predict_emotion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "predict_emotion.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coKAZLXvspj9"
      },
      "source": [
        "# Multimodal Speech Emotion Recognition\n",
        "<hr/>\n",
        "<b>Description:</b> This notebook contains a demo application of a Multimodal Speech Emotion Recognition model trained audios and texts on the IEMOCAP dataset using 4 basic emotions.<br/>\n",
        "<b>Author:</b> Jan Čuhel<br/>\n",
        "<b>Date:</b> 17.3.2021<br/>\n",
        "<b>Dataset:</b> IEMOCAP<br/>\n",
        "<b>Emotions:</b> Basic - [happy + excited, sad, angry, neutral]<br/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHBgi40Tycgh"
      },
      "source": [
        "\n",
        "###Resources \n",
        "- https://ricardodeazambuja.com/deep_learning/2019/03/09/audio_and_video_google_colab/\n",
        "- https://stackoverflow.com/questions/9031783/hide-all-warnings-in-ipython\n",
        "- https://getemoji.com/\n",
        "- https://realpython.com/python-speech-recognition/\n",
        "- https://github.com/Uberi/speech_recognition#readme"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vj-Bbk2DyK1-"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFxcN_payKfL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "798e80f8-d5f6-455e-d706-5ad5eabad542"
      },
      "source": [
        "!pip install -q ffmpeg-python SpeechRecognition gTTS pydub"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 32.8MB 110kB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzpQB2bhyNmI"
      },
      "source": [
        "### Connet to GitHub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4WHsFxYuIho",
        "outputId": "f9eabb6b-17ef-463b-8c98-b1f18dbd279d"
      },
      "source": [
        "!git clone https://github.com/HonzaCuhel/mser-thesis-app.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'mser-thesis-app'...\n",
            "remote: Enumerating objects: 51, done.\u001b[K\n",
            "remote: Counting objects: 100% (51/51), done.\u001b[K\n",
            "remote: Compressing objects: 100% (42/42), done.\u001b[K\n",
            "remote: Total 51 (delta 16), reused 44 (delta 9), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (51/51), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwAiE77ZQIIJ"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n00MeETqutfK"
      },
      "source": [
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "import speech_recognition as sr\n",
        "import gtts\n",
        "import IPython.display as display\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "sys.path.append('/content/mser-thesis-app/')\n",
        "from record_audio import get_audio\n",
        "from predict_emotion import predict_emotion"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJ156tsLQDNL"
      },
      "source": [
        "### Defining constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaZZavv3QCok"
      },
      "source": [
        "lang = 'en'\n",
        "dur = 11\n",
        "emoji_dict = {\"happy\":\"😊\", \"fear\":\"😱\", \"angry\":\"😡\", \"sad\":\"😢\", \"disgust\":\"🤮\", \"shame\":\"😳\", \"guilt\":\"😓\", \"neutral\": \"😐\"}\n",
        "NO = 'no'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zCkOrk_QbwV"
      },
      "source": [
        "### Definition of functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ai4O4LxaQa77"
      },
      "source": [
        "def recognize_emotion(audio_file):\n",
        "  # use the audio file as the audio source\n",
        "  r = sr.Recognizer()\n",
        "  with sr.AudioFile(audio_file) as source:\n",
        "      audio = r.record(source, duration=dur)  # read the entire audio file\n",
        "  \n",
        "  # Resource: https://github.com/Uberi/speech_recognition/blob/master/examples/audio_transcribe.py\n",
        "  # Recognize speech using Google Speech Recognition\n",
        "  try:\n",
        "      text = r.recognize_google(audio, language=lang)\n",
        "  except sr.UnknownValueError:\n",
        "      print(\"Google Speech Recognition could not understand audio\")\n",
        "      return \"\"\n",
        "  except sr.RequestError as e:\n",
        "      print(\"Could not request results from Google Speech Recognition service; {0}\".format(e))\n",
        "      return \"\"\n",
        "\n",
        "  # Print what the user just has said\n",
        "  print(f'You\\'ve just said: {text}')\n",
        "  \n",
        "  # Return predicted emotion\n",
        "  return predict_emotion(text, audio_file)\n",
        "\n",
        "\n",
        "# def display_prediction(pred):\n",
        "#   print(f'\\nPredicted emotion: {pred} {emoji_dict[pred]}')\n",
        "\n",
        "#   # make request to google to get synthesis\n",
        "#   tts = gtts.gTTS(f'You are {pred}', lang=lang)\n",
        "\n",
        "#   output_file = 'output_emotion.mp3'\n",
        "#   tts.save(output_file)\n",
        "\n",
        "#   # Play it\n",
        "#   display.Audio(output_file, autoplay=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETpnzv33QOzP"
      },
      "source": [
        "### Record a speech"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "RPTSXUW9u3Ak",
        "outputId": "3cdcc97b-b049-4f92-d05f-95ee10635e2c"
      },
      "source": [
        "audio, sample_rate, audio_file = get_audio()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "<script>\n",
              "var my_div = document.createElement(\"DIV\");\n",
              "var my_p = document.createElement(\"P\");\n",
              "var my_btn = document.createElement(\"BUTTON\");\n",
              "var t = document.createTextNode(\"Press to start recording\");\n",
              "\n",
              "my_btn.appendChild(t);\n",
              "//my_p.appendChild(my_btn);\n",
