notebooks/adversarial_audio_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial Audio Examples\n",
    "\n",
    "This notebook demonstrates how to use the ART library to create adversarial audio examples.\n",
    "\n",
    "---\n",
    "\n",
    "## Preliminaries\n",
    "\n",
    "Before diving into the different steps necessary, we walk through some initial work steps ensuring that the notebook will work smoothly. We will \n",
    "\n",
    "1. set up a small configuration cell,\n",
    "2. check if the test data and pretrained model are available or otherwise download them\n",
    "3. define some necessary Python classes to handle the data.\n",
    "\n",
    "**Important note:** This notebook requires `torch==1.4.0`, `torchvision==0.5.0` and `torchaudio==0.4.0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchaudio\n",
    "\n",
    "\n",
    "\n",
    "from art.attacks.evasion import ProjectedGradientDescent\n",
    "from art.estimators.classification import PyTorchClassifier\n",
    "from art import config\n",
    "from art.defences.preprocessor import Mp3Compression\n",
    "from art.utils import get_file\n",
    "\n",
    "OUTPUT_SIZE = 8000\n",
    "ORIGINAL_SAMPLING_RATE = 48000\n",
    "DOWNSAMPLED_SAMPLING_RATE = 8000\n",
    "\n",
    "# set global variables\n",
    "AUDIO_DATA_PATH = os.path.join(config.ART_DATA_PATH, \"audiomnist/test\")\n",
    "AUDIO_MODEL_PATH = os.path.join(config.ART_DATA_PATH, \"adversarial_audio_model.pt\")\n",
    "\n",
    "# set seed\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download AudioMNIST data and pretrained model\n",
    "get_file('adversarial_audio_model.pt', 'https://www.dropbox.com/s/o7nmahozshz2k3i/model_raw_audio_state_dict_202002260446.pt?dl=1')\n",
    "get_file('audiomnist.tar.gz', 'https://api.github.com/repos/soerenab/AudioMNIST/tarball');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash -s \"$AUDIO_DATA_PATH\" \"$ART_DATA_PATH\"\n",
    "mkdir -p $1\n",
    "tar -xf $2/audiomnist.tar.gz \\\n",
    "    -C $1 \\\n",
    "    --strip-components=2 */data \\\n",
    "    --exclude=**/*/{01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataloader and preprocessing classes.\n",
    "class AudioMNISTDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"Dataset object for the AudioMNIST data set.\"\"\"\n",
    "    def __init__(self, root_dir, transform=None, verbose=False):\n",
    "        self.root_dir = root_dir\n",
    "        self.audio_list = sorted(glob.glob(f\"{root_dir}/*/*.wav\"))\n",
    "        self.transform = transform\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.audio_list)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        audio_fn = self.audio_list[idx]\n",
    "        if self.verbose:\n",
    "            print(f\"Loading audio file {audio_fn}\")\n",
    "        waveform, sample_rate = torchaudio.load_wav(audio_fn)\n",
    "        if self.transform:\n",
    "            waveform = self.transform(waveform)\n",
    "        sample = {\n",
    "            'input': waveform,\n",
    "            'digit': int(os.path.basename(audio_fn).split(\"_\")[0])\n",
    "        }\n",
    "        return sample\n",
    "\n",
    "\n",
    "class PreprocessRaw(object):\n",
    "    \"\"\"Transform audio waveform of given shape.\"\"\"\n",
    "    def __init__(self, size_out=OUTPUT_SIZE, orig_freq=ORIGINAL_SAMPLING_RATE,\n",
    "                 new_freq=DOWNSAMPLED_SAMPLING_RATE):\n",
    "        self.size_out = size_out\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        transformed_waveform = _ZeroPadWaveform(self.size_out)(\n",
    "            _ResampleWaveform(self.orig_freq, self.new_freq)(waveform)\n",
    "        )\n",
    "        return transformed_waveform\n",
    "\n",
    "\n",
    "class _ResampleWaveform(object):\n",
    "    \"\"\"Resample signal frequency.\"\"\"\n",
    "    def __init__(self, orig_freq, new_freq):\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._resample_waveform(waveform)\n",
    "\n",
    "    def _resample_waveform(self, waveform):\n",
    "        resampled_waveform = torchaudio.transforms.Resample(\n",
    "            orig_freq=self.orig_freq,\n",
    "            new_freq=self.new_freq,\n",
    "        )(waveform)\n",
    "        return resampled_waveform\n",
    "\n",
    "\n",
    "class _ZeroPadWaveform(object):\n",
    "    \"\"\"Apply zero-padding to waveform.\n",
    "\n",
    "    Return a zero-padded waveform of desired output size. The waveform is\n",
    "    positioned randomly.\n",
    "    \"\"\"\n",
    "    def __init__(self, size_out):\n",
    "        self.size_out = size_out\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._zero_pad_waveform(waveform)\n",
    "\n",
    "    def _zero_pad_waveform(self, waveform):\n",
    "        padding_total = self.size_out - waveform.shape[-1]\n",
    "        padding_left = np.random.randint(padding_total + 1)\n",
    "        padding_right = padding_total - padding_left\n",
    "        padded_waveform = torch.nn.ConstantPad1d(\n",
    "            (padding_left, padding_right),\n",
    "            0\n",
    "        )(waveform)\n",
    "        return padded_waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RawAudioCNN model class\n",
    "class RawAudioCNN(nn.Module):\n",
    "    \"\"\"Adaption of AudioNet (arXiv:1807.03418).\"\"\"\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 1 x 8000\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv1d(1, 100, kernel_size=3, stride=1, padding=2),\n",
    "            nn.BatchNorm1d(100),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(3, stride=2))\n",
    "        # 32 x 4000\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv1d(100, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 64 x 2000\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 1000\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 500\n",
    "        self.conv5 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 250\n",
    "        self.conv6 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 125\n",
    "        self.conv7 = nn.Sequential(\n",
    "            nn.Conv1d(128, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 64 x 62\n",
    "        self.conv8 = nn.Sequential(\n",
    "            nn.Conv1d(64, 32, kernel_size=3, stride=1, padding=0),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.ReLU(),\n",
    "            # maybe replace pool with dropout here\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "\n",
    "        # 32 x 30\n",
    "        self.fc = nn.Linear(32 * 30, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.conv6(x)\n",
    "        x = self.conv7(x)\n",
    "        x = self.conv8(x)\n",
    "        x = x.view(x.shape[0], 32 * 30)\n",
    "        x = self.fc(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_waveform(waveform, title=\"\", sr=8000):\n",
    "    \"\"\"Display waveform plot and audio play UI.\"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    plt.plot(waveform)\n",
    "    ipd.display(ipd.Audio(waveform, rate=sr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model and Test Data\n",
    "\n",
    "In the following section we are going to load the pretrained model that we downloaded in the previous section. Let's also load the test data set from which we will generate adversarial examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load AudioMNIST test set\n",
    "audiomnist_test = AudioMNISTDataset(\n",
    "    root_dir=AUDIO_DATA_PATH,\n",
    "    transform=PreprocessRaw(),\n",
    ")\n",
    "\n",
    "# load pretrained model\n",
    "model = RawAudioCNN()\n",
    "model.load_state_dict(\n",
    "    torch.load(AUDIO_MODEL_PATH, map_location=\"cpu\")\n",
    ")\n",
    "model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Adversarial Examples\n",
    "\n",
