def main():
if sys.argv.__len__() > 1:
init_dir_name = os.path.normpath(sys.argv[1])
assert os.path.isdir(init_dir_name), 'Directory `{0}` does not exist!'.format(init_dir_name)
all_prompts = sorted(list(get_all_prompts(init_dir_name)))
accentor = Accentor()
morpho_predictor = RNNMorphPredictor()
i = 0
for cur_prompt in all_prompts[:100]:
trouble = False
unknown_words = []
for cur_subsentence in select_subsentences(cur_prompt):
morphotags = ['{0} {1}'.format(cur_morpho.pos, cur_morpho.tag)
for cur_morpho in morpho_predictor.predict_sentence_tags(cur_subsentence)]
accent_variants = accentor.do_accents(cur_subsentence, morphotags)
if len(accent_variants) > 1:
trouble = True
else:
accented_phrase = accent_variants[0]
for cur_word in accented_phrase:
vowels_counter = 0
for cur_char in cur_word.lower():
if cur_char in VOWEL_LETTERS:
vowels_counter += 1
if '+' not in cur_word and vowels_counter > 1:
unknown_words += [cur_word]
if trouble:
print('`{0}`: this phrase cannot be unambiguously accented!'.format(cur_prompt))
i += 1
if unknown_words:
for unknown_word in list(set(unknown_words)):
print('`{0}`: word `{1}` in this this phrase is unknown!'.format(cur_prompt, unknown_word))
print(i)
else:
print("Usage: input_directory_with_voxforge_ru")
def transcribe_words(source_words_list):
n_words = len(source_words_list)
n_parts = 100
part_size = n_words // n_parts
while (part_size * n_parts) < n_words:
part_size += 1
transcriptions = []
bad_words = []
to_ud2 = converters.converter('opencorpora-int', 'ud20')
morph = pymorphy2.MorphAnalyzer()
accentor = Accentor(exception_for_unknown=True, use_wiki=False)
g2p = Grapheme2Phoneme(exception_for_nonaccented=True)
russian_letters = set(
'АаБбВвГгДдЕеЁёЖжЗзИиЙйКкЛлМмНнОоПпРрСсТтУуФфХхЦцЧчШшЩщЪъЫыЬьЭэЮюЯя')
russian_consonants = set('БбВвГгДдЖжЗзЙйКкЛлМмНнПпРрСсТтФфХхЦцЧчШшЩщЪъЬь')
part_counter = 0
for word_idx in range(len(source_words_list)):
cur_word = source_words_list[word_idx].strip().lower()
err_msg = 'Word {0} is wrong!'.format(word_idx)
assert len(cur_word) > 0, err_msg + ' It is empty!'
assert set(cur_word) <= (russian_letters | {'-'}), \
err_msg + ' "{0}" contains an inadmissible characters.'.format(cur_word)
assert set(cur_word) != {'-'}, err_msg + ' It is empty!'
if (len(cur_word) > 1) and (set(cur_word) <= russian_consonants):
bad_words.append(cur_word)
else:
morpho_variants = set(
[to_ud2(str(it.tag)) for it in morph.parse(cur_word)])
try:
accentuation_variants = []
for it in morpho_variants:
accentuation_variants += accentor.do_accents(
[[cur_word, it]])[0]
variants_of_transcriptions = list(
set(
filter(
lambda it2: len(it2) > 0,
map(lambda it: tuple(g2p.word_to_phonemes(it)),
accentuation_variants))))
if len(variants_of_transcriptions) > 0:
transcriptions.append(
(cur_word, ' '.join(variants_of_transcriptions[0])))
if len(variants_of_transcriptions) > 1:
for variant_idx in range(
1, len(variants_of_transcriptions)):
transcriptions.append(
('{0}({1})'.format(cur_word, variant_idx + 1),
' '.join(
variants_of_transcriptions[variant_idx])))
else:
bad_words.append(cur_word)
except:
bad_words.append(cur_word)
if ((word_idx + 1) % part_size) == 0:
part_counter += 1
print('{0:.2%} of words have been processed...'.format(
part_counter / float(n_parts)))
if part_counter < n_parts:
print('100.00% of words have been processed...')