              "my_div.appendChild(my_btn);\n",
              "document.body.appendChild(my_div);\n",
              "\n",
              "var base64data = 0;\n",
              "var reader;\n",
              "var recorder, gumStream;\n",
              "var recordButton = my_btn;\n",
              "\n",
              "var handleSuccess = function(stream) {\n",
              "  gumStream = stream;\n",
              "  var options = {\n",
              "    //bitsPerSecond: 8000, //chrome seems to ignore, always 48k\n",
              "    mimeType : 'audio/webm;codecs=opus'\n",
              "    //mimeType : 'audio/webm;codecs=pcm'\n",
              "  };            \n",
              "  //recorder = new MediaRecorder(stream, options);\n",
              "  recorder = new MediaRecorder(stream);\n",
              "  recorder.ondataavailable = function(e) {            \n",
              "    var url = URL.createObjectURL(e.data);\n",
              "    var preview = document.createElement('audio');\n",
              "    preview.controls = true;\n",
              "    preview.src = url;\n",
              "    document.body.appendChild(preview);\n",
              "\n",
              "    reader = new FileReader();\n",
              "    reader.readAsDataURL(e.data); \n",
              "    reader.onloadend = function() {\n",
              "      base64data = reader.result;\n",
              "      //console.log(\"Inside FileReader:\" + base64data);\n",
              "    }\n",
              "  };\n",
              "  recorder.start();\n",
              "  };\n",
              "\n",
              "recordButton.innerText = \"Recording... press to stop\";\n",
              "\n",
              "navigator.mediaDevices.getUserMedia({audio: true}).then(handleSuccess);\n",
              "\n",
              "\n",
              "function toggleRecording() {\n",
              "  if (recorder && recorder.state == \"recording\") {\n",
              "      recorder.stop();\n",
              "      gumStream.getAudioTracks()[0].stop();\n",
              "      recordButton.innerText = \"Saving the recording... pls wait!\"\n",
              "  }\n",
              "}\n",
              "\n",
              "// https://stackoverflow.com/a/951057\n",
              "function sleep(ms) {\n",
              "  return new Promise(resolve => setTimeout(resolve, ms));\n",
              "}\n",
              "\n",
              "var data = new Promise(resolve=>{\n",
              "//recordButton.addEventListener(\"click\", toggleRecording);\n",
              "recordButton.onclick = ()=>{\n",
              "toggleRecording()\n",
              "\n",
              "sleep(2000).then(() => {\n",
              "  // wait 2000ms for the data to be available...\n",
              "  // ideally this should use something like await...\n",
              "  //console.log(\"Inside data:\" + base64data)\n",
              "  resolve(base64data.toString())\n",
              "});\n",
              "}\n",
              "});\n",
              "      \n",
              "</script>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TA730O8iQUiJ"
      },
      "source": [
        "### Plot the audio's waveform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "vDsY7hN1q8Ji",
        "outputId": "dbe5be67-aba8-43c8-87d4-336a1b790844"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.plot(audio)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
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ncjkPSLrPVhdY+nz/nL1Nj0/NsfQ50ZoVCbiX5m7XzI17LYjGma75xwJ9/+XF+3NERtPo1JfnbpfZ3OgsVXWZ2JV1LJ5RXqTXADgdSSIASAErrpY7wRPTcjR3S4ndYQBR/fGDtZY/54xN3k0+OIFV28Zxn2Zb8jxOZoanm0kPfLFRj3yZozmb7Zn2RaLDYxgZBiACSSIAQNxemLNdt7yxzO4wAHgEU3Hjt38kkREu+O3zB1Py2jUNAS3JLQuPlCGn0HVO/Oz+MXub1u+utDsMAA5BkggAACCGuoag3l+Wp09WWjuVDU2yLEsSOfDM22KR3c3CtYHaPJYsv393lW56ZYn21TVKEiNKPejWt1faHQIAhyBJBACIy5TsPeHbW/dW2xgJkDpFVT6N+zRbt39k/VQ2WFevzYmjM6wWrbtZqt53TlHTNt/nD1n+3HM2F2vEuMkqrvJZ/txOlKr6UQDQVSSJACAFvHBQ+Nt3VoVvX/HMPBsjAeAVVk03c/8WNn6GsT9RlLKC1c3/JuP13lmyS5K0Jr/C8ud2ogsfnaPKOr/dYbSTDolWAPEhSQQAAABbWDXdLB1askd7h1a+7VDIjFnjyEhi84WZm5qKb3v/G2zSGAxpyY4yu8MAgJhIEgEAACDldlfU67lZW+0Owz2asyiGofDQHisTK0/N2KwT7p6qmoaAnp+9VWU1DRY+e+fSIM8Xlg5JTQDuRZIIAOLGQR0AWOWnry/VjtJaS54rHbbOkTWJwtO/LEw2fLJytyRp1qa9enL6Ft3x8TrLnjs+7v4WQyEzXNi702Xd/VYBeBxJIgAAAKRcbok1CaJ01DL9K1qOyDRNNQbiLzAdCIYUCIbUMvOvoflv6xrbTz1L5gAYtw+ueX7ONr2zNC+uZUMOfLO7K+q1Nk3qQgHoGEkiAEiB/yzaaXcIaDZi3OSkv8bVz81P6CQNADoTiDL8JNpIon8v3Knj7vpSJdXxTRc7/cEZOmv8zA6XMZIwvc1rZm7aG/eyqRhJFO/3H+n6FxYmIRIAbkOSCADi1J0Lf49P3WxdIHC8jXuqlL+vzu4wAHhQZb0/ottYk4aIpPTna5qmjRVW1Mf1fNW+gPbF6LY1dX2R5m0pCe//kle+2v0JqESKe6eiJtG3SfgA6CKSRAAAJEEyT6YAtFZR59etb620O4yUqG0IhG/P2Vxi/QtEFMi+9e2VuvmNZda/RrSXdXmWyKJGfZbZHWeSEADaIkkEAEASZCSxZTSA9qZuKLI7hJQwjP3Tv+ZtSUKSyKZNlxPr9CSCbT4AryBJBABAEnC+ACAZIrubRbOuoLJ7L9BBruauz9d377k9zGkjiQCgq0gSAUCc3H2NE6nW8Wkc3Mg0TYXoXQ2bNY0k6nz70t1ENYnuxLDNB+AVJIkAAGmjuMqXstfiBMt7Xpu/Q6P+NkX7ahvtDgXoVKKzt0h/do+TtvlT1++xOwQALkaSCACQFgLBkM5+eFbKXs9JJwywxvgpmyRJhZUUhIW9urt5aQyEdO7DszR1fZQ6Ti3t7skaJcRJNYlufXuV3SEAcDGSRACQIqloeYvYjvn7lyl9PSedMADwmDg2Lx1tgspqG1RU5dN9kzZYF1Oay+CsCoBHsDkDgBQhR5ReyBF5F79l2CkQMrUmr6Jbz9FSPydaR7GWTRfreWK4MADAK0gSAUCKrM7fp9MemE49ExvYMYrrvEdma+ve6pS/LgBvu2/SBuWW1na6XEebvZZOXJF12FuWj6coNtrjcwPgFSSJACBFbnhxsSrq/Fq2s9zuUJAid3yyjmmGQAxnjxhodwiutHFPVVzLdZiz6OCx8EgiSlknJIMcEQCPIEkEAHHiXN+97PruVudVaHV+96aFwHmenrFF9Y1BSU2j1EIhNg5dcfpRA+wOwZXIRTgT3wsAryBJBAApRrIpvbQkE+Ads3OK9czMLQqFTF329FydeM9Uu0NyJ7aFXRLvrKY7Pl4nn7/j7U9pTUPs14lIe+yuSH5Hv9+/t1rLdrh3pC2rMwCvIEkEAPA8Ow/eg4wy8aRX5uXqqRmblVtSq4ZAyO5wXCcYMlWehPps6VAHLN4CyTlF1Zq0tjDh57dzizVp7W4bXx0AIJEkAgAbkDRIJ0GGjnnWv77aHr596VNf2ReICz08ZZM+Wllg+fNe8cw8y5/TaRLqohVr8xPl/qIqX6v/L84ti/91LMLmEgDsR5IIAOJEEU/3srN4dElV7OkccLfI1Sq3pPNuU9hv6voiu0NwrUSmfkVrcd8ZO2vrsJe1XzqMxgPQMZJEAADPs/PE445P1mnx9tRfkUfqzdq01+4QXKO42tf5Qui2rsx2raz3Wx8IXCMdRuMB6BhJIgAAkuyOT9baHQJS4BcTVmjEuMmak1NsdyiO5w8yZiQVujKS6IH/bkxCJPGpqGtUIEiNLwCwE0kiAIhTOtdKsHO6lhU2FlbZ+vqDD+hl6+ung9+8vdLuEMI+TkKtHaArWrbcVz47Txc9PtvWWOIxJbtId32+3u4wACCtkSQCAHTKzTmiyjq/rn9hoa0xjBjUz/WJNqf70kE1biZn79GKne5t5Q3v8DUGZZqmcoqqlV9er0AwpMIUtLPvjolrEu/IZrdPVxXoq80ldocBAJYgSQQA8DRfIGh3CPps9W69vmCH3WEghb730mK7QwA0fsomPTdra/j/D03epPMfna3yukYbo+pYV6bI2c1ro5+2FdfYHQIAG5EkAgB0yn2H7Psl0i06mTbYPOUNQHr6ZNX+6Y/ztjaNdimvcW6SyIU5ImU4ZUdjkcufnqu6xoDdYQCwCUkiAIiTVQeupimt3FXuquKcbp4qZdja0Hk/N3+GANwrctPTM7Pp0L/RwfsfN44kSnQvs6GwSqGutJ6Lg1VdFhsDzl1HACQXSSIASLHfvLNKN7y4WE/P2GJ3KGkhwxk5Ii3bUa4GB0x9Q+rQShxOEJlzycps2iAGHNxdLhAyNdVBNcbikehAolfm5SZtCvIvJqyw5HmccoEFQOqRJAIAm2wuqrY7hLg593SiYw2BoO7/wr52zpEKK326b5IzYkFqnPXQTLtDAFppOfF3+jb9Vgd1K4yH0YXpZtm7K5MQiXVqmG4GpC2SRAAQJ9Pxh9Vo64u1ezRprXM65Wwuoi5ROnHylB6kp5ZcRm0DCQArdWXEatDh0+rqWEeAtEWSCABsMiunWFU+d0xHiXYsu3LXvtQHkiA31rYAYK/vv7xY7y3LszsMy0Srh/aHD9bYEIl3daVwdbJqElmFZgtA+iJJBAA2+vtn7mibG20U1Q0vLrIhEgBIrmU7ynXnp9l2h2GZyK33ugJnT3Fyq640N3N6Q7Q/fLBGI8ZN1vzmjngA0gdJIgCw0b5a57YhdqtF20s1YtxkbSuucVzZTWdfNwbgRXsqfXaH4HldqUnkFnd8vM7uEACkGEkiAIhTOs9cctN7/6K5BtHSHWU2RwLADvO2dG3kw87SWosjQbpwShdNALACSSIAsJHbi2HHmpLx7tI8Ld5uT5KmJaHVlRoRyeamZBus4aXaNm5x8xvLEv6bSWsLdfGTX+mrzcXh+377zkqdNZ4Odegc7eIBeAlJIgBAl0U7AZ64Zrf+9lm2fvjqEhsi2p+I4ZAdTuCl2jZetr65HfnmourwfVOyi1RS3WBXSHARL48k8vBbg8sFQ6aCDi8A71aWJIkMw7jSMIzNhmFsMwxjXJTH/2QYxkbDMNYZhjHLMIyjrHhdAHC7wgp31IpIZATM/71vb9ccJ4/OWpNfodV5zu8KB6SraFuPX/xnuYqr3bGthj28XJMIcKqxD83QuY/MsjsMT+p2ksgwjExJL0i6StJJkn5oGMZJbRZbLWmsaZpjJH0s6fHuvi4AeMEOl9TAcHLipa3wSCLDmYWiv/MvusKlm4lrdtsdAjrR0Sn+rJxinT1+ljbSEhwxeDlH1DYBFgqZuvLZeZqSvcemiIAm++r8jPZMEitGEp0taZtpmrmmaTZKel/S9ZELmKY5xzTNuub/LpF0uAWvCwBAOy2Jodfm76ArCxzh/95fo8o6v91hIA6V9bG/p+0lNSmMBG7ixBp4yeILBJVTVK0/fWjvqGEAyWNFkmi4pPyI/xc03xfLLyR9acHrAoDrnTD0QLtDiIubCi63xLq1mBM6OEfITT+iNLJ1b3VTN7Tmc/wXv9oec1m+QcTi5RxR2/fWtkh3QyCoGRv3pjAiAMmW0sLVhmH8RNJYSU/EePzXhmGsMAxjRUlJ19qXAoCb9O6RaXcInlOwr67zhWz26Jc5dofgKaYLEjAvzNkmfzBkdxhpI9514opn5sXdDc0N65mXlNW4ZxpJOq8aj0zJ0a/eXKHlO8vtDgWARaxIEu2WdETE/w9vvq8VwzAul/R3SdeZphl1q2+a5iumaY41TXPskCFDLAgNAJxtTX6Flu1w/oGVm45/d5U5P0n00tzYoxWQGNM0dfuHa+0Oo1OvLdihj1YU2B1G2kj0pP3lubmdLuPzB7sYDbrizIdm2h1C3NxUt89qeeVN+1ym1ALeYUWSaLmkYw3DGGkYRk9JN0maFLmAYRinS3pZTQmiYgteEwA8Y9ynzq+b46Yr6KcdMcDuEOKyYme51hVU2B2G69U2BvXpancUhq4nyZAyyZje99dPsjV1PcV60Z6LdpEJK9hXrw2FleH/t02ItRwfeHnKHZBuup0kMk0zIOl3kqZJ2iTpQ9M0NxiG8YBhGNc1L/aEpAMkfWQYxhrDMCbFeDoASD8ePrhEbN97abGue36h3WEghdyUbHW7UJI+6jk5lENA+rnmHwvCt9mMAd6XZcWTmKY5RdKUNvfdE3H7citeBwC8yA0FbZ0f4X7pPOw/Hbkp8RJMVuYC7SRru8r2BW1tL6lRwb56u8NImZZfQNsC1gC8I6WFq2G9yjq/3l+WZ3cYQFpw0bmo5dL5vQNWIUeUOnd+mq3GgPWFwvkO0dZlT821O4SUipWYZ7oZ4B0kiVzu9o/WaNyn2a3mCgNwFy+dc9wzcb3dIZDQgmM9NjVHH63ItzuMtPDZ6t2auqGow2W60jSA7QvSXctPoN4f1PytJZ46hgHQhCSRy5XUNEqSGpJwtQwAwuI8Cnxz8a7kxtGJpbllmr5xr60xILXcdoLy+oIddodgu8KK1EzN6Wwq4vdfXpz4c7pujQOssbuiXs/N3KqHJ28K3/fT15fZGBGAZLGkJhHs0zKykytbAJLJLSdGP3hlid0hIMXctv/LKaq2OwTbnf/o7JS8TjLWDbetb4BVfjVhhTbuqWp3f355nSTJYL4Z4BmMJHK5/dtjjlqAZEtWomRXWZ1yitofeAGIA7s/xNDRNvv7LyU+ikhyV6F0wEp1jYGo928vqZUkylgDHkKSyOUYSQR4w5XPzrc7hA65YRtTWee3OwTYwC2j3CKtza+wO4S0EOpgJv6ynYnXI5LIScLZzn9klm2vzUgiwDtIErlcywaZgxYA6e5/3l5hdwhdUu3zh4frIz1c/8JCu0NIC8k4Npq4ppCusnCswkpf0p57Z1nH+ylSRIB3kCRyOTbIAFKho5Ot8ZM3piyOjizJ7drIALuNvm+6Lnp8jt1huJYbRrlF8/HKArtD8LxkTQ174L/O2OYBTsJAIsA7SBJ5hFsPkgG4Q0cnW6/Op1sT7OPW3d+fP1prdwieF22zVVHXqBHjJlv+vEC6M7h0DXgGSSKXa8nad+Vq2c//s1x/eH+1xREB3sWJQce6e+IFdAWFhJGIzRZ0lwuxzgHtMJII8A6SRC7XkrXvyuHK7Jxifb6m0NqAAHiSk0+Jnp+9VZc++ZX69sy0OxTYwMnrJpzHivWFHBHQHjkiwDtIErldeCSRvWEAsMa/F+7QPRPX2x1GO13dxmzaU2VtIFE8OX2Lcktrk/46gN2qfH7N3VJidxiuxoksnGJKdpGmbSiyOwzLvDwvV1U+uowCXkCSyOVaDnbc2AIYQGu7ymp1/xcb9ebiXXaHkrC8GF1Prnpufvj2JU9+pe/+a6HKahpSFRbSQDpdJLnjo3W65Y1lKqyotzsU16JNN5zkgS+8UwR97pYS3Tdpg91hALAASSKXM/ZniQAk2eLcsqQ+/9ef+Cp822n1fTpLRH/tic67c+0ordWqvAqd+dBM/fsFv4cAACAASURBVHddoRZsLbUqPEnplSzAful0kWTL3qZ6OuW1jRr70Ewt3GbtbygdkCNyF68XePdaTbXahoDdIQCwAEkil+tOTSIAiXn0yxy7Q7CPxRuZ3727Wj95famlQ+3TKVmACOn0tTcnODYXVau0pkGPTc3x3EkmEOnjlQWs4y7CdFjAG0gSuRxXxAC42f+8tdLuEOBy6Xj6mNF89LauoFK3vb/G3mAc7I5P1mn97krLn7cxGNI8ToZThhyRe/j8IbtDAGABkkQewQ4UQDK5YRPj9u3gnZ9mKxRy+ZtIsV1ltTrn4Vl2h5EST0zLUW5JU4H2P36wfwrOF2sLKRbbgcenbQ7f3l5SoxtfWmzJ8978xjJLngedC7l9455GLjvhELtDAGABkkQu1zKSiGkWANKd27eC7y3LUylFvROyrsD6USKpdMLdX8Y9leaFOdtjPjbmvulWheQ5kZ/vm4t22hcIuozcuXscclAvTdtQpK82F9sdCoBuyLI7AHRPuCYRO1AASeSKbYwbYuwEJ0OJWZLkYvLJ5vOHFAyZyspk7niyRI5CobOZOzGSyF1appHvfPQamyMB0FWMJHK5/SOJAHiNk4p1JnO04n/XFVryPF4YUcnJUGK+XG9d4XO7rC+s0i8nLNfqvH12h+JJoYgSKZkZJIncyMvbRa+9s/eX59sdAgALkCQCAHQqmMQhLr97d7Ulz+OF84hkfs5wpm+/sFAzNxXr9+/F/h2MGDe50+e54um5VoblGZHJY1JE7sRm0T28sB8GwHQzz3DSiAMA1jBN53QwvPCxOXaH0M6yHeXaV9cY/r8XtoJO+b6Ret0tWr61uMaiSLyl5WPNL6/ThysY5eBGXh5JBABOxEgil2N+PeBdXjks/vtn2dpX29j5ggn6/suLw7UPJJLl6chLe8Dyukb5/EG7w/Cc0poG+fxB3fTKElX5AnaHk5CGQDCuUWReZ9JV3ZXu/DTb7hAAdBFJIo/g1AjwHq8kPd5ZmqeLn/wq6a/DlIT046Wv3OcP6dp/LrA7DM/JLanVmPuna3dFvd2hJKzGZUmtZAl6ZF+Ybt5bltfh4/nldSTGAYciSeRy4auo7D8Bz/l8jTUFnZ2gst5vdwiuwLlQetvGlLGkaAy4cyjKuY/MsjsER2C6mfdsL6nRRY/P0TefnWd3KACiIEnkcvu7m7EDBbzmzx+tVaELr353xaNf5tgdAgA4ij/IsZ3k7SSRh99aTLM27dVlTzUV2t9VVmdzNACiIUnkci0jidJxJwOkA7deAU/US3O32x0CAMCBOMb1lk17qsK3e2ZlqLjap1V5+1RU6dOIcZP15uKdaggwDQ2wE0kil6NwNQAA3jH63mnh21+sLVRpTYON0aAjVz033+4Q0gJJIveriFGYvzEQ0tnjZ+m7/1qk377T1Ijinokb9Iv/rEh1iAAikCTyCHagAIB048VaV9UNTcWKy2oa9Pv3VuuXEzhZcqrIERFIHkoquNe5D89Stc+v0x6Yoe/+a1GHy67KqwjfXrCtNNmhAehAlt0BoHvC081sjQJAsviD6THdLBFeTAyga4IebmnX8t4K9qVHXTLAjap97I86UlTlU/buSknSxj1VagyE9PSMLTZHBaAzjCRyuXDhaoYSAZ50xTPzlF9OYcdIp94/XafeP93uMJKCTTnCaF8KSHL2dvFXbzLSrzM1vkD49l8+Xqt4c/s/eHlxkiIC0BmSRK7XdBTp4P0ngG56dKq9nb/sKp69rqBC65uvQKYLplWghdG8f/fwYCkgLk7+CawrSK99VFfM21oSvv1ldlHcf7d0R3kywgEQB5JELrd/JJG9ccA+DYGgahsCnS8I17K7w1mjTVPernt+oa795wJbXtsulz01VxV1jXaHAQdoGSGcSPvvn76+NGpxWMDNnDxavruhpcOFgbeX5IVvJ3o8MWLcZKvDARAHkkQuR28zHH/XVJ0c0Q0H3mN33RW2M6kTCJmau6Wk8wXhafdOXK9g89lngz/+k6r5W0u1q8zZ01NX7tpndwi2q/b5Vd9IMs8L0iHJAyD9kCTyDHZSgFcF0ny+yaq8fVpXUNH5gh7h4IvmSJEJi3epsMInSar32MigG17suMNROhh933Rd8uRXdofhGttLau0OAQDSCkkil2O