#!/usr/bin/env python
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import editdistance
import json
import logging
import numpy as np
import six
import sys
from itertools import groupby
[docs]def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):
"""End detection
desribed in Eq. (50) of S. Watanabe et al
"Hybrid CTC/Attention Architecture for End-to-End Speech Recognition"
:param ended_hyps:
:param i:
:param M:
:param D_end:
:return:
"""
if len(ended_hyps) == 0:
return False
count = 0
best_hyp = sorted(ended_hyps, key=lambda x: x['score'], reverse=True)[0]
for m in six.moves.range(M):
# get ended_hyps with their length is i - m
hyp_length = i - m
hyps_same_length = [x for x in ended_hyps if len(x['yseq']) == hyp_length]
if len(hyps_same_length) > 0:
best_hyp_same_length = sorted(hyps_same_length, key=lambda x: x['score'], reverse=True)[0]
if best_hyp_same_length['score'] - best_hyp['score'] < D_end:
count += 1
if count == M:
return True
else:
return False
# TODO(takaaki-hori): add different smoothing methods
[docs]def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0):
"""Obtain label distribution for loss smoothing
:param odim:
:param lsm_type:
:param blank:
:param transcript:
:return:
"""
if transcript is not None:
with open(transcript, 'rb') as f:
trans_json = json.load(f)['utts']
if lsm_type == 'unigram':
assert transcript is not None, 'transcript is required for %s label smoothing' % lsm_type
labelcount = np.zeros(odim)
for k, v in trans_json.items():
ids = np.array([int(n) for n in v['output'][0]['tokenid'].split()])
# to avoid an error when there is no text in an uttrance
if len(ids) > 0:
labelcount[ids] += 1
labelcount[odim - 1] = len(transcript) # count <eos>
labelcount[labelcount == 0] = 1 # flooring
labelcount[blank] = 0 # remove counts for blank
labeldist = labelcount.astype(np.float32) / np.sum(labelcount)
else:
logging.error(
"Error: unexpected label smoothing type: %s" % lsm_type)
sys.exit()
return labeldist
[docs]def get_vgg2l_odim(idim, in_channel=3, out_channel=128):
idim = idim / in_channel
idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 1st max pooling
idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 2nd max pooling
return int(idim) * out_channel # numer of channels
[docs]class ErrorCalculator(object):
"""Calculate CER and WER for E2E_ASR and CTC models during training
:param y_hats: numpy array with predicted text
:param y_pads: numpy array with true (target) text
:param char_list:
:param sym_space:
:param sym_blank:
:return:
"""
def __init__(self, char_list, sym_space, sym_blank,
report_cer=False, report_wer=False):
super(ErrorCalculator, self).__init__()
self.char_list = char_list
self.space = sym_space
self.blank = sym_blank
self.report_cer = report_cer
self.report_wer = report_wer
self.idx_blank = self.char_list.index(self.blank)
if self.space in self.char_list:
self.idx_space = self.char_list.index(self.space)
else:
self.idx_space = None
def __call__(self, ys_hat, ys_pad, is_ctc=False):
cer, wer = None, None
if is_ctc:
return self.calculate_cer_ctc(ys_hat, ys_pad)
elif not self.report_cer and not self.report_wer:
return cer, wer
seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad)
if self.report_cer:
cer = self.calculate_cer(seqs_hat, seqs_true)
if self.report_wer:
wer = self.calculate_wer(seqs_hat, seqs_true)
return cer, wer
[docs] def calculate_cer_ctc(self, ys_hat, ys_pad):
cers, char_ref_lens = [], []
for i, y in enumerate(ys_hat):
y_hat = [x[0] for x in groupby(y)]
y_true = ys_pad[i]
seq_hat, seq_true = [], []
for idx in y_hat:
idx = int(idx)
if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
seq_hat.append(self.char_list[int(idx)])
for idx in y_true:
idx = int(idx)
if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
seq_true.append(self.char_list[int(idx)])
hyp_chars = "".join(seq_hat)
ref_chars = "".join(seq_true)
if len(ref_chars) > 0:
cers.append(editdistance.eval(hyp_chars, ref_chars))
char_ref_lens.append(len(ref_chars))
cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
return cer_ctc
[docs] def convert_to_char(self, ys_hat, ys_pad):
seqs_hat, seqs_true = [], []
for i, y_hat in enumerate(ys_hat):
y_true = ys_pad[i]
eos_true = np.where(y_true == -1)[0]
eos_true = eos_true[0] if len(eos_true) > 0 else len(y_true)
# To avoid wrong higger WER than the one obtained from the decoding
# eos from y_true is used to mark the eos in y_hat
# because of that y_hats has not padded outs with -1.
seq_hat = [self.char_list[int(idx)] for idx in y_hat[:eos_true]]
seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
seq_hat_text = "".join(seq_hat).replace(self.space, ' ')
seq_hat_text = seq_hat_text.replace(self.blank, '')
seq_true_text = "".join(seq_true).replace(self.space, ' ')
seqs_hat.append(seq_hat_text)
seqs_true.append(seq_true_text)
return seqs_hat, seqs_true
[docs] def calculate_cer(self, seqs_hat, seqs_true):
char_eds, char_ref_lens = [], []
for i, seq_hat_text in enumerate(seqs_hat):
seq_true_text = seqs_true[i]
hyp_chars = seq_hat_text.replace(' ', '')
ref_chars = seq_true_text.replace(' ', '')
char_eds.append(editdistance.eval(hyp_chars, ref_chars))
char_ref_lens.append(len(ref_chars))
return float(sum(char_eds)) / sum(char_ref_lens)
[docs] def calculate_wer(self, seqs_hat, seqs_true):
word_eds, word_ref_lens = [], []
for i, seq_hat_text in enumerate(seqs_hat):
seq_true_text = seqs_true[i]
hyp_words = seq_hat_text.split()
ref_words = seq_true_text.split()
word_eds.append(editdistance.eval(hyp_words, ref_words))
word_ref_lens.append(len(ref_words))
return float(sum(word_eds)) / sum(word_ref_lens)