tutorials/training_a_model.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/pyannote/pyannote-audio/blob/develop/tutorials/training_a_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Training, fine-tuning, and transfer learning with pyannote.audio\n",
    "\n",
    "In this tutorial, you will learn how to use pyannote.audio to\n",
    "\n",
    "- train a voice activity detection model from scratch,\n",
    "- fine-tune a pretrained speaker segmentation model,\n",
    "- perform transfer learning (from speaker segmentation to overlapped speech detection)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "72ECjKLknm8D"
   },
   "source": [
    "### `Google Colab` setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mBBNyENbofJ4"
   },
   "source": [
    "If you are running this tutorial on `Colab`, execute the following commands in order to setup `Colab` environment. These commands will install `pyannote.audio` and download a mini version of the `AMI` corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 68165,
     "status": "ok",
     "timestamp": 1704810976973,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "Luhkk8D4oiir",
    "outputId": "7f43b6e3-8dba-40c1-a9e9-5b3ae4ddc891"
   },
   "outputs": [],
   "source": [
    "!pip install -qq pyannote.audio==3.1.1\n",
    "!pip install -qq ipython==7.34.0\n",
    "!git clone https://github.com/pyannote/AMI-diarization-setup.git\n",
    "%cd ./AMI-diarization-setup/pyannote/\n",
    "!bash ./download_ami_mini.sh\n",
    "%cd /content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VWo84u4ypEMc"
   },
   "source": [
    "⚠ Restart the runtime (Runtime > Restart session)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zmnD8CUUnt1N"
   },
   "source": [
    "### Non `Google Colab` setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HS7SxeZkoth1"
   },
   "source": [
    "If you are not using `Colab`, this tutorial assumes that\n",
    "* `pyannote.audio` has been installed\n",
    "* the [AMI corpus](https://groups.inf.ed.ac.uk/ami/corpus/) has already been [setup for use with `pyannote`](https://github.com/pyannote/AMI-diarization-setup/tree/main/pyannote)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 810,
     "status": "ok",
     "timestamp": 1704811019354,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "67whHDb3pOIe"
   },
   "outputs": [],
   "source": [
    "# preparing notebook for visualization purposes\n",
    "# (only show outputs between t=180s and t=240s)\n",
    "from pyannote.core import notebook, Segment\n",
    "notebook.crop = Segment(210, 240)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z4-LIYSEnj5i"
   },
   "source": [
    "## Data preparation\n",
    "\n",
    "See [`pyannote.database` documentation](https://github.com/pyannote/pyannote-database#pyannote-database) to learn how to prepare your own dataset for training with `pyannote.audio`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8vw0KmndnX8L"
   },
   "outputs": [],
   "source": [
    "from pyannote.database import registry, FileFinder\n",
    "\n",
    "registry.load_database(\"AMI-diarization-setup/pyannote/database.yml\")\n",
    "ami = registry.get_protocol('AMI.SpeakerDiarization.mini')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69qydyg6nX8M"
   },
   "source": [
    "## Training a voice activity detection model from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l0-I4_ApnX8N"
   },
   "source": [
    "Voice activity detection (VAD) is the task of detecting speech regions in a given audio stream or recording.\n",
    "\n",
    "We initialize a VAD *task* that describes how the model will be trained:\n",
    "\n",
    "* `ami` indicates that we will use files available in `ami.train()`.\n",
    "* `duration=2.` and `batch_size=128` indicates that the model will ingest batches of 128 two seconds long audio chunks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lFDktlSDnX8O"
   },
   "outputs": [],
   "source": [
    "from pyannote.audio.tasks import VoiceActivityDetection\n",
    "vad_task = VoiceActivityDetection(ami, duration=2.0, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EZ0Kje3RnX8Q"
   },
   "source": [
    "We initialize one *model* with the `PyanNet` architecture used [in that paper](https://arxiv.org/abs/2104.04045).  \n",
    "In particular, we increase the default stride of the initial `sincnet` feature extraction layer to `10`.\n",
    "\n",
    "The model is also provided with the task (`task=vad_task`) for which it is being trained:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 226,
     "status": "ok",
     "timestamp": 1704811118255,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "kSfY8Y3fnX8R"
   },
   "outputs": [],
   "source": [
    "from pyannote.audio.models.segmentation import PyanNet\n",
    "vad_model = PyanNet(task=vad_task, sincnet={'stride': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WCNd35YInX8T"
   },
   "source": [
    "Now that everything is ready, let's train with `pytorch-ligthning`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ExlUZ6DonX8U"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name              | Type             | Params | In sizes      | Out sizes                                  \n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "0 | sincnet           | SincNet          | 42.6 K | [1, 1, 32000] | [1, 60, 115]                               \n",
      "1 | lstm              | LSTM             | 589 K  | [1, 115, 60]  | [[1, 115, 256], [[4, 1, 128], [4, 1, 128]]]\n",
      "2 | linear            | ModuleList       | 49.4 K | ?             | ?                                          \n",
      "3 | classifier        | Linear           | 129    | [1, 115, 128] | [1, 115, 1]                                \n",
      "4 | activation        | Sigmoid          | 0      | [1, 115, 1]   | [1, 115, 1]                                \n",
      "5 | validation_metric | MetricCollection | 0      | ?             | ?                                          \n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "681 K     Trainable params\n",
      "0         Non-trainable params\n",
      "681 K     Total params\n",
      "2.728     Total estimated model params size (MB)\n"
     ]
    },
    {
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       "model_id": "766125242dd74f4bbd958c0488de0aeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=1` reached.\n"
     ]
    }
   ],
   "source": [
    "import pytorch_lightning as pl\n",
    "\n",
    "trainer = pl.Trainer(devices=1, max_epochs=1)\n",
    "trainer.fit(vad_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ti2Gx-z6nX8W"
   },
   "source": [
    "For the purpose of this tutorial, the model is trained for only 1 epoch. One can obviously expect better performance by training longer and on more data.\n",
    "\n",
    "See [`pytorch-lightning`](https://www.pytorchlightning.ai/) documentation to learn more about the [`Trainer` API](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html), in particular."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mo49WUfYnX8W"
   },
   "source": [
    "Once trained, the model can be applied to a test file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 358,
     "status": "ok",
     "timestamp": 1704811351433,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "b-iBNQ7mnX8X"
   },
   "outputs": [],
   "source": [
    "test_file = next(ami.test())\n",
    "# here we use a test file provided by the protocol, but it could be any audio file\n",
    "# e.g. test_file = \"/path/to/test.wav\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dRAWxFw2nX8X"
   },
   "source": [
    "Because the model was trained on 2s audio chunks and that test files are likely to be much longer than that, we wrap the `model` with an `Inference` instance: it will take care of sliding a 2s window over the whole file and aggregate the output of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "AR8douSWnX8Y",
    "outputId": "107ff5be-2c61-4a09-d64b-d1c35fd69ce3"
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   "outputs": [
    {
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Gg74/hgET8pKYNjiZ3KQYJg9K6rhni65KAv1J1Cc6Npc2ctWDyw+5eizWYaUgLY7EGDuxjvBK+1iHlRGZCVwwPpusxGObKH5/eyW/eHUr+2vDvxTpCU5CIfOQkwKTByXxzXNGMLcw/eTeaC/y+IN86f/WsGhnOKFz+ZRczh+XzZRBSRG5WTJNkyZPgE0ljTy/toSV++q6bvgHp8Zy3czBXDIphwy3C28gyNI9tdz9+rauHShnjsrg7NGZXD4lF5fd2rUyclNJI75AiMLMeFLjnGS4D39D7/EH2V/bioHRJ3qLVDd7eWNTOX99fxc1LT6GZ8Tz/FfmkBgbuR1Gz60p4bvPbTimD0GbxeD62YO5dFIuY3PcB32YtHoDVDZ5KKlvZ3NZI3urWwkEQwTN8ERjdbOXTLeTuYXpXDQhm8LMhIOObZrhFaStvnB/mBhHuEzSzspmFu+q4aNdNVQ1ezAMg+Hp8Zw2Ip2ZQ1MIBE02lTTw4c5qNpQ0AvCD80fx5dOHdee3KWr888M93PPmdiA8ds4bmc5phekMTY8nNd5BQWpc1PU8aPUG8HXUkXe77EeMf2NJA999biPbK8IrioemxfGV04dxyeQclfDpQf9dXcxv39pxUCmeGQUpXDg+m6pmD8+vKaWiKZx4G5Yex2WTcxmZ5SY3KSactDrGEoe+QIh3tlbwt/d3d/0bZye6uP3M4VwxNf9TF+MLt1dx17PrPzWZmOV28ZerJzOjIOWo5/T4g1x+/1K2ljcd8uvJsXYm5SdhMQzq2nysK2oAwGW38LmpeUzKT2ZwaizjcxN7fBdf5+dunCO8M6Syyct72yt5dMn+rjJZ3z9/FF8ZoONjT/P4g/zwhU28sK606zGnzUKm24XLbuG2M4ZzyaTcCEbYPRrb/SzYWklDmy/cX8YfxBsI4bJbGZvjZvKg5K5dKn1Fuy+cbP3tW9sJmTBvZDp/v2bKMe0yPhZFtW185ck1B40TUwYl8b3zRlGQHkdKrOOQif7Fu2p4f3sVm8saWXugviuB5LJb+O65o7j5lCFRufBKREREeseG4ga+9d8NhyyJO7MgpaOMehxJHX3YbBYDf8jEFwhR1+olEDI5UNvGmgP1bCxp6FoYMigllh9dOJpzxmT26rXIJ+czhqXHcerwNCYNSmJmQSo5STGYpsmbmyv4yctbuu49c5NiuHpGPvkpsdS1+liyu5aPdlUftkR3J4fVwhmj0imqa2fbIe71kmLtnD8uizvPKjzhxWly4qI60RETF885933I/to2JuYnce3MQeQlx4QbXyfFdOvEQJsvwPee38Qbm8q7VlqlxTu5+ZQhxDttLNtTy8IdVV3NGV/46hwmD0rutvP3lH01rfzslS18uLOaGLuVe6+YyIUTsiMd1kF8gRBPrSziL+/tOii5lOCy0e4Ldt3cpSc4+WM/X3VaXNfGFQ8so6LJw+yhqTx+y4yIZMs3FDdw2f1Lum76Tx2eRoIrXM5iUEos/mCI97dXsaOihUy3k2tmDmJsTt/tf/DUyiJ+8MIm7FaDl287lTE53TcYRoOF26v4wuOrCJlw55nD+dpJbOWMZr5AiIc+2suDi/Z2rQROjrUzOtvNrKGp3HJqQcSTnv2FNxDkxy9t5tnVJUC4VGBeciw7K5s/teI7J9HFXeeM4PIpeSe9yyYYMnl2dTF/fndXVwIlLzmGO88s5DOTcmj1Bvjze7t4YtkBACbkJXLvFRPZXdXCb9/azoHaNhw2C3+5ajLnjcs64rnufmMbDy7aS4LLxtfPKmRCXhIuu4Ulu2v5v2X7P7VzyjDgiql5fPOckce8EKOntfuC/PL1rfxnRbh00h+umMhnp+ZFOKr+pbHNz63/t5qV++owDDhzZAafn57P3ML0k+pVJt1rwdZK7nhqLR5/iAl5iTxy0/QTKnlpmia+YAiH1cI7Wyv54QubqG31kRxr58unD6MwI55TC9OOK8He7PHzwY5qHly0l02l4YUbt58xnG+fO/K44xMREZH+b1NJI1c/FF4wHu+0MT43kcGpscQ4rEzMS+KSSTnHlaSoafHy39UlPLx4X1cSYW5hGt88Z0SvzIu+uamcr/57LQB3nT2CO88aftj4G9p8PLx4H/9eUXTIyi8QLrddmJnAiMx4RmQmkJccQ7s/iNVioaKxnTnD0hiXG57fqmzy8OGOanZVNbO3upU1RfVdi+VGZSXw8u2naOFkL4vqRMfLW+r48ctbSIt38v63Tz+uchInqt0XZGt5eKvSuJzEg25Cq5o9/OD5Tby3vYqZBSk8deusPr0CuqyhnfP+tIgmTwCLAY/dPIPTRvTdnSit3gCvbCjj6VXFbCxp6CpRlRbv4OKJOdx2xvAB0Wdha1kTVzywlFZfkM9OyePeKyb0aqY8FDL53ANLWVvUwIXjs/nr1ZP79M/5sTBNk1ufWMO72yoZn5vIK7efMmBWQi7dXcPNj63CGwhx5bQ8fvvZ3v156otavAH+s+IADy7ae1Dfj6FpcTz5xZnkJGlVxskoqm3jxy9v5sOd1ViM8MXoV+YNw261UNbQzrOri9lQ3EBijJ15IzM4b1xWtyfePP4gT60s4u8L93RdjDusloNK6d04ezA/vHB014Vpmy/AnU+t591tlVgM+OPnJx1ylX0oZPKn93bxl/d2AfDAdVM4b9zBCwgCwRCrD9Szr6YViwEuu5VxuYkMS4/v1vfZXf7wzg7++v5uUuMcLPzOvF653hoIPthRxfef30RFk4cEp40Hrp/KKcP772KNaLe2qJ4vPLaK+jY/g1NjefzmGQxJO3o9ao8/yCvry9hU2sj726sobWjHYbN09RAZm+Pm4Runn3SC0zRN/vXRPn79xjYA7r92CheM71uLl0RERCSyalq8XPDnj6hq9jKzIIUHr5/WbZVC2nwB/vb+bh76aG/XDo/5YzK5ac4QgqZJSpyDMdnubp1vWF/cwOf/uQxvIMSNswfz80vGHdPrPP4gL68vZeH2ahrafSTGhBc4zh+TxejshBOOMRgyWb63ljueWkddq4/cpBh+cclYzhqdeULHk+MXtYmOuvoGPvPgGorr2vnFJWO5YfaQnjjdcSttaOeMez/AFwjx1XnD+N55oyId0mF98fFVvLutisGpsdxz+XjmDIuem+tmj5/KJi+xDivZia4BNzG7cEcVX3x8NcGQyd2XjeeamYN67dz3vbODv7y/m1iHlYXfntcneoV0h+pmL/N+v5BWXzCqJgeCIRMDTijZtGp/HTc8vJJ2f5CzR2fyj+umqJbkJ9S2eHl9Uzkef5BHl+ynvNHDxPwk/vvl2RGrOxrtNpc28tl/LMUbCGGzGDx0wzTOGJURsXjafUGeXH6Ahxfv69rhMTIzgZ9cPOaQE86BYIgfvriJZ1eXYDHgymn53H7mcPKSYzFNk02ljfz+7R18tKsGgG/PH8HtZxb26nvqCb5AiPP+tIi9Na3cc/l4rp7Re585/YVpmjy3poTd1S2MyXbz/NrSrpKhBWlx3H/tFEZnD6zdhNFob3ULNz66kuK6dlLiHDx847QjrlRsbPfzpSdWs2Jf3ae+FuuwcuOcIXz9rMJuTebe8+Y2/vnhXjLdTt771jztRBQREZEuP3ppE08uL6IwI54XvjaHhB5YwFRU28Zf3t/Fi+tKD+o9BnDWqAzuvnz8Mc0jtXoD/Pjlzby3rYqzR2dy9+XjDtodsbWsiRseWUFNi48zRqbz0A3Tjqm3Z29YuKOKO/+zjuaONgu/vGQs1/eReev+LmoTHa+s2sUdz+0gMcbO8h+c1ae29z+3poRv/3cDEN46/tV5w7qtlm93KW9sZ85v3sc04d1vnsbwjISjv0j6lM4ahGnxDhZ+e16PfED9r7c2V/CVJ9cAcN+VE7l8Sv8qYfLHBTv583u7yE2K4e27TuvzkwMvrSvlxy9vxusP8avLxnHltPxjfu364gau+9cKWrwB5ham8a8bp2lL5REU17Vx0V8X09ju53efm3Bc32sJM02TKx5YxuoD9STF2vnDFRP7zMoW0zTZV9NKRaOHqUOSj/i7EAqZ/L+XNvPUyqKux3KTYvAGgl07gBw2C7+8ZCyfn95/EgJ/eW8X9y3YyTljMnnohmmRDieq+AIhvvf8Rl78RA8OAKvF4IbZg/nuuaP61HWsHFlVs4dbHlvF5tImnDYLXzptKOeNy/rUCsW1RfV8/el1FNe147JbuGJqPvNGpjM+NxGPP0RaguOgpvLdxeMPMv+Piyiqa+Nb54zgjrOiP9kqIiIiJ6+kvo15v/+AQMjk6S/NYtbQ1B49387KZv783i7WHagnzmljT3ULITNcGmpuYTp1rT4a2/3MH5vJnWcVHrToMhgyueWxVXzYsTAIYHxuIl86bSgT8hJZuL2K3729gzZfkLE5bp758uw+N3/T5gvw2ze38/iyAxgGPHLTdM4YGblFfgNF1CY6rvn7eywpaufWuQX8vwvH9MSpTsqf393FH9/dCcDQ9Dj+7wszye1D5U7+8cEefvvWdmYMSeHZr8yOdDhyAnyBEOf+aRH7alo5Z0wm/7xuao+WkPL4g8z5zfvUtfq45ZQCfnJx3/u9O1ltvgDn/mkRxXXtXD9rML+89Ni2PUbCyn11fP7BZV0l3OIcVhZ88/RjKqtU0+LlnPs+pL7Nz6yhKTx60wxNsh2DzuRiYUY879x12oDbSXayXt1Qxh1PrSPGbuX9b58e9Y3Z1hyo4963d7Jsb23XYw6bhfPHZfH1swoZ2kfLUJ2ozaWNXPTXxcTYraz7yTkDso/PifrG0+t4aX0ZhgET85LYVNrI4NRY/nXDtH73czJQtHgDfP2pdby3varrsbzkGGYWpOINBNlb3drVYDwvOYYHrpvaVcu5N7y8vpSvP72exBg7S75/Zp+78RcREZHe1zlPOWtoCk9/qffnAXdWNvOd5zayobjhU187bUQ6D14/tese46/v7eIPC3bislu4avogXlhbQpMn8KnXnTo8jb9fO4XEmL5ZWtc0TX74YniRXHKsnXfuOp30hP5fcj+SojbRMeiuZzEcsXzw7XnHVB+3t5mmyX9Xl3DvOzuoavZy5qgMHrlpeqTDAsJNYM+890NKG9r5zeXjuUolKKLWmgP1XP3gcnzBni+V9uK6Eu56ZgO5STF88J15/bbE0ZLdNVz7rxUAPPOlWczs4VUOJyIUMrns/iVsKGnk/HFZFNe3sbm0iZvmDOFnnxl71Nff9cx6XlxXyqisBJ776hxNgByjJo+f2Xe/R6svyCu3n8KEvKRIh9Rn1bX6+OeHe8hLieVzU/KwWQ3m/f4DShvauevsEXz97P6zwriu1ce+mhZsFgsjMhP6bdLQNE3m/OZ9yhs9/PP6qZw79sjN2CWsqLaN0+9diGnCozeHV3HVtfqId9pUAi/KhUImr24s4/WN5SzaVY3HHzro6xYDLp6Ywy8uGdfrN9/BkMnZ933IvprWXi9xKiIiIn1PKGQy794PKKpr44+fn8hlkyNTncM0TZbsrmVHZTMpcXYa2vz87q0dtPuDnDc2i9vOGM672yr5c0e/wz9cMZHPTs2jqsnDI0v2887WCvbVtFKYEc/1swZzzczBWPt4z1hvIMilf1/KtvImLp+cy32fnxTpkPq1nkp09PismWnC6SPS+2SSA8AwDK6cns+Uwcmcfd+HLNxRRXFdG/kpsZEOjWdWFVPa0E6m28mlkz/dSFWix9TByfz2c+O565kN/OODPQxPj+ezU7v/AysYMnl48T4Arpqe32+THACnDE/j6hn5PLWymO89v5E3v35an5u4XL6vlg0ljcQ6rPziknFsLW/ixkdW8uK6Ur5//qgjrrR+fWM5L64rxTDgN5+doCTHcXC77JxamMbbWyr5cEe1Eh1H8MvXtnaV6XlpXSmXTc6ltKGdtHgnXzptaISj614pcQ5S4lIiHUaPMwyDC8dn86/F+3h5fakSHcfoyRUHMM3wKrXOreopcY4IRyXdwWIxuGRSLpdMyqXdF2TRrmp2VDQT77SRnehi2pCUiK3Ys1oMrpkxiF+/sY1nVhUp0SEiIjLAvbqxjKK6NhKctohexxuGwamFaZxa+HE/xNHZbq5/eAVvbangrS0VXY9/8dQCLp8SnrPMcLv4/vmj+P75owiGzD6f3Pgkp83KPZeP57L7l/DCulLuPKuwz85ly+H1yizo9bMG98ZpTsrwjHjmFqZhmvCfT9TzjpR2X5C/vr8bgNvP7N7mhxIZl03O47YzhgHwvec38vL60qO84vg9sngfm0ubSHDaBsQOoB9cMJost4v9tW1dJej6ktc3lgNw8YQc0hOcnDo8jdykGBrb/V1fO5T6Vh8/emkTAF+bN4xJ+Um9EW6/cvqI8ETlJ2uFysF2V7Xw0ifGoTUH6vnRS5uB8Od2X0scyrHrXBzx7rYq6lt9EY6m72v3BXlmVTEAN87u+9escuJiHFbOHZvFnWcVcsupBZw/PjviZQkum5KLzWKwoaSRotq2iMYiIiIikRMKmfzhnfC8xpdPH9ojPcJOxqyhqdx35STyU2LIdDsZl+vm95+bwP+7cPQhy0VHU5Kj06T8JE4fkQ5wUK9HiR49nujITnRx5qjoaOJybccqqmdXFeMNBCMay/8t3091s5fcpBg+r2a6/ca3zhnJZZNzCYRMvv70en7wwkZavJ+uX3giluyu4bdvbQfg+xeMiviNe29wu+zcfXm4P8eDi/byjafX8a+P9rKtvIkeqsp3zALBEG9tDq9yuHBCNtCxcrNjnHnoo72HjNE0TX7+6hbq2/yMzEzgG2eP6L2g+5HTRoRXnqwtqu+237H+5tUNZZgmnDkqgze/Ppe0+PDq9cGpsVyvyd6oNjbHzdgcN75AiEeX7o90OH3eKxtKaWz3k58Swzw1HpRelhbvZGLHgobVB+oiG4yIiIhEzPK9tRTVteF22bjl1IJIh3NIF0/M4aPvnsmKH57Na3fM5Ypp+f2uJ+a1M8P3ws+vLSEUiuy8khy/Hk90XDwhJ2qyeGePziTT7aS21dc1QRkJje1+/r5wDwDfOLtQtaH7EYvF4N4rJvKV04dhGPDUymJO+c37/Pat7WyvOPHJ+QVbK/nC46sIhEwumZTDNQNgN0enM0dlckPHpOxL68v41evbOP/PH1Hwgze45G+L2VHRHJG4Vuyro7bVR3KsndnDPu4fct3MwcQ5rGyvaOa5NSWfet3fF+7mpfVlWAz49WXj+nX5sZ6UlxxLdqKLkBluziyftmhXeLfL/DGZjM528/qdc/nXDdN4567TVLInyhmGwW1nDAfg8aX78QVCR3nFwGWaJo8tPQCEdzJFyzWr9C9TBycD4Z11IiIiMjC90FFS+KKJOX1uN8dAMm9kOvFOGzUtPjaXaS4h2vT4DNoZo9J7+hTdxma1cHXHBPHjEVwB+cCHe2hs9zMiM57Lp0Sm8ZD0HKvF4Pvnj+I/X5xFQVocje1+/vHBHs7700fMu/cD/vLeLp5cfoA3NpWzfG8tOyubqW720uIN4A+GupIh5Y3tPLuqmJsfXcmtT6zG4w9x+oh0fvvZCf0uo340v7hkHE9/aRa3nTGsa5shwIaSRr765JqI7NB6raM01Xnjsg5KViTG2vlaxwTkT1/ZwvK9tfiDIWpbvPzwxU3c27FV9UcXjmHakP7fT6AnTezozbG+uCGicfRFjW1+NnR8X07r+J3JdLs4e0wmTptKVvUH547NIi3eQWO7n5X7tEr8cFYfqGdbeRNOm4UrtYNWImTKICU6REREBrKqJg+vbigD4HL16I0ou9XStVh1kUphR50eTxGOzUns6VN0q2tmDuLvC3eztqiBDcUNXVvJe0t5YzuPLgk3k/7uuaO0srAfmz0slXe/eTrvbavkmVXFLN5dw4HaNu5bcOReExYj3CSp3R886LGbTynge+eNGrA7gGYNTWXW0PCH0Z7qFqqbvdz+n7XsrWnliaUHuLUXGysHgiHe7mjOdeH4nE99/SunD2PpnhqW7K7lqgeXY7MYBE2Tzg093ztvVJ/dqhpNJg1K4q0tFV0T+vKxNUV1hEwYmh5HTlJMpMORHmC1GJw1KpNnVhezYGvFQY0E5WOdC1sunZRLUqx2MklkTBmcBMCOymYa2nz6WRQRERlg/vHhHryBEFMHJ3ft9JTIOW1EOgu2VrJoVw23n1kY6XDkOPT4jKglyibqMxJcXDQhPDHZ27s6TNPkhy9swuMPMX1IMmeNVp3o/s5qMZg/NouHb5rOup+cwzfOLmTeyHTOGZPJtMHJDE2LIynWftBrQia0+4NYjHCjpDvPKuTdb57Ojy8aM2CTHP9rWHo8s4am