diff --git a/.idea/accetuation.iml b/.idea/accetuation.iml
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+++ /dev/null
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- overfitting
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- content
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- feature_dictionary
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- _convert_to_multext_east_v4
- decode_x
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- accented_vowels
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- ô
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- 36
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diff --git a/accentuate.py b/accentuate.py
new file mode 100644
index 0000000..c66d955
--- /dev/null
+++ b/accentuate.py
@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+import pickle
+import numpy as np
+from keras.models import load_model
+import sys
+
+from prepare_data import *
+
+# obtain data from parameters
+if len(sys.argv) < 3:
+ print('Please provide arguments for this script to work. First argument should be location of file with unaccented words and morphological data '
+ 'and second the name of file where you would like to save file to. Example: python accentuate.py \'test_data/unaccented_dictionary\' '
+ '\'test_data/accented_data\'')
+ raise Exception
+read_location = sys.argv[1]
+write_location = sys.argv[2]
+
+# get environment variables necessary for calculations
+pickle_input = open('preprocessed_data/environment.pkl', 'rb')
+environment = pickle.load(pickle_input)
+dictionary = environment['dictionary']
+max_word = environment['max_word']
+max_num_vowels = environment['max_num_vowels']
+vowels = environment['vowels']
+accented_vowels = environment['accented_vowels']
+feature_dictionary = environment['feature_dictionary']
+syllable_dictionary = environment['syllable_dictionary']
+
+# load models
+data = Data('l', shuffle_all_inputs=False)
+letter_location_model, syllable_location_model, syllabled_letters_location_model = data.load_location_models(
+ 'cnn/word_accetuation/cnn_dictionary/v5_3/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllables/v3_3/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllabled_letters/v3_3/20_final_epoch.h5')
+
+letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model = data.load_location_models(
+ 'cnn/word_accetuation/cnn_dictionary/v5_2/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllables/v3_2/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllabled_letters/v3_2/20_final_epoch.h5')
+
+letter_type_model, syllable_type_model, syllabled_letter_type_model = data.load_type_models(
+ 'cnn/accent_classification/letters/v3_1/20_final_epoch.h5',
+ 'cnn/accent_classification/syllables/v2_1/20_final_epoch.h5',
+ 'cnn/accent_classification/syllabled_letters/v2_1/20_final_epoch.h5')
+
+letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model = data.load_type_models(
+ 'cnn/accent_classification/letters/v3_0/20_final_epoch.h5',
+ 'cnn/accent_classification/syllables/v2_0/20_final_epoch.h5',
+ 'cnn/accent_classification/syllabled_letters/v2_0/20_final_epoch.h5')
+
+# read from data
+content = data._read_content(read_location)
+
+# format data for accentuate_word function it has to be like [['besedišči', '', 'Ncnpi', 'besedišči'], ]
+content = [[el[0], '', el[1][:-1], el[0]] for el in content[:-1]]
+
+# use environment variables and models to accentuate words
+data = Data('l', shuffle_all_inputs=False)
+location_accented_words, accented_words = data.accentuate_word(content, letter_location_model, syllable_location_model, syllabled_letters_location_model,
+ letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model,
+ letter_type_model, syllable_type_model, syllabled_letter_type_model,
+ letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model,
+ dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary, syllable_dictionary)
+
+# save accentuated words
+with open(write_location, 'w') as f:
+ for i in range(len(location_accented_words)):
+ f.write(location_accented_words[i] + ' ' + accented_words[i] + '\n')
+ f.write('\n')
\ No newline at end of file
diff --git a/accentuate_connected_text.py b/accentuate_connected_text.py
new file mode 100644
index 0000000..cbf7e95
--- /dev/null
