class_docstr: A class for computing MOS scores using an ONNX model and polynomial fitting.
      
class_docstr_tok: ["A","class","for","computing","MOS","scores","using","an","ONNX","model","and","polynomial","fitting","."]
docstr: Calculate MOS scores using polynomial fitting.
  Returns a tuple containing MOS scores for speech,
  background, and overall quality.
docstr_tok: ["Calculate","MOS","scores","using","polynomial","fitting",".","Returns","a","tuple","containing","MOS","scores","for","speech",",","background",",","and","overall","quality","."]
code: def get_polyfit_val(self, sig, bak, ovr, is_personalized_MOS):
      """Calculate MOS scores using polynomial fitting.
      Returns a tuple containing MOS scores for speech,
      background, and overall quality.
      """
      # if is_personalized_MOS:
      #     p_ovr = np.poly1d([-0.00533021,  0.005101  ,  1.18058466, -0.11236046])
      #     p_sig = np.poly1d([-0.01019296,  0.02751166,  1.19576786, -0.24348726])
      #     p_bak = np.poly1d([-0.04976499,  0.44276479, -0.1644611 ,  0.96883132])
      # else:
      p_ovr = np.poly1d([-0.06766283, 1.11546468, 0.04602535])
      p_sig = np.poly1d([-0.08397278, 1.22083953, 0.0052439])
      p_bak = np.poly1d([-0.13166888, 1.60915514, -0.39604546])
  
      sig_poly = p_sig(sig)
      bak_poly = p_bak(bak)
      ovr_poly = p_ovr(ovr)
  
      return sig_poly, bak_poly, ovr_poly
  
file_path: speechbrain/recipes/DNS/enhancement/dnsmos_local.py
imports: import argparse
  import concurrent.futures
  import glob
  import os
  import librosa
  import numpy
  import onnxruntime
  import pandas
  import soundfile
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the ComputeScore obj's underlying member method code with example usage: obj.get_polyfit_val(sig, bak, ovr, is_personalized_MOS) and returns: sig_poly,  bak_poly,  ovr_poly