return transcriptions, bad_words
class Transcription:
def __init__(self,
raise_exceptions: bool = False,
batch_size: int = 64,
verbose: bool = False,
use_wiki: bool = False):
self.__preprocessor = Preprocessor(batch_size=batch_size)
self.__accentor = Accentor(exception_for_unknown=raise_exceptions,
use_wiki=use_wiki)
self.__g2p = Grapheme2Phoneme(
exception_for_nonaccented=raise_exceptions)
self.verbose = verbose
def transcribe(self, texts: list):
all_words_and_tags = self.__preprocessor.preprocessing(texts)
if self.verbose:
print('All texts have been preprocessed...')
n_texts = len(texts)
n_data_parts = 100
part_size = n_texts // n_data_parts
while (part_size * n_data_parts) < n_texts:
part_size += 1
data_counter = 0
part_counter = 0
total_result = []
for cur_words_and_tags in all_words_and_tags:
try:
accented_text = self.__accentor.do_accents(cur_words_and_tags)
except:
accented_text = []
if len(accented_text) > 0:
tmp = ' '.join(accented_text[0])
tmp = ' ' + tmp
phonetic_words = tmp.split(' <sil>')
try:
result = []
for phonetic_word in phonetic_words:
if len(phonetic_word) != 0:
phonemes = self.__g2p.phrase_to_phonemes(
phonetic_word)
result.append(phonemes)
except:
result = []
else:
result = []
total_result.append(result)
data_counter += 1
if (part_size > 0) and self.verbose:
if (data_counter % part_size) == 0:
part_counter += 1
print('{0}% of texts have been processed...'.format(
part_counter))
if (part_counter < n_data_parts) and self.verbose:
print('100% of texts have been processed...')
return total_result
class TestRussianAccentor2(unittest.TestCase):
def setUp(self):
self.__accentor = Accentor(mode='many')
def tearDown(self):
del self.__accentor
def test_do_accents_positive01(self):
source_phrase = [['оружие'], ['для'], ['кубы']]
target_variants = [['ору+жие', 'для', 'ку+бы'],
['ору+жие', 'для', 'кубы+']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive11(self):
accentor = Accentor(exception_for_unknown=True)
source_phrase = [['зеленого'], ['камня']]
target_variants = [['зелё+ного', 'ка+мня']]
real_variants = accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive08(self):
accentor = Accentor(exception_for_unknown=True)
source_phrase = [['хракозябр'], ['впулил'], ['куздру']]
with self.assertRaises(ValueError):
_ = accentor.do_accents(source_phrase)
class TestRussianAccentor1(unittest.TestCase):
def setUp(self):
self.__accentor = Accentor()
def tearDown(self):
del self.__accentor
def test_do_accents_positive01(self):
source_phrase = [['мама'], ['мыла'], ['раму']]
target_variants = [['ма+ма', 'мы+ла', 'ра+му']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive02(self):
source_phrase_n_morphotags = [[
'привет', 'NOUN Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing'
], ['кума', 'NOUN Animacy=Anim|Case=Nom|Gender=Fem|Number=Sing']]
target_variants = [['приве+т', 'кума+']]
real_variants = self.__accentor.do_accents(source_phrase_n_morphotags)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive03(self):
source_phrase_n_morphotags = [
['подарок', 'NOUN Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing'],
['для', 'ADP _'],
['кума', 'NOUN Animacy=Anim|Case=Gen|Gender=Masc|Number=Sing']
]
target_variants = [['пода+рок', 'для', 'ку+ма']]
real_variants = self.__accentor.do_accents(source_phrase_n_morphotags)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive04(self):
source_phrase = [['оружие'], ['для'], ['кубы']]
target_variants = [['ору+жие', 'для', 'кубы']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive05(self):
