class_docstr: This class calculates Viterbi alignments in the forward method.
  
  It also records alignments and creates batches of them for use
  in Viterbi training.
  
  Arguments
  ---------
  states_per_phoneme : int
      Number of hidden states to use per phoneme.
  output_folder : str
      It is the folder that the alignments will be stored in when
      saved to disk. Not yet implemented.
  neg_inf : float
      The float used to represent a negative infinite log probability.
      Using `-float("Inf")` tends to give numerical instability.
      A number more negative than -1e5 also sometimes gave errors when
      the `genbmm` library was used (currently not in use). (default: -1e5)
  batch_reduction : string
      One of "none", "sum" or "mean".
      What kind of batch-level reduction to apply to the loss calculated
      in the forward method.
  input_len_norm : bool
      Whether to normalize the loss in the forward method by the length of
      the inputs.
  target_len_norm : bool
      Whether to normalize the loss in the forward method by the length of
      the targets.
  lexicon_path : string
      The location of the lexicon.
  
  Example
  -------
  >>> log_posteriors = torch.tensor([[[ -1., -10., -10.],
  ...                                 [-10.,  -1., -10.],
  ...                                 [-10., -10.,  -1.]],
  ...
  ...                                [[ -1., -10., -10.],
  ...                                 [-10.,  -1., -10.],
  ...                                 [-10., -10., -10.]]])
  >>> lens = torch.tensor([1., 0.66])
  >>> phns = torch.tensor([[0, 1, 2],
  ...                      [0, 1, 0]])
  >>> phn_lens = torch.tensor([1., 0.66])
  >>> aligner = HMMAligner()
  >>> forward_scores = aligner(
  ...        log_posteriors, lens, phns, phn_lens, 'forward'
  ... )
  >>> forward_scores.shape
  torch.Size([2])
  >>> viterbi_scores, alignments = aligner(
  ...        log_posteriors, lens, phns, phn_lens, 'viterbi'
  ... )
  >>> alignments
  [[0, 1, 2], [0, 1]]
  >>> viterbi_scores.shape
  torch.Size([2])
docstr: Converts alignments to 1 state per phoneme style.
  
  Arguments
  ---------
  alignments : list of ints
      Predicted alignments for a single utterance.
  
  Returns
  -------
  sequence : list of ints
      The predicted alignments converted to a 1 state per phoneme style.
  
  Example
  -------
  >>> aligner = HMMAligner(states_per_phoneme = 3)
  >>> alignments = [0, 1, 2, 3, 4, 5, 3, 4, 5, 0, 1, 2]
  >>> sequence = aligner.collapse_alignments(alignments)
  >>> sequence
  [0, 1, 1, 0]
docstr_tok: ["Converts","alignments","to","1","state","per","phoneme","style",".","Arguments","--","--","--","--","-","alignments",":","list","of","ints","Predicted","alignments","for","a","single","utterance",".","Returns","--","--","--","-","sequence",":","list","of","ints","The","predicted","alignments","converted","to","a","1","state","per","phoneme","style",".","Example","--","--","--","-",">",">",">","aligner","=","HMMAligner","(","states_per_phoneme","=","3",")",">",">",">","alignments","=","[","0",",","1",",","2",",","3",",","4",",","5",",","3",",","4",",","5",",","0",",","1",",","2","]",">",">",">","sequence","=","aligner.collapse_alignments","(","alignments",")",">",">",">","sequence","[","0",",","1",",","1",",","0","]"]
code: def collapse_alignments(self, alignments):
      """
      Converts alignments to 1 state per phoneme style.
  
      Arguments
      ---------
      alignments : list of ints
          Predicted alignments for a single utterance.
  
      Returns
      -------
      sequence : list of ints
          The predicted alignments converted to a 1 state per phoneme style.
  
      Example
      -------
      >>> aligner = HMMAligner(states_per_phoneme = 3)
      >>> alignments = [0, 1, 2, 3, 4, 5, 3, 4, 5, 0, 1, 2]
      >>> sequence = aligner.collapse_alignments(alignments)
      >>> sequence
      [0, 1, 1, 0]
      """
  
      # Filter the repetitions
      sequence = [
          v
          for i, v in enumerate(alignments)
          if i == 0 or v != alignments[i - 1]
      ]
  
      # Pick out only multiples of self.states_per_phoneme
      sequence = [
          v for v in sequence if v % self.states_per_phoneme == 0
      ]
  
      # Divide by self.states_per_phoneme
      sequence = [v // self.states_per_phoneme for v in sequence]
  
      return sequence
  
imports: import torch
  import random
  from speechbrain.utils.checkpoints import register_checkpoint_hooks
  from speechbrain.utils.checkpoints import mark_as_saver
  from speechbrain.utils.checkpoints import mark_as_loader
  from speechbrain.utils.data_utils import undo_padding
label_desc: Use image node_id 18 for calling the HMMAligner obj's underlying member method code with example usage: obj.collapse_alignments(alignments) and returns: sequence