+++ b/accentuate_connected_text.py
@@ -0,0 +1,79 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+import sys
+
+sys.path.insert(0, '../../../')
+from prepare_data import *
+
+import pickle
+
+# from keras import backend as Input
+np.random.seed(7)
+
+# obtain data from parameters
+if len(sys.argv) < 3:
+ print('Please provide arguments for this script to work. First argument should be location of file with unaccented words and morphological data, '
+ 'second the name of file where you would like to save results to and third location of ReLDI tagger. Example: python accentuate.py '
+ '\'test_data/original_connected_text\' \'test_data/accented_connected_text\' \'../reldi_tagger\'')
+ raise Exception
+read_location = sys.argv[1]
+write_location = sys.argv[2]
+reldi_location = sys.argv[3]
+
+# get environment variables necessary for calculations
+pickle_input = open('preprocessed_data/environment.pkl', 'rb')
+environment = pickle.load(pickle_input)
+dictionary = environment['dictionary']
+max_word = environment['max_word']
+max_num_vowels = environment['max_num_vowels']
+vowels = environment['vowels']
+accented_vowels = environment['accented_vowels']
+feature_dictionary = environment['feature_dictionary']
+syllable_dictionary = environment['syllable_dictionary']
+
+# get models
+data = Data('l', shuffle_all_inputs=False)
+letter_location_model, syllable_location_model, syllabled_letters_location_model = data.load_location_models(
+ 'cnn/word_accetuation/cnn_dictionary/v5_3/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllables/v3_3/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllabled_letters/v3_3/20_final_epoch.h5')
+
+letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model = data.load_location_models(
+ 'cnn/word_accetuation/cnn_dictionary/v5_2/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllables/v3_2/20_final_epoch.h5',
+ 'cnn/word_accetuation/syllabled_letters/v3_2/20_final_epoch.h5')
+
+letter_type_model, syllable_type_model, syllabled_letter_type_model = data.load_type_models(
+ 'cnn/accent_classification/letters/v3_1/20_final_epoch.h5',
+ 'cnn/accent_classification/syllables/v2_1/20_final_epoch.h5',
+ 'cnn/accent_classification/syllabled_letters/v2_1/20_final_epoch.h5')
+
+letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model = data.load_type_models(
+ 'cnn/accent_classification/letters/v3_0/20_final_epoch.h5',
+ 'cnn/accent_classification/syllables/v2_0/20_final_epoch.h5',
+ 'cnn/accent_classification/syllabled_letters/v2_0/20_final_epoch.h5')
+
+# get word tags
+tagged_words, original_text = data.tag_words(reldi_location, read_location)
+
+# find accentuation locations
+predictions = data.get_ensemble_location_predictions(tagged_words, letter_location_model, syllable_location_model, syllabled_letters_location_model,
+ letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model,
+ dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary,
+ syllable_dictionary)
+
+location_accented_text = data.create_connected_text_locations(tagged_words, original_text, predictions, vowels)
+
+# accentuate text
+location_y = np.around(predictions)
+type_predictions = data.get_ensemble_type_predictions(tagged_words, location_y, letter_type_model, syllable_type_model, syllabled_letter_type_model,
+ letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model,
+ dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary,
+ syllable_dictionary)
+
+accented_text = data.create_connected_text_accented(tagged_words, original_text, type_predictions, location_y, vowels, accented_vowels)
+
+# save accentuated text
+with open(write_location, 'w') as f:
+ f.write(accented_text)
diff --git a/learn_location_weights.py b/learn_location_weights.py
new file mode 100644
index 0000000..f2581d9
--- /dev/null
+++ b/learn_location_weights.py
@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+# text in Western (Windows 1252)
+
+import pickle
+import numpy as np
+np.random.seed(7)
+
+import sys
+from prepare_data import *
+
+# preprocess data
+# data = Data('l', allow_shuffle_vector_generation=True, save_generated_data=False, shuffle_all_inputs=True)
+data = Data('l', save_generated_data=False, shuffle_all_inputs=True)
+data.generate_data('../../internal_representations/inputs/letters_word_accentuation_train',
