avg model, compute wer, format rsl into paddlespeech.dataset

examples/aishell/asr1/path.sh

@@ -27,5 +27,3 @@ export KALDI_ROOT=${MAIN_ROOT}/tools/kaldi
 export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
 [ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present, can not using Kaldi!"
 [ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh
-
-unset GREP_OPTIONS

paddlespeech/dataset/s2t/__init__.py

@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # s2t utils binaries.
+from .avg_model import main as avg_ckpts_main
 from .build_vocab import main as build_vocab_main
 from .compute_mean_std import main as compute_mean_std_main
+from .compute_wer import main as compute_wer_main
 from .format_data import main as format_data_main
+from .format_rsl import main as format_rsl_main

paddlespeech/dataset/s2t/avg_model.py

@@ -0,0 +1,127 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import glob
+import json
+import os
+
+import numpy as np
+import paddle
+
+
+def define_argparse():
+    parser = argparse.ArgumentParser(description='average model')
+    parser.add_argument('--dst_model', required=True, help='averaged model')
+    parser.add_argument(
+        '--ckpt_dir', required=True, help='ckpt model dir for average')
+    parser.add_argument(
+        '--val_best', action="store_true", help='averaged model')
+    parser.add_argument(
+        '--num', default=5, type=int, help='nums for averaged model')
+    parser.add_argument(
+        '--min_epoch',
+        default=0,
+        type=int,
+        help='min epoch used for averaging model')
+    parser.add_argument(
+        '--max_epoch',
+        default=65536,  # Big enough
+        type=int,
+        help='max epoch used for averaging model')
+
+    args = parser.parse_args()
+    return args
+
+
+def average_checkpoints(dst_model="",
+                        ckpt_dir="",
+                        val_best=True,
+                        num=5,
+                        min_epoch=0,
+                        max_epoch=65536):
+    paddle.set_device('cpu')
+
+    val_scores = []
+    beat_val_scores = None
+    selected_epochs = None
+
+    jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
+    jsons = sorted(jsons, key=os.path.getmtime, reverse=True)
+    for y in jsons:
+        with open(y, 'r') as f:
+            dic_json = json.load(f)
+        loss = dic_json['val_loss']
+        epoch = dic_json['epoch']
+        if epoch >= args.min_epoch and epoch <= args.max_epoch:
+            val_scores.append((epoch, loss))
+    val_scores = np.array(val_scores)
+
+    if args.val_best:
+        sort_idx = np.argsort(val_scores[:, 1])
+        sorted_val_scores = val_scores[sort_idx]
+    else:
+        sorted_val_scores = val_scores
+
+    beat_val_scores = sorted_val_scores[:args.num, 1]
+    selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
+    avg_val_score = np.mean(beat_val_scores)
+    print("selected val scores = " + str(beat_val_scores))
+    print("selected epochs = " + str(selected_epochs))
+    print("averaged val score = " + str(avg_val_score))
+
+    path_list = [
+        args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
+        for epoch in sorted_val_scores[:args.num, 0]
+    ]
+    print(path_list)
+
+    avg = None
+    num = args.num
+    assert num == len(path_list)
+    for path in path_list:
+        print(f'Processing {path}')
+        states = paddle.load(path)
+        if avg is None:
+            avg = states
+        else:
+            for k in avg.keys():
+                avg[k] += states[k]
+    # average
+    for k in avg.keys():
+        if avg[k] is not None:
+            avg[k] /= num
+
+    paddle.save(avg, args.dst_model)
+    print(f'Saving to {args.dst_model}')
+
+    meta_path = os.path.splitext(args.dst_model)[0] + '.avg.json'
+    with open(meta_path, 'w') as f:
+        data = json.dumps({
+            "mode": 'val_best' if args.val_best else 'latest',
+            "avg_ckpt": args.dst_model,
+            "val_loss_mean": avg_val_score,
+            "ckpts": path_list,
+            "epochs": selected_epochs.tolist(),
+            "val_losses": beat_val_scores.tolist(),
+        })
+        f.write(data + "\n")
+
+
+def main():
+    args = define_argparse()
+    average_checkpoints(args)
+
+
+if __name__ == '__main__':
+    main()

paddlespeech/dataset/s2t/compute_wer.py

@@ -0,0 +1,558 @@
+# Copyright 2021 Mobvoi Inc. All Rights Reserved.
+# flake8: noqa
+import codecs
+import re
+import sys
+import unicodedata
+
+remove_tag = True
+spacelist = [' ', '\t', '\r', '\n']
+puncts = [
+    '!', ',', '?', '、', '。', '！', '，', '；', '？', '：', '「', '」', '︰', '『', '』',
+    '《', '》'
+]
+
+
+def characterize(string):
+    res = []
+    i = 0
+    while i < len(string):
+        char = string[i]
+        if char in puncts:
+            i += 1
+            continue
+        cat1 = unicodedata.category(char)
+        #https://unicodebook.readthedocs.io/unicode.html#unicode-categories
+        if cat1 == 'Zs' or cat1 == 'Cn' or char in spacelist:  # space or not assigned
+            i += 1
+            continue
+        if cat1 == 'Lo':  # letter-other
+            res.append(char)
+            i += 1
+        else:
+            # some input looks like: <unk><noise>, we want to separate it to two words.
+            sep = ' '
+            if char == '<': sep = '>'
+            j = i + 1
+            while j < len(string):
+                c = string[j]
+                if ord(c) >= 128 or (c in spacelist) or (c == sep):
+                    break
+                j += 1
+            if j < len(string) and string[j] == '>':
+                j += 1
+            res.append(string[i:j])
+            i = j
+    return res
+
+
+def stripoff_tags(x):
+    if not x: return ''
+    chars = []
+    i = 0
+    T = len(x)
+    while i < T:
+        if x[i] == '<':
+            while i < T and x[i] != '>':
+                i += 1
+            i += 1
+        else:
+            chars.append(x[i])
+            i += 1
+    return ''.join(chars)
+
+
+def normalize(sentence, ignore_words, cs, split=None):
+    """ sentence, ignore_words are both in unicode
+    """
+    new_sentence = []
+    for token in sentence:
+        x = token
+        if not cs:
+            x = x.upper()
+        if x in ignore_words:
+            continue
+        if remove_tag:
+            x = stripoff_tags(x)
+        if not x:
+            continue
+        if split and x in split:
+            new_sentence += split[x]
+        else:
+            new_sentence.append(x)
+    return new_sentence
+
+
+class Calculator:
+    def __init__(self):
+        self.data = {}
+        self.space = []
+        self.cost = {}
+        self.cost['cor'] = 0
+        self.cost['sub'] = 1
+        self.cost['del'] = 1
+        self.cost['ins'] = 1
+
+    def calculate(self, lab, rec):
+        # Initialization
+        lab.insert(0, '')
+        rec.insert(0, '')
+        while len(self.space) < len(lab):
+            self.space.append([])
+        for row in self.space:
+            for element in row:
+                element['dist'] = 0
+                element['error'] = 'non'
+            while len(row) < len(rec):
+                row.append({'dist': 0, 'error': 'non'})
+        for i in range(len(lab)):
+            self.space[i][0]['dist'] = i
+            self.space[i][0]['error'] = 'del'
+        for j in range(len(rec)):
+            self.space[0][j]['dist'] = j
+            self.space[0][j]['error'] = 'ins'
+        self.space[0][0]['error'] = 'non'
+        for token in lab:
+            if token not in self.data and len(token) > 0:
+                self.data[token] = {
+                    'all': 0,
+                    'cor': 0,
+                    'sub': 0,
+                    'ins': 0,
+                    'del': 0
+                }
+        for token in rec:
+            if token not in self.data and len(token) > 0:
+                self.data[token] = {
+                    'all': 0,
+                    'cor': 0,
+                    'sub': 0,
+                    'ins': 0,
+                    'del': 0
+                }
+        # Computing edit distance
+        for i, lab_token in enumerate(lab):
+            for j, rec_token in enumerate(rec):
+                if i == 0 or j == 0:
+                    continue
+                min_dist = sys.maxsize
+                min_error = 'none'
+                dist = self.space[i - 1][j]['dist'] + self.cost['del']
+                error = 'del'
+                if dist < min_dist:
+                    min_dist = dist
+                    min_error = error
+                dist = self.space[i][j - 1]['dist'] + self.cost['ins']
+                error = 'ins'
+                if dist < min_dist:
+                    min_dist = dist
+                    min_error = error
+                if lab_token == rec_token:
+                    dist = self.space[i - 1][j - 1]['dist'] + self.cost['cor']
+                    error = 'cor'
+                else:
+                    dist = self.space[i - 1][j - 1]['dist'] + self.cost['sub']
+                    error = 'sub'
+                if dist < min_dist:
+                    min_dist = dist
+                    min_error = error
+                self.space[i][j]['dist'] = min_dist
+                self.space[i][j]['error'] = min_error
+        # Tracing back
+        result = {
+            'lab': [],
+            'rec': [],
+            'all': 0,
+            'cor': 0,
+            'sub': 0,
+            'ins': 0,
+            'del': 0
+        }
+        i = len(lab) - 1
+        j = len(rec) - 1
+        while True:
+            if self.space[i][j]['error'] == 'cor':  # correct
+                if len(lab[i]) > 0:
+                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
+                    self.data[lab[i]]['cor'] = self.data[lab[i]]['cor'] + 1
+                    result['all'] = result['all'] + 1
+                    result['cor'] = result['cor'] + 1
+                result['lab'].insert(0, lab[i])
+                result['rec'].insert(0, rec[j])
+                i = i - 1
+                j = j - 1
+            elif self.space[i][j]['error'] == 'sub':  # substitution
+                if len(lab[i]) > 0:
+                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
+                    self.data[lab[i]]['sub'] = self.data[lab[i]]['sub'] + 1
+                    result['all'] = result['all'] + 1
+                    result['sub'] = result['sub'] + 1
+                result['lab'].insert(0, lab[i])
+                result['rec'].insert(0, rec[j])
+                i = i - 1
+                j = j - 1
+            elif self.space[i][j]['error'] == 'del':  # deletion
+                if len(lab[i]) > 0:
+                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
+                    self.data[lab[i]]['del'] = self.data[lab[i]]['del'] + 1
+                    result['all'] = result['all'] + 1
+                    result['del'] = result['del'] + 1
+                result['lab'].insert(0, lab[i])
+                result['rec'].insert(0, "")
+                i = i - 1
+            elif self.space[i][j]['error'] == 'ins':  # insertion
+                if len(rec[j]) > 0:
+                    self.data[rec[j]]['ins'] = self.data[rec[j]]['ins'] + 1
+                    result['ins'] = result['ins'] + 1
+                result['lab'].insert(0, "")
+                result['rec'].insert(0, rec[j])
+                j = j - 1
+            elif self.space[i][j]['error'] == 'non':  # starting point
+                break
+            else:  # shouldn't reach here
+                print(
+                    'this should not happen , i = {i} , j = {j} , error = {error}'.
+                    format(i=i, j=j, error=self.space[i][j]['error']))
+        return result
+
+    def overall(self):
+        result = {'all': 0, 'cor': 0, 'sub': 0, 'ins': 0, 'del': 0}
+        for token in self.data:
+            result['all'] = result['all'] + self.data[token]['all']
+            result['cor'] = result['cor'] + self.data[token]['cor']
+            result['sub'] = result['sub'] + self.data[token]['sub']
+            result['ins'] = result['ins'] + self.data[token]['ins']
+            result['del'] = result['del'] + self.data[token]['del']
+        return result
+
+    def cluster(self, data):
+        result = {'all': 0, 'cor': 0, 'sub': 0, 'ins': 0, 'del': 0}
+        for token in data:
+            if token in self.data:
+                result['all'] = result['all'] + self.data[token]['all']
+                result['cor'] = result['cor'] + self.data[token]['cor']
+                result['sub'] = result['sub'] + self.data[token]['sub']
+                result['ins'] = result['ins'] + self.data[token]['ins']
+                result['del'] = result['del'] + self.data[token]['del']
+        return result
+
+    def keys(self):
+        return list(self.data.keys())
+
+
+def width(string):
+    return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)
+
+
+def default_cluster(word):
+    unicode_names = [unicodedata.name(char) for char in word]
+    for i in reversed(range(len(unicode_names))):
+        if unicode_names[i].startswith('DIGIT'):  # 1
+            unicode_names[i] = 'Number'  # 'DIGIT'
+        elif (unicode_names[i].startswith('CJK UNIFIED IDEOGRAPH') or
+              unicode_names[i].startswith('CJK COMPATIBILITY IDEOGRAPH')):
+            # 明 / 郎
+            unicode_names[i] = 'Mandarin'  # 'CJK IDEOGRAPH'
+        elif (unicode_names[i].startswith('LATIN CAPITAL LETTER') or
+              unicode_names[i].startswith('LATIN SMALL LETTER')):
+            # A / a
+            unicode_names[i] = 'English'  # 'LATIN LETTER'
+        elif unicode_names[i].startswith('HIRAGANA LETTER'):  # は こ め
+            unicode_names[i] = 'Japanese'  # 'GANA LETTER'
+        elif (unicode_names[i].startswith('AMPERSAND') or
+              unicode_names[i].startswith('APOSTROPHE') or
+              unicode_names[i].startswith('COMMERCIAL AT') or
+              unicode_names[i].startswith('DEGREE CELSIUS') or
+              unicode_names[i].startswith('EQUALS SIGN') or
+              unicode_names[i].startswith('FULL STOP') or
+              unicode_names[i].startswith('HYPHEN-MINUS') or
+              unicode_names[i].startswith('LOW LINE') or
+              unicode_names[i].startswith('NUMBER SIGN') or
+              unicode_names[i].startswith('PLUS SIGN') or
+              unicode_names[i].startswith('SEMICOLON')):
+            # & / ' / @ / ℃ / = / . / - / _ / # / + / ;
+            del unicode_names[i]
+        else:
+            return 'Other'
+    if len(unicode_names) == 0:
+        return 'Other'
+    if len(unicode_names) == 1:
+        return unicode_names[0]
+    for i in range(len(unicode_names) - 1):
+        if unicode_names[i] != unicode_names[i + 1]:
