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# -*- 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)