class_docstr: Problem spec for Librispeech using clean and noisy data.
code: def generate_data(self, data_dir, tmp_dir, task_id=-1):
      train_paths = self.training_filepaths(
          data_dir, self.num_shards, shuffled=False
      )
      dev_paths = self.dev_filepaths(
          data_dir, self.num_dev_shards, shuffled=False
      )
      test_paths = self.test_filepaths(
          data_dir, self.num_test_shards, shuffled=True
      )
  
      generator_utils.generate_files(
          self.generator(data_dir, tmp_dir, self.TEST_DATASETS),
          test_paths,
      )
  
      if self.use_train_shards_for_dev:
          all_paths = train_paths + dev_paths
          generator_utils.generate_files(
              self.generator(data_dir, tmp_dir, self.TRAIN_DATASETS),
              all_paths,
          )
          generator_utils.shuffle_dataset(all_paths)
      else:
          generator_utils.generate_dataset_and_shuffle(
              self.generator(data_dir, tmp_dir, self.TRAIN_DATASETS),
              train_paths,
              self.generator(data_dir, tmp_dir, self.DEV_DATASETS),
              dev_paths,
          )
  
imports: import os
  import tarfile
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import speech_recognition
  from tensor2tensor.utils import registry
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 7 for calling the Librispeech obj's underlying member method code with example usage: obj.generate_data(data_dir, tmp_dir, task_id) without return types