8u35IwF4dMk+AsHeK92yfG8dda0+UuIczBr66V0ZVovB/ddMZUbHjo1AKJzkmJCXyL+/OJOvzhvWa7H2Z507OpTo+LQdFS1A9C1GkONzzphMINyUPNJ9i/qiyiZPV6nSG+aoL41ETkaCi+EZ8ZhmuD63iIiIDBwef7CrrPWdZxUOuAodfdGcjh0dG4ob8PfiXJKcPBV9O4Sb5gzhxXWlvLqxjO9fMIqMBFevnPf+D/awcEc1DpuFuy8br8FtgIl12A7beDoYMvEFQngDwY4/Q7hj7CTG2A/5fAm7dHIuv3t7B2WNHt7dVsl547J75byvbwpvOT13bBa2w/TYSIy18+xXZhMIhqho8hBjt5Ia3/8byPemCXmJWAwoa/RQ1eQhw907Y3k02FUZ7l0zIiM+wpFITzpleBouu4XShna2ljcpsfU//rOiiEDIZPqQZH1vJOJOGZbK7qoWluyu7bXrFREREYm8D3ZU0+wJkJ3oYu5w7cLuCwpS40iMsdPY7mdbeRMTOhZRSt+n5d+HMDE/iUn5SfiDJk+vLO7x85mmyb8+2svv394BwE8uGkNhZkKPn1eih9ViEOOwkhTrIMPtIj8lVkmOY+CyW7lqerjm+mO9tEMrGDJ5e0slABeOP/pEhc1qIS85VkmOHhDntFGYER5L1afjYLuqwjs69FnTv8U4rMwtDPdgWbC1MsLR9C2+QIj/rCwC4IbZQyIbjAjhxCTAkt01EY5EREREetPrm8L9PT8zMSfqquL0VxaLwaSOVgbrihoiGoscHyU6DuPmU4YA8OTyAz3ayLiu1ceX/28Nv3p9GxCu3X/dLJVPEOku180ajNVisHxvHdsrmnr8fOuL66lr9eF22Zh5iLJV0rs6L042lDRENI6+JBQy2d2V6NCOjv6us3zVwu1VEY6kb3lzcznVzV4yEpycOzYr0uGIMKMgfM2wt6aVxnZ/hKMRERGR3mCaZlfZyjNHqXx9XzJlULhXyrqi+ghHIsdDiY7DOH9cNhkJTqqavfzro33dfnxvIMhDi/Yy7/cLeWdrJXarwY8uHM33zhvZ7ecSGchykmKY3zHR9/jSAz1+vve2hScTTx+Zgf0wZauk90zsSHRoR8fHSurbafcHcVgtDE6JjXQ40sNO7VglvrmsiRZvIMLR9B1PLAt/Hlwzc5D6W0mfkBTrIDcpBoCtZT2/MENEREQib39tG9XNXhxWS9e9q/QNkwclAbBOcwlRRXd2h+GwWfjhBaMB+PN7u1jbTRk8byDIe9squfz+pfz6jW00eQKMykrgxa+dwhfnDlVfDpEecOOcIQC8tK6UxraeWyUZCpm80bHt9CytxugTJuaH6+5vKmlUM+YOe2rCuzkK0uIO20NG+o+cpBjyU2IIhkzWHNBqJIDNpY2sOVCPzWJwzYxBkQ5HpMvYHDcAW8oaIxyJiIiI9IZV++qA8H2ry26NcDTySZ2JpwO1bdS2eCMbjBwzzXAcwSWTcpg/JhNfIMSXnlhDcV3bCR2n1RvgzU3lfP3pdUz75bt84fHVbClrIjnWzu8+N4HX75zLuFw1wRTpKTMLUhiVlUC7P8izq3uu787i3TXsr20jwWnrKhcjkVWYkYDDaqHJE6C4rj3S4fQJe6tbARiaHhfhSKS3zBiSCsCKjm3xA90Ty/YDcP74bDLcrsgGI/IJnfcD2tEhIiIyMHQuqp42RGWv+5rEGDvDM8KlntWnI3rYIh1AX2YYBn/8/CQ+98AytpU3cd6fFnHaiHTG5SaSnxJLRoKTOIeNWKeVWIcVq8VgV2ULsQ4r+2tbWXuggbVF9WyvaCYY+nglcUaCkwvGZ/Pl04eSnRgTwXcoMjAYhsGNc4bwgxc28cTy/dxyagHWHmjy9ciScJm7z07NI86p4bUvcNgsjMxKYFNpI5vLGhmUqlJNe6s/3tEhA8OsoSk8v7aEJXuU6Ghs8/Py+jIAbpytnmjSt3Tu6NisHR0iIiIDwo7KZgDGZLsjHIkcypRBSeyuamFdcT1nazFrVNBM3FHEOW08fOM0bn50FTsqm3lzcwVvbq447uPkp8Rw3tgszhuXxeT8ZCw9MMkqIod36aRcfvvWdorr2nli2X5uPqWgW4+/vriBD3ZUY7UY3NRRKkv6hnG5iWwqbWRTaSMXjM+OdDgRt6+mc0eHGpEPFKeNSAdgY0kDda0+UuIcEY4oct7YXI43EGJEZjxTBydHOhyRg4zNCe/o2FPdiscfVAkLERGRfsw0TXZVhhehjcxKiHA0ciiTByXz7OoS7eiIIkp0HIOcpBje/PpcVu2vY11xA9vLmyhr9FDT7KXNF6TNF6DdH8QfNElPcBIMmQxOjWXKoOTwf4OTtHNDJMJiHFa+NX8kP35pM797awezhqYyuptWTQRDJj97ZQsAl03OZYhWyvcp43LD/84bSxoiG0gfodJVA0+m28WorAS2VzTz0a5qLpmUG+mQIualdaUAXDY5T33RpM/JdDtJi3dQ0+Jje0Uzk9SUVEREpN8qa/TQ4g1gtxoMSdW9WV/U2ZB8Q3EDwZDZI5VBpHsp0XGMLBaDmUNTmTk09bDPCQRDauwq0oddO2MQb2+uYPHuGm56dCX/94WZjMg8+ZUTf1+4m/XFDSQ4bXxr/ohuiFS60+T88Krt9UUNA36cbvUGqGjyADBUCbkBZd7IDLZXNPPGpvKoTXTsqW5hX3Urr28qZ3NpI5+fns8X5w495tdvKWtkxb46DAM+MymnByMVOTGGYTAmJ5FFO6vZXNqoRIeIiEg/trMiXLaqIC0Oh23g3qP2ZYUZCcQ5rLT6guyqamZUlkqM9XX6TepGA3nyTCQaWCwGf79mCiMy46ls8vK5fyxl+Uk25/2/Zfu5b8FOAH588Rjt3uqDRmYlkOC00eoLsr3jYnKg6ixblRLnICl24JYvGogumxxObry3rYqqZk+Eozk+b2wq56w/fMBZf/iQLz6xmhfXlbKrqoVfvb6NtzaXH/Nx/vb+bgAunpBDbpLGaumbOvt0bFFDchERkX6t8960sBsWX0rPsFoMJnYsPFl7oCGiscix0cy8iAwoibF2nv3ybKYMSqLJE+Cah5bzu7e24/EHj+s4pQ3t3PnUOn78crhk1a1zC7hyWn5PhCwnyWoxmNxRi3/NgfoIRxNZezv7c2g3x4AzMiuByYOSCHSU2gsEQ5EO6Zj96rWt7KluxW41GJoWx8UTc5gxJAWAR5fsP6Zj7KgI91kzDLj9zOE9GK3IyRnX0adjqxqSi4iI9GtbOj7rOxc5SN/UmejYrGuzqKBEh4gMOEmxDv79xVl8dkoeIRPu/2APs+55j3ve2EZxXdthX+cPhli8q4bb/7OW03+3kFc2lGG1GHzn3JH88ILRvfgO5HhN70h0nOwOnmi3tzrc7E79OQam7547CrvV4I1NFfxz0d5Ih3NMGtp8lDWGd6As+f6ZvP/tefz16snce8VEIJy8bPb4j3qcv7y/C4ALxmV3S8lCkZ7SOdmxraIZfxQlJEVEROT4bO3YvTm2Y5GD9E2dvV23l2u3bTRQjw4RGZBiHFb+cOVEzh6dwa/f2EZJfTv/XLSXBz/ay6yCVCbmJzE4NZbEGDut3gCbSxt5aX0Zje0fT6jNHprK988f1ZXhl77rlMI0/rBgJ4t31wzoPh2dpasK0uIjHIlEwuxhqdx92Xi+89xG/vzeLs4clcEHO6p5eX0pY7Ld/OiiMaTE9a2SZls7bijyU2LISHB1PT4oNZaCtDj21bSydE8t547NOuwxNhQ38PrGcgwD7jhLuzmkbxuUEku800aLN8Ce6hbVghYREemHWryBrt322tHRt43OCi+S2lHRTChkYlFD8j5NiQ4RGdDOH5/N/LFZLNxexRPLD7BoZzXL9tay7DAr/1PiHJw7NovrZg3SyosoMjEvicQYO43tfjaUNDB1cEqkQ4qIvdUdpau0o2PA+tzUPN7YVM7CHdWc/+ePuh7fXtHMxtJGnvvK7D7Vv2Vbebh28ehDTPaeVpjGvppWluyuOWyiwzRN7nlzGxDuU6JJY+nrLBaDMTluVu6rY0tpk35mRURE+qFtHYt5shNdpMU7IxyNHElBWhwOq4VWX5CS+nYGpcZGOiQ5AiU6RGTAs1oMzh6TydljMjtWB9ewpayJykYPje1+YhxWMt0uLpqQzdzCdKzK4Ecdq8Xg1OFpvL6pnI921QzIRIdpml07OoYp0TFgGYbBfVdO4jN/X0xxXTsAZ4xMZ2t5E7urWvjOcxt56IZpEY7yY503gZ1bxj9pekEKjy87cMTeOwt3VLF8bx0Om4VvzR/ZY3GKdKexHYmOzWWNfHZqXqTDERERkW7S5gtww8MrWd1x/TohT4sn+zqb1cLwjHi2ljextbxJiY4+TokOEZFPKEiLo0CNmvulWcNSeX1TOav3D8yG5NXNXlq8ASwGDErRz/hAlhzn4KWvncKf39uFw2rhhxeMZntFM5/522IWbK3kgx1VzBuZEekwAdhcGm76N+YQW/qndvTe2VbeRKs3QJzz4Mvadl+Qn72yFYCb5gwhNymmh6MV6R6dO0a3lKkWtIiISH/y4Y7qriQHwAXjsyMYjRyrUVkJHQvDmoHDl8yVyBuYRcpFRGTAmT4kPCm6tqiewABs8NpZAzY/JRaHTR//A11qvJNfXDKOH100pqtUzo1zhgDw81e34g0EIxsg4RVvOyvDpasmHaIXUnZiDLlJMYRMWF/c8Kmv/+ndnRTVtZGd6OLOswp7OFqR7jMuN5zY21rWRChkRjgaERER6S6Ld9cc9PezR2dGKBI5Hp2lnzvvqaXv0kyHiIgMCCMyEkhw2WjzBbvq/g8kHzci124OObSvn11IWryTfTWt/P6tHZEOh82lTYRMyHK7yHS7DvmczgTmwu1VBz2+rqiehz7aC8CvLh1HvFObmCV6DEuPx2Gz0OINUFTXFulwREREpJt8MtHxowtHf2pHsvRNQ9PjgY97XkrfpUSHiIgMCBaL0VXqZumemqM8u//ZW90CKNEhh+d22fnlJWMB+NfifTy3piSi8Wzo2KUxMf/wtYsvnJADwMsbyrp2atW2eLnt32sJmXDxxBzO0ko5iTJ2q4VRWQmAyleJiIj0F6UN7RyobcNqMdj883P54tyhkQ5JjlHnPfTe6hZMU7tt+zIlOkREZMCYNyIdgPe2VR3lmf1P546OztUoIody/vhs7jhzOAA/fGETS3ZHLinY2WR84iHKVnU6fUQ6ybF2qpu9vLG5Am8gyB1PraOs0cPQtDh+fdm4XopWpHt19unYXNYY4UhERESkO6zeXwfAuBy3dhtHmYK0OAwDmjwBalt9kQ5HjkCJDhERGTDOHhNe2b36QB11A+wCpbOe6FDt6JCjuOvsEZw/LgtfMMStT6xmbVH90V/UzYIhk2V7awGYNTT1sM9z2CzcMHsIAN99bgPn//kjlu6pJdZh5Z/XT8XtsvdGuCLdbkzOx306REREJPqt6kh0TB+SEuFI5Hi57FZyEmMAla/q65ToEBGRASMvOZaxOW5CJry1uSLS4fSaQDBEUW24zrtKV8nRWCwGf7pqEqcOT6PNF+SmR1ayrbx3J1u3ljXR2O4nwWljQu7hS1cB3HbGcMbluvH4Q+ytbiXWYeWhG6ZRmJnQS9GKdL+xHYkOla4SERGJfi+tK+XJ5UUATFOiIyqNyAxXRujt+yI5Pkp0iIjIgHLxxHBN/5fWlUY4kt5TXN9OIGQSY7eSdZimziKf5LRZefCGqUwZlESTJ8D1D6/sKn/WGz7aXQ3AzKEp2KxHvlx12Cw886XZ/OziMXzznBG8/Y3TOGV4Wm+EKdJjRme5sRhQ0+KlqskT6XBERETkBPkCIX780mYALAbMKFCiIxpNyEsCYENJQ0TjkCNTokNERAaUSyblYBiwcn9d1y6H/m5fTbgR+ZC0OCwWI8LRSLSIddh49OYZjM52U9Pi5aoHl7G7qrnHz2uaJi+vKwNg3siMY3pNnNPGTacUcOdZheSnxPZkeCK9IsZh7eqptEUrB0VERKLW0j01NHsDAHzw7TNIiXNEOCI5ERPzw7vMN5aof1pfpkSHiIgMKNmJMZzasdr7yRUHIhxN7+isI6r+HHK8EmPs/N8XZlCYEU9lk5cr/7mczaU9e3G/payJHZXNOGwWLp6Q06PnEunLxmSrT4eIiEi0e3tLJQDXzhzEoFQtyIlWnTs69lS30OzxRzYYOSwlOkREZMC5ac4QAJ5ZVUxLx+qa/qyrEXm6Eh1y/NLinTzz5dlMyEukrtXH1Q8u72qm2BP+vnA3AOeOzSIxVs3EZeD6uE+HVg6KiIhEq3VF9cCx71SWvikt3kluUgymCZt6eOGXnDglOkREZMCZNzKDoWlxNLb7eWTxvkiH0+P2dezoUCNyOVEpcQ7+/cWZzChIodkb4PqHV7Bkd023n2flvjre3FyBxYDbzxje7ccXiSZjc8IlErSjQ0REJDqFQmZXn7vOZtYSvVS+qu9TokNERAYcq8XgrnNGAPDQor3Ut/oiHFHP6ry4VqJDTkaCy84Tt8zgjJHpePwhvvD4Kpbvre2247f5Anzz2fUAfH56PiOzErrt2CLRaEzHjo79tW0qkSAiIhKFyhrb8QZC2K0GuUkxkQ5HTlJXQ/LihojGIYenRIeIiAxIF47PZky2m2ZvgH98uCfS4fSYVm+AiiYPAEPTtIpITo7LbuWB66cyryPZcctjq1jdTWWsnltTQkl9O7lJMfzwgtHdckyRaJYS5yA70QXAtvLmCEcjIiIix6uzV+Lg1DhsVk3BRruJHYkO7ejou/RbJiIiA5LFYvCdc0cC8PjS/VQ0eiIcUc/o3M2RGudQvwPpFk6blQeum8rcwjTafEFuenRVV+3hE2WaJo8v3Q/ArXMLSHDpZ1UEYHxuuETC+uKT+x0TERGR3re3ugWAodpZ3y+Mz0vEMKC0oZ3qZm+kw5FDUKJDREQGrHkj05k+JBlvIMRf3t8V6XB6hMpWSU9w2a08eP00Zg9NpcUb4IZHVrLpJFY2vbS+lD3VrcQ7bXx2al43RioS3aYOTgZg7YGGyAYiIiIix21vx73Y0HTtrO8P4p02RmSEy+uuPcmFXtIzlOgQEZEByzAMvnveKACeXVXM/o4L0f5EiQ7pKTEOKw/fNI0ZQ1Jo9gS47uEVbCk7/mTHjopmfv36NgC+dsYw7eYQ+YTORMeaonpM04xwNCIiInI8Ou/FtKOj/5jStQhFiY6+SIkOEREZ0KYPSeGMkekEQib3LdgZ6XC63e6q8HbpgnRdXEv3i3XYeOTm6UwZlERju58bH1nJno4t+sfiiWX7ufivi6lp8TE6280XTi3owWhFos+43ETsVoPqZi8l9e2RDkdERESOQ2ePjqG6F+s3uhahKNHRJynRISIiA9635od7dbyyoYytZU0RjqZ7rS9uAD6u8y7S3eKdNh67ZQZjc9zUtPi46C+L+ddHewkEQ0d83e6qZn7x6lZ8wRBzC9N46taZOG3WXopaJDq47FbG5oTHb91Qi4iIRI92X5DShvAiBZWu6j+mdSQ6NpY04vEHIxyN/C8lOkREZMAbl5vIRROyAfjDOzsiHE33qWnxUlTXhmHAxPykSIcj/ZjbZeexm2cwsyCFdn+QX72+jcvuX8rm0kOXsmr1BrjrmQ0EQiZnjEzniVtmkBTr6OWoRaKDVg6KiIhEn86yVYkxdpJjVZq1vxicGkt2ogtfMMTKfXWRDkf+hxIdIiIihHd1WC0G722vYvX+/nHBsq6oAYDCjHjc6nsgPSw9wclTt87iN5ePx+2ysam0kUv+voSfvbKFXZXNXc9r9Qb4ypNr2FTaSEqcg19cMg7DMCIYuUjfpkSHiIhI9Onqz5Eep2vdfsQwDOYWpgHw0a7qCEcj/8sW6QBERET6goK0OK6clsdTK4v53ds7eOZLs6L+gnRtUXhSbHJ+coQjkYHCYjG4asYgzhydwS9e3cprG8t5bOl+Hlu6n5xEF+4YO0V1bbT5gsTYrTxy03TyU2IjHbZIn9aZ6Nhe0USLN0C8U7dwIiIifd3ejr51Q9NUtqq/mVuYzrOrS/hoV02kQ5H/oR0dIiIiHe48qxCHzcLKfXUs3h39Fy1L99QCMKMgJcKRyECTkeDib9dM4fFbZnD26AzsVoOyRg/bK5pp8wXJTYrhoRumMUkl1USOKtPtYlBKLCETVvWTHYciIiL93faOHc3DM5To6G9OHZ6GYcD2imYqmzyRDkc+QcuBREREOmQnxnDtzEE8umQ///xwL3ML0yMd0glrbPezqaQBgDnDUyMbjAxYp49I5/QR6TR5/OyqDCc5Mt0uhqfHY7FE944pkd40Z1gqRXVtLNtTyxkjMyIdjoiIiBzFtrImAMbmuCMciXS35DgHE3IT2VDSyEe7avjc1LxIhyQdtKNDRETkE75wagFWi8Hi3TVsKTt0I+VosGJvLSEThqbFkZ0YE+lwZIBzu+xMHZzC3MJ0RmQmKMkhcpxmDwsnrJf0g92GIiIi/V2rN8C+2nCPjtHZSnT0R52LIhftVJ+OvkSJDhERkU/IS47lgvHZAPzro30RjubEvbO1EoBTOxqliYhI9OpMdGwtb6LZ449wNCIiInIk2yuaMU3ISHCSnuCMdDjSA04b0ZHo2FVNIBiKcDTSSYkOERGR/3Hr3AIAXt1QRllDe4SjOX7eQJC3t1QAcGFH0kZERKJXRoKLnEQXpglbO0phiIiISN/UWRlgjMpW9VtTBiWRHGunoc3Pqv31kQ5HOijRISIi8j8m5CUxa2gKgZDJY0v3Rzqc47ZoZw3NngCZbifTh6gRuYhIfzA2NxGAzUp0iIiI9Gkr9tUBMDk/OcKRSE+xWS2cOSoTgHe3VUY4GumkRIeIiMghfOm0oQD8Z0URje3RVSbk1Q1lAFw0IUe9EERE+onxnYmO0ujtHyUiItLfmabJ8j21AMwZnhrhaKQnnTMmA4AFWysxTTPC0Qgo0SEiInJI80ZkMCIznhZvgP9btj/S4RyzNl+ABR39OS6emBPhaEREpLt0Jjo2ljRENhARERE5rJ2VLdS2+oixW5mYlxTpcKQHzS1Mx2GzUFTXxs7KlkiHIyjRISIickgWi8FtZwwH4OHF+2j1BiIc0bF5f3sV7f4g+SkxTMxLjHQ4IiLSTSblJ2EYsKe6lapmT6TDERERkf9hmib/XV0MwIyCFBw2Tbv2Z3FOG6cMC+/aUfmqvkG/cSIiIodx4fhsBqfGUt/m56mVRZEO55h0lq26eEIOhqGyVSIi/UVynIOxHU1Nl+yuiXA0IiIi8kkt3gCn/nYh/1q8D4Cb5gyJbEDSK84ZkwXAO1uV6OgLlOgQERE5DJvVwtfmDQPgn4v24vEHIxzRkTV7/CzcUQ2E+3OIiEj/curwdAA+2qVEh4iISF+yeFcNpQ3tAEzMS2TeyPQIRyS94ezR4T4dG4obqGzSjttIU6JDRETkCC6bnEdOoovqZi//XVMS6XCOaMHWSnyBEMPS4xidnRDpcEREpJvNLUwDwpMpanopIiLSdyzdE16EMG1wMv/3xZnaXT9AZLhdTMxPAlS+qi9QokNEROQIHDYLXz49vKvjgQ/24A+GIhzR4XWVrZqoslUiIv3R1MHJOG0Wqpq9anopIiLSh3SWlfzi3ALcLnuEo5HeNH9MJgDvqnxVxCnRISIichSfn55PWryT0oZ2XlpXGulwDqm+1ddVykRlq0RE+ieX3cqMghQAPtpVHeFoREREBKChzcee6lYAZg1NjXA00tvO6Uh0LNlTS6s3EOFoBjYlOkRERI7CZbdy69wCAP7x4R6Cob5XLuStLRUEQiZjst0Mz4iPdDgiItJDuspXqSG5iIhIn7C9ohmAvOQYkmIdEY5GelthRjyDUmLxBUIs2qmFKJGkRIeIiMgxuHbWYBJj7OytbuWtzRWRDudTPlm2SkRE+q/OhuQr9tbhDQQjHI2IiIjsrAwnOkZmqk/iQGQYRlf5qndUviqilOgQERE5BvFOGzfNGQLA3xfu7lNNYKuaPSzfWwvARROyIxyNiIj0pFFZCaTFO2j3B1l7oCHS4YiIiAx4Ozp2dIzIUqJjoJo/NguA97ZV9um+nv2dEh0iIiLH6KY5Q4h1WNla3sQHfWhL6pubKgiZMCk/ifyU2EiHIyIiPchiMThleGf5qr7zWSQiIjJQde7oGKVEx4A1dXAyafEOmjwBVu2vi3Q4A5YSHSIiIscoOc7BtTMHAXD/wt0RjuZjKlslIjKwzC0Ml69atFN9OkRERCIpFDK7enQUZijRMVBZLQYzOxrRbyhujHA0A5cSHSIiIsfhi3OH4rBaWLW/npX7Ir9So7ShndUH6jEMuHC8ylaJiAwEp3U0JN9c1khtizfC0YiIiAxc+2pbafYEcNosFGbGRzociaAJuYkAbCxpiGwgA5gSHSIiIsch0+3ic9PygHCvjkh7fWN4N8eMISlkJboiHI2IiPSGDLeLUVkJmCYs3q1dHSIiIpGyobgBgHG5iditmmYdyMbndSY6tKMjUvQbKCIicpy+ctowLAZ8uLM64qs1Xt1QDqhslYjIQHP6yHD5qg/7UM8oERGRgaYz0TEpPymicUjkjevY0VHa0K4dtxGiRIeIiMhxGpQayyWTcgH4y3u7IhbH/ppWNpU2YrUYnD8uK2JxiIhI7zv9E306QiEzwtGIiIgMTOs7Eh0TlegY8NwuOyM6ypct3VMb4WgGJiU6RERETsDtZw7HYsC726rYXtEUkRhe6yhbNWdYKqnxzojEICIikTF1SDIxdis1LV62RehzSEREZCBr8vjZVBouUzRtcHKEo5G+4IxRGQC8v70qwpEMTEp0iIiInIBh6fGcOza8i+Lfy4siEsO728IXT2pCLiIy8DhtVmYPSwXCuzpERESkdy3fU0vIhKFpceQkxUQ6HOkDzhqVCcAHO6oIasdtr1OiQ0RE5ARdN2swAC+sLaGhzder565v9bGhoz/IvJEZvXpuERHpG04f0Vm+Sn06REREeltneaI5w1MjHIn0FVMGJZEYY6e+zc/64vpIhzPgKNEhIiJyguYMS2VUVgKtviAPL97Xq+detKsa04RRWQlkJbp69dwiItI3nNaR6Fh9oI5WbyDC0YiIiAwsS/eEd1TOGZYW4Uikr7BZLV0LUd7bpvJVvU2JDhERkRNkGAbfOLsQgEeX7Ke+tfd2dby5qQLQbg4RkYFsSGos+Skx+IMmy9T0UkREpNdUNXvYWdmCYcDsodrRIR87a3T4Hl2Jjt6nRIeIiMhJmD8mizHZblq8AR76aG+vnLOhzcd72ysBuGRSTq+cU0RE+h7DMDitsKN81S6VrxIREektnQsMxmS7SY5zRDga6UtOH5GOzWKwo7KZfTWtkQ5nQFGiQ0RE5CRYLB/v6nhs6X7qemFXxysbyvAHTUZnuxmd7e7x84mISN+lPh0iIiK9rzPRMWeYdnPIwZJiHcwZHi5n9sam8ghHM7Ao0SEiInKSzhmTybhcN22+IP/8cE+Pnss0Tf69vAiAK6bm9ei5RESk75s9LBWbxWB/bRtFtW2RDkdERGRAWNLZn2O4+nPIp100PhuA1zYq0dGblOgQERE5SYZh8M1zRgDw6NL9FNf13ETTmgP17KhsxmW38FklOkREBrwEl50pg5MB+FDlq0RERHpccV0bxXXt2CwGM4akRDoc6YPmj83EZjHYVt7EnuqWSIczYCjRISIi0g3OGJnBnGGp+AIhfvf2jh47z79XhHdzfGZiDokx9h47j4iIRI/O8lUf7lCiQ0REpKct7djNMSk/iTinLcLRSF+UFOvglM7yVdrV0WuU6BAREekGhmHw/y4cjWHAqxvKWHOgvtvPUdfq4/WOGp/Xzhzc7ccXEZHo1JnoWLanBl8gFOFoRERE+rf3t1cBdE1kixzKhRPC5ateV5+OXqNEh4iISDcZm5PY1TfjV69vxTTNbj3+Y0v24QuEmJCXyIS8xG49toiIRK8x2W7S4p20+oIs31sb6XBERET6rTZfgA93hndQnjs2K8LRSF927pgs7FaD7RXN7K5S+areoESHiIhIN/r2/JHEOqysK2rghbWl3XbcZo+fx5buB+Crpw/DMIxuO7aIiEQ3i8XgnDGZALy9pSLC0YiIiPRfi3ZW4/GHyE+JYXR2QqTDkT4sMdbOqR27fl5X+apeoUSHiIhIN8pwu7jtjOEA/OK1rVQ1e7rluE8uL6LJE2BYepxWDomIyKecOzac6FiwtZJQqHt3FIqIiEjYW5vDCwrOG5ulxWdyVBdNyAHghXUluj7rBUp0iIiIdLMvnTaUcbluGtv9/L8XN590Casmj5+HF+8F4GvzhmOx6IJaREQONmdYGglOG1XNXtYVN0Q6HBERkX7HFwjx3rZwf47zxmnxmRzd+eOzSHDaOFDbxjKVF+1xSnSIiIh0M7vVwr1XTMRuNViwtZL/rCw6qePd985Oalp8FKTF8ZlJOd0UpYiI9CcOm4V5ozIAeGeryledjPLGdn7+6hZ+8vJmalq8kQ5HRET6iMW7q2n2BkhPcDI5PznS4UgUiHXYuGRy+B7+qZOcF5CjU6JDRESkB4zKcvPt+SMB+PkrW9lc2nhCx9lS1sgTy/YD8ItLxmK36qNbREQOrbN81TtbKk96N+FAZZom33h6PY8u2c8Tyw7w81e3RjokERHpI55eWQzAxRNytMtejtnVMwYB4T5qtVpA0aM0WyIiItJDbp07lLNHZ+ALhrjxkZUs2Fp5XK9v9wX5zn83EjLhognZzC1M76FIRUSkP5g3MgOHzcK+mlZ2VbVEOpyo9MamClbsq+v6+6sbylhXVB/BiEREpC+oavLw3vZw2aqrZ+RHOBqJJmNzEpmQl4g/aPLs6pJIh9OvKdEhIiLSQywWgz9cMYnhGfHUtvq47T9rOVDbekyvNU2T7z6/ka3lTaTEOfjxRWN6OFoREYl28U4bpw5PA+DtzSpfdSKeWR1erXv7GcO5YmoeAL96fZt2yIiIDHD/XVNCMGQyfUgyhZkJkQ5HoswNs4cA8OiSfXgDwcgG048p0SEiItKDEmPtvHbHqcwoSMEXCHHn0+uPWu87EAzx81e38uqGMmwWg39cO4VMt6uXIhYRkWjWWb7qbfXpOG5VTR4W76oG4HNT8/j2uSOJsVtZc6CepXvUQFREZKAKhcyu/gpXTR8U4WgkGn1mYg5ZbhdVzV5eXl8W6XD6LSU6REREepjLbuU3l48nwWVjQ3EDZ977AY8dZiVHXauPGx5ZyWNL9wPwi0vGMXNoai9HLCIi0eqs0ZlYDNhc2kRpQ3ukw4kqf3hnJyETpg5OZkhaHJluF5dNyQXg5fWlEY5OREQiZfHuGkrq23G7bFw4ITvS4UgUctgs3HLqEAAeXLSXUEg7RXuCEh0iIiK9YGh6PC9+7RRGZ7tp8gT42atbmXPP+/zi1a08vbKIl9eX8otXt3L+nxexdE8tsQ4r9187hWtmasWQiIgcu7R4J9MGpwDwzpaT29VR1eTh+odXMO1XC1i0s7o7wuuzlu2p7Spb9f3zR3U9/pmJOQC8ubkCj1+lJkREBqLO3RyXT8nDZbdGOBqJVlfPGESC08buqhbe7+j3It1LiQ4REZFeMjwjntfuOJVfXTqOLLeL2lYfjyzZx/df2MTXn17PI0v2UdnkZUhqLC/ddgoXjNdqIREROX7njssC4Pm1JSfVW+L3b+/go1011LT4+McHe7orvD6nrKGd77+wEYBrZw5i+pCUrq/NGJJCblIMzZ4AL67Trg4RkYGmqtnDgq2VAFylJuRyEhJcdq6dNRiAfy7qv9dVkaREh4iISC+yWgyumzWYxd87g39eP5XrZg3i9BHpzCxI4eoZ+dx/7RTe/PppjFCDOxEROUGXTc7FabOwubSJlfvqTugYTR4/r20s7/r78n21lPWzUljeQJAHF+3h3D8t4kBtG9mJLr73id0cABaLwc2nDAFUakJEZCB6bk0JgZDJlEFJjMpyRzociXI3nzIEu9Vg1f561hw4sWs0OTwlOkRERCLAZrVw7tgsfnXpeB6/ZQbPfHk291w+gQvGZxPj0HZoERE5cSlxDi6fkgfAI0v2ndAxXl5fRrs/SGFGPDMKUjBNeP0TiY9ot7uqmYv/upi739hOsyfAhLxEnv3ybNwu+6eee/WMQSS4bOyraeWj3TURiFZERCIhEAx93IR8hkoKy8nLdLu4bHK4/9c/P9wb4Wj6HyU6RERERERE+plbOnYhvLO1kqLatuN6rWma/GdFeGLn6hmDuKCjFFZn6Y5od6C2laseXMHOyhbS4p387nMTePFrp5CfEnvI58c5bXy2I3H05PIDvRmqiIhE0JubKyiuayc51s7FE3IiHY70E186bSgAC7ZVUlJ/fNdocmRKdIiIiIiIiPQzhZkJnDYiHdOE+z/YfVyvfXdbFdvKm3DYLFw+JZezx2QCsPpAHXWtvp4It9eU1LdxzUMrqGnxMjrbzVvfmMuV0/KxWowjvu66WeGVvAu2VrKxpKEXIhURkUgyTZMHPgz3UbhxzhDtupduMzwjgVlDw7tlX15fFulw+hUlOkRERERERPqhO88cDsCzq4vZXdVyxOcGgiHWFdXzyOJ9fOPpdQBcP2swSbEO8pJjGZPtJmSGjxWtKps8XP3Qckob2hmaFsfjt0wnLd55TK8dnpHQVWrit29t78kwRUSkD/hoVw1bypqIsVu5cfaQSIcj/UxnidH/ri4mqP5f3UaJDhERERERkX5o2pAUzh6dQciEP7yz47DP8waCfO6BZVx2/1J+8dpWWn1BThmeyvfO+7gx9y2nFgDwwId7aGz393js3c0XCPGVJ9dQXNfO4NRY/nPrLDISXMd1jG/NH4HVYrBkdy07Kpp7KFIREekLOndzXDUjn+Q4R4Sjkf7mgvHZJMbY2V/bxgtrSyIdTr+hRIeIiIiIiEg/9Z1zR2EY4TrjK/bWHvI59y3YyfriBgBmDEnhRxeO5vGbZ+CwfXy7eNnkXAoz4mlo83PPG9t6I/Ru9dyaEtYVNeB22XjilhlkJR5fkgMgLzmWc0aHy3jdt2AH/mCou8MUEZE+YENxA0v31GKzGHxx7tBIhyP9ULzTxtfmDQPgjwt24vEHIxxR/6BEh4iIiIiISD81MiuBq6bnA/Cd5zbS6g0c9PXNpY08tGgvAA9eP5VnvzKbL84dis168K2i1WLw68vGA/D0qmKW7K7phei7h2maPLZ0HwB3nlXI4NS4Ez7WracNxWLA21sq+dkrW7orRBER6UP+8t4uAD4zMYfcpJgIRyP91Y1zhpCd6KKs0cMTy/ZHOpx+QYkOERERERGRfuyHF4wmNymGoro2fv2J3RiBYIjvPb+RkAkXTchm/tisIx5nRkEK188aDMD3X9hImy9wxOf3FUt217KzsoU4h5UrO5I+J2rq4GTuv3YqAP9eUcTiXdGT8BERkaNbvreW97ZXYbUY3NbR60qkJ7jsVu46ZwQAf3lvNxWNnghHFP2U6BAREREREenHElx2fv+5CQD8Z0URf1+4m8omD7c+sZotZU0kxtj56cVjj+lY3zt/FDmJLorr2rn37Z09GXa3eXRJeDfHFdPycbvsJ32888ZlcePscMLne89vpNkTfT1LRETk0/zBED99Obxb76rp+QxLj49wRNLffW5KHpPyk2jxBvjla1sjHU7UU6JDRERERESkn5szPI3vnDsSgN+/vYOZd7/Hwh3VWC0Gv7x0HOkJzmM6TrzTxt2Xh0tYPbZ0HxtLGnoq5G6xZHcN7++owjDCJSK6y3fPG0V+SgylDe187/mNmKbZbccWEZHI+O/qEnZUNpMa5+j6zBTpSRaLwd2XjcdqMXh9Uznvb6+MdEhRTYkOERERERGRAeBr84bxi0vGEmO3AjAqK4HX7zyVz0zMOa7jzBuZwSWTcgiZ8MMXNxHog025gyGTXZXNfOOZ9ZgmXDV9EAVpJ96b43/FOW386fOTsVsN3thUwQMf7u22Y4uISGQ8tbIIgK/OG0ZSrCPC0chAMSbHzS2nDAHghy9sprFNO0VPlBIdIiIiIiIiA4BhGNwwewirf3Q2H3x7Hm/cOZdRWe4TOtaPLhyD22Vjc2kTDy/e182RnhyPP8h1/1rBOX9cRHWzl8KMeH5y0ZhuP8/Uwcn87DPhkl+/e3s7C7ZqFaaISLRavreWTaWNOKwWLp+SF+lwZID55jkjKUiLo6LJw89f3RLpcKKWEh0iIiIiIiIDSJzTxpC0OCwW44SPkZ7g5AcXjAbgd2/vYMnuvtOU+1evb2XZ3loAElw2/nbNFGIc1h4517UzB3PNzEGYJnz1yTX8/NUt1Lf6euRcIiLSM3ZUNHPnU+sAuHxKLilx2s0hvSvGYeXeKyZiMeCFdaW8vaUi0iFFJSU6RERERERE5LhdNT2fyybnEgyZfO3fa9lb3RLpkGhs8/PcmhIA/nn9VFb88CxGZiX06Dl//pmxXDghm0DI5NEl+5l1z3v89q3t+PtgSS8RETnYW5vLuez+JVR17AD8cQ/sABQ5FlMHJ3PraUMB+H8vbqK62RvhiKKPEh0iIiIiIiJy3AzD4J7LxzMxP4nGdj9XP7ScHRXNEY3p6VVFePwhRmUlMH9MJrEOW4+f02618PdrpvDELTMYm+PGGwjxjw/2cNWDy9lf09rj5xcRkePn8Qe5581tfOXJtbT5gpwyPJVnvzybOGfPf26IHM5dZ49gRGY8NS0+bvv3Wi2aOE5KdIiIiIiIiMgJcdmtPHzjNAoz4qls8nL5/UtYc6A+IrGU1Lfxl/d2AXDLKQUYxomX5joRp41I57U7TuUf104hwWVjzYF6zr7vQ37wwibqVM5KRKRPME2ThTuquPAvH/HPD/cC8IVTC3j85hkkq2SVRJjLbuUf100l3mlj5f46fvXaVkzTjHRYUUOJDhERERERETlhafFOnv3ybGYWpNDqC3LToyt5ZlURoVDv3pjf88Z2Wn1Bpg9J5nNTI9NI1jAMzh+fzWt3nMrpI9IJhEyeWlnEOfd9yKsbynr9eyIiImGBYIiX15dywV8Wc/Ojq9hT3UpavJMHrpvCjy8ag82qKVLpG4alx3PflRMBeHzZAe7/YE+EI4oehtlDaaGmpiYSExNpbGzE7Xb3xClERERERESkj2jzBbjxkZWs2h/e0TE2x82V0/IZne0mO9FFVqILew9NJL2xqZyv/XsthgFv3DmX0dl94x505b46fvzSZnZUhkt6jcpK4JGbppOTFHPY15imycp9ddz/wR6qm718cW4Bl0+JTOJGRKQ/KGto57p/rWBvRznBWIeVa2YM4o4zC0mMtUc4OpFDe3jxPn752lYMA1746hwmD0qOdEjdpqfyBkp0iIiIiIiISLfwB0M8vnQ/f1ywk1Zf8KCv2SwGMwpSuOWUAs4anXFMpaVCIZP1JQ20egNsL2/GHwpx+eQ8shJdAHgDQR5evI/fv70D04RrZw7i15eN75H3dqK8gSD3L9zDI4v30ewNkBbv4K5zRvD5afmYwFubK1i1v47aVh/1rT5KG9o5UNvW9XrDgG+cNYIvnz4Ul90auTciIhJlAsEQq/bX89u3trO+uIGUOAc3zxnC9bMHkxSrMlXS933z2fW8sLaUjAQn/7huClMHp0Q6pG6hRIeIiIiIiIhEhbpWH8+vKWHRrmqK6toob/TgC3zcUHNMtptvnzuCM0dlHvYY+2pa+eqTa9j+Pw3OE2PsXDQhG18gxMIdVdS0hPtfXD9rMD+9uO+WHyltaOemR1ayq6oFALfLhsVi0NDm/9RzXXYLl07KpbLJw8Id1QDkJsVw05whfG5q3oCrI2+aJi3eADF2a5/99xWRvsEbCLJ0dy1vba5gwbbKrh5JcQ4rb3x9LoNT4yIcocixq2/18fkHl7GzsgW71eAnF43hulmDe70PWXdTokNERERERESikmmaHKht46lVRfzfsgO0dez2uHbmIOaNzCAjwUmG20lSjIOqZg9vb6ngr+/tptkbwGW3kBrnZGRWAvtrWrtKj3TKcru486xCrp6R3+dv/H2BEP9ecYC/vb+b2o7Jt7R4BxeOzyY9wUlqvBO3y868kenEOW2YpsmrG8u5541tlDd6AHDYLFw4PpszRmUwsyCFTLcrkm+p2wRDJot31/DI4n3Utnrx+EN4A0HavEEa2v0EQyYJLhvzRmZw4fhszh6doaSHyABmmiYvry9jfXED7b4gVc0eKpq8HKht7fqMAUiKtXPO6ExunDOEcbmJEYxY5MS0egN897mNvL6pHIDLp+Ty04vHkhgTvWXXlOgQERERERGRqFff6uPP7+3isaX7j/rcqYOT+cd1U8hICE/m+4Mh3thUzobiRlLi7AzPSOCs0Rk91vujpwSCIbZXNNPuDzIhLxGn7cglqdp9QV5eX8qTKw6wubSp63GbxWDK4GR+dOFoJuQl9XDU3a+q2cOKvXW8taWCRTurafYEjvm1kwclccspBZwzJlMlvUQGmFDI5I/v7uSv7+8+5Ncz3U7OHZvFeWOzmFGQoqSoRD3TNPnXR/u4581thExIcNm45ZQCrpqRT3bi4ft+9VVKdIiIiIiIiEi/sXBHFc+vKaGoro2qJi/VLV6CIROH1cL4vEQ+OyWPz0/Px2rp27s0etvGkgaeWVXM+uIGtpSFkx65STG8+83TiXFEx4T/5tJGHlm8j9c2lR9U0izBaeOSyTmcOSoDl92Ky24lxm4lOdaBO8bG9opm3tpcwZPLP94VlJ7g5LNT8rhh9uAjNnkXkegXCplsLmvk92/v4KNdNQCcMTKdSfnJZLidZCW6yE2KYXh6PBZ9dkg/tGxPLT99ZTM7K8NlMA0DJucnMWVQMpdOzmV0tjsqrpuU6BAREREREZF+KxQyafUFiHXYouImvS/YUtbItf9aQUObn7R4JxeMz2L20FSmDknu2gXTl1Q1efjjuzt5elUxnTMRo7ISmFmQwqWTcxmfm3hMK6+3lTfxzw/3sGxvLZVNXiC8u+Uzk3K4duZgxucm4rBpBbdEL9M0WbW/nv21rZwzOnNA9uU5UNvGhpIGqpu9HKhtY1t5E9srmmnxhnd+OWwWfv6ZsVw9Y1CEoxXpXcGQyeubynly+QFW7qv71NcTXDamD0lhUEosGW4nmQkuMt0uMt1OMhJcJHT0CIskJTpERERERERE5CBLdtdw+3/WUv8/Tc2HpMYybUgK0wYnMyg1ltQ4J8mxdhJj7UctldVdTNOkrNHDS+tK2VHRzNtbKvB27OD4zMQcbpwzmCmDkk+4t4o3EOT9bVU8sewAy/bWdj1utRgMSY1lVLabsTluxmS7GZuTSHqCs1ve10DlC4SwW40j/nsFgiEqmjxkJLhOKNkUCpn4giEMA+wWS8Qn43qTPxhi1b463tlayYKtlZQ2tAPhJtpXzxjEzKGp1LZ42VPdwoHaNkJmOMFntRpYDINQyMQfDOENhGjxBmjpKAVnGGAxDCwWSI93diWSsxJdZCfGEOcI756yWy2ETBMTMIADtW20eAMEQyYh0yTGbiXWaSPGbsVhs+C0hZ+/qaSREZkJ5CTF4LRZcNgs2K0WHDYDh9WK3WZgt1rwBUKU1reT4LKRHOfAajFo9gRo9vgP+rPJE+CdLRVsr2g+5PfJabMwf2wW3zxnBAVpaiwuA1tJfRsr9tbx9pYK3tlaeUyvsVoMhqbFkRhjJ8ZhxTAMPL4gDe0+6lp9NLT5sRgGDpsFm9XAZrGQEmcnJc6B22Unzmkj1mEl3mkj1mEj3mUjPcFJRoKT3KQYMt1HH/+V6BARERERERGRT/EFQny0q5qFO6pYvb+eHZXNHOlOP8ZuJSnWTmKMvetPi2HgD5o0e/wEQiapcQ7SEpykxTtx2iyYpolpQsiEoGniC4TwBUIEQyFCJoTM8GSoL2BS2eShrKGdkob2g0pTQbjvyvfPH8X0ISnd+j1YX9zAI4v38eHOahrb/Yd8TmKMvWtFa3qCE4fVgmGEJ4LBICXOzuhsNwZG1/sxTTAxCYXo+ntJfRv+kBkulWWatHiDtHoD4cllb4CQaeIPmrhdNmwWAxOwWSz4gkHafSE8/iDt/iAef5A4p424jokmA/AGQpQ3ttN0HP1KDsdqGLgcVmLsFmI6SoG57FYCIRNfIIg/aBLntJHgslHb4g3/mwZDXf+2XRPm3gCt3gD+oElavJMhqbHEOm24XTYSY+zYLAZVzV721bSyt7oVXzBEWryT0dkJXZPqncfsOv4nzvPJ/w+EDv7BtVoMYuxW8pJjaGz3Y7OGJ80d1vBkus1qYDWMjn9HA4sBBgbxrvD7Cn9XP2YxwGY18PhDuF02Jg9KJtZhJdZhw/KJebn/fV1nbidkmrR5g7T6AjR7Al0JAG8gRG2Lj4qmdoIhE4thdMVjMQy8gSAt3vBr6tt8NLUH8AVC+D/5/QiGDvq9ddosxDqsn0piDhQOq4WxuW5yk2LISYphTLab0dluhqbHRV1fJpHeUFLfht1qobLJw7qiBiqbPFQ2ealq9nT9/+E+H7tb52efaZpYLQaJMeFrjfyU2PD4HfJw/81zlegQERERERERkcNrbPeztqie1fvruiY76lp9NLb7CfXIDMDhGQZMyE3k1MI0Zg9N45ThqSe8g+NYmKZJRZOHHRXNbCtvZktZI1vLm9hX03rE5I9IX5AS5+CsURmcMyaTuYXpuOyWcD+jtaUU17WRGGNneEY8BWnhyf5AyCQYDBE0wWqA3RZOAiW47B2JHroSkcGQyfaKZgwjXNqmotFDRaOHNn8Qjy+ILxjqSkyFTMhJdJEa78DakQHy+sMJHo8/nJzx+IN4AyGy3C6qW7y0eAN4/eEEmi8YTuL4P5HIAshLjqXdH6Su1UcoZJLgsuGOCcfaGXPIBLvV4JvnjCAvOTZS/xQi/ZLHH6Smxcve6lbafAHa/UGCofACCHeMjZQ4B8mxDkzCCepAxy6xhjY/ta3h3/POZG+rN0CrL0hTu5/qZm94kUOj51MLHA4lBi/bf/tZJTpERERERERE5PiFQibN3gCNbX4a2sPlKRra/eEVnqaJ1WIhwRXukVLb4qW6xUdtixd/MNSxQv3jVfMOq7WjRE24bI6l43Gr1SAzwUVOUgx5yTFkJbr6xOrrVm+Akvp2qpvDq1urm8Pvq3NGxAT21bRSWt9+UKkfA+Pjv3e8/5Q4B7EOK+WNHiwGxDvtxDut4d0ZzvD3z2oYVLd4sVoM7BYDX9DEabMQ47B27K6w4LJbaWr34w2EunaO2CwWshJdJMc6ONl8UCBodu0e6dxB4vEHsVrC5YWcVgslDe20egMMSYvD2VGOyGENf91hsxDvtBHf8b5i7Fb2VLdQ0+KjxRugqeNnxx8Mkel2kZccw4jMBNITnCzaWd2xuyWcfOosdeSwWcLljKxG1zmcHY85usoehd+4P2gSCIaoafGxt6aFvORYzI7dMv6OHRD+QKjrHJ/cWdTU7u9qWN/JpGPCP2hisRgcqG2ltKGdNl+Qdl+QUMcPwyd/Jrpe+4mps87vR7zThsUwaPcHcdospMQ5yE6MwW4zwrufQmbXDiinLfy7FeewkRwXXtnstFm7yjzZrQYOq4XUT5SWEhGJNqZpUtfqwx80w9cMQCBkhq832nysK24AYGyajTMmFCjRISIiIiIiIiIiIiIi0aen8gaRX1YhIiIiIiIiIiIiIiJygpToEBERERERERERERGRqKVEh4iIiIiIiIiIiIiIRC0lOkREREREREREREREJGop0SEiIiIiIiIiIiIiIlFLiQ4REREREREREREREYlaSnSIiIiIiIiIiIiIiEjUUqJDRERERERERERERESilhIdIiIiIiIiIiIiIiIStZToEBERERERERERERGRqGXrqQObpglAU1NTT51CRERERERERERERESiRGe+oDN/0F16LNFRW1sLQH5+fk+dQkREREREREREREREokxzczOJiYnddrweS3SkpKQAUFRU1K0Bi0j0aGpqIj8/n+LiYtxud6TDEZEI0DggIhoHRETjgIhoHBCRznGgqKgIwzDIycnp1uP3WKLDYgm3/0hMTNQAJjLAud1ujQMiA5zGARHROCAiGgdEROOAiPRUvkDNyEVEREREREREREREJGop0SEiIiIiIiIiIiIiIlGrxxIdTqeTn/70pzidzp46hYj0cRoHRETjgIhoHBARjQMionFARHp6HDBM0zR75MgiIiIiIiIiIiIiIiI9TKWrREREREREREREREQkainRISIiIiIiIiIiIiIiUUuJDhERERERERERERERiVpKdIiIiIiIiIiIiIiISNQ6rkTHPffcw/Tp00lISCAjI4NLL72UHTt2HPScBx98kHnz5uF2uzEMg4aGhk8dp66ujmuvvRa3201SUhJf+MIXaGlpOak3IiK9o7vGgSFDhmAYxkH//eY3v+mldyEiJ+No40BdXR133HEHI0eOJCYmhkGDBnHnnXfS2Nh40HGKioq48MILiY2NJSMjg+985zsEAoHefjsicgK6axz432sBwzB4+umne/vtiMgJOJb7gi9/+csMGzaMmJgY0tPTueSSS9i+fftBz9H1gEj06q5xQNcDItHrWMaBTqZpcv7552MYBi+99NJBX+uO64HjSnR8+OGH3HbbbSxfvpwFCxbg9/uZP38+ra2tXc9pa2vjvPPO44c//OFhj3PttdeyZcsWFixYwGuvvcaiRYv40pe+dFyBi0hkdNc4APCLX/yC8vLyrv/uuOOOng5fRLrB0caBsrIyysrKuPfee9m8eTOPPfYYb731Fl/4whe6jhEMBrnwwgvx+XwsXbqUxx9/nMcee4yf/OQnkXpbInIcumMc6PToo48edD1w6aWX9vK7EZETcSz3BVOnTuXRRx9l27ZtvP3225imyfz58wkGg4CuB0SiXXeMA510PSASnY5lHOj0pz/9CcMwPvV4t10PmCehqqrKBMwPP/zwU19buHChCZj19fUHPb5161YTMFetWtX12JtvvmkahmGWlpaeTDgiEgEnMg6YpmkOHjzY/OMf/9jzAYpIjzvSONDp2WefNR0Oh+n3+03TNM033njDtFgsZkVFRddz/vGPf5hut9v0er09HrOIdK8TGQdM0zQB88UXX+yFCEWkpx3LOLBhwwYTMHfv3m2apq4HRPqbExkHTFPXAyL9yeHGgXXr1pm5ublmeXn5p37nu+t64KR6dHRuPU9JSTnm1yxbtoykpCSmTZvW9djZZ5+NxWJhxYoVJxOOiETAiYwDnX7zm9+QmprK5MmT+f3vf68t6iJR6ljGgcbGRtxuNzabDQhfD4wfP57MzMyu55x77rk0NTWxZcuWng1YRLrdiYwDnW677TbS0tKYMWMGjzzyCKZp9misItIzjjYOtLa28uijj1JQUEB+fj6g6wGR/uZExoFOuh4Q6R8ONQ60tbVxzTXX8Pe//52srKxPvaa7rgdsR3/KoYVCIb7xjW9wyimnMG7cuGN+XUVFBRkZGQcHYbORkpJCRUXFiYYjIhFwouMAwJ133smUKVNISUlh6dKl/OAHP6C8vJz77ruvh6IVkZ5wLONATU0Nv/zlLw8qU1lRUXHQRQzQ9XddD4hElxMdByBcxvLMM88kNjaWd955h6997Wu0tLRw55139kboItJNjjQO3H///Xz3u9+ltbWVkSNHsmDBAhwOB6DrAZH+5ETHAdD1gEh/cbhx4K677mLOnDlccsklh3xdd10PnHCi47bbbmPz5s0sXrz4RA8hIlHuZMaBb37zm13/P2HCBBwOB1/+8pe55557cDqd3RmmiPSgo40DTU1NXHjhhYwZM4af/exnvRuciPSKkxkHfvzjH3f9/+TJk2ltbeX3v/+9JjZEosyRxoFrr72Wc845h/Lycu69916uvPJKlixZgsvlikCkItJTTmYc0PWASP9wqHHglVde4f3332fdunU9fv4TKl11++2389prr7Fw4ULy8vKO67VZWVlUVVUd9FggEKCuru6QW1dEpG86mXHgUGbOnEkgEGD//v0nH5yI9IqjjQPNzc2cd955JCQk8OKLL2K327u+lpWVRWVl5UHP7/y7rgdEosfJjAOHMnPmTEpKSvB6vT0Vsoh0s6ONA4mJiRQWFnLaaafx3HPPsX37dl588UVA1wMi/cXJjAOHousBkehzuHHg/fffZ8+ePSQlJWGz2brK2H72s59l3rx5QPddDxxXosM0TW6//XZefPFF3n//fQoKCo7n5QDMnj2bhoYG1qxZ0/XY+++/TygUYubMmcd9PBHpXd0xDhzK+vXrsVgsnyptJyJ9z7GMA01NTcyfPx+Hw8Err7zyqVWbs2fPZtOmTQctfliwYAFut5sxY8b0+HsQkZPTHePAoaxfv57k5GTt7hSJAidyX2CaJqZpdk1e6npAJLp1xzhwKLoeEIkeRxsHvv/977Nx40bWr1/f9R/AH//4Rx599FGg+64Hjqt01W233cZ//vMfXn75ZRISErpqZCUmJhITEwOE62ZVVFSwe/duADZt2kRCQgKDBg0iJSWF0aNHc95553HrrbfywAMP4Pf7uf3227nqqqvIyck5nnBEJAK6YxxYtmwZK1as4IwzziAhIYFly5Zx1113cd1115GcnByx9yYix+Zo40Dn5GZbWxtPPvkkTU1NNDU1AZCeno7VamX+/PmMGTOG66+/nt/97ndUVFTwox/9iNtuu003NCJRoDvGgVdffZXKykpmzZqFy+ViwYIF3H333Xz729+O5FsTkWN0tHFg7969PPPMM8yfP5/09HRKSkr4zW9+Q0xMDBdccAGArgdEolx3jAO6HhCJbkcbB7Kysg65K2PQoEFdSZFuux4wjwNwyP8effTRruf89Kc/PepzamtrzauvvtqMj4833W63efPNN5vNzc3HE4qIREh3jANr1qwxZ86caSYmJpoul8scPXq0effdd5sejycyb0pEjsvRxoGFCxce9jn79u3rOs7+/fvN888/34yJiTHT0tLMb33rW6bf74/MmxKR49Id48Cbb75pTpo0yYyPjzfj4uLMiRMnmg888IAZDAYj98ZE5JgdbRwoLS01zz//fDMjI8O02+1mXl6eec0115jbt28/6Di6HhCJXt0xDuh6QCS6Hcs84aFe8+KLLx70WHdcDxgdBxcREREREREREREREYk6J9SMXEREREREREREREREpC9QokNERERERERERERERKKWEh0iIiIiIiIiIiIiIhK1lOgQEREREREREREREZGopUSHiIiIiIiIiIiIiIhELSU6REREREREREREREQkainRISIiIiIiIiIiIiIiUUuJDhEREREROWk33XQTl156aaTDEBERERGRAcgW6QBERERERKRvMwzjiF//6U9/yp///GdM0+yliERERERERD6mRIeIiIiIiBxReXl51/8/88wz/OQnP2HHjh1dj8XHxxMfHx+J0ERERERERFS6SkREREREjiwrK6vrv8TERAzDOOix+Pj4T5WumjdvHnfccQff+MY3SE5OJjMzk4ceeojW1lZuvvlmEhISGD58OG+++eZB59q8eTPnn38+8fHxZGZmcv3111NTU9PL71hERERERKKJEh0iIiIiItIjHn/8cdLS0li5ciV33HEHX/3qV7niiiuYM2cOa9euZf78+Vx//fW0tbUB0NDQwJlnnsnkyZNZvXo1b731FpWVlVx55ZURficiIiIiItKXKdEhIiIiIiI9YuLEifzoRz+isLCQH/zgB7hcLtLS0rj11lspLCzkJz/5CbW1tWzcuBGAv/3tb0yePJm7776bUaNGMXnyZB555BEWLlzIzp07I/xuRERERESkr1KPDhERERER6RETJkzo+n+r1Upqairjx4/veiwzMxOAqqoqADZs2MDChQsP2e9jz549jBgxoocjFhERERGRaKREh4iIiIiI9Ai73X7Q3w3DOOgxwzAACIVCALS0tHDxxRfz29/+9lPHys7O7sFIRUREREQkminRISIiIiIifcKUKVN4/vnnGTJkCDabblVEREREROTYqEeHiIiIiIj0Cbfddht1dXVcffXVrFq1ij179vD2229z8803EwwGIx2eiIiIiIj0UUp0iIiIiIhIn5CTk8OSJUsIBoPMnz+f8ePH841vfIOkpCQsFt26iIiIiIjIoRmmaZqRDkJEREREREREREREROREaFmUiIiIiIiIiIiIiIhELSU6REREREREREREREQkainRISIiIiIiIiIiIiIiUUuJDhERERERERERERERiVpKdIiIiIiIiIiIiIiISNRSokNERERERERERERERKKWEh0iIiIiIiIiIiIiIhK1lOgQEREREREREREREZGopUSHiIiIiIiIiIiIiIhELSU6REREREREREREREQkainRISIiIiIiIiIiIiIiUUuJDhERERERERERERERiVr/H8mwv1zpbmy1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<pyannote.core.feature.SlidingWindowFeature at 0x7f28d7caa470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyannote.audio import Inference\n",
    "vad = Inference(vad_model)\n",
    "\n",
    "vad_probability = vad(test_file)\n",
    "vad_probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vYf27Tg9nX8Z"
   },
   "source": [
    "Perfect voice activity detection output should look like that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "DRX0bew-nX8Z",
    "outputId": "77b43b35-ba67-4189-c588-43396ee49366"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Timeline(uri=IS1009a, segments=[<Segment(54.95, 60.85)>, <Segment(61.54, 80.83)>, <Segment(81.04, 82.05)>, <Segment(83.39, 86.94)>, <Segment(87.49, 88.59)>, <Segment(88.82, 89.69)>, <Segment(90.21, 90.52)>, <Segment(91.31, 107.4)>, <Segment(107.88, 118.17)>, <Segment(118.76, 119.05)>, <Segment(119.38, 119.91)>, <Segment(122.4, 132.39)>, <Segment(132.87, 146.77)>, <Segment(149.44, 162.32)>, <Segment(165.19, 166.99)>, <Segment(188.06, 188.34)>, <Segment(195.04, 195.27)>, <Segment(195.56, 195.82)>, <Segment(196.38, 202.4)>, <Segment(203.78, 218.2)>, <Segment(219.44, 222.12)>, <Segment(222.76, 227.27)>, <Segment(236.11, 238.38)>, <Segment(241.01, 242.5)>, <Segment(243.94, 245.45)>, <Segment(248.32, 251.04)>, <Segment(251.8, 252.7)>, <Segment(252.79, 254.69)>, <Segment(255.24, 257.74)>, <Segment(260.02, 272.52)>, <Segment(274.68, 275.95)>, <Segment(276.23, 279.86)>, <Segment(280.08, 285.51)>, <Segment(288.1, 297.75)>, <Segment(308.92, 310.65)>, <Segment(311.19, 318.45)>, <Segment(320.53, 327.5)>, <Segment(328.77, 333.36)>, <Segment(333.57, 335.49)>, <Segment(336.02, 336.43)>, <Segment(337.47, 339.63)>, <Segment(347.67, 349.78)>, <Segment(355.86, 364.82)>, <Segment(367.25, 375.93)>, <Segment(377.25, 386.09)>, <Segment(386.53, 387.02)>, <Segment(387.4, 393.64)>, <Segment(395.23, 398.51)>, <Segment(398.76, 399.29)>, <Segment(400.24, 400.93)>, <Segment(402.86, 412.67)>, <Segment(419.16, 427.58)>, <Segment(428.69, 434.99)>, <Segment(436.56, 449.23)>, <Segment(450.37, 495.68)>, <Segment(497.24, 502.27)>, <Segment(502.77, 503.46)>, <Segment(503.78, 506.79)>, <Segment(508.17, 551.13)>, <Segment(552.51, 565.64)>, <Segment(566.02, 581.45)>, <Segment(581.48, 637.63)>, <Segment(638.22, 641.25)>, <Segment(641.3, 661.77)>, <Segment(663.98, 665.07)>, <Segment(665.22, 683.95)>, <Segment(685.84, 703.98)>, <Segment(704.61, 711.13)>, <Segment(711.71, 715.68)>, <Segment(715.72, 744.62)>, <Segment(745.25, 750.08)>, <Segment(751.38, 758)>, <Segment(764.72, 775.95)>, <Segment(777.27, 787.09)>, <Segment(787.58, 793.95)>, <Segment(794.48, 794.68)>, <Segment(795.57, 795.88)>, <Segment(796.5, 797.81)>, <Segment(797.96, 803.68)>, <Segment(803.8, 805.72)>])>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_output = test_file[\"annotation\"].get_timeline().support()\n",
    "expected_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dzNs5vrUnX8a"
   },
   "source": [
    "## Fine-tuning a pretrained speaker segmentation model\n",
    "\n",
    "Speaker diarization is the task of partitioning a given audio stream of recording into according to the speaker identity.\n",
    "\n",
    "[`pyannote/segmentation`](https://huggingface.co/pyannote/segmentation-3.0) is a model that was pretrained to perform speaker diarization, but only locally, on 10s-long audio chunks.\n",
    "\n",
    "To load the speaker segmentation model,\n",
    "\n",
    "* accept the user conditions on [hf.co/pyannote/segmentation](https://hf.co/pyannote/segmentation-3.0).\n",
    "* login using `notebook_login` below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "0c403a6fbba048a9a5388b71a0d83259",
      "b906b2359fa84291b3b78bcced4f3028",
      "8f7afdee0292401181bd477a89ee3069",
      "05b307b893b84133a376088787ababac",
      "c258b47039a243cabadd48de545542f2",
      "15ab3c58a6014beb9c7ba5e3f5bc0b77",
      "df65aff406cc4288806b278cf83fb7df",
      "31f30fd568f84363b8dea2d40f54885c",
      "f383e9d323c64c64b528266858d6595c",
      "772e6eb169d84fcb808957fd886a59d1",
      "dcf595ee01a248f895635ad054c8ce28",
      "385eecea5fca4494be7a0e2dc34110ca",
      "c742508bbe1e4f0f9ad385896afea5eb",
      "e7aa7e431546419792ff998e10c4bbe9",
      "0e04e5e88aae4f14838356c422ba7ed5",
      "20ba5327eef34c7fa20f9f247b9042b1",
      "cc9cc24a3aea416a99897cf0c617f225",
      "98b09e7bb5d14a9d8c85e52f54c9c6cf",
      "50ed1cf2997e4929853964103bb7552c",
      "4bf2285d01ca4f6c86a117f73972c7e7",
      "d1063e27f20648ff9a43fede902a08a4",
      "3b133fae87364dcc89a34ed147995807",
      "e50f15a31e4543219ed038b88132d905",
      "6b641f7304e943d7b53466fc5aa376a2",
      "8865c7d7d01040329de63b6a13e0fdff",
      "3e816698f9f54bc28712b7e0a1f1dba5",
      "bd36d10136344d4b9965557a8783c5f1",
      "d1f2777676bf4237947a0e42ff7da6de",
      "245a4232482547579a72d10243664849",
      "d072771073ed479b8e16cd9e5b9262c7",
      "bf24ab0141ce423393972b66c6d09f94",
      "bd2757c3b84f4ec283b083d64ea52d7a"
     ]
    },
    "executionInfo": {
     "elapsed": 362,
     "status": "ok",
     "timestamp": 1704811300494,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "peG_1xclnX8b",
    "outputId": "3befc3a8-d54c-4a4b-9b40-2ce1229eeaa5"
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "84262babb03c4f7dab14b35637e29c31",
      "35f0b383bafc4425a27f36bc95874430",
      "f3d067b1a56849c19c933fb3380386b6",
      "5fb8f440470f4492a5a7a009760a0926",
      "c8348fafe0de405da831284cb39af5cc",
      "b3a67d2e0c14492788806bb221761174",
      "2f70dbe63df14dd18224e2bcb35b6e38",
      "0f59d4fccfc045b89337b4b4254de7c3",
      "c3204d520d604179923e93e0a9bfd395",
      "1cc4aeb271f4441d83c3227a0fef607e",
      "dd4a788ac0cb44ce93b6c001449d5d45",
      "bcccc0031f7245a394abc3aee4a6e1c2",
      "69c5b722ba3746aa80d87b3ee9fea08e",
      "ef96f5501bbb4a55b3aedac70d1261b5",
      "2d26c7ece1fe40178ab85f3d14408532",
      "693fc1fa7fd746ce8a089f3fd6683b51",
      "fd5fe3ad4e43456c896ca45ff6a6f155",
      "b92e36c0853f40738b51b8ef39f0377d",
      "8336d24b72414718b82c3195897ee896",
      "469ce1b8998a4d998d42fbea5f431781",
      "e7045e323ec947b3b354ffc61365f4cd",
      "b86e1bdb52414bfab9798b661e3e096d"
     ]
    },
    "executionInfo": {
     "elapsed": 3764,
     "status": "ok",
     "timestamp": 1704811326591,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "wO1fXdA9nX8c",
    "outputId": "5933da6e-d984-4ab1-9041-62b3d9c5a909"
   },
   "outputs": [],
   "source": [
    "from pyannote.audio import Model\n",