6GeB9gTTpcLZiZ/T6A9/91yJd9/zCFEdjH4OhWxCdDb2uqMpndwiuccsby7Q6z5kj0Lp7/L23qkHFrAsAHIYkkcsZFK4GPM/uk8VgirLQ33uJTiZAC7t/94CTOHUapRV7x6v/kV619xL14fJ8R9elAryIJJHLMZII8D5/0N4f+KbCqpS91tT1RY69YgznGTm4n90hJE1pTYPdIQCO4eXaP/zWO3bHJ+uo1QekWJbdAaB7wkkiD+88gXQXCKXHSCJJuvXtlZKknY9ek7LXdBqS/vHz8tXlP36w1u4QAMeweTcYm3c3QY5SWe+3OwQgrTCSCAAcLmDzSCKneGXedrtDSIlEWp6nO4MCTgBs5KSLtF5Omg8+oJfdIQBphSSRyxk0pwY8z+7aJEcc3NfW12/x8JQcu0NICQ8f51vuxGEH2h0C0txnqwvsDiEtOHWzyPYagBeRJPIIdlKAdwVC6fcDn7hmt90h2Cb9vu2uu+CYwXaH4DjffHaesgsq7Q4jbTAtMDW6Mkrm9g/XptW+hHMBAFYhSeR24ZpEALzK7q4udkx/+r/310S9/56J61McSep5ecoAUmPulmK7QwAs1ZWt4ierCmLuS6zipK21k2IB4G4kiVyuZbIZJxWAt83cuNfS5zNNU5+tLpDPH4xjWUtfulveXLzL7hCSzkEfN1wqDQcfwuscuk5z/J0afMxAapEkcjmKdlqjpiFAXQE42pbiakufb+G2Mv3xg7V6ZMqmTpelkHKK8XHHjVUzutrGgN0hAJZy6n4o1VH5/EHd9MpibdpT1T4Wh35GVrjt/dUqrKi3OwwgbZAkAiTd9Vm2/vjBWq3Jr7A7FCCqDIsTwlW+pnayExbv0n2TNnS4LKMSUuuOT9Zp+c5yu8OAi5VUNdgdAoAkWJW3T0tyy3X/Fx3vt72mvLZR4yd3flELgDVIErnc/ulmtobhekVVPklSHVdf4VB7m9dRq0RuM/6zaGcny7KBSbWXvtpudwhwsWMOPcDuEABLMXC+c17fU5uef4eAc5AkcjkjXLiaDSfgZf9euFOfrLRnSiQjiVLPqVMrnKSy3q+7Pvd+IXMAzr0Y6tS4vCgUSmz5yno/MwSALiJJ5HKMJLIGnx/c4PFpOZY9VyKJZZLQqUdirnP/mLXV7hCAbulolCYjOBFVB6uF11eZqRuKtHh7WdzL3/LGMn37hYX8loAuIEnkci2Fq9n+AclVWe+3OwSVVNtTZyTRq3dAMt07cb1GjJvMaKsOGGJujhss3Bb7hJfVGx2J9htPhws6P3x1SdzLtowi8ge9/7kAViNJ5HIcBqIFV0qSKxC0P1Ni5eiSRFYXTsZTj/obsU1YvEsSiRA4x4hxk7v0dx11oWO72z1uOyaqqGu0OwTP2sdnCySMJJFHuGtX6Dwtn5+bTzpcdjwEF2HdSr2ufOa5JTX698Id1gfjUCTS4CRdSUr4/MGYjzHlNL2c9sCMhJaPNmooXfbVD/53Y0LL98zkdBdIFL8at2spXJ0ue4YkaZnG4+aTDtYAJGJxbutpDgX76mIuyxVtd/jOvxbp/i82OmLUWyq4eHMNDwpYnNVJh6lDyeTV3dZjU5tqE7r5omZ3vb4gsYshHl0VgKQiSeRyLTsJNoDds6O01u4Quo1EYXJ57dN9d2leq/8Xd1DviCSRO1T5mupmGW7OdicgTd5ml/DZpF6gC3VPOtq0stltLdF12qsf39qCSkkkEb/zr4UdjsSLxPGx9/EdW48kkcuFd5r8Nizh5uNqhqYnl5f2P9FGDXV4spLEWBDd3iqfQvyoO5RBJgQO4re4wj/JeSC21XkVmrxuT1zJAXal3sfm0nokiVyOQ2RrufkKfLpfVUo2L32+hRW+dvf97I1lMZfnCk3q5RRV64U52xL6m3T7mjbuqbI7BMfKLalRkDOjlDItnuWZbr/nzvz1k2zNySmOe3mv77eidjfz9ltu5/aP1urN5kYGHfHS8Rui4xu2Hkkij2ADaA0X54jS7uAg1Yqr7Gk/nyrVDR112UlhIAh7asYWu0NwtL1V7ZOdaPLhigI9O5P1J5V+8vpSNQTim/4SD0YStffJqoK4l3Xjp/fB8rzOF2oW7bi/sLLeynBcYVXevs4XcuPKgIR4PSlsB5JELmeEC1fbG4dXuDhHhCR75MtNdodgG6Y9wYm6UgMmnazcFcfJEyyTvbtS63dXWvZ8bHbbS2S0txuPi//6SXa3/v6yp+ZaFIl7TFxTqKnrizpcxoWrAhLEd2w9kkQuR+FqazGSCGiPkxU4Ua4HGg4kk5v3Z25V32jhnDO2u+1ksE6HpXN3s7ZufXtlh48zKs/7+IqtR5LI5RhJhBar8vbpD++v1ohxk7mC7HGV9f5wJ6tEVdQ16snpmxP6G6az2mfr3uqE/4Zh15Ao7G0HK7eVnNi2l5nISKKI76K2g+nUTrO7Ir4pY+yX48dPyfv4PViPJJHLcQxoNfd+oD9+bak+X1MoSZqwaKe9wXiQxY1rumxjYZVOvX+6xtw3XesKKhL++9MemKFlO8oT+hsOsOxz1XPz7Q4BLpVfXmdpjRx07qevL9P2kpq4l9+0p0pr86Nvx0kStdfV5iJ7XFSr54JHZ9sdgufwS/I+NpfWI0kERPBK0s3n58TAak45YL/6H/uTBtc9vzChv+1svdgUo1uUU957Ogow1w9dtLOsTtf9M7FtBLqvs/ookV6el6vrX1io2Tl72z3GT7+9zATOWty825q+If51CE1GjJscvn3X59n6w/urw/9ndK038b0mF0kij2CYXddFnjh7JEdE62NEVVrTcYe2uz9fH/V+Vic40SnDD7I7BMfb3IXpiuieJ6ZtVkl1Yt0w88rq2t0XK2mfzgwZmrS2UPnl7T+vSMGQqaJK93Y//PVbK1XdxSnl6Wzq+iKZpqm3l+SFR9ZL7k4YIrbI75Xv2HokiVyvuXA1P44u+3BFfvh2V4cyOw2rg/Xc9BurbQho0bbShP8u1lusc1E9B6SPPj0y7Q7B8fr15DOyw1njZ3b7ObIt7JTmJbe9t1rXv9DxCLkfvrJEFz/5Vfj/btp/t2gMdDzHfUlu62njjCBvKmA9aW1hu/vd+P2jc2ar23zJVsuyOwB0T7hwtb1huJpbR92Ypik/LaBTxqk7oBPu/lKjh/fXby4+WrvK6nT/Fxv1jZMO1fSNe7X4zks1rH+f8LJdOVAqrvbpN++ssjBiwBo9Epl7kqZcuntLOzujjCRiKkV7LVOfy2sbNX1DkXr1yNTXjxvSbrllOxOru+dEiU41nrSmfXIkHT09Y0u7+5x6/IbuidxGsrm0HkdYLhce98Kvo0saAyHd/8VGu8PokmdnbtVxd31pdxhpw6k/MZ8/pOU79+l3764Or8vTNzbVt7hv0oaEnmtNlAKqO0poMw5n6t+nh90hOJ7TTo4CQYd0AEiBT1YWxL1sTZTRmiT42gtG7Ih//dZK3fLGMhujSS5/or8VbwyE77Zo+wWnHr+he8wYt2ENkkQu19XZUSGOPiRJ2btbnxS76crdRxHT5Npy0/twC6d/onWN7YeaT9uwV5V1++sadLZaRBtVl5HBkafdHkgwke30ddUq/XoxGLozPn9Ir8zbbncYYfcmmLh2s9s/Whv3sh9HSSjRMKC90ppGu0NImc6mm7WV6ZFyCd116QmHtLsvr5MaVnCn5RGdejnvsR5JIo9I9KdBx5wmbWsQuelT8Ur9JLdw6wH7T15fGr7dlfcwc1P7rjtIrTcW7rA7BEfKIoEZl4en5NgdQtiU7D12h+AaXMxrb96Wknb3ebUWT2FFYoW3OSSM7eY3lkVdd+Be1T6/fvTa/uNbtpbWI0nkcnNymjZ6gQRr07i1Dk+iAsGQ7v58vQor6qM+3vbKi5vyAB0dELjobbiGm9aNSJHFTyvqE++WUu2jaDWciZpE7kMdvfilyWFat51w99ROlwm6cAf+k9eXamlumd1huM5bi3dFvX8xn6Wn7KttfTzrwp+443GE5XK7m5MfiZ78BULpURdg2Y5yvbVkl/7ycfRh3xntMi2mtu6t7rRVuBNw1Si13Lz/qazzq6S6oUsHnAzWgFMdfnCfzheCo0SrvYPoTj7sILtDcI2GQMejifLLo18odLpEOtxxTNikrDb6lMQXv3LOtFs7rSuo0ONTnTO6tKuqfG3Oe918kO5QJIk8ItG5mFv2VicpEmdp+VRifTwZbX4Bpild8cw8XRrROtWpDKoUppaLL1OcNX6mzho/U4982fmBwcbCqlb/z2r7I4EtGCrfHiMt4HTdOdY6sDeF2eN1/F1TO7y4t66gfVMGN+isNER2wf4kEoX8EY/rnl+of3213fXTWdsmido2adhYWKWSaudf8Hcyjv49ItHpYz94eUmSInGWzs7r205XaFm8ygVTbDoa4eHifIZjufkjbUygS8rV/5ivtRFdzjIZSuQINyfQxWfu5vRIKD3mgauhqZJbUmN3CGnpG8/M6/LfurUOnl1++MoSVdRFH0Xi1pPFzo7tv/X8gvDtNJkgAIs0JFgY3Wmq6lufp7XdXF79j/m64pm5KYzIe0gSeUSiw0zTpXD1y81dXXaVxdfZwE3HZDvjfE+whpvWje66/oWFOnv8TC3cVqqsTJJEbvPLN1fYHQIc5tKnOFi2S6Jdqlq4sY6OnbYW1+iafyyI+tj7y2N3g3WyJ6Zt1tr8Cm0r7jzJu2xneafLAC38Ls8qVrcZSXT6gzP0P2+1PvapqEu8Dif2I0kET5u/tVTS/tpNXhHralkLDi2tl25XdYurG/Tj15aqB9PNHOOeievjXvZPH65JYiRwo89Wt2+zjuQ77q4v41puxLjJrf5PS+fEJetYr6ymQS/P3a4X5mxLyvN35PoXFuryp+fqx6/FngHQGAjplXm5KYzKfU4cRo2vSIk2PHKaTXvaT+WdtoFuvFbi6N8jOJbomrZjJGa5pN231R2n7p24vt0BKlpL198YHaSc480YXVui+XTV7iRGAjf64wfRGzjAObZHTAtMYJYwkuzMh5pq+j0xbbNtU9cWbovdeGI5o4g6NXwAjQ4iBVy+gdlRGnt0XWSCfeWufbp34nqS7l3A0b9HsOpb4+U0vRIzIYGTz3SVrr+xHllMN3OSb3ajxgkwYtxkDpYd7OW5+zswpdvoVatsK/ZuY5Zov13TNFVU6bMhGrfh9xTJ7/KyI//vgpExH/NHjJK64cVFmrB4V8K1e0GSyDMi9xuvzY+e6Kioa5Q/SuY4FDLT9sfT0bsur23U5U/P1cJtpSmLJ16d1aDK2VOl619YmHD3gmjrB5qk64kV082cZXOadKZE8tzy7+V66L8bVeixadhOFllTpqYh9kjgD1cUhLv27K3ixL8rLn86eiK9o889EZHHX99IcWHcL9cXtbvvgkdn6/aPGCXYmfxytneR3D6SKNb5SlGlL2qzlkQauKAJR/8e9NDkTZKakj9lNQ3KKWpqaX3aAzP0h/fb16kY9bcpOumeqWoMhLQizYasdnSl7ou1hdpWXKN/zt6awojiY3SSJSqubtDa/ArNjdE2+7xHZumix2drwqKd+nDF/oKOL8zZ5vq2mMmSpjkivb2UUWZudetbK+0OAQ40b0uJXluwQ3//LNvuUNLG5U/PVXltUy3B0k6mK/1ywgqFQqbumbghFaGljRkb2ydYumvL3tR2DfztO6va3VfIKKK4cIGlNb/LaxLFShK9tWSn6huD7e73+UkSJYokkUeYUcbEvDBnm858aKaufHZ+OFkwOXtP1L9vCIT0yJeb9L2XFmtp7v55z42BUPjHZpqmPlqRr4XbSpVf7o3OWh2d+K/fXSlJWpLrvMRZvImc3RX1aggE221M91T6lF9er3snbdAdH68L3//szK36YIU7u4Akm1VXId