class_docstr: Compute CTC label sequence scores
  
  which is based on Algorithm 2 in WATANABE et al.
  "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
  but extended to efficiently compute the probablities of multiple labels
  simultaneously
class_docstr_tok: ["Compute","CTC","label","sequence","scores","which","is","based","on","Algorithm","2","in","WATANABE","et","al",".","``","HYBRID","CTC\/ATTENTION","ARCHITECTURE","FOR","END-TO-END","SPEECH","RECOGNITION",",","''","but","extended","to","efficiently","compute","the","probablities","of","multiple","labels","simultaneously"]
code: def __init__(self, x, blank, eos, xp):
      self.xp = xp
      self.logzero = -10000000000.0
      self.blank = blank
      self.eos = eos
      self.input_length = len(x)
      self.x = x
  
file_path: espnet/espnet/nets/ctc_prefix_score.py
imports: import numpy
  import torch
label_desc: Use image node_id 1 to create a new CTCPrefixScore object from inherited base classes: object with example: obj = CTCPrefixScore(x, blank, eos, xp)