code: def dataio_prep(hparams):
      """Creates data processing pipeline"""
  
      # 1. Define datasets
      train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
          csv_path=hparams["train_data"],
          replacements={"data_root": hparams["data_folder_nspks"]},
      )
  
      valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
          csv_path=hparams["valid_data"],
          replacements={"data_root": hparams["data_folder_nspks"]},
      )
  
      test_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
          csv_path=hparams["test_data"],
          replacements={"data_root": hparams["data_folder_nspks"]},
      )
  
      datasets = [train_data, valid_data, test_data]
  
      # 2. Provide audio pipelines
  
      @sb.utils.data_pipeline.takes("mix_wav")
      @sb.utils.data_pipeline.provides("mix_sig")
      def audio_pipeline_mix(mix_wav):
          mix_sig = sb.dataio.dataio.read_audio(mix_wav)
          return mix_sig
  
      @sb.utils.data_pipeline.takes("s1_wav")
      @sb.utils.data_pipeline.provides("s1_sig")
      def audio_pipeline_s1(s1_wav):
          s1_sig = sb.dataio.dataio.read_audio(s1_wav)
          return s1_sig
  
      @sb.utils.data_pipeline.takes("s2_wav")
      @sb.utils.data_pipeline.provides("s2_sig")
      def audio_pipeline_s2(s2_wav):
          s2_sig = sb.dataio.dataio.read_audio(s2_wav)
          return s2_sig
  
      if hparams["num_spks"] == 3:
  
          @sb.utils.data_pipeline.takes("s3_wav")
          @sb.utils.data_pipeline.provides("s3_sig")
          def audio_pipeline_s3(s3_wav):
              s3_sig = sb.dataio.dataio.read_audio(s3_wav)
              return s3_sig
  
      if hparams["use_wham_noise"]:
  
          @sb.utils.data_pipeline.takes("noise_wav")
          @sb.utils.data_pipeline.provides("noise_sig")
          def audio_pipeline_noise(noise_wav):
              noise_sig = sb.dataio.dataio.read_audio(noise_wav)
              return noise_sig
  
      sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_mix)
      sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s1)
      sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline_s2)
      if hparams["num_spks"] == 3:
          sb.dataio.dataset.add_dynamic_item(
              datasets, audio_pipeline_s3
          )
  
      if hparams["use_wham_noise"]:
          print("Using the WHAM! noise in the data pipeline")
          sb.dataio.dataset.add_dynamic_item(
              datasets, audio_pipeline_noise
          )
  
      if (hparams["num_spks"] == 2) and hparams["use_wham_noise"]:
          sb.dataio.dataset.set_output_keys(
              datasets,
              ["id", "mix_sig", "s1_sig", "s2_sig", "noise_sig"],
          )
      elif (hparams["num_spks"] == 3) and hparams["use_wham_noise"]:
          sb.dataio.dataset.set_output_keys(
              datasets,
              [
                  "id",
                  "mix_sig",
                  "s1_sig",
                  "s2_sig",
                  "s3_sig",
                  "noise_sig",
              ],
          )
      elif (hparams["num_spks"] == 2) and not hparams["use_wham_noise"]:
          sb.dataio.dataset.set_output_keys(
              datasets, ["id", "mix_sig", "s1_sig", "s2_sig"]
          )
      else:
          sb.dataio.dataset.set_output_keys(
              datasets, ["id", "mix_sig", "s1_sig", "s2_sig", "s3_sig"]
          )
  
      return train_data, valid_data, test_data
  
imports: import os
  import csv
  import sys
  import torch
  import torchaudio
  import numpy
  from tqdm import tqdm
  import speechbrain
  import torch.nn.functional
  import speechbrain.nnet.schedulers
  from speechbrain.utils.distributed import run_on_main
  from hyperpyyaml import load_hyperpyyaml
  import logging
  from speechbrain.core import AMPConfig
label_desc: Use image node_id 1 for calling a global function with example usage: dataio_prep(hparams) and returns: train_data,  valid_data,  test_data, mix_sig, s1_sig, s2_sig, s3_sig, noise_sig