0m3s6AcJ6pG4o0eV30bT7A9YDU2lzUdJLa2TSyZTvKNepvU1IRUloprGifTGkIBPW3z7JVWhN/nSFD0qY9VYy8dqHy2kbd9MpiFVeTWAu4vLtZrCTXvC2lOmv8zHb3+/ztE0foGEkij9pWXK1PV+8vXHrLG8vCtx/878aof7OxsGnE0Q9eWaIR4yZr5J2Tdd3zC3TiPVMlSdM37tVfPl6nH7+2VBc9PkdlNQ2unwe9r4MuYZFT8Oobg3pn6S7HTDmKt5jwfZM26Pi7purYv3fcXaVPj8zw7ednp757hxt4rUMe3OvmiO15i9oYScwpMS4MAHO3lGjEuMkJnSB3x4yN7mgMkSz5+5oS7iTn7LGjtFaSVFhRr8ZA0wny1PVFendpXszj4mge/O9GXfXcfN323uqkxInkOePBGVqSW67Lnpornz+oNfkV2tm8XqSbV+bl6r5J7h2tGCtJm918gb8tnz+ovVW+8G8fncuyOwBYpM1BR6w52ZL0+oIdUe9fuqP1iBnTlHKar3xF63x15kNNmdqdj14jSZq2oUiXn3ioMjO6V+g2p6hKeyp8uuSEQyQ1jZp5bGqOrhkzTKOH99fMTcUqqvLpp+ce1eHzNARaZ41X7tqnM486uNV9P3p1acy/r23cf9LVkig7bEAfXXL8IQm9n2RoGbbemUCbo9Hy2ka9uXhnu+UiZ6/trqjXXz9ep9suP1YXPDpbkrT875dryIG9uhouAAvNaz65l5o6tjz07VP0//6zPOqyk7P36IUYzxMKmTKMzqevwtvGPjRT//rxGbp69LCkvs6v3lyR1Od3ujs+XqcGf1Bnjxxkdyhp6eOVBdpb5dP8raW6ZswwvfCjM8IXfyauKVSNL6DXbhnb6fbw8zWFkqLXB4I7VPsCevC/G/XO0jxJ+89jvO7af84P327pgHrfdSfbFU63TFi0M6Hlq3wBXfrUXI0a3E+z/3xxUmLyGkuSRIZhXCnpOUmZkl4zTfPRNo/3kvSmpDMllUn6gWmaO614bTSx88LUiHGTdfaIgVoWUc9o9u1f19tL8nT7N47Tb95ZpatPGaobxx6hLXurVe0LaEdpjY4a1E+hkKlTjxigfr2yVFnnV/6+Ol37zwXh5zmsf+/wfOu2nce+e/pw9evVtApPXLNbB/TK0mUnHqrpG4r02vwdreKRmircP3njqfpznAX+WpJmA/r2UEVdUyFJmU3JJ58/pP59ekhqmpJXWtOgwwb00ZfZe3T+0YN187+XaW1+hSTprmtO1PWnDVdlvV9HDuyrnlkZ4Ux2bUNAB/fr2ep188vr1CMzQ5Oz92jimt362rFD1BAI6oYzD9cJQw+SJN0zcX1c7yHS9pIaXfZU9CKLdW3m736wIr/VtLOzxs/U5Sceqpmb9urFH5+hM0ccrEMO7K26xoAKK+o1oG9P1fgC6tcrSz2zMpSZYcgfCKnaF1B5XaNOO2KAJCm3pEY9MjM0sF9PLdxWql8310w5YeiBuv+6k3XOqEEKBEPaVV6no4ccEDVW0zRV1xgMf/dAOttdUa87PlnX4TIP/XejXluwQ3/55vH630uOCd/fMqWl5QA5GDITTvIHgk2/87bbMattL6nRyEH96E+TJC21Tv5+9Ykac3h/nTOq40TG6rx9OnHYQeodMQoVnbt74gZ98+RD7Q4jbc3f2tSIZPK6PXrhR2pVu2RWTrG+//JifXTr+XaFhxRqSRBJUnGVTzM27VXIlE49vL/GHD4g5t/VNASUW1KjdQWVuu60w1TXENSAvj0S3hYGgiFN37hXV50yNGZi8oYXF2nlrn3aNv4qZcU5gyCW2oaA1u+uand/fWNQfXqmdjtumqaW79yns0Yc3OWLVGsLoo8YiuXfC5sGSOR2MnJs8fYyDT6gp4499MAuxeUlRnenzxiGkSlpi6QrJBVIWi7ph6ZpboxY5reSxpimeathGDdJ+o5pmj/o6HnHjh1rrliR3led4tFyNflXF43Uq/OjjxAC7Hb2iIEqr2ts1eElmhvPPFwfrSwI///D/zlPeyrrNXJwP60tqFRJlU8fryxQYaVPj39vjP7+WbZe/PGZ+mXzFeqXfnKGLjp2iCrq/RrYt6eenblFW4trdGDvLD130+mSpK17q3XFM/O06u4r1LtHhnpnZSojw9DO0lpV+wKqbQxo4bZSXTvmMB01qK9Kqhv0l4/XOrI2FWCFoQf1VlFzJ6U+PTI1+89f14RFu/Tjc46UPxjSpc3J5UMP6qW9VQ168PqTdXeborqjhvRTbkmtFo67VJK0atc+/b55OsarN4/V+MkbdceVJ4STEcMH9NHRhxygk4YdpJci2n6PGtJPVfV+XX/acN183lG6d9IG9c7K1NQNXLVPtdHD++vXXxulwop6bSuuUc+sDF075jD96cM12lPp08XHD1FWhqHRwwfoGycfqienbVaVz6/fXnyM/t9/luv9X5+rHpkZ+jJ7jwb07aEnp2+x+y0BnbrprCO0p9KnJ288VWeNn6mzRw7Ush3s/9PNSz85Qx+uKFDPzAwdN/RAnTtyoGblFMecjXHhMYP1wPUna8Sgfvr5hOW645snqEemIcMwtK6gQnsqfXpi2mb9YOwR+mBFvob17609lT7dfe1J6t+nhy46drAO7J2l/67bo6OHHKDPVhfo7SX7E1lbHrpK2bsrVFLdoBOHHaRDD+qtgn1N2+Zh/Xtr0AE9lVdep/OPHtwuNn8wpCenbW53sb2ta0YP06gh/fTRigL98Ypj9e7SPF147GD9/IKRen7ONhVXN+iw/r115KB+yjCkK08eqqyMDO0qr9XrC3aoMRBSRZ1fZ40cqBvPPFzPzNii315yjPr36aFtxTV6ZuYWnXnUwdpcVK3ZOcWtXnvU4H6a9sev6fUFO3T9aYfpey8uVq+sDF099BhR+AAAIABJREFUepiuGj1UhgyddFjThfJQyEy4btvFxw/RV5ub6vO2XPxvOW5p8d6vztUPX10iSbrvWyfpvi826q1fnK2lueU6buiB4Smmt379aP31yuM9MQrbMIyVpmmOjfqYBUmi8yTdZ5rmN5v/f6ckmab5SMQy05qXWWwYRpakIklDzA5e3CtJosZASMHmIf1S07SeDMOQoaYh/kbzfV1Z0fLL63TR43MkSb+8cKRei7HhAgAAAAAA3fPF7y7U6MP72x1Gt3WUJLJivsZwSZHtkAoknRNrGdM0A4ZhVEoaJKnUgtd3tPGTN2rC4vhbSBuGWiWQMprvCCeTZISXqY0YJkuCCAAAAACA5PnFhOVa9vfL7Q4jqRxV1MMwjF9L+rUkHXnkkTZHY40rThqqof37yJQZbrdumqZCZlNh6Jb7zaYHZKr9/aHmG2bz37bcX1Hn1yerCmK9NAAAAAAAsEhxdWq6gtrJiiTRbklHRPz/8Ob7oi1T0DzdrL+aCli3YprmK5JekZqmm1kQm+0uPHawLjy2/fxQq5AkAtob2K9nuAPc7y89Rv+cvU2SdPjBfdQQCKmkeeN+1KC+OuPIg/XZ6rabLABAd1x2wiGa1abuBABgv+MPPVA9sgz16ZGp5Tv3dbjsGUcO0Kq8Cv3lm8frqembFdnA+BcXjtTrC3boz984TpL09pI81TYEVN0QiPFs9hg5uJ92dFI8uq3rTj1MPz3vKD32ZY6Kqnwq2FefpOji99crT7A7hKSzoiZRlpoKV1+mpmTQckk/Mk1zQ8Qy/ytpdETh6u+apvn9jp7XKzWJki1aa3q7/fXKE3Tq4f31o9eW6tunHaYZG/eGp8Y9fsMYLdlRFm692OKjW8/T2vwKHdS7R7tOPQ9++xTd/Xn7bl7/e8nR+t9LjtGX2UW6PaJj2aTfXaCRg/tp9H3To8bXUoysO7Lv+4ZenZerYQP66IiD+6qwsl53fNxxh6Hvjz1cYw4foLua38ugfj31528er/rGoOr9QZVUN2j4gD76/tgj1L9vj/B3m/PglbrtvdXaVlKjm846QqcfebBufGlxpzFedcrQpLRoPePIAXr8e2PUKyszXBNLkr5z+nD9/IKRenjKJr3+s7HqmZmhJ6dv0YXHDNYFxwzSsh3lOmfUIH2xtlBnHnWwzn90dqvnnfDzs3XLG8v03q/O1XlHD1J9Y1D+UEgH9e6hYMjUrE17dfbIgXpo8iZ9f+wROnvkQPmDIZXVNGrcp+v00k/OtKzTTrXPr4o6v44Y2FeSNHdLiW55Y5klzw3Y5fITD9Ut5x+lbcU1ur95G/jt0w7TyMEHyB8M6XeXHqNeWRmtauQFQ6ZyS5qKFk/JLtJVpwzV0P69VVrToGpfQCcOO0g7Sms1YdFO/fKikTr84KbfTChkauqGIj0zY4suPHawjj/0QP3grCNU1xjU7op6Vfv8OvOogZKapmVfNXqY8svrNH9rqZ688dTw6786L1f9+/bQlacMVZ8emarxBXT6gzNS+Kmln5vOOkI/PucoZWUaOmpQX01YtEu3fn1UXLUT/cGQahsCCpnSwX17aHV+hU4/YoAMw3Dk8QrS1+I7L9V5j8yO+tizPzhN144ZFu4oxbqbXi4/8RANObCXfnnRKI0Y1E+NgVC4A9jfP8vWyl37dN1ph2nkoH5atrNcd19zkjJidAYtq2nQoAN6hf9vmqZW7tqnsSMGtlvWNM3wdnbx9rJwEeVff22UThneX9edeljC7yUUMsOx/enDNe3OvyL99/cX6uTDDtK24hod0DtLB/ftGfW4urCiXit3NSW0rh0zTBsKq3TK8P4qrWlQnx6ZyswwVNcYVGFFfXNR7V6t/j6/vE61jQEdd8iBWltQodOPPDj8mD/YVMu3o+P5K56eq62dNMKJ5bPfnq83F+/S7y89Rocc1FtfrC3U0P69Nax/b727NE//d9mxqm0IqrYxoJGD+4XjME1T9f6g+vbMUllNgwb260nh6jhf4GpJz0rKlPSGaZrjDcN4QNIK0zQnGYbRW9Jbkk6XVC7pJtM0OyyxTpIoPk7YcX3ym/N0w4tNSYv5d1wSPrFuEQyZCoRC6pXV/gd/78T1OuaQA/TT80a0fs6VBbr9o7U69KBeWvq3y9UQCKqwwqeRg/uF33NL22ZJyiur04xNe/WLC0e2ep7Iz+e2S4/RHy4/TvX+oE6+d1rM93PkwL7KK69rd//iOy/VwX17qrLer0MP6h31b+saA1qwtVTnjBqkx6fm6O5rT9Li3DId1r+Pjh/a1E4xEAzp7SW79KNzjlLPrNgtLat9fmUYRtRW728u3ql72nQXauuJ743RX5oTV9eMHqbfXHy0ThneP+51ZvbtXw93NYoU+blbYV1Bhd5ZkqfHvjfG0ue1UmWdX6c+ED3pCNht56PX6Lb3VmvS2sKoj+945GqVVDfokIjtVkMgKNOUK1uYO2G/5zXnjByopTvK9c8fnq5vdeFEJB58b00dkP78zeP17RcW2h1K2tv56DXtjhHPHTVIZ48c2K7dOOuuN+U8eKVOuHuqJOlPVxyn2y471uaIWvP5g5buo9seJ/z7Z2dpcHMCZ9SQflHPN5xo6voi3fr2yi79rdXnMG6X7MLVMk1ziqQpbe67J+K2T9KNVrwWOnfyYQdpQ2GVZc/38wtG6o2F+wtjT/rdBXpq+haNGtJPf73yBPXukanJt12orIyMdgkiScrMMJSZEX0jd//1p0S9/4YzD9e1pw5TU/luqVdWpkYO7idJevdX57RqWShJRw7q2y5B1NZvLzlGGRlNSZd7rj1JZ48cqGv/uaDdclmZ+zPDLW0SJWlY/z6SOj6p6tszS984eagkafx3RkuSLjn+kDbPn6GfXdBxrJJ0YO8eMR87qIPH9r/O/vfx5I2nhq+CdGbKbRepyufXqCEH6KkbT9XtH63Vd08froe/O1rJSJqPOXyAxnxvgPVPbKH