#!/usr/bin/env python3
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# encoding: utf-8
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# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Common functions for ASR."""
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from typing import List, Optional, Tuple
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import json
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import logging
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import sys
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from itertools import groupby
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import numpy as np
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import six
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import torch
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from funasr.modules.beam_search.beam_search_transducer import BeamSearchTransducer
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from funasr.models.rnnt_predictor.abs_decoder import AbsDecoder
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from funasr.models.joint_net.joint_network import JointNetwork
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def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):
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"""End detection.
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described in Eq. (50) of S. Watanabe et al
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"Hybrid CTC/Attention Architecture for End-to-End Speech Recognition"
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:param ended_hyps:
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:param i:
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:param M:
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:param D_end:
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:return:
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"""
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if len(ended_hyps) == 0:
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return False
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count = 0
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best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0]
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for m in six.moves.range(M):
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# get ended_hyps with their length is i - m
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hyp_length = i - m
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hyps_same_length = [x for x in ended_hyps if len(x["yseq"]) == hyp_length]
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if len(hyps_same_length) > 0:
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best_hyp_same_length = sorted(
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hyps_same_length, key=lambda x: x["score"], reverse=True
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)[0]
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if best_hyp_same_length["score"] - best_hyp["score"] < D_end:
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count += 1
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if count == M:
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return True
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else:
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return False
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# TODO(takaaki-hori): add different smoothing methods
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def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0):
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"""Obtain label distribution for loss smoothing.
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:param odim:
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:param lsm_type:
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:param blank:
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:param transcript:
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:return:
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"""
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if transcript is not None:
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with open(transcript, "rb") as f:
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trans_json = json.load(f)["utts"]
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if lsm_type == "unigram":
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assert transcript is not None, (
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"transcript is required for %s label smoothing" % lsm_type
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)
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labelcount = np.zeros(odim)
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for k, v in trans_json.items():
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ids = np.array([int(n) for n in v["output"][0]["tokenid"].split()])
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# to avoid an error when there is no text in an uttrance
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if len(ids) > 0:
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labelcount[ids] += 1
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labelcount[odim - 1] = len(transcript) # count <eos>
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labelcount[labelcount == 0] = 1 # flooring
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labelcount[blank] = 0 # remove counts for blank
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labeldist = labelcount.astype(np.float32) / np.sum(labelcount)
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else:
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logging.error("Error: unexpected label smoothing type: %s" % lsm_type)
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sys.exit()
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return labeldist
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def get_vgg2l_odim(idim, in_channel=3, out_channel=128):
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"""Return the output size of the VGG frontend.
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:param in_channel: input channel size
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:param out_channel: output channel size
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:return: output size
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:rtype int
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"""
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idim = idim / in_channel
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idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 1st max pooling
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idim = np.ceil(np.array(idim, dtype=np.float32) / 2) # 2nd max pooling
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return int(idim) * out_channel # numer of channels
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class ErrorCalculator(object):
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"""Calculate CER and WER for E2E_ASR and CTC models during training.
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:param y_hats: numpy array with predicted text
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:param y_pads: numpy array with true (target) text
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:param char_list:
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:param sym_space:
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:param sym_blank:
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:return:
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"""
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def __init__(
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self, char_list, sym_space, sym_blank, report_cer=False, report_wer=False
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):
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"""Construct an ErrorCalculator object."""
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super(ErrorCalculator, self).__init__()
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self.report_cer = report_cer
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self.report_wer = report_wer
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self.char_list = char_list
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self.space = sym_space
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self.blank = sym_blank
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self.idx_blank = self.char_list.index(self.blank)
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if self.space in self.char_list:
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self.idx_space = self.char_list.index(self.space)
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else:
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self.idx_space = None
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def __call__(self, ys_hat, ys_pad, is_ctc=False):
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"""Calculate sentence-level WER/CER score.
