vui_notebook.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Artificial Intelligence Nanodegree\n",
    "\n",
    "## Voice User Interfaces\n",
    "\n",
    "## Project: Speech Recognition with Neural Networks\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following blocks of code will require additional functionality which you must provide. Please be sure to read the instructions carefully! \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.\n",
    "\n",
    "The rubric contains _optional_ \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. If you decide to pursue the \"Stand Out Suggestions\", you should include the code in this Jupyter notebook.\n",
    "\n",
    "---\n",
    "\n",
    "## Introduction  \n",
    "\n",
    "In this notebook, you will build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline!  Your completed pipeline will accept raw audio as input and return a predicted transcription of the spoken language.  The full pipeline is summarized in the figure below.\n",
    "\n",
    "<img src=\"images/pipeline.png\">\n",
    "\n",
    "- **STEP 1** is a pre-processing step that converts raw audio to one of two feature representations that are commonly used for ASR. \n",
    "- **STEP 2** is an acoustic model which accepts audio features as input and returns a probability distribution over all potential transcriptions.  After learning about the basic types of neural networks that are often used for acoustic modeling, you will engage in your own investigations, to design your own acoustic model!\n",
    "- **STEP 3** in the pipeline takes the output from the acoustic model and returns a predicted transcription.  \n",
    "\n",
    "Feel free to use the links below to navigate the notebook:\n",
    "- [The Data](#thedata)\n",
    "- [**STEP 1**](#step1): Acoustic Features for Speech Recognition\n",
    "- [**STEP 2**](#step2): Deep Neural Networks for Acoustic Modeling\n",
    "    - [Model 0](#model0): RNN\n",
    "    - [Model 1](#model1): RNN + TimeDistributed Dense\n",
    "    - [Model 2](#model2): CNN + RNN + TimeDistributed Dense\n",
    "    - [Model 3](#model3): Deeper RNN + TimeDistributed Dense\n",
    "    - [Model 4](#model4): Bidirectional RNN + TimeDistributed Dense\n",
    "    - [Models 5+](#model5)\n",
    "    - [Compare the Models](#compare)\n",
    "    - [Final Model](#final)\n",
    "- [**STEP 3**](#step3): Obtain Predictions\n",
    "\n",
    "<a id='thedata'></a>\n",
    "## The Data\n",
    "\n",
    "We begin by investigating the dataset that will be used to train and evaluate your pipeline.  [LibriSpeech](http://www.danielpovey.com/files/2015_icassp_librispeech.pdf) is a large corpus of English-read speech, designed for training and evaluating models for ASR.  The dataset contains 1000 hours of speech derived from audiobooks.  We will work with a small subset in this project, since larger-scale data would take a long while to train.  However, after completing this project, if you are interested in exploring further, you are encouraged to work with more of the data that is provided [online](http://www.openslr.org/12/).\n",
    "\n",
    "In the code cells below, you will use the `vis_train_features` module to visualize a training example.  The supplied argument `index=0` tells the module to extract the first example in the training set.  (You are welcome to change `index=0` to point to a different training example, if you like, but please **DO NOT** amend any other code in the cell.)  The returned variables are:\n",
    "- `vis_text` - transcribed text (label) for the training example.\n",
    "- `vis_raw_audio` - raw audio waveform for the training example.\n",
    "- `vis_mfcc_feature` - mel-frequency cepstral coefficients (MFCCs) for the training example.\n",
    "- `vis_spectrogram_feature` - spectrogram for the training example. \n",
    "- `vis_audio_path` - the file path to the training example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2136 total training examples.\n"
     ]
    }
   ],
   "source": [
    "from data_generator import vis_train_features\n",
    "\n",
    "# extract label and audio features for a single training example\n",
    "vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code cell visualizes the audio waveform for your chosen example, along with the corresponding transcript.  You also have the option to play the audio in the notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Audio Signal** : (138033,)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Transcript** : he was young no spear had touched him no poison lurked in his wine"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
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type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "from data_generator import vis_train_features, plot_raw_audio\n",
    "from IPython.display import Audio\n",
    "%matplotlib inline\n",
    "\n",
    "# plot audio signal\n",
    "plot_raw_audio(vis_raw_audio)\n",
    "# print length of audio signal\n",
    "display(Markdown('**Shape of Audio Signal** : ' + str(vis_raw_audio.shape)))\n",
    "# print transcript corresponding to audio clip\n",
    "display(Markdown('**Transcript** : ' + str(vis_text)))\n",
    "# play the audio file\n",
    "Audio(vis_audio_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step1'></a>\n",
