window hops (n_frame).
          """
          return F.spectrogram(
              waveform,
              self.pad,
              self.window,
              self.n_fft,
              self.hop_length,
              self.win_length,
              self.power,
              self.normalized,
              self.center,
              self.pad_mode,
              self.onesided,
              self.return_complex,
          )
  
  
  class GriffinLim(torch.nn.Module):
      r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
  
      Implementation ported from
      *librosa* [:footcite:`brian_mcfee-proc-scipy-2015`], *A fast Griffin-Lim algorithm* [:footcite:`6701851`]
      and *Signal estimation from modified short-time Fourier transform* [:footcite:`1172092`].
  
      Args:
          n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
          n_iter (int, optional): Number of iteration for phase recovery process. (Default: ``32``)
          win_length (int or None, optional): Window size. (Default: ``n_fft``)
          hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
          window_fn (Callable[..., Tensor], optional): A function to create a window tensor
              that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
          power (float, optional): Exponent for the magnitude spectrogram,
              (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
          wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
          momentum (float, optional): The momentum parameter for fast Griffin-Lim.
              Setting this to 0 recovers the original Griffin-Lim method.
              Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: ``0.99``)
          length (int, optional): Array length of the expected output. (Default: ``None``)
          rand_init (bool, optional): Initializes phase randomly if True and to zero otherwise. (Default: ``True``)
      """
      __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power',
                       'length', 'momentum', 'rand_init']
  
      def __init__(self,
                   n_fft: int = 400,
                   n_iter: int = 32,
                   win_length: Optional[int] = None,
                   hop_length: Optional[int] = None,
                   window_fn: Callable[..., Tensor] = torch.hann_window,
                   power: float = 2.,
                   wkwargs: Optional[dict] = None,
                   momentum: float = 0.99,
                   length: Optional[int] = None,
                   rand_init: bool = True) -> None:
          super(GriffinLim, self).__init__()
  
          assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum)
          assert momentum >= 0, 'momentum={} < 0'.format(momentum)
  
          self.n_fft = n_fft
          self.n_iter = n_iter
          self.win_length = win_length if win_length is not None else n_fft
          self.hop_length = hop_length if hop_length is not None else self.win_length // 2
          window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
          self.register_buffer('window', window)
          self.length = length
          self.power = power
          self.momentum = momentum / (1 + momentum)
          self.rand_init = rand_init
  
      def forward(self, specgram: Tensor) -> Tensor:
          r"""
          Args:
              specgram (Tensor):
                  A magnitude-only STFT spectrogram of dimension (..., freq, frames)
                  where freq is ``n_fft // 2 + 1``.
  
          Returns:
              Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.
          """
          return F.griffinlim(specgram, self.window, self.n_fft, self.hop_length, self.win_length, self.power,
                              self.n_iter, self.momentum, self.length, self.rand_init)
  
  
  class AmplitudeToDB(torch.nn.Module):
      r"""Turn a tensor from the power/amplitude scale to the decibel scale.
  
      This output depends on the maximum value in the input tensor, and so
      may return different values for an audio clip split into snippets vs. a
      a full clip.
  
      Args:
          stype (str, optional): scale of input tensor ('power' or 'magnitude'). The
              power being the elementwise square of the magnitude. (Default: ``'power'``)
          top_db (float, optional): minimum negative cut-off in decibels.  A reasonable number
              is 80. (Default: ``None``)
      """
      __constants__ = ['multiplier', 'amin', 'ref_value', 'db_multiplier']
  
      def __init__(self, stype: str = 'power', top_db: Optional[float] = None) -> None:
          super(AmplitudeToDB, self).__init__()
          self.stype = stype
          if top_db is not None and top_db < 0:
              raise ValueError('top_db must be positive value')
          self.top_db = top_db
          self.multiplier = 10.0 if stype == 'power' else 20.0
          self.amin = 1e-10
          self.ref_value = 1.0
          self.db_multiplier = math.log10(max(self.amin, self.ref_value))
  
      def forward(self, x: Tensor) -> Tensor:
          r"""Numerically stable implementation from Librosa.
  
          https://librosa.org/doc/latest/generated/librosa.amplitude_to_db.html
  
          Args:
              x (Tensor): Input tensor before being converted to decibel scale.
  
          Returns:
              Tensor: Output tensor in decibel scale.
          """
          return F.amplitude_to_DB(x, self.multiplier, self.amin, self.db_multiplier, self.top_db)
  
  
  class MelScale(torch.nn.Module):
      r"""Turn a normal STFT into a mel frequency STFT, using a conversion
      matrix.  This uses triangular filter banks.
  
      User can control which device the filter bank (`fb`) is (e.g. fb.to(spec_f.device)).
  
      Args:
          n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
          sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
          f_min (float, optional): Minimum frequency. (Default: ``0.``)
          f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
          n_stft (int, optional): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`. (Default: ``201``)
          norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band
          (area normalization). (Default: ``None``)
          mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
      """
      __constants__ = ['n_mels', 'sample_rate', 'f_min', 'f_max']
  
      def __init__(self,
                   n_mels: int = 128,
                   sample_rate: int = 16000,
                   f_min: float = 0.,
                   f_max: Optional[float] = None,
                   n_stft: int = 201,
                   norm: Optional[str] = None,
                   mel_scale: str = "htk") -> None:
          super(MelScale, self).__init__()
          self.n_mels = n_mels
          self.sample_rate = sample_rate
          self.f_max = f_max if f_max is not None else float(sample_rate // 2)
          self.f_min = f_min
          self.norm = norm
          self.mel_scale = mel_scale
  
          assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max)
          fb = F.melscale_fbanks(
              n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm,
              self.mel_scale)
          self.register_buffer('fb', fb)
  
      def forward(self, specgram: Tensor) -> Tensor:
          r"""
          Args:
              specgram (Tensor): A spectrogram STFT of dimension (..., freq, time).
  