source_phrase = [['машинисты'], ['любят'], ['кофе']]
target_variants = [['машини+сты', 'лю+бят', 'ко+фе']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive06(self):
source_phrase = [['во-первых'], ['кто-то'], ['вот-вот']]
target_variants = [['во-пе+рвых', 'кто+-то', 'вот-во+т']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive07(self):
source_phrase = [['хракозябр'], ['впулил'], ['куздру']]
target_variants = [['хракозябр', 'впулил', 'куздру']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive08(self):
accentor = Accentor(exception_for_unknown=True)
source_phrase = [['хракозябр'], ['впулил'], ['куздру']]
with self.assertRaises(ValueError):
_ = accentor.do_accents(source_phrase)
def test_do_accents_positive09(self):
source_phrase = [['серебристо-белый'], ['цвет']]
target_variants = [['серебри+сто-бе+лый', 'цве+т']]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive10(self):
source_phrase = [['озеро'], ['так'], ['серебристо'], ['в'], ['свете'],
['солнца']]
target_variants = [[
'о+зеро', 'та+к', 'серебри+сто', 'в', 'све+те', 'со+лнца'
]]
real_variants = self.__accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_positive11(self):
accentor = Accentor(exception_for_unknown=True)
source_phrase = [['зеленого'], ['камня']]
target_variants = [['зелё+ного', 'ка+мня']]
real_variants = accentor.do_accents(source_phrase)
self.assertEqual(target_variants, real_variants)
def test_do_accents_negative01(self):
source_phrase_n_morphotags = [[
'подарок', 'NOUN Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing'
], ['для', 'NOUN Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing'],
['кума']]
target_err_msg = re.escape(
'`подарок для кума`: morphotags do not correspond to words!')
with self.assertRaisesRegex(AssertionError, target_err_msg):
self.__accentor.do_accents(source_phrase_n_morphotags)
def test_do_accents_negative02(self):
source_phrase = [['подарок'], [''], ['кума']]
target_err_msg = re.escape(
'`(\'подарок\', \'\', \'кума\')`: this phrase is wrong!')
with self.assertRaisesRegex(AssertionError, target_err_msg):
self.__accentor.do_accents(source_phrase)
def test_do_accents_negative03(self):
source_phrase = []
target_err_msg = re.escape('Source phrase is empty!')
with self.assertRaisesRegex(AssertionError, target_err_msg):
self.__accentor.do_accents(source_phrase)
def test_do_accents_negative04(self):
source_phrase = [['а-зе']]
target_err_msg = re.escape('Word `а-зе` is unknown!')
accentor = Accentor(exception_for_unknown=True, use_wiki=False)
with self.assertRaisesRegex(ValueError, target_err_msg):
accentor.do_accents(source_phrase)
def test_check_source_wordform_positive01(self):
self.assertTrue(self.__accentor.check_source_wordform('абвг'))
self.assertTrue(self.__accentor.check_source_wordform('аб-вг'))
self.assertFalse(self.__accentor.check_source_wordform('-'))
self.assertFalse(self.__accentor.check_source_wordform(''))
self.assertFalse(self.__accentor.check_source_wordform('-абвг'))
self.assertFalse(self.__accentor.check_source_wordform('аб--вг'))
self.assertFalse(self.__accentor.check_source_wordform('abcабвг'))
self.assertFalse(self.__accentor.check_source_wordform('abc'))
self.assertFalse(self.__accentor.check_source_wordform('abcабвг123'))
def test_check_accented_wordform_positive01(self):
self.assertTrue(self.__accentor.check_accented_wordform('абвг'))
self.assertTrue(self.__accentor.check_accented_wordform('аб-вг'))
