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)
code: def __getitem__(self, index):
      import torchaudio
      import torchaudio.compliance.kaldi as kaldi
  
      tgt_item = self.tgt[index] if self.tgt is not None else None
  
      path = self.aud_paths[index]
      if not os.path.exists(path):
          raise FileNotFoundError(
              "Audio file not found: {}".format(path)
          )
      sound, sample_rate = torchaudio.load_wav(path)
      output = kaldi.fbank(
          sound,
          num_mel_bins=self.num_mel_bins,
          frame_length=self.frame_length,
          frame_shift=self.frame_shift,
      )
      output_cmvn = data_utils.apply_mv_norm(output)
  
      return {"id": index, "data": [output_cmvn.detach(), tgt_item]}
  
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 2 for calling the AsrDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: dict