code: def audio_melspec(
      self,
      audio,
      n_mels=120,
      frame_size=320,
      hop_length=160,
      sr=16000,
      to_db=True,
  ):
      if self.convert_to_torch:
          specgram = self.spectrogram(audio)
          fb = torch.as_tensor(
              librosa.filters.mel(
                  sr=sr, n_fft=frame_size + 1, n_mels=n_mels
              ).T,
              dtype=audio.dtype,
              device=audio.device,
          )
          mel_spec = torch.matmul(
              specgram.transpose(-1, -2), fb
          ).transpose(-1, -2)
          if to_db:
              self.to_db.db_multiplier = math.log10(
                  max(self.to_db.amin, torch.max(mel_spec))
              )
              mel_spec = (self.to_db(mel_spec) + 40) / 40
      else:
          mel_spec = librosa.feature.melspectrogram(
              y=audio,
              sr=sr,
              n_fft=frame_size + 1,
              hop_length=hop_length,
              n_mels=n_mels,
          )
          if to_db:
              mel_spec = (
                  librosa.power_to_db(mel_spec, ref=np.max) + 40
              ) / 40
      return mel_spec.T
  
file_path: espnet/espnet2/enh/layers/dnsmos.py
imports: import json
  import math
  import librosa
  import numpy
  import requests
  import torch
  import torchaudio
label_desc: Use image node_id 2 for calling the DNSMOS_local obj's underlying member method code with example usage: obj.audio_melspec(audio, n_mels, frame_size, hop_length, sr, to_db) and returns: mel_spec