code: def llr(ref_wav, deg_wav, srate):
      """Calculate Log Likelihood Ratio (LLR) distortion measure
      between reference and degraded audio signals. This function
      computes the LLR distortion measure between reference and
      degraded audio signals using LPC analysis and autocorrelation
      logs.
      """
      clean_speech = ref_wav
      processed_speech = deg_wav
      clean_length = ref_wav.shape[0]
      processed_length = deg_wav.shape[0]
      assert clean_length == processed_length, clean_length
  
      winlength = round(30 * srate / 1000.0)  # 240 wlen in samples
      skiprate = np.floor(winlength / 4)
      if srate < 10000:
          # LPC analysis order
          P = 10
      else:
          P = 16
  
      # For each frame of input speech, calculate the Log Likelihood Ratio
      num_frames = int(clean_length / skiprate - (winlength / skiprate))
      start = 0
      time = np.linspace(1, winlength, winlength) / (winlength + 1)
      window = 0.5 * (1 - np.cos(2 * np.pi * time))
      distortion = []
  
      for frame_count in range(num_frames):
          # (1) Get the Frames for the test and reference speeech.
          # Multiply by Hanning window.
          clean_frame = clean_speech[start : start + winlength]
          processed_frame = processed_speech[start : start + winlength]
          clean_frame = clean_frame * window
          processed_frame = processed_frame * window
  
          # (2) Get the autocorrelation logs and LPC params used
          # to compute the LLR measure
          R_clean, Ref_clean, A_clean = lpcoeff(clean_frame, P)
          R_processed, Ref_processed, A_processed = lpcoeff(
              processed_frame, P
          )
          A_clean = A_clean[None, :]
          A_processed = A_processed[None, :]
  
          # (3) Compute the LLR measure
          numerator = A_processed.dot(toeplitz(R_clean)).dot(
              A_processed.T
          )
          denominator = A_clean.dot(toeplitz(R_clean)).dot(A_clean.T)
  
          if (numerator / denominator) <= 0:
              print(f"Numerator: {numerator}")
              print(f"Denominator: {denominator}")
  
          log_ = np.log(numerator / denominator)
          distortion.append(np.squeeze(log_))
          start += int(skiprate)
      return np.nan_to_num(np.array(distortion))
  
file_path: speechbrain/recipes/DNS/enhancement/composite_eval.py
imports: from scipy.linalg import toeplitz
  from tqdm import tqdm
  from pesq import pesq
  import librosa
  import numpy
  import os
  import sys
label_desc: Use image node_id 7 for calling a global function with example usage: llr(ref_wav, deg_wav, srate) and returns: np