    "## STEP 1: Acoustic Features for Speech Recognition\n",
    "\n",
    "For this project, you won't use the raw audio waveform as input to your model.  Instead, we provide code that first performs a pre-processing step to convert the raw audio to a feature representation that has historically proven successful for ASR models.  Your acoustic model will accept the feature representation as input.\n",
    "\n",
    "In this project, you will explore two possible feature representations.  _After completing the project_, if you'd like to read more about deep learning architectures that can accept raw audio input, you are encouraged to explore this [research paper](https://pdfs.semanticscholar.org/a566/cd4a8623d661a4931814d9dffc72ecbf63c4.pdf).\n",
    "\n",
    "### Spectrograms\n",
    "\n",
    "The first option for an audio feature representation is the [spectrogram](https://www.youtube.com/watch?v=_FatxGN3vAM).  In order to complete this project, you will **not** need to dig deeply into the details of how a spectrogram is calculated; but, if you are curious, the code for calculating the spectrogram was borrowed from [this repository](https://github.com/baidu-research/ba-dls-deepspeech).  The implementation appears in the `utils.py` file in your repository.\n",
    "\n",
    "The code that we give you returns the spectrogram as a 2D tensor, where the first (_vertical_) dimension indexes time, and the second (_horizontal_) dimension indexes frequency.  To speed the convergence of your algorithm, we have also normalized the spectrogram.  (You can see this quickly in the visualization below by noting that the mean value hovers around zero, and most entries in the tensor assume values close to zero.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Spectrogram** : (625, 161)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from data_generator import plot_spectrogram_feature\n",
    "\n",
    "# plot normalized spectrogram\n",
    "plot_spectrogram_feature(vis_spectrogram_feature)\n",
    "# print shape of spectrogram\n",
    "display(Markdown('**Shape of Spectrogram** : ' + str(vis_spectrogram_feature.shape)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mel-Frequency Cepstral Coefficients (MFCCs)\n",
    "\n",
    "The second option for an audio feature representation is [MFCCs](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum).  You do **not** need to dig deeply into the details of how MFCCs are calculated, but if you would like more information, you are welcome to peruse the [documentation](https://github.com/jameslyons/python_speech_features) of the `python_speech_features` Python package.  Just as with the spectrogram features, the MFCCs are normalized in the supplied code.\n",
    "\n",
    "The main idea behind MFCC features is the same as spectrogram features: at each time window, the MFCC feature yields a feature vector that characterizes the sound within the window.  Note that the MFCC feature is much lower-dimensional than the spectrogram feature, which could help an acoustic model to avoid overfitting to the training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dWmGHZ00ErXnvzz/BWkFNDDVj3jt/BaTW7SRId0Qe1e4/2FsdsdxaQU07VlsrqJjNHtYKaqp9v5QBWBxBqkgMOemnc5e1gpqz+Lu1gopZ9LNWUNMF35MuAXZ2Hl8AVDkP0qWeWUD+P+0aj+tz3ZkVjOYhaw0VF7b/o7WCmq++uNXdhbKCr3b07Q/w9ICh1gpqbuLH1gpqejHHWkFNT3zvjAE8c1bW9VZuEq/d7DuNE+DP+RdYK6jxvgoNcO77d1sr6FhXO/1OgBaWket2roN0HaS3Zp373D3vq9DA/4bgOmX50FbWCmryIxiis5KdrRXUFEXQzrMygt0A781RKtb7/xmszvefshMDHfsuslZQsar1hlq3RSAvCdJ9EBD3WzltYwjSv2YtoKPjgvXWCmoGdPPfn7u62vdrGeCh3NHWCmp+wxXWCnr6WgvoWP9hR2sFNYWD/XecGlUx3lpBzfj8UdYKKqZTuzWvCLTw/1HRaFwH6TXkuC+w6f7sZ9YKepx/ntw46GxrBTXDeN5aQc3wXP+t/4ojyMOdSX9rBT1trQV07Dd4csN3ynImMMJaQY33xhQAq52/GGrIqXXbfCV9O+P6VNfR2n2BzWGT/b8Zf3a17zeBGHrVx3AOZ3K7tYIa7+9HEMfk130vm2KtoOLNqf67fr250v85TB3uv9f7KYyzVlCxJKN41zwnfTvj+lSFQC7OJy2ushbQ067cd1L67AL/3V1i6M8dQy70kTxpraAmhpX02znTWkHFvoN9X2QAvP3qAdYKakbhP90lOgScZzk3CddBegHl/oMT/5+HrCxob62gwvvgE4CH8J8LPQP/g72mfniwtYKaY/r+xlpBzcJf9bZWULFwgG9/gJO+47sNJuB/ERDcd8B7hBW1D2TzyNFtgOtTzaGG1qy11tDhfzeNifieyraMQmsFNT0jaP0XQ6rII32PsFZQ8/Zs/yug3jOnrl3ivx3pD6pvs1ZQc2Xu5dYKavacPc9aQcf6jAnISZDuhxqESu8TwQqsBfSMqva9JfjH3AutFdQcxVPWCmoW0M1aQU0MFxre2xcC7L3kDWsFFaN43FpBzb9z/W8Tx1CfkdOh3FpBRU1uTd2DriPXpuH6VHOpcd9n/Le9f26toOb/rviTtYKKJ35zlLWCmmu52FpBTQwdamJIO/r6kBetFdS8Pv9AawUV93c/wVpBze9XXWStoGb9p85blwHk1Z3Y6YqanIbvEzGug/T8mkpKyudba6h4quBIawU1/3e77yC9w2/8j36eHUFe/bE8aq2g5jTuslZQ891Vj1grqPl2d987S+9EUJ+R38r/gLWf9fGf7vK76VdaK+jILAtICkf9IJXQaoG1hY43xX+bKu90LPU/zOiWoh9ZK6iJYbDXU/jflenT/j/WCmraOK9Vmsaghu+U5ayatqu1gpp5Q0qsFdQcNPBf1goqprf5vPaBJCfdETFcUfn+LEnhvfjV/7BOcr/lvwvBCwyzVlAz1f2LAeaW97RWUHN5ge/Vw1fZ31pBzwH+P9yWhiJrBTUr6WCtoKI6M8hLgnQ/VOS34KPena01VHybf1gr6LnUWkCJ/3lS9N3Ld9oXwFnF/lu2TS3zH6QPK/RfG/ASB1srqPh09pesFfT81FpAz2DetFZQ471N9c/rm+LsfXG2CWyzIF1E7gSOAEpDCHulj3UEHgJKgHnAcSGEFSIiwI3ACFJry6eFEN5u6Dkqaem+I8RFXGutoCY4/zyR96wNEgCmzD/YWkHNz7v7fz2fg//WeXvc5zwP8kNrAT2vX+8/r/5r771rraBG3ndeOLoiacG4rbgbuBlqzaS9GHghhPB7Ebk4ffsi4NtA7/SfwcCtNCKJIiB1t0Kc4X0rCkDqudB1xTRrgWbgW9YCzcDtLawN1Cy9yvfOHsD9+O8s4r6NpP8OjMy8yn8LxsHF/oP0tj0+s1ZQsfbaqtoHkiC9eQghvCwiJRmHR8IX+5BjgUmkgvSRwLgQQgDeEJEOItIlhLBkS8/RgkqK69sKcUSV84sMgDf22ttaQcWAa/2/ES8o2M1aQU+JtYCee6efZa2g5o6BEQTpxzovBu/QytpAzT8ZYa2g5qyye60V1Izu7Xvi6BM5y+se9B82NZrtfT3SeZPA+1Ng47JTV2DT/cmF6WNbDNJzqYmiI4R3qp1f1o4vGGmtoOYrzLRW0OO/GQRt+/petQI4Gt/DyQDGdx1lraBi8Km+84gBZuJ/JZ27rQX09Lx6rrWCinwqah9IVtK3DyGEICJNTpYSkbOBswF22x33w4w6Lna+4gPMKN7HWkHFedW3WCuoOSH3PmsFNacefqu1gpp+zLJWUHM+N1krqDmT260VVIy87FlrBTU3Xn22tYKaP1x9nrWCmj9W+56ovYqM4WpJkL5NWboxjUVEugCl6eOLoFYF6G7pY3UIIYwBxgAM3FdCm3LnQa7/znnuRycvv7mrtYKax8/3vXII8GcusFZQ0zqCnqor2dlaQc3RYydaK6gIvcVaQc0tnGutoKYXvlehAZb/1vnn2+KMWqUkSN+mPAmcCvw+/fcTmxw/T0QeJFUwuqqhfHSAIFCd53xkbHmNtYEa74ND8J/+SSFl1gpqWuJ/QmEMXLP4KmsFNb9b6btP+qfnt7dWUFNa4b+I+mf511srqHnm02OsFXRU1XMsyUnXIyIPkCoS7SQiC4ErSAXnD4vIGcB84Lj03SeQar84h1QLxtMb8xw1IlTkt2xm8+1LwX+d7wQAa/u2sVbQEUEudAdWWiuo8V4EDv7T7wDwn2kBa6wFdEyIoOhy1b/8v7FOHDncWkHPYdYCSp7LuJ2spDcPIYTjN/OlQ+u5bwCaPNe8hlxW066p35ZVdMzzH6RX4PtCiQEbrA3UxBAc9i/337C+1SfWBs1AD2uBZuDr1gIJe45scNRJ1vMzbrBWUPNEr82FYk7Iz7idBOl+qCKPMjpZa6jonu+/G0TnL0oLnJLnvzAghpX0Vv4X0okg6wjvL2eAnL7l1goq9mC2tYKaGNoLV3pfgAJ2/fLH1goqlrWup7tLlv5qiUg3UrOBOgMBGBNCuFHzmK6D9HW0dt/mad+iD6wV1BzJk9YKOtZnXqr7owT/S7h39na+4gN8/w8PWCvoecNaQE/NeQXWCipeOGeYtYKaURFMZJpNH2sFNfsww1pBxausq30gu1fSq4CfhxDeFpF2wHQReS6EsNVtv7L3VBtBJS1ZUKspjEN8L/gAMJde1goq9u7pvydxDK3//hPBByK/tBZoBvy3SWfoOU9bK6g4mEnWCmpmMMBaQc1SiqwV1BzIK9YKKv5dXypnlkau6YYnS9L/Xi0iH5Ca+bNjBumpdJdCaw0dEXQW6ckcawUV7rvTEMcwo+kMslbQE8FFdwy8OP0IawUVwwf6biEJcVx0n8D91gpqvBchVzkNU0WkBNgHUK0C+jz7NFXksRTfbZ6W9/UfpedX+G6d1z/ff4BbFEEicQy7Ad7XDAAm/3I/awU9zjfH3O8QA6PxPY4e4ug4tZhiawUVG6inT7ptTnonEZm2ye0x6fk9XyAibYHHgJ+GED7XPJnrIL06gu4u1b5/BAAUzPTdoWbaoIHWCmp2edZ5zzmg9bf872jEwNClLzZ8pyxn18G+i+ViKAQ/H1W9XFYw6zz/nw0PjPq+tYKO1X+tfds+J31ZCGGz274i0oJUgH5fCOEf2idzHSGWU8BUBltrqIghb2/1IN8XSu28N1UGnE9BB6DNt9Y1fKcsZ3k3/ztjrzLEWkHN16/2XSzX6TL/bYLGcqq1gp7zrAX07NrX9wXrsnb1dHfJ0shVRAS4A/gghPDn5njMLD3VxlFdlcvKsg7WGirmFPa0VlBTTIPDYbOaziy1VtCzu7WAnhguWGNo2bYsgpyd3S77yFpBRQnzrBXUDFjlf+5BnR7dDmmT2R3FGTnUM5U9eyPXIcDJwEwR2bhScGkIYcLWPmD2nmojkJxAfquKhu+YxXRjgbWCGu9Begx5hzFcZ8TwWjh6le+uIgCz2ve2VlDjvb7h+boz/9wxs73v9sgAS9v77+4yjOetFVT8I7O7i31O+mYJIUwhZdhsuA7Sc3KqaVvge9JiZQSX6pM42FpBRUUEPwNWWQvo8V5fAtDC/3UGPdrOs1ZQszrX9+9SDD3Ghy5+3VpBzWnFf234TlmO95V0IWQecB65Ng3Xp5r6WfmeFum+hST+Bz547yMLRNHK8xUOtFZQ02Ev/wV/3lehAV5/dai1goo/DrnQWkHN0uJx1goJwFx8p9TWWURLgnQ/bFibz8LpvrdmKwb6X8U9mJesFVSUOm/jCbD+LmsDPQOcT8YD+CvnWiuoGcFWp09mDR2/tshaQUVLfKdxArzG/tYKaiYy3FpBzVE8Za2g4m3qafGcpeku2wLXQToV4L2+5vmB/nMPh/GCtYKKGNIsxheMtFZQE0NtQAyTItvVN+HPGe1yfZ9Dfn2BiTNm8hVrBTU9vAcYwFraWCuoqCGn9oFkJd0R1eC9nWwZnawV1KyltbWCigG8Y62g5ojF/ntbbyiwNtCzuP0u1gpqYpgU6b07Sgx90isi6HQUw4WG94vuOhNHkyDdERH8sFbiu4UkQCd89/T1XlgDRDGOvrS4o7WCmjUR7MrkOq/zAf/vq4XO31MB9zNMAPrzb2sFNd7ngORRVftABHFfU9iBTjU7iWG1YanznO7qCBLc+vX2X+wXQ3/uV/iGtYKa/Ajyob23nfsGL1srqJnKftYKavrxgbWCGu8F+fXOnvD/kd1ofAfpATIvsryxcwTbmiV8Yq2gYh49rBXUxFCk5X31E2AxxdYKamL4ORSyzFpBRZcI6jM6U2qtoGYQ06wV1AxnorWCirPJKAJPVtIdEcEPK4Ye3d4/1GOYOBpD27xZ9LNWUNOLOdYKaqYxyFpBzQz2sVZQcUgEBcj785q1gpoYurt4r88oz/SPIO5rCjvQqWYnMWwte895i+Fn0Jq11gpqvLfyhDh2NGLIw811vsXqvdgP4nhf9T4DBPwH6fWSpLs4oQZYby2hI4b2f95X0mP4QIxhcu30CD4QvV+wQhydRYpZYq2gIoYV3BhqfWLYZa03p9sRNUjtA8lKuiMi+GHF0EnBex5uJ+f5qxDHhYb3AmSI40Pd+xRngH2ct1UdvnyytYKaRzseYa2gxns+N8AnlFgrqGjlfSVWifMQ1z9tIkhT8B6YxPAzmON89DPE8XOIYRXa+/ATgJedd9lp29H/RXcMLRj/yQhrBTXeB2Ot5u3aByJYnG0KO9CpJmwrvO8GtI6gT3oM6S4xtCNdQDdrhQT850PH0MqzyPniDcA4TrFWUDOaB60VVAg1mQeSnHQ37GBXVNmK99VD7zl7AP2Zaa2gppAu1gpqkiA9O3iJQ6wVVBwSQRF1lfNJ1BBHKqT3lfQcQu0DO1jc5/tUI+iTXhXBJeE652/G3nPqIY6uIjEQQ23A/ZxgraDGe3cX7wsfEEdThCN5ylpBTUvnu0o5mSvp4D1ybRI70KlmJ9UR/Ai8vxl3Y4G1gpoYhm7EUDgaAwOYYa2wwxPDz+CPXGitoGYGA6wV1Ax0/tmwNnMRMFlJT9ieeF/xAf8DmbxfZEAcxX7ex1dDHCugcyMoQp5Jf2sFFaUUWSuoiSFlZ4DzLkEAvZhrraDi3cz31CQn3RERXFHFsJLuvR9uDAFundUGh3gvQAZYGkFwFcfrwfc5eF/4APi38wulWPCeClmWdHdxTA7QylpCh/cAF/ynWsSw+rn34o+sFdTkFfsP0h9ktLWCmp7MsVZQ4z1Ij6EAeR49rBXUzKKftYIa7zsaefVlG/iOXJuE71OtBu8D/mIoNPPewjCGD8QuxYutFdS8HEG6y+4R1Dd4fz2D/4FMXfD/ej6Ql60V1JzCOGsFNcsotFZQ8SrltQ8k6S5+6NppAT8642fWGiq89zAFOIn7rBVUPM3h1gpqbhX/U9kuLRtrraDmsI6PWyuo+R2XWCuo8b67dyr+XwsxdC4bxgvWCmp6Od8Zq3PBnaS7+CGfCve/gF+6+1NrBTWvfetQawUV/yo+yFpBzaVl/seI39fxO9YKag7kFWsFNddykbWCmvFlo6wVVBQW+u/PfTgTrBXUTGS4tYIa/znpU2srfqa8AAAgAElEQVQfSIJ0P7SkghLmWWvo8N3CNIX3nVn/bdKR8obvk+20i2AU+mz6WCuomVDufxT6hk93slZQsaaV/w+GiQX+A9yF7/e2VlAz9MtPWyuokMxhRpCkuyRsR5zn1APwobWAjopB/jspcK21gJ6JN/v/UPe+swewZtou1gp62loL6Fiz0n9b2EsLrrFWUHNNyaXWCmpeW+V7Jb2iuqD2gWQlvXkQkW7AOKAzqdmgY0IIN4pIR+AhoASYBxwXQlghIgLcCIwA1gKnhRDeru+xN5JLjf/Cy2OsBfRM7rGftYKKuzjNWkHN/je/Zq2gJoYuO79+3//V0qkH3WqtoGbs9B9aK6i4pKv/ADeGQUCjCx6yVlBzxy3nWSvoKMsIU5MgvdmoAn4eQnhbRNoB00XkOeA04IUQwu9F5GLgYuAi4NtA7/SfwcCt6b83Sws2UFztO9eivDjHWkGN98EbT0w/3lpBzU8G/sVaQU2x+7wpOP7Ld1orqBnORGsFNWPb+g7Sf8JN1gpqdv1olbWCmvm9/e8q3dHLeZCe2WY7CdKbhxDCEmBJ+t+rReQDoCswEjg4fbexwCRSQfpIYFwIIQBviEgHEemSfpx6yakOtF6zYVudwnahxefWBno+6VZiraDDd8oeAPMGllgrqIlhR+NE7rdWUPMVZlor6LnYWkDHtPEDrRXUdOvtvx3p3i/6nz+B94ZT9W2wZnFOuojcCRwBlIYQ9tI+3na5HhGREmAfYCrQeZPA+1NS6TCQCuA3fVUvTB/bbJAecqCyle+V6BZLaqwV1LhPOYrgqjyGgsUF7G6toGYAM6wV1KzGfz40u1oL6Hgc391pAAY6b4MJsPdOEQTpF9ZTeOmJKRm3s38l/W7gZmieJvvb/FRFpC3wGPDTEMLnqdTzFCGEICJN+g0SkbOBswF22x0q8ls2p+52p2CV//7WI7y32nL+gQ5xjKPPrW+ynDM6s9RaQc2X7/3YWkHP36wFdLS71fnCB/AUR1krqDmzt/9+9Te0/4G1goo/5c+3VmgSIYSX0wvTzcI2DdJFpAWpAP2+EMI/0oeXbkxjEZEuQGn6+CKoNfpxt/SxWoQQxgBjAPoPahFW0mGb+W8POq7y3ye9+92fWSvo+Jq1gJ6Z9LdWUDM4sx+uQ3qWRxDg+p/mjvfMqRj6c3eLYPru3PbdrRXUHMxL1goqxmTu1Gf/Snqzsi27uwhwB/BBCOHPm3zpSeBU4Pfpv5/Y5Ph5IvIgqYLRVVvKRweoIYd1tGl29+2K/8VD97Tabbm1gpo+/MdaQU2Z8/HVAK38/yoxesjd1gp6nG9QfjB5X2sFNR9M8X8O37/Mf+rXa7f5HjbYur41QNuc9E4ismku15j04vE2YVtejwwBTgZmisjGRM1LSQXnD4vIGcB84Lj01yaQar84h1QLxtO3oVtCc+J89sk32vufEnkgL1srqHF/wQ0s6tbRWkFNUQQpO+5xPnsCIIIOjJzJ7dYKenzP9aobkNuvpC8LIQzaXk+2Lbu7TCH131kfdS7t0l1dftSU56gh13+Rk/9aOXjWWkBHt5P8b8v+8AH/uZMDjn/dWkHNBR/57zHeobf/fvUMe9LaQMfd/vO59zx8i2NOXPA8w6wV1PQ/3ne3pvI/ZQyIsw/StyuuT7UGoQLf0yLH7nVcw3fKck6d+bC1gooTImibF8PF3ijGWyuoeau3uuOWOTEU/HG380KTYc7zdYCTucdaQc015f4njs4t6GmtoOK/qT4h/yPLg3QReYBUm/FOIrIQuCKEcMfWPl4Wn2rDtK1Zy+DyN601VBy8LLO/kD9GHec7uBo6w/8KbgSd/zh2yKPWCmraem9HCpTwibWCmn6nzrJWUHE5V1orqLmAPzd8pywnhomjr7G/tYKKmnoS0EMW90kPITTrdETXQbqshlaTrS2UPNXC2kDNTpf6Hij1wICR1gpqigdsscbaBe777QPvsI+1gpp8Kq0V1HSodwKKH57kSGsFNS2psFZQc3QEu3uz6GetoCKX6lq3g0C168i1afg+1WrA++Rh57uyQN1hA86Ycbz/wKra+UsZ4KQFj1krqOnQbYW1gprp+J92eSC+i8FjuGCNoRD8iE9etFZQc32Pn1krqMghY+BjEqQnbFf+z1qgGdjqbKvsoH8EY9DX0dpaQc3ybq2sFdSUfjFA2S9TGWytoGYPZlsrqBi1/BlrBTVXdrzcWkHNFfzBWkHNm+W+X89rawpq3Q4CVbmWk+a375R430F6K2BPawkla6wF9Lz1Ld/Fcqtpa62gphdzrRXULKHYWkFNDANcYhgq9cMPfXc7er/vl6wV1HgfogPwWQ//nw2/db4S+Kec2jMtgwjVeZah6/ZNB3QdpK9r05L3B+xmraHDf60ci50HV8N4wVpBTe+pC60V1EwevJ+1gpovf+h/4uihfZ+3VlDzVl/fCwc/wH8rz8u5ylpBzWK6WCuouZ8TrBVUlDGpzrHq3CyuHG1mXAfpc+nFKOft8w449DlrhR2eztURDG+JYHJtMYutFfQUNHyXbKcTZdYKasYzylpBxbzqHtYKakpzi6wV1By13PkQEODojo9bK6hYlFEEHhCqjUeObk9cB+kVZa35aOze1hoqOp/qP0hf6zwf+qXcQ6wV1Izs4f/DJIZiuRe7fd1aQU0MP4du/NdaQcWoXP9dRbzPMAH4QccbrBXUeF/8qMmYiRkQqpIg3Qe9C2dz06kHW2vs8FzHhdYKKq7jl9YKevy3tmZX962a4PBi/4Vmb197gLWCHuensHBIobWCmpv4ibWCmvvLfaeKAJxb8FdrBRU11C0SjaGbWWNxfaZ5VFFEqbWGiv6rPrBWUPNJ+xJrBRW9quc0fKdsx/UrOUWXYv/Fr21YZ62g5tsX/cNaQc0z04+xVlBxNZdZK6i59ccXWCuoGfmXB6wV1Hjv1lTOfbVuJ+kujsil2v3W7OL2u1grqBmF75y3nR72PYwJoPwYy5ZUzcOnY/13tBhz6snWCmq6ON8eB3jmad9B+qMDj7VW0HOYtYCey7jGWkGN9/qM2Rkt8JIg3REx/LDW0M5aQU01a60VdFT7T7MoWLV9e7duC1odvdxaQc2rzkdwA3wlgrkBX7rifWsFFR//6svWCmqOu8p3G0yABXSzVlDjvT6jZQQTkDW4DtKFUGdkrDe8+wNM4HBrBRXnDXU+jQmg3FpAT2XwX2g2ds3p1gpq/tr1B9YKatp4XziIoFvTk6uOtFZQc2T7p6wV1Hj/fF5Vz/Rg74uzTcF1kA6BXOfvZjHksFbS0lpBh//rJPCfOknNwAj6F/p+OwJgQdfdrRXUeE+D5EFrAT3r6WitoOakwx6zVlCz7iDf3ddm1tOCMenukrDd2PVV/6kW/x7S31pBxaXdfmWtoOaaVf4Hh3QctqjhO2U5leudX7ACExhhraDmOb5praCi6PnPrRUSgPW7WhvoKWGetYKKzHSXVJrzjhO6uj7TQA6V3nux+q/Rcj+S/lfzf2etoOaaF/0H6QfmvmytoOaJ9463VlDz7uP+i9l3+fGahu+Uxfy1p/+0qXMvudtaQc//WQvoOQXftQHLGFnnWJLu4oSAUOE91WKqtYCe/t91Xmh2QwtrAz3Or1UBfsJfrBXUTOjlO/8TYAM7WSuoea7Yd6P0H77oO7ACOLfv3dYKalq9am2gp9+3ZlkrqHiL9bVux9AwpCm4D9Ldb3v4n5xMqfeTOMlaoBmI4D3LfR4xcEjhS9YKap7tUHflyhsTGW6toOKbH0+xVlBz/Jl3WiuouZVTrRXUzKWXtYKKzMm1AZKcdC+spxWz6GetoWLfIf6HGXVjgbWCiv0GTrZW0HOUtYCeW/54rrWCmqPxP8795IvusVZQM4hp1goqnj5zqLWCmgce+r61gpq/Heu/01Flru9t1j+xNONIBIuzTcD1meZQ43/1rYe1gJ7bOdNaQcWf8D8ZL4JGClH0JB7Ca9YKak66139Hi4wdcndcceZvrBXUdB/9obWCmlv4kbWCmktKb7BWUHFPRsesJN3FGd5/WPOL/RdpjWCCtYKKJRRbK+jp8ba1gRrvBcgAK+hgraDnYWsBPQ886TtlJ4YdGfcLaJHwSNER1goqVuQlfdLdIgRaUmGtoaJ41WfWCmq8D3zwvhMAwHHWAnq8j68GOJ27rBXUfPyk/75z3rfD+/NvawU1J8z1f6Hxl55nWSuo8f6+ujJjAnKyku6IXKrpkNHo3hstPrI20HPboHOsFVR4z6kHoLe1gJ7PpvofojNvcIm1gpp7OMVaQU2x8962nSm1VlDTqtMKawU1A3jHWkGN94B2SsY47WSYkSOqyWWl9+3lCOLDmYN8DzOKYfXTeZ0cAL1/9K61gpoY8uq9B7gAc5x3tHiJQ6wV1AxqP91aQc3jzlehwf/rOSB1jnnfKWsKrs+0gnzmea+8LG/4LtnOQy+eZq2g4rOhba0V9ETQ3eUX/NFaQc3iCOobltLZWkHNTHwvHMxc6tsfoH9n5/MzgNastVZQkzmx0xtCqHU7SXdxxBra8goHWmuoyD2pquE7ZTk/PMb34I1d8D2dEGDsUP9J6ftH0BnF+wciwG34Tl8DmFdRYq2gol9n3wNoII5Ukcc41lpBzX7OJyau5blat5Mg3RGVtHS/vbySna0V9HzXWkBJZhtWhwzjeWsFNd5nHsTCnyZGMAu9lbWAjlWt/BfvvvfTr1or6Lk3NHyfLKdLT9/pLpX1TJVPctKd0J15/B3f1dd7LXrfWkHNzON9b832x/+2bBTFrxEQw8XSC8P3t1ZQ471WyXseMcAFr//ZWkHNPzjGWkHNBEZYK6iYy/Jat6OYNN8EXJ+pkOrw4pmcPN/+AP3wvTW7mnbWCmoW08VaQc1R+G7lCbDrglXWCmrWdvvEWkGN9x3KUoqsFdR4/1wAuAX/U5CH8YK1gop8Z5PJROQw4EYgF7g9hPB7zeO5DtI30MJ9odarnYdYK6iZTR9rBRXeU6YATuduawU1MfSrL4kgwJ3OIGsFNdcv/Zm1gorq6f6L2X8x4jprBTXvzfX/Wnil5zesFVQs5ie1bmdzTrqI5AK3AN8EFgJviciTIYStvmJ1HaTnsYHOzhOKv1z6sbWCmtVFvleir8f3BzrAoAh6MM6lp7WCmp+V+x7BDTCowH/rvD9Ncp5Xv8xaQE8MU1OX9Sy0VlDjPfWrvoA8W4N0YD9gTgjhYwAReRAYCVu/rdToIF1E2oQQsqofUauaSnqWOw9yI2jB+H/81lpBxbsTv2atoObVCPKIn2eYtYKaSwqusVZQE0MLxpNG/91aQcd71gJ6Ksm3VlAzisetFdTcessF1go6Sgtq3czyYUZdqT39ZiEwWPOADQbpIrI/cDvQFthdRPYGzgkhmCdrBYHqvBxrDR3/rbE2ULOih+8rdfbylfNWH7+o9t9j/Ib1/nc03inYx1pBzVE8aa2gxn0Km+80YgBm77WHtYKaE7nfWkHNrQOcB+ltat/MgsLRTiKy6db1mBDCmG31ZI050+uB4ZB65w4hvCsiWZHktEHyWJrfyVpDxZcKP7VWULMPM6wVVBzV1X/BYhavLDSangVzrRXUTJ5+mLWCmuqB/n+XTmGctYKKRed3tFZQ88Qi/9M6h3X1f7V03BDfc0yea1tW55hxusuyEMLmihUWQa0Vgt3Sx7aaRl2OhBAWiNQazdpgSxIRaQW8DOSnn+fREMIVItIDeBAoBKYDJ4cQKkUkHxgHDATKgNEhhHlbeo7Pac9EhjfmFLKWH5b7fgEBDHSeD/2L5TdbK6iRD6wN9OQPqbBWUHP3wNHWCmpiaCM5jlOsFVTcxenWCmru6Pp9awU13n+PwH+noHW0rnU7mwtHgbeA3uk4dxHwPeAEzQM2JkhfkE55CSLSAjgfaExIUAEMDSGsSX/fFBF5BrgAuD6E8KCI/A04A7g1/feKEEIvEfkecC2wxU+8Dqx037atZS//LdsuQtVhyByJoC7A+fswEEe/+iMjSBX5j/NuTeC/U1AMKUfjONlaQY33+AJgjvOC/MUZLRizOUgPIVSJyHnARFItGO8MIaiG4TQmSP8BqZ6PXUldGTwL/KihbwohBPhi3nqL9J8ADOV/VxZjgV+TCtJHpv8N8Chws4hI+nHqpUVFNV0/Wr65L7tgw7idrBXUrLzKeU76G9YCzYD/Gi0m9va9KwYwwHnqF8B5E+6wVlAzesRD1goqvHctA+hMqbWCmsFMtVZQU0jddBFPTKunu0Y2p3eGECYAE5rr8RoM0kMIy4ATt+bB0z0jpwO9SPWOnAusDCFUpe+ykFTwD5tUxaavRlaRSomJoBnVFvDfhpVillgrqAiHWhvomd2xu7WCmpO5x1pBTQwr6Rv8j26os0XujQN5xVpBjfviXeLY3fM+rC+Pqlq3s6BwdLvSmO4uPYAfAyWb3j+EcFRD3xtCqAYGiEgHYDzQd6tN/+dzNnA2wK675/F2797ah7TlJw3fJdspHek710L8ZxzR98r51gpqSm/w/XsEcQQm3ad/Zq2g5uWhB1orqCiKYBW6gpbWCmr6Tvb/vnrbQedYK6hYx8u1bmdzusu2oDGXI48DdwBPAVvVLzCEsFJEXgK+DnQQkbz0avqmla8bq2IXikge0B7q7tOkW92MAWgxqH84nCe2Rilr2O2Zj6wV1Lgf/3yjtUAz8HNrAT0xbC0fMOFtawU9ztc9APKptFZQkZuxeuiRmXzFWkFN/4PetFZQ8970r1or6Fj7WJ1DSZBem/UhhJua+sAisguwIR2gtyY1JvVa4CXgWFIdXk6FL6LsJ9O3X09//cUt5aMDdGUxl+N7stzVXGatoObsRb4Hh7S+YZ21gpqZ9LdWUNOO1dYKevxvBhD8D1nkAb5nraBiLr2sFdSU4bs9MsTxnlQ40HdO+vQ2n9e6neXDjJqdxgTpN4rIFaQKRr/okRZCaGjJqAswNp2XngM8HEJ4WkRmAQ+KyG+Bd0it0pP++x4RmQMsh4bfZYVAbsPdILOay7nSWkHNiK7NViNhwi5Prmn4TlnOp0f5b5t3Fr4v9gAK91loraBmcK7/HQ3vbWFj6LDz8OxTrRXUlPXxXdsAcC63WCuoGErt1rxJTnpd+gMnk+rKsjHdZWOXls0SQvg3UGf8XgjhY2C/eo6vB77bCJ8vqCaXlfjuLHIsj1orqNllse8g94GjRlorqDl+gu+0L4CnVzfp5Z+dDLAW0DO6j+/OKACv4DsnPYbUr1l9+lkrqOm42P806hOKfXdrWlDPQmaS7lKb7wJfCiFkXZJfFXksw/febGvWWivocT6D5jGOtVZQc7zz2gwAvrbB2kDN17v778oxovm6h5kxyPlK+trMWegO+fn0v1orqLl54BnWCmoeeMH5UKnVtX+PksLRurwHdIAIys2zkI7T/F+pzx+0i7WCilfZ31pBzaIR/seIH4T/Edw/ocnlO1nHbPawVlDjfRJ1DFwy8HJrBTUxtGB0PmuQ+jo8J0F6bToAH4rIW9TOSW+wBeO25nN24gWGWWuouCrvd9YKatY5X/X5Hv6397ue7HuoF8B19/zCWkFNDC0YY8iHftZ5kL6YYmsFNW+zr7WCml0O953KCbDXc29ZK6iYMyiGkeBbT2OC9Cu2ucVWkscGirxPZmtvLaCnpNx3L9kBBf6nRPKitYCeGALcC/iztYKaGOpk3px8kLWCjlbWAnp+MfgP1gpq7r78XGsFNe/Nd14oU1l7ETDp7pJBCGHy9hDZGqpoQSmdrTVUlBfnWCuoWZnvu3i3MoKhG1MWWxvoiWHK4gDesVZQU4z/X6ZWA3zvLPVoP89aQY33SdQAEbSrp+NuvjOVV7VMJo7Wi4hMCSEcICKrSXVz+eJLQAgh7LTN7Roghu4uC/L9rx4udX6hFAMl1gLNwGsR1AbMwn9Hi3n0sFZQs37ZztYKKn7T3n/7whheCx8O6W6toGb5r7paK+hY3KLOoSQnPUUBQAih3XZyaTIVVfnMKfM99OGVQt+twgD3W0+PM8paQc1Zb9xrraCmTcb4Z4+cwj3WCmp6MsdaQc1TPc1LplQcN/8f1gpqdu2+wFpBzaMRdP7iCGsBJRnZd0l3l/+xxWmf2UBYl8uGGeYL+irWHuq76BJgNVl7HdcovA/EAvhscFtrBTXtyv0XaQ0o8J/ushLfq9AA/ZhlraDinu7HWyuomc5AawU1DzLaWkHNrwdfZK2g4raC2gPikpz0/1EkIhds7oshBPsKqUrA+YC/hyJ4EziZcdYKKg7mJWsFNW0q/Pfbn1mwl7WCmhgC3BhWqbyfQwwtJO9ddLK1gpozut5uraBmtvNuTRX1VFEnOekpcoG2pHLQE7YRMQyt8N6CsXMEIwCq83wHJeB/Rwbi6DHeiTJrBTXTGGStoGIYz1srqOnTdba1gpoYFtH25zVrBRU5Xwy6T5Gku/yPJSGEuvNYswmhcU0ksxjvha/gP12kzPnUWoDFuf77KsfAVyIYflIUwUWr99VD7++pAM87n2ECMND55Frw/7skGZnXSZD+P5IV9O1AB1ZaK6hp+b8ZVy5pwzprBTUxvGl5zyMG/8EhQCeWWSvs8MTQFjaG13OvCIqovXdfy6nnIiOGz7vGsqUg/dDtZrEDUxjBB+JcfHfYKYxge39FBDsyMaS7/CeCdJcY8L74EcNuhvfgEGAZnawV1HgfTvYkK2rdTgpH04QQsn8ahOB+Mls+ldYKarzn1cfwYeL9jRiguML/EJ05+b4vWAEmcbC1gpqZ9LdWUDGcidYKavKd77ACvMQh1gpqpjnvsrM2YyhWMszIG84ngrXBf1cO71uzMYyjX4z/nPTKfN+/RxDHtM659LRWUNMN3z26SymyVlATw3Cyziy1VlDTh/9YK6hoxfo6x5J0F084P4MuEXyoe+8GEUOAe1Dpm9YKapYXOd8Ww39XEfA/nAxgBgOsFVSM5iFrBTU9mWutoCaGbk3eC0czR/YkhaOeyMF9uksMebjee0PHsJvhfUcJoGNp3RUTb7QrWm2toKYH86wV1HgvWoxhN2MW/awV1HhvXxgjSU66JyJowRhDbpX3IDeGreUpxftaKyQA7fAfpHvP5wZ4mQOtFVQMZqq1gprxHG2toCaGlXTv70lLuazOsRjipsbi+0xroJ50JVeso7W1gpoOGdXX3mjt/CID4siFLougk8IM9rFWUBND+lcv56kWezrfCQD4dJH/36PvdfWfduS9LWwgJ+N2ku7ih4D7bX7/+WKQ5/wcvBeZxUIMr4W1EVx0x1BI/Y7znPTpEdQ2PNX1cGsFNTGk7FSQb62gYloEXYI0+A/Sna+ke29fCHH0GfdOaQRtJL1vywIMxn8Br/caE/B/oRHDa8H74g1A5wj61Vc6D9JbsKHW7WQlPWG7EsPKm/cPlBgulLz/DCCOlfRc71t7wDd42VpBjffCyyN5ylpBzS4L1lgrqHmk2xHWCmrmUWKtoKK+Fs9eC0dF5LvAr4E9gf1CCNMa+h7fQXoE6S4x5OF6DxBjyOduGcFQrBhyoZ+PYFCz95aq4P936SFGWyuomdBthLWCmjIKrRXU7OG8T3pNvTnpbkPX94BjgNsa+w1uzzQhe/Ce7uJ9GBPEcQ4xfCDuwwxrBTUx/BxKnLeR7MBKawU13n8GAEURDDNaSQdrBRXVERWOhhA+ABCRRn9PEqQbE0NwtZgu1goq2uF/WzYGYmj9F0OnoBgKqb2/r3ZimbWCmunOx9EDtGadtcIOT32r5l6D9K3Bd5AeQbqL91Vo8N/uzPvWOMQRHJ7T+B3ArGUcJ1srqHmTwdYKarzndMdQJ7PC+QouwKG8YK2gxvsF64eU17qdBSvpnURk01zyMSGEMRtviMjzwK71fN9lIYQnmvpkvoP0CCaOet+KAv8roDFsLcfQkeMVvmGtoMZ7VxGIIyfd+xCaUTxuraCmj/NcaPBfdAn+p5qvzwjyUmuzpkH6shDCZnukhhCGNeeTuQ7S8zpW0OnEj601VPyZC6wV1OzBbGsFFfPoYa2gpl/5B9YKaroV+E+zGMEEawU13i+6wX+QHkNweB2/sFZQE8PPwXvKzn/4NOOI68LRJuP6TLvwKb/kGmsNFd98YIq1gp6DrAV0rCn2vdIA0Gq8tYGeu076jrWCmhh6Qy+LoOPU485H0i+NYO7BvZxoraDmen5mrbDD8yk/r3U7C9JdthoRGQX8BdgF+KeIzAghDN/S97gO0tuxmgO99/R1HuACXFr8K2sFFdc8eZW1gp7/WgvouT+CD/Wfcb21gpqXI0g7enOq7zfW9wb77vMOcaR+PTt5pLWCnoXWAkqW1/189hqkhxDGA01aUnMdpAuBfO/9oT+3FtDTq3iOtYKKHx91rbWCmr8suchaQU0M9RlznA/RAeiF79czAI9aC+j4cnvfaZwAE/tucYHQBccdNNZaQc3D759qraAjY95jQKxz0rcrzoP0GlpSYa2hY721gB7vnUWms9kaED/0thbQ4/61DBzCJGsFNTF0O/L9yQZv993TWkHNBQtutVZQM6XbVGsFNeN3HWWtoKKqRe0UQufDjJqM6zMN5FBJvrWGDv/1fjDAWkCH94sMgDeG7m2toKY0gjzcrz7ynrWCmne/6/9iqcWFvrcoL+F31gpqJi71XRcAsLKb/9293xdebK2g4vq8RXWOeU132RpcB+m5VLsfSZ/RAtQlxSyxVlDh/kIPGI/v1RKIY7pfDOz90UfWCmo2jNnJWkFFyXXzrBXU/GuQ77oA8N++EPx3nLqTVbVuey4c3Rq2eZAuIrnANGBRCOEIEekBPAgUAtOBk0MIlSKSD4wDBgJlwOgQwryGHj/XezcF5/qA+wultZlJbw6JoUgrhoulGNKOQqG1QTNw3gZrAxUxpBzFEOCWRdDpyPsckMwYLyBU1yRBenNyPqmkjo1LG9cC14cQHhSRvwFnALem/14RQuglIt9L3290Q0EIuWEAACAASURBVA/u/orK+cRUgH2W+87ZmVvQy1pBTXW+600xAN6b/lVrBT0RvJ7F/5BF2KOFtYGK3O7+f5GOm+176ivAY31GWCuo8V7rI9TUPhCgqsp53NcEtuknu4jsBhwOXA1cICICDAVOSN9lLPBrUkH6yPS/IVWbf7OISAghbO7xq8h13xGia9VyawU13l8v7fJ97wQA5EYQHe460H9Hiw2rGr5PtuM7vE3Rca+6eaye6BdBsdKkkswhNP7oGUGnozcZbK2gYi3Ta90OQaiu8r8o1Vi29ZneAPwSvtj3KgRWhhA2RhQLga7pf3cFFgCEEKpEZFX6/ss29+DV5LHCeZDuuyogRQvngyLbtfcfpMcwyn0dbawV1Mxs778rx74F/gPE5c93bfhOWcwpw8dZK6i5fv1PrRXUzM33v8u61vn7alUMQZKCbXb2InIEUBpCmC4iBzfj454NnA1QtHs+a7znvS22FmgGZloL6Gi3l/8g3XtdAEBL7zMPiCMPlxnWAs2A89qAbhXOVz6A9Y92tFZQU3SG/2L2v/ATawUVK+tdSXe+fd8EtuUlyhDgKBEZAbQilZN+I9BBRPLSq+m7ARv3JRcB3YCFIpIHtIe6y4MhhDHAGIBOg7qHBxtOW89qDiuabK2gx/mF7sncY62gph+zrBXUFG5+08wNe7/qvzPKuyc5j3CBA3jOWkHFidxrraDmgDN8/wwAvssj1gpqvuJ8Fa1O3WEgCdKbgxDCJcAlAOmV9AtDCCeKyCPAsaQ6vJwKPJH+lifTt19Pf/3FLeWjA6ymLa94H2H9XWuBZuBZawEd/Z2/iQF0iiDAnYP/reVOQ/ynHeVF0HJqAO9YK6iIoVvTUoqsFdR8+u6XrBXUFO/te7s+M90lBKFqQxKkb0suAh4Ukd8C7wB3pI/fAdwjInOA5cD3GnqgqpoWlJb7fiN4v9j/m8CX8V3wN6himrWCmoLFNQ3fKctp28N/ys4E/HeDmEU/awU17VhjraAihiB9wSr/58A8awE9s0p8v54rqltlHBFqqp1v3zeB7XKmIYRJkJqXHUL4GNivnvusp4nryjWf57Hm+V2awdCO60ZeaK2g5u5Dz7VWUFGR39JaQU1BxXprBTXdSz+zVlBTUeS/1/sezLZWUDOTr1grqBjF49YKar7R/hVrBTWfjCyxVlDjvdf7jNyMieAB/y3lmoDvy5EacL5gwtwItvjf7eY7hzWGYr8Dct+2VlDzUdFu1gpqekXQsq0b/osWvQfpMbT+855yBPAox1orqMl3XpD/PhmDyYIkQboXeneczU0nHmytoeIlDrFWUPND/matoKI1axu+U5bz995nWSuocT+YDDh0lf9C8Bh2khcXdLFWUHEtF1srqBnORGsFNT/gNmsFNbfwI2sFFUJGaWIAqsTExQLXb8dV5Lrvkz6T/tYKan7DFdYKKr51o/9t2SHnv2qtoOZwJlgrqPlNe9+vBYCu1/gfsNbn0v9YK6h4fMEJDd8pyzm2m/+uWd4DXICjeNJaQcVrrKx70P/svkbjOkgX/HciKIxgCM03P5piraDjiC02EXKB99cBQEkEVVpdJ/sPcHnDWkCP91HozjcnAVh6dWdrBTUxvCd5nz9R/0q6iYoJ0kCXw6xmz0Gtw7hpvnO6v/ree9YKepwvgHb7pe9VN4CFk33XBQDOlwxSTBriewQ3wEGlb1orqJGn/H6uAZSd0dpaQU3hxHXWCmo6DlvU8J2ynLVrfP8uVRw8lJp3ZnyR3yL9BgXuNezINlCmhxAGba+nc/2xmEeV/wEo/idwwwBrAR0D8d+Cce0Bvkc/Ayy/1/cod4Bv7Ok/wOUTa4FmwPl7Uscr/Xdrcj5nEIDf5l5mraBmdXvfjRFuys3o8x4gs5Y0ZlwH6blU06G+fCVP9LAW0LN8UGYfU19MKDvcWkFNr0L/3SCW4z9Il1Jrg2agvbVAM+C8Hv/OJcdbK+jx38mT08vHWiuoKSvoaK2g4j5W1T4QIILszkbjOkgHodr7KRRbC+i5jR9YK6ho09Z/d5cjecpaQc2cI3ynrgHwjLWAnotO+rW1gh7nK+mz6WOtoOaZPgdbK6hp5b8OnNm/8f27tJ5/1z24A+WkO49w/fNx8a7WCmoW47vdWbd8/32hry39tbWCmklFB1srqPnVSZdYK6iZxMHWCnqct7eOoR3p4hhWoPw3d+G71Y9YK6hYlTnFeQcrHE2CdGNyI9i36YPvwst+zLJW0HOQtYCeNy/2fxJv7ur/HOYO933RDXDMGeOtFVTEsJJ+e8WZ1gpqvs8D1gpqXs0dYq2g4liW1D6QBOkJ25MYVkxWOu9Vfy63WCvo8d/tDPayFtCz18C3rBXUvIbvD3XwP+1y7D9/aK2gpvfh71or6HHeuQxgwmkjGr5TFrOKR2sfSIJ0P9SQw1p8txdayc7WCmr6OK8Q2nl5BJ0UIghwW5R8bq2gZi3+u+zEsLN0P/6HAXnno/v2tlbQE8H76kPO2+ws5wVrBVPcB+nrnH8ozqPEWkFNO1ZbK6iQCAaH4LvBDgAHFr5sraBmFv2sFdSUUWitoMZ7W9Vn+o6yVtAzw//o9ucGHWCtoOZgJlkrqJifGV8kK+l+EIL7nO7b8Z+3N+G271gr6PC9GwjA5AH7WSuouZhrrRXUfJ87rRXUeF95A6gg31pBxU97/t5aQc2ynp2sFdTE8Fo4k9utFVQ8WV+b7SRI90HL6g2UrFporaHimWnHWCvoiSBbxDuVtLRWUPPNG6dYK6i59/yTrBXUPIrzi27g9sU/tlZQ8edi/znpMRBDh5pPnO/WVzCv9gHHw4xE5DrgSKASmAucHkLY4rAf10G61ECLcmsLJTEEuN4nFPpvyBEHEQz2KqXIWkHNDPaxVlDzr2LfL+pHvfeQxH8aJEBLKq0V1NzPidYKKpYzs/YB38OMngMuCSFUici1wCXARVv6BtdBenmL1rxRvIe1hoqrii+0VtDzVWsBHR8X+e9V3xP/E0ffOMp/oVkMH+pTXv2mtYKaqUOet1ZQcSF/tFZQMyGCPMIDecVaQc1pUx+yVtBRnpEG6TgnPYTw7CY336AREx1cB+nV5LKadtYaKi5b/idrBTVPFw21VlAxk69YK6i5ZOoN1gpqXhn8DWsFNd7fjwD+NORcawU13leiz+E2awU1MRRRj+NkawU9b1gLKMnMlnAcpGfwfaDBKyjXQXoMhaMSQbrLEud5ex1YYa2gp6O1gB7vr2Xw358b4AWGWSuoieF3yTuDmWqtoObKssutFfR43yhukXHbPkjvJCKbto8aE0IYs/GGiDxP/f/rl4UQnkjf5zJSZ3FfQ0/mOkiPYSWdj6wF9Kwt9t0Gcxi+t8YBmGwtoGde7xJrBTUnTXvMWkHN3YNOt1ZQM+Ux3yk793/Hf5/3Zfjv7nJ54ZXWCmomjh5uraBixnX1zM+wDdKXhRAGbe6LIYQtrnKIyGnAEcChIYTQ0JO5DtKjoMJaQM+xmRPBnBFDHjH+P0tYeqb/osunB/lO/YI4Cv68X3f/7TvnWCuo+Ql/sVZQ0z+zaNEh3neVPmFV7QP2K+lbjYgcBvwSOCiEsLYx35ME6dY4/WXblMLy5dYKKvIjuFDiS9YCehbQzVpBzVx6WSuo6cYCawU9X7MW0NEjs+2cQ2K42JtJf2sFNd7PYV3mVHnHQTpwM5APPCciAG+EEH6wpW9wHaTnUEO+96XoCD4Pywp8J0TPLuhjraBmaO7r1gpqVrKztYKaE7jfWkHNX/FfOOq8NTQvrzrQWkHNye3vsVZQE0OfdO8r6XVw3Cc9hNDkVRzXQXoU7GQtoKfrZN8r6fcdNNBaQc3Qk/0H6aMYb62gZi49rRXUlESwikuJ00/xiLiL06wV1BSzxFpBTT9mWSuoeIl1tQ/47pPeZFwH6ULwn08cwy9bK2sBHZXOR4gD7rf3ATrUN/7ZGd63lgHeiWCY0b7d/XcW8c6kpYdYK6gp7rzYWkFNH2ZbK6gISN2DftNdmkwEQbrzdJcYyLUW0BFD7mQMLRjLKLRWUFMRwQVfDP2t3371AGsFHZ9aCzQD/pu7MLDztIbvlOXMxnc65/rMVUDfOelNxn2Qnud9Kdp/8Ti8YC2g47W79rdWUHP+jWMavlOW0/PqudYKamLYDfC+PQ4we4jvSdRlZf4j3A3H+s/l/NlL/ofEXcFvrBVUVNKy9oEkSPdDDeJ/5WqAtUAz0NtaQMcezrcDAd6/2n97lxhWcGPouf8UR1orqPl0rPPXwxprgWbAdXSR4iTutVZQ0xP/ix+1cFw4ujW4fhnlEPx3d/GfwsqLe33dWkHFKB63VlAznlHWCmpiSDv66pXvWSuomXD5CGsFPc6zFNr/0X++y6rdvI+6hAN5xVpBjffdvVmZhaM7GK6D9JZU+u9EUGYtoMf7ytsN8y+0VlBzR/dTrRXUnDHRf/vC8yv8px1NxPeEQgCOthbQMTjff+Hrs8tGWiuouX/4GdYKavpNnG6toGI1t9c+kHR38YNQ479w1H+tnP/xz3NaWBuoKe7uvwvBScP/bq2gJ4Kdsdcn+p+auvfwN6wVVNzLSdYKaopu9r8ztvYjawM9++H7gq+U8roHk5x0HwRy/LfPK1tvbaCmhE+sFXT09f8zWEsbawU1p3OXtYIe52kWAFxlLaBn/+GvWSuo2OWnESSlD7IW0NOmvbWBnk7Ot+vzMiPypHDUF9Xe+//5H2hGKZ2tFVTs3XWGtYKaGIL0KMbRR7CSzofWAnq8/y6Fy60N9OzbcYq1gh7/Hw08zzBrBRWfc1/tA0nhqB+qyWU17aw1dLT3Pa0T4EietFZQcX/5CdYKakbl+Z/WWTCxxlpBzdijjrNW0OM8ew3gQF62VlAhpdYGeg7pOMlaQY/z9sIA717ifNLdRwW1byc56X5YQ1te4UBrDRUlBfOtFdQcsfhFawUVazZ0sFZQU1DpP8DlD9YCelYe5f93iXkPWRuo6YzvKPfpvv7rAm4rP8daQc2In0+wVtDzL2sBJaGe20m6S/MgIvOA1aSue6pCCINEpCPwEFACzAOOCyGsEBEBbgRGAGuB00IIb2/p8VewM49y7LY7ge3AWR/478PqvXb3p0P+aK2gprwix1pBzTtT/A8NqMgcvOGQViu/aa2g5n587465n/8BfEaRtYKartULrRXUXPvcj60VVNw06L91DyZBerNySAhh2Sa3LwZeCCH8XkQuTt++CPg2qbE4vYHBwK3pvzfLelq5H3k7ZdC+1gpqVuJ79fD60kutFfRcZi2gp/Lv/gOTJREUmaxftrO1gpqK9r5/l/bhHWsFNYcVPGOtoCY3gryKpzjKWkHFKibWPpDkpG9zRgIHp/89FphEKkgfCYwLIQTgDRHpICJdQghLNvdAlRtasmBpt22su22Z2dl/pVm176wpPizqbq2gpu8b/tOmhk573VpBzdRBW1xXcMFJPW9v+E5ZTg/nHacW4PtzDWDyLw6zVlBz3HVjrRXUFDrv7jI7c5hRkpPerATgWREJwG0hhDFA500C70/hi9YgXaFWSf7C9LHNBunU5FCzxndXi306+y8f995Z5Hp+Zq2gpufMOdYKar7CTGsFNZe8d4O1gprgfzOAwzs+Zq2g4plF/qe+fv0637VKAPeuOs1aQc017X9praAiL3PZPMlJb1YOCCEsEpEi4DkRqdXcK4QQ0gF8oxGRs4GzAQp3b8Mfep7YfLYGzIygZ9s05w1xb7vxp9YKeiIYivX2SXtaK6i5eS//Ewpns4e1gprR+C5+XdnVdwohxDH3YHr7va0V1Myhl7WCigpa1T6QBOnNRwhhUfrvUhEZD+wHLN2YxiIiXeCLMvxFUGuPb7f0sczHHAOMAdhzUOvQ3/nqm/d+vgA9mWutoOKz89taK6jZZYb/4Sc/5FZrBTVvTj/IWkHNVQMvtFZQcxO+i+XaZG7xO+Ts4++xVlBz7QO+f4/Af4zRksraB5Kc9OZBRAqAnBDC6vS/vwVcCTwJnAr8Pv33E+lveRI4T0QeJFUwumpL+egAOdTQhrXb6hS2C+0q/I9OLslP8j+t2WXGB9YKevw3d4lihedcbrFWUOP9c2Hqlnsm+OAAawE9g5huraDmNfa3VlCRQz3thZOc9GahMzA+1VmRPOD+EMK/ROQt4GEROQOYD2yc/jGBVPvFOaRaMJ7e0BMI/quvW6733996Vn4/awUVK/HfzWLfIv9BuvcVH4DVg7fYNdYFM9jHWkHNYudddvZgtrWCHv8ZO/SJ4OcwjYHWCiokM0hP0l2ahxDCx0CdhK4QQhlwaD3HA/CjpjxHNTnuJ4628F83Sv5BlQ3fKYs5aYHvIjMAVlkL6ClhnrWCmqVf1MH75XxutFZQczlXWiuoOLHsvobvlOXkDCu3VlAzjlOsFdRM+qKZnk9Wk9HKMwnS/RDDSrrzTmEA9DtolrWCire67WWtoOart7xnraCm+v/bu/c4q8tqj+Of5XARUEFBEJCEvIAE3kLxSN6CvCdapnnHNI+Vl0wrL3nKeqWWnFNU5iXMNEkxvB4jUQmro4kiXlAURSVAkIsmItDADOv8sX/oMLOZGXyAtdcz6/16+YL5zW9mvtsfs/faz+951nNilXWEZAsy2MClP75/