    "After loading the test set and model, we are ready to employ the ART library. We will first load a sample, which here will have label 1. The classification model correctly classifies it as such. We will then use ART and perform a Projected Gradient Descent attack. The attack will corrupt the spoken audio and will be misclassified as 9. However, there is almost no hearable difference in the original audio file and the adversarial audio file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wrap model in a ART classifier\n",
    "classifier_art = PyTorchClassifier(\n",
    "    model=model,\n",
    "    loss=torch.nn.CrossEntropyLoss(),\n",
    "    optimizer=None,\n",
    "    input_shape=[1, DOWNSAMPLED_SAMPLING_RATE],\n",
    "    nb_classes=10,\n",
    "    clip_values=(-2**15, 2**15 - 1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction (ground truth):\t1 (1)\n",
      "Adversarial prediction:\t\t\t9\n"
     ]
    }
   ],
   "source": [
    "# load a test sample\n",
    "sample = audiomnist_test[3559]\n",
    "\n",
    "waveform = sample['input']\n",
    "label = sample['digit']\n",
    "\n",
    "# craft adversarial example with PGD\n",
    "epsilon = .2\n",
    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
    "adv_waveform = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that for the given test sample, the model correctly classified it as **1**. Applying PGD, we can create an adversarial example that is now classified as **9**.\n",
    "\n",
    "Now we can qualitatively explore the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display adversarial example\n",
    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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qAE8C9UQkx7buWwFrbP01QGtgtYjkAHWBsk0SpVRc5GQ7vaoHi8qm9uygHldt+ftHhVv+xpiRxphWxpg8YBgw2RjzW2AKcL6tdjnwiX08xk5j50822tGoVMJUscm/oKhsv3/w4TY928c/Enme//8BfxKRpTh9+i/a8heBhrb8T8CIBMaglO9l2QTv1cTKCjrVQpth/hHXgd2MMVOBqfbxcqCnR539wAXxXK9Sytu6Hfv4aM6akPOz9EQ739JRPZXKYL99YSbLN+0JOT849+tvgX/o8A5KZbBNOw+UPC4qLi5zHr+g2d6vNPkrlclcuf3VGSt5ZMKSsNVfnp7Ptj0HExyUqgw0+SuVwYLb9f+auozhH8xj2abdgPfZPXePWZCEyFSqafJXKoNlBZ/OA7w3azX9//HfkM/Zd7AokSGpSkKTv1IZ6qcNu9i+N/wNWrTP3780+SuVoQY+/lXY+V5j/Sj/0OSvlE9dNPrbEFf06pVefqDJXymlfEiTv1I+tr/Ae5x/lfk0+SvlYz+u2VGmTMf38QdN/kqpUjT3+4Mmf6WU8iFN/kop5UOa/JVSyoc0+SullA9p8ldKlaJ3VfUHTf5KZaC8EWPL/VxN/f6gyV8ppXxIk79SSvmQJn/lK/sOFpE3Yiwvfr2C2Su3pTqcuNuxr4Bb3plToWVol78/6A3cla9s2ePc0/a+zxYC8MmNJ9Gldb1UhhRXL329gk/mrk11GCoNaMtf+dqmXQciV0ojRcXabFfR0eSvVATGmLQ5/VH0xlwqSpr8lYqg418+Z/CT01IdBgB//WQ+Q5/+OuT8eOT+9PiZUxWlff5KRXCwsJjF63elNIbComIeGLeIV2esDFtP4tD0T5e9HFUx2vJXKg1MW7qZl6fnl0w/Mekn9hwoLFNPu31UtDT5K1/7eulmVmze4znv+a+Wc+qjU5MbUAhFRaVb409M+pnHJ/5UqmzHvgJWbtmbzLBUGtPkr3ztlW/yQyb4B8YtCvnDkGxeLfq9BUWlpoc+/TUfzVmTpIhUutM+f6XiZOnGXazeto9+HZskZX3GOP3z+Vv2cqCwiHxt9asYaPJXvvJ9/taELXvAY18BkD/qjLgud8KC9Xz10yaPOYY3Zv7CXz6eH9f1KX/Q5K985fUIZ8vEQ++HvqR+jaqMu6VPXJZ33euzPcuLi2FOBg5RoZJD+/yVirN1O/azcN1O7vroR3buLwhZr+vfviBvxFgmLFjP5t2xX2n87qxV/Ef7+FU5afJXGWH1tr28MG15xHrJPIP9zZm/8NSkn0PO377X+WG47vXZDBv9bbLCUgrQbh+VAa565XsmL94IwBnHNqd53eopjuiQgqLiMmVLN+7i3k8XBpXtTlZIEc39ZTsHCovIzclOdSgqgbTlr9KaMaYk8QMsXLszhdGUVegx0NqAx75i2s+bUxBNdHYdKOSeMQsjV1RpTZO/SmvLNpVuMQeGavYy9JnpzPlle6JDKqWwKD2HSvhhdXLfJ5V8FU7+ItJaRKaIyEIRWSAit9jyBiIyUUR+tv/r23IRkadEZKmI/CAix1U0BuVfwcPQhEu181ZFl9CWbtxFn4cnlz8oF6+WfzrQoaEzXzxa/oXAbcaYTkAv4EYR6QSMAL40xnQAvrTTAIOBDvbvWuDZOMSgfCpeY9lc9cr3rN2+D4Dn/rucVVv3lakzZt5aCj368AuLinlo3CLP5RYWl62vVGVQ4eRvjFlnjPmffbwLWAS0BIYCr9pqrwLn2MdDgdeM41ugnog0r2gcKrMUFBUzceGGKEaYLJ39t+4+yHnPfsPGnftjWt/kxRt5ZMKSsHVufnsOd31U9oKqzxes599feZ9pNGPZFtbtOPRDMn/NjqhjOlhYzOfz10ddX6lYxLXPX0TygG7ATKCpMWadnbUeaGoftwRWuZ622pYFL+taEZklIrM2bfK6ulGlo6JiE1WXwtOTl3LNa7OYuiT8Zx/c8t91oJDZK7fx6oz8mGOLZlycd2etKlP27fItIetv3HWA3g9NLtmrOPOfocfiHzZ6BnsPHhqp84LnvuH6N7wv8FKqouKW/EWkFvAhcKsxptQpF8ZpvsXUiWiMGW2M6WGM6dG4ceN4halSZP2O/cxfs4PD7xzHhf+eEbF+IFlu3BVbCz4e3C11Lz3un8TWPQdLprftDX0hV0A0F3F9u3wrM5Yd+iGZtzr6vQSlYhWX5C8iVXAS/5vGmP/Y4g2B7hz7P3A+3hqgtevprWyZymB9H5lS0uqdHcWQBFm2SR9pJyFUl7+U855W+w4WMX1p6JY8OIn8iwWHumMOFkbu1482nsrSzaP3c8l88TjbR4AXgUXGmMdcs8YAl9vHlwOfuMovs2f99AJ2uLqHVIaKJkG6Zdktc8e+AvKTOKzyUXd/HlU9d26MKvlH+Vv0/uzVXG/H8umZ1yC6JylVDvG4wvck4FLgRxGZa8vuBEYB74nI1cBK4EI7bxwwBFgK7AWujEMMKuM42XLU+MWMGr/Yc6TMBWt38MyUpckODIBiV9PY6yreYBeP/paqOdG1tT63exV6Vy6VSBVO/saYrwm9993fo74BbqzoelV6W7V1Ly3qVSc7y3vTCVFcyhlPhT546rZ2+z6mLNkYuWIM3N0i0ST/XQcKIcax2zT5q0TSK3xVQq3aupfb359XprzPw1N49IvQp1YGJ77nv1rOhAXR94e7n3/R6Bmep2hWRKzdPrFat2NfxOMdiWSSOgSeSgVN/iqhHhq/iA9mr/acN+3n0qdxrtq6t+Qsmqyg7P/AuEUhx7WPZMPO2IdLjsjV9D+YgCEcej80me9WJO7GM0pp8lcV8tkPa8nfvIfvVmz1vCAr3Ng2wb0lfR6eQu+HvgTKJv/Kxv2qoun2Uaqy0SGdVYXc9Nacksf/uKAL53VvVWp+rDn8QGExeSPGxiO0QzHEdWmOYlefjI6Do9KRJn8VNyu3lD0ls7zn24ezettePpm7Nu7LjcW7s1bToFYuZ3dpQU40R6fTjJ7nn/m020fFjVe+iHfvTd6IsZz89ykRx+F5duoyVm3dCzh7E/G2aN1Obn7b2eu54qS8uC9fqUTT5K/KZX9BUakhDsC7tZiqrvvCYsMVL3+X8PW8Mn0Fm3Yl4IByiu0+UEjeiLF8NMf7YL1Kf9rto8rlgudm8GPQCJU79hWwfNNu2jWuVVIWrtsn8oidFbNlz0HOf/abhK7jnk8z845X63Y4Yyo9N3U553ZrFaG2Skfa8lflEpz4AV7/diWn/eO/JdO79hek9Hzx7XsLmBXFOEIqtGLt/M9Y2vJXMQu+daKXDTv3c8KDXyYhGpVImvwzl7b8VUy+W7GV/q7WfSiBbgOV3vQs1sylyV/FZHkUrX6A7CiO9BYUFTM3yvvqqtTQaxgylyZ/FZNoUsHfPl3ImzNXRqy3bNMeznlmOnN+2Zbwg7+qfHSMn8ylff4q7l6aviKm+heN/jYhg6MppULTlr+KSSIa6Jr4K69EXKGtKgdN/kqpkLTbJ3Np8lcx0WSgVGbQ5K9iosdllcoMmvxVTDT3K5UZNPkrpULSPb3MpclfxUazgVIZQc/zV1Eb/OQ0Fq3bmeowVJIZY1i7Yz8t61VPdSgqjrTlr6Kmid9/duwrYOqSTZw0ajI3vDGb3QcKUx2SihNN/kqpkHbtL+TKV74HYPz89Twx8aeIz1m8fic79xckOjRVQdrto5SK2qbdoe9atnn3AYY8OY2Nuw5wdMs6fPaHPkmMTMVKW/5KqaiFG+Vz6pJNbLS3tJy/RrsIKztN/kqpqIW7uUtW0DBA932Wmbe4zBSa/JVSUQu0/AuKipmVv7WkfNXWvTwWdDzgxa9XMPyDeUmNT0VPk79SKmqBXp+HP1/M+c/NYMFa517O9366kNXb9pWp/96s1RQW6aitlZEmf6VU1HbuK+DD2at5fppzz4bNuw8CsHVP6APBewuKkhKbio0mf6VU1Gau2Mpt7x/qygkcA8gKc9vOfQdDJ/+9BwsZ/sE88kaMZcKC9fELVEWkyV8pVW4miuS/P0zL/7N563hv1moAnp26LL7BqbA0+Sulyq3YdueHyf2c8shU5q7aXqZ8yfpdDP/wh5Lpuau2c81rsygOczrpxp37mfPLtnLHqw7R5K+UKrfnpy2P6mrec56Zzh/fnVsybYzhkQmLy9SbuHADSzftDrmcIU9N49x/fVO+YFUpmvwz1Iad+8kbMZa8EWPZVY5L7T/7YS2XvjgTgBWb99Dj/onxDlFlgJkrtvLe96uiqvvRnDUlj+/9dCGTFm30rDfw8a+Ysth7XuAAc4GeQVRhmvwzkDGGEx78smT6mHu+CNvv6vbdiq3kjRjLTW/NYdrPm1m2aTevzcgv+dIpFeznDaFb6qG88k1+2Pnvzy77g+I+ZXTHPh07qKI0+aeRV6av4NN5ayPWW7C27KX1R/7l86jWceG/Z5Sa7v+P//Ly9Pyonqv86eulm5m5YmvkijHIziqbmrrfP6nksSb/itPkn0bu+XQhf3h7Dk9O+jlsvetenw3AxD/2ZfJtp5SU540Yy/a9oVvw4cZtUSqUNdvLXtwVTqgDup1b1Cl5XCW77BFkd8I/UKDdPhWVsuQvIoNEZImILBWREamKIx09PumnklPsvAS+jO2b1KJd41pMH3Faybyufwvdd3/4nePiF6RSIfzh7Tme5WNvPjQK6Lrt+9kT5t4BB7XPv8JSMqSziGQDzwC/AlYD34vIGGNMXEeCMsawL4OuLmxUq2pJ3/tPG3bTsVntMnUCl9vfcXpHxJ5/17JedaYNP5U+D08BYPLiDZx2ZNNSz3MfFH7+sh4c07IuvR76EqXi6ctFGxj747qI9WYs38J5z37D57f29Zy/a38Bew/648YyWSJUq5Id9+Wmajz/nsBSY8xyABF5BxgKxDX5b91zsFQ/YSY4Pq8+3+dv49N5a+nYrGOZ+Ze84JyhM+joZqXKWzeowZ/POIr7xy7iqldmkT/qjFLzb3nHOQ3v4p5t+FWnptoFpBLi6ldneZb/bWhnAF658niueNm5eczi9bsAOFBYRMc/lz5mdemL3yUwysqla+t6fHzjSXFfbqqSf0vAfTh/NXCCu4KIXAtcC9CmTZtyraRG1RxGDj6ynCFWPiIwqHNz+j4yhaenLOWWAR2okn2o527Dzv1s2+u04A9vXKvM83/Xpx33j10EQNe/fcHcuwcC8N6sVUy2p9aNHOK8X9nB4/MqlUCX9c4DoF/HJnRuUafUSQvBZxM1qZ3L1ScflszwUqpJndyELLfS3snLGDMaGA3Qo0ePcjVDq1fN5rpTDo9rXJVB1ewsDhYV0+Gu8aVa8Gf+82sALukV+cdy+94CduwroEbVbIZ/cOgqyzrVqpQ8/u8d/Rj5nx/5ZtmWOEavVHjuvc5Nuw6U2QsddnzrjPxeJ1uqDviuAVq7plvZMhWFz289dGAscPaOMYZN9i5K9559dMjnrnhoCOd0bQFAl3u/oMNd40vmTQjqX23bsCbd2tSLW9zKX9o3qcV3d/YnJ8a9SPcNY45/YBJTl2wqNb95vepxic/vUpX8vwc6iMhhIlIVGAaMSVEsaadd41rcd46T4AOndR420jlT57zjWoXtshERHrmgS5nyPw44wvMAsntPQKlY3HXGUTSpU41Yd9t/CurmeXzSoZvEXNSjNRf1aB38FFUOKUn+xphC4CZgArAIeM8YsyAVsaSrS05wunZmrtjKhc8dujDrr2d3ivjcKtlZzPrzgFJlZ3Vp7lm3tiZ/VU5V7fGoBjWrhq3n3pMFeOPqE0LUhIt6tiZLj0fFRcrO8zfGjDPGHGGMOdwY80Cq4khXIsLFPZ0W0Hf2dnqvXHl81C31RrVyyR91BssfHMKCe0+nnccBYoDa1SrtYSFVyQW6ex67sOyeptuRzeqUmj65QyPev763Z91aubo9xote4ZvGHjz3mFLT/To2iXkZWVlCzTBfqDrVteWvDnnukuOiqlc7N4cjmztJvU+HxgwfVPa05HBaePTrd2lVl9b1a8S0HBWaJv80JiK8elVPWjeoztIHBidkHSe3b8TtA49IyLJV+hl0tHf3YLAvbzuFuq6GQ7i7eXlpWa86428p3R30yU0nU71q/C928itN/mnulCMaM/znSGQAABLUSURBVG34aeRkJ+ajzM4SbjqtQ0n/rVJ3nB65FR+8PZ7dpQUdmtSid7uGUa/nqOaHuoPaNNAWf7zpN1pFJdydmpS/3Hhq+4h1gs8469C0NhP/dAq1YjyGdO/ZnXnmN8fx1fBTY3qeikyTv4pKuHu0Kv/5v0GHrpx/9rdljwN4jcoJcPvA0nsNfz7jqLDrufzEPM44NrquJhUbTf4qKjrWj3K7od+hK2zbNCzbJZPjMR4/QMdmtXn1qp4A9OnQiN/1aZeYAFVEmvxVVAqLdQhd5c3reFColj9AoEeoOMyw5CrxNPmrqGjD37+eHNY17Hyv4YYlTDdhhybOleTndmtVscBUhegVE0qpsNx32PKSWyW2NmSzutVY+sDghJ2hpqKjyV9VSn84rT1Du7agWd3qHHPPBLSHIHVCteLvP+doalfLoXo5bjSiiT/1NPmrqNTOzWFXiNvqvXNtL974diWf/RD5Dk3Rus11VkhuThb79Z6tKRPqTK9LerUFoCDolorvXNsr4TGpitOfXxWVj286KWTfb692DSMO3lUReoFZakUaR61K0OfTK4YLuVTq6LdKReXwxrUY2rVlqbJLe7UtGZExmm6ZP59xFA+fd2zM685NwP1LVfSyRHj4vGNLhhH3MulP3vfaVZWXdvuocuvYrHbJiIwmilHbB3ZqRpuGNcjKEnbsK+C+z6K7ZXNujrZRUkkELjw+/Bj67ZuUvReEqtz0W6XKpU+HRpzVpUXJdGFR5OQfOK/7/O6t6NQ8/Bkkbpr8U0uv7s5M2vJX5fJ60A03CsIk/9ycLJrWqUazutVKynLCXAQUrGqOdvukkib/zKTJX8XMa2TGcFcA9+vYmH9f2qNUWaiE0jOvAVf3OaxUWbirRVXiRZv7P7+1D7n6Q502NPmrmCy493SqenTDhBty1+uewqESyqtX9SwzZnu4q0UDnrukO3//fDErNu+JWFfFJtqWf/AduVTlpp2pKiY1c3PKnNoHcHP/DrxmB+wK5pW8i0OMF+E1HtjNp0UeQrhPh0a6h5AgesvczKTJX8VFlews+h7R2HOeV8sx1DGCbI+6/Y9qSv6oM8KuX0T7phPFa89NpT9N/irhvHJHqBtxlzeBa+JPnGi63VT60eSv4qpx7dwyZV6t+WNa1eXta3qVOVaQVc5WprZOE0ff2sykyV/F1YRb+zL19n4l02cc05zhrrs+ufU+PH7DAGSL6OBvCaI/rJlJz/ZRcdWgZtVS4/w843GLv0Qo7x6Diky71DKTJn+VEC3qVuOUjt4HgN1q2NM6G9fOZdOuAzGv59ObTuaYVnUBPE9BVRWnuT8zafJXCfHNyP5R1Xv0gi68+k0+D/76mHK1MN1PaVW/Oj+u2RHzMlR4XsdsVPrT5K9S6uiWdXnkgi7lfn7taroJJ5p2+2Qm3U9WaeP87q04v7tz39fsLOGda3vRtmHNFEeV+TT3ZyZtNqm08egFXdixr4APZq+mWk5WmZuGaJJKDD3PPzNpy1+lJa+EpKd6KhU9Tf4qrdTOzWFgp6Y8f1mPMvOKQowXFA9XnJhH97b1E7Z8pZJNk79KK1lZwujLenheIBa4oXgijBh8JC9dcXxcl9m0TtmroSuboV1bRK6k0pImf5Ux+h7ROOIAcOUlEv9jCjPvHBDfBSbAk8O6pToElSCa/JWKgiDoYU+VSTT5KxUFp+Uf//Q/6tfHcOaxzeO+XKUi0eSvVBTE/sXbsJ5tePo3yRn/SCk3Pc9fqSiICNGcS9QzrwH//E03Vm3dy7a9BVzz2izPeke3rNy3PLyubzu+WbYl1WGoBKpQ8heRR4CzgIPAMuBKY8x2O28kcDVQBNxsjJlgywcBTwLZwAvGmFEViUGpZDFRXEiQnSU0rVONpnWqse9gkWedr+44laZ1YzvTp1qVLAZ2asbxefX5yycLYnpuedx+ekfP23WqzFHRT3cicLQx5ljgJ2AkgIh0AoYBnYFBwL9EJFtEsoFngMFAJ+BiW1epSum6U9rx8Y0nkZ0VXcvfPfZ9qEME9WtWITcn23tmCI+c34WnLu7Gpb3zYnpeeenB7cxXoeRvjPnCGFNoJ78FWtnHQ4F3jDEHjDErgKVAT/u31Biz3BhzEHjH1lWqUqpTrQpdW9cDoGbVHFrUrRa+fvVDO9OhBkQrz4HjZF+8rIO5Zb547tddBYy3j1sCq1zzVtuyUOVliMi1IjJLRGZt2rQpjmEqVT7ZWcI3I/vTqJZ3l83wQR158NxjSqZD3V/Gq3z8LX0YPqhjPMKMC839mS9i8heRSSIy3+NvqKvOXUAh8Ga8AjPGjDbG9DDG9GjcOPJNQZSK5NYBHTiyWe2YnhNLEvx9v/bUq3HoLmYhW/4enSpHNa9DQ9cd0Nx6tK1PP9eNcT696WTaNqzhWTdedDC3zBfxgK8xJuxliCJyBXAm0N8cOiK2BmjtqtbKlhGmXKmEunXAEVx/yuHsOVDIfZ8tZNW2fcxeuS3m5VSrEt0Oc1aW8JczO/HRnNXMX7OzpDxUXg11GdkHN5xYavqYVnXp2roeK7fsjS5gpTxUqNvHnrkzHDjbGOPeEscAw0QkV0QOAzoA3wHfAx1E5DARqYpzUHhMRWJQKhpdbL99tSrZNKyVyxPDupXcGyBWdw05Kuq6V598GO9e25vHL+pCk9pOd1HVUGfRxNDY1na5qqiKnuf/NJALTLS7id8aY643xiwQkfeAhTjdQTcaY4oAROQmYALOqZ4vGWMSf96a8qWv7jiVcfPXMWr8Yo5uUfa8+vIOAV23epWY6tfMzeHcbq3o26ExP23YHZebzesBWVVRFUr+xpj2YeY9ADzgUT4OGFeR9SoVjTYNa3Bd33ZUy8niwuNbl5lvojiHxqsr5vAmtQC4/5yj6d62PoOfnBZVPA1r5dI7xMFigOIYhqTWPnlVUXqFr8poIsIVJx3mOS+alr9XD03TOtUSMnpooUfyP7l9I8+6mvtVRWnyVyqMZHavdLZdUy3qVmPr3oPMHDmA6lW9LwaLQ8+R8jlN/sq3cqLIoMnsXunWpj7z7h5I3RqRjynoANOqonTwDuVbvz6uFdf1bRdy/kntG3LecZ7XIJbSp0MjauXGpx0VTeIHyNJvrqogbfkr36qak8XIIUfx5sxf2H2gsMz8N3/XK6rlvH71CfEOLSI94KsqStsPyveiGa2zsomU+gd1bpaUOFT60uSvfC/9Uj8M6NQ07PwOTWslKRKVrjT5K99Lw4Y/p3ZswrIHh4Scn46vSSWX9vmrjPOf35/I6m37oq4fzcVelVG2x9lKDWtW5a1revHpvLUpiEilE03+KuMc16Y+x7WpH3X9TGold21dj47NatOgZh6TF29k4bqdkZ+kfEm7fZTycHxe9D8elUlOtrM30Lh2Lm9dk/yzkFT60Ja/8r3ghv8Xf+xLi3rVUxJLRbnvu6sXgqlwNPkrFZT9j2ga2w1fKpOL3APYae5XYWi3j/K9dD3gC3DlSXklj5c/OIQ+HQ7d8UuvA1PhaPJXvpfOB3z/elbnksfByV5zvwpHk79SLrk56fuVCB7yQYeAUOFon7/yvUDDf97dA6mSk34J88lhXXlpen6Z8nQctkIlT/o2c5SKk0CSrJmbTY2q6dceGtq1JZ/ceFKZ8trVqvDCZT1iXt5Hvz8xciWV9tJvS1cqzt6//kQ+mbvG84rZdBdpDCAv3WK4QE6lL03+yve6t61P97aa8JS/aLePUkr5kCZ/pZTyIU3+SinlQ5r8lVLKhzT5K6WUD2nyV0opH9Lkr5RPNa9bLdUhqBTS5K+UUj6kyV8pn8q865lVLDT5K+VTIsJP9w9OdRgqRTT5K+VjVdN4CGtVMfrJK6WUD2nyV8qn9F4v/qbJXymf0uTvb5r8lVLKhzT5K+VToid7+pomf6Uy3IJ7T/cs124ff4tL8heR20TEiEgjOy0i8pSILBWRH0TkOFfdy0XkZ/t3eTzWr5QKrWau9w37zu7SokzZxT3bJDocVUlUOPmLSGtgIPCLq3gw0MH+XQs8a+s2AP4KnAD0BP4qInr/PKWS7ILurfjjgCMAOLdby5Lyh359TKpCUkkWj5b/48BwwLjKhgKvGce3QD0RaQ6cDkw0xmw1xmwDJgKD4hCDUioGNXNzyLI3rH/8oq4pjkalQoVu4C4iQ4E1xph5UroDsSWwyjW92paFKvda9rU4ew20aaO7okrFU5Xs0h3+04afSnaWHgTwk4jJX0QmAc08Zt0F3InT5RN3xpjRwGiAHj16mAjVlVIxyM3JLjXdukGNFEWiUiVi8jfGDPAqF5FjgMOAQKu/FfA/EekJrAFau6q3smVrgH5B5VPLEbdSqgLO7lr2YK/yl3J3+xhjfgSaBKZFJB/oYYzZLCJjgJtE5B2cg7s7jDHrRGQC8KDrIO9AYGS5o1dKxWTq7f3Ia1Qz1WGoSqBCff5hjAOGAEuBvcCVAMaYrSJyH/C9rfc3Y8zWBMWglAqiiV8FxC35G2PyXI8NcGOIei8BL8VrvUqpyD68oTc/b9id6jBUJZKolr9SqhLp3rYB3ds2SHUYqhLR4R2UUsqHNPkrpZQPafJXSikf0uSvlFI+pMlfKaV8SJO/Ukr5kCZ/pZTyIU3+SinlQ+JcjFu5icgmYGUFFtEI2ByncOJJ44qNxhUbjSs2mRhXW2NMY68ZaZH8K0pEZhljeqQ6jmAaV2w0rthoXLHxW1za7aOUUj6kyV8ppXzIL8l/dKoDCEHjio3GFRuNKza+issXff5KKaVK80vLXymllIsmf6WU8qGMTv4iMkhElojIUhEZkYT1vSQiG0VkvqusgYhMFJGf7f/6tlxE5Ckb2w8icpzrOZfb+j+LyOVxiKu1iEwRkYUiskBEbqkMsYlINRH5TkTm2bjuteWHichMu/53RaSqLc+100vt/DzXskba8iUicnpF4nItM1tE5ojIZ5UlLhHJF5EfRWSuiMyyZZVhG6snIh+IyGIRWSQivVMdl4h0tO9T4G+niNya6rjs8v5ot/n5IvK2/S4kd/syxmTkH5ANLAPaAVWBeUCnBK+zL3AcMN9V9jAwwj4eAfzdPh4CjAcE6AXMtOUNgOX2f337uH4F42oOHGcf1wZ+AjqlOja7/Fr2cRVgpl3fe8AwW/4ccIN9/HvgOft4GPCufdzJfr65wGH2c8+Ow+f5J+At4DM7nfK4gHygUVBZZdjGXgV+Zx9XBepVhrhc8WUD64G2qY4LaAmsAKq7tqsrkr19xSXpVcY/oDcwwTU9EhiZhPXmUTr5LwGa28fNgSX28b+Bi4PrARcD/3aVl6oXpxg/AX5VmWIDagD/A07AuZoxJ/hzBCYAve3jHFtPgj9bd70KxNMK+BI4DfjMrqcyxJVP2eSf0s8RqIuTzKQyxRUUy0BgemWICyf5r8L5Mcmx29fpyd6+MrnbJ/AGB6y2ZcnW1Bizzj5eDzS1j0PFl9C47S5jN5xWdspjs10rc4GNwESc1st2Y0yhxzpK1m/n7wAaJiIu4AlgOFBspxtWkrgM8IWIzBaRa21Zqj/Hw4BNwMu2m+wFEalZCeJyGwa8bR+nNC5jzBrgUeAXYB3O9jKbJG9fmZz8Kx3j/Dyn7NxaEakFfAjcaozZ6Z6XqtiMMUXGmK44Le2ewJHJjiGYiJwJbDTGzE51LB5ONsYcBwwGbhSRvu6ZKfocc3C6O581xnQD9uB0p6Q6LgBs3/nZwPvB81IRlz3GMBTnR7MFUBMYlMwYILOT/xqgtWu6lS1Ltg0i0hzA/t9oy0PFl5C4RaQKTuJ/0xjzn8oUG4AxZjswBWd3t56I5Hiso2T9dn5dYEsC4joJOFtE8oF3cLp+nqwEcQVajRhjNgIf4fxgpvpzXA2sNsbMtNMf4PwYpDqugMHA/4wxG+x0quMaAKwwxmwyxhQA/8HZ5pK6fWVy8v8e6GCPoFfF2e0bk4I4xgCBswMux+lvD5RfZs8w6AXssLuiE4CBIlLfthAG2rJyExEBXgQWGWMeqyyxiUhjEalnH1fHOQ6xCOdH4PwQcQXiPR+YbFtuY4Bh9qyIw4AOwHfljcsYM9IY08oYk4ez3Uw2xvw21XGJSE0RqR14jPP+zyfFn6MxZj2wSkQ62qL+wMJUx+VyMYe6fALrT2VcvwC9RKSG/W4G3q/kbl/xOJhSWf9wjt7/hNOPfFcS1vc2Th9eAU5r6GqcvrkvgZ+BSUADW1eAZ2xsPwI9XMu5Clhq/66MQ1wn4+za/gDMtX9DUh0bcCwwx8Y1H7jblrezG/FSnF31XFtezU4vtfPbuZZ1l413CTA4jp9pPw6d7ZPSuOz659m/BYFtOtWfo11eV2CW/Sw/xjkrpjLEVROnlVzXVVYZ4roXWGy3+9dxzthJ6valwzsopZQPZXK3j1JKqRA0+SullA9p8ldKKR/S5K+UUj6kyV8ppXxIk79SSvmQJn+llPKh/wcwByvMGqr4/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display original example\n",
    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a second adversarial example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction (ground truth):\t5 (5)\n",
      "Adversarial prediction:\t\t\t6\n"
     ]
    }
   ],
   "source": [
    "# load a test sample\n",
    "sample = audiomnist_test[3773]\n",
    "\n",
    "waveform = sample['input']\n",
    "label = sample['digit']\n",
    "\n",
    "# craft adversarial example with PGD\n",
    "epsilon = 0.5\n",
    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
    "adv_waveform = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we observe that for the given test sample, the model correctly classified it as **5**. Applying PGD, we can create an adversarial example that is now classified as **6**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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H3Qja/xG5GvsI/aF8oDwoeIh4Mbt5+zk5CjnzOON4r3vGvIs7annUO1NLeU7pAWn6pnon/SbEG0U6f8TCzwbCBQ+B0oAIPwACCEU7RJkDTEEx/lrBIETvEAVNWftjtuY7frzL/r58ezeOe5w+Lrkqdxn55rgt+l3/jX3HvRI6F3qSyO2L+H+SPBI9e39ABFIES4BihHcIrcbvw0vBqkDmQ0TFgMW2hMpEpgNPx4JQ1EnyPDA7sn/bgLsCcD8qenY7rHqm95Q4n/p2fGT9pL7hvpT8tPqueDN8CUfkz5ZKRcQM/s2+VsDhg3GEfsebSpdJckWQQjP+uT8lg7AHYUhYxtwEBoHQwGwBIkRlhjMKqcQWOfz4ZHqQutm9nn1Eu5i7zXpJtgs3Cv45/96+KbznO9gFQM90xf176PycQJXD+QSgQq7C90d7BzKDDcF2gSsB8URGhPRC/oGiAdhAwELjQ1PRfg6bula2Z/qNOgS+Rn7Q+jT/4P8S9lN1Bne5+EmBq4IJOv57NLwFPGLEd0aUQBTAZIIrv2C+pL9xvfnBf0S5AoFBxkG+f8RAdwAOv0l/koEHQZ1Cl8LvgXxAgcDRgBxEdH8xteM4b704eyh5nfkV9tO5pzhnNZu7gbxgt6D3SDkkvUaG98Qaey/6RfyjPWa/6D+tP/QEekTkQRs+iv0B/h1BaAHUgEeBAUFKQAy/IsFgSlrF1fm5dIP5XrrMOtD4hPiAewm5YLHXMV23mf8K/6e7qvfzeO47nn8xf4W+sj8bQRzACD2UfKD9yMAWAQgBAEEEgP0AhEA6vpB+2X8gfnv9uL77v2h/6/60f2GAUb+AADg+0QRLg4x3QrZEe1a74rvuujZ22Ls+ej33MTu1fC05jvrsej676ELeAVO8T73SQESAH//B/+oACkNJg9mA9oD0QlYBbkAGP5Y/WEGUwXf+9f9WwOK//X9xwFE+yP5aPsR9uj1d/y59z//Jfv+4rrkPPCp6oDmK+oI8Xz58/U16Wjo9PEpAm8U3RA7ApMDtgNV/gb9HQDNCCEUUhSyDKMLqQiUAaf9a/te/QoEWwHi/tP/+v9NAb4ApgI9Auv/ff3i+1/+pf+l/c3/Nf6E