class_docstr: Class for sound class embedding training" 
docstr: interpration test with overlapped audio
docstr_tok: ["interpration","test","with","overlapped","audio"]
code: def overlap_test(self, batch):
      """interpration test with overlapped audio"""
      wavs, _ = batch.sig
      wavs = wavs.to(self.device)
  
      s1 = wavs[0]
      s2 = wavs[1]
  
      # create the mixture with s2 being the noise (lower gain)
      mix = (s1 + (s2 * 0.2)).unsqueeze(0)
  
      # get the interpretation spectrogram, phase, and the predicted class
      X_int, X_stft_phase, pred_cl = self.interpret_computation_steps(
          mix
      )
  
      X_stft_phase_sb = torch.cat(
          (
              torch.cos(X_stft_phase).unsqueeze(-1),
              torch.sin(X_stft_phase).unsqueeze(-1),
          ),
          dim=-1,
      )
  
      temp = X_int.transpose(0, 1).unsqueeze(0).unsqueeze(-1)
  
      X_stft_phase_sb = X_stft_phase_sb[:, : temp.shape[1], :, :]
      X_wpsb = temp * X_stft_phase_sb
      x_int_sb = self.modules.compute_istft(X_wpsb)
  
      # save reconstructed and original spectrograms
      # epoch = self.hparams.epoch_counter.current
      current_class_ind = batch.class_string_encoded.data[0].item()
      current_class_name = self.hparams.label_encoder.ind2lab[
          current_class_ind
      ]
      predicted_class_name = self.hparams.label_encoder.ind2lab[pred_cl]
  
      noise_class_ind = batch.class_string_encoded.data[1].item()
      noise_class_name = self.hparams.label_encoder.ind2lab[
          noise_class_ind
      ]
  
      out_folder = os.path.join(
          self.hparams.output_folder,
          "overlap_test",
          f"tc_{current_class_name}_nc_{noise_class_name}_pc_{predicted_class_name}",
      )
      makedirs(
          out_folder,
          exist_ok=True,
      )
  
      torchaudio.save(
          os.path.join(out_folder, "mixture.wav"),
          mix.cpu(),
          self.hparams.sample_rate,
      )
  
      torchaudio.save(
          os.path.join(out_folder, "source.wav"),
          s1.unsqueeze(0).cpu(),
          self.hparams.sample_rate,
      )
  
      torchaudio.save(
          os.path.join(out_folder, "noise.wav"),
          s2.unsqueeze(0).cpu(),
          self.hparams.sample_rate,
      )
  
      torchaudio.save(
          os.path.join(out_folder, "interpretation.wav"),
          x_int_sb.cpu(),
          self.hparams.sample_rate,
      )
  
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
label_desc: Use image node_id 3 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.overlap_test(batch) without return types