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funasr_local/models/e2e_asr_common.py

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+#!/usr/bin/env python3
+# encoding: utf-8
+
+# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Common functions for ASR."""
+
+import json
+import logging
+import sys
+
+from itertools import groupby
+import numpy as np
+import six
+
+
+def end_detect(ended_hyps, i, M=3, D_end=np.log(1 * np.exp(-10))):
+    """End detection.
+
+    described 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
+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
+
+
+def get_vgg2l_odim(idim, in_channel=3, out_channel=128):
+    """Return the output size of the VGG frontend.
+
+    :param in_channel: input channel size
+    :param out_channel: output channel size
+    :return: output size
+    :rtype int
+    """
+    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
+
+
+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
+    ):
+        """Construct an ErrorCalculator object."""
+        super(ErrorCalculator, self).__init__()
+
+        self.report_cer = report_cer
+        self.report_wer = report_wer
+
+        self.char_list = char_list
+        self.space = sym_space
+        self.blank = sym_blank
+        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):
+        """Calculate sentence-level WER/CER score.
+
+        :param torch.Tensor ys_hat: prediction (batch, seqlen)
+        :param torch.Tensor ys_pad: reference (batch, seqlen)
+        :param bool is_ctc: calculate CER score for CTC
+        :return: sentence-level WER score
+        :rtype float
+        :return: sentence-level CER score
+        :rtype float
+        """
+        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
+
+    def calculate_cer_ctc(self, ys_hat, ys_pad):
+        """Calculate sentence-level CER score for CTC.
+
+        :param torch.Tensor ys_hat: prediction (batch, seqlen)
+        :param torch.Tensor ys_pad: reference (batch, seqlen)
+        :return: average sentence-level CER score
+        :rtype float
+        """
+        import editdistance
+
+        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
+
+    def convert_to_char(self, ys_hat, ys_pad):
+        """Convert index to character.
+
+        :param torch.Tensor seqs_hat: prediction (batch, seqlen)
+        :param torch.Tensor seqs_true: reference (batch, seqlen)
+        :return: token list of prediction
+        :rtype list
+        :return: token list of reference
+        :rtype list
+        """
+        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]
+            ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
+            # NOTE: padding index (-1) in y_true is used to pad y_hat
+            seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
+            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
+
+    def calculate_cer(self, seqs_hat, seqs_true):
+        """Calculate sentence-level CER score.
+
+        :param list seqs_hat: prediction
+        :param list seqs_true: reference
+        :return: average sentence-level CER score
+        :rtype float
+        """
+        import editdistance
+
+        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)
+
+    def calculate_wer(self, seqs_hat, seqs_true):
+        """Calculate sentence-level WER score.
+
+        :param list seqs_hat: prediction
+        :param list seqs_true: reference
+        :return: average sentence-level WER score
+        :rtype float
+        """
+        import editdistance
+
+        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)