+TX3s/hU90X3APpA/OwACP3F9+H0j/qy/64ONhb5Dn8OcQwIB4EANv3gAn8NCA3eCwYNjQ6XCJ7/nvnr/m4HEwi7Bu8JjgrVBlL+BvreAcAE2P2z/Mz6kPaq8hzzOfovAuP9V/jP9GP0uPI89ej9lg4hFHsNvgdDCPII4wOn+hT9FgdoC+0EqwP8BrIGnAH6+ar5BP0jAiECsAD1AjEG4QYFBHcDGQMZANsBaP8P+QH3kflq9h38+AMh/8r2D/ai8TnzwvGI7Ezz3P5mBRALhQgDCLgQFBB+BXkDRwXLBd8IggkACuQLigR+99Lx8vF08j/wsO+U8Izyq/Re83XvOO8r8IbyuvAN657qvfId/JcHcAuABgIL0AlLAyD/JP26/tUDRQiWCNAItgZVAfz7Hfb58LjvKu3W7NTyq/iy9CXzbPHd79Du+eyb57ntzvF29JX9uATuCCwQzg6iCcQGxQE0ABYB3QMXB+oH5ASLAeT5hPBH7HLrDOwo717yS/UU+ST5HvjM9/33dvXf75bxOfZ5/dUGQQsjELUSrA/0CZYFXP4w/Vv/qAAfBGcGFgPZ/mf5t/RX8ybypO3m8RX3wPuS+r/4+fg9+9b7TvfZ9+T5JPw2AvcG8QxRFTgWRxKPD2wJOAQGA2UEWQgGCnAI/QVaBTMAvPzL9zr2hPgn/Pz8c/41A+oBrv8UAnAEaf/8/Kj7MQFjCysPdxNnFhAVPxVxFLgM4wZ0CTwJjgoBC2kKewrvCW0Dbf0a/T/7w/3FAOj+dQRuB6sCbgWmBzcCKAU3BeEB1gliESMTlh3DHoMZjhsJGN8PLg72Cv8K6xG2EQcOzQ0eC20F8QOFAV/8ZgBXA+EDvgerCOcIbgkVCHcEogUBBY0G1g02FO8Y/x2mHBYaWxofF8MROxAyDqwO2RA5EHcQyw92Cr0JIQit/4T7Nf98//kDzwfpBj8H5QcYB7UFHwR3BbQJ+gtSE6sZJRlmGc0Z7hUJEcIPyQvhCl4MSwtrCZYKigjxBtAFkwKu/H78Zv7mAHgD6wWtBrwGcwa0BXEFmgd8BS8IEg5WEbET0BTdE6sUPxNfD1EOMwymCRMKIAloB20HtgRaAxUB2/zq/ID8QPua+mz8Pv6+/xABuwBYAkQC/gI8BMEEvgk4Cw4MMg42EfkOeg+0DosMEwkwBv8E5AXrBK4ALP9V/Dr6FvnR91D2U/VT9QP2qPWj9DX1vfWc9tv3MPna+h/+l/6a/28CPAKTArkDrgTUBRkHPQXhA4kDHANwACf+DPyo+tv59/ZP9IL0bfST9Gn0bfI/8A7ug+0G7Xruh++Y8xP1TfcW+hL5Zf3X/sX8xv+EArz/lv+P/iT/5ABE/7776vlp+2D7/veo9PDx3PC570XwI+9K77Xwi/GB80zxye5t71Lz4/K58Qv0+PSn+ef7ZviY+yoAjv9N/GP9p/wz/3/9Bf71/VH/o/+S+RX2+vYj+M/zsPEP8l3yz/Jz8Z7x4/Rr9p710fKZ8831L/RX9IP3YvyL/oX+Rv0R/0YDfwGAADgCGgBx/xT/mvwz/IP9RfxV/hAAS/y8+Wj76fkF+FL4Mfhu+a36uPpZ+kH8SPxA+0L7w/lR+0j9+v1//Xr/RgDyANEBsQL8ATYE1AGdAMYCBwO1Ai4CHgJeAvgCPgFI/5X/Uf8QAK8CqQIeAYkBZQFDA78GdQZBBtIEdQXiAwkErgPgASwEzAWeA1YF7AStA90GVQh2BNsE7wb+BU4GeQanBa4ErAbWBDIFbAbgBWgFVwl1BvcGBgdyBGYEZwYHB64EpQfRByIISwjXB9QJwQnOBwkIKwqdBnwHZwhJBqMFVgXPB4IHSAiDBx8GVgvAB88HUQYaCIcISQqXB5wHQQTFA60I2whxC4MGogdKCQoE8QUZCCoGIQmGCq4EswYuBqYIrAQABCEI1wXUByIEbQXcBBsGIARlA5oDGAkMBKABrANiBIIFCAJYA8QGpwTvBVcEvgJIBvIACQS3A+gDfwHhAdf/ewNOAAD/CQE0ADQBRACx/3MDmAPV/CT9kfzi+/38f/tl/Pv8cQDl9pH5rPxj/rT2kvzD+l78pPkE+Zr7xfuF/rT2e/xW+FX8FvdN/LT2A/qK+WH3P/jV91b3GfWl+vrwMPZ29rX5nPPi8o71PfSW9Tnzavc08yb5d/LT+Pz1jPZR9nX13PQC8p/2Xu4M73TzCvKw7VLxxe5Z8iXvPe418ofzF/FV7jzr2+5n74Howu506wbuUvIK7w/uTfET7i/tWu7b7EPui+0U7aDqqfFC7tnoDu1X7lzrZe6t7Hzoku5t7jPtC+mV7FTs/evg7P7ndeto89zsVuwr8Bnr9PMh8MLu3fYw8+/yLfKm8/j0Z/IW8/HyQfC28jfxH+4N8HXuFPHB7AbzDvLS8V3wHfMe8YXyA/TV74L1APn18XDySPVb8tn0OPT18gbzJ/s/84751Pbo+Qr54PdU9Tb7F/tA98n33/Z89ez7qPUY9bj8Kfvz+sv7Q/Z/9/35cfc5/uH1O/tH+238T/dh+53/oP9K/or/2f+PAHMEiP+r/58A4f4x/oX/of4BBWL9Hv8tASv9WwS1Ar3+SP/NBJcEowOC//wDbAMeBwIG6gYdCuoIkwgXBZUJHglfCAMLWwcwCckLIgv0AmUFzQaeCd8EnAqPAxMJiQbMBXUHSgrNCP8HEwwdCB8L4AgPDWkMmA02D3cLpA3vDH8P9Q9MDdQO1RAsDvEQ8ww1EBARuQoUC34MpQ+QCvIK3whlDrYS3ggVDCkSwQ7qDLkI2ArnFOsO1Q1ZD0oQ8hGoDhgNaA1vFX8NAg44DJMQYQ8NDnQJYQ3kDRwKCQcoC0MMRwg5CnAE2woIBicHVAWXByoF8wqnBDAEdgxVAxgKNgryCG8KXwu6A00H6AKcB9cGbAXEB9gDtAUxAx4H7wQ7Ar0Cmwj3A+39BwNLBK0AU/1iAFz8SwEb/lf98gII/6gBh/5aAvT/mv/m/78Aiv8h/zr8KwAe/j39OwDQ/Jz+iPnq/LX8SPuO94r7bfl8+Dz6avdx9tH4Sfke9oz4ff2A+RP5aPoH/dr5u/2l+A73s/kM/D/3PPqU+KH6c/ho8Xz5bfhQ9uX0R/Z09aH16fTa9Vb0d/as9CzyK/MW89nzEPAC9Qn3P/Yc9DP1Bfpz9lD4uvVm+A35hPbT8cn49fW++B33bvqZ+Nb1GPpX97L0tPhu+Cb2Xvhf9m33Avh1+mP6i/5V/o35TfzG/ML7if6n/XsACQF0/nYBXv+H/mEB/AICAHUBvwJhAKr/pwLvAVgFZAFFAxQEEAMEAvEDqwA9BhgE1gU6BEoGtQckBIcHsggRBokG2wV1Bv4FuQjeBr0H4QYOBwQLzQm4A1YJFQixBTYJ7gVfBgUIWgeGCdAH1AcPDMcL+gt6D68MtAuhCTQN4w2gDfQPORElEG0OjA8RE4wTyhDxEXUPyxErEnAOrRKQD1IRbhIDEPcU2BJKEEIQKw6YEtYT7g4rESMVpRJ/EJkQzxM7ElAV8xG+DxgS1xTwE8kQBBEUFuEUZxAzEYMVDROqESkT2BP7E/YS3Q90EygUHRM5EyoPnw9sFM8PEBH7EWQT6w+cE9QOgxL3DwgQPRFnECYQKRPlDh0PKxAtD+8P/Q/qEAQPeRUZDaAPJA4eDsQO1Q7UCnsOBQ+9CMcNpQyGDA8MjguLCqwLjAcuBmII7ATRBIEGcQlqCAAI6wYGBgYG8gD+BMsAGQNaBQMAJ/+wAgj/uv0rAUH+8P2R/Dz/lvuy/WX3UPn9+oD5Gfo0+YP2s/jb+HP1OfrP9rX4HvhS9+X3d/Zp9Qv11fQc9SP55vUS9cj5j/Ux9qT49/dm9Mvzn/eT85zzrfSW8LftG/I+8bLwfPA889DwQe6i7w/wFfD28SnwOO1f8ZLsYuqw76DwSOvg627sG/Bb64bqDO+X6tTq/u796IDr8+1u6YLphOx+69Ls9upT6y3uYej06CDtb+qt6snsX+mE7Obrcepx6iXu++yo7fvtg+lS6ufwiezp6q7tKO7861XrgO9u63zsm+6/7znwRPLc7VPvpfKj8XzuA+/m8jPx8+4Q8Y7vnfNN8Yz2avQ78i7wKPR19ULxC/Tj86f2X/f+9Xz03vNf+bP2gfkX9+T14/Q59573CPrq9Tf5aPpU+Pz6JwAK/Ev9jf4D/hYArfzk/Jv+rP0p/SgBvvq3/kMDMPwl/uQAVwC1AlsBTv7U/0f9RQFNAlwAZQIAAwABRwNRBM0IwQVPBXMFpgjpCSYFNwvuBu4HtAibC/QJzAYXCUMJ1AfCB+cGegoDDLIHqwgYCJkLfwlYDA4K1AbFDUMKRwZGChAKtQXPB2oMfQxzDLAKygVyC+AM+Ql2DG8IrwmXDMEGPgXVCQ4KzgnODNwHhAmHCnkJFwxMCggLOwmmB+sIAg4pBkgJiwiFD+oLsAwkDsgQJwr6CZQK3gqvDBkMgwqKDZYKrAnEC0QL3gu7B2YIDQ3KBF4I8wfHCKEMkwjoBXEMrgYcCFQJQwYEC6sKZQkJB3oIQwinCb4FmAbmBZIClAMeAyQFOAMeAwP/6v8fAoMBuP2MAC7/9/ysAIH9G/64/gr8D/5u/pb7ffzn/cL2v/mO+/z3evNn+475APit9ez0VfQd+A340PQV8vbwNfRr8j/wfvFQ71HwAPOS8Jrx+++g8Z3yhO+972nx0PFH723xOfGV8vHyBPEp8tL0ePKN8wP0uPCh9bDwRvF19nr10/Wt9cbxPPS58/7w0vYo8UHyR/fX80fyavQx8tbwQPS68PHvLfJv8RLwgO+97bvwTOzM6yjt7e5V8Uru9Oob7Gvvb+2C7X3sOOjf6pLsEulN6ofr/euJ6R/vn+s+7CvrYOc26ezpBuur7LfpCu/o7+vunO+178rxefBH80bvOvX58VvvBO8M9RP0OfYc8rr31vMS9Ev1pfY/+Qf6Mfq/9Sr5Bvi29yH7oviL+qX4ufll95z7ZvtU+IL6eP20+yv84/urAaD9Mf1IAKz8lwDr/vL+GgGtAXL99ACoAP0AtgfKAsb/xgIjBCoAAAL//ZkErgPtA4gAMwJCAXsFrgYMBZsGPQQwBukDRAQkBw4DywVBB5IFogiHDMUHmwpgCVMH7AwGCi8LlA7ODrcIKAndDo4TCA5BD8IUIxF6EVwSSRPHEJMU1hPjD2QS8hOfEDgTrBJ6FJEXThJRE+AXMRJFD+sSchCeDR0SnQ4tDsUSKQ1vC6cPtA6WEYUPLw2nEWIOYgzGDIYMGBDqDa0Mfg45CQ4McAseC9AQ3w3iByUK+QrNB6YLJAkiDfcMVwlLCNALYgw5CTUKogzYBywKHQwIDOsKgQ6XCrELvwyBC0sKRgzNC7cMWAuMCCUNgQ36CaYK5g+VCKoKlAlXCPgKNwr4ClkLLwgRBwcLSQgcCBkGVAkgCssH1wb/CKwE5QaxCFcF3QO5BUoCOwakCH0FDgPABSkDFQYtBDL/hwHeB/sCjgQSBH4CrwFLBcQCTAMIAQ383vj9++j8iPhO+JL3wfsa/wb9E/hk+xn78fsw/or6n/zZ/ar6p/uP+n391/wS+cX8xPtg+rr7sPvP+M/6c/2v+Tz4jv0t/Dj4//pR+Lb62flj9yz2XfZ/92/2QPTE8hb1k/WM9NHy1fSZ9G/08vW+9zz33PXK9HL09vZv9fT0SvcO9bX11PUv9T31u/c89WH1effG96/1wvaX9zH3aveR9x73FvcQ/Kr2e/Zp+qv7pftA/W/9mPxS/iT6pv0K/ef9gP7Y//D/DwA8/iX/+wG7AWsBWAH7AWMBNQM0AdkAJgaWBCkCEwNoAyYCZgV5A6UDeQONAWIBVQDSADr/lwApAHn+DgBhALD+e/+yAlr/N/9lAToA/P6OATkAUQGIAXkCYASJAngBQgT4Ag0AMQE2AuAEkgE+Aq0EeALuA9cCBAYnBccGrwRUBUkH0AVpBIwFCAn1CQ4IDwnSBbYHPwfFB8EFUQkTDJwLYQjmCqQKogtwDecLyA2pC7gM4ArYC2EMugnADBAKBwvsCrUM3AvvCZoJqQt4B+4F6QmHCWoHTAtECYwFkwnqB2IH4wh4BZ0GjAftA/AFNAWdBHMHSgdxBxEJbQkdBicHYgfxBcwGewXyBcIH/gWjBssEPwV8BJUF/wadBfgFIgfoBJkF+gXTBeYE/gXTBkQEcgTiAs4DlwOgA1YBZwP/A5QFdwUKA+QDZAMrBDAFUASsBF0D/AJcAdID7gLMAuEBywOLAQIBkAKVAm8BmgCkAoYAHQDf/pcAaP9a/nn/B/7C+0v+Rv00/LX8H/vl+4b8Gvq9++r6pvoG/ov+aPxS+/X86vwL/W391vzj+Wb9Lfx9+uX7xvva+wX6o/hy+HD2nPR59q71PvRj9O3zZvLJ8kb03fOV8lPzr/JF8yTyIfMs8o/zc/Nv9A30mfLY8qnx6PAv8r7xTfLU83vy8PTf8xD0Afdb99T3RPap98T3nvZk9/34Evdx+OL4Xvgk+fn35ve49jH3U/VF91j1wPX+9wf3YPkC+Qz5qfcq+gf7DPqZ+ov3pvjL9Tj4MfZd9rT0fvTF9cXyUPFN8mXyRvFo9LbyWvMh9Ub1Efj499n4hfem95D3wPl6+bj4w/qt+n376/l5+lv5Afo5+fD4bvl3+IX6F/o6+h/5S/sI+/r6KfwV/an8RPt//iX+qv+6AS0DwQMKAsIC/QBtA6YBAwHJAWgAFgNTAOD+rQDPAKP/ZALvAWsCKAH6AlQEyAKIA1wDTAa3Bu0GXAcgBgAGjgiUCB4HdwmVB4YIQAYlAx8DgwPQAOADmAM0AGYB6QBFAd//AgTlBMMESAVaBDEFRgPrBHkGzwWbA30FVwQTAlIB5gGjAbMCrAFaA5MDGQLwAq8CtAMmB5QFsQTCBeUF5wX8BggHHAhqBokHxwZeB0wH1wXjBBsGTAdUBakETwJ7BN0EKQIQA2cBRQJFAyUD9gM4A+ECbwUMBjMC4AKQAZ4C0gQHBNcEvALLAoYCmQJEAhMA5gLb/z8B/P54/6/+Hf/P/vL9pf6T/SD9q/rq+2T7CP4z/dP8Cv2K+0L+3/xx/jv9jP5B/UP++v4s/cD7ZP3K/oX9cv0G/PH9pPyb/RD8/fsq+mr9wv5o/nz+tftY/Pv8H//i/sb/Jf4+/yUB0/7x/aj/M/+M/6cAEwG2/9n/M/9+/6cAcP5a/yP/yfxx/SL88Psx+1j8f/2Z+sn5yflb+3z5qfvd+mT7afl++Sj7fPgK+oP5oPiQ+CX5nfd9+Xb39fZD93H2hPg89wH2UPjU9mT2avh5+Cr3tfcc9z76L/lk92P4A/fD93z5KfmZ+BP5S/i5/7n/uf+5/7n/uf85ALn/uf/k/7n/RwDk/w4ARwC5/0cAuf9HALn/RwC5/0cAuf85ALn/RwArAMf/RwC5/7n/RwBHALn/RwC5/0cARwC5/0cAuf9HALn/RwC5/0cAuf+5/0cAuf+5/0cAuf9HALn/RwC5/0cAuf9HALn/RwC5/0cAuf9HALn/RwC5/0cAuf9HAEcAuf9HALn/RwC5/7n/RwC5/0cAuf9HALn/RwC5/0cAuf+5/0cAuf+5/0cAuf9HALn/RwC5/0cAuf9HALn/RwDH/0cAuf8rALn/RwC5/0cAuf9HALn/RwC5/0cAuf9HAEcAuf9HALn/RwC5/7n/RwC5/0cAuf85ANX/uf9HALn/RwC5/0cAuf9HALn/uf9HALn/RwC5/7n/RwC5/xwARwC5/0cAuf9HALn/RwC5/0cAx/9HALn/OQC5/0cAuf9HALn/RwC5/0cAuf9HALn/RwC5/0cAuf9HALn/uf9HALn/RwC5/7n/RwC5/0cAuf9HALn/RwC5/0cAuf9HALn/RwC5/0cAuf85ALn/RwC5/0cAuf9HALn/RwC5/0cA5P+5/0cAuf9HALn/RwBHALn/RwC5/0cAuf9HALn/RwBHALn/RwC5/0cARwC5/0cAuf9HANX/RwC5/0cAuf9HAMf/uf9HALn/uf9HALn/RwC5/0cAuf/k/0cAuf9HALn/uf9HALn/RwC5/0cAuf9HAAAAuf9HALn/RwC5/zkA1f9HAMf/RwC5/zkAx/85AMf/OQC5/zkAx/8cAEcAx/9HALn/RwC5/0cAx/+5/0cAuf9HABwAuf9HALn/RwC5/zkAuf9HALn/RwC5/0cA8v/H/0cAuf9HALn/5P/y/8f/OQAAALn/OQDH/0cARwC5/0cARwC5/0cAuf+5/0cA8v8cAEcAuf/V/7n/RwC5/xwARwC5/7n/x/85AEcARwA5ALn/x/+5/zkARwDH/7n/5P8cADkARwA5ADkAx/+5/xwAOQDV/ysARwBHADkAHAA5AEcARwA5AAAAuf/H//L/OQBHABwAAADH/8f/x/8AADkAOQAAAOT/uf/H/8f/HAArAEcARwA5ADkAOQBHADkARwA5ADkAHAAAAMf/x/+5/+T/AAAcADkAOQA5AOT/uf/H/7n/5P8AADkAOQA5ABwADgArACsAOQA5AEcAOQBHADkAOQBHADkAAADH/8f/AAAcADkAOQBHAEcAHADk/7n/x/8AAAAAHAAcABwAx/+5/8f/AAAcADkAHADk/8f/uf/k/xwAOQBHABwAAADH/7n/x//k/+T/AADk/+T/uf+5/9X/5P8cADkAOQAcAAAAx//H/7n/HABHAEcARwAAAMf/5P8AABwAHAAcAAAAAADk/wAAAAAAAAAAHAAAAOT/x/8AABwAHAAAAAAA5P8AAAAAHAAAAOT/uf/H/+T/HAA5AEcAHADk/7n/uf/k/wAAHAAcADkAOQA5AEcAOQA5ADkAHAAcAAAA5P8AAAAAHAAcABwAAADk/8f/x//V/+T/HAAcABwAAAAcABwAOQBH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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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IOAT4GGhnjFltD60B2tntTsAK12krbZiiKIpSi6RNAYhIc+BF4ApjzGb3MeO4W6TsciEi40WkRERKysrK0iSpoiiKAmlSACLSAKfwf9IY85INXhsx7dj/dTZ8FdDFdXpnGxaHMeZ+Y0yxMaa4sLAwHaIqSiW7yisoKd2QbTEUJWukwwtIgIeA+caY212HXgXOs9vnAa+4wsdZb6AhwCaXqUhRao31W3dx+n0fsnjdlmyLoihZoSANaRwBnAvMEZEvbNi1wCTgORG5AFgO/NgemwKMARYD24Hz0yCDolSbjdt3Z1sERckKNVYAxpj/AX4ja471iG+AS2t6XUVRFKVm6EhgRVGUkKIKQFEUJaSoAlAURQkpqgCU0BHbYaVzwylhRRWAoihKSFEFoCiKElJUASiKooQUVQCKoighRRWAoihKSFEFoCiKElJUASj1ls07drNs/bZsi6EoOYsqAKXecsrdMzn6tnezLYai5CyqAJR6y9KyZGv/OhJMCSeqABRFUUKKKgBFSX21UkWpF6gCUBRFCSmqAJTQoZO/KYpDuhaFf1hE1onIXFfYDSKySkS+sL8xrmMTRWSxiCwUkZHpkEFRqo9qBCWcpKsF8CgwyiP878aY/vY3BUBE+gJnAv3sOfeISH6a5FAURVGSJC0KwBjzPrAhyehjgWeMMTuNMctwFocflA45FCUZjPb5KgqQ+T6Ay0RktjURtbZhnYAVrjgrbZiiZIwVG7Yz6fUFGC39FaWSTCqAe4F9gf7AauBvqSYgIuNFpERESsrKytItnxIiLnvqM+57bwnzV2+J6wTWTmElrGRMARhj1hpjyo0xFcADVJl5VgFdXFE72zCvNO43xhQbY4oLCwszJaoSAnaVOzX/Cm0BKEolGVMAItLBtfsjIOIh9Cpwpog0EpHuQC/gk0zJoSgAeQG1/AdnLOX+95fUnjCKkiMUpCMREXkaGA60FZGVwB+A4SLSH2eYZSlwEYAxZp6IPAd8BewBLjXGlKdDDkXxI2Lm8WoBTJmzhilz1jD+yH1rWSpFyS5pUQDGmLM8gh8KiH8zcHM6rq0oyZBnNYBagBSlCh0JrISCiAVIy39FqUIVgBIObAtAO4EVpQpVAEookKht/x7hKXNWUzRhMms378i8UIqSZVQBKIrliQ9Lue3NhQAsWLMlu8IoSi2Qlk5gRakPXP/KvGyLoCi1irYAlFDgtvzryF9FcVAFoIQKQV1BFSWCKgBFSYEvV2xkwZrN2RZDUdKC9gEoocKQnAnIL8rYu2cCUDrphLTJpCjZQlsASihI1ewfsRIZY5ixqIyKCrUbKfUPVQBKKKhu8T113hrOfegTHv2gNJ3iKEpOoApACRVC8q2BdZt3sHDNVgC+2bA9YzIpSrbQPgAldCTTGhBg0C3TMy2KomQVbQEoShIsKduabREUJe2oAlBCRXX7AmYsWs/cVZvSKouiZBtVAEooEJ/tVLjhVZ0qQqlfqAJQ6iWj7ng/aj/Vmr+X10/J8u+rL5Ci5CBpUQAi8rCIrBORua6wNiLylogssv+tbbiIyJ0islhEZovIgHTIoChu/Gbz3L4zudVH316wLp3iKEpOkq4WwKPAqJiwCcB0Y0wvYLrdBxiNsxB8L2A8cG+aZFAUX3budgr+i/89S1cFUxRLWhSAMeZ9YENM8FjgMbv9GHCKK/xx4/AR0EpEOqRDDkXxY3d5BQBbdu7JsiSKkjtksg+gnTFmtd1eA7Sz252AFa54K21YHCIyXkRKRKSkrKwsc5Iq9R7ROaAVJY5a6QQ2xhiq4YFnjLnfGFNsjCkuLCzMgGRKWNDiX1HiyaQCWBsx7dj/SK/aKqCLK15nG6YoiqLUIplUAK8C59nt84BXXOHjrDfQEGCTy1SkKGmnvMJox6+ieJCWuYBE5GlgONBWRFYCfwAmAc+JyAXAcuDHNvoUYAywGNgOnJ8OGRTFj3vfXYzRZcAUJY60KABjzFk+h471iGuAS9NxXUVJhqXrt0W1AFQXKIqDjgRWwoEW+ooShyoAJRRo+a8o8agCUEKB9gEoSjyqAJRQoMW/osSjCkAJBdoAUJR4VAEo9R5B2OqaA0hHBSiKgyoApd7z9dotbNi2K9tiKErOoQpAqffMydBSjjMXr+ewW6fzw67k1hhQlFxDFYASOmalaWWvmyfPZ/WmHbpgvFJnUQWghI7Lnvo82yIoSk6gCkBRFCWkqAJQFEUJKaoAFKWa6CJjSl1HFYCiKEpIUQWgKIoSUlQBKIqihBRVAIqiKCEl4wpAREpFZI6IfCEiJTasjYi8JSKL7H/rTMuh1C1mr9zI3AyN4E03NZlobv3WnRRNmMyDM5ayecfu9AmlKElQWy2Ao40x/Y0xxXZ/AjDdGNMLmG73FaWSk++ayYn//F+2xcg4X6/ZAsBNk+dzwp0zsiyNEjayZQIaCzxmtx8DTsmSHEotYYxhT3lFtsXIPVyupCs2/JA9OZRQUhsKwABvisgsERlvw9oZY1bb7TVAO68TRWS8iJSISElZWVktiKokS0WFSWmVrWc/XUHP617n2431o5BbsWE7877dXON08nQwgZJFakMBDDXGDABGA5eKyJHug8YpRTxLEmPM/caYYmNMcWFhYS2IqiRLj2unMOHFOUnHf+WLbwFYtn5bpkSqVYb95Z20pBNb/KdrojpFSYaMKwBjzCr7vw54GRgErBWRDgD2f12m5VDSz7MlK9iSRMflvG838eHS7wD4YsXGTItVp8jLi1YBp937QZYkUcJIRhWAiDQTkRaRbeB4YC7wKnCejXYe8Eom5VDSR3mF4UhX7ff2t75OeM64hz6p3P7r1IWUV+iKXBHCbABau3kHp937Abu1byhrFGQ4/XbAy+LYOQuAp4wxb4jIp8BzInIBsBz4cYblUNLEyu+3882G7ZX7iT5eYwzfxazG5Vj96k/R57XE5I7d5TQqyEN8bPx7yivYuaeChWu3ZFq8nGXwLdMBeGDGUi4Z3jPL0oSTjCoAY8xS4GCP8O+AYzN5bSV5dpdXcMuU+VwwtDudWzcNjBtbeZcEBblXZX/brnJaNqk/YxBj8+DbjT9w+KS3+dPYfpx7WJHnOeMe/oQPlnxXC9LFs2n7bhas2czgHnsHxlu/dSfFN03j3CHdGH1Aew7v2TYj8mzdsSdxJCUj1J+vUKk2v3txNo/MLGXonxN3bE6Zszpqf09FcAvAazDXuIc+Tk3AOsRTH3/D4x8uB+Dlz1exfutOz3hBhf+5D32c0UFhB//xTX5y/0fs2B28lGXxTdMAeOKj5fz0wcw9s/y8+tMarGuoAgg581dv5qXPViUdP7Zg+mjphsD4y13moghfrgwe4fvsp98kLY8Xf3ljQY3OrwnXvjyH+95bAsBn32yk+KZpKfd5zFi0ns+/yXxneUWAG+8Kj+eW1mu78kRdYbOHKoCQc93Lybtybt+1h3+9tzQqbNn6bXy81L82m8pYgQhPfpy6Api1fAOfljrK6J53l6R8fiaJVQDnPfyJT8wqqpNvyZBsy+LUDHsjfbmySsH9Y/qijF5L8UcVQMj5LKamOX+1/+CmG16d5xn+m2e/8D3Hr5YZVMOcnaCF4MVp937IGfd9mPJ56WDF99t5rmSF7/HYTuL3vk48qPH5kpVs2p5+M9Cb89ZWyRWgY8q2eJuukiGRaQlgj3qC5QSqAJQoRv/Dfz6a1Zt2eIZ/6xMO/oWMn/dQacxAsY8CWhde7NyTuPBJN5c8+RnXvDDbt9Z+0ROzKJowGWMMj85cllSak+es5srnv0ynmABc5Upz7Wb/51Zdlq3fRp/r3+D5AIUI0SYggH9OX5QReZRgVAEoGcWvoudXA3z58+j+iIf+l7jAdJs1duzKnk+5n+np3YVOjX/uqs3c8N+vkk5v3ZbMFogzF69PKf7G7bsSxnnG9t88PLM0MF7s4//bW19XuoUqtYcqACWOr3zmuNm5O/XC9SqfWuyvnvo8qfMTmcLXbd7BQTe8Wbl/8B/fDIidWf46dWHg8V0pDngK6qRNB9+naGI6PYGJbfl32yr7iIJMiQBPfrw8pWsDLFq7RVsJaUYVgBLHGJ9pib0GPAUR5P3iNwAq9oxp89d6xouw4vu6M7lcqs4uCTxsUybWRJXMKO7bf1w1jCeRbf+ov76btCwrq/Hcjvv7+9pKSDOqAJSMUZ15f5LpQHSz+Ye6s4jK6o2p1V6TaQFs2LaLTUnmgZe7r5eSdiuKUwd0pnEDp5gIKrS9Oqz/9Z6/N1aq74b7vfAbW6GkjiqAEBPUTF9atjUuLFWLRHVMGI9/WJpS/PMf/TTla2SLS5/6LKX4ibJvzspNDPjTWxx8Y3JmL69O5Q89BqQ9X7Iyar93uxYJ0/Yyvd36eurjMfy8j9z9Fcm0XJTkUAWQI+zcU87f3lzID7v8a8DvLFhH0YTJaZtR8/tt/p16XtM2p1qgx3p6JOLdhevYUY1+hvpKUH5XVBhOuqtqxbRFCeYUWuKh0AHeWRg/Ee81L86O2j97cLfAtNPJtT7jUt6Yu6Zy+6kkxoksXreF5d/Vj6nHM4kqgBzhuU9X8M+3F3P7W/4diZHa7il3z0zZVOLFwwEuiV5eOrFjBtx87VEApVL8G2P42SPetfmLnijxDJ/2VXD/QF0nSAH8OmbsxXF/fz8wrXcWeM+4/sRHiTtj27ZomDBOunjL55k+Pyu6VeL1vrkZcfv7HPXXd9m+a4+uRBeAKoAcYdE6p4b2wAzvQjm2A6/P9W/U+JrT5vsvw/ByjL145ffBUwOcdk/8yNFEDQa3EgtqLEyd510o/OJxb8VQX9i6c4/vYLD/fvltSmn5ud3u2lMRuA8wtGd6F2N6wVWY33P2gLjjGwJaphESjTOI0Pf3U+l53evJCxcyVAHkCJ1bNwk8Hjulck3ZtjN4BsY35q2J2r/9zWC7a0F+vItLoukMFq+rMkukal7K1FQJucTazTs9beuv+hT+WwOeqbtg/+9lQ33jff5N/IpkDQuqiol05LvbNfiYPvvQolH0pMQlpVXzS63ZtMNz1PgDM5axfZf3/XoNMkxHi7k+ogogR+jXsSUAhxa19jx+yt0zAejRtlllWDKTjK3futPTFr9onbdN2I/Y+PedM5CfDu5aue81o2Mi8dwzi6aqABJNKFefufxp7zEU/xcwr5O7T6dZo3zfeNe/Mrdy++dHdI87HqRkqkPjBvnMuXEk/75gcGWYu7Iz5Nbpvstv+o272O7Rj3bpk6l1wIcFVQA5QqT881oT1hhT6YJ3ydFVC2fse+2UwDSLb5pG8U3TGHxrvO90qjWi2FrVqAPac9PYAyr3vRTA8g3BnXBPf1LVmZeqz3tYa3RBBbDfestbduyOGmFdtHczz3gAX6+tUvRXjdwv7vg/pmVm4rYjelatTTDxpeQmKHzEZ7TxN9/Ftxim+/SBhB1VADlCpAbsVWueu6rKXXP0Ae2jzEV+njbT56+t9Jcu27Izbo4cr4ndvvz98Qnlc+Nez3bt5mj3vcXrtnDdy1W1yStG9PJIs2o7kW93bGsnjL7gz326guF/9V+zwa9V9J8vok1GsesQ+71DBXlVxUPklA0e00FETDanD+xM6aQTAt8jP/xWToula5voBYti+0h+2FUe5R2lBJM1BSAio0RkoYgsFpEJ2ZIjVwgygbz1VZU9vlmjAp7/5WGV+1e9EO/b/cmyDVzwWHQH6QWPRu8vWFPlRXHfOQN57qLDaNm0ga8Mqc7e+G3MoKdhvQqjTEYAm37YTdGEyfzuhdmBs2kC3DEtug/isiSnkqgPFE2YTHmF4ZoXZ7N+a1UBfPLBHZlxzdHVSvOh84orty9/Jj4vrx7ZO8r2v29hcwBPN+XIFBGRzl33e5Tu9Z/f/M2RjOrXvnJ/wkvRLqtbdvoPivPq4A47WVEAIpIP3A2MBvoCZ4lI32zIkiu4y/9YV7inPnEKx/5dWgHQoWVVC2C6hyfPj/8VP2fL/wIm/hp1QHsGdW8TKF+qPv1eFbrrT/B+xM+WrEjoMfTPtxendP36htcsp+cM6UaXNsFLeEL06ssfTXRWYj12/3aVYa/Ndvpi3Ga1nvs0j0qjSUOn3yDVZSxj3U9nLa/q4B3WK3iJyS0eaxc0bpDP2UOqKhKvz5GB+DkAABpzSURBVI12VthT7v8ifbT0O4omTObbjXVn+pBMk+lF4f0YBCy2awYjIs8AY4Hkp0pMkoNumMrmJNYc3btZw7R72lSXCx8voXTSCZX7EXPHnWceUhn23tXDOeqv7yY9DYAfFx3ZI2r/mD778HbMR7tlx25KXXbVpy4cTBBe3hkiVYWIF+4FQqrDhNF9mFSNkad1hb6/nxoXFlHab1wxjFF3OPM3fVq6gUOLopW539Tbsbife2w/wamHdGL2yk1x75tbabTyaEH+afJXjOhbpWwWrqnqYzigU8uouD0Km7G0bBvH2/jbdkYrvcuPcfq/hgasTRxUyx9nF+I5fNLb9Ou4F/N8Jj3MNVo0KmDW9cdFtcjSRbZMQJ0Ad5t/pQ2LQkTGi0iJiJSUlSVeRCOWbTv3JFX4Q/rdLDNB172ranuxttAI7ib3e1cPZ/HNoyv3vVz4Cls0itof2a9dXJxY+/7h+1Z9gH077BV17L2vy+j7+6l8Whrdmd2/cytPeSPMWJTa1MSxnHRwxxqdX9do7nKd7NO+6hm8EDONA8DNk+dXbjcN8ADa6LKn924fPf2DuxB386VrVPpYj2ewPKZD1j2h4Mkx8fex7+KbtgUcO9bhQltZERFmTjjGU57YGVffuWq4Z7y6UvgDdC9slpHCH7LXAkgKY8z9wP0AxcXFKRsTmzUqYNmtY6gwjpeKMQYRYU95BQX5eVRUGEQc+3ZBnlT+Q1XY7nJDg3yJ6rAsrzAYDA3y8qgwhjyRSpPHngqDAD/sLqdJA+dji3jIRNKI2Pt37qmgeaMC9pRXICLkCXSfGO3Z4zeK0a/TzO0Z1C2mFjfv280c0KllVC3p9IGdo+KMObADv3vR8cJ46bOVnDqgc+Asln3at+Ar15xCkbllXptd9fFeO6ZPXMdjdVi8bis992nOEZPejjsWtlVlX77kcM/wZ0tW8OfTD4oKc/ff7NXYu59n8uzV/HWqfwvK3SHs5gdXC6BRgyrlctqAzrz4WbwycldQYusjd/90AAPtQvQvf76Sm6fMjzrewiV7O1fF5Z0F6zi6zz5AVQugRaMC5tw4EoBfH9srbtnJPu1b0G6vxtw/biD5UvV9Rl7Tgvw8jDHsLjfk5znfZiQfC/KE8grnuy+3339+nlSWJ+UVzjnlFYY9FYYG+XkIsLuignwRRKrKImNMnAddJO0ImVwyOVstgFVAF9d+ZxuWdsQ+nMg2OA8XHG8IEXEekOvfvd2wIK8yjcivYUEejQryycsTCvLzKtOJnFeQn0eLxg0osNuRY5HzG+Tn0SA/r7IWV5CfR75NI5a5SdRUIs1wt53+jJiC3Y17wrWWTaILBPdH9tvnnA7mDwL6D2JljuwuLatySfQrPPyI9HXE8ox1G13lYcPdq4l/B3Z9pFcSE7SlwqVPfVa5PsCfxvaLO962ufd0EO4W9v4dqmQasf8+nvHvcvXluOMD7N28qlD/zbNVzg33nj2AJbeMiYob+YYhekLAyHKb156wf2XYuMPi5zKqMIamDfNpVJBf+Z02LMir3AYqv/98VzkRKRci330D++1CVXlS4IrTuEG+o0DypPJa+bbciPxHyofIL5J25Jesh1R1yJYC+BToJSLdRaQhcCbwapZkyUluf9MZ5BLxunjkZ4f6xo3UwF/5skqH/p+rw3X0AY7XRGSmxZtcJoFkXq7rX/FeC9g5P2bfI04Dj1HCQTTM934tH/zfMoomTPY81rxRAbNvSN39sL7j7vB88eLoVsP1J3p3yr/3dbzCL/B5Ju4+gVP6V1lxR7o8ddyss+9gv457JV2w9Shs7jnOJJbd5RWVg8PmrqpyiW3aMN7QsWtPRVJp1neyogCMMXuAy4CpwHzgOWOMfykTQu60NaXxdiK0Lm3ip4roUeiYeKZaT4hXXf7eble8SEvj/Ec/5c9vpLej1P0Jfb12S1TTNYLbPh/xQknE2P7OOX8+7cCkZfEzb+Q6zRsVMPnyoeTnCe9dPbxaaezv6otxr5p1uMtcdkCn6P6anx9R5JmW28smERtdfWfuAj2Rye/Osw4JPO4mmYL6gyXr6eWa8+fI/armL/I6v/S77ZXm3jCTtXEAxpgpxpj9jDH7GmNuzpYcucy6LTvYYpvYnVvHd/redZYzkda/3neW4Yt0gI2P8ezp0KpKedzrWre2V4yrX4Rj+1Q133/3wmzPOBHc5f1FT8zytFe2alplPmjfsnFgehEmnXoQt51xMCceFNy5O7Bb68oWTl0kP08o+b8R9OvYkiW3jKHb3s1SKhwjuDtU/VbNyo8z13kXgM0bBXcNDv1zlVL5WzXn5vdr5XnhZ35yF/I/feDjqGPHuN7hBvnCr47pSSyxA+TCiI4EzjEeGFc1QMc92Klxg3jvjX32ivbgifhoX3zUvlHhx+3v7cHxowFxjldA9If1bMwArdgyw93UX7Z+W3o6Y63L6OkDOwfW/rrt3ZQXLz6ce88ZmI6rZoUmDfLjnu3JB3f09MaK4FVAlycxl0ayJo8BXb3no4pQneUcY0llgJi7AuHmgXHOc28X8x0ANHApGBHhyuN7M/fGkYzw+RbCiiqAHOO4vu34zQhnDpZPlm0IjNvW1WlWNGFypVdFi8bRBUS/jtFN/whek31B/CAgN7G1yGNjPqh0dFj5TYgXy71n192CP4JfbgW57t94cnwn7S6PAVCxC6Ik+2yqYxsPUli3v7mQO6Z9HTX7a8dWwbPfJkOjgnxG7N8uzk3Zj+aNCrj3nPjpp8OMKoAc5PJjo5urT1wwKKXzYzvs8vIkamBZBK9WBcARAQNtvGz8bjZ7jN5MhelXHsVvj+tdud+oII/BPqOUmwYMLKsz+GTnVp8pDa4Y0YvTPDy8Duka7Tm1bP22pBZp/+1x8RO+JYu7g7lvh5a+8e58ezF3TFvE9PlVI9z9/Nqfu+gwz3A/ps2PXyvi/av9p8dwtwwuOzreLBQ2VAHkICLCqYdUmWe6t/WfvTGWw/fdO3GkGmASrPPlN0NjMnRs2Zh9Yzw+RIQHXPPWuOmUYA2FuoCfd9dHS71bf36twqN77xMTL7kpGy46qkfiSJZOMbV2tyzFSbTaklkjeFD3Nlx+bPzEgamwV5PkhjddNbJ34kj1HFUAOcrtP+nPXT89hAuGdvfsAI7wz5gOwyd/4T9NwwuuSeS8fL2ToVFBarVuP39wL17xWajEy7vnT6ccEFWbq4tcdfx+FBd5t25+7VMI+rljAtzxk/6V27FmET9F06gg39PDzIvXflX1fLbs2B01geHAbsmZ7ZKhmatll6zXmJtE3mClk07wbBGHkbr9BdVzTjyoo6+vdgS3i+V7Vw8PtPO6CxsvM0IyeJmNOgZ49jTx8MH2I3ZaCjdf3zQ6aj92FalcI5kCJmgCvCtG9OKR8+ML7SDvmdbNqjpLb4/xzjm6j78i7tyqqoJx1iB/F1B3+iWl30dN9+FnTqwObiWXrNeYm3SMOg8LqgDqAS9efBhjDmwfN/WDF5Haj9fgmOoSpHSuP3F/32OpEGszbpbjCiAZgoxpIsJBneLt6qcc4u8Wm47lGpsHzBXk5vxHP41a0MeLC4d5Oxkc3Tt4jeHIZHCx04d74efKrCSHKoB6wMBubbinljxibvnRAXFhQSaETA3OSjSVcC5QOukElt06JnFEH7w6SoOm7a6uSWyoKy/HHVZUrTS86OpTIbkoxk05li5tmlI66QRu+VHiQYCv/3pYtWRTHFQBKJ54uYJOOvVAjvcY4p8Od8wDPWq7fjRtGO87n6sEtY4SVdhjFcCs/xvBPi38TSKH9aieA4B73Egy6wsky2k+40zSOQK3ID+PB8cVc9sZB/Par4by9IVD0pZ2GKj77WglIzwzfgjFdmbGRLhtw8lw1fH7sbRsGy+51qmNnafGiw8nHkN+ngQWgnWJRB5VsfZ+92RpXvjZvh8c5+1F5T5v6hVHJrVuwF6NC+KmWL/Px7e+iY+STrf5zm+qaiUx2gJQPGnrUdika3G/y47pxe0/6R9lI05mvvMOLZvUm8If4gfVxeJuPfxuVJ+k0rz06HjzSjK1+t7tW8Qt0OJF98L4lqHfxG8iwpe/P543rog20+zfwXtgolL7qAJQkuao/YI777wIMnMkU+CkitvVNZc5Z0hXfj7Uu5M0lv5dWnHx8GC7eQT3ILoI6VxM5GGPMRlBZq6WTRtELVij5BaqABRf2sSYdqozfL9xA/9XLJUJwZKluKgN141Jj+dRuvi/E+LluemUA5MyhXw48ZiES3C68ZrGIZWBhIlIZIZS6haqABRfUp3HP5YHxhUH1g4jtttzh8Qv2FETMrmCUnX4xbDo0bbHpWCz7tCySY1cdq/W0a5KAKoAFF/co4pPOKhDYNx9PAZxJargN8jPo3TSCfzplHjX0pqQiwt9zLiman6aS5I056SDI3ulbrZLhVtPTX69BiX3UAWg+NJznxbMuOZonh0/JGqaAS9+PaJm87ekk0QT1mWDLm2aVk7YVpGGAVvJkomscE8J8ZPiLgExlVxHFYASSJc2TRncY++Eg4zcywFG2O0xRXFtkO2pALxs/gDD7Cyrhc0z68k045qjOd1O9bGvh9dOTTmgU0se//kgplw+LOm8ftROa3FEz8xOVqikRsbGAYjIDcCFQJkNutYYM8UemwhcAJQDlxtjpmZKDqV2aOThaZKMX3kmyLYFyM+76YoR+3FGcZe0Drbyokubptx2xsHcdsbBGbvGkSl6hA3vvQ8PjivmUJ/J75TskOmBYH83xtzmDhCRvjiLwPcDOgLTRGQ/Y0x5hmVRMojXLJXH983OUo3ZNgEd3LmVZ3henmS88M9ldMBW7pENE9BY4BljzE5jzDJgMZDaiidKnSCd/uepEDTA6ppRmfeKycEuCEXxJNNf6GUiMltEHhaRyIThnQD3QrMrbVgcIjJeREpEpKSsrMwriqLEk+UCWBWAUleokQIQkWkiMtfjNxa4F9gX6A+sBv6WavrGmPuNMcXGmOLCwsy6syn1h0QmoMjC4CLBM5lWh/vPHZjyojmKki1q1AdgjBmRTDwReQB4ze6uAty+Y51tmKKkhaDiXxA6tnK8cP5wYl/WbtnJve8uSdu1vWZLVZRcJWMmIBFxjxz6ETDXbr8KnCkijUSkO9AL+CRTcii1R5/2LbItAgB5AW+1SNVAsXKTdWuRomSVTPYB/EVE5ojIbOBo4DcAxph5wHPAV8AbwKXqAaSkE0lQrEc6icsrKnzNRQv+NCrtcilKrpExN1BjzLkBx24Gbs7UtZXscOupB3Lr6wsY0mNverfLXmsgUSdsZQugwn/MgHvBmatH9mZYr7acfNfMdImoKDmBLgijpI1DurbmuYuyPx1z0AR0UDVSuMKYpFx2TjyoQ1LrLStKXUOnglDqHYmK9MhKVXmSyFjkkGzhf/kxPZOKpyi5grYAlHpHkBuoABcO68G2XXs4/4gi7nvP3wPogwnHpLQK2lG91VVZqVuoAlDqHUFzAYlAk4b5TBydeNGYVBfAyfYUFIqSKmoCUuod1S2Hrzxuv2pf8/JjetK/i/ccQIqSq6gCUOodQZ3AQVb/Xx3bi1H92lPg04R4/OeDKO7W2vPYb4/vnbDzWVFyDVUASr2jOsXwgXYK5/vOHcjiW8Z4xjlyv0La7eWMItZ57ZX6gCoApd4RWxN/+8qj/ONadXF0kh24xnYLnzWoa9rXMlaU2kYVgFLviLXgNGlYNairplaaH9slEA/p6m0KUpS6hHoBKfWO2EI+Ge+cZN09h/feh9JJJwDQO0fmPlKU6qItAKXeEWsCataowPdYTTh7cNe0paUo2UAVgFLviC3imzfKTENXvX6Uuo4qAKXeEVQwx/YPtGziKIe9GjfIpEiKkpNoH4BS7wgcCRyzf86QbuTnCWcOUnOOEj5UASj1jlSmZCjIz+Pcw4oyJ4yi5DBqAlLqHUHlv9rtFaUKVQBKvSO5SZ4VRamRAhCRM0RknohUiEhxzLGJIrJYRBaKyEhX+CgbtlhEJtTk+oriRXALoPbkUJRcp6YtgLnAqcD77kAR6QucCfQDRgH3iEi+iOQDdwOjgb7AWTauoqSNoDJey39FqaJGncDGmPngaVcdCzxjjNkJLBORxcAge2yxMWapPe8ZG/ermsihKG6CRvUW5KvVU1EiZOpr6ASscO2vtGF+4Z6IyHgRKRGRkrKysowIqtQ/Koy/CjhtQOdalERRcpuECkBEponIXI/f2EwLZ4y53xhTbIwpLizU5faU5Di0qI3vdM0NC7QFoCgREpqAjDEjqpHuKqCLa7+zDSMgXFHSQoP8PB4671D6XP9Gxq91wdDuHLWfVk6UukmmBoK9CjwlIrcDHYFewCc4fXC9RKQ7TsF/JvDTDMmghJjGDfITR0oD15+oPgxK3aVGCkBEfgT8EygEJovIF8aYkcaYeSLyHE7n7h7gUmNMuT3nMmAqkA88bIyZV6M7UBRFUapFTb2AXgZe9jl2M3CzR/gUYEpNrqsoiqLUHO0RUxRFCSmqABRFUUKKKgBFUZSQogpAURQlpKgCUBRFCSmqABRFUUKKKgBFUZSQogpAURQlpKgCUBRFCSmqABRFUUKKKgBFUZSQogpAURQlpKgCUBRFCSmqABRFUUKKKgBFUZSQogpAURQlpNRIAYjIGSIyT0QqRKTYFV4kIj+IyBf2d5/r2EARmSMii0XkThGRmsigKIqiVI+atgDmAqcC73scW2KM6W9/v3SF3wtciLNOcC9gVA1lUBRFUapBjRSAMWa+MWZhsvFFpAOwlzHmI2OMAR4HTqmJDIqiKEr1yGQfQHcR+VxE3hORYTasE7DSFWelDVMURVFqmYSLwovINKC9x6HrjDGv+Jy2GuhqjPlORAYC/xGRfqkKJyLjgfEAXbt2TfV0Rank3xcMZsaismyLoSg5RUIFYIwZkWqixpidwE67PUtElgD7AauAzq6onW2YXzr3A/cDFBcXm1TlUJQIQ3u1ZWivttkWQ1FyioQKoDqISCGwwRhTLiI9cDp7lxpjNojIZhEZAnwMjAP+mQkZFOWRnx3Kjt3l2RZDUXKWGikAEfkRTgFeCEwWkS+MMSOBI4E/ishuoAL4pTFmgz3tEuBRoAnwuv0pSto5us8+2RZBUXIacZxxcp/i4mJTUlKSbTEURVHqDCIyyxhT7HdcRwIriqKEFFUAiqIoIUUVgKIoSkhRBaAoihJSVAEoiqKEFFUAiqIoIUUVgKIoSkipM+MARKQMWF7N09sC69MoTrpQuVJD5UoNlSs16qNc3YwxhX4H64wCqAkiUhI0GCJbqFypoXKlhsqVGmGUS01AiqIoIUUVgKIoSkgJiwK4P9sC+KBypYbKlRoqV2qETq5Q9AEoiqIo8YSlBaAoiqLEoApAURQlpNRrBSAio0RkoYgsFpEJtXC9h0VknYjMdYW1EZG3RGSR/W9tw0VE7rSyzRaRAa5zzrPxF4nIeWmQq4uIvCMiX4nIPBH5dS7IJiKNReQTEfnSynWjDe8uIh/b6z8rIg1teCO7v9geL3KlNdGGLxSRkTWRy5Vmvoh8LiKv5YpcIlIqInNE5AsRKbFhufCOtRKRF0RkgYjMF5HDsi2XiPS2+RT5bRaRK7Itl03vN/adnysiT9tvofbfL2NMvfwB+cASoAfQEPgS6Jvhax4JDADmusL+Akyw2xOAP9vtMTiroQkwBPjYhrcBltr/1na7dQ3l6gAMsNstgK+BvtmWzabf3G43wFkmdAjwHHCmDb8PuNhuXwLcZ7fPBJ61233t820EdLfPPT8Nz/O3wFPAa3Y/63IBpUDbmLBceMceA35htxsCrXJBLpd8+cAaoFu25QI6AcuAJq736mfZeL9qnLG5+gMOA6a69icCE2vhukVEK4CFQAe73QFYaLf/BZwVGw84C/iXKzwqXppkfAU4LpdkA5oCnwGDcUY9FsQ+R2AqcJjdLrDxJPbZuuPVQJ7OwHTgGOA1e51ckKuUeAWQ1ecItMQp0CSX5IqR5XhgZi7IhaMAVuAolAL7fo3MxvtVn01AkUyOsNKG1TbtjDGr7fYaoJ3d9pMvo3Lb5uMhOLXtrMtmzSxfAOuAt3BqMRuNMXs8rlF5fXt8E7B3JuQC7gCuwVnTGnudXJDLAG+KyCwRGW/Dsv0cuwNlwCPWZPagiDTLAbncnAk8bbezKpcxZhVwG/ANsBrnfZlFFt6v+qwAcg7jqOms+d2KSHPgReAKY8xm97FsyWaMKTfG9MepcQ8C+tS2DLGIyInAOmPMrGzL4sFQY8wAYDRwqYgc6T6YpedYgGP6vNcYcwiwDce0km25ALC29JOB52OPZUMu2+cwFkdxdgSaAaNqU4YI9VkBrAK6uPY727DaZq2IdACw/+tsuJ98GZFbRBrgFP5PGmNeyiXZAIwxG4F3cJq+rUSkwOMalde3x1sC32VAriOAk0WkFHgGxwz0jxyQK1J7xBizDngZR2lm+zmuBFYaYz62+y/gKIRsyxVhNPCZMWat3c+2XCOAZcaYMmPMbuAlnHeu1t+v+qwAPgV62Z71hjhNwFezIMerQMRr4Dwc+3skfJz1PBgCbLLN0qnA8SLS2tYUjrdh1UZEBHgImG+MuT1XZBORQhFpZbeb4PRLzMdRBKf7yBWR93TgbVuDexU403pLdAd6AZ9UVy5jzERjTGdjTBHOe/O2MebsbMslIs1EpEVkGyf/55Ll52iMWQOsEJHeNuhY4Ktsy+XiLKrMP5HrZ1Oub4AhItLUfpuR/Kr99ysdHSy5+sPp1f8ax658XS1c72kcm95unFrRBTi2uunAImAa0MbGFeBuK9scoNiVzs+BxfZ3fhrkGorTzJ0NfGF/Y7ItG3AQ8LmVay7wexvew77Ii3Ga7Y1seGO7v9ge7+FK6zor70JgdBqf6XCqvICyKpe9/pf2Ny/yTmf7Odr0+gMl9ln+B8dbJhfkaoZTW27pCssFuW4EFtj3/gkcT55af790KghFUZSQUp9NQIqiKEoAqgAURVFCiioARVGUkKIKQFEUJaSoAlAURQkpqgAURVFCiioARVGUkPL/+OwgarTxkkIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display adversarial example\n",