self.assertFalse(self.__accentor.check_accented_wordform('-'))
self.assertFalse(self.__accentor.check_accented_wordform(''))
self.assertFalse(self.__accentor.check_accented_wordform('-абвг'))
self.assertFalse(self.__accentor.check_accented_wordform('аб--вг'))
self.assertFalse(self.__accentor.check_accented_wordform('abcабвг'))
self.assertFalse(self.__accentor.check_accented_wordform('abc'))
self.assertFalse(self.__accentor.check_accented_wordform('abcабвг123'))
self.assertTrue(self.__accentor.check_accented_wordform('а+бвг'))
self.assertTrue(self.__accentor.check_accented_wordform('а+бвгде+жз'))
self.assertTrue(self.__accentor.check_accented_wordform('а+б-вг'))
self.assertFalse(self.__accentor.check_accented_wordform('-'))
self.assertFalse(self.__accentor.check_accented_wordform('+-'))
self.assertFalse(self.__accentor.check_accented_wordform('+'))
self.assertFalse(self.__accentor.check_accented_wordform(''))
self.assertFalse(self.__accentor.check_accented_wordform('-а+бвг'))
self.assertFalse(self.__accentor.check_accented_wordform('а+б--вг'))
self.assertFalse(self.__accentor.check_accented_wordform('a+bcа+бвг'))
self.assertFalse(self.__accentor.check_accented_wordform('a+bc'))
self.assertFalse(
self.__accentor.check_accented_wordform('a+bcа+бвг123'))
def test_check_morphotag_positive01(self):
self.assertTrue(self.__accentor.check_morphotag('a,b c,d,e'))
self.assertTrue(self.__accentor.check_morphotag('12'))
self.assertTrue(self.__accentor.check_morphotag('a,b c,d,e(2)'))
self.assertFalse(self.__accentor.check_morphotag('a,b c,d,e()'))
self.assertFalse(self.__accentor.check_morphotag('a,b(1) c,d,e(2)'))
self.assertFalse(self.__accentor.check_morphotag('a,1,b c,d,e'))
self.assertFalse(self.__accentor.check_morphotag('a,&,b c,d,e'))
self.assertTrue(self.__accentor.check_morphotag('a|b c|d|e'))
self.assertTrue(self.__accentor.check_morphotag('a|b c|d|e(2)'))
self.assertFalse(self.__accentor.check_morphotag('a|b c|d|e()'))
self.assertFalse(self.__accentor.check_morphotag('a|b(1) c|d|e(2)'))
self.assertFalse(self.__accentor.check_morphotag('a|1|b c|d|e'))
self.assertFalse(self.__accentor.check_morphotag('a|&|b c|d|e'))
self.assertTrue(
self.__accentor.check_morphotag(
'VERB Aspect=Perf|Mood=Ind|Number=Sing|Person=3|Tense=Fut|VerbForm=Fin'
))
def test_prepare_morphotag_positive01(self):
self.assertEqual('a,b c,d,e',
self.__accentor.prepare_morphotag('a,b c,d,e(2)'))
self.assertEqual('a,b c,d,e',
self.__accentor.prepare_morphotag('a,b c,d,e'))
self.assertNotEqual('a,b c,d,e',
self.__accentor.prepare_morphotag('a,b c,d(2)'))
self.assertNotEqual('a c,d,e',
self.__accentor.prepare_morphotag('a,b c,d,e(2)'))
def test_calculate_morpho_similarity_positive01(self):
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'1', 'a,b'),
0.0,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'a,b c,d,e', 'a,b c,d,e'),
1.0,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'a,b c,d,e', 'f,g h,i,j'),
0.0,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'a,b c,d,e', 'f,b h,d,j'),
0.25,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'1', 'a|b'),
0.0,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'a|b c|d|e', 'a|b c|d|e'),
1.0,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'a|b c|d|e', 'f|g h|i|j'),
0.0,
places=7)
self.assertAlmostEqual(self.__accentor.calculate_morpho_similarity(
'a|b c|d|e', 'f|b h|d|j'),
0.25,
places=7)
def test_do_accents_negative04(self):
source_phrase = [['а-зе']]
target_err_msg = re.escape('Word `а-зе` is unknown!')
