code: def split_process_librispeech(
      datasets,
      transforms,
      language_model,
      root,
      folder_in_archive,
  ):
      def create(tags, cache=True):
          if isinstance(tags, str):
              tags = [tags]
          if isinstance(transforms, list):
              transform_list = transforms
          else:
              transform_list = [transforms]
  
          data = torch.utils.data.ConcatDataset(
              [
                  Processed(
                      LIBRISPEECH(
                          root,
                          tag,
                          folder_in_archive=folder_in_archive,
                          download=False,
                      ),
                      transform,
                      language_model.encode,
                  )
                  for tag, transform in zip(tags, transform_list)
              ]
          )
  
          data = MapMemoryCache(data)
          return data
  
      # For performance, we cache all datasets
      return tuple(create(dataset) for dataset in datasets)
  
file_path: audio/examples/pipeline_wav2letter/datasets.py
imports: import torch
  from torchaudio.datasets import LIBRISPEECH
label_desc: Use image node_id 1 for calling a global function with example usage: split_process_librispeech(datasets, transforms, language_model, root, folder_in_archive) and returns: tuple, data