    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display original example\n",
    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We present a final third example. For this example observe that the model correctly classifies it as **8**, but the adversarial example is classified as **3**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction (ground truth):\t8 (8)\n",
      "Adversarial prediction:\t\t\t3\n"
     ]
    }
   ],
   "source": [
    "# load a test sample\n",
    "sample = audiomnist_test[5905]\n",
    "\n",
    "waveform = sample['input']\n",
    "label = sample['digit']\n",
    "\n",
    "# craft adversarial example with PGD\n",
    "epsilon = 0.5\n",
    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
    "adv_waveform = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display adversarial example\n",
    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display original example\n",
    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply MP3 compression defense\n",
    "\n",
    "Next we are going to apply a simple input preprocessing defense, namely `Mp3Compression`. Ideally, we want this defense to result in correct predictions when applied both to the original and the adversarial audio waveforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.28it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction with MP3 compression (ground truth):\t8 (8)\n",
      "Adversarial prediction with MP3 compression:\t\t\t8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# initialize Mp3Compression defense\n",
    "mp3_compression = Mp3Compression(sample_rate=DOWNSAMPLED_SAMPLING_RATE, channels_first=True)\n",
    "\n",
    "# apply defense to original input\n",
    "waveform_mp3 = mp3_compression(torch.unsqueeze(waveform, 0).numpy())[0]\n",
    "\n",
    "\n",
    "# apply defense to adversarial sample\n",
    "adv_waveform_mp3 = mp3_compression(adv_waveform)[0]\n",
    "\n",
    "# evaluate the classifier on the adversarial example\n",
    "with torch.no_grad():\n",
    "    _, pred_mp3 = torch.max(model(torch.Tensor(waveform_mp3)), 1)\n",
    "    _, pred_adv_mp3 = torch.max(model(torch.Tensor(adv_waveform_mp3)), 1)\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction with MP3 compression (ground truth):\\t{pred_mp3.tolist()[0]} ({label})\")\n",
    "print(f\"Adversarial prediction with MP3 compression:\\t\\t\\t{pred_adv_mp3.tolist()[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply adaptive whitebox attack to defeat MP3 compression defense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wrap model and MP3 defense in a ART classifier\n",
    "classifier_art_def = PyTorchClassifier(\n",
    "    model=model,\n",
    "    loss=torch.nn.CrossEntropyLoss(),\n",
    "    optimizer=None,\n",
    "    input_shape=[1, DOWNSAMPLED_SAMPLING_RATE],\n",
    "    nb_classes=10,\n",
    "    clip_values=(-2**15, 2**15 - 1),\n",
    "    preprocessing_defences=[mp3_compression],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.22it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.76it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.77it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.85it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.85it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.68it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.80it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.74it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.65it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.42it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.26it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.58it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.26it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.15it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.83it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.60it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.05it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.44it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.43it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.51it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.20it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.48it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.32it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.24it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.41it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.83it/s]\n",
      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  7.00it/s]\n",
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      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original prediction with adaptive classifier (ground truth):\t8 (8)\n",
      "Adversarial prediction with adaptive classifier:\t\t3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# craft adversarial example with PGD\n",
    "epsilon = 0.5\n",
    "pgd = ProjectedGradientDescent(classifier_art_def, eps=epsilon, eps_step=0.1, max_iter=40)\n",
    "adv_waveform_def = pgd.generate(\n",
    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
    ")\n",
    "\n",
    "pred_def = np.argmax(classifier_art_def.predict(torch.unsqueeze(waveform, 1).numpy()), axis=1)[0]\n",
    "pred_adv_def = np.argmax(classifier_art_def.predict(adv_waveform_def), axis=1)[0]\n",
    "\n",
    "# print results\n",
    "print(f\"Original prediction with adaptive classifier (ground truth):\\t{pred_def} ({label})\")\n",
    "print(f\"Adversarial prediction with adaptive classifier:\\t\\t{pred_adv_def}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this notebook we have demonstrated how we can apply the ART library to audio data. By providing a pretrained PyTorch model and loading it via ART's `PyTorchClassifier` we can easily plug in several off the shelf attacks like Projected Gradient Descent.\n",
    "\n",
    "Furthermore, we have demonstrated how to apply the `Mp3Compression` defense and demonstrated how to circumvent it with an adaptive whitebox attack.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduce CNN\n",
    "\n",
    "Our goal is to make it as easy as possible to reproduce or modify our modified AudioMNIST classifier, which we provided as a pretrained fixture in the notebook. Therefore, we provide in the following the original code that we used for training the AudioMNIST classifier.\n",
    "\n",
    "**Training script `train.py`**\n",
    "\n",
    "```python\n",
    "#!/usr/bin/env python\n",
    "\n",
    "\"\"\"Train a simple AudioNet-like classifier for AudioMNIST.\"\"\"\n",
    "\n",
    "import logging\n",
    "import time\n",
    "\n",
    "import torch\n",
    "\n",
    "from dataloader import AudioMNISTDataset, PreprocessRaw\n",
    "from model import RawAudioCNN\n",
    "\n",
    "# set global variables\n",
    "AUDIO_DATA_TRAIN_ROOT = \"data/audiomnist/train\"\n",
    "AUDIO_DATA_TEST_ROOT = \"data/audiomnist/test\"\n",
    "\n",
    "\n",
    "def _is_cuda_available():\n",
    "    return torch.cuda.is_available()\n",
    "\n",
    "\n",
    "def _get_device():\n",
    "    return torch.device(\"cuda\" if _is_cuda_available() else \"cpu\")\n",
    "\n",
    "\n",
    "def main():\n",
    "    # Step 0: parse args and init logger\n",
    "    logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "    generator_params = {\n",
    "        'batch_size': 64,\n",
    "        'shuffle': True,\n",
    "        'num_workers': 6\n",
    "    }\n",
    "\n",
    "    # Step 1: load data set\n",
    "    train_data = AudioMNISTDataset(\n",
    "        root_dir=AUDIO_DATA_TRAIN_ROOT,\n",
    "        transform=PreprocessRaw(),\n",
    "    )\n",
    "    test_data = AudioMNISTDataset(\n",
    "        root_dir=AUDIO_DATA_TEST_ROOT,\n",
    "        transform=PreprocessRaw(),\n",
    "    )\n",
    "\n",
    "    train_generator = torch.utils.data.DataLoader(\n",
    "        train_data,\n",
    "        **generator_params,\n",
    "    )\n",
    "    test_generator = torch.utils.data.DataLoader(\n",