accentor = Accentor(exception_for_unknown=True, use_wiki=False)
with self.assertRaisesRegex(ValueError, target_err_msg):
accentor.do_accents(source_phrase)
class PhoneticIndex(object):
def __init__(self,
path_to_w2v='modelphonemes.model',
path_to_annoy='annoy_index.ann',
path_to_dict='data.pkl'):
self.your_transcriptor = Transcription()
self.your_accentor = Accentor()
if os.path.isfile(path_to_w2v):
self.model = gensim.models.Word2Vec.load(path_to_w2v)
else:
raise IOError("File {} does not exist!".format(path_to_w2v))
if os.path.isfile(path_to_dict):
with open(path_to_dict, 'rb') as f:
self.dict_of_acc = pickle.load(f)
else:
raise IOError("File {} does not exist!".format(path_to_dict))
self.accents = list(self.dict_of_acc.keys())
f = len(self.accents[0])
self.t = AnnoyIndex(f, 'hamming')
if os.path.isfile(path_to_annoy):
self.t.load(path_to_annoy)
else:
raise IOError("File {} does not exist!".format(path_to_annoy))
def transform(self, sentence, acc_number=10, sent_number=1):
assert acc_number >= sent_number, "number of variants for nearest neighbors should be bigger than number of nearest sentences"
phonemes = self.get_phonemes(sentence)
accents = self.get_accents(sentence)
closest_vectors = self.get_closest_vecs(accents, number=acc_number)
closest_sentences = self.get_embeddings(closest_vectors,
phonemes,
number=sent_number)
return closest_sentences
def get_phonemes(self, sentence):
# выдает транскрипцию
with warnings.catch_warnings():
warnings.simplefilter('ignore')
new_sentence = self.transcriptor(sentence)
text = []
for string in new_sentence[0]:
for phoneme in string:
text.append(phoneme)
if len(text) != 0:
try:
# строит эмбеддинги пакетно
phoneme_sent = self.model[text]
except:
# если символа нет в словаре эмбеддингов, строит поэлементно, заменяя неизвестный на вектор из 100 нулей
phoneme_sent = []
for word in text:
try:
phoneme_word = self.model[word]
except:
print("unknown word", word)
phoneme_word = np.zeros(100)
phoneme_sent.append(phoneme_word)
phoneme_sent = np.array(phoneme_sent)
if len(phoneme_sent) < 100:
# приведение к единому размеру 100
difference = 100 - len(phoneme_sent)
part = np.zeros((difference, 100))
phoneme_sent = np.concatenate((part, phoneme_sent))
assert len(phoneme_sent
) == 100, "len of vector is inappropriate: {}".format(
sentence)
else:
phoneme_sent = np.zeros((100, 100))
return phoneme_sent
def get_accents(self, sentence):
# выдает вектор из 0 и 1 - ударений в предложении
vector = []
sentence = sentence.translate(
sentence.maketrans(
'', '', '!&?\./(){}[]"$%^*+=@№<>|–—_€£±•`≠…§~«»₽,:;')).lower()
for word in sentence.split():
# ставит ударение в слове, если слово неизвестное, возвращается без ударения
try:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
accents = self.accentor(word)
except:
#print("unknown accent word: ", word)
accents = [[word]]
s = accents[0][0]
vowels = "эоуиаеёюыяЭОУАЕИЁЫЮЯ"
for letter, next_letter in zip(s, s[1:] + " "):
# преобразование слов в бинарные вектора, где ударная гласная - 1, безударная 0
if letter in vowels:
if next_letter == "+":
vector.append(1)
else:
vector.append(0)
if len(vector) < 29:
# приведение векторов к стандартному размеру - 29
difference = 29 - len(vector)
part = [0 for n in range(difference)]
vector = part + vector
assert len(vector) == 29, "len of vector is inappropriate: {}".format(
sentence)
return tuple(vector)
def get_closest_vecs(self, vector, number=10):
# возвращает список ближайших векторов в количестве number
closest = [
self.t.get_item_vector(x)
for x in self.t.get_nns_by_vector(vector, number)