+            return 'Other'
+    return unicode_names[0]
+
+
+def usage():
+    print(
+        "compute-wer.py : compute word error rate (WER) and align recognition results and references."
+    )
+    print(
+        "         usage : python compute-wer.py [--cs={0,1}] [--cluster=foo] [--ig=ignore_file] [--char={0,1}] [--v={0,1}] [--padding-symbol={space,underline}] test.ref test.hyp > test.wer"
+    )
+
+
+def main():
+    # python utils/compute-wer.py --char=1 --v=1 ref hyp > rsl.error
+    if len(sys.argv) == 1:
+        usage()
+        sys.exit(0)
+    calculator = Calculator()
+    cluster_file = ''
+    ignore_words = set()
+    tochar = False
+    verbose = 1
+    padding_symbol = ' '
+    case_sensitive = False
+    max_words_per_line = sys.maxsize
+    split = None
+    while len(sys.argv) > 3:
+        a = '--maxw='
+        if sys.argv[1].startswith(a):
+            b = sys.argv[1][len(a):]
+            del sys.argv[1]
+            max_words_per_line = int(b)
+            continue
+        a = '--rt='
+        if sys.argv[1].startswith(a):
+            b = sys.argv[1][len(a):].lower()
+            del sys.argv[1]
+            remove_tag = (b == 'true') or (b != '0')
+            continue
+        a = '--cs='
+        if sys.argv[1].startswith(a):
+            b = sys.argv[1][len(a):].lower()
+            del sys.argv[1]
+            case_sensitive = (b == 'true') or (b != '0')
+            continue
+        a = '--cluster='
+        if sys.argv[1].startswith(a):
+            cluster_file = sys.argv[1][len(a):]
+            del sys.argv[1]
+            continue
+        a = '--splitfile='
+        if sys.argv[1].startswith(a):
+            split_file = sys.argv[1][len(a):]
+            del sys.argv[1]
+            split = dict()
+            with codecs.open(split_file, 'r', 'utf-8') as fh:
+                for line in fh:  # line in unicode
+                    words = line.strip().split()
+                    if len(words) >= 2:
+                        split[words[0]] = words[1:]
+            continue
+        a = '--ig='
+        if sys.argv[1].startswith(a):
+            ignore_file = sys.argv[1][len(a):]
+            del sys.argv[1]
+            with codecs.open(ignore_file, 'r', 'utf-8') as fh:
+                for line in fh:  # line in unicode
+                    line = line.strip()
+                    if len(line) > 0:
+                        ignore_words.add(line)
+            continue
+        a = '--char='
+        if sys.argv[1].startswith(a):
+            b = sys.argv[1][len(a):].lower()
+            del sys.argv[1]
+            tochar = (b == 'true') or (b != '0')
+            continue
+        a = '--v='
+        if sys.argv[1].startswith(a):
+            b = sys.argv[1][len(a):].lower()
+            del sys.argv[1]
+            verbose = 0
+            try:
+                verbose = int(b)
+            except:
+                if b == 'true' or b != '0':
+                    verbose = 1
+            continue
+        a = '--padding-symbol='
+        if sys.argv[1].startswith(a):
+            b = sys.argv[1][len(a):].lower()
+            del sys.argv[1]
+            if b == 'space':
+                padding_symbol = ' '
+            elif b == 'underline':
+                padding_symbol = '_'
+            continue
+        if True or sys.argv[1].startswith('-'):
+            #ignore invalid switch
+            del sys.argv[1]
+            continue
+
+    if not case_sensitive:
+        ig = set([w.upper() for w in ignore_words])
+        ignore_words = ig
+
+    default_clusters = {}
+    default_words = {}
+
+    ref_file = sys.argv[1]
+    hyp_file = sys.argv[2]
+    rec_set = {}
+    if split and not case_sensitive:
+        newsplit = dict()
+        for w in split:
+            words = split[w]
+            for i in range(len(words)):
+                words[i] = words[i].upper()
+            newsplit[w.upper()] = words
+        split = newsplit
+
+    with codecs.open(hyp_file, 'r', 'utf-8') as fh:
+        for line in fh:
+            if tochar:
+                array = characterize(line)
+            else:
+                array = line.strip().split()
+            if len(array) == 0: continue
+            fid = array[0]
+            rec_set[fid] = normalize(array[1:], ignore_words, case_sensitive,
+                                     split)
+
+    # compute error rate on the interaction of reference file and hyp file
+    for line in open(ref_file, 'r', encoding='utf-8'):
+        if tochar:
+            array = characterize(line)
+        else:
+            array = line.rstrip('\n').split()
+        if len(array) == 0: continue
+        fid = array[0]
+        if fid not in rec_set:
+            continue
+        lab = normalize(array[1:], ignore_words, case_sensitive, split)
+        rec = rec_set[fid]
+        if verbose:
+            print('\nutt: %s' % fid)
+
+        for word in rec + lab:
+            if word not in default_words:
+                default_cluster_name = default_cluster(word)
+                if default_cluster_name not in default_clusters:
+                    default_clusters[default_cluster_name] = {}
+                if word not in default_clusters[default_cluster_name]:
+                    default_clusters[default_cluster_name][word] = 1
+                default_words[word] = default_cluster_name
+
+        result = calculator.calculate(lab, rec)
+        if verbose:
+            if result['all'] != 0:
+                wer = float(result['ins'] + result['sub'] + result[
+                    'del']) * 100.0 / result['all']
+            else:
+                wer = 0.0
+            print('WER: %4.2f %%' % wer, end=' ')
+            print('N=%d C=%d S=%d D=%d I=%d' %
+                  (result['all'], result['cor'], result['sub'], result['del'],
+                   result['ins']))
+            space = {}
+            space['lab'] = []
+            space['rec'] = []
+            for idx in range(len(result['lab'])):
+                len_lab = width(result['lab'][idx])
+                len_rec = width(result['rec'][idx])
+                length = max(len_lab, len_rec)
+                space['lab'].append(length - len_lab)
+                space['rec'].append(length - len_rec)
+            upper_lab = len(result['lab'])
+            upper_rec = len(result['rec'])
+            lab1, rec1 = 0, 0
+            while lab1 < upper_lab or rec1 < upper_rec:
+                if verbose > 1:
+                    print('lab(%s):' % fid.encode('utf-8'), end=' ')
+                else:
+                    print('lab:', end=' ')
+                lab2 = min(upper_lab, lab1 + max_words_per_line)
+                for idx in range(lab1, lab2):
+                    token = result['lab'][idx]
+                    print('{token}'.format(token=token), end='')
+                    for n in range(space['lab'][idx]):
+                        print(padding_symbol, end='')
+                    print(' ', end='')
+                print()
+                if verbose > 1:
+                    print('rec(%s):' % fid.encode('utf-8'), end=' ')
+                else:
+                    print('rec:', end=' ')
+                rec2 = min(upper_rec, rec1 + max_words_per_line)
+                for idx in range(rec1, rec2):
+                    token = result['rec'][idx]
+                    print('{token}'.format(token=token), end='')
+                    for n in range(space['rec'][idx]):
+                        print(padding_symbol, end='')
+                    print(' ', end='')
+                print('\n', end='\n')
+                lab1 = lab2
+                rec1 = rec2
+
+    if verbose:
+        print(
+            '==========================================================================='
+        )
+        print()
+
+    result = calculator.overall()
+    if result['all'] != 0:
+        wer = float(result['ins'] + result['sub'] + result[
+            'del']) * 100.0 / result['all']
+    else:
+        wer = 0.0
+    print('Overall -> %4.2f %%' % wer, end=' ')
+    print('N=%d C=%d S=%d D=%d I=%d' %
+          (result['all'], result['cor'], result['sub'], result['del'],
+           result['ins']))
+    if not verbose:
+        print()
+
+    if verbose:
+        for cluster_id in default_clusters:
+            result = calculator.cluster(
+                [k for k in default_clusters[cluster_id]])
+            if result['all'] != 0:
+                wer = float(result['ins'] + result['sub'] + result[
+                    'del']) * 100.0 / result['all']
+            else:
+                wer = 0.0
+            print('%s -> %4.2f %%' % (cluster_id, wer), end=' ')
+            print('N=%d C=%d S=%d D=%d I=%d' %
+                  (result['all'], result['cor'], result['sub'], result['del'],
+                   result['ins']))
+        if len(cluster_file) > 0:  # compute separated WERs for word clusters
+            cluster_id = ''
+            cluster = []
+            for line in open(cluster_file, 'r', encoding='utf-8'):
+                for token in line.decode('utf-8').rstrip('\n').split():
+                    # end of cluster reached, like </Keyword>
+                    if token[0:2] == '</' and token[len(token)-1] == '>' and \
+                       token.lstrip('</').rstrip('>') == cluster_id :
+                        result = calculator.cluster(cluster)
+                        if result['all'] != 0:
+                            wer = float(result['ins'] + result['sub'] + result[
+                                'del']) * 100.0 / result['all']
+                        else:
+                            wer = 0.0
+                        print('%s -> %4.2f %%' % (cluster_id, wer), end=' ')
+                        print('N=%d C=%d S=%d D=%d I=%d' %
+                              (result['all'], result['cor'], result['sub'],
+                               result['del'], result['ins']))
+                        cluster_id = ''
+                        cluster = []
+                    # begin of cluster reached, like <Keyword>
+                    elif token[0] == '<' and token[len(token)-1] == '>' and \
+                         cluster_id == '' :
+                        cluster_id = token.lstrip('<').rstrip('>')
+                        cluster = []
+                    # general terms, like WEATHER / CAR / ...
+                    else:
+                        cluster.append(token)
+        print()
+        print(
+            '==========================================================================='
+        )
+
+
+if __name__ == '__main__':
+    main()

