stress_asignment/sloleks_accetuation2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from __future__ import unicode_literals\n",
    "\n",
    "import numpy as np\n",
    "from keras.models import load_model\n",
    "import sys\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "from prepare_data import *\n",
    "\n",
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Data('l', shuffle_all_inputs=False)\n",
    "content = data._read_content('data/SlovarIJS_BESEDE_utf8.lex')\n",
    "dictionary, max_word, max_num_vowels, vowels, accented_vowels = data._create_dict(content)\n",
    "feature_dictionary = data._create_slovene_feature_dictionary()\n",
    "syllable_dictionary = data._create_syllables_dictionary(content, vowels)\n",
    "accented_vowels = ['ŕ', 'á', 'ä', 'é', 'ë', 'ě', 'í', 'î', 'ó', 'ô', 'ö', 'ú', 'ü']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "environment = {}\n",
    "environment['dictionary'] = dictionary\n",
    "environment['max_word'] = max_word\n",
    "environment['max_num_vowels'] = max_num_vowels\n",
    "environment['vowels'] = vowels\n",
    "environment['accented_vowels'] = accented_vowels\n",
    "environment['feature_dictionary'] = feature_dictionary\n",
    "environment['eng_feature_dictionary'] = feature_dictionary\n",
    "environment['syllable_dictionary'] = syllable_dictionary\n",
    "output = open('environment.pkl', 'wb')\n",
    "pickle.dump(environment, output)\n",
    "output.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "407\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for el in syllable_dictionary:\n",
    "    if el == \"da\":\n",
    "        print(i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run prepare_data.py\n",
    "\n",
    "data = Data('l', shuffle_all_inputs=False)\n",
    "letter_location_model, syllable_location_model, syllabled_letters_location_model = data.load_location_models(\n",
    "    'cnn/word_accetuation/cnn_dictionary/v5_3/20_final_epoch.h5',\n",
    "    'cnn/word_accetuation/syllables/v3_3/20_final_epoch.h5',\n",
    "    'cnn/word_accetuation/syllabled_letters/v3_3/20_final_epoch.h5')\n",
    "\n",
    "letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model = data.load_location_models(\n",
    "    'cnn/word_accetuation/cnn_dictionary/v5_2/20_final_epoch.h5',\n",
    "    'cnn/word_accetuation/syllables/v3_2/20_final_epoch.h5',\n",
    "    'cnn/word_accetuation/syllabled_letters/v3_2/20_final_epoch.h5')\n",
    "\n",
    "letter_type_model, syllable_type_model, syllabled_letter_type_model = data.load_type_models(\n",
    "    'cnn/accent_classification/letters/v3_1/20_final_epoch.h5',\n",
    "    'cnn/accent_classification/syllables/v2_1/20_final_epoch.h5',\n",
    "    'cnn/accent_classification/syllabled_letters/v2_1/20_final_epoch.h5')\n",
    "\n",
    "letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model = data.load_type_models(\n",
    "    'cnn/accent_classification/letters/v3_0/20_final_epoch.h5',\n",
    "    'cnn/accent_classification/syllables/v2_0/20_final_epoch.h5',\n",
    "    'cnn/accent_classification/syllabled_letters/v2_0/20_final_epoch.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_input = [['uradni', '', 'Agpmpn', 'uradni'], ['podatki', '', 'Ncmpn', 'podatki'], ['policije', '', 'Ncfsg', 'policije'], ['kažejo', '', 'Vmpr3p', 'kažejo'], ['na', '', 'Sa', 'na'], ['precej', '', 'Rgp', 'precej'], ['napete', '', 'Appfpa', 'napete'], ['razmere', '', 'Ncfpa', 'razmere'], ['v', '', 'Sl', 'v'], ['piranskem', '', 'Agpmsl', 'piranskem'], ['zalivu', '', 'Ncmsl', 'zalivu'], ['je', '', 'Va-r3s-n', 'je'], ['danes', '', 'Rgp', 'danes'], ['poročala', '', 'Vmpp-sf', 'poročala'], ['oddaja', '', 'Ncfsn', 'oddaja'], ['do', '', 'Sg', 'do'], ['danes', '', 'Rgp', 'danes'], ['se', '', 'Px------y', 'se'], ['je', '', 'Va-r3s-n', 'je'], ['zgodilo', '', 'Vmep-sn', 'zgodilo']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accented_vowels = ['ŕ', 'á', 'ä', 'é', 'ë', 'ě', 'í', 'î', 'ó', 'ô', 'ö', 'ú', 'ü']\n",
