class_docstr: Librispeech dataset.
class_docstr_tok: ["Librispeech","dataset","."]
code: def _split_generators(self, dl_manager):
      archive_path = dl_manager.download(_DL_URLS[self.config.name])
      # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
      local_extracted_archive = (
          dl_manager.extract(archive_path)
          if not dl_manager.is_streaming
          else {}
      )
  
      if self.config.name == "clean":
          train_splits = [
              datasets.SplitGenerator(
                  name="train.100",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "train.100"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["train.100"]
                      ),
                  },
              ),
              datasets.SplitGenerator(
                  name="train.360",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "train.360"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["train.360"]
                      ),
                  },
              ),
          ]
          dev_splits = [
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION,
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "dev"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["dev"]
                      ),
                  },
              )
          ]
          test_splits = [
              datasets.SplitGenerator(
                  name=datasets.Split.TEST,
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "test"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["test"]
                      ),
                  },
              )
          ]
      elif self.config.name == "other":
          train_splits = [
              datasets.SplitGenerator(
                  name="train.500",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "train.500"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["train.500"]
                      ),
                  },
              )
          ]
          dev_splits = [
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION,
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "dev"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["dev"]
                      ),
                  },
              )
          ]
          test_splits = [
              datasets.SplitGenerator(
                  name=datasets.Split.TEST,
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "test"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["test"]
                      ),
                  },
              )
          ]
      elif self.config.name == "all":
          # train_splits = [
          #    datasets.SplitGenerator(
          #        name="train.clean.100",
          #        gen_kwargs={
          #            "local_extracted_archive": local_extracted_archive.get("train.clean.100"),
          #            "files": dl_manager.iter_archive(archive_path["train.clean.100"]),
          #        },
          #    ),
          #    datasets.SplitGenerator(
          #        name="train.clean.360",
          #        gen_kwargs={
          #            "local_extracted_archive": local_extracted_archive.get("train.clean.360"),
          #            "files": dl_manager.iter_archive(archive_path["train.clean.360"]),
          #        },
          #    ),
          #    datasets.SplitGenerator(
          #        name="train.other.500",
          #        gen_kwargs={
          #            "local_extracted_archive": local_extracted_archive.get("train.other.500"),
          #            "files": dl_manager.iter_archive(archive_path["train.other.500"]),
          #        },
          #    ),
          # ]
          dev_splits = [
              datasets.SplitGenerator(
                  name="validation.clean",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "dev.clean"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["dev.clean"]
                      ),
                  },
              ),
              datasets.SplitGenerator(
                  name="validation.other",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "dev.other"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["dev.other"]
                      ),
                  },
              ),
          ]
          test_splits = [
              datasets.SplitGenerator(
                  name="test.clean",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "test.clean"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["test.clean"]
                      ),
                  },
              ),
              datasets.SplitGenerator(
                  name="test.other",
                  gen_kwargs={
                      "local_extracted_archive": local_extracted_archive.get(
                          "test.other"
                      ),
                      "files": dl_manager.iter_archive(
                          archive_path["test.other"]
                      ),
                  },
              ),
          ]
  
      return train_splits + dev_splits + test_splits
  
file_path: BigDL/python/llm/test/benchmark/whisper/librispeech_asr.py
imports: import os
  import datasets
  from datasets.tasks import AutomaticSpeechRecognition
label_desc: Use image node_id 2 for calling the LibrispeechASR obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: unknown