+ '../../internal_representations/inputs/letters_word_accentuation_test',
+ '../../internal_representations/inputs/letters_word_accentuation_validate',
+ content_location='../accetuation/data/',
+ content_name='SlovarIJS_BESEDE_utf8.lex',
+ inputs_location='../accetuation/cnn/internal_representations/inputs/',
+ content_shuffle_vector='content_shuffle_vector',
+ shuffle_vector='shuffle_vector')
+
+# combine all data (if it is unwanted comment code below)
+data.x_train = np.concatenate((data.x_train, data.x_test, data.x_validate), axis=0)
+data.x_other_features_train = np.concatenate((data.x_other_features_train, data.x_other_features_test, data.x_other_features_validate), axis=0)
+data.y_train = np.concatenate((data.y_train, data.y_test, data.y_validate), axis=0)
+
+# build neural network architecture
+nn_output_dim = 10
+batch_size = 16
+actual_epoch = 20
+num_fake_epoch = 20
+
+conv_input_shape=(23, 36)
+othr_input = (140, )
+
+conv_input = Input(shape=conv_input_shape, name='conv_input')
+x_conv = Conv1D(115, (3), padding='same', activation='relu')(conv_input)
+x_conv = Conv1D(46, (3), padding='same', activation='relu')(x_conv)
+x_conv = MaxPooling1D(pool_size=2)(x_conv)
+x_conv = Flatten()(x_conv)
+
+othr_input = Input(shape=othr_input, name='othr_input')
+
+x = concatenate([x_conv, othr_input])
+x = Dense(256, activation='relu')(x)
+x = Dropout(0.3)(x)
+x = Dense(256, activation='relu')(x)
+x = Dropout(0.3)(x)
+x = Dense(256, activation='relu')(x)
+x = Dropout(0.3)(x)
+x = Dense(nn_output_dim, activation='sigmoid')(x)
+
+model = Model(inputs=[conv_input, othr_input], outputs=x)
+opt = optimizers.Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
+model.compile(loss='mean_squared_error', optimizer=opt, metrics=[actual_accuracy,])
+# model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
+
+
+# start learning
+history = model.fit_generator(data.generator('train', batch_size, content_name='SlovarIJS_BESEDE_utf8.lex', content_location='../accetuation/data/'),
+ data.x_train.shape[0]/(batch_size * num_fake_epoch),
+ epochs=actual_epoch*num_fake_epoch,
+ validation_data=data.generator('test', batch_size),
+ validation_steps=data.x_test.shape[0]/(batch_size * num_fake_epoch))
+
+
+# save generated data
+name = 'test_data/20_epoch'
+model.save(name + '.h5')
+output = open(name + '_history.pkl', 'wb')
+pickle.dump(history.history, output)
+output.close()
diff --git a/prepare_data.py b/prepare_data.py
index 250807e..75eefcb 100644
--- a/prepare_data.py
+++ b/prepare_data.py
@@ -7,6 +7,7 @@ import h5py
import math
import keras.backend as K
import os.path
+from os import remove
import codecs
from copy import copy
@@ -666,7 +667,7 @@ class Data:
loc += batch_size
# generator for inputs for tracking of data fitting
- def _syllable_generator(self, orig_x, orig_x_additional, orig_y, batch_size, translator, accented_vowels, oversampling):
+ def _syllable_generator(self, orig_x, orig_x_additional, orig_y, batch_size, translator, accented_vowels, oversampling=np.ones(13)):
size = orig_x.shape[0]
while 1:
loc = 0
@@ -1655,6 +1656,95 @@ class Data:
return location_accented_words, accented_words
+ def tag_words(self, reldi_location, original_location):
+ # generates text with every word in new line
+ with open(original_location) as f:
+ original_text = f.readlines()
+ original_text = ''.join(original_text)
+ # print(original_text)
+ text_with_whitespaces = original_text.replace(',', ' ,').replace('.', ' .').replace('\n', ' ').replace("\"", " \" ").replace(":",
+ " :").replace(
+ "ć", "č").replace('–', '-')
+ # print('-------------------------------------------------')
+ text_with_whitespaces = '\n'.join(text_with_whitespaces.split())
+ text_with_whitespaces += '\n\n'
+ # print(text_with_whitespaces)
+ with open('.words_with_whitespaces', "w") as text_file:
+ text_file.write(text_with_whitespaces)
+
+ # generates text with PoS tags
+ import subprocess
+
+ myinput = open('.words_with_whitespaces', 'r')
+ myoutput = open('.word_tags', 'w')
+ # print(myinput.readlines())
+ python3_command = reldi_location + "/tagger.py sl" # launch your python2 script using bash
+
+ process = subprocess.run(python3_command.split(), stdin=myinput, stdout=myoutput)
+
+ # generates interesting words
+ pointless_words = ['.', ',', '\"', ':', '-']
