class_docstr: Time invariant MLSA filter.
  
  This module is used to perform noise shaping described in
  `An investigation of noise shaping with perceptual
   weighting for WaveNet-based speech generation`_.
  
  Args:
      coef (ndaaray): MLSA filter coefficient (D,).
      alpha (float): All pass constant value.
      n_shift (int): Shift length in points.
  
  .. _`An investigation of noise shaping with perceptual
      weighting for WaveNet-based speech generation`:
      https://ieeexplore.ieee.org/abstract/document/8461332
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code: def __init__(self, coef, alpha, n_shift):
      self.coef = coef
      self.n_shift = n_shift
      self.mlsa_filter = pysptk.synthesis.Synthesizer(
          pysptk.synthesis.MLSADF(order=coef.shape[0] - 1, alpha=alpha),
          hopsize=n_shift,
      )
  
file_path: espnet/utils/generate_wav_from_fbank.py
imports: import argparse
  import logging
  import os
  import time
  import h5py
  import numpy
  import pysptk
  import torch
  from scipy.io.wavfile import write
  from sklearn.preprocessing import StandardScaler
  from espnet.nets.pytorch_backend.wavenet import WaveNet, decode_mu_law, encode_mu_law
  from espnet.utils.cli_readers import file_reader_helper
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 1 to create a new TimeInvariantMLSAFilter object from inherited base classes: object with example: obj = TimeInvariantMLSAFilter(coef, alpha, n_shift)