          Returns:
              Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time).
          """
  
          # (..., time, freq) dot (freq, n_mels) -> (..., n_mels, time)
          mel_specgram = torch.matmul(specgram.transpose(-1, -2), self.fb).transpose(-1, -2)
  
          return mel_specgram
  
  
  class InverseMelScale(torch.nn.Module):
      r"""Solve for a normal STFT from a mel frequency STFT, using a conversion
      matrix.  This uses triangular filter banks.
  
      It minimizes the euclidian norm between the input mel-spectrogram and the product between
      the estimated spectrogram and the filter banks using SGD.
  
      Args:
          n_stft (int): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.
          n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
          sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
          f_min (float, optional): Minimum frequency. (Default: ``0.``)
          f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
          max_iter (int, optional): Maximum number of optimization iterations. (Default: ``100000``)
          tolerance_loss (float, optional): Value of loss to stop optimization at. (Default: ``1e-5``)
          tolerance_change (float, optional): Difference in losses to stop optimization at. (Default: ``1e-8``)
          sgdargs (dict or None, optional): Arguments for the SGD optimizer. (Default: ``None``)
          norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band
              (area normalization). (Default: ``None``)
          mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
      """
      __constants__ = ['n_stft', 'n_mels', 'sample_rate', 'f_min', 'f_max', 'max_iter', 'tolerance_loss',
                       'tolerance_change', 'sgdargs']
  
      def __init__(self,
                   n_stft: int,
                   n_mels: int = 128,
                   sample_rate: int = 16000,
                   f_min: float = 0.,
                   f_max: Optional[float] = None,
                   max_iter: int = 100000,
                   tolerance_loss: float = 1e-5,
                   tolerance_change: float = 1e-8,
                   sgdargs: Optional[dict] = None,
                   norm: Optional[str] = None,
                   mel_scale: str = "htk") -> None:
          super(InverseMelScale, self).__init__()
          self.n_mels = n_mels
          self.sample_rate = sample_rate
          self.f_max = f_max or float(sample_rate // 2)
          self.f_min = f_min
          self.max_iter = max_iter
          self.tolerance_loss = tolerance_loss
          self.tolerance_change = tolerance_change
          self.sgdargs = sgdargs or {'lr': 0.1, 'momentum': 0.9}
  
          assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max)
  
          fb = F.melscale_fbanks(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate,
                                 norm, mel_scale)
          self.register_buffer('fb', fb)
  
      def forward(self, melspec: Tensor) -> Tensor:
          r"""
          Args:
              melspec (Tensor): A Mel frequency spectrogram of dimension (..., ``n_mels``, time)
  
          Returns:
              Tensor: Linear scale spectrogram of size (..., freq, time)
          """
          # pack batch
          shape = melspec.size()
          melspec = melspec.view(-1, shape[-2], shape[-1])
  
          n_mels, time = shape[-2], shape[-1]
          freq, _ = self.fb.size()  # (freq, n_mels)
          melspec = melspec.transpose(-1, -2)
          assert self.n_mels == n_mels
  
          specgram = torch.rand(melspec.size()[0], time, freq, requires_grad=True,
                                dtype=melspec.dtype, device=melspec.device)
  
          optim = torch.optim.SGD([specgram], **self.sgdargs)
  
          loss = float('inf')
          for _ in range(self.max_iter):
              optim.zero_grad()
              diff = melspec - specgram.matmul(self.fb)
              new_loss = diff.pow(2).sum(axis=-1).mean()
              # take sum over mel-frequency then average over other dimensions
              # so that loss threshold is applied par unit timeframe
              new_loss.backward()
              optim.step()
              specgram.data = specgram.data.clamp(min=0)
  
              new_loss = new_loss.item()
              if new_loss < self.tolerance_loss or abs(loss - new_loss) < self.tolerance_change:
                  break
              loss = new_loss
  
          specgram.requires_grad_(False)
          specgram = specgram.clamp(min=0).transpose(-1, -2)
  
          # unpack batch
          specgram = specgram.view(shape[:-2] + (freq, time))
          return specgram
  
  
  class MelSpectrogram(torch.nn.Module):
      r"""Create MelSpectrogram for a raw audio signal. This is a composition of Spectrogram
      and MelScale.
  
      Sources
          * https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe
          * https://timsainb.github.io/spectrograms-mfccs-and-inversion-in-python.html
          * http://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
  
      Args:
          sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
          n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
          win_length (int or None, optional): Window size. (Default: ``n_fft``)
          hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
          f_min (float, optional): Minimum frequency. (Default: ``0.``)
          f_max (float or None, optional): Maximum frequency. (Default: ``None``)
          pad (int, optional): Two sided padding of signal. (Default: ``0``)
          n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
          window_fn (Callable[..., Tensor], optional): A function to create a window tensor
              that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
          power (float, optional): Exponent for the magnitude spectrogram,
              (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
          normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``)
          wkwargs (Dict[..., ...] or None, optional): Arguments for window function. (Default: ``None``)
          center (bool, optional): whether to pad :attr:`waveform` on both sides so
              that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
              (Default: ``True``)
          pad_mode (string, optional): controls the padding method used when
              :attr:`center` is ``True``. (Default: ``"reflect"``)
          onesided (bool, optional): controls whether to return half of results to
              avoid redundancy. (Default: ``True``)
          norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band
              (area normalization). (Default: ``None``)
          mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
  