+fTv/vIFUWva3y/TO0jzd+vWjJUn/+OHpUZNEpww/SIZhtEoQSYqatEd6uuuaE/XLi0Yl/XV+/bVReqWTFsxe9/Yvz1GNw6Z/pIvHbxij+77YoLqIpi+R/vSN41McEexwzehhGjm4n75+/BD17pGpeX+5RD2zMjS0f/SLv3ay+iJO22OES044JMaSznbx8UN09eihmpK9f7bEwX17aF/z+VpbOx+9Rg98sVF94zwPQhN3pAyRkNOPHKDvnD5cD03eFPXxMYf317qCyvD/jxjYR/nlTfM7bzzzcH20sqnO0aUnHKIThh6oP1x+nG7/xnFavL1MQw7spTGHD9CEn5/d6jlPPsz6NoCxTmLOP3qwzj868TpPkRvbn3eSUGrxm68frYH9eqoxGEr49ZKpf5/OkxYZERmdWCOWWuYwRzrpsIPCt79z+nCFTFPfPn24emTGHvUEIHV+eeFIHXJQb/3xiuM6XfbD/zkvBRHBzZI1cqitTXusu3jlRnP/cnHcy/7lm8friWmbkxdMGvrWqYfp0P69dcsby/TYDaNbPbbgr5fYFBVS6btnDNfT3z+t1X1HDmp/cRvO1rtHpv714zN19viZ4QLSB/TOipkkkqR7vnVSqsLzDJJEHpRhGLpq9LCYSaJnfnCaLouYRnTGkQervjGkl35yhsaOGKgfnnOk9lT4dM2YYeFlemZl6PKTDk167E4QOVjmmycP1YjmEUxOcvHxQzpdZtam/QVDMyPmSn/w63O1t7pBB/bO0rkjB7VLEkXKyDB049gjYj4OIPXOOOrgzhdq1rcnu3nElsqh9xstHOHsRkcNajqW6GxA7sq7LtegA3qRJLJYn56Z+vpxQ1qt8/16Zqq2MRiupRaP3158tD5bvVt7Kn067tADtGVv12qjILUuO+EQ3futk+0Ow1ZL7rxM5z4yy+4w4BIcPXpQhmFo+IA+WnHX5Rr70MxWj62463INPqCXvnv6cH3a3NGpf58eWnHX5eFlzjjyYOnIlIacNOeNGqTFue0a6XUoshCZExNEkuIqllZR71evrAw1BFqPgjpn1KBW/1/2t8uUmWHozDbrClo79pADulwoD7DK/3x9lK4ePazT5Z676TQdc8gBKYgIbvTVny9WaU1qW/geclBvlTV3nUw3V50yNO5l2xZ5RfLM/+ulqk1w+t8dV56gn5x7lNbmV8gXCOqPH6zt/I+S6LbLjtU/Zm21NQY3eP1nZ9kdgu2G9u+t7Q9fraP/NqXzhV2oR6Yhf9ATzdEdgSSRyxmGFKv2+OCIA43ch69W0DTDU4Yi/yTDA9XZYzn84D4dPt7RHFa3u+GM4Rr/7VO0raTjxEbbWiUAnOuUOKf2XnfqYZ7ovAHrfe24IRoxuF/KL4JcM3poWk45O2/UID130+lxLfvbi49OcjSINLBfTw3s1zPhvztsQB8dNqCPPl8du1NUKjx2w2hdeOwQkkSIW2aMLmxe0CsrU/7/396dh9lVlfke/62a53lIjalUqjInlaEyksocEggCMsoYkIDMxAgICig4kO6mW67dTigqTo2KXvU6oICNohdtcUAb0QYl2nSjtCja6nWAWvePOlWpSp2qOsPee+3h+3mePHWGfc5+U7Wn9e53rfXi4aRvf0uVVmVReY2JGGQk4tLt6umSPgUFZsKYMsPjMktxbkfMlABLN86OFzP+hcHqngZ1NVRMGjgbyMbWDLo2IjjTzYg4HgkipPPRi9bpvXvTTmTiu31DvWMTUCTJgrbqCfvtdLvmtbsXBBBRvC3r9H6MzFGuD6vvPnfivnvSyk511JXrW6/dMcUngGQwMvrEJRsmzFB334HNOnhyeGdODjuSRDF0ZJJ4acf0J8w4VxLN9F9L93+3kn78xt164k3H+BNUQDJtTCIzMd5NpvSKzb165zmrXIeBcZroioIcre6p19rexknTewelrLhQl2/tc7Jul0YnBoG/Duycp8dv2a39OyZPY96T5+DEL1vdpYHOWj11q7vpsz97xUbtPGJs0NFr2OZqzgvITnsIZ3PL1/xZ1Xplav9fPG4SHuSG7mYxVDAuS/TVa7aqoWpyKe34Ypk4lx7O1LC/bOtc3fjpxya+aOMxPXS2s5G9+aVLfb0Dh2h580uX6sy1MRmcLCa2zG/OqHR6eVddANEgaoZDUCSbxGR7SVEC/9MOjFYQ9DVXT3rvim2TE0fZcF2NcP+BzWnHmGPLQi7u3T+klur4JYmkkSrqey5ez5iMHqDUIOKqy0amQl8w6/BJcfxJo7uxQlWlk3OB468V43zRNn7a+3TOWd8z6bUQXEd7oiTLJNGZa7u1ZIaqM0TT+8/PfsDG9rp4XkBE2Y6Fmc0wme9dc8TTxr4m1yHE+nojnd6mSt160sQEg6Fp76t0U5oXFUb7dz7VfpO0/QneWDCrJqexuMJmqu1/sKdBdRXR//+5RpIo4malBh2ecJcjg5PG+HF3CmN8lulvGUmeZVNWORyTMYmKI35RFDZRvrDfMr8lq+6Txox8BuGSaeL35FWdPkeCqLlwaI6u2p5fNQWyt2+oV7XlxRktG4YkHsJpqmEhGHsOGMGu4D2SRBE3P1VB1DguI5zJGEPj8yBxHpOorHhkE18zpyHjz8QkRxTrboTIXnFhgdb1ZrYf/J/LN/ocDbK1srtOJ67oyGjZRW30xcdEK7rrJ3RFdyXKyfZszWut0stWd2W8/If2rfUxGvghxpfPABKOMYki7m9OXqYz1nSrq+FweW0m5yw7rlNVCK4bfTM4e6RRnGnjSopHJdG7zlnFHSZMsnd9j77x019Pu8xTtx7LthNC1x2zkJnNkJP9O/p1zJJZrsOQlKxG9eeuHEqbmEvS7yAon79yyHUIvopD1yAA0UKSKOLKSwq1fm7jhNeyriSKcZaou7FChw5mNxtFDHJE2rU4HA0CuDe+iq41g26XJBiij78gxjtuWRv7tQNx7sofNotiPJPRv752e8ZdFjFiZXedvvPz512HAUQa3c1iKJPrkqR0NwO8Eubd5KXTVMrddf6ascdxSIBiZnUVNChw2DO//ZPrEMYkKVmVzQ24jrpyHyOJt0yGE1g7p3HGZXIRxDm1gQF4s/bJS49yHQIClpwzS3BIEiVUUrqb5cJGuCXN3abk6W2uVGXpyCx+Szpq1DauWqi6rEjlJdPP8IdoyOa4lKSGOGa2rLPOdQiJ886zV2a1/M5Fmc1ciMnSHRsbj+ie5dcYjTaA+XAZXzI79x/Y7DoEIBZIEsVQJg2E8edUGhQTDUc3R6Tv3bRTT916rOswEKA1PQ1jg8GeNtilh6/fPvZeEReXQKI9etPR3DxwYPeStqyWTzcb6e2nL/cqnFi75YQlk1778qu2xGa2OK7Rs9PXUuU6BCAWSBLFUGYDVx9Gd7OJXoxIJdHD12/TSUd0MzLGcEGREOt7R8rnL93SN/bakZvuR1+xfsJzLp6iK85jx8E/hqu80Mn0FJ3NhBtJMn6cyUVtNVqYZjbH2opinTrY6Xssfl4ufvzi9VmPqZl0zdWlrkMAYoPLhxjKdkyiQraCCYYjUkrUVluuqjLGnk+qU1Z16tDBPepurNC2BS2SpBXdE7uVzGutnvA83d1qRMOsmpkHHQeOFLabQFHuzo3wOGfdbEnS0Yun7qZXmuFskPnwY3MuLjR659mrtLpn5rGWcNjSjlp9cf+msed37h10GA2CYMaVRXCD3Hu0MGPIZFlLFLaLSNeiUkmEZBu/225d0KIfv3G3SovyG3/opuMW5RkV/PCFq4bU1VDhOgxEEDNsBW97KmmfDS47slNTPtJ8mb5L9eH36iMymP+nLjtKy7sYQywXc5sr1TBuLKr5s6qnWRrATKghiaFMrgmHGZNoSi++GJ2rNS4sMSrfBJEkNVYxi0oYdZMgQo44vQfvzvNWT/t+uht5Q/Oa/Qonlpa010qSFsya3NUsnSKfSuYrS729106v4twd2SWb62MgPySJYiiTk8z4km9OShO9EJHuZki2mS6AFnAXLTaK6CaY1qVb5roOIfTCVin82//3V9chhMqCWdX68Rt3azNJoqwcs7RND169RTummRUuiGvbXYtb1dPoXRI/m/11cXtmCbK4aqws0bLO2rHnvU2VDqOBa+E608UD3c1iKKPZzcY/JicywYskiRBiOxa2qqGyWHuWTT17zkPXblV9ZfZVQUzDHE5FBdzPSefa3Qv09gd/4jqMUAvbOGQ/e+6PrkMIlRXd9Z5UgSZRzwxJgcEAxvQxxujMtd168+d/5Mn3ZTPd/cuPmuPJOqPKSnr3uYP60g9/qa76cg31T0y00rYB8kOSKKHGHzyHOZJO8MLwsOsQgCn94xkrVF4yfaMi1/FrKko4JYQR1Z7IRXVZUei6k//fnzznOgTnQvYniZT7D2yaeaGUoMbj8vISevoxliZK6nb0vvNX6/z3fUvWWrXWlI0NYn4kK9o2cZfUfSAo3J6MoYxmNxv/mOPoGGOkGxm8FyFGUUmyXLx5buga+mHQ1VDuOoTQ+8Hrd7kOYRJmN5uIXTs7fS2Zd6OOYpIgmwrgpG47s1M3wfpaqqZdrqWaGUGBfHDbOIYymd1s/IUalUSHPXXrHtchZCWKF0HIT9jGGIG/rjtmgesQQue7N+5UaTHZ0iiaqQoy6r59w44Zlxl/BO+foaGL5Ni3cY6aqkozXj6zmYzjp66iRO8/f/WMs8CVlxTqoWu3auhv/yWgyOBUMncHX3GVFUPZdk1gyncgOvIpoS8rKtSithod2DnPw4iAYNVXltA1MqLKi+OdJGrMopEvSWvnNPoUCYLi1RV0R31+1ZH3vTLzrnhRVlhgtGV+i+oqmI0V8BNJohjKqLvZuLMagyYC0XHkNK/ZfvbzVw1p95JZk9774v5kXGACcCfOlcsb5maf8KEa2D9R29Tyjbe/NRkzmmYzuHc2ywKYiCRRDGXU3Sx1YbK+t3HKQd8QflG7CEI4tNZM7qtfW17sIBIASRLnc9Z7z1ud9WeS2mUIkw101c680DhJ7XmezeDeJInijb+uv0gSxcgFG0emw8zkxDE6gdfl2/pUUsRmACRJbXmxDp601HUYABImrjmipqpSleXQlS6pDf0gRGlb+9+XbtCq2Q0ZLfuSgXafowmvjX1NKs2izcIYjsnBX9p7dOqPkWzKuEcridipgOj4yIVrfftuuj0A8F1MDzPZtEWZrRBHyqaSN8kzBH5oX3bXQNlUHQGYiBKSGBk9b2R1AcLxc8yDV29xHULWknupkEwb5jZ59l3HL2/XnqVtnn0fvPeRLC+IgbCL65hEuV5KZTObFeIrm+v20T2IZOPM8hnDEUg6kkQxlMkxcSyhRJZoTE9TpesQgMBUlBTpbWetHHtez0whobOhz7ukIBAGsU0SZXEpNf4arbmaJFHUebFJZ3MlXlI40nTLZ6bTpKCSKDlImnqP7mYxMlqCmslucrjqyL944L+YXm/DgVzG03CttKhAf35h2HUYADIU11NWNjfcaMzgSNlsEjcdt0iNlSXatbjVv4BigoGrk4O/tPdIEsVINiWojEkUFyN/x5cMtOsVm3odx4IoevDqLbFtuAEIl9kNFfruz593HYbnyPuET01ZdJo42QywXF9ZohuOW+RjNPHBwNVA7uhuFiOjZdyZHBNzGr8IoTP6d1zf26glHdlNn4rsnLyy03UIvuhpqtSckHS17A1JHAD80VpT5joEX3Al5b+vXLMlq+WLCoNp4jDpQzC+9MpNWX+G7mbJQULQeySJYuTwOEMZLJv6yT4VbdsXjpQbL++qcxxJ/O0bmuM6hFg7blmbPnX5UXro2q0Zf4ZL84neeOISXbNrvuswEALffM121yGkFdd9lhtu/pvdGN+bCGw+M5vXWp31Zxi4OjnYh7wXnVpMzGjs4iuT7mapjBLHz2jbuahVT7zpGBUHdMcsyWgE+OufzhwZRLumLPOpgOPsrpevyfozZ6+b7UMkiKK4VuyE1VtOX+46BKSxY2GrTlzR7joM5Cmfy6+NfU06Y023d8EgNLgu9xdJohjJppJo+HBGyadoEBQSRMnw6OuOdrbupqpS/er3f3a2/lHHD7TrM4/+1+EX4lqWIGnzvGbXIQCeW9xe4zoEz524vF1r5jS4DgNpvGfvoOsQ4IF8uhJ9aN9aDyMBkoPWZaxkPibRKJKwQDTUlrursLn/QPZjAfjhrWes0KGDe1yHASBHJyzvoHs0YoVZZv33qUuPch0CQo6qIu+RJIqR0RNVJhl3zmkAMlVXUeLr93fUlfv6/QDCIywD5XuFxgngnx0LW7S0k4lZMD2Owt4jSRQjY7ObZbLw2JhE7FYA3FrdU5/T55Z0xK/rChB3ltILYIKSIppjU/nzC8OuQ0AE0Jz1HkelGDk8rf3Myw5nMX4RAPjpyDvxTVWZVS697/w1+sQl6/0ICQAA373tzJVqqWag+an89UWSRJgZSSLvkSSKkbHET0bdzbIfvwgAvNZUVaoLh3onvPbF/Zv0hauGZvxsbXmxVs1mwFggSuJWR8RlFPJx9OJW1yGE2hXb+l2HgJCiDesvkkQxMpb4yWTZsUoi9jAA7jxyww4tOmLGo8aqUi1soysZkuvklZ1jj6/Y1pfVZz960Tqvw/FU3HqbnbmW6bWRO67Cp/aPZ6zQUX1NrsNABNCe9R5JohjJauDqLLqmxd09F6/X285c6ToMIHGYNhqY7NItc/W3pyzL6bNVpUVa29vocUTeilOO6ObjF2uwh+MYcsfA51P70S9+5zoERAS7kfeKXAcA74wOXF2QQepv9CKNnUpc4AGO9LdUuQ4BCJVrds3XxZvnqrDg8Mk5m9P0+M8hvL53007ufEMSlUTTaatl5lNMbXwblv3Ie1QSxUh2lUSjXdPYrQC4sWFubmXkDZWZDWwNRM2W+c15JXqikCRidjOprqJEtRXFrsNACHCzdmqb5zW7DgFRwY7kOSqJYmS0kojSVQBRsGdZW0bLlRYVTJgG9979Q/rP3/y/sef1FcX6zR//6nl8QNDyzZ9kcpPItTiliCLw64bP8k16cs2e3pNvOkZFhdQyAK6QJIoRm8W09oxJBCAqvnLNVv3yd38ae95SXTZhyuB/vmiddt/+kIvQAP9lcaK+dtd8HwPxxu//9ILrEACEXBQS3nBrfG8YthbvkaKNkbExiTLpbqbMlwXg1oGd81yH4LmvXLMl42Vn1ZZpoKtuyvcXzKrRGWu6PIgKCI/7XrlJbz8r80kVzlzbrdNWh38/iFMlEQDvHTq4RwUR6DqL8KA56z2SRDFyOEmUybIjP9mpgPC7cnu/6xA8N7ux0tPvO25Zu6ffB7gwvudKf2u1jl2aWZdMiTupAIBk4vznPZJEMXK4C1k2A1cDQPTVljMILKLv0aefn/RapufpJ579vbfB+CROA1dzDRWM6SpJER1fvWbrjMuUFdM0BcKAMYliZHhsdrOZlx2bCY1yTgAxEKcLy446pv1NqoVtNTl/dmNfbrMFInfxSXeFV3VpkT68b63rMOCB6e5hv2JTry4YmqP6CmYvRfYYAN578bmqxuHqoAx2lBezGL8IAMKur6XadQieeejame+2Ip4aKnNrIB3YOU+Xb+3zOBp/BF1ItG1Bi2/fTcPEf70tVaoqDe897RgVxvmuubpUp6zq1McvXj/h9Xv3D+n6YxeqpbpMxcxohgyNP/xyJPYee2KMjJ6nMtlR/v7UAW2Y26jOeu5YAwinAzvnaW5z5mMXnbqq08dogkOFZzIVFRi11ZZNen30QvgVm3un/OxRfY1sN1N4z7mDeufZK3X76cs9/+6nf/1Hz78TE7FZx0dhgdFtpw5odU/D2GuHDu7Rglm5V1AiucYfGsjXe48kUYy8kOpvVlg4854y2NOgj1y4jow9gNC6cnu/HnjVFtdhAIF48s3Hqqy4cMr3zRS3gG4/fblWzW5I+x5Gkq67l7TpxBUdnn/31bvme/6dmIi2X3zwt4Rf6BnjPTIEMfLi8LCZc7LSAAAgAElEQVQkqbiAPyuA5GmuLnUdAuC5qZJDo05YHq2Z/WxMRvJ56Nqt3GgLQNi79IVpa/7EJRtchzCtsP8tEV0kibzH2S1G/vpiqpKI2lwACXTCcu8rBYCwmOoamIaXG/zag8GvOXOrZte7DmFaNE/gF+ojvMevNEYu2TxXkrQoj9lRAABAePzsuT9Ikn74X7+b9N6jNx0ddDh5cznQ71lruz37Lu5cB4Nfc3yMT2hfvrVPC2bFZ8IJuDW/lbav10gSxcjWBS06dHCPaiuKXYcCAJKkMz1slAFJNCs1mPXi9skXwZzvp/f5K4cmPH/TS5d69t0kL4IxU3fLKPvujTtdhxCY95+/esLzq3fN1737NzmKBnEwmnS86bhFuu6YBY6jiR+SRAAA37zZw0YZEFffyaCxWD7NoNZREmRyZVGaxJpXqCQKRth/zflUxtVXlngXSMhtmd/iOgTE1Jo5DSopIqXhtSLXAQAApvbxi9e7DgGAzxqmaSyOjjM4/iL4oWu36rk//MX3uDC1sCcv4oLfM4DpcIzwB2k3AAip8uJCre5hamsgyS7ePFfnbejR3g09Y691NVRoeVedu6BAJVFAmIwFwHTi3CXVJSqJACCk4toGefe5g65DAELjX1+zfdr3K0uL9PrjFwcUDTJFkigYr3sJ2z4ABI1KIgBAoHYuanUdAhAKt5++XC01ZRkvf8662frIvrU+RhR/D1+/zZPvIUXkv1ds6tW8VmbAAjDZ6DGYfL0/qCQCAPiqrLhAf/rrsOswgFC545xVOnrxrKw+84YTl/gUTXBcdw1oqy1XgZGG8xhwWKJhAmSqt6nSdQiIo9QxmGOxP0gSAUBIxeW89/VXb9P//OkFbbntQV/XY5Vnqw8IyOeu3KjF7bWuw3AiLjM6uU52IRw478zsy1dvcR0CYoxjsT/obgYA8FVjVal6uJOY1vreRtchwIHuhgrXITjTWV/uOgRPFBbSMAEA16gk8gdJIgAIgSQ3Gr1SWRK94tiXDLRPeD7QmczqkqRhxqZo+9uTl6mqNHrHm8hhN4m8V+6Y5zoExByHCX+QJAKAEHjrGSvUXjtxANtdS7IbryRo7ztvtesQJuhqqNAHL1jjOoysHJkruOeSDW4CQaCYGct/d+6dehbFfDsInba6K89vQEYi0JPLRiBGV7bMb9ZVO/pdh4GY43TqD5JEABACVaWF2r/z8B23hsoS/c3JyxxGNLOtC1qmff+GPQsDiuSwDXObAl9nLi4cmqPCAqO2uoldb4oLOS0nAZVE/upqKNf2hVPPoriyuz7AaAAAiBauRgEgLMbdkWyqKolEwuD+A5umfG/fUO+E51+9Zqs+cqG/03dHpel97NI2/eTNx6q8uNB1KAjYPRevj8S+7Zcw7KPvO3+17jhnldqOqN4EAERDGM4lcUaHagBAzvpaqjNetruxQt2N/o69RNkxwm6wp8F1CLE302w3NWXFOnrxLBljdOEHHgkoKmQrChV39DabWvj/eogy9j1/JfdWFgCEGFN65sYYoy+/arPrMDJGUgsA0otCkgiAI2NZIo4TfiBJBAAhYIxRTTnFnV7oba5yHcKM5jRVSmLQU8APJF/jgQHeo+2lKztdh4AYG7184jDhD1okAODQJy5Zr28+9Wv1NlWqt6lSF2ycozu/9hQnvRj70Rt2q4yxiJBQYTq2hSgUpNHXEv6EP5n+9P75wnVaP7fRdRhIAI7j/qCSCAAcWjW7QZdu6ZMxRsYY7RuaI0laOyc645bcfyA63bvCYHwjOUwNZiAIC9tqXIcwhuZ9eN1++nK9ZKDddRjIkWXvQkDY0vxBJREAhEhbbbkeeNVmdTf4O8CzlyJxtxdAKBy3rF1tteU6+R3/13UoGs6hCuTRm472IRIcaWlnresQMkIDdaJ1vQ36xk9/7ToMJIBNHb/pluoPKokAIGTmNlcleorsuGNQ8uQa6m9yHUIo9Lf6m1jOdA8bzRHtXNSa8XfXVhRnHxBii95mE63vHTnGtdWWO44EcTec2ve4ovIHlUQAAASIm17J9cEL1roOIRT83gVMxjvZ6J1o/2JBvNGtaqIrtvXppJUd6opQNTSiyYpKIj9xqxoAgAAVTnFBs7K7LuBIADcyT+L46/Cd6HDEc6S7L1rnOgT45B9OG3Adgi8KCgwJIgRieHjkZ0hOJ7GTV5LIGNNgjLnPGPNE6md9mmWWG2MeNsY8Zoz5vjHm9HzWCQBAlKW7oCkvLtT7X74m+GCABBvtKpRpI2NpR7Dj5KzrbdR3btwZ6DrDIu7duE5ienggL8WFZIf8lG8l0XWSHrDW9kt6IPX8SH+UdK61drGk3ZJuN8ZwuxQAYqy6lN7MU0lXRbGovUY1ZYx1gmTwvbtZhssNZznw6f+5YmOOEeWuobIk8HWGQzSyRHFPZgFhded5q/WKTb3qrGf8Kz/kmyQ6QdJdqcd3STrxyAWstf9urX0i9fi/JD0rqTnP9QIAQuwHN+9yuv5HX8cMRAiXr716q+sQkiPDLNFo+57uCuFD8gXAdOY2V+n6YxeGpvty3OSbJGq11j6TevwLSdNOD2GMWSOpRNJP8lwvAMChL1w15DqEadWWR6MqZ7QhxMC58ddZzzgdo8JyTb9lfrOWdNRo/455rkPBEaKSI4pKnACQjRn7Axhj7pc0K81brx3/xFprjTFTHiuNMW2SPihpr7V2eIplLpJ0kSR1d3fPFBoAwJGFbTWuQ4ikpqqJXUdGu7uEdeBcIM5qyor12SvCnfBOqqhUEoUtzsXtNXrsv37nOgwAETdjkshau2Oq94wxvzTGtFlrn0klgZ6dYrkaSZ+T9Fpr7TemWdcdku6QpMHBwZAddgEAmVgwq9p1CKH1yA0TB6Eda2CQI0KC+J0U9XJ8r419Tfrak7/y7PuQmYqSQtchRNKW+c0kiQDkLd/uZp+RtDf1eK+kTx+5gDGmRNL/lvQBa+09ea4PABBihw7u0b37N7kOIzJGK4uWBTxrEhBXpUUFetc5qzz7vrbaMs++C5n54AVrIjONug1Zh7Oj+ppchwAgBvJNEh2UtNMY84SkHannMsYMGmPek1rmNEmbJJ1njPle6t/yPNcLAEDk9bdW69OXHaVXH7PAdShAYPwck2jf0By11nif2GEGneAM9TO/Ta42zG3S4na6gwPIT15zFFtrn5O0Pc3rj0jal3r8IUkfymc9AABk6wevP1pLX/8l12HMaKCrznUIQOKdsaZbPY0VOnjvjyaNM/O1V29VTUQGwweKmAkBQJ7yShIBAHLzlWu2qKgw32JOTKfaw3FJAMTbrSctlSRtX9iq6z7xfT3ys99IGjmOMDMdphSu3maSQhkSgIghSQQADsxurHQdAgA44Wd3s3z1tVTp4xev16//8Bfd/a3/0PlH9bgOCSEWxoRM2GZcAxA9JIkAAHnbvXiWnn7+j67DAIC8GWPUWFWqy7b2uQ4FIdcdkQG2ASAbJIkAAHl7p4ezCcXVw9dvcx0CEApGIS4lArJw1tpuff/p5/WxR552HcqYsM24BiB6GBADAIAAtNUyOxIAxIkxRhvmMu08gHghSQQAAIDAhHlMIiDqGJMIQL5IEgEAAABADJAkApAvkkQAELCdi1pdhwAAzlBIlJ2SwuRcrj/5pmNchxB55IgA5IuBqwEgQIcO7nEdAoCA3LBnod74ucddh4GIS9JAxEURTIjRfRJA3ETvSAwAABABK7rrJUlb5zc7jiRcDK1qwDfWUX+z4wfanawXgPeoJAIAAPDJT998LJUGR+DXkR0jIzoRIezeesYK1yEA8AhJIgAAAF9YFRSQEgkSg/YiaGxz0umDXa5DAOAhupsBAGJrwaxqtdWWuQ4DwDh+VlYVULaFhAs6aXX51j4dPHlpsCsF4CuSRACA2Lp3/yY9fP1212Hob09Z5joEIBHIEaV326kDrkNAQIYDzhIVGMYZA+KGJBEAICsPXr3FdQiRcxql+AlFwykdGpS5Wd5Vl/NnW2tKPYwEABBnJIkAAFnpaap0HQIgSbr5+MWuQ5hWcSHJkKDFOQF19dHzc/rcoYN7VFnKMKQAgMyQJAIAAJG0d0OP6xDSak+Ng1VUwGXWVL57407Pv3Nld50uHJrj+feGRWlx7tsTgysnx6rZ9YGuj00LiB+uXgAAADzUWDXStYdKomAdPHmZqsuKXYcRSr1UgCbG8cvbXYcAIOJIEgFAQC7bOtd1CAAC8PazVurK7f3qa6lyHQpi4IbjFkqSigtzv2yvryzRoYN7vAoJABBjJIkAIAALZlXrml0LXIcBwGf/dvMudTVU6MDOebEeHyeM4vrbPnd9jw4d3BPb/x88Rv8vAHkiSQQAAOCRKgYIRoj94xkrpn2/gEwUssR4V0D8kCQCAADIQ09jhesQgIy8ZGDieDXHLp3lKJLJ3n3uoOsQAAAiSQQAAJCz8zb06P4Dm12HETnlJYWuQ4icuPde3Lmo1XUIAACRJAIAJMAxS8JztxzeKi92m2x49e4FKios0M3HL9ZHL1rnNJYoKXP8d4sir7r1PH7L7rHHZpqRjob6m7xZYczNa612HQIAeIokEQAg9t56xgp976adOm9Dj+tQ4LGvXrs1sHVVTlP9sndDj9b2NgYWCyZjaJTMUMXlrUXtNXrvecntKmfZ84DYIUkEAIi94sIC1VWUxL67RhI1V5cGtq4NfYcrK0qLRi6h2KYQB7MZVysvteXFrkMYQ8oGQL5IEgFAAG4+frHrEAB4qKkquOQUMkO+LnMPXbtVn79yaKwK5Mpt/ZOWYdaqzPG7AhAnzNMKAD776EXr6IaSYKt76l2HAB/cfdE6PfyT5xhbB5HU1TBSOTQ8PPK8IM1tY7oRZS4pv6nTBjv1sUeedh0GAJ9RSQQAPiNBlGwfv3iD6xDgg66GCp22ust1GEBeRhNBBal+k+OTHVTHZC5Mv6v+lipfvve4ZW1644lLffluAOFCkggAfFRXEZ5xCgAgzkLUTo+MW05YotMHu7R9IdPP56Orodx1CGNaasp0517vB9Ke3VihkiKajkASsKcDABIjTHd7ET07F9GQhntLO2o9+67WmjL9zSnLVFw4eUQnjpeZa6st12M373Idhq+2zm9xHQKAgDAmEQD46LZTBlyHAMADbz9rpY5d2qbKkiI1VpW4DgdpJGXgaj9m1DNpfnuMSZSdytL4Nqsev2W3ykvSj79GMhGIHyqJAMAnH7lwrXbEpPLgXeesch2CJ5iuHLkabSDtWdamdYwzhhi7cnu/ThvspPEPSdKhg3vSJogu3jzXQTQAghDflDcAOJbuzmxU7Vo8y3UInqDRg1zFZ28GJhtNoBtJB3bOkySd9q6H3QUUY/9y9RbXIeTla6/equf/+Fc9+ONnXYcCwCdUEgGAD7bMb2bqcwBAJKRNgqaS6lvmNwcZSqx11JVrTlOl7+vxs2q2s75CSzpqZSjNBWKLJBEAZOh956/OeNk3vXSpigo5xIaNpZQIOaJBhDgb3b476ysmvffSFR1BhwMAcIjuZgCQoS3zuJsadaSIgPgij5e7wgKjt525Uitn1429xsDV2WusLNFzf/iL6zAAIC/c5gYAH9BWCScKiZAr9unwi3u115zmkW5Kr9jkz4DBe5a1qa22fOw5x8vsffvGna5DCBybCRA/JIkAAIkxTKsHiK3qsngXyNeUFevQwT3as6wtkPVxtASAZCJJBAAeuWHPQtchYAY0epCrhsoS1yFgGvfuH1JLdZnrMGLl1pOWavuCFi3tqHUdCkJocXuNJGkZ2wcQOySJAMAj+4Z6NauGRkqYUUiEXNx90TotoSEUagtm1bgOIXTyraya11qtO89breIAJmFY2MbfL2q2zG/R1169VccsDaayDUBwSBIBgA9iPjRGhAWbJdrY1xTo+uCPdb2NrkMAMvL3pw6MPf76ddv0rdfuyPs7/U6ut9aU6pOXbPB3JfBFutnwAERfvDtvA0DAmA0m3IKuJLrr5WuCXSE89fgtu/Wr3//ZdRhATmrKiiUPilv9PK9VlRbp05dtVHlJoW/rAABkh0oiAPCBYS6kUAo6SVRYwHYQZeUlhepq4E45osOPKtZhH4+bZ6+brVm1dNP22ny6XwLIA0kiAPBQnMe8uXxrn04b7HQdRl6o9AKA7Ng4n9hiqqOuXD96w27XYQCIKLqbAYAP4jgm0dW75rsOIW+0deLp7Wet1KUf/o7rMADnolZJROIeAMKHSiIAQGLQHImnY5e2aXB2veswgJjiyJk0HXXlrkMA4BCVRACAxBimlAgAssJhM3lWdNfpTS9domd++yfXoQBwgCQRAHiIa+lwu3bXAn3yO//pOgwAiIyuhgo1VZXoV7//i+tQEIAdC1v1d6cMMOMckGB0NwMAH8RwSKJYmFVbpreescJ1GADgCz9m1iwrLtQjN+z0/HslZgINo4HOWhJEQMJRSQQAHqIsP/yYqQczaast03XHLHAdBgAEjjMkAJJEAOAHbo4CkbRjYYves3e16zCARCjgXAkAoUN3MwDwFPfggCij0AxxUF3q/X3gksICz2cRLDBkicKGvwgAKokAwAeMswBE07W76WaGaLvt1AHPkzmS9O9vOkaS1HPd5zz7TiqJACB8qCQCAA9dsLFXklRdRg4+rIKqFPnkpRuCWRE885bTBzR/VrXrMBKjq6HcdQixdMqqTvU0VboOIyOGSiIACB2SRADgoUu2zNWhg3tUVszMIEk2u7FCK7u9v5MPf1EBGKyHrt3mOoTYOH6gXZIUtZxLHLub3X3ROtchAEBeSBIBABJl28IW39cRv2YPgLAZ6Kx1HULeasvjV3W7rrfRdQgAkBeSRACQIcri46GmrNj3O71sK9HEnw1Rcf+BzfrQvrVjz6M43vrxA+06e91s12EEKshjDMczALmKX/oeAIAZMINV/OTbIOpprNDmec3eBAP4rK+lynUIefn7Uwd00sqOxCXUgzz3cJ4DkCuSRAAAeCxZzZ5wyLdB9OA1W70JBIH71GVHuQ4BWTp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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhbvAYwJP2Ym"
      },
      "source": [
        "#### Emotion recognition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJ7Fcv6WJx2A",
        "outputId": "8dbf722e-7585-4c5d-edc0-47fd71fe6134"
      },
      "source": [
        "print('Welcome to the Multimodal Speech Emotion Recognizer app from audio and text!')\n",
        "print('-'*80)\n",
        "print('Help:')\n",
        "print(' - record a speech and the program will recognize your emotion')\n",
        "# print(f' - to end the program type \"{NO}\"')\n",
        "\n",
        "print('Recognizing emotion...')\n",
        "# Recognize the emotion\n",
        "pred_emotion = recognize_emotion(audio_file)\n",
        "\n",
        "# Check whether the emotion is not empty string (= the recognition was not succesful)\n",
        "if pred_emotion == '':\n",
        "  print('Something went wrong... Try again, please...')\n",
        "else:\n",
        "  # Display the results\n",
        "  # display_prediction(pred_emotion)\n",
        "  print(f'\\nPredicted emotion: {pred_emotion} {emoji_dict[pred_emotion]}')\n",
        "\n",
        "print('='*60)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Welcome to the Multimodal Speech Emotion Recognizer app from audio and text!\n",
            "--------------------------------------------------------------------------------\n",
            "Help:\n",
            " - record a speech and the program will recognize your emotion\n",
            "Recognizing emotion...\n",
            "You've just said: I feel like I am going to die\n",
            "\n",
            "Predicted emotion: happy 😊\n",
            "============================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUHmdj8nPytt"
      },
      "source": [
        "#### Audio output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 61
        },
        "id": "mhlVyWoaM-5O",
        "outputId": "719cf00f-74b8-406f-bdb6-09b04f31eea9"
      },
      "source": [
        "# make request to google to get synthesis\n",
        "tts = gtts.gTTS(f'You are {pred_emotion}', lang=lang)\n",
        "\n",
        "output_file = 'output_emotion.mp3'\n",
        "tts.save(output_file)\n",
        "\n",
        "# Play it\n",
        "display.Audio(output_file, autoplay=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source src=\"data:audio/mpeg;base64,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\" type=\"audio/mpeg\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CleIaKi0PNLk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}