    "pretrained = Model.from_pretrained(\"pyannote/segmentation-3.0\", token=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zm1-d-IsnX8d"
   },
   "source": [
    "Let's visualize how it performs on our test file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "beFpRrN-nX8d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZfP8lERRNrUF5nZkmAAAp0rOsNdLb5QNA31XWoFxmx0bK8jQpHGKeQhkraN7k+y+JoGjNDcrLzA4NygEANiazozUSWL0DAOtUltkxvqWb4KF+k8rMjgQblBcHqzI7oDnpZnbUsLnO13p2aFAOAHAGLnu0Rnq7fADou/IgaTjb4xRfn+ChfpPakNkxnPVnD0xtsndFUpkddcwLo5UYjMtYyewAADiDsq+m9U/qEli9AwDrFPXQI2ar0V6WsVo8+9/rmTYEO2R2QHMmD/0XU5g/6wwu5CtlGavRZj9vbPYBgL7Rs6w1BDsAIDWTmRizHG5pUL6hImsixWBHUVpLzw5ozmSwMYnMjjoblI+G5YZwmG8yc2SgjBUA0DMyW1sjgdU7ALDO5CH8LIdJGpRvqDjITOIQ8xQyO6B5k8HGJIKitTYoX4nFfDW1Y9PzTnGz0WYfAOgLPctaI71dPgD03WRwYqbMDg3KN1IcZCZxiHmKIgAjswOaUxz6Z5FFlmUNjyZqblA+KjeEm553bPYBgL6R2dEagh0AkJrKMzt83E8qMzsSfF1kdkDzigblyQREa87sWIitZnbY7AMAPeOyR2ukt8sHgL6rLLNDz46NyOwAzia5Une1ZnasxGDcoHzzmR0alAMAPVNclHMpLXmJrOABgFJVmR3jRtx6dqxX3NpO5iBzQhGAWXGICI0pGnUvpDJ3Fpvr0SYbiG/FaBjFd7npeUeDcgCgb8rLJzWsx6hUert8AOi7wSAixnXiKyljtTjzkLoiz/PIxyVbUszsKA5XlbGC5hTvv2TmiLIheA3zwmglBlstY5UpYwUA9EyxHnPZI3mCHQCQoipqoitjdZrJMi3JHGROUMYKmle8/5LJ/qq5QfnCVstY2ewDAH2jZ1lrJLKCBwDWqeJwq7ilm2Aj7qZM3lzWoBzYyGiUWmZH3Q3Kx/+oQTkAwMY0KG+N9Hb5AIDMjprI7ADOpV+ZHSsxyFdTOzQoBwA4Aw3KWyORFTwAsE4lmR1Fz470DvWbsi6zI5WDzAkyO6B56fXsmFNmx2irmR3mKQCgJ2R2tEZ6u3wAYO3myCyLqdFw9c9UDuwSMCxek0joIHNCEYCZHCcwX2VmRyql7urMpBgNy8yOYb7Jeafc7JunAICeGFj/tEUiK3gAYJ2iAWwVZayKx2JdxkSKwY7F8c9KZgc0pwh2JDNH1NkQPJ+mZ4cG5QBAz1SxP2cunH5Qm/ccfE988I6/b3oYAK20eN72yLYtxco7/n3E9vOmeozs8J0x2LcUo1iOlQ/+j4pH2E5HhkfKfz52chRZljc4mtPleRYREe888Fdx99H7Gh5Ntf6fx7wkti9sb3oYNODIySNxw0d/o+lhbNpth2+LiIgsBnHkRPO39xZGETsiYnTfgRje9FPVPvbn/qoMdvzVF94Vh44fOevfj4jIbv/7WNy3FPn9/xijP3xxpeMBoPue+NCvj69+3EuaHgYNGI1Gcc+xw00PYyrZ8aOxM8siP3k4hv/nJ5oeTustHz5W22NneZ7XsstfXl6Offv2xX333RdLS0t1PAWJe917fzr+v0/8etPDAIDT5KPtcf8t/7npYZxmx4N+P7af//6mh1GLd37bu+PC3XubHgYNuO3Ql+Lq//WspoexZSvHHhRHPvNvmx5GPG3w0fit7a+t7fF/7KIL4neXvDcBmI+X7nlkvOpb3tT0MGjAXUcOxTN/72lND4MErBxdiY+//OO1xA1kdlCbr7roUXHi3ic1PQyAVro07on92Z0zP04eWXwqvyzuiT0VjKo7Vu6/sukhbOjEXV8XMdoWMTjZ9FAqN0ilJBBzt22wvX1rwnwQw+XHNT2KiIj429FXxs+d/JZ4UHZXLY+/5+4stg33xuFNljxciFE8Mvtc7IwTtYwHgG678sse1/QQgA6T2UFt8jyPoyfVsgMAInZtW4gsy5oeBg2wJgQACtaE/dXmMlZU69DycjzsQVfI7KBdsiyL3dv9igEA9Jk1IQAAg8EgLlLWlojYNqyvd+agtkcGAAAAAACYA8EOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVFut64DzPIyJieXm5rqcAAAAAAABaoogXFPGDKtUW7Dh06FBEROzfv7+upwAAAAAAAFrm0KFDsW/fvkofM8vrCKFExGg0ioMHD8bevXsjy7I6ngJI3PLycuzfvz8OHDgQS0tLTQ8HaIB5AIgwFwDmAcA8AKzNAzfffHNceeWVMRhU22WjtsyOwWAQV1xxRV0PD7TI0tKShQz0nHkAiDAXAOYBwDwARFx++eWVBzoiNCgHAAAAAABaTrADAAAAAABoNcEOoDY7duyI6667Lnbs2NH0UICGmAeACHMBYB4AzANA/fNAbQ3KAQAAAAAA5kFmBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtNpiXQ88Go3i4MGDsXfv3siyrK6nAQAAAAAAWiDP8zh06FBcdtllMRhUm4tRW7Dj4MGDsX///roeHgAAAAAAaKEDBw7EFVdcUelj1hbs2Lt3b0SsDnppaamupwEAAAAAAFpgeXk59u/fX8YPqlRbsKMoXbW0tCTYAQAAAAAARETU0vpCg3IAAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUWa3+GE4cjTizU/jQkbNvuiCxrehQ05b5bIyJvehSwSVnE0mXdm7PyPGL5YHgvrpfneXzx2N0xykdND+Wsdi3ujH3b9zY9jDU79kbs3Dfzwxw5eSSWTyxXMCDaYmn7UuzetrvpYQAA1OP4oYhj9zU9ivnZeX7Ejj1NjwLWqT/Y8bNXRuzo2KERW/MjByO2n9f0KGjKLz814niPPuxpv6ueF/EvfqPpUVTr97474uY/bHoUyXndhRfEb+1LKIhwBgt5Hr94xxfjGUePNT2UVYNtES/5s4j9T576IW4/fHs87w+fF0eHRyscGKl73T95XTz3y57b9DAAAKp3x8cifuVZESvHmx7J/Czuinj5uyIuenjTI4FS/cEOoN8WtkUsbG96FHBu+ShiNIz4wvubHkn1vvC+1T8HixGZCpaFD+/cGRGrwYRUX5VhRKxkWXxs5654xokEMlBWTkaMTkbc9uGZgh3/eM8/loGObYNtVY2OxA3MPwBAV93+kbVARx/OQFZORAyPrgZ5BDtISP3Bju+/JWJpqfanIWHKFfTbD36q6RHA5tz+kYg3fG1EvtL0SKo3Gn9PL70p4kGPaXYsCRn9ybdH3P3x+KVnvyG+9vKvbXo4G/rxv/nxeNMtb4rR1/1AxOOubXo4q1lCH3vz2u/UlIrSYY+66FHx29/82xUMDAAAGlSsj7/82RHf9fvNjmUerv+miM+9q5v7Z1qt/mDH9vOUMAIgfdm4v9SMh7hJKhagAz20Jq2MX5eUb5svjH8vV1L5vSzeJzNuasrXfpDuaw8AAJvWtz1XsYdKZZ8CY3aYABCxtijt4s2UYgGa9WThvUlFdsFCwq9LEYhJpon6oJqgYBteewAA2LS+7bnK/XMi+xQYE+wAgIiJzI4OLtb6dstok9qU2ZFMsKPqzI6EX3sAANi0cs/Vk/Vtlysj0Go9eQcCwDkUi9LRsNlx1KG8ZeRjf1JRGirl7IKizNMwT+T3sqL3SRteewAA2LS+ZnZ0cf9Mqzn1AICIiMG4jVWXy1gN6m/V1SZFdsFCwhkvi9nqzyyZzI7id2jGDKjyte/LZhAAgG7r256ry/tnWk2wAwAiup2Gq4zVhtrQN6Io89S1BuXFa69BOQAAndC3PZcG5STKDhMAIjQo76E29I1IrmeHBuUAAHC6vu25NCgnUenu7gFgnrKJxVqeNzuWqvXtltEmteHAvczsSCUIp0E5AACcToNySEJP3oEAcA6TgYAu3U6Z7K2Q8KF+E8pSSgkfuBf9RGR2AABAwop9V1/Wt12ujECrpbu7B4B5mjzw7tLtlMnFZ19uGW1SG5pkp5fZMf4dmjH4IrMDAIBO6Vs2vcwOEmWHCQAR6xelo2Fz46ja5PeS8KF+E4qm3ykfuBeBmGSCHYPF1T9nfI8Ur33KgSYAANi0Yn3cl/VtmfHdob0znZDu7h4A5qk4xI3oViru5E2bye+RtcyOhG9flQ3KR90qY9WG1x4AADatWB/3Zc+ljBWJEuwAgIj1N3C6lIq7royVg+VJbegbkV4Zq2o2NW147QEAYNN6W8YqkUtZMCbYAQARHW5QPnEo7WB5nTb0jSgzO1L5naw4syPl1x4AADatWB/3ZX0rs4NE9eQdCADn0NkG5ROH5H25ZbRJbcguGAxSy+woGpTL7AAAgFKx7+rLnkuDchIl2AEAERFZVtlBblLKxWe2+j0SERF5npcH7ilnF6Sb2THbeGR2AADQKWVmR0+CHTI7SJQdJgAUytspw2bHUaXie+nLDaNNmsyUSDm7oAgGDPNEfieLhoszvkdWRoIdAAB0SN/2XcU6vkt7ZzrBDhMAChX1I0hK3rMbRps0mSlRlIpKUZnZkUrjPw3KAQDgdH3bd1WU8Q1VS3d3DwDzVtxa71IqbhG4Kb43ImJ9Zsdilu5rk24Zq2oalC/05eYbAADdVu67erK+7eLemU4Q7ACAQtbB2yl9a5S3SesyOxIupaRBOQAAtEDf9l0alJOodHf3ADBvgw43KE/4QL8JbenZkW5mhwblAABQ0qAckmCHCQCFLt5OyXuWTr1Jkz0wUj5wL8aWTmaHnh0AAHCavu27urh3phPS3d0DwLx18XZK324YbdJk8CDlYEd6mR3j2ryz9uwYyewAAKBD+rbv6mJVBDrBDhMACuXtlGGz46hS8b305YbRJk2WUcqyrOHRnFkRDBim8js5qOY9UjYo78tmEACAbuvbvktmB4kS7ACAQnlrPZFb9FUo06kXmx1HYtpSRmkxW/25JZPZUXUZq75sBgEA6La+NSivKOMbqibYAQCFLqbiFoEb5YLWaUtmwWCQWM+O4j2iQTkAAKzpXRmrDpaAphPsMAGg0MVU3L41ytukokF56oftyfXs0KAcAABO17d9Vxf3znRC2jt8AJinLt5O6dsNo01qTWbHOBiTTLBjUM2mRmYHAACd0rd9VxerItAJdpgAUOji7ZS+3TDapCJ4UJSJSlURjEmnjNW4Nu+smR0jmR0AAHRI3/Zd5d45kUtZMJb2Dh8A5qm8tT5sdhxVKr4Xh8rrDPPV1yX1w/Yi82EllQBcVs17pHj9ZXYAANAJZWZHT9a3Xdw70wk9eQcCwCYUC9NUSgZVobhpk3gGw7yVmR2Jb0bSy+yopkG5nh0AAHTKqKeZHansU2As7R0+AMxTUaInlVv0VSjTqRebHUdi2tKzY2HQzQbl5evfl80gAADd1rd9Vxf3znSCYAcAFDQo74229Iwoy1il8jtZUYNymR0AAHRK3/ZdXdw70wmCHQBQ0KC8N4rgQVvKWHU1syP11x8AADalb/uurJrytlA1O0wAKHTxdkrfbhhtUplZkPhmJL3MjmrS1YvMGsEOAAA6oW/7ri7unekEO0wAKJS3Uzq0YOvbDaNNaktmQXKZHRVtapSxAgCgU4r1+iDt/UVlulgVgU7oyTsQADahon4ESSlvGPnIn9SWBuVlZkcqv5MVBQSH+TAiIgZ92QwCANBto9X1be8yO4rvGxJhhwkAhaJET5dScYtD6eJ7IyLa06B8cfxzS6eMVbUNyhczv5cAAHRA3/ZdXdw70wmCHQBQ6GIqrjJWG2pLGatifMmUsdKgHAAATte3fZcG5STKDhMACl1ssta3Rnmb1JaeEek1KK8os6MlmTUAALApfdt3dXHvTCcIdgBAQYPy3igzCxLvGTEZDEgiu6OidHWZHQAAdIoG5ZCEnrwDAWATytspCRwqV0WD8g21LbMjIpHsjorS1dvy+gMAwKbI7IAkOPkAgEIXb6eUN4x6sujepLZkFqSX2VFxz46+3HwDAKDb+pZR38W9M51ghwkAhbIfwbDZcVSp+F76csNok1bGi/LUMwvWZXaksJHIqnmPFMGO1F9/AADYlL7tuyrq5QdVE+wAgEJF/QiSUiw+i++NiGjPYfvixM8tiTJWVTUoV8YKAIAu6du+SxkrEiXYAQCFLqbi9i2depOKw/bUyyhNZnYkUcYqq6iM1agdZcQAAGBT+lY+uIt7ZzrBDhMACsXBd5dup2hQvqG2ZBZMji+NzI6Jm2ozNClvy+sPAACb0rd9l8wOEtWTdyAAbEJ5OyWBG/RV6dsNo01qS4PyLMsiiywiEsnsmMyEmWFjo0E5AACd0reMepkdJMoOEwAKXbydUt4w6smie5PalFlQjDGpBuURM21s2vT6AwDAOfVt39XFvTOdINgBAIXydsqw2XFUqfhe+nLDaJOG49cl9cyOiLUxplHGajLYMf37pC2ZNQAAsCl923cV6/gULmTBBDtMACgU/Qi6tGAr06kXz/73eqbILFjM0n9dFsYbpiSCHZM31SooY9WG1x8AAM4qzyMiX/3nvuy7urh3phMEOwCgoEF5b7SpZ0RR6imNnh0VlbEa98WR2QEAQOtNrov7sr5VxopE9eQdCACboEF5b7SpZ0RSZazWZXZM/z4pvpc2vP4AAHBWk+v0vuy7NCgnUYIdAFDo4u2UvjXK26Q29YwoMztSCMINBhGRrf5zBQ3K25BZAwAAZ7Uus6Mn+64u7p3pBDtMACh08XZK2bOjJ4vuTZLZMYMKNjYyOwAA6Iw+Z3ZEdKsyAq0n2AEAhS7eTpHZsaFWZnak0LMjopKgYJnZ0YLXHwAAzqqXmR0T6/gu7Z9pPTtMACiUh7jDZsdRpeJ76csNo01aGbUn2FGUekous2PK90me560KNgEAwFmN+p7Z0aH9M61nhwkAhUEXG5QrY7WRIrNgcbDY8EjOrcjsSCbYUWxspsw0mcxQWczSf/0BAOCsJtfpfbnMM7mP6lIZaFqvJ+9AANiETpaxGh8s9yWdepPalFmQXBmrwWxlrCa/Dw3KAQBovcnSwVnW7FjmZfIyXZf2z7SeHSYAFDQo741WNihP5fdyxqDgZIZKG15/AAA4qz7uudaVsUpknwIh2AEAazqZ2aFB+UZkdsxgxqDgusyOFrz+AABwVn3cc63L7EhknwIh2AEAa2R29EarMjtSbVAuswMAAPq558qyiBiX7OrS/pnWE+wAgELRP2A0bHYcVSq+Fzfo1xmOX5c2ZBYk26B8yvfJZDmuNrz+AABwVn3M7IiY6OXXof0zrWeHCQCFweLqn11Kwy0alBffGxExkdnRgttXyZWxKoOC041HZgcAAJ0y6mFmR8TE/jmRS1kQgh0AsEYZq95oUxmrMrMjld/LGTc1xWs/yAaRZVlVowIAgGb0dc/Vxf0zrSfYAQAFDcp7o00NyosxJpPZMeOmpk2vPQAAnFNf91zl/jmRfQqEYAcArOnizZS+3jI6hzZldhRBgWR6dswYFGzTaw8AAOfU1z1XcXmpS/tnWq/2At53HzkUJxeVKOizC3aeF4OBuFof5XkeR0/60KM9FkYROyJiZWUYx090o8najpVhLETE8VHESke+pyqcGK6+FiujiCPJvy6rn6FHT55MYqw7YxCDiDh24kSMphjP4RMnImI1iJPC98P87Nq2oHRZT41Go7jn2OGmhwEAtRgcPRQ7sixG2SCOHznU9HDmZudgdW137Ohy5Fv4vp0TUqcsz/O8jgdeXl6Offv2xVWvvyoWdvUsssk67/i2d8dFu/c2PQwacOTEMJ78m0+NGBxveiiwKVnkkUUtH4uNy8ffHauybPXnfOz2b46T93xtw6M5u10P/p+xeN4nIyIiz5v/GQ4ij5jhfZKPD7v3jEbxrs/eWtGoaIOTL3hD7Hj8dzQ9DBpw15FD8czfe1rTwwAAEvDXD3lx7HvmDzY9DBpUxA3uu+++WFpaqvSxa8/sACgOFaENuhwQ6GogZ1r5aDFGx65oehjntHLkoWWwI4X5dHUEs79PnnDseAwS+H4AAADohtozOz5z2xdib8URGtpFelp/5Xketx76YtQ0zUA9hkcjTnSs1Mb28yIWdzU9iuTsWtwVu7ftbnoYm3Lf8XtjmFIt3KP3TN2zo3Dh9vOVNOqZXXvOj2x7O95zVGs0GsU9d38+YqSBKQAdlUXE7gdE9Gl9m+cRR7605aTvC5YujsFOFWD6rNWZHRfu3htLShhBL2VZFlcsPbDpYQC03u7tD2h6COvtu6TpEQAtMhgM4qIHPLTpYQAAVTvPBXfS4ro9AAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtJpgBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrLdb1wHmeR0TE8vJyXU8BAAAAAAC0RBEvKOIHVaot2HHo0KGIiNi/f39dTwEAAAAAALTMoUOHYt++fZU+ZpbXEUKJiNFoFLfccks88pGPjAMHDsTS0lIdTwMkbnl5Ofbv328egB4zDwDmAcA8AJgHgGIeuPnmm+PKK6+MwaDaLhu1ZXYMBoO4/PLLIyJiaWnJJAY9Zx4AzAOAeQAwDwDmAeDyyy+vPNARoUE5AAAAAADQcoIdAAAAAABAq9Ua7NixY0dcd911sWPHjjqfBkiYeQAwDwDmAcA8AJgHgLrngdoalAMAAAAAAMyDMlYAAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtJpgBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqi3U98Gg0ioMHD8bevXsjy7K6ngYAAAAAAGiBPM/j0KFDcdlll8VgUG0uRm3BjoMHD8b+/fvrengAAAAAAKCFDhw4EFdccUWlj1lbsGPv3r0RsTropaWlup4GAAAAAABogeXl5di/f38ZP6hSbcGOonTV0tKSYAcAAAAAABARUUvrCw3KAQAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWW6z9GU4cjjixUPvTAAnbtjsiy5oeBQAAAADQUfUHO372yogdDjmh137kYMT285oeBQ35ib/5iTgyPNL0MDb0vIc/L57yoKc0PYxa3PDRG+If7/3HpofRTsPjEXd8LGLleDPPv7gr4tJHRwzSuCxyxZ4r4mWPfVkMspoTgu+4OeK9r49YOVnv88AMblk5HL994rY4GaNan+cbFi+KZ227aOP/mA0iHveiiIc+fboH/9InI97z31bnullc/sSIJ790tscAgC4ZrUS886ci7v1c0yOhLl/+DRGP/tamR8FZ1B/sAKDX3vbZt8U9x+9pehgbuuXuW+L3n/f7TQ+jcgeWD8TPfuBnmx4G0zoZEZ/5fNOjWOfplz89HnPxY+p9kr/62YiPdu/9SLf8ysUXxZ/vqf8Cx98cvS2edeDgmf/CF2+JeOn/me7B3/PfIj5ww3RfO+nDvx3xVf884rwzBGUAoG++8L6Id76u6VFQp5v/SLAjcfUHO77/loilpdqfBkjYtt1Nj4AGvfxxL4/js94erdit998av3PL78TR4dGmh1KLIpNm9+LuePljX97waFroc++KuOUtEUsPirj0sfN97i/8bcSRuyMe+y8iLnn0fJ97Azd87Ia469hd83mvnBxngD3y+RGXP6n+54MpHD341ogjB+Ib93xZPGrHxZU//j0rx+KN9344jm7fHfHsHzv9L9z96YgPXL/2fpnGifHXfsU1EQ952nSP8X/+U8RoOB6HYAcARMRqKf+IiD2XRjz12mbHQrVOHF4NZJ08EpHnSrUnrP5gx/bzlK8B6LEXfuULmx7CaT78xQ/H79zyO7GSrzQ9lFoU39eebXviux/13c0Opo3uvTdi+XcjHvbNEd/8hvk+9w3fHHH75yIe+NSIRzV/Y+hPPv0ncdexu2JlNIf3ymi4+ueXPzviCS+u//lgCis3fiziyIF4xuO+J5738OdV/vgHlg/EG9/8TTEcLEQ8/VWn/4XP/NVqsKN4v0yj+Nov+7qIr5kyIP6On1x9nFnGAQBdU6yZ916y8ec47XXk7rWsndFKxIJiSamqufgyAKRnMVtdmHQ12DHKV2vJLyTS86F1it+LJl6/wXjRPI/gwiYsZKuvwVzeK8X3PLBxIF3Fe6F4b1StmLeLefw0VcwReQXvteJrzzROAOijKj5jSdPk3rCj5whdIdgBQO8UjZZHo24e0hSHcbU3lO6q4veipsPMsyoW0YksoIsD3TMevFapySATbFIZTK5pfijm7TMGGKuYI4pAySyfEcXXJhKYBYAklJ+x1rOdM/kztf5JmlMQAHrnnIdJLVf3YVznNXnoXvzMEllADwZzfK9UcQALNStKutUVTD5ngLGcI2YIQBaPPcscl1hgFgCS4PJOd8nsaA27SQB6Z6631RtQ92Fc5zV5IyuxA8T5ZnZUcAALNZtXZscoH0We5xv8hfG8XklmxwzfQ2KBWQBIgsyO7pLZ0RpOQQDonbneVm+AzI4ZNZrZkVZpmLlmQdkc0gLF/Fp3Zsfkc61TRZChijkuscAsACShvLzjuLVz1mV2dPPSZFd49wHQO53P7Ch6dlhkTyeFzI5Egh16dsB6ZYPymn5PJ+ftDd93lfbskNkBAJVyeae7Ji+6WP8kzSkIAL3T9Z4d5WGcRfZ0RsPVP5sIFmVp3ZYu3ivD4jWpU/Ecfm9JWBlMnkNmxzDf4H1XBhlmeE+Wc9wsmR1pZaEBQBKq+IwlTVk2kYU/h70RUxPsAKB3FrPFiFjrbdE1yljNqEw/X5z/cxfPmcjvZnF7fS6ZHcX33MTrDptUdzD5nGWsyjmi6Qbl43EkEpgFgCTk1rOdZv3TCoIdAPTOZAPYLiqCOIIdU0qhjFUiC+hmGpRbnpKu0ajeYPLk426YfahBOQCkSxmrbrP+aQW7SQB6p7it3tUyVnU30O28RhuUp7WA1qAc1qu7jNXk4442yt7QoBwA0lV+xtqHdZL1Tyt49wHQO8VhUh555Hne8GiqV/dhXOc1mtlRwa3tCmlQDuuVZQLralA+MW9vnNmhQTkAJKu4qODyTjeV659uVojoCqcgAPTOOcuEtFzdh3Gd1+SNrMQOEGV2wHp1B5OzLDt7qcVkMjvSCswCQBJc3uk2659WEOwAoHfOeXO25Yb5MCJkdkwthZ4diQQ7isDgyjzGM1r9vbU5JGV1NyiPOEeQcTKzY9rMxHKOm+EzIrHALAAkoVjPurzTTeX6Z9jsODgrpyAA9M66zI4OHtQUt4EXs8WGR9JSxe/EoIHXr3jORIJwc+1vUzYotzkkXXU3KI9Ym7s3DnZMzEvTlperYo4rvraDn6EAMLUm9xHUz/qnFQQ7AOidyfJOc+lFMGdFAEdmx5Q0KC/NtWeHMla0wDx6IpVlrDZsUD7xvNPOExqUA0A9NCjvNuufVvDuA6B3ul7GquzZ4dB4OhqUl+bas0ONY1pgHvNrWT7ubGWsIqafJzQoB4B6aFDebRqUt4JgBwC9M3lI1cnMjjncPO60JDI70vi9lNkB65Xza403NovHPmuD8giZHQCQGpd3ui2xi2lszCkIAL3Tm8wOi+zpVNG8d1plg/I0mt7J7ID1upHZUcGt02J+TCQwCwBJcHmn22S2toJgBwC9NNcb63Mms2NGTTbKztK6LV32DphLZoe0f9I3154dtWd2zPA9yOwAgNO5vNNt1j+t4BQEgF4qb6x38FaGBuUzKrIqGunZkdZtofKG+TzGU7zuGjqSsOK9UGdmRzF3D/MNMrwGFQQ7qpjjsrSy0AAgCU3uI6if9U8r2E0C0EuLg8WI6HYZq8VsseGRtFRxgDho4PVL7LZQUQptvmWs/N6SrnmUsSrm7tFGJaKybK2E1KwNymd5rxVfm0hgFgCSMJLZ0WnWP60g2AFAL821PM+cKWM1Iw3KSxqUw3rF/FpnT6Rz9sqZtV60BuUAUI8my+FSv7JBeRp7NTbmFASAXppr4+U506B8Rik0KE/k91KDcliT53nkkUdEvcHkYu4+Y5Bx1nmi0gblacxVAJAEl3e6TYPyVhDsAKCXNCjnjFJoUJ5IHdi5vU/yfO11tzkkUZNBv3n07Kg/s6OKBuXd+wwFgKm5vNNtiV1MY2NOQQDopV5kdjg0nk6TN7ISa1Bevk/qHs/kganNIYmaDPrVmtlxriDjrIGGKuY4NxsB4HQyO7rN+qcVBDsA6KXiMKn2Q9wGDMdZATI7plRkVTSZ2ZFIEK58n9Q9nslMFr+3JGo48Xs6l8yOM30+lSWkpswAq2KOG6SVhQYASSg/Y61nO8n6pxW8+wDopXPWRG8xmR0zKtPPF+f/3IlldsztfTL5/TbxusMmTL4P6uyJdM4gY/EembmM1QzvteJrEwnMAkASynK41rOdpIxnKwh2ANBLc7ux3gDBjhmlUMYqkQX03Mq9TT6+MlYkavJ9kEYZqynel1X1x9GgHABOp4xVtylj1QqCHQD0UnFQ1cXMjrJBufTp6ZQ3shp4/RJbQM+tQfnk92tzSKLWZXbUWcZqUGOD8qr64yQWmAWAJGhQ3m0alLeCUxAAeklmB2eUQmZHInVg55fZoUE56etEZse6wOIM30NigVkASILMjm6z/mkFwQ4AeqkXmR0aPU+nyRtZiTYoH43mmdnh95Y0zSuQfM4gY1lCaor3ZVUl49xsBIDTyezoNuufVrCbBKCX5nZjvQEyO2aURGZHGr+Xc+/ZkQ0isqze54IpFXNr3YHk+WV2zNKzI625CgCSMJpY09I9epa1gncfAL1UlrHq4EJlOC6BJLNjSkUJqUYyO8Y/s0QyjuZW7q14zQXoSFgxt84rs2N4pnJ22Qzl7ia/porMjkRK7gFAEkYyOzotsYtpbMwpCAC9tDDofs+OxcFiwyNpqbJBeROZHeOfWSIL6Lm9T8qNod9Z0lVmzdU8NxSPf+bMjhnmiXX9cWZ4v2lQDgCny61pO634uXbwDKFLBDsA6KVzlglpMT07ZpRCGatEFtBze5+ob0wLzGtu1aAcAFpKg/Jus/5pBacgAPSSnh2cUQoNyhMpDTO390nRaNnvLAnrVIPyWfvjJBaYBYAkuMDTbdY/rSDYAUAvyezgjFLI7EjktlDxOzSa5lB1K8qNod9Z0tWpzI5Z5zc3GwHgdBqUd5v1Tyt49wHQS33I7BDsmFKjmR1pNiivPSgo5Z8WSCezY4aNdlXzm5uNAHC6Jnv/Ub/iYpb1T9KcggDQS8Vh1UoHb2UU35MyVlOS2VEajBf0w7zmslpF2S4bQxJWzK3zyuw44+fTYIZyd8XXzJzZUZTSSqPkHgAkoarPWdIks6MVBDsA6KWyPE8iN+irpIzVjEYNllTK0rotPfcG5TaGJKyYW5PJ7JiqjFVFN07LgEv3PkMBYGqjBjPEqV9iF9PYmFMQAHppYbxQ6XIZq8XBYsMjaamyzEsDr1/xnIksoMsb5vNqUO53loSVZaxqPsAo5u5z9uxotIzV4vrHAwCa3UdQP+ufVhDsAKCXNCjnjJIoY5VGaZjyfaJBOaST2VH2y5jifalBOQDURx+6brP+aQU7SgB6qQ8NyvXsmFKjDcpnOMSswdzeJzaGtEAxt86rZ8cZg/EalANAmsoG5Y5bO0mD8lbw7gOgl+Z2Y70BMjtmkOdrm5RGMjuKpr9pLKDn3rNDfWMSNq+5dfOZHdP07KgqsyOtuQoAkuACT7eVF066d4bQJU5BAOglmR1saPJQv9HMjjR+LwcDmR1QKILj8ypj1Y7MDpt9ACi5wNNtMltbQbADgF6aW+PlBqyMZHZMbbJXRhOv3yyNh2swvwbl49fdxpCEDfPV39N5lbFaOdM8MEtvnzKwOOP3kM0wBgDoquJz0QWebrL+aQWnIAD00sKg+w3KFxwcb93k4eJgcf7PXzxnIkG4+ZWxKuob+50lXcX7YLHmuaGYu2ttUD7r91B8fSKBWQBIQlWfs6TJ+qcVBDsA6CVlrNjQ5O9Dk2WsEllAl++TusejjBUtMK+eHRqUA0BLaVDebRqUt4J3HwC9NLcb6w3QoHwGk4eHGpRrUA4T5hVIbkeD8rQCswCQBBd4uk2D8lZwCgJAL83txnoDZHbMQIPydTQohzUyOyaUNxtt9gGg5AJPt8lsbQXBDgB6SWYHG1qX2aFBucwOWDMazTmz41wNyqfK7Bi/lytrUJ7GXAUASXCBp9usf1rBKQgAvVQcJg3zYcMjqV5xQCazYwqj8e9DNojIsvk/f2qZHfPqbVNuDC1NSde8MzvO+L4rnn80xedX8TVV9eyYZgwA0FVVfc6SJuufVrCjBKCXFgbdzewovieZHVPIG76NVd7YHkXkeTNjmHDOQ9eqjGR2kL4y2FFz09Fi7j7j59NghnrRVc1xiQVmASAJxWe3S2fdZP3TCk5BAOiluR3iNqD4nhYHiw2PpIXKQ/eGXrvJ500gPbosY1V3E7684dcdNqEIPixm9f6eFsH4MzcoHz//LA3KZ32vFV+fwDwFAMlwgafbEis5zMYEOwDopbkd4jZAZscMmu4dMfkzSyAQN/fMDrfgSJgG5RPKBuXNz1MAkIym9xLUazILn2Q5BQGgl+bWi6ABxfekZ8cURg2nnk9ujBK4MVSU69GgHBpoUH7GzI5ZGpRX1B8nm6GUFgB0lQs83aZBeSsIdgDQS+esid5ixYGczI4plIfuDb12kxujBAJx88/s8DtLuuae2XGmQELZoHyazI7xY1bVoDyBeQoAkuECT7dZ/7SCHSUAvXTOmugtNq8DuU5q+jZWapkd8woKVnUACzUq3gfpZHZM8b6sao5zsxEA1stzDcq7zvqnFZyCANBLylixodFw9c/GenZMZnY0n3U0+T7J87y+Jyped7+zJKwMJNec+XXOjKpyoz3c+oNXNccNZhgDAHTR5AG4CzzdZP3TCoIdAPTSYrYYEd0sY1UGOyyyt65MPV9s5vnXZXY0v4gu3icRNb9XRg2/7rAJ8woknzPzsHifzNSgfMb3WvH1HbwwAABTyQU7Ok+D8lYQ7ACgl8ob6x1MQZ1XqZVOarpBeZbNVo+/YpM32GsNdqhvTAvMa24te3ac6T2XRIPyYp6y2QeAiFi/drcP6yZlrFpBsAOAXjrnYVKLFd+Tnh1TaLpBecTaIjqBG9OTh7q1lnxrulcKbEIRHK97bj1nmcVZNtoalANAPWR2dJ/1Tys4BQGglzrds2OkZ8fUUjh0H6RzY2jyUHc+mR2WpqRr3g3KR2fKmijeJzNldmhQDgCVktnRfdY/rWBHCUAvFTXRZXawTgrllHqZ2dFw+TDYhLJBec1z6zkzD8uN9hSfX1XNcW42AsB6k5/bMju6yfqnFZyCANBLnc7s0KB8eklldjQfiJt/ZoffWdJVZnbU/Hta9Mo5c4PyKnp2yOwAgEqty+xw3NpJepa1gncfAL1U3JztWoPyUT6KPPKIkNkxldFw9c9GMzuKRfSwuTGMTWZ2DOscT/HYMjtI2DBf/T2dV2bHuXt2TPGerGqOS6jcHgAkoVzPDiKyrNmxUI/BDGsw5sYpCAC91NUG5ZOHY3p2TKGq5r2zGCyOx9L8IWKWZWv9A+p8rxQHpsX3DgkqemgsZvX+np4z2FG8T6ZqUK6MFQDUIree7byE9mmcWe3vwLuPHIqTiyKa0GcX7DyvLMlAv+R5Hiff+bORnTza9FBOkx/5TEREfOq298cv/sG/aHg01RnG2oH06B0/GycH2xocTftk93w6FiNiJQZx/EQzN3Z2ZQuRRcTwPW+I/LwHNjKGSYM8YhQRv/pnL4s9WT2/T9l9n4vs/H0xOnRLrLz/F2t5DpjV++/4u4iIWBlFHKlxfhiO98+33X9b/NwG74fBnR+KhfP3RX7vhyPf4udXtvyF1ffakc/O9F7Lji/H4vn7IrIsRlsYwzd81QvjkV/5gqmfl/YajUZx6D2/HNn9X2x6KAD1OHFfLGZZ5IOFOHbkUNOjoQaDE8djR5ZFfuSLsfK265oeTquNHvTU2h47y/M8r+OBl5eXY9++fXHV66+KhV1ulkKfvePb3h0X7d7b9DBowJETwzj6Ew+Ni7L0Fns37t4V33fJxU0PozaLeR7v/eyB2N70QFrqr1e+Kr7r5L9v5Ln/z/bvj4cPbmvkuTfyTx58edy7YC0HheN3fmOcuOtZtT3+wq5Px+6H/kptj9+k6y67Jr712T/V9DBowF1HDsUzf+9pTQ8DAEjAn13wgnjw83887rvvvlhaWqr0seVWAVCrN608K3bHsaaHcZqTh0bx9IW74/BiN+tt7j+6O35reFXTw2ilUQziD1ae0djz//DJ/yeuWfjbyKKW+yhb9vW33x+fPu/+2p9nGItx8+ghcUyIjoTlo51x8t6vrvU5Vo4+NI7dcU0Mtt274X9fjJX4quyzsSM7Md3jx0J8fPSQOBI7ZhhlxJdlB+MB2X1b+pqHPO6xMz0nAADtN7rkUbU9du2ZHZ+57Quxt+IIDdAuylj1V57ncfSkepYAQMSubQuRadraS6PRKO45drjpYQAACVg4sRIXXHBBOzM7Lty9N5aUrwHopSzLYvd2SYQAAH02GAyUtQUAIiJiebhc22O7ag0AAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtJpgBwAAAAAA0GqLdT1wnucREbG8vFzXUwAAAAAAAC1RxAuK+EGVagt2HDp0KCIi9u/fX9dTAAAAAAAALXPo0KHYt29fpY+Z5XWEUCJiNBrFLbfcEo985CPjwIEDsbS0VMfTAIlbXl6O/fv3mwegx8wDgHkAMA8A5gGgmAduvvnmuPLKK2MwqLbLRm2ZHYPBIC6//PKIiFhaWjKJQc+ZBwDzAGAeAMwDgHkAuPzyyysPdERoUA4AAAAAALScYAcAAAAAANBqtQY7duzYEdddd13s2LGjzqcBEmYeAMwDgHkAMA8A5gGg7nmgtgblAAAAAAAA86CMFQAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtNpiXQ88Go3i4MGDsXfv3siyrK6nAQAAAAAAWiDP8zh06FBcdtllMRhUm4tRW7Dj4MGDsX///roeHgAAAAAAaKEDBw7EFVdcUelj1hbs2Lt3b0SsDnppaamupwEAAAAAAFpgeXk59u/fX8YPqlRbsKMoXbW0tCTYAQAAAAAARETU0vpCg3IAAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUWa3+GE4cjTizU/jQAJGzb7ogsa3oUNOWez0WsnGx6FJt2cnQybj1yZ9PDiKVte+LCHfuaHgYwdnI0jFuP3DG357tgx77Yt23P6v+58GERgwr3VCcORyzfNuXAHhKxsK26sQBAF933hYiTx5oeBVXLsogLHhYxkD+QqvqDHT97ZcQOB1wAvfYjByO2n9f0KGjKb78w4s6PNT2KTXvRZZfGx3dsb3oYMcjzeONtd8YTjx9veihARLz4skviYzt2zO35to/y+KNbD8YVw5WIL3tWxL/8w2oe+MSRiF94bMThL0739Zc9PuJfvaOasQBAF/3N6yPe+kNNj4K6POY7Iv75/2h6FJxB/cEOAKDfduyJaEmGQh5RBjrOG+WN1fs8mkUMsyz+YfdSPDFONDQKYNLHt89vbjicRZwYZPHpXUtxxaF7Im77cHUPfui2tUDHVubmfBRx4lC1YwGALio+Kxd2RCzubHYsVGc0jDh52FoocfUHO77/loilpdqfBoCEbdvd9Aho0vf8edMj2LTRaCXiNx4XERFvfeFfx/k7z29kHD/wzh+It372rTG6+j9FXPWiRsYArMnzPEa//piIiPjf3/HOuGjXRbU+33f+7++Mj3zpIzF6zmsjfu/lEflKdQ+ej1b/3Lkv4oc+v/mvu/+LET/z5atfn+fKUwLAmYzGn9v/9DURT3tFs2OhOp/964gbnlvtuozK1R/s2H6e0iUAQCuMikPAiBg0WId1kK0+98rIQhpSMDk3LGT19yMs54DIxwMYneVvb1Exr2z1+5jsGZKPtv71ANAXxWF4lf22aF6x9rFHS5puKgAAYysTt3TmcaB5JsVzTx6wAs2ZdyC0nAOKf1FpZseUBzDZxPdtkw8AZzbtxQLSVqydZHYkTbADAGBs3YFmlkBmh4U0JGGYD8t/biazo8K5oJLMDnMTAJxRebHAsWunlJkdLqSlzLsOAGBs3geaZ7IwPlQU7IA0zLuMVfEc5QwwGp7x725Z8VhbzuyY+PtVjgcAukZmRzcVwSvroKQJdgAAjI1G8z3QPJPyoFOwA5Iw7xJ3RcBzFOMm4HU0KN9qsGMw0e5RGSsAOLPic3JQf6tk5qj4edqjJU2wAwBgbPJAM4UyVnp2QBomA6HzmBtOK2O1OohqHryqBuUAwMY0KO8mDcpbQbADAGCsCC4MskFkWdbYOMrMDgtpSMK8A6FrDcongh1V3SLUoBwA6qWMVTdpUN4Kgh0AAGPFgWaTWR2Tzy+zA9Iw70DoWmbH5CAq2lhPewCTZWsBD5t8ADgzmR3dpEF5Kwh2AACMFQeaTfbrmHx+wQ5Iw7wDoWsNyifmgKYzOyKUbwCAzSgOwxu+QEXFBi59tIF3HQDAWFE2qvHMjvFCepgPGx0HsKoIdixm82k0WmZ2TFSxilFF80HxONMEdYsASVVjAYAuKj4nZXZ0S2Yd1AaCHQAAY/M+0DyT4vlldkAaigblc8vsGBQ9OyYHUVUZq/GjTnMAMxjPjW40AsCZlVmUze4pqFjx85ThmjTBDgCAsbIu/yCNnh0alEMaikDovErcbdygvKLgZyVlrARiAeCMNCjvJg3KW0GwAwBgbN4HmmeiZwekZd6B0LUG5aOIGDdEb7pBeYRa1QCwGRqUd1OxdspHEXl+9r9LYwQ7AADGygPNpnt2lPX6HShCChrL7MhH1d8i1KAcAOqlQXk3Ta6dXEpLlncdAMBYcaDZdLCjrNdvEQ1JmPfcUDzPcDSsPsAwU2aH8g0AcE4yO7ppch3o4keyBDsAAMaK4ELTZaxkdkBaupXZUTQon2IrKLMDAM5Nz45uWpfZYS2UKsEOAICx4WgYEQlkdow3RoIdkIbRaL6B0HUBz8ozO1bnuZkyOwQ7AODMis9amR3dMrl2Kn7GJEewAwBgrMjsWBwsNjqO8lb3SBkrSMG8y1gVc9C6zI6qy1hNM88pYwUA55bP8FlLuiZ/ni5+JEuwAwBgLJWeHcpYQVrKEndzuqG5bg7QoBwA2qVsUC6zo1M0KG8FwQ4AgLFUenasq9cPNG7egdB12V0alANAu5QXCxy7dooG5a3gXQcAMJZMZsdAZgekZN4NyueT2aFBOQDUQoPybsqytYCHfVqyBDsAAMZkdgAb6VaD8hlKa8jsAIBzm6VkJGlz8SN5gh0AAGMro0QyO8bPP8yHjY4DWDX3MlaDiYBnkYFRWbBjPK9M1bOj4rEAQBcVn7UyO7qnWD+N7NNSJdgBADBWlqpp+BbWunr9QOPmnfVVPM9qGavF1X9ZeRmrxa1/bfE1gh0AcGbFGn6az1rSVvW6jMoJdgAAjCljBWykzOyYU6PRdXOABuUA0C4alHdXuS6zT0uVdx0AwJgG5cBG5h0ILXt2jDQoB4DW0aC8uwYalKdOsAMAYExmB7CRon/O3Hp2TJax0qAcANpFg/LucvEjeYIdAABjyWR2ZDI7ICVF/5zFbD61t9fNAVXfIJzlAEaDcgA4N5kd3eXiR/IEOwAAxmR2ABuZdyB0YbBRz46K5oNKenaYmwBgQ3kus6PLZHYkT7ADAGBsZZRWZsdwNGx0HMCqIvA4rwblxRwwykdrByVVzQfF40yV2VHxWACgayYvBMjs6J6BYEfqBDsAAMaK29sLDd/CKkrlyOyANMw766vs2TFaiRiMS2dVXsZqipJcxdfY4APAxiY/I2V2dI8yVskT7AAAGEuljFVxe1zPDkjD3MtYZRuVsaqqQXlRxmqK78UGHwDOLhfs6DRlrJIn2AEAMJZKg3I9OyAtRYm7eQVCy1J2+bD6AEMxr2hQDgDVm/yMVMaqe1z8SJ5gBwDAWDKZHZP1+oHGlSXu5lXGal2D8iLAoEE5ACRPZke3yexInmAHAMBYKg3Ky3r9bgxBEuYdCC3moJV8pYbMjqJnxywNys1NALAhmR3dJrMjeYIdAABj8769fSblQacDRUhCWeJu0GTPjmE1D148ziyZHVWNBQC6RoPyblPSM3mCHQAAY2Wwo+GNSfH8MjsgDfPO7Cizu0YrEYPF8SAqblA+zTxXjMXcBAAbKz4js0FEljU7FqpX9bqMygl2AACMpdKzQ4NySEuZ2TGnEnfr5oCkGpQrYwUAZzVLbyzSp4xV8gQ7AADG5n2geSbr6vUDjStKyjXSs6PqcgkzNSgfj8XcBAAbm6U3Fulz8SN5gh0AAGNFJkXTwQ6ZHZCWuZexmixlV94grGg+qKRBubkJADYks6PbZHYkT7ADAGBs3re3z0RmB6Rl3llfxfOsb1CeQmaHDT4AnNUs5SJJnwblyRPsAAAYk9kBbKTM7JjTwcW6gGflPTuqyOywwQeADY0mGpTTPVVn3FI57zwAgLHi9nYymR0OFCEJjTUoH01mdgyrefBZDmEGFY8FALqm+IyU2dFNVa/LqJxgBwDAWBnsaHhzsjhYjAhlrCAVc+/ZkW3Qs6PqMlbjeWZLlLECgLPLZ/icJX3Fz9WltGQJdgAAjM37QPNM1tXrBxo3HN/em1tmhwblANBOGpR3m4sfyRPsAAAYK8pGNd2zQ4NySMu8A6EalANAS81yqYD0aVCePMEOAICxVDI7Jp9fdgc0b94l7jYsY6VBOQCkr8h+1KC8mzQoT553HgDA2LybEJ/J5PPL7oDmFUHHec0NZWbHqM7MjhkalJuXAGBjMju6zcWP5Al2AACMpdKgfDKzY8VCGhqXRoPyYTUPPqois6OisQBA1xSfkXp2dFPV6zIqJ9gBADCWTBmrgTJWkJJ5Z30Vc9AoH9XYoHxx619bbvDNSwCwodEMn7Okr/i5ynJNlmAHAMBYKmWs1mV2WEhD44oMq7k1KB+szkHDfKhBOQC0iTJW3aaMVfIEOwAAxkajNDI7JoMtMjugeWWJuzmXsVqf2aFBOQAkT4PybhtfSHHxI13eeQAAYzI7gI2UJe7mdEuzbFCe19mgXGYHAFROZke3ZUp6pk6wAwBgLJWeHVmWRRZZRMjsgBQ01bNjZbRSQ2bHeE4ZTPG9yOwAgLOb5VIB6XPxI3mCHQAAY+WB5jSHgBVbd9gJNGregdD1mR3j+SiJzA6lGwDgrGR2dJuLH8lrficPAJCIedflP5visFMZK2heY5kd+URmR2XBjuHqn3p2AED1is/ZBPYT1KBclw2bHQdnJNgBADCWUrCj6A0g2AHNKzKs5tagfPz+zyOPUVZXg/LFrX9t8TWCHQCwsZHMjk5Txip5gh0AAGOjcaO5phuUR6wdqurZAc2bd4PyyaDKSpaNB5FCGSsbfAA4q7I3lmBHJ2lQnrzmd/IAAIlIpUF5hDJWkJJ5l7GafJ5RVnGfDA3KAaA+GpR3m4sfyRPsAAAYS7FB+citIWjcvAOhMjsAoKU0KO82Fz+S1/xOHgAgETI7gI00m9kxDnZUVdJulkOYYlyCsACwMZkd3ebiR/IEOwAAxob5MCLS6tkh2AHNazazowgwDKt58OJxZsnsqGosANA1xWekzI5uyqyFUtf8Th4AIBFFyagkMjvGpbQ0KIfmrYyay+yorYzVVJkdbjMCwFkVa/cELk9Rg4Es19R55wEAjBVZFCkEO2R2QDqK9+HiYHEuzzefBuVTfC/F16hTDQAbKy8VzGfNwJwVP1d7tGQJdgAAjKXUs6NsUC6zAxpXvA/nldmRZdlawDPqalA+xfeiTjUAnJ0G5d2mQXnyBDsAAMbKJsSD5pdIZYNyC2lo3LwblE8+16h4Sg3KASB9GpR3m4sfyWt+Jw8AkAiZHcBGmpgb1jI7igBD1ZkdMzQot8EHgI3J7Og2mR3Jq72A3N1HDsXJxazupwEgYRfsPC+Jm/LMX57nsXzsWOSRNz2UTTk5GkZExIlhHkdODBsdSzY+4Lzv+OG49+iRRscCfXdyZTj+c35zQ5HZsTw8ESciYmXleBw/dM/Mj7srXy2MdeTE8Ygtzi2DkydjZ0SMhsfjWAVjoX+ybBBL5y1Fljkj6KPR8GTcc+S+pocBtVo8en9sy7JYGY3ixJFDTQ+Hii0OT67+fE8ejRPLX2p6OK11aLm+90aW53ktpw/Ly8uxb9++uOr1V8XCLtFMgD57x7e9Oy7avbfpYdCAIyeG8aTrr4mFnbc3PZQtOfzZl8fo6EMaHcPuh/1CLOy8rdExAOsd+ey/jpWjD53Lc+35iv8Y2cKxuTwXzMsjjg7iN//vD8Tu7Rr39tG97/+1+Ccf+5mmhwFAw1aOrsTHX/7xuO+++2JpaanSx3bNFgBgwujk3hgdf2DTw4iVw49oegjAhNFwb6zMcW4YmgMAAGBLas/s+MxtX4i9FUdoAGgXZaz6K8/z+NKR5chb1Hdi5+KuWBykceP08MnDrXrtoMvmPTfkeR6HT96/+n9WhhHDo9U9+Pbz1pqNb9VoJeKk0npMZ7CwGBfte5AyVj01Onks7jl0V9PDgPotLEZs2930KKjL8Pjq/5jaoeVD8bCHXVVLZkftq/ULd++NJaVLAKCXsiyLi8/b1/QwWmv3dq8d9Nl5Oy5oeggAlRls2xkXXXh508MAmJFz7lltW1yu7bFdswUAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaLXFuh44z/OIiFheXq7rKQAAAAAAgJYo4gVF/KBKtQU77rrrroiI2L9/f11PAQAAAAAAtMxdd90V+/btq/Qxawt2XHjhhRER8fnPf77yQQPtsby8HPv3748DBw7E0tJS08MBGmAeAMwDgHkAMA8AERH33XdfPPjBDy7jB1WqLdgxGKy2A9m3b58JDIilpSVzAfSceQAwDwDmAcA8AESsxQ8qfczKHxEAAAAAAGCOBDsAAAAAAIBWqy3YsWPHjrjuuutix44ddT0F0ALmAsA8AJgHAPMAYB4AIuqdC7I8z/PKHxUAAAAAAGBOlLECAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWW6zrgUejURw8eDD27t0bWZbV9TQAAAAAAEAL5Hkehw4dissuuywGg2pzMWoLdhw8eDD2799f18MDAAAAAAAtdODAgbjiiisqfczagh179+6NiNVBLy0t1fU0AAAAAABACywvL8f+/fvL+EGVagt2FKWrlpaWBDsAAAAAAICIiFpaX2hQDgAAAAAAtJpgBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaou1P8P/elnE7u21Pw101gO+ImLbrojbPtz0SPppcWfE17464oFXRXzk9yM+/idNj4iqZFnEY74j4srnND2S+fnHGyM+9P9F5Pn8n/sbXxux7/L5Py9p+F8vixgea3oUMJM/Ht4V7xzdN78n3LY74oFfFRF5PGf53nj2yraIp74i4kGP2fJD/eM9/xjXf/T6OL5yfOZhZVkWz3v48+IZVzxj5scCgM7465+POPjBpkdB3a76ZxGP/tamR8FZ1B/suOV/R+zIan8agNosbIt4/i9FvOUHI47c1fRoqNLtH+1XsOPG10TceXMzz/3MH27meUnDJ/404sT9TY8CZvLjD7kijg7mmBi/cm/E5w9GRMSHhsN49oGDEfko4lv+55Yf6jdu/o34k09Xd2Hjk/d8UrADAArLByP+4rqmR8E8fPavBDsSV3+w49k/HrFnV+1PA510009EHL1n9Z8Xd0Zc/ePNjqdvPvOXER//44iTR1f//4kjq38+80cidl/Y3LiY3b2fj3j3L679bPvixOHVP5/6iogLHjrf597zwPk+H2m5+scjRsOmRwEzOfrxX4yIiH978VPjvEHNmeuffkfE0Xvirsd+e/yPW/8ijmXjy2NTfm4dHa5+3bMf8uz46ku/euphHbz/YNzwsRvi2IpMLQAoFZ/PCzsivvEnmh0L9Thyd8Q7Xtu/M4QWqj/Y8aTvjlhaqv1poJPe80trwY5tuyKe/NJmx9M3+Wg12JGvjP//+M/HfWfE+fubGxezO/ih1WBH8TPti3y0+udX/fOIK57Y7Fjolye9pOkRwExG+ShiHOz4lq//L3HBzgvqfcJf/acRdx6Iz1702Pgft/5FjIpgx2i6z62V8efdky55UrzwK1849bA+9qWPxQ0fu6F8PAAg1j6ft+10btNV9x5YDXZMuRZjfjQoh5QNFjf+Z+YjG0+RxYdZ8aefRfsNFlb/7NtCpfwdXmh2HAAtM3m4P8jmsIUarzUWxi2WymefMsgwGge7F2dcwxTf+2g0mulxAKBTcmcFnVfsoV34SJ5gB6QsW9j4n5mP8sNsvKHPHRR3RtbThYrfYYCpFMGCiIiFeazJxvP0QqxGO8pnnzGzY9ZATfH1MjsAYELx+ezcpruynl6YbCHBDkjZ5IGkw8n5m/wwm7zBaAHTfn3P7PA7DLAlK6M5Z3aMn2OQrwY7VooyVjNmdswaqCm+fjL4AwC951JZ95U/2zxivD4jTYIdkDKZHc2aTFOcPFwYmDpbLzsla6cvLMIBprIus2Mec2iqmR0DmR0AcBqXyrpvcg3Vt0uTLePEDlI2eajugH3+ysyO4foPMwuY9huc0o+lLyzCAaYy954d43l6MM4sHWXZathjyiB90WNj1rEXmR2CHQAwofh8dm7TXZOXXUbD5sbBOXkXQspkdjSraC42Wln/YeZWfPuVP9ueLVLKBuU+/gG2YvJwf649OybKJKxETP25VYxfGSsAqEHx+ezcprsmm8+79JE0px2QssnJdPKfmY/iQDgfnVLGys+i9XrfoNzvMMBWFIf7WWTzyewYz9NFGauIcSmrGctYzVqCq8zs6FtmJACczcg+q/MmA1nWQUkT7ICUaVDerHUNypWx6hQNypsdB0DLFIf7c8nqiCjrQi9MZFCsZFnjDcqLQI/MDgCYoDdi903+bPt2abJlBDsgZcpYNWtdg/LR6f+e9irfT3nERImQzrMIB5hKcbg/l6yOiHKeHkx8RFWR2TFzz46Bnh0AcBqXyrpvXWaHSx8pE+yAlGlQ3qwNMzuyiCxrbEhUZNDDFNQ8XwvaWYQDbElVZaA2LSt6dkxmdsTMDcqryuzII5fdAQCFXG/Ezpv82br0kTTvQkiZzI5mlaWOhm7Ed03Ww4XKZFDH7zHAllSVGbFpRWbHxNw9iqz5zI6J9ajsDgAYG7lU1gvZxBkRyRLsgJTp2dGsyTJW0lK7ZbJxXG8yOyb7zvj4B9iKMrNjbj07ijJWE5kdEY337Jj8epkdADDmcmQ/FOcIfTlDaCmnHZAymR3NKqP2o7XIvcVLN6wrY9WTWxkyOwCmVlUZqE0bz9NZPopBrJbPXMmyqT+zysyOGctrTGaGrNjoA8Cq4vPZuU23TV6IJVmCHZCyydvnk//MfGzUoNzPoRsmF6F9WahMfp9+jwG2pKkyVpGvxMI42FFFg/KZMzsGMjsA4DTF57N9VrdN9nUlWYIdkDINypu1UYNy5X+6YV1mR08OayYXZG4cAWxJVWWgNm0iu3QhKzI7YvoG5RWNf11mR18uCwDAuShj1Q/FuZwLH0lzagcpU8aqWesyOyxeOqWPDconF2R+jwG2pAgWzFoGatMm1iCDMrNjhgblo+oblMvsAICxskG5Y9ZOk9nRCt6FkDINyptVfpANNSjvmixbW4j2ZaEyWefdIhxgS4b56hw6/8yOYVnGKoUG5TI7AGADLkf2w2DijIhkOe2AlMnsaNZgrYSExUsHFfVU+3JYM1mKbVwSBYDNmXsZq8HazcFiwzbKYuaeHVVkpixmq5+fGpQDwJjLkf3QtzOElhLsgJTJ7GjWZBmrInJv8dIdWc9uZeQW4ADTqqoM1KYVzzPRoHwY2dSfWVU1KI9Yew2UsQKAseLz2blNtylj1QqCHZAywY5mrWtQPt7Q+zl0x6BnC5Xi+yxuowCwafPP7BjP1RMNykcJNCiPiFgYf34qYwUAY7m9Vi9oUN4Kgh2QMmWsmqVBebcV76m+LFT8DgNMrcoyUJuyrkH5eAwRM5exktkBADWYLBlMd8nsaAXvQkiZzI5mrcvsUAKocwZ9a1A+PpTyOwywZXPP7JhYgxRlrEaRTd+gfPwZUEUZruIxZHYAwFiuEkQvTF6IJVmCHZAymR3Nmixz5FZ892Q9W6iUdWR99ANsVZnZMa8bm+UaZLiW2VFBg/JKyliNH0ODcgAYczmyH/rW97OlnHhAymR2NGtdg3KLl84p66H35LBGg3KAqRUH+4vZnGpxTwTkK8nsGN84raIMVxns6MtlAQA4F5cj+6FvZwgtJdgBKZPZ0axso8wO02Zn9C0FdWQBDjCtMlgwt8yOotTiaH1mRz6KyPMtP1wdmR16dgDAmMuR/VA2KO/JGUJLObWDlE0erDtknz+ZHd3Wt+ZiMjsApjb3MlbrMjvGYyj+2xSfW1WOv8gOEewAgDElg/uhPEOwBkqZdyGkbLC48T8zHxs1KPdz6I6+Nij3OwywZWWD8nllx02USVgYJ3KMstVyVlu9TZjneaUN1pWxAoBT5PZavdC36hAtJdgBKVPGqlnlgUY+cVPDz6Ez+tagXCk2gKk11qA8X1krY1X8ty0G6SczMKoIdhSvgcwOABhTCaIf+lYdoqWceEDKNChv1uSBxsqJ8b/zc+iMQc8WKkXAzu8wwJZV2fNiU8rN9LAsY1WGFrYYpJ8MSlTZoHxYfK4AQN9pUN4Pg7X1GekS7ICUyexo1mQK6srJ8b/zc+iM4ufbl8wODcoBprYymnOwY6LU4lqD8qz8d1sxWW5Kg3IAqIHMjn7o2xlCSwl2QMo0KG/W5KHwyvHVP+dVvoL69S0FVYNygKlV2fNiU8pSi6O1nh3Ff9tikGFdZkeFDcr17ACAMZkd/VCsozQoT5pTO0iZzI5mTb7mMju6p3cNyi3AAabVWM+OicyOYZnZsbXSCcN87e/L7ACAGpSZHY5ZO02D8lbwLoSUTZZRmvxn5mNdZse4Z4efQ3f0rkH5+FBKsANgy8rMjnnNoRNlEhZjNbWjDC1stUH5qJ4G5TI7AGCsvFjmvKDT+lYdoqUEOyBlGpQ3a/JAYHj89H9Hu/WuQbkyVgDTmntmx8RmejAuY1V+Wm0xyDAZlKhi/DI7AOAUylj1g8yOVhDsgJQpY9WsyT4pylh1T98yO4qyJ36HAbasaFA+/zJWw3LDNpqyQXkRlBhkg8iKx5hlaEVmR18uCwDAubhY1g/Z2vqMdAl2QMrWZXZ4uzaiSEMtylipwdkdxc+2L4c1GpQDTK0IGCxmcypPUaw38lEs5KeUsZoys6OqQE2R2aGMFQCMyezoh75Vh2gpp3aQsslNqQPKZhSve9mzw8+hMwZrB0m9oEE5wNRSaFC+UiRljLb2uVX2G6loLVn0LVHGCgDGNCjvh7KMlTVQyrwLIWV6djRvcEqwQ9CpO/rWXKxYkFmAA2zZ3BuUT5RaXMvsyMp/txVVB2o0KAeAUxR7Lec23da3M4SWcuIBKdOzo3kyO7pr0LN6mzI7AKbWbGbHarCj/LTa4udW0VujsswODcoBYL3is9m5TbdpUN4Kgh2QssHixv/M/BSljoZFsMPPoTP61qC8rCPrdxhgq6ouBXVOxVydr8RikdkxY4PyqrJSZHYAwClG9lq9ILOjFQQ7IGXKWDXv1MwOJYC6o2/Nxdw2ApjacDyHzi2zo3ie0UoMxsGO8tNqyjJWVWd2rPTl8xMAzkWD8n4oLsP2pTpESzm1g5Sta1Du7dqI4maGMlbd07cUVGWsAKY2/8yOtYD8QhSZHcVgpmtQXlWgpgx29OXzEwDORYPyfigzb5XyTJl3IaRMZkfzygblJ1f/dCu+O8oU1J4sVHILcIBpzT3YMVFqcS2zI40G5UU5LD07AGBMg/J+UMaqFZx4QMo0KG9eWcbq+OqfFi/d0bvMDgtwgGmVAYPB/BuULxQ9O4r/Nm3PjorWknp2AMApyswOe61O69sZQksJdkDKZHY0rzjUKHt2+Dl0Rnkroyf1NnMLcIBpJZHZUZax2trnVtX9RorHkdkBAGPFZ7Nzm26T2dEKgh2QsqIe4Kn/zPwUH2ZDPTs6p3cNyoueHeYSgK2quhTUORVz9WhUZnZMW8aqCEosVjT/FwEfwQ4AGMvttXpBZkcrCHZAytaVsfJ2bUTZs0Owo3Oyni1U3DYCmNrKOGA8vwbl43XfaBgL46DCWoPyZnt2FI8z7EtmJACcizJW/VCspfpyYbKlnJ5CyibrQjugbEZxM0OD8u4pMzt6cjNVGSuAqRUBg2bLWBX/bmufW1WX4CoyRGR2AMBY2aDcMWunlZm3gh0p8y6ElGlQ3rxMZkdn9S0FdWQBDjCtMmAwr3XAZIPyGDcon3KDXVdmhwblADAms6Mf+naG0FJOPCBlGpQ3T4Py7upbczGZHQBTm3vPjsnMjnGweqVYk0zZs6OqzA49OwDgFGXPDnutTuvbGUJLCXZAymR2NE9mR3f17VbGyAIcYFpVBwzOaaLUYpnZMeUGuxi7zA4AqInMjn7o2xlCSwl2QMpkdjSveN2Hx1f/1Ci+O8pDo540WJXZATC1+Wd2rGVxFJkdw8F0n1tFI3GZHQBQk+Kz2blNt2lQ3gpO7SBlgh3NKzM7xg3Ki3rZtN+gZwuVcgHudxhgq1ZGcw52lP05hrEwDiqMygDIlA3KK1pLlpkdffn8BIBzUcaqHwbKWLWBYAekTBmr5hWHDcpYdU/xs+3LzdSyjJWPfoCtKgIGi9mcAsaTDcqLYMeUQfqqs1KKoIkyVgAwNs7CdG7TceUZgjVQypx4QMpkdjRvcErPDouX7uhbc7HcAhxgWmXAYF4B48kG5flqz46VTINyAEiSzI5+6NsZQksJdkDKZHY079SDBYuX7uhbczENygGm1liD8nyiQXkimR0alAPAKTQo74e+nSG0lGAHpExmR/NOfd01KO+Ovt3K0KAcYGrzb1C+NlcP8vEYZHYAQJpcjuwHDcpbwakdpExmR/NOfd0tXrqjvCE7bHYc8yKzA2Bq88/sWNumFc9YBju2+LlVdXN1mR0AcIris9m5TbdNZN6SLsEOSJnMjuad+roP5tSYlPplPVuoFAtwcwnAllUdMDinifXGQtmzI1v9F6OtfW4VQYmFiub/IuCz4lYjAKwqPpvttbqtrA7RkwuTLSXYASkT7GjeqcENNzW6o/jZ9uWwRoNygKlVHTA4p4m5uvinUSplrMavgcwOABhTxqof+naG0FKCHZAyZayad+oNTouX7uhbczFlrACm1liD8ogYxKmZHc02KNezAwBOoUF5P/TtDKGlBDsgZTI7mndag3I/h87QoByATWqyQfnCuEH5qAh2NJzZoWcHAJxCZkc/9O0MoaUEOyBlMjuad1qDctNmZ/TtVobMDoCpNZvZsar8tNpqZkdNDcpldgDAmMyOfijOg6yBkubUDlI2eShZ3OZjvmR2dFdx6NOXWxkyOwCmNv/MjiwiVtd+g3GD8nJb3XTPDmWsAGC9XIPyXpDZ0QqCHZCyyQ31eKPLnJ2W2WHx0hmDni1UyswOH/0AW1VkR8wtsyOi/JwqnnHazI5hPlx9uIozO4ajYSWPBwCtV3wmzutSBM0ozxCsgVLmXQgpGyw2PQJODW74mXRH8bPsXRkrv8MAW1VkdizM89LDeL5eGF94mTbYUWRgLFY0/xePI7MDAMbstfqhb2cILVX7u/DuI4fi5KLyOzCVk0dj17h81bFjhyM/cqjhAfXPtlEeixMlxI6fOBEjP4dOWBieiO1ZFqPhiTjeg5/pjuGJGGRZnBiejJUGvt8Ldp4XA1klvZTneRw9aUNAuw3HhxgnhxFHTsznNt+ubCGymMzsyMdjOBkntzCGY8OTERExyqsZ+3D8dj6xMpzbawF0y65tC5Ep09xLo9Eo7jl2uOlhVG5njCLLsjh2/Khzmw4bnDgRO7IsRisne3GGUKdDNb5+WZ7XUxtneXk59u3bF1e9/qpY2KXsCwD02Tu+7d1x0e69TQ+DBhw5MYzH/bfviywT8KC9tp3/vsgWjsaRz74sVo4+bC7P+fc7vieWsqPx9t274t9ecnGcN1yMb7v/7rgzzo/b8gs3/Ti37TwSt+86Eo++98L4ui9ePvO4PrH3nviLS78Qe09uiy+/f9/Mjwf0y7GjD4kbvv9/xu7tbsD30T03/1k8433/b9PDABq2cnQlPv7yj8d9990XS0tLlT62TxcAAGq1/YJ3RbZwoulhwMzy0c65PddynBdLcTT2jlbLRR1eHMYN5y/FaqvyL2358Z4Sn4mXLX5o5nG9I3bFX8TFcWjbyfjgBVsfB9Bvj1k40vQQaNDgtg82PQSg42oPdvzZC26MvRVHaKBPBp++KbLhsVj5imuaHko/3f/FWPzwb0Y2PBL50hUxfNy/jJBy3Q0rJ2Lxg78W2eE7mx7J3OS7L47h4/+viMUdc3/uC3aeN/fnJA27ti3E//2YfxknV042PRSYyRV798cLvuOfz630yuDzN8TJT74tHp3n8e/27IrbB3kM7vhIxBTvpV3ZQnzrAx4aJxdmD9Y8OR/F9x35THxpdGzmxwL658oHPzp2bVP9o6/2PuRp8a4T3Sz/M7r08bFy5Tc1PQzqlOex+KFfj2z5C02PpPWWjxyP/fHxWh679jJWdaSjAAAAAAAA7VJn3ECnUAAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWm2xrgfO8zwiIpaXl+t6CgAAAAAAoCWKeEERP6hSbcGOu+66KyIi9u/fX9dTAAAAAAAALXPo0KHYt29fpY9ZW7DjwgsvjIiIz3/+85UPGmiH5eXl2L9/fxw4cCCWlpaaHg7QEHMBYB4AzAOAeQAo5oGbb745Lrvsssofv7Zgx2Cw2g5k3759JjDouaWlJfMAYC4AzAOAeQAwDwBx+eWXl/GDKmlQDgAAAAAAtJpgBwAAAAAA0Gq1BTt27NgR1113XezYsaOupwASZx4AIswFgHkAMA8A5gGg/nkgy/M8r+WRAQAAAAAA5kAZKwAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGi1LQU7fvInfzK++qu/Ovbu3RsPfOAD4wUveEHccsst6/7Or/zKr8Qzn/nMWFpaiizL4t577z3tce6+++540YteFEtLS3H++efH93zP98T9998/0zcCzEdV88BDH/rQyLJs3f9e97rXzem7AGZxrnng7rvvjle+8pVx5ZVXxq5du+LBD35wvOpVr4r77rtv3eN8/vOfj+c+97mxe/fueOADHxg/8AM/EMPhcN7fDjClquaCU9cDWZbF7/zO78z72wGmsJm9wcte9rJ4+MMfHrt27YqLL744nv/858cnPvGJdX/HmgDaq6p5wHoA2msz80Ahz/O45pprIsuy+MM//MN1/62K9cCWgh3vfOc749prr42/+Zu/iRtvvDFOnjwZV199dRw+fLj8O0eOHInnPOc58SM/8iNnfJwXvehF8bGPfSxuvPHG+NM//dP4y7/8y/hX/+pfbWngQDOqmgciIv7zf/7Pcdttt5X/e+UrX1n38IEKnGseOHjwYBw8eDB+5md+Jj760Y/GDTfcEG9961vje77ne8rHWFlZiec+97lx4sSJePe73x2/9mu/FjfccEO85jWvaerbAraoirmgcP31169bE7zgBS+Y83cDTGMze4MnPvGJcf3118fHP/7xeNvb3hZ5nsfVV18dKysrEWFNAG1XxTxQsB6AdtrMPFD4+Z//+ciy7LR/X9l6IJ/BnXfemUdE/s53vvO0/3bTTTflEZHfc8896/79zTffnEdE/r73va/8d295y1vyLMvyW2+9dZbhAA2YZh7I8zx/yEMekv/cz/1c/QMEane2eaDwu7/7u/n27dvzkydP5nme53/2Z3+WDwaD/Pbbby//zutf//p8aWkpP378eO1jBqo3zVyQ53keEfmb3/zmOYwQqNtm5oEPf/jDeUTkn/zkJ/M8tyaArplmHshz6wHokjPNAx/84Afzyy+/PL/ttttOe89XtR6YqWdHkYJ+4YUXbvpr3vOe98T5558fT3rSk8p/9w3f8A0xGAzive997yzDARowzTxQeN3rXhcXXXRRPP7xj4+f/umflqoOLbWZeeC+++6LpaWlWFxcjIjV9cCjH/3ouOSSS8q/843f+I2xvLwcH/vYx+odMFCLaeaCwrXXXhsPeMAD4slPfnK88Y1vjDzPax0rUI9zzQOHDx+O66+/Ph72sIfF/v37I8KaALpmmnmgYD0A3bDRPHDkyJH4zu/8zvjv//2/x6WXXnra11S1Hlg891/Z2Gg0ile/+tXx9Kc/PR71qEdt+utuv/32eOADH7h+EIuLceGFF8btt98+7XCABkw7D0REvOpVr4onPOEJceGFF8a73/3u+OEf/uG47bbb4r/+1/9a02iBOmxmHvjSl74UP/ZjP7auZOXtt9++bhETEeX/tx6A9pl2LohYLWv59V//9bF79+748z//8/g3/+bfxP333x+vetWr5jF0oCJnmwd++Zd/OX7wB38wDh8+HFdeeWXceOONsX379oiwJoAumXYeiLAegK440zzwvd/7vfG0pz0tnv/852/4dVWtB6YOdlx77bXx0Y9+NP76r/962ocAWm6WeeD7vu/7yn9+zGMeE9u3b4+Xvexl8ZM/+ZOxY8eOKocJ1Ohc88Dy8nI897nPjUc+8pHxH//jf5zv4IC5mWUu+A//4T+U//z4xz8+Dh8+HD/90z/tcANa5mzzwIte9KJ49rOfHbfddlv8zM/8THz7t397vOtd74qdO3c2MFKgLrPMA9YD0A0bzQN//Md/HG9/+9vjgx/8YO3PP1UZq1e84hXxp3/6p3HTTTfFFVdcsaWvvfTSS+POO+9c9++Gw2HcfffdG6awAGmaZR7YyFOe8pQYDofx2c9+dvbBAXNxrnng0KFD8ZznPCf27t0bb37zm2Pbtm3lf7v00kvjjjvuWPf3i/9vPQDtMstcsJGnPOUp8YUvfCGOHz9e15CBip1rHti3b1884hGPiGc84xnx+7//+/GJT3wi3vzmN0eENQF0xSzzwEasB6B9zjQPvP3tb49PfepTcf7558fi4mJZ0vZbvuVb4pnPfGZEVLce2FKwI8/zeMUrXhFvfvOb4+1vf3s87GEP28qXR0TEU5/61Lj33nvjAx/4QPnv3v72t8doNIqnPOUpW348YL6qmAc28qEPfSgGg8FpZe6A9GxmHlheXo6rr746tm/fHn/8x3982s3Npz71qfGRj3xk3QWIG2+8MZaWluKRj3xk7d8DMLsq5oKNfOhDH4oLLrhApie0wDR7gzzPI8/z8gDTmgDarYp5YCPWA9Ae55oHfuiHfij+/u//Pj70oQ+V/4uI+Lmf+7m4/vrrI6K69cCWylhde+218Vu/9VvxR3/0R7F3796yXta+ffti165dEbFaQ+v222+PT37ykxER8ZGPfCT27t0bD37wg+PCCy+Mq666Kp7znOfES1/60njDG94QJ0+ejFe84hXxHd/xHXHZZZdtZThAA6qYB97znvfEe9/73njWs54Ve/fujfe85z3xvd/7vfFd3/VdccEFFzT2vQGbc655oDjcPHLkSPzmb/5mLC8vx/LyckREXHzxxbGwsBBXX311PPKRj4wXv/jF8VM/9VNx++23x4/+6I/Gtddea0MDLVHFXPAnf/Incccdd8TXfM3XxM6dO+PGG2+M1772tfHv/t2/a/JbAzbpXPPApz/96XjTm94UV199dVx88cXxhS98IV73utfFrl274pu+6ZsiIqwJoOWqmAesB6DdzjUPXHrppRtmZzz4wQ8uAyOVrQfyLYiIDf93/fXXl3/nuuuuO+ffueuuu/IXvvCF+Z49e/KlpaX8JS95SX7o0KGtDAVoSBXzwAc+8IH8KU95Sr5v3758586d+VVXXZW/9rWvzY8dO9bMNwVsybnmgZtuuumMf+czn/lM+Tif/exn82uuuSbftWtX/oAHPCD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      "text/plain": [