      Example
          >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
          >>> transform = transforms.MelSpectrogram(sample_rate)
          >>> mel_specgram = transform(waveform)  # (channel, n_mels, time)
      """
      __constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad', 'n_mels', 'f_min']
  
      def __init__(self,
                   sample_rate: int = 16000,
                   n_fft: int = 400,
                   win_length: Optional[int] = None,
                   hop_length: Optional[int] = None,
                   f_min: float = 0.,
                   f_max: Optional[float] = None,
                   pad: int = 0,
                   n_mels: int = 128,
                   window_fn: Callable[..., Tensor] = torch.hann_window,
                   power: float = 2.,
                   normalized: bool = False,
                   wkwargs: Optional[dict] = None,
                   center: bool = True,
                   pad_mode: str = "reflect",
                   onesided: bool = True,
                   norm: Optional[str] = None,
                   mel_scale: str = "htk") -> None:
          super(MelSpectrogram, self).__init__()
          self.sample_rate = sample_rate
          self.n_fft = n_fft
          self.win_length = win_length if win_length is not None else n_fft
          self.hop_length = hop_length if hop_length is not None else self.win_length // 2
          self.pad = pad
          self.power = power
          self.normalized = normalized
          self.n_mels = n_mels  # number of mel frequency bins
          self.f_max = f_max
          self.f_min = f_min
          self.spectrogram = Spectrogram(n_fft=self.n_fft, win_length=self.win_length,
                                         hop_length=self.hop_length,
                                         pad=self.pad, window_fn=window_fn, power=self.power,
                                         normalized=self.normalized, wkwargs=wkwargs,
                                         center=center, pad_mode=pad_mode, onesided=onesided)
          self.mel_scale = MelScale(
              self.n_mels,
              self.sample_rate,
              self.f_min,
              self.f_max,
              self.n_fft // 2 + 1,
              norm,
              mel_scale
          )
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time).
          """
          specgram = self.spectrogram(waveform)
          mel_specgram = self.mel_scale(specgram)
          return mel_specgram
  
  
  class MFCC(torch.nn.Module):
      r"""Create the Mel-frequency cepstrum coefficients from an audio signal.
  
      By default, this calculates the MFCC on the DB-scaled Mel spectrogram.
      This is not the textbook implementation, but is implemented here to
      give consistency with librosa.
  
      This output depends on the maximum value in the input spectrogram, and so
      may return different values for an audio clip split into snippets vs. a
      a full clip.
  
      Args:
          sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
          n_mfcc (int, optional): Number of mfc coefficients to retain. (Default: ``40``)
          dct_type (int, optional): type of DCT (discrete cosine transform) to use. (Default: ``2``)
          norm (str, optional): norm to use. (Default: ``'ortho'``)
          log_mels (bool, optional): whether to use log-mel spectrograms instead of db-scaled. (Default: ``False``)
          melkwargs (dict or None, optional): arguments for MelSpectrogram. (Default: ``None``)
      """
      __constants__ = ['sample_rate', 'n_mfcc', 'dct_type', 'top_db', 'log_mels']
  
      def __init__(self,
                   sample_rate: int = 16000,
                   n_mfcc: int = 40,
                   dct_type: int = 2,
                   norm: str = 'ortho',
                   log_mels: bool = False,
                   melkwargs: Optional[dict] = None) -> None:
          super(MFCC, self).__init__()
          supported_dct_types = [2]
          if dct_type not in supported_dct_types:
              raise ValueError('DCT type not supported: {}'.format(dct_type))
          self.sample_rate = sample_rate
          self.n_mfcc = n_mfcc
          self.dct_type = dct_type
          self.norm = norm
          self.top_db = 80.0
          self.amplitude_to_DB = AmplitudeToDB('power', self.top_db)
  
          melkwargs = melkwargs or {}
          self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate, **melkwargs)
  
          if self.n_mfcc > self.MelSpectrogram.n_mels:
              raise ValueError('Cannot select more MFCC coefficients than # mel bins')
          dct_mat = F.create_dct(self.n_mfcc, self.MelSpectrogram.n_mels, self.norm)
          self.register_buffer('dct_mat', dct_mat)
          self.log_mels = log_mels
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: specgram_mel_db of size (..., ``n_mfcc``, time).
          """
          mel_specgram = self.MelSpectrogram(waveform)
          if self.log_mels:
              log_offset = 1e-6
              mel_specgram = torch.log(mel_specgram + log_offset)
          else:
              mel_specgram = self.amplitude_to_DB(mel_specgram)
  
          # (..., time, n_mels) dot (n_mels, n_mfcc) -> (..., n_nfcc, time)
          mfcc = torch.matmul(mel_specgram.transpose(-1, -2), self.dct_mat).transpose(-1, -2)
          return mfcc
  
  
  class LFCC(torch.nn.Module):
      r"""Create the linear-frequency cepstrum coefficients from an audio signal.
  
      By default, this calculates the LFCC on the DB-scaled linear filtered spectrogram.
      This is not the textbook implementation, but is implemented here to
      give consistency with librosa.
  
      This output depends on the maximum value in the input spectrogram, and so
      may return different values for an audio clip split into snippets vs. a
      a full clip.
  
