class_docstr: The audio part of CREMA-D dataset for emotion recognition.
class_docstr_tok: ["The","audio","part","of","CREMA-D","dataset","for","emotion","recognition","."]
docstr: Returns SplitGenerators.
docstr_tok: ["Returns","SplitGenerators","."]
code: def _split_generators(self, dl_manager):
      """Returns SplitGenerators."""
      dl_manager.download_checksums(_CHECKSUMS_URL)
      csv_path = dl_manager.download(
          {"summary_table": SUMMARY_TABLE_URL}
      )
      all_wav_files = []
      speaker_ids = []
      wav_names = []
      # These are file names which do do not exist in the github
      bad_files = set(
          [
              "FileName",
              "1040_ITH_SAD_XX",
              "1006_TIE_NEU_XX",
              "1013_WSI_DIS_XX",
              "1017_IWW_FEA_XX",
          ]
      )
      with tf.io.gfile.GFile(csv_path["summary_table"]) as f:
          for line in f:
              wav_name = line.strip().split(",")[1].replace('"', "")
              if (not wav_name) or (wav_name in bad_files):
                  continue
              wav_path = os.path.join(WAV_DATA_URL, "%s.wav" % wav_name)
              all_wav_files.append(wav_path)
              speaker_ids.append(wav_name.split("_")[0])
              wav_names.append(wav_name)
      all_wav_files = dl_manager.download({"all_files": all_wav_files})
      all_wav_files = list(zip(all_wav_files["all_files"], wav_names))
      wav_and_speaker_ids = list(zip(all_wav_files, speaker_ids))
      split_probs = [("train", 0.7), ("validation", 0.1), ("test", 0.2)]
      splits = _get_inter_splits_by_group(
          wav_and_speaker_ids, split_probs, 0
      )
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs={"file_paths_and_names": splits["train"]},
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              gen_kwargs={"file_paths_and_names": splits["validation"]},
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              gen_kwargs={"file_paths_and_names": splits["test"]},
          ),
      ]
  
file_path: datasets/tensorflow_datasets/audio/crema_d.py
imports: from __future__ import annotations
  import collections
  import os
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the CremaD obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list