docstr: Compute ITD and ILD errors
docstr_tok: ["Compute","ITD","and","ILD","errors"]
code: def cal_interaural_error(self, predictions, targets):
      """Compute ITD and ILD errors"""
  
      EPS = 1e-8
      s_target = targets[0]  # [T,E,C]
      s_prediction = predictions[0]  # [T,E,C]
  
      # ITD is computed with generalized cross-correlation phase transform (GCC-PHAT)
      ITD_target = [
          tdoa(
              s_target[:, 0, i].cpu().numpy(),
              s_target[:, 1, i].cpu().numpy(),
              fs=self.hparams.sample_rate,
          )
          * 10**6
          for i in range(s_target.shape[-1])
      ]
      ITD_prediction = [
          tdoa(
              s_prediction[:, 0, i].cpu().numpy(),
              s_prediction[:, 1, i].cpu().numpy(),
              fs=self.hparams.sample_rate,
          )
          * 10**6
          for i in range(s_prediction.shape[-1])
      ]
      ITD_error1 = np.mean(
          np.abs(np.array(ITD_target) - np.array(ITD_prediction))
      )
      ITD_error2 = np.mean(
          np.abs(np.array(ITD_target) - np.array(ITD_prediction)[::-1])
      )
      ITD_error = min(ITD_error1, ITD_error2)
  
      # ILD  = 10 * log_10(||s_left||^2 / ||s_right||^2)
      ILD_target_beforelog = torch.sum(s_target[:, 0] ** 2, dim=0) / (
          torch.sum(s_target[:, 1] ** 2, dim=0) + EPS
      )
      ILD_target = 10 * torch.log10(ILD_target_beforelog + EPS)  # [C]
      ILD_prediction_beforelog = torch.sum(
          s_prediction[:, 0] ** 2, dim=0
      ) / (torch.sum(s_prediction[:, 1] ** 2, dim=0) + EPS)
      ILD_prediction = 10 * torch.log10(
          ILD_prediction_beforelog + EPS
      )  # [C]
  
      ILD_error1 = torch.mean(torch.abs(ILD_target - ILD_prediction))
      ILD_error2 = torch.mean(
          torch.abs(ILD_target - ILD_prediction.flip(0))
      )
      ILD_error = min(ILD_error1.item(), ILD_error2.item())
  
      return ITD_error, ILD_error
  
imports: import os
  import sys
  import torch
  import torch.nn.functional
  import torchaudio
  import speechbrain
  import speechbrain.nnet.schedulers
  from speechbrain.utils.distributed import run_on_main
  from hyperpyyaml import load_hyperpyyaml
  import numpy
  from tqdm import tqdm
  import csv
  import logging
  from pyroomacoustics.experimental.localization import tdoa
  from speechbrain.processing.features import STFT, spectral_magnitude
  from torch.nn import Conv1d
  from speechbrain.core import AMPConfig
label_desc: Use image node_id 9 for calling the Separation obj's underlying member method code with example usage: obj.cal_interaural_error(predictions, targets) and returns: ITD_error,  ILD_error