      Args:
          sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
          n_filter (int, optional): Number of linear filters to apply. (Default: ``128``)
          n_lfcc (int, optional): Number of lfc coefficients to retain. (Default: ``40``)
          f_min (float, optional): Minimum frequency. (Default: ``0.``)
          f_max (float or None, optional): Maximum frequency. (Default: ``None``)
          dct_type (int, optional): type of DCT (discrete cosine transform) to use. (Default: ``2``)
          norm (str, optional): norm to use. (Default: ``'ortho'``)
          log_lf (bool, optional): whether to use log-lf spectrograms instead of db-scaled. (Default: ``False``)
          speckwargs (dict or None, optional): arguments for Spectrogram. (Default: ``None``)
      """
      __constants__ = ['sample_rate', 'n_filter', 'n_lfcc', 'dct_type', 'top_db', 'log_lf']
  
      def __init__(self,
                   sample_rate: int = 16000,
                   n_filter: int = 128,
                   f_min: float = 0.,
                   f_max: Optional[float] = None,
                   n_lfcc: int = 40,
                   dct_type: int = 2,
                   norm: str = 'ortho',
                   log_lf: bool = False,
                   speckwargs: Optional[dict] = None) -> None:
          super(LFCC, self).__init__()
          supported_dct_types = [2]
          if dct_type not in supported_dct_types:
              raise ValueError('DCT type not supported: {}'.format(dct_type))
          self.sample_rate = sample_rate
          self.f_min = f_min
          self.f_max = f_max if f_max is not None else float(sample_rate // 2)
          self.n_filter = n_filter
          self.n_lfcc = n_lfcc
          self.dct_type = dct_type
          self.norm = norm
          self.top_db = 80.0
          self.amplitude_to_DB = AmplitudeToDB('power', self.top_db)
  
          speckwargs = speckwargs or {}
          self.Spectrogram = Spectrogram(**speckwargs)
  
          if self.n_lfcc > self.Spectrogram.n_fft:
              raise ValueError('Cannot select more LFCC coefficients than # fft bins')
  
          filter_mat = F.linear_fbanks(
              n_freqs=self.Spectrogram.n_fft // 2 + 1,
              f_min=self.f_min,
              f_max=self.f_max,
              n_filter=self.n_filter,
              sample_rate=self.sample_rate,
          )
          self.register_buffer("filter_mat", filter_mat)
  
          dct_mat = F.create_dct(self.n_lfcc, self.n_filter, self.norm)
          self.register_buffer('dct_mat', dct_mat)
          self.log_lf = log_lf
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Linear Frequency Cepstral Coefficients of size (..., ``n_lfcc``, time).
          """
          specgram = self.Spectrogram(waveform)
  
          # (..., time, freq) dot (freq, n_filter) -> (..., n_filter, time)
          specgram = torch.matmul(specgram.transpose(-1, -2), self.filter_mat).transpose(-1, -2)
  
          if self.log_lf:
              log_offset = 1e-6
              specgram = torch.log(specgram + log_offset)
          else:
              specgram = self.amplitude_to_DB(specgram)
  
          # (..., time, n_filter) dot (n_filter, n_lfcc) -> (..., n_lfcc, time)
          lfcc = torch.matmul(specgram.transpose(-1, -2), self.dct_mat).transpose(-1, -2)
          return lfcc
  
  
  class MuLawEncoding(torch.nn.Module):
      r"""Encode signal based on mu-law companding.  For more info see the
      `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
  
      This algorithm assumes the signal has been scaled to between -1 and 1 and
      returns a signal encoded with values from 0 to quantization_channels - 1
  
      Args:
          quantization_channels (int, optional): Number of channels. (Default: ``256``)
  
      Example
         >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
         >>> transform = torchaudio.transforms.MuLawEncoding(quantization_channels=512)
         >>> mulawtrans = transform(waveform)
  
      """
      __constants__ = ['quantization_channels']
  
      def __init__(self, quantization_channels: int = 256) -> None:
          super(MuLawEncoding, self).__init__()
          self.quantization_channels = quantization_channels
  
      def forward(self, x: Tensor) -> Tensor:
          r"""
          Args:
              x (Tensor): A signal to be encoded.
  
          Returns:
              x_mu (Tensor): An encoded signal.
          """
          return F.mu_law_encoding(x, self.quantization_channels)
  
  
  class MuLawDecoding(torch.nn.Module):
      r"""Decode mu-law encoded signal.  For more info see the
      `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
  
      This expects an input with values between 0 and quantization_channels - 1
      and returns a signal scaled between -1 and 1.
  
      Args:
          quantization_channels (int, optional): Number of channels. (Default: ``256``)
      """
      __constants__ = ['quantization_channels']
  
      def __init__(self, quantization_channels: int = 256) -> None:
          super(MuLawDecoding, self).__init__()
          self.quantization_channels = quantization_channels
  
      def forward(self, x_mu: Tensor) -> Tensor:
          r"""
          Args:
              x_mu (Tensor): A mu-law encoded signal which needs to be decoded.
  
          Returns:
              Tensor: The signal decoded.
          """
          return F.mu_law_decoding(x_mu, self.quantization_channels)
  
  
  class Resample(torch.nn.Module):
      r"""Resample a signal from one frequency to another. A resampling method can be given.
  
      Note:
          If resampling on waveforms of higher precision than float32, there may be a small loss of precision
          because the kernel is cached once as float32. If high precision resampling is important for your application,
          the functional form will retain higher precision, but run slower because it does not cache the kernel.
          Alternatively, you could rewrite a transform that caches a higher precision kernel.
  