paddlespeech/dataset/s2t/format_data.py

@@ -1,5 +1,4 @@
-#!/usr/bin/env python3
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

paddlespeech/dataset/s2t/format_rsl.py

@@ -0,0 +1,127 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+format ref/hyp file for `utt text` format to compute CER/WER/MER
+"""
+import argparse
+
+import jsonlines
+
+
+def trans_hyp(origin_hyp, trans_hyp=None, trans_hyp_sclite=None):
+    """
+    Args:
+        origin_hyp: The input json file which contains the model output
+        trans_hyp: The output file for caculate CER/WER
+        trans_hyp_sclite: The output file for caculate CER/WER using sclite
+    """
+    input_dict = {}
+
+    with open(origin_hyp, "r+", encoding="utf8") as f:
+        for item in jsonlines.Reader(f):
+            input_dict[item["utt"]] = item["hyps"][0]
+    if trans_hyp is not None:
+        with open(trans_hyp, "w+", encoding="utf8") as f:
+            for key in input_dict.keys():
+                f.write(key + " " + input_dict[key] + "\n")
+    if trans_hyp_sclite is not None:
+        with open(trans_hyp_sclite, "w+") as f:
+            for key in input_dict.keys():
+                line = input_dict[key] + "(" + key + ".wav" + ")" + "\n"
+                f.write(line)
+
+
+def trans_ref(origin_ref, trans_ref=None, trans_ref_sclite=None):
+    """
+    Args:
+        origin_hyp: The input json file which contains the model output
+        trans_hyp: The output file for caculate CER/WER
+        trans_hyp_sclite: The output file for caculate CER/WER using sclite
+    """
+    input_dict = {}
+
+    with open(origin_ref, "r", encoding="utf8") as f:
+        for item in jsonlines.Reader(f):
+            input_dict[item["utt"]] = item["text"]
+    if trans_ref is not None:
+        with open(trans_ref, "w", encoding="utf8") as f:
+            for key in input_dict.keys():
+                f.write(key + " " + input_dict[key] + "\n")
+
+    if trans_ref_sclite is not None:
+        with open(trans_ref_sclite, "w") as f:
+            for key in input_dict.keys():
+                line = input_dict[key] + "(" + key + ".wav" + ")" + "\n"
+                f.write(line)
+
+
+def define_argparse():
+    parser = argparse.ArgumentParser(
+        prog='format ref/hyp file for compute CER/WER', add_help=True)
+    parser.add_argument(
+        '--origin_hyp', type=str, default=None, help='origin hyp file')
+    parser.add_argument(
+        '--trans_hyp',
+        type=str,
+        default=None,
+        help='hyp file for caculating CER/WER')
+    parser.add_argument(
+        '--trans_hyp_sclite',
+        type=str,
+        default=None,
+        help='hyp file for caculating CER/WER by sclite')
+
+    parser.add_argument(
+        '--origin_ref', type=str, default=None, help='origin ref file')
+    parser.add_argument(
+        '--trans_ref',
+        type=str,
+        default=None,
+        help='ref file for caculating CER/WER')
+    parser.add_argument(
+        '--trans_ref_sclite',
+        type=str,
+        default=None,
+        help='ref file for caculating CER/WER by sclite')
+    parser_args = parser.parse_args()
+    return parser_args
+
+
+def format_result(origin_hyp=None,
+                  trans_hyp=None,
+                  trans_hyp_sclite=None,
+                  origin_ref=None,
+                  trans_ref=None,
+                  trans_ref_sclite=None):
+
+    if origin_hyp is not None:
+        trans_hyp(
+            origin_hyp=origin_hyp,
+            trans_hyp=trans_hyp,
+            trans_hyp_sclite=trans_hyp_sclite, )
+
+    if origin_ref is not None:
+        trans_ref(
+            origin_ref=origin_ref,
+            trans_ref=trans_ref,
+            trans_ref_sclite=trans_ref_sclite, )
+
+
+def main():
+    args = define_argparse()
+    format_result(**vars(args))
+
+
+if __name__ == "__main__":
+    main()

paddlespeech/s2t/exps/deepspeech2/bin/export.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.deepspeech2.model import DeepSpeech2Tester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/deepspeech2/bin/test.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.deepspeech2.model import DeepSpeech2Tester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/deepspeech2/bin/test_export.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.deepspeech2.model import DeepSpeech2ExportTester as ExportTester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/deepspeech2/bin/test_wav.py