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:param torch.Tensor ys_hat: prediction (batch, seqlen)
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:param torch.Tensor ys_pad: reference (batch, seqlen)
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:param bool is_ctc: calculate CER score for CTC
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:return: sentence-level WER score
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:rtype float
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:return: sentence-level CER score
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:rtype float
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"""
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cer, wer = None, None
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if is_ctc:
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return self.calculate_cer_ctc(ys_hat, ys_pad)
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elif not self.report_cer and not self.report_wer:
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return cer, wer
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seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad)
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if self.report_cer:
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cer = self.calculate_cer(seqs_hat, seqs_true)
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if self.report_wer:
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wer = self.calculate_wer(seqs_hat, seqs_true)
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return cer, wer
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def calculate_cer_ctc(self, ys_hat, ys_pad):
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"""Calculate sentence-level CER score for CTC.
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:param torch.Tensor ys_hat: prediction (batch, seqlen)
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:param torch.Tensor ys_pad: reference (batch, seqlen)
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:return: average sentence-level CER score
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:rtype float
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"""
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import editdistance
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cers, char_ref_lens = [], []
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for i, y in enumerate(ys_hat):
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y_hat = [x[0] for x in groupby(y)]
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y_true = ys_pad[i]
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seq_hat, seq_true = [], []
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for idx in y_hat:
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idx = int(idx)
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if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
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seq_hat.append(self.char_list[int(idx)])
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for idx in y_true:
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idx = int(idx)
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if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
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seq_true.append(self.char_list[int(idx)])
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hyp_chars = "".join(seq_hat)
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ref_chars = "".join(seq_true)
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if len(ref_chars) > 0:
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cers.append(editdistance.eval(hyp_chars, ref_chars))
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char_ref_lens.append(len(ref_chars))
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cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
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return cer_ctc
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def convert_to_char(self, ys_hat, ys_pad):
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"""Convert index to character.
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:param torch.Tensor seqs_hat: prediction (batch, seqlen)
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:param torch.Tensor seqs_true: reference (batch, seqlen)
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:return: token list of prediction
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:rtype list
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:return: token list of reference
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:rtype list
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"""
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seqs_hat, seqs_true = [], []
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for i, y_hat in enumerate(ys_hat):
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y_true = ys_pad[i]
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eos_true = np.where(y_true == -1)[0]
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ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
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# NOTE: padding index (-1) in y_true is used to pad y_hat
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seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
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seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
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seq_hat_text = "".join(seq_hat).replace(self.space, " ")
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seq_hat_text = seq_hat_text.replace(self.blank, "")
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seq_true_text = "".join(seq_true).replace(self.space, " ")
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seqs_hat.append(seq_hat_text)
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seqs_true.append(seq_true_text)
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return seqs_hat, seqs_true
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def calculate_cer(self, seqs_hat, seqs_true):
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"""Calculate sentence-level CER score.
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:param list seqs_hat: prediction
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:param list seqs_true: reference
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:return: average sentence-level CER score
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:rtype float
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"""
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import editdistance
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char_eds, char_ref_lens = [], []
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for i, seq_hat_text in enumerate(seqs_hat):
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seq_true_text = seqs_true[i]
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hyp_chars = seq_hat_text.replace(" ", "")
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ref_chars = seq_true_text.replace(" ", "")
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char_eds.append(editdistance.eval(hyp_chars, ref_chars))
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char_ref_lens.append(len(ref_chars))
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return float(sum(char_eds)) / sum(char_ref_lens)
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def calculate_wer(self, seqs_hat, seqs_true):
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"""Calculate sentence-level WER score.
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:param list seqs_hat: prediction
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:param list seqs_true: reference
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:return: average sentence-level WER score
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:rtype float
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"""
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import editdistance
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word_eds, word_ref_lens = [], []
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for i, seq_hat_text in enumerate(seqs_hat):
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seq_true_text = seqs_true[i]
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hyp_words = seq_hat_text.split()
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ref_words = seq_true_text.split()
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word_eds.append(editdistance.eval(hyp_words, ref_words))
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word_ref_lens.append(len(ref_words))
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return float(sum(word_eds)) / sum(word_ref_lens)
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class ErrorCalculatorTransducer:
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"""Calculate CER and WER for transducer models.
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Args:
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decoder: Decoder module.
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joint_network: Joint Network module.
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token_list: List of token units.
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sym_space: Space symbol.
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sym_blank: Blank symbol.
