code: def eval_composite(ref_wav, deg_wav):
      ref_wav = ref_wav.reshape(-1)
      deg_wav = deg_wav.reshape(-1)
  
      alpha = 0.95
      len_ = min(ref_wav.shape[0], deg_wav.shape[0])
      ref_wav = ref_wav[:len_]
      deg_wav = deg_wav[:len_]
  
      # Compute WSS measure
      wss_dist_vec = wss(ref_wav, deg_wav, 16000)
      wss_dist_vec = sorted(wss_dist_vec, reverse=False)
      wss_dist = np.mean(
          wss_dist_vec[: int(round(len(wss_dist_vec) * alpha))]
      )
  
      # Compute LLR measure
      LLR_dist = llr(ref_wav, deg_wav, 16000)
      LLR_dist = sorted(LLR_dist, reverse=False)
      LLRs = LLR_dist
      LLR_len = round(len(LLR_dist) * alpha)
      llr_mean = np.mean(LLRs[:LLR_len])
  
      # Compute the SSNR
      snr_mean, segsnr_mean = SSNR(ref_wav, deg_wav, 16000)
      segSNR = np.mean(segsnr_mean)
  
      # Compute the PESQ
      pesq_raw = PESQ(ref_wav, deg_wav)
  
      Csig = (
          3.093 - 1.029 * llr_mean + 0.603 * pesq_raw - 0.009 * wss_dist
      )
      Csig = trim_mos(Csig)
      Cbak = (
          1.634 + 0.478 * pesq_raw - 0.007 * wss_dist + 0.063 * segSNR
      )
      Cbak = trim_mos(Cbak)
      Covl = (
          1.594 + 0.805 * pesq_raw - 0.512 * llr_mean - 0.007 * wss_dist
      )
      Covl = trim_mos(Covl)
  
      return {"csig": Csig, "cbak": Cbak, "covl": Covl}
  
file_path: speechbrain/recipes/Voicebank/MTL/ASR_enhance/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 1 for calling a global function with example usage: eval_composite(ref_wav, deg_wav) and returns: dict