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80 lines
3.9 KiB
80 lines
3.9 KiB
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import sys
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sys.path.insert(0, '../../../')
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from prepare_data import *
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import pickle
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# from keras import backend as Input
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np.random.seed(7)
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# obtain data from parameters
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if len(sys.argv) < 3:
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print('Please provide arguments for this script to work. First argument should be location of file with unaccented words and morphological data, '
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'second the name of file where you would like to save results to and third location of ReLDI tagger. Example: python accentuate.py '
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'\'test_data/original_connected_text\' \'test_data/accented_connected_text\' \'../reldi_tagger\'')
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raise Exception
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read_location = sys.argv[1]
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write_location = sys.argv[2]
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reldi_location = sys.argv[3]
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# get environment variables necessary for calculations
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pickle_input = open('preprocessed_data/environment.pkl', 'rb')
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environment = pickle.load(pickle_input)
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dictionary = environment['dictionary']
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max_word = environment['max_word']
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max_num_vowels = environment['max_num_vowels']
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vowels = environment['vowels']
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accented_vowels = environment['accented_vowels']
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feature_dictionary = environment['feature_dictionary']
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syllable_dictionary = environment['syllable_dictionary']
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# get models
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data = Data('l', shuffle_all_inputs=False)
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letter_location_model, syllable_location_model, syllabled_letters_location_model = data.load_location_models(
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'cnn/word_accetuation/cnn_dictionary/v5_3/20_final_epoch.h5',
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'cnn/word_accetuation/syllables/v3_3/20_final_epoch.h5',
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'cnn/word_accetuation/syllabled_letters/v3_3/20_final_epoch.h5')
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letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model = data.load_location_models(
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'cnn/word_accetuation/cnn_dictionary/v5_2/20_final_epoch.h5',
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'cnn/word_accetuation/syllables/v3_2/20_final_epoch.h5',
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'cnn/word_accetuation/syllabled_letters/v3_2/20_final_epoch.h5')
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letter_type_model, syllable_type_model, syllabled_letter_type_model = data.load_type_models(
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'cnn/accent_classification/letters/v3_1/20_final_epoch.h5',
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'cnn/accent_classification/syllables/v2_1/20_final_epoch.h5',
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'cnn/accent_classification/syllabled_letters/v2_1/20_final_epoch.h5')
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letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model = data.load_type_models(
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'cnn/accent_classification/letters/v3_0/20_final_epoch.h5',
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'cnn/accent_classification/syllables/v2_0/20_final_epoch.h5',
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'cnn/accent_classification/syllabled_letters/v2_0/20_final_epoch.h5')
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# get word tags
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tagged_words, original_text = data.tag_words(reldi_location, read_location)
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# find accentuation locations
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predictions = data.get_ensemble_location_predictions(tagged_words, letter_location_model, syllable_location_model, syllabled_letters_location_model,
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letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model,
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dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary,
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syllable_dictionary)
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location_accented_text = data.create_connected_text_locations(tagged_words, original_text, predictions, vowels)
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# accentuate text
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location_y = np.around(predictions)
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type_predictions = data.get_ensemble_type_predictions(tagged_words, location_y, letter_type_model, syllable_type_model, syllabled_letter_type_model,
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letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model,
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dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary,
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syllable_dictionary)
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accented_text = data.create_connected_text_accented(tagged_words, original_text, type_predictions, location_y, vowels, accented_vowels)
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# save accentuated text
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with open(write_location, 'w') as f:
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f.write(accented_text)
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