code: def logmelspc_to_linearspc(
      lmspc, fs, n_mels, n_fft, fmin=None, fmax=None
  ):
      """Convert log Mel filterbank to linear spectrogram.
  
      Args:
          lmspc (ndarray): Log Mel filterbank (T, n_mels).
          fs (int): Sampling frequency.
          n_mels (int): Number of mel basis.
          n_fft (int): Number of FFT points.
          f_min (int, optional): Minimum frequency to analyze.
          f_max (int, optional): Maximum frequency to analyze.
  
      Returns:
          ndarray: Linear spectrogram (T, n_fft // 2 + 1).
  
      """
      assert lmspc.shape[1] == n_mels
      fmin = 0 if fmin is None else fmin
      fmax = fs / 2 if fmax is None else fmax
      mspc = np.power(10.0, lmspc)
      mel_basis = librosa.filters.mel(
          sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax
      )
      inv_mel_basis = np.linalg.pinv(mel_basis)
      spc = np.maximum(EPS, np.dot(inv_mel_basis, mspc.T).T)
  
      return spc
  
file_path: espnet/utils/convert_fbank_to_wav.py
imports: import argparse
  import logging
  import os
  import librosa
  import numpy
  from packaging.version import parse
  from scipy.io.wavfile import write
  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 for calling a global function with example usage: logmelspc_to_linearspc(lmspc, fs, n_mels, n_fft, fmin, fmax) and returns: spc