      Args:
          orig_freq (float, optional): The original frequency of the signal. (Default: ``16000``)
          new_freq (float, optional): The desired frequency. (Default: ``16000``)
          resampling_method (str, optional): The resampling method to use.
              Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``)
          lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper
              but less efficient. (Default: ``6``)
          rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist.
              Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``)
          beta (float or None): The shape parameter used for kaiser window.
          dtype (torch.device, optional):
              Determnines the precision that resampling kernel is pre-computed and cached. If not provided,
              kernel is computed with ``torch.float64`` then cached as ``torch.float32``.
              If you need higher precision, provide ``torch.float64``, and the pre-computed kernel is computed and
              cached as ``torch.float64``. If you use resample with lower precision, then instead of providing this
              providing this argument, please use ``Resample.to(dtype)``, so that the kernel generation is still
              carried out on ``torch.float64``.
  
      Example
          >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
          >>> transform = transforms.Resample(sample_rate, sample_rate/10)
          >>> waveform = transform(waveform)
      """
  
      def __init__(
              self,
              orig_freq: float = 16000,
              new_freq: float = 16000,
              resampling_method: str = 'sinc_interpolation',
              lowpass_filter_width: int = 6,
              rolloff: float = 0.99,
              beta: Optional[float] = None,
              *,
              dtype: Optional[torch.dtype] = None,
      ) -> None:
          super().__init__()
  
          self.orig_freq = orig_freq
          self.new_freq = new_freq
          self.gcd = math.gcd(int(self.orig_freq), int(self.new_freq))
          self.resampling_method = resampling_method
          self.lowpass_filter_width = lowpass_filter_width
          self.rolloff = rolloff
          self.beta = beta
  
          if self.orig_freq != self.new_freq:
              kernel, self.width = _get_sinc_resample_kernel(
                  self.orig_freq, self.new_freq, self.gcd,
                  self.lowpass_filter_width, self.rolloff,
                  self.resampling_method, beta, dtype=dtype)
              self.register_buffer('kernel', kernel)
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Output signal of dimension (..., time).
          """
          if self.orig_freq == self.new_freq:
              return waveform
          return _apply_sinc_resample_kernel(
              waveform, self.orig_freq, self.new_freq, self.gcd,
              self.kernel, self.width)
  
  
  class ComplexNorm(torch.nn.Module):
      r"""Compute the norm of complex tensor input.
  
      Args:
          power (float, optional): Power of the norm. (Default: to ``1.0``)
  
      Example
          >>> complex_tensor = ... #  Tensor shape of (…, complex=2)
          >>> transform = transforms.ComplexNorm(power=2)
          >>> complex_norm = transform(complex_tensor)
      """
      __constants__ = ['power']
  
      def __init__(self, power: float = 1.0) -> None:
          warnings.warn(
              'torchaudio.transforms.ComplexNorm has been deprecated '
              'and will be removed from future release.'
              'Please convert the input Tensor to complex type with `torch.view_as_complex` then '
              'use `torch.abs` and `torch.angle`. '
              'Please refer to https://github.com/pytorch/audio/issues/1337 '
              "for more details about torchaudio's plan to migrate to native complex type."
          )
          super(ComplexNorm, self).__init__()
          self.power = power
  
      def forward(self, complex_tensor: Tensor) -> Tensor:
          r"""
          Args:
              complex_tensor (Tensor): Tensor shape of `(..., complex=2)`.
  
          Returns:
              Tensor: norm of the input tensor, shape of `(..., )`.
          """
          return F.complex_norm(complex_tensor, self.power)
  
  
  class ComputeDeltas(torch.nn.Module):
      r"""Compute delta coefficients of a tensor, usually a spectrogram.
  
      See `torchaudio.functional.compute_deltas` for more details.
  
      Args:
          win_length (int): The window length used for computing delta. (Default: ``5``)
          mode (str): Mode parameter passed to padding. (Default: ``'replicate'``)
      """
      __constants__ = ['win_length']
  
      def __init__(self, win_length: int = 5, mode: str = "replicate") -> None:
          super(ComputeDeltas, self).__init__()
          self.win_length = win_length
          self.mode = mode
  
      def forward(self, specgram: Tensor) -> Tensor:
          r"""
          Args:
              specgram (Tensor): Tensor of audio of dimension (..., freq, time).
  
          Returns:
              Tensor: Tensor of deltas of dimension (..., freq, time).
          """
          return F.compute_deltas(specgram, win_length=self.win_length, mode=self.mode)
  
  
  class TimeStretch(torch.nn.Module):
      r"""Stretch stft in time without modifying pitch for a given rate.
  
      Args:
          hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
          n_freq (int, optional): number of filter banks from stft. (Default: ``201``)
          fixed_rate (float or None, optional): rate to speed up or slow down by.
              If None is provided, rate must be passed to the forward method. (Default: ``None``)
      """
      __constants__ = ['fixed_rate']
  
      def __init__(self,
                   hop_length: Optional[int] = None,
                   n_freq: int = 201,
                   fixed_rate: Optional[float] = None) -> None:
          super(TimeStretch, self).__init__()
  
          self.fixed_rate = fixed_rate
  
          n_fft = (n_freq - 1) * 2
          hop_length = hop_length if hop_length is not None else n_fft // 2
          self.register_buffer('phase_advance', torch.linspace(0, math.pi * hop_length, n_freq)[..., None])
  
      def forward(self, complex_specgrams: Tensor, overriding_rate: Optional[float] = None) -> Tensor:
          r"""
          Args:
              complex_specgrams (Tensor):
                  Either a real tensor of dimension of ``(..., freq, num_frame, complex=2)``
                  or a tensor of dimension ``(..., freq, num_frame)`` with complex dtype.
              overriding_rate (float or None, optional): speed up to apply to this batch.
                  If no rate is passed, use ``self.fixed_rate``. (Default: ``None``)
  
          Returns:
              Tensor:
                  Stretched spectrogram. The resulting tensor is of the same dtype as the input
                  spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``.
          """
          if overriding_rate is None:
              if self.fixed_rate is None:
                  raise ValueError(
                      "If no fixed_rate is specified, must pass a valid rate to the forward method.")
              rate = self.fixed_rate
          else:
              rate = overriding_rate
          return F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
  
  
  class Fade(torch.nn.Module):
      r"""Add a fade in and/or fade out to an waveform.
  
