class_docstr: Class for sound class embedding training" 
class_docstr_tok: ["Class","for","sound","class","embedding","training","''"]
docstr: The helper function to create debugging images
docstr_tok: ["The","helper","function","to","create","debugging","images"]
code: def debug_files(self, X_stft, xhat, X_stft_logpower, batch, wavs):
      """The helper function to create debugging images"""
      X_stft_phase = spectral_phase(X_stft)
      temp = xhat[0].transpose(0, 1).unsqueeze(0).unsqueeze(-1)
      Xspec_est = torch.expm1(temp.permute(0, 2, 1, 3))
      xhat_tm = self.invert_stft_with_phase(Xspec_est, X_stft_phase)
  
      Tmax = Xspec_est.shape[1]
      if self.hparams.use_mask_output:
          X_masked = xhat[0] * X_stft_logpower[0, :Tmax, :]
      else:
          th = xhat[0].max() * 0.15
          X_masked = (xhat[0] > th) * X_stft_logpower[0, :Tmax, :]
  
      X_est_masked = torch.expm1(X_masked).unsqueeze(0).unsqueeze(-1)
      xhat_tm_masked = self.invert_stft_with_phase(
          X_est_masked, X_stft_phase
      )
  
      plt.figure(figsize=(10, 5), dpi=100)
  
      plt.subplot(141)
      X_target = (
          X_stft_logpower[0].permute(1, 0)[:, : xhat.shape[1]].cpu()
      )
      plt.imshow(X_target)
      plt.colorbar()
  
      plt.subplot(142)
      input_masked = X_target > (
          X_target.max(keepdim=True, dim=-1)[0].max(
              keepdim=True, dim=-2
          )[0]
          * self.hparams.mask_th
      )
      plt.imshow(input_masked)
      plt.title("input masked")
      plt.colorbar()
  
      plt.subplot(143)
      if self.hparams.use_mask_output:
          mask = xhat[0]
      else:
          mask = xhat[0] > th  # (xhat[0] / xhat[0] + 1e-10)
      X_masked = mask * X_stft_logpower[0, :Tmax, :]
      plt.imshow(X_masked.permute(1, 0).data.cpu())
      plt.colorbar()
      plt.title("masked")
  
      plt.subplot(144)
      plt.imshow(mask.permute(1, 0).data.cpu())
      plt.colorbar()
      plt.title("mask")
  
      out_folder = os.path.join(
          self.hparams.output_folder,
          "reconstructions/" f"{batch.id[0]}",
      )
      makedirs(
          out_folder,
          exist_ok=True,
      )
  
      plt.savefig(
          os.path.join(out_folder, "reconstructions.png"),
          format="png",
      )
      plt.close()
  
      torchaudio.save(
          os.path.join(out_folder, "reconstruction.wav"),
          xhat_tm.data.cpu(),
          self.hparams.sample_rate,
      )
  
      torchaudio.save(
          os.path.join(out_folder, "reconstruction_masked.wav"),
          xhat_tm_masked.data.cpu(),
          self.hparams.sample_rate,
      )
  
      torchaudio.save(
          os.path.join(out_folder, "true.wav"),
          wavs[0:1].data.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
  import matplotlib.pyplot
label_desc: Use image node_id 7 for calling the InterpreterESC50Brain obj's underlying member method code with example usage: obj.debug_files(X_stft, xhat, X_stft_logpower, batch, wavs) without return types