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71 lines
3.4 KiB
71 lines
3.4 KiB
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import pickle
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import numpy as np
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from keras.models import load_model
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import sys
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from prepare_data import *
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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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'and second the name of file where you would like to save file to. Example: python accentuate.py \'test_data/unaccented_dictionary\' '
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'\'test_data/accented_data\'')
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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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# 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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# load 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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# read from data
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content = data._read_content(read_location)
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# format data for accentuate_word function it has to be like [['besedišči', '', 'Ncnpi', 'besedišči'], ]
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content = [[el[0], '', el[1][:-1], el[0]] for el in content]
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# use environment variables and models to accentuate words
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data = Data('l', shuffle_all_inputs=False)
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location_accented_words, accented_words = data.accentuate_word(content, 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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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, syllable_dictionary)
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# save accentuated words
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with open(write_location, 'w') as f:
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for i in range(len(location_accented_words)):
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f.write(location_accented_words[i] + ' ' + accented_words[i] + '\n')
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f.write('\n') |