      Args:
          fade_in_len (int, optional): Length of fade-in (time frames). (Default: ``0``)
          fade_out_len (int, optional): Length of fade-out (time frames). (Default: ``0``)
          fade_shape (str, optional): Shape of fade. Must be one of: "quarter_sine",
              "half_sine", "linear", "logarithmic", "exponential". (Default: ``"linear"``)
      """
  
      def __init__(self,
                   fade_in_len: int = 0,
                   fade_out_len: int = 0,
                   fade_shape: str = "linear") -> None:
          super(Fade, self).__init__()
          self.fade_in_len = fade_in_len
          self.fade_out_len = fade_out_len
          self.fade_shape = fade_shape
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Tensor of audio of dimension (..., time).
          """
          waveform_length = waveform.size()[-1]
          device = waveform.device
          return self._fade_in(waveform_length).to(device) * \
              self._fade_out(waveform_length).to(device) * waveform
  
      def _fade_in(self, waveform_length: int) -> Tensor:
          fade = torch.linspace(0, 1, self.fade_in_len)
          ones = torch.ones(waveform_length - self.fade_in_len)
  
          if self.fade_shape == "linear":
              fade = fade
  
          if self.fade_shape == "exponential":
              fade = torch.pow(2, (fade - 1)) * fade
  
          if self.fade_shape == "logarithmic":
              fade = torch.log10(.1 + fade) + 1
  
          if self.fade_shape == "quarter_sine":
              fade = torch.sin(fade * math.pi / 2)
  
          if self.fade_shape == "half_sine":
              fade = torch.sin(fade * math.pi - math.pi / 2) / 2 + 0.5
  
          return torch.cat((fade, ones)).clamp_(0, 1)
  
      def _fade_out(self, waveform_length: int) -> Tensor:
          fade = torch.linspace(0, 1, self.fade_out_len)
          ones = torch.ones(waveform_length - self.fade_out_len)
  
          if self.fade_shape == "linear":
              fade = - fade + 1
  
          if self.fade_shape == "exponential":
              fade = torch.pow(2, - fade) * (1 - fade)
  
          if self.fade_shape == "logarithmic":
              fade = torch.log10(1.1 - fade) + 1
  
          if self.fade_shape == "quarter_sine":
              fade = torch.sin(fade * math.pi / 2 + math.pi / 2)
  
          if self.fade_shape == "half_sine":
              fade = torch.sin(fade * math.pi + math.pi / 2) / 2 + 0.5
  
          return torch.cat((ones, fade)).clamp_(0, 1)
  
  
  class _AxisMasking(torch.nn.Module):
      r"""Apply masking to a spectrogram.
  
      Args:
          mask_param (int): Maximum possible length of the mask.
          axis (int): What dimension the mask is applied on.
          iid_masks (bool): Applies iid masks to each of the examples in the batch dimension.
              This option is applicable only when the input tensor is 4D.
      """
      __constants__ = ['mask_param', 'axis', 'iid_masks']
  
      def __init__(self, mask_param: int, axis: int, iid_masks: bool) -> None:
  
          super(_AxisMasking, self).__init__()
          self.mask_param = mask_param
          self.axis = axis
          self.iid_masks = iid_masks
  
      def forward(self, specgram: Tensor, mask_value: float = 0.) -> Tensor:
          r"""
          Args:
              specgram (Tensor): Tensor of dimension (..., freq, time).
              mask_value (float): Value to assign to the masked columns.
  
          Returns:
              Tensor: Masked spectrogram of dimensions (..., freq, time).
          """
          # if iid_masks flag marked and specgram has a batch dimension
          if self.iid_masks and specgram.dim() == 4:
              return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis + 1)
          else:
              return F.mask_along_axis(specgram, self.mask_param, mask_value, self.axis)
  
  
  class FrequencyMasking(_AxisMasking):
      r"""Apply masking to a spectrogram in the frequency domain.
  
      Args:
          freq_mask_param (int): maximum possible length of the mask.
              Indices uniformly sampled from [0, freq_mask_param).
          iid_masks (bool, optional): whether to apply different masks to each
              example/channel in the batch. (Default: ``False``)
              This option is applicable only when the input tensor is 4D.
      """
  
      def __init__(self, freq_mask_param: int, iid_masks: bool = False) -> None:
          super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks)
  
  
  class TimeMasking(_AxisMasking):
      r"""Apply masking to a spectrogram in the time domain.
  
      Args:
          time_mask_param (int): maximum possible length of the mask.
              Indices uniformly sampled from [0, time_mask_param).
          iid_masks (bool, optional): whether to apply different masks to each
              example/channel in the batch. (Default: ``False``)
              This option is applicable only when the input tensor is 4D.
      """
  
      def __init__(self, time_mask_param: int, iid_masks: bool = False) -> None:
          super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks)
  
  
  class Vol(torch.nn.Module):
      r"""Add a volume to an waveform.
  