nwH3m9ytem4r6wjJHhvqf8rOM85HoQHepLd1hCQNNvaKOel+lNplOn9hP8Q6QDrvo4d7P+y/wHXeYAeAdhksluuTwd2AFc5bqgL05VXrCEm2G/qGdYRkYznBOkKygbxgHSHZTs4bOyzwvqV5ItdF+irauF/0t7yH/xfEA198yjpCmjK7DrtzonWAdCtoZx0h2fWcYx0hmff53AC/5HzrCEnePvWT1hGSDfv9o9YRkk3gUOsIydz33F/147U/js2MwqbkvQsB8FETTa/8T53M4jHkMJLufY0MwGde8b/49dx+fa0jpHml6VMqXQ4LwQcxxTpCsls2b7IHR0WrkTLNNWJOug9V1NKJ96xjJMnhicz9wFs/6wAbwOPWAdL9nf2tIySb2HBNvDu/6neudYRkBzPJOkKS57fY1zpCsq/wW+sIyc7nF9YRkl3ceaR1hCS3tFqw9oFYOOpHDkX6dvP8t+V4q9821hGS5LDr62Ev/tU6QrJuX1rQ9EkV7u4/nWIdIdnSI/3fDfD+urDPJP+/z8++47+V5+jOZ1lHSDaUidYRNqxYOOqHIqykjXWMNBnMrapxvni3wepxj3a2DpBuP56wjpDs0WHDrCMk+0L1PdYRkvVoO886QpIr+b51hGTHv/eAdYRkU2r8r8h/apbzXZCXjV/7Y8dz0kXkR8BwYDWlicIjVLXRJyvXRXorVtEV36Nvl/W6wjpCsqtu/5F1hCQ7DHnYOkKyqafsah0h2bcWXm8dIVn3rr6LQ4CTvnqfdYRkT/7G93SRz/GIdYRkd+14tHWEZF+t/o11hGRdB8+0jpBkdod6a5V8T3e5VlWvABCR84H/gsa7Dbgu0tvU1tBrydvWMZL8evOvW0dIdtXvfRfpOezAncOUnb2q/e+amoPNR75rHSGZ964cF/Iz6wjJvs211hGSXdjW/3Xw3gpT0IYHnRbpqvp+nQ87QLkHtzbXRXpNVRULO3a0jpHE+9xJAEZYB0iUQWeUHDaguafX4dYRkg1msnWEZMM6+p/DehunWkdIcg43WkdIdhzjrCMkm8TB1hGSfdH5dbiZf619wH5OehcRqdv25yZVvam5XywiPwZOo7RPeJP/wFwX6cvowGTnw6CPM8Q6QrL3j29tHSHJlCr/8w4HV/svDh9o+3nrCMm+sPDPTZ9U4c7oeot1hGRfvHl80ydVsDFnfmAdIVkOdwNu4zTrCMmOxvfagAa7ytvPSV+squssGkTkUWC7Mp+6XFXvV9XLgctF5FLgXGh8AYrrIn0zVrMlS61jJOk5yv+t5bcu8N3dZTT+V/D3b+t/JP1l+ltHCMCwWv+b0Oxwpu9G43+9+TDrCMn6njnDOkKyHHYcbed8L5bNqNcnvcLnpKtqc7sHjAHGk3ORLqymDSutY6TxX6PTc7zvB7HgiK7WEZJ1XuK/leezHf23bLu16/HWEZKdPucu6wjJBvaaZh0hyQlnjrWOkOynL/nvUDP8U3dYR0j2mPMpO0up1460wov0xojIzqr6WvHhcJqxbZnrIr01NXRz3t2FDtYB0r1/qO/pLnPoZR0h2aUdnS/eBXrzpnWEZPPc7+wFzjuqAjCP7tYRkuSwtoHnrAOke/1TO1lHSDYQ329YGywctZ+TnuIaEelLqQXjP2misws4L9JzaMGYgxlVvrfgfo+trSMky6G7S19etY6Q7Pxq/zsU8kPrAOk63eB7QX4Ov8/cYB0g3fRh/qfgXd3tEusISZ4uN93AaZ90Vf3i+n6N6yJ9NVUsxffueNv0+bd1hGRPsJ91hCSLxnzCOkKyridPsI6QrMrrPcw67m17rHWEZKecerd1hGR/ue4o6whpjvI7VPihLawDpDu0m//n1V9yvnWEJAu5qOHBJhsX5sN1kS4orby+pVpjtnWAdO7vZmxuHSBdDu3OcijSD65+zDpCuuusA2wAvtukc/wOf7COkOyu4063jpDsu/zEOkKypc7fLb3BQusIplwX6Yq435I+B/O9z8P1XxuykjbWEZLNo7d1hGTd2vp/Qdm7w4vWEdI9ZB0gzS6n+++MwjD/dwNyWCdzAaOsIyRZlME1SOG6SM9CBlfgTefFVeth7zd9UoXbBf8v6ovpbB0h2d53ZFDg+t4frsR5V9VBPGMdId2DvhsKAGz5Dd8tngG68I51hCStcxhFS+C6RFSElbS1jpHG+SA0wKv4Xji6avFW1hGS9eg83zpCsrbe26mC82fUgvMCF2CLXousIyTJYifqg6wDpNtmiv81Y7f18b0hU03NzdYRTLl+SanNYOFoDqNW7qccbeH/iXjbKf53KOw0KIPCpI91gA3Ad8c2ALr3m2cdIcmetc9aR0iXwU0l9rcOkG7VSOeDUAvq1xe+ezCuL9dF+mo2YzntrGOkcX4jAOC5Wt+b0OzeM4OGvv4H0unNLOsI6fw3Cspi4OBkfC+8nFnlvz+38/WKADzY47PWEdLdZx0gUYOxG8e7GX0Mrov0lbRhlvOhq890nGodIdkRVeOtIyR5lKHWEdL5n87Ne3SyjpDsn123tY6QbId3fU8VATgC389Je4162TpCun2tA6TzvlsnAHdaB0h0Uv0DMZLuRg2teMd7dZLBG8Ie+L61vHUO8z8nWgdI9/pZO1pHSOZ++h2ww+iHrSMkqx7pu9vRqhHWCTYA/11hGTv4BOsI6ZrceL7CNZiNGiPpbmQxJ935ewzwX6T3Z7p1hHT+O/9xC2dYR0h2NZdaRwjACtpbR0gyreOu1hHSZVBHDWaydYRkVSf43ktm/rX1q/QYSXdDEaqd94d+/xMZtKnCd5uqK3LYBz2DafX/mOh//ucPh15hHSHZxCmft46QzHtL0ku5xjpCuketA6QbN/hU6wjJ5Ebn23POrr/bYBTpbqyiNQvpZh2jxevsvA/rTtWvW0dIl8FiP9/PRiU5zKvPoQXj2fzGOkKSExhrHSHZlD9+2jpCutHWATYA7wt4Nyt3MIPbNM3k+mWxtHC0t3WMJFvN8/+OcFCvKdYRklTVrLaOkC6DdWZHHfhH6wjJvE/9AmCIdYB0k6sHW0dIMukD/wsW9+zsv43khWddZR0h2Q7OJ6XPnxTTXdxaSWvm5bAbkHM9J79rHSHJ84N3to6QbPc9XrOOkOwI/mQdIZn7zdWA/+zzc+sIyY5zvmpxbI3/BYsXM9I6QrIc2sKO4BbrCElOYna9I7Fw1I1aWrm/vfx8L/8F4sJevqcczXC+YyrAK9f5L9K/dtGt1hGSndXnl9YRku2RwQKH79deaR0hyT4d/C9YnMIg6wjJ3DemAJY7X0S9lPvrHYmRdDdW62Ysrfb9S/RUW9+3ZQEWOF8X4H3xMcC5h1gnSPd0nwHWEZJVZzCSPo2B1hGSHVo1wTpCkhnsYh0hWW/etI4QgAV0tY6QpKZBmRoj6W7U1rRiyWLfI+mde/pedAnwpvMNpbKYR5xBK8+/Z7AHd1/nXUUAdmSmdYRk93GsdYQkrfDdNg+gPSusIyTL4bXBe4vhcSypdyRG0v2oFphVvz2PL9N6+h+1mkMv6whJvE+ZAuAC6wDpvvXK9dYRkt3Rb7h1hGRH1vrerRNgZZXvOxoTONQ6QrKx+J9Xn8Ob7mO4zzpCktVIvSMxku7HKmCudYg0z7KHdYRkRzrfgjsLVdYB0r3bz/cbboD5GSxkn1Dlv0D0vndDDgsWJ7OPdYRk3TMYSf81X7eOkGQRP6h3JEbSNxgR6USp0+gASv9nvwLMAMYCvYFZwPGq+i8REWAUcASwHBihqlMb/QGrKbNlrC85LEzpxHvWEZLkMI84h+kuOXRG8b6JDuTxnPQow6wjJDmWe60jJGvPcusIyd6hi3WEZL2YYx0hSRtW1jsSI+kb0ijgIVU9TkTaAO2By4CJqnqNiFwCXAJ8Fzgc2Ln4bzBwffFn1nIoTLwXud1YYB0hne9LAEAbqq0jJGvb4AXFn3cy+MfkfYO1XZ3PIwb4GwdYR0iWw1RI73vJrKBdvSMxkr5BiEhH4ABgBICqrgRWishw4KDitFuBxygV6cOB21RVgSdFpJOIdFfV+ev8IRm8oVre4B+gP7XO51p4fxIDeKOH/8V+VRkslsvh39JMdrKOkOwd57eWchi8GcgL1hGS3c1x1hGSeX+jUeO8vki1MUfS+wCLgFtEZHfgGUrL27rVKbzfhg/79/WEte7LzC2OrbtI3wxwPo211vmyAPA/Aup9/irkcVvW+7QpgF141TpCshxGccdzpHWEJN7b5gH0aOSl24uGUy38OYk/WEdI8t+U2yzR+ejsetiYFWIrYC/gPFWdLCKjKE1t+ZCqqojo+nxTETkbOBuAzp/Y9ftklgAAC55JREFUQFHtrMygR/cK55sl5NAqzPvdDPDfJQj8j1pBHnPS/5fPW0dIkkPrvxzk8Lz6BPtZR0jyAQ/XOxLTXTaUucBcVV2zddo4SkX6gjXTWESkO7Cw+PxbsNar9PbFsbWo6k3ATQDSe5B6Xzja9cOH79c85x0tchgtOXRZ/Scyf+Z02N46QrIcNnB5giHWEZJ1db7OJIfiMN50V4aBTLOOkKT8wtEo0pOp6tsiMkdE+qrqDGAoML3473TgmuLPNXu+PgCcKyJ3UlowuqTR+eiQxXQX73Mnwf/mJ90yeKPU1veMIwC6be67sAL4W5X/DZlyuLvnfU734nhdqAg5bLDm/Y1GwzesGSxGXA8be0L0ecCYorPLG8AZlErru0TkTOCfwPHFueMptV+cSakF4xlNfvcMrlUOi+W8746Xw/xPWWadIN2b2/S2jhDIYxTXewvD/XjCOkKyBR8uN/NrHyY3fVKF835HY1WDMtX/SLqIXASMBLZV1cWNnbtRi3RVfQ4YVOZTQ8ucq8A31u8H4L5Iz4H36S7VGYwcPtlrd+sIyXKYC52DHBbwPu58Hm4OBa73hgLgfwAK4FX6WkdIUt1guoTvwk9EegGHALObc77v1iIZTHfxfisqB12c91SGPG6Pe1+ADLA8g8eQw0j6NAZaR0jyTgadUXJ40+19FBrgOMZZR0hya4NBA/cj6T8DvsNHU70b5btIz2DH0Rza/3l/DDkUVt7n4IL/LgQAn+YZ6wjJvP8+AxzMY9YRkhzEJOsIycZl0GN8KVtYR0jmfUqtNDjidyS92CfoLVV9XqThIyvHd5GewUi6918ggHYZbP/sXf8Melt73yUS4HV2tI6QzPs24uB/ClsOHXb6MMs6QsB/c4qaypuT3kVEptT5+Kai6yAAIvIosF2Zr7scuIzSVJdm812k1wIfWIdIk8N0l2nsZh0hSSf+ZR0hWQ7zP3PoKpLDXZkc7mi8x9bWEZLksHeD97VKkMcbDe8DgeVbMJqOpC9W1XJrLQFQ1WHljovIQEqbfK4ZRd8emCoi