code: def compute_melspec(wav, sample_rate=SAMPLE_RATE):
      melspec = librosa.feature.melspectrogram(
          y=wav,
          sr=sample_rate,
          n_fft=N_FFT,
          fmin=F_MIN,
          fmax=F_MAX,
          n_mels=N_MELS,
          hop_length=HOP_LEN,
      )
      melspec = librosa.power_to_db(melspec).astype(np.float32)
      return melspec
  
file_path: gradio/demo/musical_instrument_identification/data_setups.py
imports: import os
  import librosa
  import torch
  import numpy
  from torchaudio.transforms import Resample
label_desc: Use image node_id 5 for calling a global function with example usage: compute_melspec(wav, sample_rate) and returns: melspec