      Args:
          gain (float): Interpreted according to the given gain_type:
              If ``gain_type`` = ``amplitude``, ``gain`` is a positive amplitude ratio.
              If ``gain_type`` = ``power``, ``gain`` is a power (voltage squared).
              If ``gain_type`` = ``db``, ``gain`` is in decibels.
          gain_type (str, optional): Type of gain. One of: ``amplitude``, ``power``, ``db`` (Default: ``amplitude``)
      """
  
      def __init__(self, gain: float, gain_type: str = 'amplitude'):
          super(Vol, self).__init__()
          self.gain = gain
          self.gain_type = gain_type
  
          if gain_type in ['amplitude', 'power'] and gain < 0:
              raise ValueError("If gain_type = amplitude or power, gain must be positive.")
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Tensor of audio of dimension (..., time).
          """
          if self.gain_type == "amplitude":
              waveform = waveform * self.gain
  
          if self.gain_type == "db":
              waveform = F.gain(waveform, self.gain)
  
          if self.gain_type == "power":
              waveform = F.gain(waveform, 10 * math.log10(self.gain))
  
          return torch.clamp(waveform, -1, 1)
  
  
  class SlidingWindowCmn(torch.nn.Module):
      r"""
      Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
  
      Args:
          cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600)
          min_cmn_window (int, optional):  Minimum CMN window used at start of decoding (adds latency only at start).
              Only applicable if center == false, ignored if center==true (int, default = 100)
          center (bool, optional): If true, use a window centered on the current frame
              (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false)
          norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false)
      """
  
      def __init__(self,
                   cmn_window: int = 600,
                   min_cmn_window: int = 100,
                   center: bool = False,
                   norm_vars: bool = False) -> None:
          super().__init__()
          self.cmn_window = cmn_window
          self.min_cmn_window = min_cmn_window
          self.center = center
          self.norm_vars = norm_vars
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Tensor of audio of dimension (..., time).
          """
          cmn_waveform = F.sliding_window_cmn(
              waveform, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars)
          return cmn_waveform
  
  
  class Vad(torch.nn.Module):
      r"""Voice Activity Detector. Similar to SoX implementation.
      Attempts to trim silence and quiet background sounds from the ends of recordings of speech.
      The algorithm currently uses a simple cepstral power measurement to detect voice,
      so may be fooled by other things, especially music.
  
      The effect can trim only from the front of the audio,
      so in order to trim from the back, the reverse effect must also be used.
  
      Args:
          sample_rate (int): Sample rate of audio signal.
          trigger_level (float, optional): The measurement level used to trigger activity detection.
              This may need to be cahnged depending on the noise level, signal level,
              and other characteristics of the input audio. (Default: 7.0)
          trigger_time (float, optional): The time constant (in seconds)
              used to help ignore short bursts of sound. (Default: 0.25)
          search_time (float, optional): The amount of audio (in seconds)
              to search for quieter/shorter bursts of audio to include prior
              to the detected trigger point. (Default: 1.0)
          allowed_gap (float, optional): The allowed gap (in seconds) between
              quiteter/shorter bursts of audio to include prior
              to the detected trigger point. (Default: 0.25)
          pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve
              before the trigger point and any found quieter/shorter bursts. (Default: 0.0)
          boot_time (float, optional) The algorithm (internally) uses adaptive noise
              estimation/reduction in order to detect the start of the wanted audio.
              This option sets the time for the initial noise estimate. (Default: 0.35)
          noise_up_time (float, optional) Time constant used by the adaptive noise estimator
              for when the noise level is increasing. (Default: 0.1)
          noise_down_time (float, optional) Time constant used by the adaptive noise estimator
              for when the noise level is decreasing. (Default: 0.01)
          noise_reduction_amount (float, optional) Amount of noise reduction to use in
              the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35)
          measure_freq (float, optional) Frequency of the algorithm’s
              processing/measurements. (Default: 20.0)
          measure_duration: (float, optional) Measurement duration.
              (Default: Twice the measurement period; i.e. with overlap.)
          measure_smooth_time (float, optional) Time constant used to smooth
              spectral measurements. (Default: 0.4)
          hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied
              at the input to the detector algorithm. (Default: 50.0)
          lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied
              at the input to the detector algorithm. (Default: 6000.0)
          hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used
              in the detector algorithm. (Default: 150.0)
          lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used
              in the detector algorithm. (Default: 2000.0)
  
      Reference:
          - http://sox.sourceforge.net/sox.html
      """
  
      def __init__(self,
                   sample_rate: int,
                   trigger_level: float = 7.0,
                   trigger_time: float = 0.25,
                   search_time: float = 1.0,
                   allowed_gap: float = 0.25,
                   pre_trigger_time: float = 0.0,
                   boot_time: float = .35,
                   noise_up_time: float = .1,
                   noise_down_time: float = .01,
                   noise_reduction_amount: float = 1.35,
                   measure_freq: float = 20.0,
                   measure_duration: Optional[float] = None,
                   measure_smooth_time: float = .4,
                   hp_filter_freq: float = 50.,
                   lp_filter_freq: float = 6000.,
                   hp_lifter_freq: float = 150.,
                   lp_lifter_freq: float = 2000.) -> None:
          super().__init__()
  