       "<pyannote.core.feature.SlidingWindowFeature at 0x7f28ceef7940>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spk_probability = Inference(pretrained, step=2.5)(test_file)\n",
    "spk_probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hrJ9pDbTnX8e"
   },
   "source": [
    "A perfect output would look like that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "bLcE98G4nX8f",
    "outputId": "7bb6ed3d-8076-4ebf-fa89-95a8c5943d27"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyannote.core.feature.SlidingWindowFeature at 0x7f28ceef4cd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_file[\"annotation\"].discretize(notebook.crop, resolution=0.010)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9DY3NIY5nX8f"
   },
   "source": [
    "We are going to fine-tune this pretrained model on the AMI dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 1183,
     "status": "ok",
     "timestamp": 1704811457994,
     "user": {
      "displayName": "Clément PAGES",
      "userId": "11757386314069785178"
     },
     "user_tz": -60
    },
    "id": "GJ2BWqqknX8g"
   },
   "outputs": [],
   "source": [
    "from pyannote.audio.tasks import SpeakerDiarization\n",
    "seg_task = SpeakerDiarization(ami, duration=10.0, max_speakers_per_chunk=3, max_speakers_per_frame=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y6e77EJ-nX8g"
   },
   "source": [
    "To check that fine-tuning was actually helpful, we need to evaluate the performance of the pretrained model, and compute the average local diarization error rate on a 10s window sliding over the whole test set. To do so, we need to create a helper function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "XxJs0DtCnX8h"
   },
   "outputs": [],
   "source": [
    "def test(model, protocol, subset=\"test\"):\n",
    "    from pyannote.audio.utils.signal import binarize\n",
    "    from pyannote.audio.utils.metric import DiscreteDiarizationErrorRate\n",
    "    from pyannote.audio.pipelines.utils import get_devices\n",
    "\n",
    "    (device,) = get_devices(needs=1)\n",
    "    metric = DiscreteDiarizationErrorRate()\n",
    "    files = list(getattr(protocol, subset)())\n",
    "\n",
    "    inference = Inference(model, device=device)\n",
    "\n",
    "    for file in files:\n",
    "        reference = file[\"annotation\"]\n",
    "        hypothesis = binarize(inference(file))\n",
    "        uem = file[\"annotated\"]\n",
    "        _ = metric(reference, hypothesis, uem=uem)\n",
    "\n",
    "    return abs(metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nkdm0dR0nX8h"
   },
   "source": [
    "We can then evaluate the model and see its local DER:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "LH0CGdmnnX8i",
    "outputId": "b07ba927-6f23-403a-d70b-74cf8685df06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local DER (pretrained) = 19.7%\n"
     ]
    }
   ],
   "source": [
    "der_pretrained = test(model=pretrained, protocol=ami, subset=\"test\")\n",
    "print(f\"Local DER (pretrained) = {der_pretrained * 100:.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUCdup8QnX8i"
   },
   "source": [
    "Next, we prepare the model for fine-tuning, simply by overriding its `task` attribute..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "5i5Bv-7enX8j"
   },
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "finetuned = deepcopy(pretrained)\n",
    "finetuned.task = seg_task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nDPEYDZKnX8j"
   },
   "source": [
    "... and we train it (for just one epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "",
      "634877439d6744d88223cfd1313e5fc1"
     ]
    },
    "id": "AIUsJ3MqnX8k",
    "outputId": "6078a5dd-5cb2-4780-db4e-e3516d1cce8e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name              | Type             | Params | In sizes       | Out sizes                                  \n",
      "----------------------------------------------------------------------------------------------------------------------\n",
      "0 | sincnet           | SincNet          | 42.6 K | [1, 1, 160000] | [1, 60, 589]                               \n",
      "1 | lstm              | LSTM             | 1.4 M  | [1, 589, 60]   | [[1, 589, 256], [[8, 1, 128], [8, 1, 128]]]\n",
      "2 | linear            | ModuleList       | 49.4 K | ?              | ?                                          \n",
      "3 | classifier        | Linear           | 903    | [1, 589, 128]  | [1, 589, 7]                                \n",
      "4 | activation        | LogSoftmax       | 0      | [1, 589, 7]    | [1, 589, 7]                                \n",
      "5 | powerset          | Powerset         | 0      | ?              | ?                                          \n",
      "6 | validation_metric | MetricCollection | 0      | ?              | ?                                          \n",
      "----------------------------------------------------------------------------------------------------------------------\n",
      "1.5 M     Trainable params\n",
      "0         Non-trainable params\n",
      "1.5 M     Total params\n",
      "5.893     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc1c93f0b95649b1ae7a7135c9c04d62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c98a5f8cd8f4d90bf790cd1fa59855a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20211f5562804ebe87a67b587cab6ac7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=1` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(devices=1, max_epochs=1)\n",
    "trainer.fit(finetuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jSotawK0nX8z"
   },
   "source": [
    "We now evaluate the performance of the fine-tuned model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "VNmCSkdfnX80",
    "outputId": "7bfcef09-901d-441b-b73a-9b20f5b29b86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local DER (finetuned) = 18.8%\n"
     ]
    }
   ],
   "source": [
    "der_finetuned = test(model=finetuned, protocol=ami, subset=\"test\")\n",
    "print(f\"Local DER (finetuned) = {der_finetuned * 100:.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5v6fQptnX81"
   },
   "source": [
    "... to confirm that it actually improved its performance on the AMI test set.\n",
    "\n",
    "\n",
    "## Transfer learning\n",
    "\n",
    "What if you are only interested in detecting overlapped speech regions?\n",
    "\n",
    "Looking at the output of the `pyannote/segmentation-3.0` model, it seems that it would be a good starting point for training such a dedicated model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "3bRoUeAVnX81",
    "outputId": "b9db7010-5e4e-4028-b2a1-95e3525db93b"
   },
   "outputs": [
    {
     "data": {
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ZfP8lERRNrUF5nZkmAAAp0rOsNdLb5QNA31XWoFxmx0bK8jQpHGKeQhkraN7k+y+JoGjNDcrLzA4NygEANiazozUSWL0DAOtUltkxvqWb4KF+k8rMjgQblBcHqzI7oDnpZnbUsLnO13p2aFAOAHAGLnu0Rnq7fADou/IgaTjb4xRfn+ChfpPakNkxnPVnD0xtsndFUpkddcwLo5UYjMtYyewAADiDsq+m9U/qEli9AwDrFPXQI2ar0V6WsVo8+9/rmTYEO2R2QHMmD/0XU5g/6wwu5CtlGavRZj9vbPYBgL7Rs6w1BDsAIDWTmRizHG5pUL6hImsixWBHUVpLzw5ozmSwMYnMjjoblI+G5YZwmG8yc2SgjBUA0DMyW1sjgdU7ALDO5CH8LIdJGpRvqDjITOIQ8xQyO6B5k8HGJIKitTYoX4nFfDW1Y9PzTnGz0WYfAOgLPctaI71dPgD03WRwYqbMDg3KN1IcZCZxiHmKIgAjswOaUxz6Z5FFlmUNjyZqblA+KjeEm553bPYBgL6R2dEagh0AkJrKMzt83E8qMzsSfF1kdkDzigblyQREa87sWIitZnbY7AMAPeOyR2ukt8sHgL6rLLNDz46NyOwAzia5Une1ZnasxGDcoHzzmR0alAMAPVNclHMpLXmJrOABgFJVmR3jRtx6dqxX3NpO5iBzQhGAWXGICI0pGnUvpDJ3Fpvr0SYbiG/FaBjFd7npeUeDcgCgb8rLJzWsx6hUert8AOi7wSAixnXiKyljtTjzkLoiz/PIxyVbUszsKA5XlbGC5hTvv2TmiLIheA3zwmglBlstY5UpYwUA9EyxHnPZI3mCHQCQoipqoitjdZrJMi3JHGROUMYKmle8/5LJ/qq5QfnCVstY2ewDAH2jZ1lrJLKCBwDWqeJwq7ilm2Aj7qZM3lzWoBzYyGiUWmZH3Q3Kx/+oQTkAwMY0KG+N9Hb5AIDMjprI7ADOpV+ZHSsxyFdTOzQoBwA4Aw3KWyORFTwAsE4lmR1Fz470DvWbsi6zI5WDzAkyO6B56fXsmFNmx2irmR3mKQCgJ2R2tEZ6u3wAYO3myCyLqdFw9c9UDuwSMCxek0joIHNCEYCZHCcwX2VmRyql7urMpBgNy8yOYb7Jeafc7JunAICeGFj/tEUiK3gAYJ2iAWwVZayKx2JdxkSKwY7F8c9KZgc0pwh2JDNH1NkQPJ+mZ4cG5QBAz1SxP2cunH5Qm/ccfE988I6/b3oYAK20eN72yLYtxco7/n3E9vOmeozs8J0x2LcUo1iOlQ/+j4pH2E5HhkfKfz52chRZljc4mtPleRYREe888Fdx99H7Gh5Ntf6fx7wkti9sb3oYNODIySNxw0d/o+lhbNpth2+LiIgsBnHkRPO39xZGETsiYnTfgRje9FPVPvbn/qoMdvzVF94Vh44fOevfj4jIbv/7WNy3FPn9/xijP3xxpeMBoPue+NCvj69+3EuaHgYNGI1Gcc+xw00PYyrZ8aOxM8siP3k4hv/nJ5oeTustHz5W22NneZ7XsstfXl6Offv2xX333RdLS0t1PAWJe917fzr+v0/8etPDAIDT5KPtcf8t/7npYZxmx4N+P7af//6mh1GLd37bu+PC3XubHgYNuO3Ql+Lq//WspoexZSvHHhRHPvNvmx5GPG3w0fit7a+t7fF/7KIL4neXvDcBmI+X7nlkvOpb3tT0MGjAXUcOxTN/72lND4MErBxdiY+//OO1xA1kdlCbr7roUXHi3ic1PQyAVro07on92Z0zP04eWXwqvyzuiT0VjKo7Vu6/sukhbOjEXV8XMdoWMTjZ9FAqN0ilJBBzt22wvX1rwnwQw+XHNT2KiIj429FXxs+d/JZ4UHZXLY+/5+4stg33xuFNljxciFE8Mvtc7IwTtYwHgG678sse1/QQgA6T2UFt8jyPoyfVsgMAInZtW4gsy5oeBg2wJgQACtaE/dXmMlZU69DycjzsQVfI7KBdsiyL3dv9igEA9Jk1IQAAg8EgLlLWlojYNqyvd+agtkcGAAAAAACYA8EOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVFut64DzPIyJieXm5rqcAAAAAAABaoogXFPGDKtUW7Dh06FBEROzfv7+upwAAAAAAAFrm0KFDsW/fvkofM8vrCKFExGg0ioMHD8bevXsjy7I6ngJI3PLycuzfvz8OHDgQS0tLTQ8HaIB5AIgwFwDmAcA8AKzNAzfffHNceeWVMRhU22WjtsyOwWAQV1xxRV0PD7TI0tKShQz0nHkAiDAXAOYBwDwARFx++eWVBzoiNCgHAAAAAABaTrADAAAAAABoNcEOoDY7duyI6667Lnbs2NH0UICGmAeACHMBYB4AzANA/fNAbQ3KAQAAAAAA5kFmBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtNpiXQ88Go3i4MGDsXfv3siyrK6nAQAAAAAAWiDP8zh06FBcdtllMRhUm4tRW7Dj4MGDsX///roeHgAAAAAAaKEDBw7EFVdcUelj1hbs2Lt3b0SsDnppaamupwEAAAAAAFpgeXk59u/fX8YPqlRbsKMoXbW0tCTYAQAAAAAARETU0vpCg3IAAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUWa3+GE4cjTizU/jQkbNvuiCxrehQ05b5bIyJvehSwSVnE0mXdm7PyPGL5YHgvrpfneXzx2N0xykdND+Wsdi3ujH3b9zY9jDU79kbs3Dfzwxw5eSSWTyxXMCDaYmn7UuzetrvpYQAA1OP4oYhj9zU9ivnZeX7Ejj1NjwLWqT/Y8bNXRuzo2KERW/MjByO2n9f0KGjKLz814niPPuxpv6ueF/EvfqPpUVTr97474uY/bHoUyXndhRfEb+1LKIhwBgt5Hr94xxfjGUePNT2UVYNtES/5s4j9T576IW4/fHs87w+fF0eHRyscGKl73T95XTz3y57b9DAAAKp3x8cifuVZESvHmx7J/Czuinj5uyIuenjTI4FS/cEOoN8WtkUsbG96FHBu+ShiNIz4wvubHkn1vvC+1T8HixGZCpaFD+/cGRGrwYRUX5VhRKxkWXxs5654xokEMlBWTkaMTkbc9uGZgh3/eM8/loGObYNtVY2OxA3MPwBAV93+kbVARx/OQFZORAyPrgZ5BDtISP3Bju+/JWJpqfanIWHKFfTbD36q6RHA5tz+kYg3fG1EvtL0SKo3Gn9PL70p4kGPaXYsCRn9ybdH3P3x+KVnvyG+9vKvbXo4G/rxv/nxeNMtb4rR1/1AxOOubXo4q1lCH3vz2u/UlIrSYY+66FHx29/82xUMDAAAGlSsj7/82RHf9fvNjmUerv+miM+9q5v7Z1qt/mDH9vOUMAIgfdm4v9SMh7hJKhagAz20Jq2MX5eUb5svjH8vV1L5vSzeJzNuasrXfpDuaw8AAJvWtz1XsYdKZZ8CY3aYABCxtijt4s2UYgGa9WThvUlFdsFCwq9LEYhJpon6oJqgYBteewAA2LS+7bnK/XMi+xQYE+wAgIiJzI4OLtb6dstok9qU2ZFMsKPqzI6EX3sAANi0cs/Vk/Vtlysj0Go9eQcCwDkUi9LRsNlx1KG8ZeRjf1JRGirl7IKizNMwT+T3sqL3SRteewAA2LS+ZnZ0cf9Mqzn1AICIiMG4jVWXy1gN6m/V1SZFdsFCwhkvi9nqzyyZzI7id2jGDKjyte/LZhAAgG7r256ry/tnWk2wAwAiup2Gq4zVhtrQN6Io89S1BuXFa69BOQAAndC3PZcG5STKDhMAIjQo76E29I1IrmeHBuUAAHC6vu25NCgnUenu7gFgnrKJxVqeNzuWqvXtltEmteHAvczsSCUIp0E5AACcToNySEJP3oEAcA6TgYAu3U6Z7K2Q8KF+E8pSSgkfuBf9RGR2AABAwop9V1/Wt12ujECrpbu7B4B5mjzw7tLtlMnFZ19uGW1SG5pkp5fZMf4dmjH4IrMDAIBO6Vs2vcwOEmWHCQAR6xelo2Fz46ja5PeS8KF+E4qm3ykfuBeBmGSCHYPF1T9nfI8Ur33KgSYAANi0Yn3cl/VtmfHdob0znZDu7h4A5qk4xI3oViru5E2bye+RtcyOhG9flQ3KR90qY9WG1x4AADatWB/3Zc+ljBWJEuwAgIj1N3C6lIq7royVg+VJbegbkV4Zq2o2NW147QEAYNN6W8YqkUtZMCbYAQARHW5QPnEo7WB5nTb0jSgzO1L5naw4syPl1x4AADatWB/3ZX0rs4NE9eQdCADn0NkG5ROH5H25ZbRJbcguGAxSy+woGpTL7AAAgFKx7+rLnkuDchIl2AEAERFZVtlBblLKxWe2+j0SERF5npcH7ilnF6Sb2THbeGR2AADQKWVmR0+CHTI7SJQdJgAUytspw2bHUaXie+nLDaNNmsyUSDm7oAgGDPNEfieLhoszvkdWRoIdAAB0SN/2XcU6vkt7ZzrBDhMAChX1I0hK3rMbRps0mSlRlIpKUZnZkUrjPw3KAQDgdH3bd1WU8Q1VS3d3DwDzVtxa71IqbhG4Kb43ImJ9Zsdilu5rk24Zq2oalC/05eYbAADdVu67erK+7eLemU4Q7ACAQtbB2yl9a5S3SesyOxIupaRBOQAAtEDf9l0alJOodHf3ADBvgw43KE/4QL8JbenZkW5mhwblAABQ0qAckmCHCQCFLt5OyXuWTr1Jkz0wUj5wL8aWTmaHnh0AAHCavu27urh3phPS3d0DwLx18XZK324YbdJk8CDlYEd6mR3j2ryz9uwYyewAAKBD+rbv6mJVBDrBDhMACuXtlGGz46hS8b305YbRJk2WUcqyrOHRnFkRDBim8js5qOY9UjYo78tmEACAbuvbvktmB4kS7ACAQnlrPZFb9FUo06kXmx1HYtpSRmkxW/25JZPZUXUZq75sBgEA6La+NSivKOMbqibYAQCFLqbiFoEb5YLWaUtmwWCQWM+O4j2iQTkAAKzpXRmrDpaAphPsMAGg0MVU3L41ytukokF56oftyfXs0KAcAABO17d9Vxf3znRC2jt8AJinLt5O6dsNo01qTWbHOBiTTLBjUM2mRmYHAACd0rd9VxerItAJdpgAUOji7ZS+3TDapCJ4UJSJSlURjEmnjNW4Nu+smR0jmR0AAHRI3/Zd5d45kUtZMJb2Dh8A5qm8tT5sdhxVKr4Xh8rrDPPV1yX1w/Yi82EllQBcVs17pHj9ZXYAANAJZWZHT9a3Xdw70wk9eQcCwCYUC9NUSgZVobhpk3gGw7yVmR2Jb0bSy+yopkG5nh0AAHTKqKeZHansU2As7R0+AMxTUaInlVv0VSjTqRebHUdi2tKzY2HQzQbl5evfl80gAADd1rd9Vxf3znSCYAcAFDQo74229Iwoy1il8jtZUYNymR0AAHRK3/ZdXdw70wmCHQBQ0KC8N4rgQVvKWHU1syP11x8AADalb/uurJrytlA1O0wAKHTxdkrfbhhtUplZkPhmJL3MjmrS1YvMGsEOAAA6oW/7ri7unekEO0wAKJS3Uzq0YOvbDaNNaktmQXKZHRVtapSxAgCgU4r1+iDt/UVlulgVgU7oyTsQADahon4ESSlvGPnIn9SWBuVlZkcqv5MVBQSH+TAiIgZ92QwCANBto9X1be8yO4rvGxJhhwkAhaJET5dScYtD6eJ7IyLa06B8cfxzS6eMVbUNyhczv5cAAHRA3/ZdXdw70wmCHQBQ6GIqrjJWG2pLGatifMmUsdKgHAAATte3fZcG5STKDhMACl1ssta3Rnmb1JaeEek1KK8os6MlmTUAALApfdt3dXHvTCcIdgBAQYPy3igzCxLvGTEZDEgiu6OidHWZHQAAdIoG5ZCEnrwDAWATytspCRwqV0WD8g21LbMjIpHsjorS1dvy+gMAwKbI7IAkOPkAgEIXb6eUN4x6sujepLZkFqSX2VFxz46+3HwDAKDb+pZR38W9M51ghwkAhbIfwbDZcVSp+F76csNok1bGi/LUMwvWZXaksJHIqnmPFMGO1F9/AADYlL7tuyrq5QdVE+wAgEJF/QiSUiw+i++NiGjPYfvixM8tiTJWVTUoV8YKAIAu6du+SxkrEiXYAQCFLqbi9i2depOKw/bUyyhNZnYkUcYqq6iM1agdZcQAAGBT+lY+uIt7ZzrBDhMACsXBd5dup2hQvqG2ZBZMji+NzI6Jm2ozNClvy+sPAACb0rd9l8wOEtWTdyAAbEJ5OyWBG/RV6dsNo01qS4PyLMsiiywiEsnsmMyEmWFjo0E5AACd0reMepkdJMoOEwAKXbydUt4w6smie5PalFlQjDGpBuURM21s2vT6AwDAOfVt39XFvTOdINgBAIXydsqw2XFUqfhe+nLDaJOG49cl9cyOiLUxplHGajLYMf37pC2ZNQAAsCl923cV6/gULmTBBDtMACgU/Qi6tGAr06kXz/73eqbILFjM0n9dFsYbpiSCHZM31SooY9WG1x8AAM4qzyMiX/3nvuy7urh3phMEOwCgoEF5b7SpZ0RR6imNnh0VlbEa98WR2QEAQOtNrov7sr5VxopE9eQdCACboEF5b7SpZ0RSZazWZXZM/z4pvpc2vP4AAHBWk+v0vuy7NCgnUYIdAFDo4u2UvjXK26Q29YwoMztSCMINBhGRrf5zBQ3K25BZAwAAZ7Uus6Mn+64u7p3pBDtMACh08XZK2bOjJ4vuTZLZMYMKNjYyOwAA6Iw+Z3ZEdKsyAq0n2AEAhS7eTpHZsaFWZnak0LMjopKgYJnZ0YLXHwAAzqqXmR0T6/gu7Z9pPTtMACiUh7jDZsdRpeJ76csNo01aGbUn2FGUekous2PK90me560KNgEAwFmN+p7Z0aH9M61nhwkAhUEXG5QrY7WRIrNgcbDY8EjOrcjsSCbYUWxspsw0mcxQWczSf/0BAOCsJtfpfbnMM7mP6lIZaFqvJ+9AANiETpaxGh8s9yWdepPalFmQXBmrwWxlrCa/Dw3KAQBovcnSwVnW7FjmZfIyXZf2z7SeHSYAFDQo741WNihP5fdyxqDgZIZKG15/AAA4qz7uudaVsUpknwIh2AEAazqZ2aFB+UZkdsxgxqDgusyOFrz+AABwVn3cc63L7EhknwIh2AEAa2R29EarMjtSbVAuswMAAPq558qyiBiX7OrS/pnWE+wAgELRP2A0bHYcVSq+Fzfo1xmOX5c2ZBYk26B8yvfJZDmuNrz+AABwVn3M7IiY6OXXof0zrWeHCQCFweLqn11Kwy0alBffGxExkdnRgttXyZWxKoOC041HZgcAAJ0y6mFmR8TE/jmRS1kQgh0AsEYZq95oUxmrMrMjld/LGTc1xWs/yAaRZVlVowIAgGb0dc/Vxf0zrSfYAQAFDcp7o00NyosxJpPZMeOmpk2vPQAAnFNf91zl/jmRfQqEYAcArOnizZS+3jI6hzZldhRBgWR6dswYFGzTaw8AAOfU1z1XcXmpS/tnWq/2At53HzkUJxeVKOizC3aeF4OBuFof5XkeR0/60KM9FkYROyJiZWUYx090o8najpVhLETE8VHESke+pyqcGK6+FiujiCPJvy6rn6FHT55MYqw7YxCDiDh24kSMphjP4RMnImI1iJPC98P87Nq2oHRZT41Go7jn2OGmhwEAtRgcPRQ7sixG2SCOHznU9HDmZudgdW137Ohy5Fv4vp0TUqcsz/O8jgdeXl6Offv2xVWvvyoWdvUsssk67/i2d8dFu/c2PQwacOTEMJ78m0+NGBxveiiwKVnkkUUtH4uNy8ffHauybPXnfOz2b46T93xtw6M5u10P/p+xeN4nIyIiz5v/GQ4ij5jhfZKPD7v3jEbxrs/eWtGoaIOTL3hD7Hj8dzQ9DBpw15FD8czfe1rTwwAAEvDXD3lx7HvmDzY9DBpUxA3uu+++WFpaqvSxa8/sACgOFaENuhwQ6GogZ1r5aDFGx65oehjntHLkoWWwI4X5dHUEs79PnnDseAwS+H4AAADohtozOz5z2xdib8URGtpFelp/5Xketx76YtQ0zUA9hkcjTnSs1Mb28yIWdzU9iuTsWtwVu7ftbnoYm3Lf8XtjmFIt3KP3TN2zo3Dh9vOVNOqZXXvOj2x7O95zVGs0GsU9d38+YqSBKQAdlUXE7gdE9Gl9m+cRR7605aTvC5YujsFOFWD6rNWZHRfu3htLShhBL2VZFlcsPbDpYQC03u7tD2h6COvtu6TpEQAtMhgM4qIHPLTpYQAAVTvPBXfS4ro9AAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtJpgBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrLdb1wHmeR0TE8vJyXU8BAAAAAAC0RBEvKOIHVaot2HHo0KGIiNi/f39dTwEAAAAAALTMoUOHYt++fZU+ZpbXEUKJiNFoFLfccks88pGPjAMHDsTS0lIdTwMkbnl5Ofbv328egB4zDwDmAcA8AJgHgGIeuPnmm+PKK6+MwaDaLhu1ZXYMBoO4/PLLIyJiaWnJJAY9Zx4AzAOAeQAwDwDmAeDyyy+vPNARoUE5AAAAAADQcoIdAAAAAABAq9Ua7NixY0dcd911sWPHjjqfBkiYeQAwDwDmAcA8AJgHgLrngdoalAMAAAAAAMyDMlYAAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtJpgBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqi3U98Gg0ioMHD8bevXsjy7K6ngYAAAAAAGiBPM/j0KFDcdlll8VgUG0uRm3BjoMHD8b+/fvrengAAAAAAKCFDhw4EFdccUWlj1lbsGPv3r0RsTropaWlup4GAAAAAABogeXl5di/f38ZP6hSbcGOonTV0tKSYAcAAAAAABARUUvrCw3KAQAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWW6z9GU4cjjixUPvTAAnbtjsiy5oeBQAAAADQUfUHO372yogdDjmh137kYMT285oeBQ35ib/5iTgyPNL0MDb0vIc/L57yoKc0PYxa3PDRG+If7/3HpofRTsPjEXd8LGLleDPPv7gr4tJHRwzSuCxyxZ4r4mWPfVkMspoTgu+4OeK9r49YOVnv88AMblk5HL994rY4GaNan+cbFi+KZ227aOP/mA0iHveiiIc+fboH/9InI97z31bnullc/sSIJ790tscAgC4ZrUS886ci7v1c0yOhLl/+DRGP/tamR8FZ1B/sAKDX3vbZt8U9x+9pehgbuuXuW+L3n/f7TQ+jcgeWD8TPfuBnmx4G0zoZEZ/5fNOjWOfplz89HnPxY+p9kr/62YiPdu/9SLf8ysUXxZ/vqf8Cx98cvS2edeDgmf/CF2+JeOn/me7B3/PfIj5ww3RfO+nDvx3xVf884rwzBGUAoG++8L6Id76u6VFQp5v/SLAjcfUHO77/loilpdqfBkjYtt1Nj4AGvfxxL4/js94erdit998av3PL78TR4dGmh1KLIpNm9+LuePljX97waFroc++KuOUtEUsPirj0sfN97i/8bcSRuyMe+y8iLnn0fJ97Azd87Ia469hd83mvnBxngD3y+RGXP6n+54MpHD341ogjB+Ib93xZPGrHxZU//j0rx+KN9344jm7fHfHsHzv9L9z96YgPXL/2fpnGifHXfsU1EQ952nSP8X/+U8RoOB6HYAcARMRqKf+IiD2XRjz12mbHQrVOHF4NZJ08EpHnSrUnrP5gx/bzlK8B6LEXfuULmx7CaT78xQ/H79zyO7GSrzQ9lFoU39eebXviux/13c0Opo3uvTdi+XcjHvbNEd/8hvk+9w3fHHH75yIe+NSIRzV/Y+hPPv0ncdexu2JlNIf3ymi4+ueXPzviCS+u//lgCis3fiziyIF4xuO+J5738OdV/vgHlg/EG9/8TTEcLEQ8/VWn/4XP/NVqsKN4v0yj+Nov+7qIr5kyIP6On1x9nFnGAQBdU6yZ916y8ec47XXk7rWsndFKxIJiSamqufgyAKRnMVtdmHQ12DHKV2vJLyTS86F1it+LJl6/wXjRPI/gwiYsZKuvwVzeK8X3PLBxIF3Fe6F4b1StmLeLefw0VcwReQXvteJrzzROAOijKj5jSdPk3rCj5whdIdgBQO8UjZZHo24e0hSHcbU3lO6q4veipsPMsyoW0YksoIsD3TMevFapySATbFIZTK5pfijm7TMGGKuYI4pAySyfEcXXJhKYBYAklJ+x1rOdM/kztf5JmlMQAHrnnIdJLVf3YVznNXnoXvzMEllADwZzfK9UcQALNStKutUVTD5ngLGcI2YIQBaPPcscl1hgFgCS4PJOd8nsaA27SQB6Z6631RtQ92Fc5zV5IyuxA8T5ZnZUcAALNZtXZscoH0We5xv8hfG8XklmxwzfQ2KBWQBIgsyO7pLZ0RpOQQDonbneVm+AzI4ZNZrZkVZpmLlmQdkc0gLF/Fp3Zsfkc61TRZChijkuscAsACShvLzjuLVz1mV2dPPSZFd49wHQO53P7Ch6dlhkTyeFzI5Egh16dsB6ZYPymn5PJ+ftDd93lfbskNkBAJVyeae7Ji+6WP8kzSkIAL3T9Z4d5WGcRfZ0RsPVP5sIFmVp3ZYu3ivD4jWpU/Ecfm9JWBlMnkNmxzDf4H1XBhlmeE+Wc9wsmR1pZaEBQBKq+IwlTVk2kYU/h70RUxPsAKB3FrPFiFjrbdE1yljNqEw/X5z/cxfPmcjvZnF7fS6ZHcX33MTrDptUdzD5nGWsyjmi6Qbl43EkEpgFgCTk1rOdZv3TCoIdAPTOZAPYLiqCOIIdU0qhjFUiC+hmGpRbnpKu0ajeYPLk426YfahBOQCkSxmrbrP+aQW7SQB6p7it3tUyVnU30O28RhuUp7WA1qAc1qu7jNXk4442yt7QoBwA0lV+xtqHdZL1Tyt49wHQO8VhUh555Hne8GiqV/dhXOc1mtlRwa3tCmlQDuuVZQLralA+MW9vnNmhQTkAJKu4qODyTjeV659uVojoCqcgAPTOOcuEtFzdh3Gd1+SNrMQOEGV2wHp1B5OzLDt7qcVkMjvSCswCQBJc3uk2659WEOwAoHfOeXO25Yb5MCJkdkwthZ4diQQ7isDgyjzGM1r9vbU5JGV1NyiPOEeQcTKzY9rMxHKOm+EzIrHALAAkoVjPurzTTeX6Z9jsODgrpyAA9M66zI4OHtQUt4EXs8WGR9JSxe/EoIHXr3jORIJwc+1vUzYotzkkXXU3KI9Ym7s3DnZMzEvTlperYo4rvraDn6EAMLUm9xHUz/qnFQQ7AOidyfJOc+lFMGdFAEdmx5Q0KC/NtWeHMla0wDx6IpVlrDZsUD7xvNPOExqUA0A9NCjvNuufVvDuA6B3ul7GquzZ4dB4OhqUl+bas0ONY1pgHvNrWT7ubGWsIqafJzQoB4B6aFDebRqUt4JgBwC9M3lI1cnMjjncPO60JDI70vi9lNkB65Xza403NovHPmuD8giZHQCQGpd3ui2xi2lszCkIAL3Tm8wOi+zpVNG8d1plg/I0mt7J7ID1upHZUcGt02J+TCQwCwBJcHmn22S2toJgBwC9NNcb63Mms2NGTTbKztK6LV32DphLZoe0f9I3154dtWd2zPA9yOwAgNO5vNNt1j+t4BQEgF4qb6x38FaGBuUzKrIqGunZkdZtofKG+TzGU7zuGjqSsOK9UGdmRzF3D/MNMrwGFQQ7qpjjsrSy0AAgCU3uI6if9U8r2E0C0EuLg8WI6HYZq8VsseGRtFRxgDho4PVL7LZQUQptvmWs/N6SrnmUsSrm7tFGJaKybK2E1KwNymd5rxVfm0hgFgCSMJLZ0WnWP60g2AFAL821PM+cKWM1Iw3KSxqUw3rF/FpnT6Rz9sqZtV60BuUAUI8my+FSv7JBeRp7NTbmFASAXppr4+U506B8Rik0KE/k91KDcliT53nkkUdEvcHkYu4+Y5Bx1nmi0gblacxVAJAEl3e6TYPyVhDsAKCXNCjnjFJoUJ5IHdi5vU/yfO11tzkkUZNBv3n07Kg/s6OKBuXd+wwFgKm5vNNtiV1MY2NOQQDopV5kdjg0nk6TN7ISa1Bevk/qHs/kganNIYmaDPrVmtlxriDjrIGGKuY4NxsB4HQyO7rN+qcVBDsA6KXiMKn2Q9wGDMdZATI7plRkVTSZ2ZFIEK58n9Q9nslMFr+3JGo48Xs6l8yOM30+lSWkpswAq2KOG6SVhQYASSg/Y61nO8n6pxW8+wDopXPWRG8xmR0zKtPPF+f/3IlldsztfTL5/TbxusMmTL4P6uyJdM4gY/EembmM1QzvteJrEwnMAkASynK41rOdpIxnKwh2ANBLc7ux3gDBjhmlUMYqkQX03Mq9TT6+MlYkavJ9kEYZqynel1X1x9GgHABOp4xVtylj1QqCHQD0UnFQ1cXMjrJBufTp6ZQ3shp4/RJbQM+tQfnk92tzSKLWZXbUWcZqUGOD8qr64yQWmAWAJGhQ3m0alLeCUxAAeklmB2eUQmZHInVg55fZoUE56etEZse6wOIM30NigVkASILMjm6z/mkFwQ4AeqkXmR0aPU+nyRtZiTYoH43mmdnh95Y0zSuQfM4gY1lCaor3ZVUl49xsBIDTyezoNuufVrCbBKCX5nZjvQEyO2aURGZHGr+Xc+/ZkQ0isqze54IpFXNr3YHk+WV2zNKzI625CgCSMJpY09I9epa1gncfAL1UlrHq4EJlOC6BJLNjSkUJqUYyO8Y/s0QyjuZW7q14zQXoSFgxt84rs2N4pnJ22Qzl7ia/porMjkRK7gFAEkYyOzotsYtpbMwpCAC9tDDofs+OxcFiwyNpqbJBeROZHeOfWSIL6Lm9T8qNod9Z0lVmzdU8NxSPf+bMjhnmiXX9cWZ4v2lQDgCny61pO634uXbwDKFLBDsA6KVzlglpMT07ZpRCGatEFtBze5+ob0wLzGtu1aAcAFpKg/Jus/5pBacgAPSSnh2cUQoNyhMpDTO390nRaNnvLAnrVIPyWfvjJBaYBYAkuMDTbdY/rSDYAUAvyezgjFLI7EjktlDxOzSa5lB1K8qNod9Z0tWpzI5Z5zc3GwHgdBqUd5v1Tyt49wHQS33I7BDsmFKjmR1pNiivPSgo5Z8WSCezY4aNdlXzm5uNAHC6Jnv/Ub/iYpb1T9KcggDQS8Vh1UoHb2UU35MyVlOS2VEajBf0w7zmslpF2S4bQxJWzK3zyuw44+fTYIZyd8XXzJzZUZTSSqPkHgAkoarPWdIks6MVBDsA6KWyPE8iN+irpIzVjEYNllTK0rotPfcG5TaGJKyYW5PJ7JiqjFVFN07LgEv3PkMBYGqjBjPEqV9iF9PYmFMQAHppYbxQ6XIZq8XBYsMjaamyzEsDr1/xnIksoMsb5vNqUO53loSVZaxqPsAo5u5z9uxotIzV4vrHAwCa3UdQP+ufVhDsAKCXNCjnjJIoY5VGaZjyfaJBOaST2VH2y5jifalBOQDURx+6brP+aQU7SgB6qQ8NyvXsmFKjDcpnOMSswdzeJzaGtEAxt86rZ8cZg/EalANAmsoG5Y5bO0mD8lbw7gOgl+Z2Y70BMjtmkOdrm5RGMjuKpr9pLKDn3rNDfWMSNq+5dfOZHdP07KgqsyOtuQoAkuACT7eVF066d4bQJU5BAOglmR1saPJQv9HMjjR+LwcDmR1QKILj8ypj1Y7MDpt9ACi5wNNtMltbQbADgF6aW+PlBqyMZHZMbbJXRhOv3yyNh2swvwbl49fdxpCEDfPV39N5lbFaOdM8MEtvnzKwOOP3kM0wBgDoquJz0QWebrL+aQWnIAD00sKg+w3KFxwcb93k4eJgcf7PXzxnIkG4+ZWxKuob+50lXcX7YLHmuaGYu2ttUD7r91B8fSKBWQBIQlWfs6TJ+qcVBDsA6CVlrNjQ5O9Dk2WsEllAl++TusejjBUtMK+eHRqUA0BLaVDebRqUt4J3HwC9NLcb6w3QoHwGk4eHGpRrUA4T5hVIbkeD8rQCswCQBBd4uk2D8lZwCgJAL83txnoDZHbMQIPydTQohzUyOyaUNxtt9gGg5AJPt8lsbQXBDgB6SWYHG1qX2aFBucwOWDMazTmz41wNyqfK7Bi/lytrUJ7GXAUASXCBp9usf1rBKQgAvVQcJg3zYcMjqV5xQCazYwqj8e9DNojIsvk/f2qZHfPqbVNuDC1NSde8MzvO+L4rnn80xedX8TVV9eyYZgwA0FVVfc6SJuufVrCjBKCXFgbdzewovieZHVPIG76NVd7YHkXkeTNjmHDOQ9eqjGR2kL4y2FFz09Fi7j7j59NghnrRVc1xiQVmASAJxWe3S2fdZP3TCk5BAOiluR3iNqD4nhYHiw2PpIXKQ/eGXrvJ500gPbosY1V3E7684dcdNqEIPixm9f6eFsH4MzcoHz//LA3KZ32vFV+fwDwFAMlwgafbEis5zMYEOwDopbkd4jZAZscMmu4dMfkzSyAQN/fMDrfgSJgG5RPKBuXNz1MAkIym9xLUazILn2Q5BQGgl+bWi6ABxfekZ8cURg2nnk9ujBK4MVSU69GgHBpoUH7GzI5ZGpRX1B8nm6GUFgB0lQs83aZBeSsIdgDQS+esid5ixYGczI4plIfuDb12kxujBAJx88/s8DtLuuae2XGmQELZoHyazI7xY1bVoDyBeQoAkuECT7dZ/7SCHSUAvXTOmugtNq8DuU5q+jZWapkd8woKVnUACzUq3gfpZHZM8b6sao5zsxEA1stzDcq7zvqnFZyCANBLylixodFw9c/GenZMZnY0n3U0+T7J87y+Jyped7+zJKwMJNec+XXOjKpyoz3c+oNXNccNZhgDAHTR5AG4CzzdZP3TCoIdAPTSYrYYEd0sY1UGOyyyt65MPV9s5vnXZXY0v4gu3icRNb9XRg2/7rAJ8woknzPzsHifzNSgfMb3WvH1HbwwAABTyQU7Ok+D8lYQ7ACgl8ob6x1MQZ1XqZVOarpBeZbNVo+/YpM32GsNdqhvTAvMa24te3ac6T2XRIPyYp6y2QeAiFi/drcP6yZlrFpBsAOAXjrnYVKLFd+Tnh1TaLpBecTaIjqBG9OTh7q1lnxrulcKbEIRHK97bj1nmcVZNtoalANAPWR2dJ/1Tys4BQGglzrds2OkZ8fUUjh0H6RzY2jyUHc+mR2WpqRr3g3KR2fKmijeJzNldmhQDgCVktnRfdY/rWBHCUAvFTXRZXawTgrllHqZ2dFw+TDYhLJBec1z6zkzD8uN9hSfX1XNcW42AsB6k5/bMju6yfqnFZyCANBLnc7s0KB8eklldjQfiJt/ZoffWdJVZnbU/Hta9Mo5c4PyKnp2yOwAgEqty+xw3NpJepa1gncfAL1U3JztWoPyUT6KPPKIkNkxldFw9c9GMzuKRfSwuTGMTWZ2DOscT/HYMjtI2DBf/T2dV2bHuXt2TPGerGqOS6jcHgAkoVzPDiKyrNmxUI/BDGsw5sYpCAC91NUG5ZOHY3p2TKGq5r2zGCyOx9L8IWKWZWv9A+p8rxQHpsX3DgkqemgsZvX+np4z2FG8T6ZqUK6MFQDUIree7byE9mmcWe3vwLuPHIqTiyKa0GcX7DyvLMlAv+R5Hiff+bORnTza9FBOkx/5TEREfOq298cv/sG/aHg01RnG2oH06B0/GycH2xocTftk93w6FiNiJQZx/EQzN3Z2ZQuRRcTwPW+I/LwHNjKGSYM8YhQRv/pnL4s9WT2/T9l9n4vs/H0xOnRLrLz/F2t5DpjV++/4u4iIWBlFHKlxfhiO98+33X9b/NwG74fBnR+KhfP3RX7vhyPf4udXtvyF1ffakc/O9F7Lji/H4vn7IrIsRlsYwzd81QvjkV/5gqmfl/YajUZx6D2/HNn9X2x6KAD1OHFfLGZZ5IOFOHbkUNOjoQaDE8djR5ZFfuSLsfK265oeTquNHvTU2h47y/M8r+OBl5eXY9++fXHV66+KhV1ulkKfvePb3h0X7d7b9DBowJETwzj6Ew+Ni7L0Fns37t4V33fJxU0PozaLeR7v/eyB2N70QFrqr1e+Kr7r5L9v5Ln/z/bvj4cPbmvkuTfyTx58edy7YC0HheN3fmOcuOtZtT3+wq5Px+6H/kptj9+k6y67Jr712T/V9DBowF1HDsUzf+9pTQ8DAEjAn13wgnjw83887rvvvlhaWqr0seVWAVCrN608K3bHsaaHcZqTh0bx9IW74/BiN+tt7j+6O35reFXTw2ilUQziD1ae0djz//DJ/yeuWfjbyKKW+yhb9vW33x+fPu/+2p9nGItx8+ghcUyIjoTlo51x8t6vrvU5Vo4+NI7dcU0Mtt274X9fjJX4quyzsSM7Md3jx0J8fPSQOBI7ZhhlxJdlB+MB2X1b+pqHPO6xMz0nAADtN7rkUbU9du2ZHZ+57Quxt+IIDdAuylj1V57ncfSkepYAQMSubQuRadraS6PRKO45drjpYQAACVg4sRIXXHBBOzM7Lty9N5aUrwHopSzLYvd2SYQAAH02GAyUtQUAIiJiebhc22O7ag0AAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtJpgBwAAAAAA0GqLdT1wnucREbG8vFzXUwAAAAAAAC1RxAuK+EGVagt2HDp0KCIi9u/fX9dTAAAAAAAALXPo0KHYt29fpY+Z5XWEUCJiNBrFLbfcEo985CPjwIEDsbS0VMfTAIlbXl6O/fv3mwegx8wDgHkAMA8A5gGgmAduvvnmuPLKK2MwqLbLRm2ZHYPBIC6//PKIiFhaWjKJQc+ZBwDzAGAeAMwDgHkAuPzyyysPdERoUA4AAAAAALScYAcAAAAAANBqtQY7duzYEdddd13s2LGjzqcBEmYeAMwDgHkAMA8A5gGg7nmgtgblAAAAAAAA86CMFQAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaoIdAAAAAABAqwl2AAAAAAAArSbYAQAAAAAAtNpiXQ88Go3i4MGDsXfv3siyrK6nAQAAAAAAWiDP8zh06FBcdtllMRhUm4tRW7Dj4MGDsX///roeHgAAAAAAaKEDBw7EFVdcUelj1hbs2Lt3b0SsDnppaamupwEAAAAAAFpgeXk59u/fX8YPqlRbsKMoXbW0tCTYAQAAAAAARETU0vpCg3IAAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUWa3+GE4cjTizU/jQAJGzb7ogsa3oUNOWez0WsnGx6FJt2cnQybj1yZ9PDiKVte+LCHfuaHgYwdnI0jFuP3DG357tgx77Yt23P6v+58GERgwr3VCcORyzfNuXAHhKxsK26sQBAF933hYiTx5oeBVXLsogLHhYxkD+QqvqDHT97ZcQOB1wAvfYjByO2n9f0KGjKb78w4s6PNT2KTXvRZZfGx3dsb3oYMcjzeONtd8YTjx9veihARLz4skviYzt2zO35to/y+KNbD8YVw5WIL3tWxL/8w2oe+MSRiF94bMThL0739Zc9PuJfvaOasQBAF/3N6yPe+kNNj4K6POY7Iv75/2h6FJxB/cEOAKDfduyJaEmGQh5RBjrOG+WN1fs8mkUMsyz+YfdSPDFONDQKYNLHt89vbjicRZwYZPHpXUtxxaF7Im77cHUPfui2tUDHVubmfBRx4lC1YwGALio+Kxd2RCzubHYsVGc0jDh52FoocfUHO77/loilpdqfBoCEbdvd9Aho0vf8edMj2LTRaCXiNx4XERFvfeFfx/k7z29kHD/wzh+It372rTG6+j9FXPWiRsYArMnzPEa//piIiPjf3/HOuGjXRbU+33f+7++Mj3zpIzF6zmsjfu/lEflKdQ+ej1b/3Lkv4oc+v/mvu/+LET/z5atfn+fKUwLAmYzGn9v/9DURT3tFs2OhOp/964gbnlvtuozK1R/s2H6e0iUAQCuMikPAiBg0WId1kK0+98rIQhpSMDk3LGT19yMs54DIxwMYneVvb1Exr2z1+5jsGZKPtv71ANAXxWF4lf22aF6x9rFHS5puKgAAYysTt3TmcaB5JsVzTx6wAs2ZdyC0nAOKf1FpZseUBzDZxPdtkw8AZzbtxQLSVqydZHYkTbADAGBs3YFmlkBmh4U0JGGYD8t/biazo8K5oJLMDnMTAJxRebHAsWunlJkdLqSlzLsOAGBs3geaZ7IwPlQU7IA0zLuMVfEc5QwwGp7x725Z8VhbzuyY+PtVjgcAukZmRzcVwSvroKQJdgAAjI1G8z3QPJPyoFOwA5Iw7xJ3RcBzFOMm4HU0KN9qsGMw0e5RGSsAOLPic3JQf6tk5qj4edqjJU2wAwBgbPJAM4UyVnp2QBomA6HzmBtOK2O1OohqHryqBuUAwMY0KO8mDcpbQbADAGCsCC4MskFkWdbYOMrMDgtpSMK8A6FrDcongh1V3SLUoBwA6qWMVTdpUN4Kgh0AAGPFgWaTWR2Tzy+zA9Iw70DoWmbH5CAq2lhPewCTZWsBD5t8ADgzmR3dpEF5Kwh2AACMFQeaTfbrmHx+wQ5Iw7wDoWsNyifmgKYzOyKUbwCAzSgOwxu+QEXFBi59tIF3HQDAWFE2qvHMjvFCepgPGx0HsKoIdixm82k0WmZ2TFSxilFF80HxONMEdYsASVVjAYAuKj4nZXZ0S2Yd1AaCHQAAY/M+0DyT4vlldkAaigblc8vsGBQ9OyYHUVUZq/GjTnMAMxjPjW40AsCZlVmUze4pqFjx85ThmjTBDgCAsbIu/yCNnh0alEMaikDovErcbdygvKLgZyVlrARiAeCMNCjvJg3KW0GwAwBgbN4HmmeiZwekZd6B0LUG5aOIGDdEb7pBeYRa1QCwGRqUd1OxdspHEXl+9r9LYwQ7AADGygPNpnt2lPX6HShCChrL7MhH1d8i1KAcAOqlQXk3Ta6dXEpLlncdAMBYcaDZdLCjrNdvEQ1JmPfcUDzPcDSsPsAwU2aH8g0AcE4yO7ppch3o4keyBDsAAMaK4ELTZaxkdkBaupXZUTQon2IrKLMDAM5Nz45uWpfZYS2UKsEOAICx4WgYEQlkdow3RoIdkIbRaL6B0HUBz8ozO1bnuZkyOwQ7AODMis9amR3dMrl2Kn7GJEewAwBgrMjsWBwsNjqO8lb3SBkrSMG8y1gVc9C6zI6qy1hNM88pYwUA55bP8FlLuiZ/ni5+JEuwAwBgLJWeHcpYQVrKEndzuqG5bg7QoBwA2qVsUC6zo1M0KG8FwQ4AgLFUenasq9cPNG7egdB12V0alANAu5QXCxy7dooG5a3gXQcAMJZMZsdAZgekZN4NyueT2aFBOQDUQoPybsqytYCHfVqyBDsAAMZkdgAb6VaD8hlKa8jsAIBzm6VkJGlz8SN5gh0AAGMro0QyO8bPP8yHjY4DWDX3MlaDiYBnkYFRWbBjPK9M1bOj4rEAQBcVn7UyO7qnWD+N7NNSJdgBADBWlqpp+BbWunr9QOPmnfVVPM9qGavF1X9ZeRmrxa1/bfE1gh0AcGbFGn6az1rSVvW6jMoJdgAAjCljBWykzOyYU6PRdXOABuUA0C4alHdXuS6zT0uVdx0AwJgG5cBG5h0ILXt2jDQoB4DW0aC8uwYalKdOsAMAYExmB7CRon/O3Hp2TJax0qAcANpFg/LucvEjeYIdAABjyWR2ZDI7ICVF/5zFbD61t9fNAVXfIJzlAEaDcgA4N5kd3eXiR/IEOwAAxmR2ABuZdyB0YbBRz46K5oNKenaYmwBgQ3kus6PLZHYkT7ADAGBsZZRWZsdwNGx0HMCqIvA4rwblxRwwykdrByVVzQfF40yV2VHxWACgayYvBMjs6J6BYEfqBDsAAMaK29sLDd/CKkrlyOyANMw766vs2TFaiRiMS2dVXsZqipJcxdfY4APAxiY/I2V2dI8yVskT7AAAGEuljFVxe1zPDkjD3MtYZRuVsaqqQXlRxmqK78UGHwDOLhfs6DRlrJIn2AEAMJZKg3I9OyAtRYm7eQVCy1J2+bD6AEMxr2hQDgDVm/yMVMaqe1z8SJ5gBwDAWDKZHZP1+oHGlSXu5lXGal2D8iLAoEE5ACRPZke3yexInmAHAMBYKg3Ky3r9bgxBEuYdCC3moJV8pYbMjqJnxywNys1NALAhmR3dJrMjeYIdAABj8769fSblQacDRUhCWeJu0GTPjmE1D148ziyZHVWNBQC6RoPyblPSM3mCHQAAY2Wwo+GNSfH8MjsgDfPO7Cizu0YrEYPF8SAqblA+zTxXjMXcBAAbKz4js0FEljU7FqpX9bqMygl2AACMpdKzQ4NySEuZ2TGnEnfr5oCkGpQrYwUAZzVLbyzSp4xV8gQ7AADG5n2geSbr6vUDjStKyjXSs6PqcgkzNSgfj8XcBAAbm6U3Fulz8SN5gh0AAGNFJkXTwQ6ZHZCWuZexmixlV94grGg+qKRBubkJADYks6PbZHYkT7ADAGBs3re3z0RmB6Rl3llfxfOsb1CeQmaHDT4AnNUs5SJJnwblyRPsAAAYk9kBbKTM7JjTwcW6gGflPTuqyOywwQeADY0mGpTTPVVn3FI57zwAgLHi9nYymR0OFCEJjTUoH01mdgyrefBZDmEGFY8FALqm+IyU2dFNVa/LqJxgBwDAWBnsaHhzsjhYjAhlrCAVc+/ZkW3Qs6PqMlbjeWZLlLECgLPLZ/icJX3Fz9WltGQJdgAAjM37QPNM1tXrBxo3HN/em1tmhwblANBOGpR3m4sfyRPsAAAYK8pGNd2zQ4NySMu8A6EalANAS81yqYD0aVCePMEOAICxVDI7Jp9fdgc0b94l7jYsY6VBOQCkr8h+1KC8mzQoT553HgDA2LybEJ/J5PPL7oDmFUHHec0NZWbHqM7MjhkalJuXAGBjMju6zcWP5Al2AACMpdKgfDKzY8VCGhqXRoPyYTUPPqois6OisQBA1xSfkXp2dFPV6zIqJ9gBADCWTBmrgTJWkJJ5Z30Vc9AoH9XYoHxx619bbvDNSwCwodEMn7Okr/i5ynJNlmAHAMBYKmWs1mV2WEhD44oMq7k1KB+szkHDfKhBOQC0iTJW3aaMVfIEOwAAxkajNDI7JoMtMjugeWWJuzmXsVqf2aFBOQAkT4PybhtfSHHxI13eeQAAYzI7gI2UJe7mdEuzbFCe19mgXGYHAFROZke3ZUp6pk6wAwBgLJWeHVmWRRZZRMjsgBQ01bNjZbRSQ2bHeE4ZTPG9yOwAgLOb5VIB6XPxI3mCHQAAY+WB5jSHgBVbd9gJNGregdD1mR3j+SiJzA6lGwDgrGR2dJuLH8lrficPAJCIedflP5visFMZK2heY5kd+URmR2XBjuHqn3p2AED1is/ZBPYT1KBclw2bHQdnJNgBADCWUrCj6A0g2AHNKzKs5tagfPz+zyOPUVZXg/LFrX9t8TWCHQCwsZHMjk5Txip5gh0AAGOjcaO5phuUR6wdqurZAc2bd4PyyaDKSpaNB5FCGSsbfAA4q7I3lmBHJ2lQnrzmd/IAAIlIpUF5hDJWkJJ5l7GafJ5RVnGfDA3KAaA+GpR3m4sfyRPsAAAYS7FB+citIWjcvAOhMjsAoKU0KO82Fz+S1/xOHgAgETI7gI00m9kxDnZUVdJulkOYYlyCsACwMZkd3ebiR/IEOwAAxob5MCLS6tkh2AHNazazowgwDKt58OJxZsnsqGosANA1xWekzI5uyqyFUtf8Th4AIBFFyagkMjvGpbQ0KIfmrYyay+yorYzVVJkdbjMCwFkVa/cELk9Rg4Es19R55wEAjBVZFCkEO2R2QDqK9+HiYHEuzzefBuVTfC/F16hTDQAbKy8VzGfNwJwVP1d7tGQJdgAAjKXUs6NsUC6zAxpXvA/nldmRZdlawDPqalA+xfeiTjUAnJ0G5d2mQXnyBDsAAMbKJsSD5pdIZYNyC2lo3LwblE8+16h4Sg3KASB9GpR3m4sfyWt+Jw8AkAiZHcBGmpgb1jI7igBD1ZkdMzQot8EHgI3J7Og2mR3Jq72A3N1HDsXJxazupwEgYRfsPC+Jm/LMX57nsXzsWOSRNz2UTTk5GkZExIlhHkdODBsdSzY+4Lzv+OG49+iRRscCfXdyZTj+c35zQ5HZsTw8ESciYmXleBw/dM/Mj7srXy2MdeTE8Ygtzi2DkydjZ0SMhsfjWAVjoX+ybBBL5y1Fljkj6KPR8GTcc+S+pocBtVo8en9sy7JYGY3ixJFDTQ+Hii0OT67+fE8ejRPLX2p6OK11aLm+90aW53ktpw/Ly8uxb9++uOr1V8XCLtFMgD57x7e9Oy7avbfpYdCAIyeG8aTrr4mFnbc3PZQtOfzZl8fo6EMaHcPuh/1CLOy8rdExAOsd+ey/jpWjD53Lc+35iv8Y2cKxuTwXzMsjjg7iN//vD8Tu7Rr39tG97/+1+Ccf+5mmhwFAw1aOrsTHX/7xuO+++2JpaanSx3bNFgBgwujk3hgdf2DTw4iVw49oegjAhNFwb6zMcW4YmgMAAGBLas/s+MxtX4i9FUdoAGgXZaz6K8/z+NKR5chb1Hdi5+KuWBykceP08MnDrXrtoMvmPTfkeR6HT96/+n9WhhHDo9U9+Pbz1pqNb9VoJeKk0npMZ7CwGBfte5AyVj01Onks7jl0V9PDgPotLEZs2930KKjL8Pjq/5jaoeVD8bCHXVVLZkftq/ULd++NJaVLAKCXsiyLi8/b1/QwWmv3dq8d9Nl5Oy5oeggAlRls2xkXXXh508MAmJFz7lltW1yu7bFdswUAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaLXFuh44z/OIiFheXq7rKQAAAAAAgJYo4gVF/KBKtQU77rrrroiI2L9/f11PAQAAAAAAtMxdd90V+/btq/Qxawt2XHjhhRER8fnPf77yQQPtsby8HPv3748DBw7E0tJS08MBGmAeAMwDgHkAMA8AERH33XdfPPjBDy7jB1WqLdgxGKy2A9m3b58JDIilpSVzAfSceQAwDwDmAcA8AESsxQ8qfczKHxEAAAAAAGCOBDsAAAAAAIBWqy3YsWPHjrjuuutix44ddT0F0ALmAsA8AJgHAPMAYB4AIuqdC7I8z/PKHxUAAAAAAGBOlLECAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWk2wAwAAAAAAaDXBDgAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWW6zrgUejURw8eDD27t0bWZbV9TQAAAAAAEAL5Hkehw4dissuuywGg2pzMWoLdhw8eDD2799f18MDAAAAAAAtdODAgbjiiisqfczagh179+6NiNVBLy0t1fU0AAAAAABACywvL8f+/fvL+EGVagt2FKWrlpaWBDsAAAAAAICIiFpaX2hQDgAAAAAAtJpgBwAAAAAA0GqCHQAAAAAAQKsJdgAAAAAAAK0m2AEAAAAAALSaYAcAAAAAANBqgh0AAAAAAECrCXYAAAAAAACtJtgBAAAAAAC0mmAHAAAAAADQaou1P8P/elnE7u21Pw101gO+ImLbrojbPtz0SPppcWfE17464oFXRXzk9yM+/idNj4iqZFnEY74j4srnND2S+fnHGyM+9P9F5Pn8n/sbXxux7/L5Py9p+F8vixgea3oUMJM/Ht4V7xzdN78n3LY74oFfFRF5PGf53nj2yraIp74i4kGP2fJD/eM9/xjXf/T6OL5yfOZhZVkWz3v48+IZVzxj5scCgM7465+POPjBpkdB3a76ZxGP/tamR8FZ1B/suOV/R+zIan8agNosbIt4/i9FvOUHI47c1fRoqNLtH+1XsOPG10TceXMzz/3MH27meUnDJ/404sT9TY8CZvLjD7kijg7mmBi/cm/E5w9GRMSHhsN49oGDEfko4lv+55Yf6jdu/o34k09Xd2Hjk/d8UrADAArLByP+4rqmR8E8fPavBDsSV3+w49k/HrFnV+1PA510009EHL1n9Z8Xd0Zc/ePNjqdvPvOXER//44iTR1f//4kjq38+80cidl/Y3LiY3b2fj3j3L679bPvixOHVP5/6iogLHjrf597zwPk+H2m5+scjRsOmRwEzOfrxX4yIiH978VPjvEHNmeuffkfE0Xvirsd+e/yPW/8ijmXjy2NTfm4dHa5+3bMf8uz46ku/euphHbz/YNzwsRvi2IpMLQAoFZ/PCzsivvEnmh0L9Thyd8Q7Xtu/M4QWqj/Y8aTvjlhaqv1poJPe80trwY5tuyKe/NJmx9M3+Wg12JGvjP//+M/HfWfE+fubGxezO/ih1WBH8TPti3y0+udX/fOIK57Y7Fjolye9pOkRwExG+ShiHOz4lq//L3HBzgvqfcJf/acRdx6Iz1702Pgft/5FjIpgx2i6z62V8efdky55UrzwK1849bA+9qWPxQ0fu6F8PAAg1j6ft+10btNV9x5YDXZMuRZjfjQoh5QNFjf+Z+YjG0+RxYdZ8aefRfsNFlb/7