@@ -27,8 +27,8 @@ from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils import mp_tools
 from paddlespeech.s2t.utils.checkpoint import Checkpoint
 from paddlespeech.s2t.utils.log import Log
-from paddlespeech.s2t.utils.utility import print_arguments
 from paddlespeech.s2t.utils.utility import UpdateConfig
+from paddlespeech.utils.argparse import print_arguments
 
 logger = Log(__name__).getlog()
 

paddlespeech/s2t/exps/deepspeech2/bin/train.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.deepspeech2.model import DeepSpeech2Trainer as Trainer
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/u2/bin/alignment.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2.model import U2Tester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/u2/bin/export.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2.model import U2Tester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/u2/bin/test.py

@@ -18,7 +18,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2.model import U2Tester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/u2/bin/train.py

@@ -19,7 +19,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2.model import U2Trainer as Trainer
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 # from paddlespeech.s2t.exps.u2.trainer import U2Trainer as Trainer
 

paddlespeech/s2t/exps/u2_kaldi/bin/test.py

@@ -18,7 +18,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 model_test_alias = {
     "u2": "paddlespeech.s2t.exps.u2.model:U2Tester",

paddlespeech/s2t/exps/u2_kaldi/bin/train.py

@@ -19,7 +19,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 model_train_alias = {
     "u2": "paddlespeech.s2t.exps.u2.model:U2Trainer",

paddlespeech/s2t/exps/u2_st/bin/export.py

@@ -16,7 +16,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2_st.model import U2STTester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/u2_st/bin/test.py

@@ -18,7 +18,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2_st.model import U2STTester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/u2_st/bin/train.py

@@ -19,7 +19,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.u2_st.model import U2STTrainer as Trainer
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/wav2vec2/bin/test.py

@@ -18,7 +18,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.wav2vec2.model import Wav2Vec2ASRTester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

paddlespeech/s2t/exps/wav2vec2/bin/train.py

@@ -19,7 +19,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.exps.wav2vec2.model import Wav2Vec2ASRTrainer as Trainer
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
+from paddlespeech.utils.argparse import print_arguments
 
 
 def main_sp(config, args):

utils/avg_model.py

@@ -12,105 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import argparse
-import glob
-import json
-import os
-
-import numpy as np
-import paddle
-
-
-def main(args):
-    paddle.set_device('cpu')
-
-    val_scores = []
-    beat_val_scores = None
-    selected_epochs = None
-
-    jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
-    jsons = sorted(jsons, key=os.path.getmtime, reverse=True)
-    for y in jsons:
-        with open(y, 'r') as f:
-            dic_json = json.load(f)
-        loss = dic_json['val_loss']
-        epoch = dic_json['epoch']
-        if epoch >= args.min_epoch and epoch <= args.max_epoch:
-            val_scores.append((epoch, loss))
-    val_scores = np.array(val_scores)
-
-    if args.val_best:
-        sort_idx = np.argsort(val_scores[:, 1])
-        sorted_val_scores = val_scores[sort_idx]
-    else:
-        sorted_val_scores = val_scores
-
-    beat_val_scores = sorted_val_scores[:args.num, 1]
-    selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
-    avg_val_score = np.mean(beat_val_scores)
-    print("selected val scores = " + str(beat_val_scores))
-    print("selected epochs = " + str(selected_epochs))
-    print("averaged val score = " + str(avg_val_score))
-
-    path_list = [
-        args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
-        for epoch in sorted_val_scores[:args.num, 0]
-    ]
-    print(path_list)
-
-    avg = None
-    num = args.num
-    assert num == len(path_list)
-    for path in path_list:
-        print(f'Processing {path}')
-        states = paddle.load(path)
-        if avg is None:
-            avg = states
-        else:
-            for k in avg.keys():
-                avg[k] += states[k]
-    # average
-    for k in avg.keys():
-        if avg[k] is not None:
-            avg[k] /= num
-
-    paddle.save(avg, args.dst_model)
-    print(f'Saving to {args.dst_model}')
-
-    meta_path = os.path.splitext(args.dst_model)[0] + '.avg.json'
-    with open(meta_path, 'w') as f:
-        data = json.dumps({
-            "mode": 'val_best' if args.val_best else 'latest',
-            "avg_ckpt": args.dst_model,
-            "val_loss_mean": avg_val_score,
-            "ckpts": path_list,
-            "epochs": selected_epochs.tolist(),
-            "val_losses": beat_val_scores.tolist(),
-        })
-        f.write(data + "\n")
-
+from paddlespeech.dataset.s2t import avg_ckpts_main
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='average model')
-    parser.add_argument('--dst_model', required=True, help='averaged model')
-    parser.add_argument(
-        '--ckpt_dir', required=True, help='ckpt model dir for average')
-    parser.add_argument(
-        '--val_best', action="store_true", help='averaged model')
-    parser.add_argument(
-        '--num', default=5, type=int, help='nums for averaged model')
-    parser.add_argument(
-        '--min_epoch',
-        default=0,
-        type=int,
-        help='min epoch used for averaging model')
-    parser.add_argument(
-        '--max_epoch',
-        default=65536,  # Big enough
-        type=int,
-        help='max epoch used for averaging model')
-
-    args = parser.parse_args()
-    print(args)
-
-    main(args)
+    avg_ckpts_main()

