docstr: Computes the oracle si-snrs
docstr_tok: ["Computes","the","oracle","si-snrs"]
code: def compute_oracle_sisnrs(self, predictions, targets):
      """Computes the oracle si-snrs"""
  
      snr1_1 = self.hparams.loss(
          targets[:, :, 0:1], predictions[:, :, 0:1]
      )
      snr1_2 = self.hparams.loss(
          targets[:, :, 1:2], predictions[:, :, 0:1]
      )
      snr1, ind1 = torch.stack([snr1_1, snr1_2]).min(0)
  
      ind2 = 1 - ind1
      snr2 = self.hparams.loss(
          targets[:, :, ind2], predictions[:, :, 1:2]
      )
      return torch.stack([-snr1, -snr2], dim=1)
  
imports: import os
  import sys
  import torch
  import speechbrain
  import speechbrain.nnet.schedulers
  from speechbrain.utils.distributed import run_on_main
  from hyperpyyaml import load_hyperpyyaml
  import itertools
  from tqdm import tqdm
  import numpy
  import logging
  import csv
  from speechbrain.core import AMPConfig
label_desc: Use image node_id 4 for calling the Separation obj's underlying member method code with example usage: obj.compute_oracle_sisnrs(predictions, targets) and returns: torch