    "words = [[\"Gorejevemu\", \"\", \"Psnsed\", \"Gorejevemu\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gorejévemu']\n",
      "['Gorejěvemu']\n",
      "[['Gorejevemu', '', 'Psnsed', 'Gorejevemu']]\n"
     ]
    }
   ],
   "source": [
    "pos = 4282\n",
    "print(location_accented_words)\n",
    "print(accented_words)\n",
    "print(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Data('s', shuffle_all_inputs=False)\n",
    "new_content = data._read_content('data/sloleks-sl_v1.2.tbl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "words = [[el[0], '', el[2], el[0]] for el in new_content][1146450:1146550]\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['adventistovo', '', 'Psnzeo', 'adventistovo'], ['adventistovo', '', 'Psnzet', 'adventistovo'], ['adventistu', '', 'Somed', 'adventistu'], ['adventistu', '', 'Somem', 'adventistu'], ['adventiven', '', 'Ppnmein', 'adventiven'], ['adventiven', '', 'Ppnmetn', 'adventiven'], ['adventivna', '', 'Ppnmdi', 'adventivna'], ['adventivna', '', 'Ppnmdt', 'adventivna'], ['adventivna', '', 'Ppnsmi', 'adventivna'], ['adventivna', '', 'Ppnsmt', 'adventivna'], ['adventivna', '', 'Ppnzei', 'adventivna'], ['adventivne', '', 'Ppnmmt', 'adventivne'], ['adventivne', '', 'Ppnzer', 'adventivne'], ['adventivne', '', 'Ppnzmi', 'adventivne'], ['adventivne', '', 'Ppnzmt', 'adventivne'], ['adventivnega', '', 'Ppnmer', 'adventivnega'], ['adventivnega', '', 'Ppnmet', 'adventivnega'], ['adventivnega', '', 'Ppnser', 'adventivnega'], ['adventivnem', '', 'Ppnmem', 'adventivnem'], ['adventivnem', '', 'Ppnsem', 'adventivnem'], ['adventivnemu', '', 'Ppnmed', 'adventivnemu'], ['adventivnemu', '', 'Ppnsed', 'adventivnemu'], ['adventivni', '', 'Ppnmeid', 'adventivni'], ['adventivni', '', 'Ppnmetd', 'adventivni'], ['adventivni', '', 'Ppnmmi', 'adventivni'], ['adventivni', '', 'Ppnsdi', 'adventivni'], ['adventivni', '', 'Ppnsdt', 'adventivni'], ['adventivni', '', 'Ppnzdi', 'adventivni'], ['adventivni', '', 'Ppnzdt', 'adventivni'], ['adventivni', '', 'Ppnzed', 'adventivni'], ['adventivni', '', 'Ppnzem', 'adventivni'], ['adventivnih', '', 'Ppnmdm', 'adventivnih'], ['adventivnih', '', 'Ppnmdr', 'adventivnih'], ['adventivnih', '', 'Ppnmmm', 'adventivnih'], ['adventivnih', '', 'Ppnmmr', 'adventivnih'], ['adventivnih', '', 'Ppnsdm', 'adventivnih'], ['adventivnih', '', 'Ppnsdr', 'adventivnih'], ['adventivnih', '', 'Ppnsmm', 'adventivnih'], ['adventivnih', '', 'Ppnsmr', 'adventivnih'], ['adventivnih', '', 'Ppnzdm', 'adventivnih'], ['adventivnih', '', 'Ppnzdr', 'adventivnih'], ['adventivnih', '', 'Ppnzmm', 'adventivnih'], ['adventivnih', '', 'Ppnzmr', 'adventivnih'], ['adventivnima', '', 'Ppnmdd', 'adventivnima'], ['adventivnima', '', 'Ppnmdo', 'adventivnima'], ['adventivnima', '', 'Ppnsdd', 'adventivnima'], ['adventivnima', '', 'Ppnsdo', 'adventivnima'], ['adventivnima', '', 'Ppnzdd', 'adventivnima'], ['adventivnima', '', 'Ppnzdo', 'adventivnima'], ['adventivnim', '', 'Ppnmeo', 'adventivnim'], ['adventivnim', '', 'Ppnmmd', 'adventivnim'], ['adventivnim', '', 'Ppnseo', 'adventivnim'], ['adventivnim', '', 'Ppnsmd', 'adventivnim'], ['adventivnim', '', 'Ppnzmd', 'adventivnim'], ['adventivnimi', '', 'Ppnmmo', 'adventivnimi'], ['adventivnimi', '', 'Ppnsmo', 'adventivnimi'], ['adventivnimi', '', 'Ppnzmo', 'adventivnimi'], ['adventivno', '', 'Ppnsei', 'adventivno'], ['adventivno', '', 'Ppnset', 'adventivno'], ['adventivno', '', 'Ppnzeo', 'adventivno'], ['adventivno', '', 'Ppnzet', 'adventivno'], ['adventna', '', 'Ppnmdi', 'adventna'], ['adventna', '', 'Ppnmdt', 'adventna'], ['adventna', '', 'Ppnsmi', 'adventna'], ['adventna', '', 'Ppnsmt', 'adventna'], ['adventna', '', 'Ppnzei', 'adventna'], ['adventne', '', 'Ppnmmt', 'adventne'], ['adventne', '', 'Ppnzer', 'adventne'], ['adventne', '', 'Ppnzmi', 'adventne'], ['adventne', '', 'Ppnzmt', 'adventne'], ['adventnega', '', 'Ppnmer', 'adventnega'], ['adventnega', '', 'Ppnmet', 'adventnega'], ['adventnega', '', 'Ppnser', 'adventnega'], ['adventnem', '', 'Ppnmem', 'adventnem'], ['adventnem', '', 'Ppnsem', 'adventnem'], ['adventnemu', '', 'Ppnmed', 'adventnemu'], ['adventnemu', '', 'Ppnsed', 'adventnemu'], ['adventni', '', 'Ppnmeid', 'adventni'], ['adventni', '', 'Ppnmetd', 'adventni'], ['adventni', '', 'Ppnmmi', 'adventni'], ['adventni', '', 'Ppnsdi', 'adventni'], ['adventni', '', 'Ppnsdt', 'adventni'], ['adventni', '', 'Ppnzdi', 'adventni'], ['adventni', '', 'Ppnzdt', 'adventni'], ['adventni', '', 'Ppnzed', 'adventni'], ['adventni', '', 'Ppnzem', 'adventni'], ['adventnih', '', 'Ppnmdm', 'adventnih'], ['adventnih', '', 'Ppnmdr', 'adventnih'], ['adventnih', '', 'Ppnmmm', 'adventnih'], ['adventnih', '', 'Ppnmmr', 'adventnih'], ['adventnih', '', 'Ppnsdm', 'adventnih'], ['adventnih', '', 'Ppnsdr', 'adventnih'], ['adventnih', '', 'Pp
     ]
    }
   ],
   "source": [
    "print(words.append['nadnaravno', '', 'Ppnsei'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing data from 0 onward.\n",
      "Elapsed time: 46.20 minutes\n",
      "Writing data from 100000 onward.\n",
      "Elapsed time: 89.81 minutes\n",
      "Writing data from 200000 onward.\n",
      "Elapsed time: 134.45 minutes\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 10 is out of bounds for axis 0 with size 10",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-3dd0379916f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m                                 \u001b[0mletter_type_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllable_type_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllabled_letter_type_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m                                 \u001b[0mletter_type_co_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllable_type_co_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msyllabled_letter_type_co_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m                                 dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary, syllable_dictionary)\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developement/accetuation/prepare_data.py\u001b[0m in \u001b[0;36maccentuate_word\u001b[0;34m(self, input_words, 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)\u001b[0m\n\u001b[1;32m   1635\u001b[0m                                                              \u001b[0msyllabled_letters_location_co_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1636\u001b[0m                                                              \u001b[0mdictionary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_word\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_num_vowels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccented_vowels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_dictionary\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1637\u001b[0;31m                                                              syllable_dictionary)\n\u001b[0m\u001b[1;32m   1638\u001b[0m         \u001b[0;31m#print(predictions)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'A'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developement/accetuation/prepare_data.py\u001b[0m in \u001b[0;36mget_ensemble_location_predictions\u001b[0;34m(input_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)\u001b[0m\n\u001b[1;32m   1465\u001b[0m         \u001b[0mletter_location_co_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mletter_location_co_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1467\u001b[0;31m         \u001b[0mletter_location_co_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse_predictions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mletter_location_co_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1469\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m's'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle_all_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_multext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Developement/accetuation/prepare_data.py\u001b[0m in \u001b[0;36mreverse_predictions\u001b[0;34m(self, predictions, words, vowels)\u001b[0m\n\u001b[1;32m   1503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1504\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1505\u001b[0;31m                 \u001b[0mnew_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword_len\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1507\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 10 is out of bounds for axis 0 with size 10"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "#Words proccesed: 650250\n",
    "#Word indeks: 50023\n",
    "#Word number: 50023\n",
    "\n",
    "#done_lexical_entries = 33522\n",
    "\n",
    "#new_content = data._read_content('sloleks-sl_v1.2.tbl')\n",
    "rate = 100000\n",
    "start_timer = time.time()\n",
    "with open(\"data/new_sloleks/new_sloleks.tab\", \"a\") as myfile:\n",
    "    for index in range(0, len(new_content), rate):\n",
    "        if index+rate >= len(new_content):\n",
    "            words = [[el[0], '', el[2], el[0]] for el in new_content][index:len(new_content)]\n",
    "        else:\n",
    "            words = [[el[0], '', el[2], el[0]] for el in new_content][index:index+rate]\n",
    "        data = Data('l', shuffle_all_inputs=False)\n",
    "        location_accented_words, accented_words = data.accentuate_word(words, letter_location_model, syllable_location_model, syllabled_letters_location_model,\n",
    "                                letter_location_co_model, syllable_location_co_model, syllabled_letters_location_co_model,\n",
    "                                letter_type_model, syllable_type_model, syllabled_letter_type_model,\n",
    "                                letter_type_co_model, syllable_type_co_model, syllabled_letter_type_co_model,\n",
    "                                dictionary, max_word, max_num_vowels, vowels, accented_vowels, feature_dictionary, syllable_dictionary)\n",
    "\n",
    "        res = ''\n",
    "        for i in range(index, index + len(words)):\n",
    "            res += new_content[i][0] + '\\t' + new_content[i][1] + '\\t' + new_content[i][2] + '\\t' \\\n",
    "            + new_content[i][3][:-1] + '\\t' + location_accented_words[i-index] + '\\t' + accented_words[i-index] + '\\n'\n",
    "\n",
    "        print('Writing data from ' + str(index) + ' onward.')\n",
    "        end_timer = time.time()\n",
    "        print(\"Elapsed time: \" + \"{0:.2f}\".format((end_timer - start_timer)/60.0) + \" minutes\")\n",
    "        myfile.write(res)"
   ]
  }
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