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report_cer: Whether to compute CER.
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report_wer: Whether to compute WER.
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"""
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def __init__(
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self,
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decoder: AbsDecoder,
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joint_network: JointNetwork,
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token_list: List[int],
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sym_space: str,
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sym_blank: str,
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report_cer: bool = False,
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report_wer: bool = False,
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) -> None:
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"""Construct an ErrorCalculatorTransducer object."""
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super().__init__()
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self.beam_search = BeamSearchTransducer(
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decoder=decoder,
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joint_network=joint_network,
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beam_size=1,
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search_type="default",
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score_norm=False,
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)
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self.decoder = decoder
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self.token_list = token_list
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self.space = sym_space
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self.blank = sym_blank
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self.report_cer = report_cer
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self.report_wer = report_wer
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def __call__(
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self, encoder_out: torch.Tensor, target: torch.Tensor
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) -> Tuple[Optional[float], Optional[float]]:
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"""Calculate sentence-level WER or/and CER score for Transducer model.
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Args:
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encoder_out: Encoder output sequences. (B, T, D_enc)
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target: Target label ID sequences. (B, L)
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Returns:
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: Sentence-level CER score.
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: Sentence-level WER score.
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"""
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cer, wer = None, None
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batchsize = int(encoder_out.size(0))
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encoder_out = encoder_out.to(next(self.decoder.parameters()).device)
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batch_nbest = [self.beam_search(encoder_out[b]) for b in range(batchsize)]
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pred = [nbest_hyp[0].yseq[1:] for nbest_hyp in batch_nbest]
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char_pred, char_target = self.convert_to_char(pred, target)
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if self.report_cer:
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cer = self.calculate_cer(char_pred, char_target)
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if self.report_wer:
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wer = self.calculate_wer(char_pred, char_target)
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return cer, wer
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def convert_to_char(
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self, pred: torch.Tensor, target: torch.Tensor
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) -> Tuple[List, List]:
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"""Convert label ID sequences to character sequences.
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Args:
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pred: Prediction label ID sequences. (B, U)
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target: Target label ID sequences. (B, L)
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Returns:
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char_pred: Prediction character sequences. (B, ?)
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char_target: Target character sequences. (B, ?)
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"""
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char_pred, char_target = [], []
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for i, pred_i in enumerate(pred):
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char_pred_i = [self.token_list[int(h)] for h in pred_i]
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char_target_i = [self.token_list[int(r)] for r in target[i]]
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char_pred_i = "".join(char_pred_i).replace(self.space, " ")
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char_pred_i = char_pred_i.replace(self.blank, "")
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char_target_i = "".join(char_target_i).replace(self.space, " ")
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char_target_i = char_target_i.replace(self.blank, "")
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char_pred.append(char_pred_i)
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char_target.append(char_target_i)
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return char_pred, char_target
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def calculate_cer(
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self, char_pred: torch.Tensor, char_target: torch.Tensor
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) -> float:
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"""Calculate sentence-level CER score.
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Args:
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char_pred: Prediction character sequences. (B, ?)
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char_target: Target character sequences. (B, ?)
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Returns:
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: Average sentence-level CER score.
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"""
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import editdistance
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distances, lens = [], []
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for i, char_pred_i in enumerate(char_pred):
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pred = char_pred_i.replace(" ", "")
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target = char_target[i].replace(" ", "")
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distances.append(editdistance.eval(pred, target))
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lens.append(len(target))
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return float(sum(distances)) / sum(lens)
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def calculate_wer(
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self, char_pred: torch.Tensor, char_target: torch.Tensor
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) -> float:
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"""Calculate sentence-level WER score.
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Args:
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char_pred: Prediction character sequences. (B, ?)
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char_target: Target character sequences. (B, ?)
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Returns:
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: Average sentence-level WER score
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"""
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import editdistance
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distances, lens = [], []
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for i, char_pred_i in enumerate(char_pred):
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pred = char_pred_i.replace("▁", " ").split()
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target = char_target[i].replace("▁", " ").split()
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distances.append(editdistance.eval(pred, target))
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lens.append(len(target))
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return float(sum(distances)) / sum(lens)
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