utils/compute-wer.py

@@ -1,554 +1,5 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 # Copyright 2021 Mobvoi Inc. All Rights Reserved.
-import codecs
-import re
-import sys
-import unicodedata
-
-remove_tag = True
-spacelist = [' ', '\t', '\r', '\n']
-puncts = [
-    '!', ',', '?', '、', '。', '！', '，', '；', '？', '：', '「', '」', '︰', '『', '』',
-    '《', '》'
-]
-
-
-def characterize(string):
-    res = []
-    i = 0
-    while i < len(string):
-        char = string[i]
-        if char in puncts:
-            i += 1
-            continue
-        cat1 = unicodedata.category(char)
-        #https://unicodebook.readthedocs.io/unicode.html#unicode-categories
-        if cat1 == 'Zs' or cat1 == 'Cn' or char in spacelist:  # space or not assigned
-            i += 1
-            continue
-        if cat1 == 'Lo':  # letter-other
-            res.append(char)
-            i += 1
-        else:
-            # some input looks like: <unk><noise>, we want to separate it to two words.
-            sep = ' '
-            if char == '<': sep = '>'
-            j = i + 1
-            while j < len(string):
-                c = string[j]
-                if ord(c) >= 128 or (c in spacelist) or (c == sep):
-                    break
-                j += 1
-            if j < len(string) and string[j] == '>':
-                j += 1
-            res.append(string[i:j])
-            i = j
-    return res
-
-
-def stripoff_tags(x):
-    if not x: return ''
-    chars = []
-    i = 0
-    T = len(x)
-    while i < T:
-        if x[i] == '<':
-            while i < T and x[i] != '>':
-                i += 1
-            i += 1
-        else:
-            chars.append(x[i])
-            i += 1
-    return ''.join(chars)
-
-
-def normalize(sentence, ignore_words, cs, split=None):
-    """ sentence, ignore_words are both in unicode
-    """
-    new_sentence = []
-    for token in sentence:
-        x = token
-        if not cs:
-            x = x.upper()
-        if x in ignore_words:
-            continue
-        if remove_tag:
-            x = stripoff_tags(x)
-        if not x:
-            continue
-        if split and x in split:
-            new_sentence += split[x]
-        else:
-            new_sentence.append(x)
-    return new_sentence
-
-
-class Calculator:
-    def __init__(self):
-        self.data = {}
-        self.space = []
-        self.cost = {}
-        self.cost['cor'] = 0
-        self.cost['sub'] = 1
-        self.cost['del'] = 1
-        self.cost['ins'] = 1
-
-    def calculate(self, lab, rec):
-        # Initialization
-        lab.insert(0, '')
-        rec.insert(0, '')
-        while len(self.space) < len(lab):
-            self.space.append([])
-        for row in self.space:
-            for element in row:
-                element['dist'] = 0
-                element['error'] = 'non'
-            while len(row) < len(rec):
-                row.append({'dist': 0, 'error': 'non'})
-        for i in range(len(lab)):
-            self.space[i][0]['dist'] = i
-            self.space[i][0]['error'] = 'del'
-        for j in range(len(rec)):
-            self.space[0][j]['dist'] = j
-            self.space[0][j]['error'] = 'ins'
-        self.space[0][0]['error'] = 'non'
-        for token in lab:
-            if token not in self.data and len(token) > 0:
-                self.data[token] = {
-                    'all': 0,
-                    'cor': 0,
-                    'sub': 0,
-                    'ins': 0,
-                    'del': 0
-                }
-        for token in rec:
-            if token not in self.data and len(token) > 0:
-                self.data[token] = {
-                    'all': 0,
-                    'cor': 0,
-                    'sub': 0,
-                    'ins': 0,
-                    'del': 0
-                }
-        # Computing edit distance
-        for i, lab_token in enumerate(lab):
-            for j, rec_token in enumerate(rec):
-                if i == 0 or j == 0:
-                    continue
-                min_dist = sys.maxsize
-                min_error = 'none'
-                dist = self.space[i - 1][j]['dist'] + self.cost['del']
-                error = 'del'
-                if dist < min_dist:
-                    min_dist = dist
-                    min_error = error
-                dist = self.space[i][j - 1]['dist'] + self.cost['ins']
-                error = 'ins'
-                if dist < min_dist:
-                    min_dist = dist
-                    min_error = error
-                if lab_token == rec_token:
-                    dist = self.space[i - 1][j - 1]['dist'] + self.cost['cor']
-                    error = 'cor'
-                else:
-                    dist = self.space[i - 1][j - 1]['dist'] + self.cost['sub']
-                    error = 'sub'
-                if dist < min_dist:
-                    min_dist = dist
-                    min_error = error
-                self.space[i][j]['dist'] = min_dist
-                self.space[i][j]['error'] = min_error
-        # Tracing back
-        result = {
-            'lab': [],
-            'rec': [],
-            'all': 0,
-            'cor': 0,
-            'sub': 0,
-            'ins': 0,
-            'del': 0
-        }
-        i = len(lab) - 1
-        j = len(rec) - 1
-        while True:
-            if self.space[i][j]['error'] == 'cor':  # correct
-                if len(lab[i]) > 0:
-                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
-                    self.data[lab[i]]['cor'] = self.data[lab[i]]['cor'] + 1
-                    result['all'] = result['all'] + 1
-                    result['cor'] = result['cor'] + 1
-                result['lab'].insert(0, lab[i])
-                result['rec'].insert(0, rec[j])
-                i = i - 1
-                j = j - 1
-            elif self.space[i][j]['error'] == 'sub':  # substitution
-                if len(lab[i]) > 0:
-                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
-                    self.data[lab[i]]['sub'] = self.data[lab[i]]['sub'] + 1
-                    result['all'] = result['all'] + 1
-                    result['sub'] = result['sub'] + 1
-                result['lab'].insert(0, lab[i])
-                result['rec'].insert(0, rec[j])
-                i = i - 1
-                j = j - 1
-            elif self.space[i][j]['error'] == 'del':  # deletion
-                if len(lab[i]) > 0:
-                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
-                    self.data[lab[i]]['del'] = self.data[lab[i]]['del'] + 1
-                    result['all'] = result['all'] + 1
-                    result['del'] = result['del'] + 1
-                result['lab'].insert(0, lab[i])
-                result['rec'].insert(0, "")
-                i = i - 1
-            elif self.space[i][j]['error'] == 'ins':  # insertion
-                if len(rec[j]) > 0:
-                    self.data[rec[j]]['ins'] = self.data[rec[j]]['ins'] + 1
-                    result['ins'] = result['ins'] + 1
-                result['lab'].insert(0, "")
-                result['rec'].insert(0, rec[j])
-                j = j - 1
-            elif self.space[i][j]['error'] == 'non':  # starting point
-                break
-            else:  # shouldn't reach here
-                print(
-                    'this should not happen , i = {i} , j = {j} , error = {error}'.
-                    format(i=i, j=j, error=self.space[i][j]['error']))
-        return result
-
-    def overall(self):
-        result = {'all': 0, 'cor': 0, 'sub': 0, 'ins': 0, 'del': 0}
-        for token in self.data:
-            result['all'] = result['all'] + self.data[token]['all']
-            result['cor'] = result['cor'] + self.data[token]['cor']
-            result['sub'] = result['sub'] + self.data[token]['sub']
-            result['ins'] = result['ins'] + self.data[token]['ins']
-            result['del'] = result['del'] + self.data[token]['del']
-        return result
-
-    def cluster(self, data):
-        result = {'all': 0, 'cor': 0, 'sub': 0, 'ins': 0, 'del': 0}
-        for token in data:
-            if token in self.data:
-                result['all'] = result['all'] + self.data[token]['all']
-                result['cor'] = result['cor'] + self.data[token]['cor']
-                result['sub'] = result['sub'] + self.data[token]['sub']
-                result['ins'] = result['ins'] + self.data[token]['ins']
-                result['del'] = result['del'] + self.data[token]['del']
-        return result
-
-    def keys(self):
-        return list(self.data.keys())
-
-
-def width(string):
-    return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)
-
-
-def default_cluster(word):
-    unicode_names = [unicodedata.name(char) for char in word]
-    for i in reversed(range(len(unicode_names))):
-        if unicode_names[i].startswith('DIGIT'):  # 1
-            unicode_names[i] = 'Number'  # 'DIGIT'
-        elif (unicode_names[i].startswith('CJK UNIFIED IDEOGRAPH') or
-              unicode_names[i].startswith('CJK COMPATIBILITY IDEOGRAPH')):
-            # 明 / 郎
-            unicode_names[i] = 'Mandarin'  # 'CJK IDEOGRAPH'
-        elif (unicode_names[i].startswith('LATIN CAPITAL LETTER') or
-              unicode_names[i].startswith('LATIN SMALL LETTER')):
-            # A / a
-            unicode_names[i] = 'English'  # 'LATIN LETTER'
-        elif unicode_names[i].startswith('HIRAGANA LETTER'):  # は こ め
-            unicode_names[i] = 'Japanese'  # 'GANA LETTER'
-        elif (unicode_names[i].startswith('AMPERSAND') or
-              unicode_names[i].startswith('APOSTROPHE') or
-              unicode_names[i].startswith('COMMERCIAL AT') or
-              unicode_names[i].startswith('DEGREE CELSIUS') or
-              unicode_names[i].startswith('EQUALS SIGN') or
-              unicode_names[i].startswith('FULL STOP') or
-              unicode_names[i].startswith('HYPHEN-MINUS') or
-              unicode_names[i].startswith('LOW LINE') or
-              unicode_names[i].startswith('NUMBER SIGN') or
-              unicode_names[i].startswith('PLUS SIGN') or
-              unicode_names[i].startswith('SEMICOLON')):
-            # & / ' / @ / ℃ / = / . / - / _ / # / + / ;
-            del unicode_names[i]
-        else:
-            return 'Other'
-    if len(unicode_names) == 0:
-        return 'Other'
-    if len(unicode_names) == 1:
-        return unicode_names[0]
-    for i in range(len(unicode_names) - 1):
-        if unicode_names[i] != unicode_names[i + 1]:
-            return 'Other'
-    return unicode_names[0]
-
-
-def usage():
-    print(
-        "compute-wer.py : compute word error rate (WER) and align recognition results and references."
-    )
-    print(
-        "         usage : python compute-wer.py [--cs={0,1}] [--cluster=foo] [--ig=ignore_file] [--char={0,1}] [--v={0,1}] [--padding-symbol={space,underline}] test.ref test.hyp > test.wer"
-    )
-
+from paddlespeech.dataset.s2t import compute_wer_main
 