NtCpfwdXmh2HAAtM3m4P8jmsIUarzUWxi2WymefMsgwGge7F2dcwxTf+2g0mulxAKBTcmcFnVfsoV34SJ5gB6QsW9j4n5mP8sNsvKHPHRR3RtbThYrfYYCpFMGCiIiFeazJxvP0QqxGO8pnnzGzY9ZATfH1MjsAYELx+ezcpruynl6YbCHBDkjZ5IGkw8n5m/wwm7zBaAHTfn3P7PA7DLAlK6M5Z3aMn2OQrwY7VooyVjNmdswaqCm+fjL4AwC951JZ95U/2zxivD4jTYIdkDKZHc2aTFOcPFwYmDpbLzsla6cvLMIBprIus2Mec2iqmR0DmR0AcBqXyrpvcg3Vt0uTLePEDlI2eajugH3+ysyO4foPMwuY9huc0o+lLyzCAaYy954d43l6MM4sHWXZathjyiB90WNj1rEXmR2CHQAwofh8dm7TXZOXXUbD5sbBOXkXQspkdjSraC42Wln/YeZWfPuVP9ueLVLKBuU+/gG2YvJwf649OybKJKxETP25VYxfGSsAqEHx+ezcprsmm8+79JE0px2QssnJdPKfmY/iQDgfnVLGys+i9XrfoNzvMMBWFIf7WWTzyewYz9NFGauIcSmrGctYzVqCq8zs6FtmJACczcg+q/MmA1nWQUkT7ICUaVDerHUNypWx6hQNypsdB0DLFIf7c8nqiCjrQi9MZFCsZFnjDcqLQI/MDgCYoDdi903+bPt2abJlBDsgZcpYNWtdg/LR6f+e9irfT3nERImQzrMIB5hKcbg/l6yOiHKeHkx8RFWR2TFzz46Bnh0AcBqXyrpvXWaHSx8pE+yAlGlQ3qwNMzuyiCxrbEhUZNDDFNQ8XwvaWYQDbElVZaA2LSt6dkxmdsTMDcqryuzII5fdAQCFXG/Ezpv82br0kTTvQkiZzI5mlaWOhm7Ed03Ww4XKZFDH7zHAllSVGbFpRWbHxNw9iqz5zI6J9ajsDgAYG7lU1gvZxBkRyRLsgJTp2dGsyTJW0lK7ZbJxXG8yOyb7zvj4B9iKMrNjbj07ijJWE5kdEY337Jj8epkdADDmcmQ/FOcIfTlDaCmnHZAymR3NKqP2o7XIvcVLN6wrY9WTWxkyOwCmVlUZqE0bz9NZPopBrJbPXMmyqT+zysyOGctrTGaGrNjoA8Cq4vPZuU23TV6IJVmCHZCyydvnk//MfGzUoNzPoRsmF6F9WahMfp9+jwG2pKkyVpGvxMI42FFFg/KZMzsGMjsA4DTF57N9VrdN9nUlWYIdkDINypu1UYNy5X+6YV1mR08OayYXZG4cAWxJVWWgNm0iu3QhKzI7YvoG5RWNf11mR18uCwDAuShj1Q/FuZwLH0lzagcpU8aqWesyOyxeOqWPDconF2R+jwG2pAgWzFoGatMm1iCDMrNjhgblo+oblMvsAICxskG5Y9ZOk9nRCt6FkDINyptVfpANNSjvmixbW4j2ZaEyWefdIhxgS4b56hw6/8yOYVnGKoUG5TI7AGADLkf2w2DijIhkOe2AlMnsaNZgrYSExUsHFfVU+3JYM1mKbVwSBYDNmXsZq8HazcFiwzbKYuaeHVVkpixmq5+fGpQDwJjLkf3QtzOElhLsgJTJ7GjWZBmrInJv8dIdWc9uZeQW4ADTqqoM1KYVzzPRoHwY2dSfWVU1KI9Yew2UsQKAseLz2blNtylj1QqCHZAywY5mrWtQPt7Q+zl0x6BnC5Xi+yxuowCwafPP7BjP1RMNykcJNCiPiFgYf34qYwUAY7m9Vi9oUN4Kgh2QMmWsmqVBebcV76m+LFT8DgNMrcoyUJuyrkH5eAwRM5exktkBADWYLBlMd8nsaAXvQkiZzI5mrcvsUAKocwZ9a1A+PpTyOwywZXPP7JhYgxRlrEaRTd+gfPwZUEUZruIxZHYAwFiuEkQvTF6IJVmCHZAymR3Nmixz5FZ892Q9W6iUdWR99ANsVZnZMa8bm+UaZLiW2VFBg/JKyliNH0ODcgAYczmyH/rW97OlnHhAymR2NGtdg3KLl84p66H35LBGg3KAqRUH+4vZnGpxTwTkK8nsGN84raIMVxns6MtlAQA4F5cj+6FvZwgtJdgBKZPZ0axso8wO02Zn9C0FdWQBDjCtMlgwt8yOotTiaH1mRz6KyPMtP1wdmR16dgDAmMuR/VA2KO/JGUJLObWDlE0erDtknz+ZHd3Wt+ZiMjsApjb3MlbrMjvGYyj+2xSfW1WOv8gOEewAgDElg/uhPEOwBkqZdyGkbLC48T8zHxs1KPdz6I6+Nij3OwywZWWD8nllx02USVgYJ3KMstVyVlu9TZjneaUN1pWxAoBT5PZavdC36hAtJdgBKVPGqlnlgUY+cVPDz6Ez+tagXCk2gKk11qA8X1krY1X8ty0G6SczMKoIdhSvgcwOABhTCaIf+lYdoqWceEDKNChv1uSBxsqJ8b/zc+iMQc8WKkXAzu8wwJZV2fNiU8rN9LAsY1WGFrYYpJ8MSlTZoHxYfK4AQN9pUN4Pg7X1GekS7ICUyexo1mQK6srJ8b/zc+iM4ufbl8wODcoBprYymnOwY6LU4lqD8qz8d1sxWW5Kg3IAqIHMjn7o2xlCSwl2QMo0KG/W5KHwyvHVP+dVvoL69S0FVYNygKlV2fNiU8pSi6O1nh3Ff9tikGFdZkeFDcr17ACAMZkd/VCsozQoT5pTO0iZzI5mTb7mMju6p3cNyi3AAabVWM+OicyOYZnZsbXSCcN87e/L7ACAGpSZHY5ZO02D8lbwLoSUTZZRmvxn5mNdZse4Z4efQ3f0rkH5+FBKsANgy8rMjnnNoRNlEhZjNbWjDC1stUH5qJ4G5TI7AGCsvFjmvKDT+lYdoqUEOyBlGpQ3a/JAYHj89H9Hu/WuQbkyVgDTmntmx8RmejAuY1V+Wm0xyDAZlKhi/DI7AOAUylj1g8yOVhDsgJQpY9WsyT4pylh1T98yO4qyJ36HAbasaFA+/zJWw3LDNpqyQXkRlBhkg8iKx5hlaEVmR18uCwDAubhY1g/Z2vqMdAl2QMrWZXZ4uzaiSEMtylipwdkdxc+2L4c1GpQDTK0IGCxmcypPUaw38lEs5KeUsZoys6OqQE2R2aGMFQCMyezoh75Vh2gpp3aQsslNqQPKZhSve9mzw8+hMwZrB0m9oEE5wNRSaFC+UiRljLb2uVX2G6loLVn0LVHGCgDGNCjvh7KMlTVQyrwLIWV6djRvcEqwQ9CpO/rWXKxYkFmAA2zZ3BuUT5RaXMvsyMp/txVVB2o0KAeAUxR7Lec23da3M4SWcuIBKdOzo3kyO7pr0LN6mzI7AKbWbGbHarCj/LTa4udW0VujsswODcoBYL3is9m5TbdpUN4Kgh2QssHixv/M/BSljoZFsMPPoTP61qC8rCPrdxhgq6ouBXVOxVydr8RikdkxY4PyqrJSZHYAwClG9lq9ILOjFQQ7IGXKWDXv1MwOJYC6o2/Nxdw2ApjacDyHzi2zo3ie0UoMxsGO8tNqyjJWVWd2rPTl8xMAzkWD8n4oLsP2pTpESzm1g5Sta1Du7dqI4maGMlbd07cUVGWsAKY2/8yOtYD8QhSZHcVgpmtQXlWgpgx29OXzEwDORYPyfigzb5XyTJl3IaRMZkfzygblJ1f/dCu+O8oU1J4sVHILcIBpzT3YMVFqcS2zI40G5UU5LD07AGBMg/J+UMaqFZx4QMo0KG9eWcbq+OqfFi/d0bvMDgtwgGmVAYPB/BuULxQ9O4r/Nm3PjorWknp2AMApyswOe61O69sZQksJdkDKZHY0rzjUKHt2+Dl0Rnkroyf1NnMLcIBpJZHZUZax2trnVtX9RorHkdkBAGPFZ7Nzm26T2dEKgh2QsqIe4Kn/zPwUH2ZDPTs6p3cNyoueHeYSgK2quhTUORVz9WhUZnZMW8aqCEosVjT/FwEfwQ4AGMvttXpBZkcrCHZAytaVsfJ2bUTZs0Owo3Oyni1U3DYCmNrKOGA8vwbl43XfaBgL46DCWoPyZnt2FI8z7EtmJACcizJW/VCspfpyYbKlnJ5CyibrQjugbEZxM0OD8u4pMzt6cjNVGSuAqRUBg2bLWBX/bmufW1WX4CoyRGR2AMBY2aDcMWunlZm3gh0p8y6ElGlQ3rxMZkdn9S0FdWQBDjCtMmAwr3XAZIPyGDcon3KDXVdmhwblADAms6Mf+naG0FJOPCBlGpQ3T4Py7upbczGZHQBTm3vPjsnMjnGweqVYk0zZs6OqzA49OwDgFGXPDnutTuvbGUJLCXZAymR2NE9mR3f17VbGyAIcYFpVBwzOaaLUYpnZMeUGuxi7zA4AqInMjn7o2xlCSwl2QMpkdjSveN2Hx1f/1Ci+O8pDo540WJXZATC1+Wd2rGVxFJkdw8F0n1tFI3GZHQBQk+Kz2blNt2lQ3gpO7SBlgh3NKzM7xg3Ki3rZtN+gZwuVcgHudxhgq1ZGcw52lP05hrEwDiqMygDIlA3KK1pLlpkdffn8BIBzUcaqHwbKWLWBYAekTBmr5hWHDcpYdU/xs+3LzdSyjJWPfoCtKgIGi9mcAsaTDcqLYMeUQfqqs1KKoIkyVgAwNs7CdG7TceUZgjVQypx4QMpkdjRvcErPDouX7uhbc7HcAhxgWmXAYF4B48kG5flqz46VTINyAEiSzI5+6NsZQksJdkDKZHY079SDBYuX7uhbczENygGm1liD8nyiQXkimR0alAPAKTQo74e+nSG0lGAHpExmR/NOfd01KO+Ovt3K0KAcYGrzb1C+NlcP8vEYZHYAQJpcjuwHDcpbwakdpExmR/NOfd0tXrqjvCE7bHYc8yKzA2Bq88/sWNumFc9YBju2+LlVdXN1mR0AcIris9m5TbdNZN6SLsEOSJnMjuad+roP5tSYlPplPVuoFAtwcwnAllUdMDinifXGQtmzI1v9F6OtfW4VQYmFiub/IuCz4lYjAKwqPpvttbqtrA7RkwuTLSXYASkT7GjeqcENNzW6o/jZ9uWwRoNygKlVHTA4p4m5uvinUSplrMavgcwOABhTxqof+naG0FKCHZAyZayad+oNTouX7uhbczFlrACm1liD8ogYxKmZHc02KNezAwBOoUF5P/TtDKGlBDsgZTI7mndag3I/h87QoByATWqyQfnCuEH5qAh2NJzZoWcHAJxCZkc/9O0MoaUEOyBlMjuad1qDctNmZ/TtVobMDoCpNZvZsar8tNpqZkdNDcpldgDAmMyOfijOg6yBkubUDlI2eShZ3OZjvmR2dFdx6NOXWxkyOwCmNv/MjiwiVtd+g3GD8nJb3XTPDmWsAGC9XIPyXpDZ0QqCHZCyyQ31eKPLnJ2W2WHx0hmDni1UyswOH/0AW1VkR8wtsyOi/JwqnnHazI5hPlx9uIozO4ajYSWPBwCtV3wmzutSBM0ozxCsgVLmXQgpGyw2PQJODW74mXRH8bPsXRkrv8MAW1VkdizM89LDeL5eGF94mTbYUWRgLFY0/xePI7MDAMbstfqhb2cILVX7u/DuI4fi5KLyOzCVk0dj17h81bFjhyM/cqjhAfXPtlEeixMlxI6fOBEjP4dOWBieiO1ZFqPhiTjeg5/pjuGJGGRZnBiejJUGvt8Ldp4XA1klvZTneRw9aUNAuw3HhxgnhxFHTsznNt+ubCGymMzsyMdjOBkntzCGY8OTERExyqsZ+3D8dj6xMpzbawF0y65tC5Ep09xLo9Eo7jl2uOlhVG5njCLLsjh2/Khzmw4bnDgRO7IsRisne3GGUKdDNb5+WZ7XUxtneXk59u3bF1e9/qpY2KXsCwD02Tu+7d1x0e69TQ+DBhw5MYzH/bfviywT8KC9tp3/vsgWjsaRz74sVo4+bC7P+fc7vieWsqPx9t274t9ecnGcN1yMb7v/7rgzzo/b8gs3/Ti37TwSt+86Eo++98L4ui9ePvO4PrH3nviLS78Qe09uiy+/f9/Mjwf0y7GjD4kbvv9/xu7tbsD30T03/1k8433/b9PDABq2cnQlPv7yj8d9990XS0tLlT62TxcAAGq1/YJ3RbZwoulhwMzy0c65PddynBdLcTT2jlbLRR1eHMYN5y/FaqvyL2358Z4Sn4mXLX5o5nG9I3bFX8TFcWjbyfjgBVsfB9Bvj1k40vQQaNDgtg82PQSg42oPdvzZC26MvRVHaKBPBp++KbLhsVj5imuaHko/3f/FWPzwb0Y2PBL50hUxfNy/jJBy3Q0rJ2Lxg78W2eE7mx7J3OS7L47h4/+viMUdc3/uC3aeN/fnJA27ti3E//2YfxknV042PRSYyRV798cLvuOfz630yuDzN8TJT74tHp3n8e/27IrbB3kM7vhIxBTvpV3ZQnzrAx4aJxdmD9Y8OR/F9x35THxpdGzmxwL658oHPzp2bVP9o6/2PuRp8a4T3Sz/M7r08bFy5Tc1PQzqlOex+KFfj2z5C02PpPWWjxyP/fHxWh679jJWdaSjAAAAAAAA7VJn3ECnUAAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGg1wQ4AAAAAAKDVBDsAAAAAAIBWE+wAAAAAAABaTbADAAAAAABoNcEOAAAAAACg1QQ7AAAAAACAVhPsAAAAAAAAWm2xrgfO8zwiIpaXl+t6CgAAAAAAoCWKeEERP6hSbcGOu+66KyIi9u/fX9dTAAAAAAAALXPo0KHYt29fpY9ZW7DjwgsvjIiIz3/+85UPGmiH5eXl2L9/fxw4cCCWlpaaHg7QEHMBYB4AzAOAeQAo5oGbb745Lrvsssofv7Zgx2Cw2g5k3759JjDouaWlJfMAYC4AzAOAeQAwDwBx+eWXl/GDKmlQDgAAAAAAtJpgBwAAAAAA0Gq1BTt27NgR1113XezYsaOupwASZx4AIswFgHkAMA8A5gGg/nkgy/M8r+WRAQAAAAAA5kAZKwAAAAAAoNUEOwAAAAAAgFYT7AAAAAAAAFpNsAMAAAAAAGi1LQU7fvInfzK++qu/Ovbu3RsPfOAD4wUveEHccsst6/7Or/zKr8Qzn/nMWFpaiizL4t577z3tce6+++540YteFEtLS3H++efH93zP98T9998/0zcCzEdV88BDH/rQyLJs3f9e97rXzem7AGZxrnng7rvvjle+8pVx5ZVXxq5du+LBD35wvOpVr4r77rtv3eN8/vOfj+c+97mxe/fueOADHxg/8AM/EMPhcN7fDjClquaCU9cDWZbF7/zO78z72wGmsJm9wcte9rJ4+MMfHrt27YqLL744nv/858cnPvGJdX/HmgDaq6p5wHoA2msz80Ahz/O45pprIsuy+MM//MN1/62K9cCWgh3vfOc749prr42/+Zu/iRtvvDFOnjwZV199dRw+fLj8O0eOHInnPOc58SM/8iNnfJwXvehF8bGPfSxuvPHG+NM//dP4y7/8y/hX/+pfbWngQDOqmgciIv7zf/7Pcdttt5X/e+UrX1n38IEKnGseOHjwYBw8eDB+5md+Jj760Y/GDTfcEG9961vje77ne8rHWFlZiec+97lx4sSJePe73x2/9mu/FjfccEO85jWvaerbAraoirmgcP31169bE7zgBS+Y83cDTGMze4MnPvGJcf3118fHP/7xeNvb3hZ5nsfVV18dKysrEWFNAG1XxTxQsB6AdtrMPFD4+Z//+ciy7LR/X9l6IJ/BnXfemUdE/s53vvO0/3bTTTflEZHfc8896/79zTffnEdE/r73va/8d295y1vyLMvyW2+9dZbhAA2YZh7I8zx/yEMekv/cz/1c/QMEane2eaDwu7/7u/n27dvzkydP5nme53/2Z3+WDwaD/Pbbby//zutf//p8aWkpP378eO1jBqo3zVyQ53keEfmb3/zmOYwQqNtm5oEPf/jDeUTkn/zkJ/M8tyaArplmHshz6wHokjPNAx/84Afzyy+/PL/ttttOe89XtR6YqWdHkYJ+4YUXbvpr3vOe98T5558fT3rSk8p/9w3f8A0xGAzive997yzDARowzTxQeN3rXhcXXXRRPP7xj4+f/umflqoOLbWZeeC+++6LpaWlWFxcjIjV9cCjH/3ouOSSS8q/843f+I2xvLwcH/vYx+odMFCLaeaCwrXXXhsPeMAD4slPfnK88Y1vjDzPax0rUI9zzQOHDx+O66+/Ph72sIfF/v37I8KaALpmmnmgYD0A3bDRPHDkyJH4zu/8zvjv//2/x6WXXnra11S1Hlg891/Z2Gg0ile/+tXx9Kc/PR71qEdt+utuv/32eOADH7h+EIuLceGFF8btt98+7XCABkw7D0REvOpVr4onPOEJceGFF8a73/3u+OEf/uG47bbb4r/+1/9a02iBOmxmHvjSl74UP/ZjP7auZOXtt9++bhETEeX/tx6A9pl2LohYLWv59V//9bF79+748z//8/g3/+bfxP333x+vetWr5jF0oCJnmwd++Zd/OX7wB38wDh8+HFdeeWXceOONsX379oiwJoAumXYeiLAegK440zzwvd/7vfG0pz0tnv/852/4dVWtB6YOdlx77bXx0Y9+NP76r/962ocAWm6WeeD7vu/7yn9+zGMeE9u3b4+Xvexl8ZM/+ZOxY8eOKocJ1Ohc88Dy8nI897nPjUc+8pHxH//jf5zv4IC5mWUu+A//4T+U//z4xz8+Dh8+HD/90z/tcANa5mzzwIte9KJ49rOfHbfddlv8zM/8THz7t397vOtd74qdO3c2MFKgLrPMA9YD0A0bzQN//Md/HG9/+9vjgx/8YO3PP1UZq1e84hXxp3/6p3HTTTfFFVdcsaWvvfTSS+POO+9c9++Gw2HcfffdG6awAGmaZR7YyFOe8pQYDofx2c9+dvbBAXNxrnng0KFD8ZznPCf27t0bb37zm2Pbtm3lf7v00kvjjjvuWPf3i/9vPQDtMstcsJGnPOUp8YUvfCGOHz9e15CBip1rHti3b1884hGPiGc84xnx+7//+/GJT3wi3vzmN0eENQF0xSzzwEasB6B9zjQPvP3tb49PfepTcf7558fi4mJZ0vZbvuVb4pnPfGZEVLce2FKwI8/zeMUrXhFvfvOb4+1vf3s87GEP28qXR0TEU5/61Lj33nvjAx/4QPnv3v72t8doNIqnPOUpW348YL6qmAc28qEPfSgGg8FpZe6A9GxmHlheXo6rr746tm/fHn/8x3982s3Npz71qfGRj3xk3QWIG2+8MZaWluKRj3xk7d8DMLsq5oKNfOhDH4oLLrhApie0wDR7gzzPI8/z8gDTmgDarYp5YCPWA9Ae55oHfuiHfij+/u//Pj70oQ+V/4uI+Lmf+7m4/vrrI6K69cCWylhde+218Vu/9VvxR3/0R7F3796yXta+ffti165dEbFaQ+v222+PT37ykxER8ZGPfCT27t0bD37wg+PCCy+Mq666Kp7znOfES1/60njDG94QJ0+ejFe84hXxHd/xHXHZZZdtZThAA6qYB97znvfEe9/73njWs54Ve/fujfe85z3xvd/7vfFd3/VdccEFFzT2vQGbc655oDjcPHLkSPzmb/5mLC8vx/LyckREXHzxxbGwsBBXX311PPKRj4wXv/jF8VM/9VNx++23x4/+6I/Gtddea0MDLVHFXPAnf/Incccdd8TXfM3XxM6dO+PGG2+M1772tfHv/t2/a/JbAzbpXPPApz/96XjTm94UV199dVx88cXxhS98IV73utfFrl274pu+6ZsiIqwJoOWqmAesB6DdzjUPXHrppRtmZzz4wQ8uAyOVrQfyLYiIDf93/fXXl3/nuuuuO+ffueuuu/IXvvCF+Z49e/KlpaX8JS95SX7o0KGtDAVoSBXzwAc+8IH8KU95Sr5v3758586d+VVXXZW/9rWvzY8dO9bMNwVsybnmgZtuuumMf+czn/lM+Tif/exn82uuuSbftWtX/oAHPCD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      "text/plain": [
       "<pyannote.core.feature.SlidingWindowFeature at 0x7f2873347a60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Inference('pyannote/segmentation-3.0', token=True, step=2.5)(test_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "DvpRY7H1nX82",
    "outputId": "ce284d08-e0f1-4a58-da1e-0b7c62fa2f31"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<pyannote.core.annotation.Annotation at 0x7f28d69fefb0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_file[\"annotation\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hjw4nIE7nX83"
   },
   "source": [
    "Let's update the model so that it specifically addresses overlapped speech detection.  \n",
    "This is achieved very simply by updating the `task` attribute..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "xhmDk1qJnX83"
   },
   "outputs": [],
   "source": [
    "from pyannote.audio.tasks import OverlappedSpeechDetection\n",
    "osd_task = OverlappedSpeechDetection(ami, duration=2.0)\n",
    "\n",
    "osd_model = Model.from_pretrained(\"pyannote/segmentation-3.0\", token=True)\n",
    "osd_model.task = osd_task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wbb0MeQanX84"
   },
   "source": [
    "... optionally freeezing a bunch of layers..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "tuE1xNjunX84",
    "outputId": "2211d836-8bda-4550-a5e2-7646c764c0af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sincnet',\n",
       " 'sincnet.wav_norm1d',\n",
       " 'sincnet.conv1d',\n",
       " 'sincnet.conv1d.0',\n",
       " 'sincnet.conv1d.0.filterbank',\n",
       " 'sincnet.conv1d.1',\n",
       " 'sincnet.conv1d.2',\n",
       " 'sincnet.pool1d',\n",
       " 'sincnet.pool1d.0',\n",
       " 'sincnet.pool1d.1',\n",
       " 'sincnet.pool1d.2',\n",
       " 'sincnet.norm1d',\n",
       " 'sincnet.norm1d.0',\n",
       " 'sincnet.norm1d.1',\n",
       " 'sincnet.norm1d.2',\n",
       " 'lstm']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osd_model.freeze_up_to('lstm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DrKqQyXonX85"
   },
   "source": [
    "... and training it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "",
      "17ca908b800f4d34841bc2c7d5ccdff3"
     ]
    },
    "id": "z9PIQ4oUnX85",
    "outputId": "ed98a898-d9a2-4895-d523-9660abf79f1e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name              | Type             | Params | In sizes      | Out sizes                                  \n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "0 | sincnet           | SincNet          | 42.6 K | [1, 1, 32000] | [1, 60, 115]                               \n",
      "1 | lstm              | LSTM             | 1.4 M  | [1, 115, 60]  | [[1, 115, 256], [[8, 1, 128], [8, 1, 128]]]\n",
      "2 | linear            | ModuleList       | 49.4 K | ?             | ?                                          \n",
      "3 | classifier        | Linear           | 129    | [1, 115, 128] | [1, 115, 1]                                \n",
      "4 | activation        | Sigmoid          | 0      | [1, 115, 1]   | [1, 115, 1]                                \n",
      "5 | validation_metric | MetricCollection | 0      | ?             | ?                                          \n",
      "---------------------------------------------------------------------------------------------------------------------\n",
      "49.5 K    Trainable params\n",
      "1.4 M     Non-trainable params\n",
      "1.5 M     Total params\n",
      "5.890     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a9dfccd597f477bb2990e2746433375",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f24057dd69a449a19e04d400305e137d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "434c310479c8425ea51772167a0ef10b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=1` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(devices=1, max_epochs=1)\n",
    "trainer.fit(osd_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B5adPAy8nX86"
   },
   "source": [
    "Et voilà! A brand new overlapped speech detection model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "jZHEnmw7nX86",
    "outputId": "3856fee4-7547-4297-a56b-bbc59544be52"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyannote.core.feature.SlidingWindowFeature at 0x7f2a11452c20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyannote.audio.utils.signal import binarize\n",
    "binarize(Inference(osd_model)(test_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "P_7CB2wgnX87",
    "outputId": "a5e3a05f-8695-4880-9380-fbde264ca06f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<pyannote.core.annotation.Annotation at 0x7f28d69fefb0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_file[\"annotation\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A4OM7DzwnX88"
   },
   "source": [
    "## Going further\n",
    "\n",
    "This tutorial only scratched the surface of the training API.\n",
    "\n",
    "Every task supports an `augmentation` parameter for training with data augmentation via [`torch-audiomentations`](https://github.com/asteroid-team/torch-audiomentations) library:\n",
    "\n",
    "```python\n",
    "from torch_audiomentations import AddBackgroundNoise\n",
    "augmentation = AddBackgroundNoise(\"/path/to/background/noise/directory\")\n",
    "vad_task = VoiceActivityDetection(ami, augmentation=augmentation)\n",
    "```\n",
    "\n",
    "We also benefit from all the nice things [`pytorch-lightning`](https://lightning.ai/docs/pytorch/stable/) has to offer (like multi-gpu training, for instance).\n",
    "\n",
    "```python\n",
    "trainer = Trainer(devices=4, accelerator=\"gpu\", strategy='ddp')\n",
    "trainer.fit(model)\n",
    "```\n",
    "\n",
    "Default optimizer (`Adam` with default parameters) is automatically set up for you.  \n",
    "Customizing optimizer (and scheduler) requires overriding [`model.configure_optimizers`](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#configure-optimizers) method:\n",
    "\n",
    "```python\n",
    "from types import MethodType\n",
    "from torch.optim import SGD\n",
    "from torch.optim.lr_scheduler import ExponentialLR\n",
    "def configure_optimizers(self):\n",
    "    return {\"optimizer\": SGD(self.parameters()),\n",
    "            \"lr_scheduler\": ExponentialLR(optimizer, 0.9)}\n",
    "model.configure_optimizers = MethodType(configure_optimizers, model)\n",
    "trainer.fit(model)\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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