+ with open('.word_tags', "r") as text_file:
+ tagged_input_words = []
+ for x in text_file.readlines()[:-1]:
+ splited_line = x[:-1].split('\t')
+ if splited_line[0] not in pointless_words and not any(char.isdigit() for char in splited_line[0]):
+ tagged_input_words.append([splited_line[0].lower(), '', splited_line[1], splited_line[0].lower()])
+
+ remove(".words_with_whitespaces")
+ remove(".word_tags")
+ return tagged_input_words, original_text
+
+ def create_connected_text_locations(self, tagged_input_words, original_text, predictions, vowels):
+ if 'A' not in vowels:
+ vowels.extend(['A', 'E', 'I', 'O', 'U'])
+ accented_words = [self.assign_location_stress(tagged_input_words[i][0][::-1], self.decode_y(predictions[i]), vowels)[::-1] for i in
+ range(len(tagged_input_words))]
+
+ # print(accented_words[:20])
+ # print(tagged_input_words[:20])
+
+ words_and_accetuation_loc = [[tagged_input_words[i][0], self.decode_y(predictions[i])] for i in range(len(tagged_input_words))]
+
+ original_text_list = list(original_text)
+ original_text_lowercase = original_text.lower()
+ end_pos = 0
+ for word in words_and_accetuation_loc:
+ posit = original_text_lowercase.find(word[0], end_pos)
+ if posit != -1:
+ start_pos = posit
+ end_pos = start_pos + len(word[0])
+
+ original_text_list[start_pos:end_pos] = list(
+ self.assign_location_stress(''.join(original_text_list[start_pos:end_pos][::-1]), word[1], vowels)[::-1])
+
+ return ''.join(original_text_list)
+
+ def create_connected_text_accented(self, tagged_input_words, original_text, type_predictions, location_y, vowels, accented_vowels):
+
+ input_words = [el[0] for el in tagged_input_words]
+ words = self.assign_stress_types(type_predictions, input_words, location_y, vowels, accented_vowels)
+
+ # print(original_text)
+
+ original_text_list = list(original_text)
+ original_text_lowercase = original_text.lower()
+ end_pos = 0
+ for i in range(len(words)):
+ posit = original_text_lowercase.find(input_words[i], end_pos)
+ if posit != -1:
+ start_pos = posit
+ end_pos = start_pos + len(words[i])
+
+ orig_word = original_text_list[start_pos:end_pos]
+ new_word = list(words[i])
+ for j in range(len(orig_word)):
+ if orig_word[j].isupper():
+ new_word[j] = new_word[j].upper()
+
+ original_text_list[start_pos:end_pos] = new_word
+
+ return ''.join(original_text_list)
# def count_vowels(content, vowels):
# num_all_vowels = 0
# for el in content:
diff --git a/preprocessed_data/environment.pkl b/preprocessed_data/environment.pkl
new file mode 100644
index 0000000..7912fd3
Binary files /dev/null and b/preprocessed_data/environment.pkl differ
diff --git a/test_data/accented_connected_text b/test_data/accented_connected_text
new file mode 100644
index 0000000..6e078f8
--- /dev/null
+++ b/test_data/accented_connected_text
@@ -0,0 +1 @@
+Izbrúhi na sóncu só žé vëčkrat pokazáli zóbe nášim satelítom, poslédično nášim mobílnim telefónom, navigáciji, celo eléktričnemu omréžju. Á vesóljskega vreména šë në morémo napovédati – kakó bî ga láhko, se tá téden na Blédu pogovárja okóli 70 znánstvenikov Evrópske vesóljske agéncije, ki jé sebój pripeljála svôjo näjvéčjo ikóno, británca Mátta Taylorja.
diff --git a/test_data/accented_data b/test_data/accented_data
new file mode 100644
index 0000000..c193212
--- /dev/null
+++ b/test_data/accented_data
@@ -0,0 +1,6 @@
+absolutístični absolutístični
+spoštljívejše spoštljívejše
+tresóče tresóče
+razneséna raznesěna
+žvížgih žvížgih
+
diff --git a/test_data/original_connected_text b/test_data/original_connected_text
new file mode 100644
index 0000000..cd04ba3
--- /dev/null
+++ b/test_data/original_connected_text
@@ -0,0 +1 @@
+Izbruhi na soncu so že večkrat pokazali zobe našim satelitom, posledično našim mobilnim telefonom, navigaciji, celo električnemu omrežju. A vesoljskega vremena še ne moremo napovedati – kako bi ga lahko, se ta teden na Bledu pogovarja okoli 70 znanstvenikov Evropske vesoljske agencije, ki je seboj pripeljala svojo največjo ikono, britanca Matta Taylorja.
diff --git a/test_data/unaccented_dictionary b/test_data/unaccented_dictionary
new file mode 100644
index 0000000..ed1c01b
--- /dev/null
+++ b/test_data/unaccented_dictionary
@@ -0,0 +1,6 @@
+absolutistični Afpmsay-n
+spoštljivejše Afcfsg
+tresoče Afpfsg
+raznesena Vmp--sfp
+žvižgih Ncmdl
+