code: def wer_details_for_batch(ids, refs, hyps, compute_alignments=False):
      """Convenient batch interface for ``wer_details_by_utterance``.
  
      ``wer_details_by_utterance`` can handle missing hypotheses, but
      sometimes (e.g. CTC training with greedy decoding) they are not needed,
      and this is a convenient interface in that case.
  
      Arguments
      ---------
      ids : list, torch.tensor
          Utterance ids for the batch.
      refs : list, torch.tensor
          Reference sequences.
      hyps : list, torch.tensor
          Hypothesis sequences.
      compute_alignments : bool, optional
          Whether to compute alignments or not. If computed, the details
          will also store the refs and hyps. (default: False)
  
      Returns
      -------
      list
          See ``wer_details_by_utterance``
  
      Example
      -------
      >>> ids = [['utt1'], ['utt2']]
      >>> refs = [[['a','b','c']], [['d','e']]]
      >>> hyps = [[['a','b','d']], [['d','e']]]
      >>> wer_details = []
      >>> for ids_batch, refs_batch, hyps_batch in zip(ids, refs, hyps):
      ...     details = wer_details_for_batch(ids_batch, refs_batch, hyps_batch)
      ...     wer_details.extend(details)
      >>> print(wer_details[0]['key'], ":",
      ...     "{:.2f}".format(wer_details[0]['WER']))
      utt1 : 33.33
      """
      refs = _batch_to_dict_format(ids, refs)
      hyps = _batch_to_dict_format(ids, hyps)
      return wer_details_by_utterance(
          refs,
          hyps,
          compute_alignments=compute_alignments,
          scoring_mode="strict",
      )
  
file_path: speechbrain/speechbrain/utils/edit_distance.py
imports: import collections
label_desc: Use image node_id 7 for calling a global function with example usage: wer_details_for_batch(ids, refs, hyps, compute_alignments) and returns: wer_details_by_utterance