"""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)