class_docstr: Mozilla Common Voice Dataset.
class_docstr_tok: ["Mozilla","Common","Voice","Dataset","."]
docstr: Returns generators for each split in the dataset.
docstr_tok: ["Returns","generators","for","each","split","in","the","dataset","."]
code: def _split_generators(self, dl_manager, pipeline):
      """Returns generators for each split in the dataset."""
      beam = tfds.core.lazy_imports.apache_beam
  
      language = self.builder_config.language
      dl_path = dl_manager.download(_download_url(language))
  
      examples_per_file = {}
      for path_within_archive, file_obj in dl_manager.iter_archive(
          dl_path
      ):
          if not path_within_archive.endswith(".tsv"):
              continue
          examples_per_file[path_within_archive] = _parse_examples(
              file_obj.read().decode("utf-8")
          )
  
      clips_pipeline = self._clips_pipeline(
          pipeline=pipeline,
          name="clips",
          dl_manager=dl_manager,
          dl_path=dl_path,
      )
  
      generators = {}
      generate_examples = beam.ptransform_fn(self._generate_examples)
      for split, split_filename in _SPLITS.items():
          filename = (
              _DATA_FOLDER_IN_ARCHIVE
              / language
              / f"{split_filename}.tsv"
          )
          examples = examples_per_file.get(str(filename), None)
          if not examples:
              continue
          generators[
              split
          ] = pipeline | f"{split}-{language}" >> generate_examples(
              clips_pipeline=clips_pipeline, examples=examples
          )
      return generators
  
imports: import csv
  import dataclasses
  import functools
  import io
  import re
  from typing import Any, Mapping, Sequence
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the CommonVoice obj's underlying member method code with example usage: obj._split_generators(dl_manager, pipeline) and returns: generators