80 lines
3.9 KiB
Python
80 lines
3.9 KiB
Python
|
# -*- 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)
|