code: def window_sumsquare(
      window,
      n_frames,
      hop_length=200,
      win_length=800,
      n_fft=800,
      dtype=np.float32,
      norm=None,
  ):
      """
      # from librosa 0.6
      Compute the sum-square envelope of a window function at a given hop length.
  
      This is used to estimate modulation effects induced by windowing
      observations in short-time fourier transforms.
  
      Parameters
      ----------
      window : string, tuple, number, callable, or list-like
          Window specification, as in `get_window`
  
      n_frames : int > 0
          The number of analysis frames
  
      hop_length : int > 0
          The number of samples to advance between frames
  
      win_length : [optional]
          The length of the window function.  By default, this matches `n_fft`.
  
      n_fft : int > 0
          The length of each analysis frame.
  
      dtype : np.dtype
          The data type of the output
  
      Returns
      -------
      wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
          The sum-squared envelope of the window function
      """
      if win_length is None:
          win_length = n_fft
  
      n = n_fft + hop_length * (n_frames - 1)
      x = np.zeros(n, dtype=dtype)
  
      # Compute the squared window at the desired length
      win_sq = get_window(window, win_length, fftbins=True)
      win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2
      win_sq = librosa_util.pad_center(win_sq, n_fft)
  
      # Fill the envelope
      for i in range(n_frames):
          sample = i * hop_length
          x[sample : min(n, sample + n_fft)] += win_sq[
              : max(0, min(n_fft, n - sample))
          ]
      return x
  
file_path: textlesslib/textless/vocoders/tacotron2/audio_processing.py
imports: import torch
  import numpy
  from scipy.signal import get_window
  import librosa.util
label_desc: Use image node_id 1 for calling a global function with example usage: window_sumsquare(window, n_frames, hop_length, win_length, n_fft, dtype, norm) and returns: x