class_docstr: Class for sound class embedding training" 
class_docstr_tok: ["Class","for","sound","class","embedding","training","''"]
docstr: Pre-process wavs.
docstr_tok: ["Pre-process","wavs","."]
code: def preprocess(self, wavs):
      """Pre-process wavs."""
      X_stft = self.modules.compute_stft(wavs)
      X_stft_power = sb.processing.features.spectral_magnitude(
          X_stft, power=self.hparams.spec_mag_power
      )
      X_stft_logpower = torch.log1p(X_stft_power)
  
      return X_stft_logpower, X_stft, X_stft_power
  
imports: import os
  import sys
  import torch
  import torchaudio
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from esc50_prepare import prepare_esc50
  from speechbrain.utils.metric_stats import MetricStats
  from os import makedirs
  import torch.nn.functional
  from speechbrain.processing.NMF import spectral_phase
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.preprocess(wavs) and returns: X_stft_logpower,  X_stft,  X_stft_power