 if __name__ == '__main__':
-    if len(sys.argv) == 1:
-        usage()
-        sys.exit(0)
-    calculator = Calculator()
-    cluster_file = ''
-    ignore_words = set()
-    tochar = False
-    verbose = 1
-    padding_symbol = ' '
-    case_sensitive = False
-    max_words_per_line = sys.maxsize
-    split = None
-    while len(sys.argv) > 3:
-        a = '--maxw='
-        if sys.argv[1].startswith(a):
-            b = sys.argv[1][len(a):]
-            del sys.argv[1]
-            max_words_per_line = int(b)
-            continue
-        a = '--rt='
-        if sys.argv[1].startswith(a):
-            b = sys.argv[1][len(a):].lower()
-            del sys.argv[1]
-            remove_tag = (b == 'true') or (b != '0')
-            continue
-        a = '--cs='
-        if sys.argv[1].startswith(a):
-            b = sys.argv[1][len(a):].lower()
-            del sys.argv[1]
-            case_sensitive = (b == 'true') or (b != '0')
-            continue
-        a = '--cluster='
-        if sys.argv[1].startswith(a):
-            cluster_file = sys.argv[1][len(a):]
-            del sys.argv[1]
-            continue
-        a = '--splitfile='
-        if sys.argv[1].startswith(a):
-            split_file = sys.argv[1][len(a):]
-            del sys.argv[1]
-            split = dict()
-            with codecs.open(split_file, 'r', 'utf-8') as fh:
-                for line in fh:  # line in unicode
-                    words = line.strip().split()
-                    if len(words) >= 2:
-                        split[words[0]] = words[1:]
-            continue
-        a = '--ig='
-        if sys.argv[1].startswith(a):
-            ignore_file = sys.argv[1][len(a):]
-            del sys.argv[1]
-            with codecs.open(ignore_file, 'r', 'utf-8') as fh:
-                for line in fh:  # line in unicode
-                    line = line.strip()
-                    if len(line) > 0:
-                        ignore_words.add(line)
-            continue
-        a = '--char='
-        if sys.argv[1].startswith(a):
-            b = sys.argv[1][len(a):].lower()
-            del sys.argv[1]
-            tochar = (b == 'true') or (b != '0')
-            continue
-        a = '--v='
-        if sys.argv[1].startswith(a):
-            b = sys.argv[1][len(a):].lower()
-            del sys.argv[1]
-            verbose = 0
-            try:
-                verbose = int(b)
-            except:
-                if b == 'true' or b != '0':
-                    verbose = 1
-            continue
-        a = '--padding-symbol='
-        if sys.argv[1].startswith(a):
-            b = sys.argv[1][len(a):].lower()
-            del sys.argv[1]
-            if b == 'space':
-                padding_symbol = ' '
-            elif b == 'underline':
-                padding_symbol = '_'
-            continue
-        if True or sys.argv[1].startswith('-'):
-            #ignore invalid switch
-            del sys.argv[1]
-            continue
-
-    if not case_sensitive:
-        ig = set([w.upper() for w in ignore_words])
-        ignore_words = ig
-
-    default_clusters = {}
-    default_words = {}
-
-    ref_file = sys.argv[1]
-    hyp_file = sys.argv[2]
-    rec_set = {}
-    if split and not case_sensitive:
-        newsplit = dict()
-        for w in split:
-            words = split[w]
-            for i in range(len(words)):
-                words[i] = words[i].upper()
-            newsplit[w.upper()] = words
-        split = newsplit
-
-    with codecs.open(hyp_file, 'r', 'utf-8') as fh:
-        for line in fh:
-            if tochar:
-                array = characterize(line)
-            else:
-                array = line.strip().split()
-            if len(array) == 0: continue
-            fid = array[0]
-            rec_set[fid] = normalize(array[1:], ignore_words, case_sensitive,
-                                     split)
-
-    # compute error rate on the interaction of reference file and hyp file
-    for line in open(ref_file, 'r', encoding='utf-8'):
-        if tochar:
-            array = characterize(line)
-        else:
-            array = line.rstrip('\n').split()
-        if len(array) == 0: continue
-        fid = array[0]
-        if fid not in rec_set:
-            continue
-        lab = normalize(array[1:], ignore_words, case_sensitive, split)
-        rec = rec_set[fid]
-        if verbose:
-            print('\nutt: %s' % fid)
-
-        for word in rec + lab:
-            if word not in default_words:
-                default_cluster_name = default_cluster(word)
-                if default_cluster_name not in default_clusters:
-                    default_clusters[default_cluster_name] = {}
-                if word not in default_clusters[default_cluster_name]:
-                    default_clusters[default_cluster_name][word] = 1
-                default_words[word] = default_cluster_name
-
-        result = calculator.calculate(lab, rec)
-        if verbose:
-            if result['all'] != 0:
-                wer = float(result['ins'] + result['sub'] + result[
-                    'del']) * 100.0 / result['all']
-            else:
-                wer = 0.0
-            print('WER: %4.2f %%' % wer, end=' ')
-            print('N=%d C=%d S=%d D=%d I=%d' %
-                  (result['all'], result['cor'], result['sub'], result['del'],
-                   result['ins']))
-            space = {}
-            space['lab'] = []
-            space['rec'] = []
-            for idx in range(len(result['lab'])):
-                len_lab = width(result['lab'][idx])
-                len_rec = width(result['rec'][idx])
-                length = max(len_lab, len_rec)
-                space['lab'].append(length - len_lab)
-                space['rec'].append(length - len_rec)
-            upper_lab = len(result['lab'])
-            upper_rec = len(result['rec'])
-            lab1, rec1 = 0, 0
-            while lab1 < upper_lab or rec1 < upper_rec:
-                if verbose > 1:
-                    print('lab(%s):' % fid.encode('utf-8'), end=' ')
-                else:
-                    print('lab:', end=' ')
-                lab2 = min(upper_lab, lab1 + max_words_per_line)
-                for idx in range(lab1, lab2):
-                    token = result['lab'][idx]
-                    print('{token}'.format(token=token), end='')
-                    for n in range(space['lab'][idx]):
-                        print(padding_symbol, end='')
-                    print(' ', end='')
-                print()
-                if verbose > 1:
-                    print('rec(%s):' % fid.encode('utf-8'), end=' ')
-                else:
-                    print('rec:', end=' ')
-                rec2 = min(upper_rec, rec1 + max_words_per_line)
-                for idx in range(rec1, rec2):
-                    token = result['rec'][idx]
-                    print('{token}'.format(token=token), end='')
-                    for n in range(space['rec'][idx]):
-                        print(padding_symbol, end='')
-                    print(' ', end='')
-                print('\n', end='\n')
-                lab1 = lab2
-                rec1 = rec2
-
-    if verbose:
-        print(
-            '==========================================================================='
-        )
-        print()
-
-    result = calculator.overall()
-    if result['all'] != 0:
-        wer = float(result['ins'] + result['sub'] + result[
-            'del']) * 100.0 / result['all']
-    else:
-        wer = 0.0
-    print('Overall -> %4.2f %%' % wer, end=' ')
-    print('N=%d C=%d S=%d D=%d I=%d' %
-          (result['all'], result['cor'], result['sub'], result['del'],
-           result['ins']))
-    if not verbose:
-        print()
-
-    if verbose:
-        for cluster_id in default_clusters:
-            result = calculator.cluster(
-                [k for k in default_clusters[cluster_id]])
-            if result['all'] != 0:
-                wer = float(result['ins'] + result['sub'] + result[
-                    'del']) * 100.0 / result['all']
-            else:
-                wer = 0.0
-            print('%s -> %4.2f %%' % (cluster_id, wer), end=' ')
-            print('N=%d C=%d S=%d D=%d I=%d' %
-                  (result['all'], result['cor'], result['sub'], result['del'],
-                   result['ins']))
-        if len(cluster_file) > 0:  # compute separated WERs for word clusters
-            cluster_id = ''
-            cluster = []
-            for line in open(cluster_file, 'r', encoding='utf-8'):
-                for token in line.decode('utf-8').rstrip('\n').split():
-                    # end of cluster reached, like </Keyword>
-                    if token[0:2] == '</' and token[len(token)-1] == '>' and \
-                       token.lstrip('</').rstrip('>') == cluster_id :
-                        result = calculator.cluster(cluster)
-                        if result['all'] != 0:
-                            wer = float(result['ins'] + result['sub'] + result[
-                                'del']) * 100.0 / result['all']
-                        else:
-                            wer = 0.0
-                        print('%s -> %4.2f %%' % (cluster_id, wer), end=' ')
-                        print('N=%d C=%d S=%d D=%d I=%d' %
-                              (result['all'], result['cor'], result['sub'],
-                               result['del'], result['ins']))
-                        cluster_id = ''
-                        cluster = []
-                    # begin of cluster reached, like <Keyword>
-                    elif token[0] == '<' and token[len(token)-1] == '>' and \
-                         cluster_id == '' :
-                        cluster_id = token.lstrip('<').rstrip('>')
-                        cluster = []
-                    # general terms, like WEATHER / CAR / ...
-                    else:
-                        cluster.append(token)
-        print()
-        print(
-            '==========================================================================='
-        )
+    compute_wer_main()