          self.sample_rate = sample_rate
          self.trigger_level = trigger_level
          self.trigger_time = trigger_time
          self.search_time = search_time
          self.allowed_gap = allowed_gap
          self.pre_trigger_time = pre_trigger_time
          self.boot_time = boot_time
          self.noise_up_time = noise_up_time
          self.noise_down_time = noise_down_time
          self.noise_reduction_amount = noise_reduction_amount
          self.measure_freq = measure_freq
          self.measure_duration = measure_duration
          self.measure_smooth_time = measure_smooth_time
          self.hp_filter_freq = hp_filter_freq
          self.lp_filter_freq = lp_filter_freq
          self.hp_lifter_freq = hp_lifter_freq
          self.lp_lifter_freq = lp_lifter_freq
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)`
                  Tensor of shape `(channels, time)` is treated as a multi-channel recording
                  of the same event and the resulting output will be trimmed to the earliest
                  voice activity in any channel.
          """
          return F.vad(
              waveform=waveform,
              sample_rate=self.sample_rate,
              trigger_level=self.trigger_level,
              trigger_time=self.trigger_time,
              search_time=self.search_time,
              allowed_gap=self.allowed_gap,
              pre_trigger_time=self.pre_trigger_time,
              boot_time=self.boot_time,
              noise_up_time=self.noise_up_time,
              noise_down_time=self.noise_down_time,
              noise_reduction_amount=self.noise_reduction_amount,
              measure_freq=self.measure_freq,
              measure_duration=self.measure_duration,
              measure_smooth_time=self.measure_smooth_time,
              hp_filter_freq=self.hp_filter_freq,
              lp_filter_freq=self.lp_filter_freq,
              hp_lifter_freq=self.hp_lifter_freq,
              lp_lifter_freq=self.lp_lifter_freq,
          )
  
  
  class SpectralCentroid(torch.nn.Module):
      r"""Compute the spectral centroid for each channel along the time axis.
  
      The spectral centroid is defined as the weighted average of the
      frequency values, weighted by their magnitude.
  
      Args:
          sample_rate (int): Sample rate of audio signal.
          n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
          win_length (int or None, optional): Window size. (Default: ``n_fft``)
          hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
          pad (int, optional): Two sided padding of signal. (Default: ``0``)
          window_fn (Callable[..., Tensor], optional): A function to create a window tensor
              that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
          wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
  
      Example
          >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
          >>> transform = transforms.SpectralCentroid(sample_rate)
          >>> spectral_centroid = transform(waveform)  # (channel, time)
      """
      __constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad']
  
      def __init__(self,
                   sample_rate: int,
                   n_fft: int = 400,
                   win_length: Optional[int] = None,
                   hop_length: Optional[int] = None,
                   pad: int = 0,
                   window_fn: Callable[..., Tensor] = torch.hann_window,
                   wkwargs: Optional[dict] = None) -> None:
          super(SpectralCentroid, self).__init__()
          self.sample_rate = sample_rate
          self.n_fft = n_fft
          self.win_length = win_length if win_length is not None else n_fft
          self.hop_length = hop_length if hop_length is not None else self.win_length // 2
          window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
          self.register_buffer('window', window)
          self.pad = pad
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: Spectral Centroid of size (..., time).
          """
  
          return F.spectral_centroid(waveform, self.sample_rate, self.pad, self.window, self.n_fft, self.hop_length,
                                     self.win_length)
  
  
  class PitchShift(torch.nn.Module):
      r"""Shift the pitch of a waveform by ``n_steps`` steps.
  
      Args:
          waveform (Tensor): The input waveform of shape `(..., time)`.
          sample_rate (float): Sample rate of `waveform`.
          n_steps (int): The (fractional) steps to shift `waveform`.
          bins_per_octave (int, optional): The number of steps per octave (Default : ``12``).
          n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins (Default: ``512``).
          win_length (int or None, optional): Window size. If None, then ``n_fft`` is used. (Default: ``None``).
          hop_length (int or None, optional): Length of hop between STFT windows. If None, then ``win_length // 4``
              is used (Default: ``None``).
          window (Tensor or None, optional): Window tensor that is applied/multiplied to each frame/window.
              If None, then ``torch.hann_window(win_length)`` is used (Default: ``None``).
  
      Example
          >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
          >>> transform = transforms.PitchShift(sample_rate, 4)
          >>> waveform_shift = transform(waveform)  # (channel, time)
      """
      __constants__ = ['sample_rate', 'n_steps', 'bins_per_octave', 'n_fft', 'win_length', 'hop_length']
  
      def __init__(self,
                   sample_rate: int,
                   n_steps: int,
                   bins_per_octave: int = 12,
                   n_fft: int = 512,
                   win_length: Optional[int] = None,
                   hop_length: Optional[int] = None,
                   window_fn: Callable[..., Tensor] = torch.hann_window,
                   wkwargs: Optional[dict] = None) -> None:
          super(PitchShift, self).__init__()
          self.n_steps = n_steps
          self.bins_per_octave = bins_per_octave
          self.sample_rate = sample_rate
          self.n_fft = n_fft
          self.win_length = win_length if win_length is not None else n_fft
          self.hop_length = hop_length if hop_length is not None else self.win_length // 4
          window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
          self.register_buffer('window', window)
  
      def forward(self, waveform: Tensor) -> Tensor:
          r"""
          Args:
              waveform (Tensor): Tensor of audio of dimension (..., time).
  
          Returns:
              Tensor: The pitch-shifted audio of shape `(..., time)`.
          """
  
          return F.pitch_shift(waveform, self.sample_rate, self.n_steps, self.bins_per_octave, self.n_fft,
                               self.win_length, self.hop_length, self.window)
  