]
closest_int = [[int(x) for x in vector] for vector in closest]
return closest_int
def get_embeddings(self, vectors, source_embedding, number=1):
# возвращает список ближайших предложений в количестве number
possible_sentences = []
for vector in vectors:
possible_sentences += self.dict_of_acc[tuple(vector)]
possible_embs = []
embs_sentences = {}
for sentence in possible_sentences:
emb_sentence = self.get_phonemes(sentence)
full_emb = np.concatenate(tuple(emb_sentence))
possible_embs.append(full_emb)
full_emb = tuple(full_emb)
if full_emb not in embs_sentences:
embs_sentences[full_emb] = list()
embs_sentences[full_emb].append(sentence)
else:
embs_sentences[full_emb].append(sentence)
assert len(
possible_embs
) >= number, "Number of nearest neighbors should be less than number of possible neighbors"
source_embedding = np.concatenate(tuple(source_embedding))
final_sentences = []
neigh = NearestNeighbors(number)
neigh.fit(possible_embs)
nearest_neighbors = neigh.kneighbors([source_embedding],
return_distance=False).tolist()
for element in nearest_neighbors[0]:
for sentence in embs_sentences[tuple(possible_embs[element])]:
final_sentences.append(sentence.replace('\xa0', ' '))
return final_sentences
@functools.lru_cache(maxsize=None)
def accentor(self, word):
return self.your_accentor.do_accents([[word]])
@functools.lru_cache(maxsize=None)
def transcriptor(self, sentence):
return self.your_transcriptor.transcribe([sentence])
class Transcription:
"""[summary]
"""
def __init__(self,
raise_exceptions: bool = False,
batch_size: int = 64,
verbose: bool = False,
use_wiki: bool = False):
"""[summary]
Args:
raise_exceptions (bool, optional): [description]. Defaults to False.
batch_size (int, optional): [description]. Defaults to 64.
verbose (bool, optional): [description]. Defaults to False.
use_wiki (bool, optional): [description]. Defaults to False.
"""
self.__preprocessor = Preprocessor(batch_size=batch_size)
self.__accentor = Accentor(exception_for_unknown=raise_exceptions,
use_wiki=use_wiki)
self.__g2p = Grapheme2Phoneme(
exception_for_nonaccented=raise_exceptions)
self.verbose = verbose
def __call__(self, texts: list):
"""[summary]
Args:
texts (list): [description]
Returns:
[type]: [description]
"""
return self.transcribe(texts)
def transcribe(self, texts: list) -> list:
"""[summary]
Args:
texts (list): [description]
Returns:
list: [description]
"""
all_words_and_tags = self.__preprocessor.preprocessing(texts)
if self.verbose:
print('All texts have been preprocessed...')
n_texts = len(texts)
n_data_parts = 100
part_size = n_texts // n_data_parts
while (part_size * n_data_parts) < n_texts:
part_size += 1
data_counter = 0
part_counter = 0
total_result = []
for cur_words_and_tags in all_words_and_tags:
try:
accented_text = self.__accentor.do_accents(cur_words_and_tags)
except:
accented_text = []
if len(accented_text) > 0:
tmp = ' '.join(accented_text[0])
tmp = ' ' + tmp
phonetic_words = tmp.split(' <sil>')
try:
result = []
for phonetic_word in phonetic_words:
if len(phonetic_word) != 0:
phonemes = self.__g2p.phrase_to_phonemes(
phonetic_word)
result.append(phonemes)
except:
result = []
else:
result = []
total_result.append(result)
data_counter += 1
if (part_size > 0) and self.verbose:
if (data_counter % part_size) == 0:
part_counter += 1
print(f'{part_counter}% of texts have been processed...')
if (part_counter < n_data_parts) and self.verbose:
print('100% of texts have been processed...')
return total_result
def bad_words(self):
return sorted(list(set(self.__accentor.get_bad_words())))