Source code for espnet2.asr.transducer.error_calculator

"""Error Calculator module for Transducer."""

from typing import List, Tuple

import torch

from espnet2.asr.decoder.abs_decoder import AbsDecoder
from espnet2.asr.transducer.beam_search_transducer import BeamSearchTransducer


[docs]class ErrorCalculatorTransducer(object): """Calculate CER and WER for transducer models. Args: decoder: Decoder module. token_list: List of tokens. sym_space: Space symbol. sym_blank: Blank symbol. report_cer: Whether to compute CER. report_wer: Whether to compute WER. """ def __init__( self, decoder: AbsDecoder, joint_network: torch.nn.Module, token_list: List[int], sym_space: str, sym_blank: str, report_cer: bool = False, report_wer: bool = False, ): """Construct an ErrorCalculatorTransducer.""" super().__init__() self.beam_search = BeamSearchTransducer( decoder=decoder, joint_network=joint_network, beam_size=2, search_type="default", score_norm=False, ) self.decoder = decoder self.token_list = token_list self.space = sym_space self.blank = sym_blank self.report_cer = report_cer self.report_wer = report_wer def __call__(self, encoder_out: torch.Tensor, target: torch.Tensor): """Calculate sentence-level WER/CER score for Transducer model. Args: encoder_out: Encoder output sequences. (B, T, D_enc) target: Target label ID sequences. (B, L) Returns: : Sentence-level CER score. : Sentence-level WER score. """ cer, wer = None, None batchsize = int(encoder_out.size(0)) batch_nbest = [] encoder_out = encoder_out.to(next(self.decoder.parameters()).device) for b in range(batchsize): nbest_hyps = self.beam_search(encoder_out[b]) batch_nbest.append(nbest_hyps) pred = [nbest_hyp[0].yseq[1:] for nbest_hyp in batch_nbest] char_pred, char_target = self.convert_to_char(pred, target) if self.report_cer: cer = self.calculate_cer(char_pred, char_target) if self.report_wer: wer = self.calculate_wer(char_pred, char_target) return cer, wer
[docs] def convert_to_char( self, pred: torch.Tensor, target: torch.Tensor ) -> Tuple[List, List]: """Convert label ID sequences to character sequences. Args: pred: Prediction label ID sequences. (B, U) target: Target label ID sequences. (B, L) Returns: char_pred: Prediction character sequences. (B, ?) char_target: Target character sequences. (B, ?) """ char_pred, char_target = [], [] for i, pred_i in enumerate(pred): char_pred_i = [self.token_list[int(h)] for h in pred_i] char_target_i = [self.token_list[int(r)] for r in target[i]] char_pred_i = "".join(char_pred_i).replace(self.space, " ") char_pred_i = char_pred_i.replace(self.blank, "") char_target_i = "".join(char_target_i).replace(self.space, " ") char_target_i = char_target_i.replace(self.blank, "") char_pred.append(char_pred_i) char_target.append(char_target_i) return char_pred, char_target
[docs] def calculate_cer( self, char_pred: torch.Tensor, char_target: torch.Tensor ) -> float: """Calculate sentence-level CER score. Args: char_pred: Prediction character sequences. (B, ?) char_target: Target character sequences. (B, ?) Returns: : Average sentence-level CER score. """ import editdistance distances, lens = [], [] for i, char_pred_i in enumerate(char_pred): pred = char_pred_i.replace(" ", "") target = char_target[i].replace(" ", "") distances.append(editdistance.eval(pred, target)) lens.append(len(target)) return float(sum(distances)) / sum(lens)
[docs] def calculate_wer( self, char_pred: torch.Tensor, char_target: torch.Tensor ) -> float: """Calculate sentence-level WER score. Args: char_pred: Prediction character sequences. (B, ?) char_target: Target character sequences. (B, ?) Returns: : Average sentence-level WER score """ import editdistance distances, lens = [], [] for i, char_pred_i in enumerate(char_pred): pred = char_pred_i.split() target = char_target[i].split() distances.append(editdistance.eval(pred, target)) lens.append(len(target)) return float(sum(distances)) / sum(lens)