class_docstr: A dataset representing speech and corresponding transcription.
  
  Args:
      aud_paths: (List[str]): A list of str with paths to audio files.
      aud_durations_ms (List[int]): A list of int containing the durations of
          audio files.
      tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
          of target transcriptions.
      tgt_dict (~fairseq.data.Dictionary): target vocabulary.
      ids (List[str]): A list of utterance IDs.
      speakers (List[str]): A list of speakers corresponding to utterances.
      num_mel_bins (int): Number of triangular mel-frequency bins (default: 80)
      frame_length (float): Frame length in milliseconds (default: 25.0)
      frame_shift (float): Frame shift in milliseconds (default: 10.0)
docstr: Return an ordered list of indices. Batches will be constructed based
  on this order.
docstr_tok: ["Return","an","ordered","list","of","indices",".","Batches","will","be","constructed","based","on","this","order","."]
code: def ordered_indices(self):
      """Return an ordered list of indices. Batches will be constructed based
      on this order."""
      return np.arange(len(self))
  
file_path: fairseq/examples/speech_recognition/data/asr_dataset.py
imports: import os
  import numpy
  from fairseq.data import FairseqDataset
  from .None import data_utils
  from .collaters import Seq2SeqCollater
label_desc: Use image node_id 7 for calling the AsrDataset obj's underlying member method code with example usage: obj.ordered_indices() and returns: np