utils/format_rsl.py

@@ -11,96 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import argparse
+from paddlespeech.dataset.s2t import format_rsl_main
 
-import jsonlines
-
-
-def trans_hyp(origin_hyp, trans_hyp=None, trans_hyp_sclite=None):
-    """
-    Args:
-        origin_hyp: The input json file which contains the model output
-        trans_hyp: The output file for caculate CER/WER
-        trans_hyp_sclite: The output file for caculate CER/WER using sclite
-    """
-    input_dict = {}
-
-    with open(origin_hyp, "r+", encoding="utf8") as f:
-        for item in jsonlines.Reader(f):
-            input_dict[item["utt"]] = item["hyps"][0]
-    if trans_hyp is not None:
-        with open(trans_hyp, "w+", encoding="utf8") as f:
-            for key in input_dict.keys():
-                f.write(key + " " + input_dict[key] + "\n")
-    if trans_hyp_sclite is not None:
-        with open(trans_hyp_sclite, "w+") as f:
-            for key in input_dict.keys():
-                line = input_dict[key] + "(" + key + ".wav" + ")" + "\n"
-                f.write(line)
-
-
-def trans_ref(origin_ref, trans_ref=None, trans_ref_sclite=None):
-    """
-    Args:
-        origin_hyp: The input json file which contains the model output
-        trans_hyp: The output file for caculate CER/WER
-        trans_hyp_sclite: The output file for caculate CER/WER using sclite
-    """
-    input_dict = {}
-
-    with open(origin_ref, "r", encoding="utf8") as f:
-        for item in jsonlines.Reader(f):
-            input_dict[item["utt"]] = item["text"]
-    if trans_ref is not None:
-        with open(trans_ref, "w", encoding="utf8") as f:
-            for key in input_dict.keys():
-                f.write(key + " " + input_dict[key] + "\n")
-
-    if trans_ref_sclite is not None:
-        with open(trans_ref_sclite, "w") as f:
-            for key in input_dict.keys():
-                line = input_dict[key] + "(" + key + ".wav" + ")" + "\n"
-                f.write(line)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        prog='format hyp file for compute CER/WER', add_help=True)
-    parser.add_argument(
-        '--origin_hyp', type=str, default=None, help='origin hyp file')
-    parser.add_argument(
-        '--trans_hyp',
-        type=str,
-        default=None,
-        help='hyp file for caculating CER/WER')
-    parser.add_argument(
-        '--trans_hyp_sclite',
-        type=str,
-        default=None,
-        help='hyp file for caculating CER/WER by sclite')
-
-    parser.add_argument(
-        '--origin_ref', type=str, default=None, help='origin ref file')
-    parser.add_argument(
-        '--trans_ref',
-        type=str,
-        default=None,
-        help='ref file for caculating CER/WER')
-    parser.add_argument(
-        '--trans_ref_sclite',
-        type=str,
-        default=None,
-        help='ref file for caculating CER/WER by sclite')
-    parser_args = parser.parse_args()
-
-    if parser_args.origin_hyp is not None:
-        trans_hyp(
-            origin_hyp=parser_args.origin_hyp,
-            trans_hyp=parser_args.trans_hyp,
-            trans_hyp_sclite=parser_args.trans_hyp_sclite, )
-
-    if parser_args.origin_ref is not None:
-        trans_ref(
-            origin_ref=parser_args.origin_ref,
-            trans_ref=parser_args.trans_ref,
-            trans_ref_sclite=parser_args.trans_ref_sclite, )
+if __name__ == '__main__':
+    format_rsl_main()