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: Merge a list of samples to form a mini-batch.
  
  Args:
      samples (List[int]): sample indices to collate
  
  Returns:
      dict: a mini-batch suitable for forwarding with a Model
docstr_tok: ["Merge","a","list","of","samples","to","form","a","mini-batch",".","Args",":","samples","(","List","[","int","]",")",":","sample","indices","to","collate","Returns",":","dict",":","a","mini-batch","suitable","for","forwarding","with","a","Model"]
code: def collater(self, samples):
      """Merge a list of samples to form a mini-batch.
  
      Args:
          samples (List[int]): sample indices to collate
  
      Returns:
          dict: a mini-batch suitable for forwarding with a Model
      """
      return self.s2s_collater.collate(samples)
  
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 4 for calling the AsrDataset obj's underlying member method code with example usage: obj.collater(samples) and returns: self