    "        test_data,\n",
    "        **generator_params,\n",
    "    )\n",
    "\n",
    "    # Step 2: prepare training\n",
    "    device = _get_device()\n",
    "    logging.info(device)\n",
    "\n",
    "    model = RawAudioCNN()\n",
    "    if _is_cuda_available():\n",
    "        model.to(device)\n",
    "\n",
    "    criterion = torch.nn.CrossEntropyLoss()\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "    # Step 3: train\n",
    "    n_epochs = 60\n",
    "    for epoch in range(n_epochs):\n",
    "        # training loss\n",
    "        training_loss = 0.0\n",
    "        # validation loss\n",
    "        validation_loss = 0\n",
    "        # accuracy\n",
    "        correct = 0\n",
    "        total = 0\n",
    "\n",
    "        model.train()\n",
    "        for batch_idx, batch_data in enumerate(train_generator):\n",
    "            inputs = batch_data['input']\n",
    "            labels = batch_data['digit']\n",
    "            if _is_cuda_available():\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "            # Model computations\n",
    "            optimizer.zero_grad()\n",
    "            # forward + backward + optimize\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            # sum training loss\n",
    "            training_loss += loss.item()\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for batch_idx, batch_data in enumerate(test_generator):\n",
    "                inputs = batch_data['input']\n",
    "                labels = batch_data['digit']\n",
    "                if _is_cuda_available():\n",
    "                    inputs = inputs.to(device)\n",
    "                    labels = labels.to(device)\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                # sum validation loss\n",
    "                validation_loss += loss.item()\n",
    "                # calculate validation accuracy\n",
    "                predictions = torch.max(outputs.data, 1)[1]\n",
    "                total += labels.size(0)\n",
    "                correct += (predictions == labels).sum().item()\n",
    "\n",
    "        # calculate final metrics\n",
    "        validation_loss /= len(test_generator)\n",
    "        training_loss /= len(train_generator)\n",
    "        accuracy = 100 * correct / total\n",
    "        logging.info(f\"[{epoch+1}] train-loss: {training_loss:.3f}\"\n",
    "                     f\"\\tval-loss: {validation_loss:.3f}\"\n",
    "                     f\"\\taccuracy: {accuracy:.2f}\")\n",
    "    logging.info(\"Finished Training\")\n",
    "\n",
    "    # Step 4: save model\n",
    "    torch.save(\n",
    "        model,\n",
    "        f\"model/model_raw_audio_{time.strftime('%Y%m%d%H%M')}.pt\"\n",
    "    )\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "**Dataloader module `dataloader.py`:**\n",
    "\n",
    "```python\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchaudio\n",
    "\n",
    "\n",
    "OUTPUT_SIZE = 8000\n",
    "ORIGINAL_SAMPLING_RATE = 48000\n",
    "DOWNSAMPLED_SAMPLING_RATE = 8000\n",
    "\n",
    "\n",
    "class AudioMNISTDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"Dataset object for the AudioMNIST data set.\"\"\"\n",
    "    def __init__(self, root_dir, transform=None, verbose=False):\n",
    "        self.root_dir = root_dir\n",
    "        self.audio_list = glob.glob(f\"{root_dir}/*/*.wav\")\n",
    "        self.transform = transform\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.audio_list)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        audio_fn = self.audio_list[idx]\n",
    "        if self.verbose:\n",
    "            print(f\"Loading audio file {audio_fn}\")\n",
    "        waveform, sample_rate = torchaudio.load_wav(audio_fn)\n",
    "        if self.transform:\n",
    "            waveform = self.transform(waveform)\n",
    "        sample = {\n",
    "            'input': waveform,\n",
    "            'digit': int(os.path.basename(audio_fn).split(\"_\")[0])\n",
    "        }\n",
    "        return sample\n",
    "\n",
    "\n",
    "class PreprocessRaw(object):\n",
    "    \"\"\"Transform audio waveform of given shape.\"\"\"\n",
    "    def __init__(self, size_out=OUTPUT_SIZE, orig_freq=ORIGINAL_SAMPLING_RATE,\n",
    "                 new_freq=DOWNSAMPLED_SAMPLING_RATE):\n",
    "        self.size_out = size_out\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        transformed_waveform = _ZeroPadWaveform(self.size_out)(\n",
    "            _ResampleWaveform(self.orig_freq, self.new_freq)(waveform)\n",
    "        )\n",
    "        return transformed_waveform\n",
    "\n",
    "\n",
    "class _ResampleWaveform(object):\n",
    "    \"\"\"Resample signal frequency.\"\"\"\n",
    "    def __init__(self, orig_freq, new_freq):\n",
    "        self.orig_freq = orig_freq\n",
    "        self.new_freq = new_freq\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._resample_waveform(waveform)\n",
    "\n",
    "    def _resample_waveform(self, waveform):\n",
    "        resampled_waveform = torchaudio.transforms.Resample(\n",
    "            orig_freq=self.orig_freq,\n",
    "            new_freq=self.new_freq,\n",
    "        )(waveform)\n",
    "        return resampled_waveform\n",
    "\n",
    "\n",
    "class _ZeroPadWaveform(object):\n",
    "    \"\"\"Apply zero-padding to waveform.\n",
    "\n",
    "    Return a zero-padded waveform of desired output size. The waveform is\n",
    "    positioned randomly.\n",
    "    \"\"\"\n",
    "    def __init__(self, size_out):\n",
    "        self.size_out = size_out\n",
    "\n",
    "    def __call__(self, waveform):\n",
    "        return self._zero_pad_waveform(waveform)\n",
    "\n",
    "    def _zero_pad_waveform(self, waveform):\n",
    "        padding_total = self.size_out - waveform.shape[-1]\n",
    "        padding_left = np.random.randint(padding_total + 1)\n",
    "        padding_right = padding_total - padding_left\n",
    "        padded_waveform = torch.nn.ConstantPad1d(\n",
    "            (padding_left, padding_right),\n",
    "            0\n",
    "        )(waveform)\n",
    "        return padded_waveform\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "**Model module `model.py`:**\n",
    "\n",
    "```python\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "class RawAudioCNN(nn.Module):\n",
    "    \"\"\"Adaption of AudioNet (arXiv:1807.03418).\"\"\"\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 1 x 8000\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv1d(1, 100, kernel_size=3, stride=1, padding=2),\n",
    "            nn.BatchNorm1d(100),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(3, stride=2))\n",
    "        # 32 x 4000\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv1d(100, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 64 x 2000\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 1000\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 500\n",
    "        self.conv5 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 250\n",
    "        self.conv6 = nn.Sequential(\n",
    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 128 x 125\n",
    "        self.conv7 = nn.Sequential(\n",
    "            nn.Conv1d(128, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "        # 64 x 62\n",
    "        self.conv8 = nn.Sequential(\n",
    "            nn.Conv1d(64, 32, kernel_size=3, stride=1, padding=0),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.ReLU(),\n",
    "            # maybe replace pool with dropout here\n",
    "            nn.MaxPool1d(2, stride=2))\n",
    "\n",
    "        # 32 x 30\n",
    "        self.fc = nn.Linear(32 * 30, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = self.conv6(x)\n",
    "        x = self.conv7(x)\n",
    "        x = self.conv8(x)\n",
    "        x = x.view(x.shape[0], 32 * 30)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "```\n",
    "\n",
    "---"
   ]
  }
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