+6jq2+v6fr6LdMH7I8hi/qf3eXvz6G4dIdlz7GkdIZn3ER+AnTJoOzfD+UIz8D9lJ4fXhRyK9Cl82jpCMu93xpZxT70j5iPpH4uqToOPWsmJyCxgkGl3l40ugyLd+4sJwEBesI6QZOsMull437ACYDr9rSMky6HAzeE6eO9vfTEjrSMkm0Vv6wjJXl3m//d5RQffd/dy6+6yvqTU+dAnEVlEqdf6xtIFaPRdTtgk4jpUhrgO9uIaVIa4DvbiGlSGjX0ddlDVbdd8ICIPFT/TymJVPWxT/TDXRfrGJiJTGpt7FDaNuA6VIa6DvbgGlSGug724BpUhrsPGtZl1gBBCCCGEEMLaokgPIYQQQgihwkSR3ribmj4lbAJxHSpDXAd7cQ0qQ1wHe3ENKkNch40o5qSHEEIIIYRQYWIkPYQQQgghhAoTRfo6iMhhIjJDRGaKyCXWeVoiEeklIpNEZLqIvCQiF1hnaqlEpEpEnhWRB62ztFQi0klExonIKyLysoj8h3WmlkZELiyei14UkTtEpH4T57ARiMhvRWShiLxY59g2IvKIiLxW/Ol7m1kH1nEdri2ek14QkXtFxP826hUkivQyRKQKuA44HOgPnCgi/nf48KcGuEhV+wP7At+I62DmAuBl6xAt3CjgIVXtB+xOXI9NSkR6AudT2iVwAFAFfNk2VYvxO6B+b+pLgImqujMwsfg4bFy/o+F1eAQYoKq7Aa8Cl27qUDmLIr28fYCZqvqGqq4E7gSGG2dqcVR1vqpOLf6+lFJR0tM2VcsjItsDRwKjrbO0VCLSETgAuBlAVVeqqv+tcv1pBbQTkVZAe2CecZ4WQVX/Brxb7/Bw4Nbi77cCx2zSUC1Queugqg+r6potQJ8Ett/kwTIWRXp5PYE5dT6eSxSHpkSkN7AnMNk2SYv0c+A7wGrrIC1YH2ARcEsx7Wi0iHSwDtWSqOpbwEhgNjAfWKKqD9umatG6qer84u9vA90swwQAvgL82TpETqJIDxVPRLYA7ga+qarvW+dpSUTkKGChqj5jnaWFawXsBVyvqnsCy4jb+5tUMed5OKU3TD2ADiJyim2qAKClNnXRqs6QiFxOaYrqGOssOYkivby3gF51Pt6+OBY2MRFpTalAH6Oq91jnaYGGAEeLyCxK074+KyK320ZqkeYCc1V1zZ2kcZSK9rDpDAPeVNVFqroKuAfYzzhTS7ZARLoDFH8uNM7TYonICOAo4GSNvt4bVBTp5T0N7CwifUSkDaXFQQ8YZ2pxREQozcF9WVX/xzpPS6Sql6rq9qram9LvwV9UNUYPNzFVfRuYIyJ9i0NDgemGkVqi2cC+ItK+eG4aSizetfQAcHrx99OB+w2ztFgichil6ZBHq+py6zy5iSK9jGIRxLnABEpPwnep6ku2qVqkIcCplEZvnyv+O8I6VAhGzgPGiMgLwB7AVcZ5WpTiLsY4YCowjdLrZ+y2uAmIyB3AP4C+IjJXRM4ErgE+JyKvUbrLcY1lxpZgHdfhV8CWwCPFa/QNpiEzEzuOhhBCCCGEUGFiJD2EEEIIIYQKE0V6CCGEEEIIFSaK9BBCCCGEECpMFOkhhBBCCCFUmCjSQwghhBBCqDBRpIcQAiAiWnejJhFpJSKLROTB4uMRxcdr2oHeVufci0XkleL40yJyWnG8tYhcIyKvichUEfmHiBxe5mc367xmPo79ReSlIks7Ebm2+PhaETlnTbZ1fG0PERn3cX5u8fXfFJH2H/frQwghfKSVdYAQQqgQy4ABItJOVVcAn6PhTsNjVfXcugdE5Jzi3H1U9X0R2Qo4tvj0j4DuwABVrRaRbsCBZX52c89rjpOBq1X19iLf2cA2qlrb1Beq6jzguI/5cwG+CdwOxKYmIYSQKPqkhxACICIfAL8ApqrquGKk/CVgf1U9qtj6elCZIn02cJCqvlHveHtgDtBHVd9v5Oc2ep6InAhcBgjwJ1X9bnH8EOBKoC3wOnAGpV1hfwosAZ6gtMnIkZQ237ka2BX4QFVHishOwA3AtkAt8KXizwdVdYCIVFHaIOag4mdcp6o3ishBwA+AxcAA4BngFEqbLY0EZgCLVfXgdT3mEEIITYvpLiGE8JE7gS+LyObAbsDkep8/oc50lzOKUfMt6xfohZ2A2Y0V6E2dJyI9gJ8An6W0y+jeInKMiHQBvgcMU9W9gCnAt1R1NKXt0r+tqier6tHAClXdQ1XH1vv2YygV3rsD+wHz633+TGCJqu4N7A18VUT6FJ/bk9KoeX/gk8AQVf0FMA84OAr0EEJIF9NdQgihoKoviEhv4ERgfJlT1pruUhTpG9PewGOquqj4eWOAA4AaSgXy4yIC0IbSdt3NIiJbAj1V9V4AVf13cbzuaYcAu4nImukvHYGdgZXAU6o6t/ia54DewP99rEcYQgihrCjSQwhhbQ9QmrZxENC5sROLOegfiMgny4ymzwQ+ISJbNTGa3tzz6hLgEVU9sZnnfxwCnKeqE9Y6WJruUl3nUC3xWhJCCBtcTHcJIYS1/Ra4UlWnNfP8q4Hr1oyqi8gWInKaqi4HbgZGiUib4nPbisiX6n5xE+c9BRwoIl2KOeInAn8FngSGFPPKEZEOIrJLcx+gqi4F5orIMcXXty3TlWUC8DURaV2cs4uIdGjiWy+lNA8+hBBCoijSQwihDlWdW8yvbq7rgUnA0yLyIvB3YHXxue8Bi4DpxeceBMqNlpc9T1XnA5cU3/954BlVvb+Y/jICuENEXqA01aXf+j1STgXOL77+CWC7ep8fDUwHphaZbqTpEfObgIdEZNJ6ZgkhhFBPdHcJIYQQQgihwsRIegghhBBCCBUmivQQQgghhBAqTBTpIYQQQgghVJgo0kMIIYQQQqgwUaSHEEIIIYRQYaJIDyGEEEIIocJEkR5CCCGEEEKFiSI9hBBCCCGECvP/yV0wIuU9fJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of MFCC** : (625, 13)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from data_generator import plot_mfcc_feature\n",
    "\n",
    "# plot normalized MFCC\n",
    "plot_mfcc_feature(vis_mfcc_feature)\n",
    "# print shape of MFCC\n",
    "display(Markdown('**Shape of MFCC** : ' + str(vis_mfcc_feature.shape)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you construct your pipeline, you will be able to choose to use either spectrogram or MFCC features.  If you would like to see different implementations that make use of MFCCs and/or spectrograms, please check out the links below:\n",
    "- This [repository](https://github.com/baidu-research/ba-dls-deepspeech) uses spectrograms.\n",
    "- This [repository](https://github.com/mozilla/DeepSpeech) uses MFCCs.\n",
    "- This [repository](https://github.com/buriburisuri/speech-to-text-wavenet) also uses MFCCs.\n",
    "- This [repository](https://github.com/pannous/tensorflow-speech-recognition/blob/master/speech_data.py) experiments with raw audio, spectrograms, and MFCCs as features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step2'></a>\n",
    "## STEP 2: Deep Neural Networks for Acoustic Modeling\n",
    "\n",
    "In this section, you will experiment with various neural network architectures for acoustic modeling.  \n",
    "\n",
    "You will begin by training five relatively simple architectures.  **Model 0** is provided for you.  You will write code to implement **Models 1**, **2**, **3**, and **4**.  If you would like to experiment further, you are welcome to create and train more models under the **Models 5+** heading.  \n",
    "\n",
    "All models will be specified in the `sample_models.py` file.  After importing the `sample_models` module, you will train your architectures in the notebook.\n",
    "\n",
    "After experimenting with the five simple architectures, you will have the opportunity to compare their performance.  Based on your findings, you will construct a deeper architecture that is designed to outperform all of the shallow models.\n",
    "\n",
    "For your convenience, we have designed the notebook so that each model can be specified and trained on separate occasions.  That is, say you decide to take a break from the notebook after training **Model 1**.  Then, you need not re-execute all prior code cells in the notebook before training **Model 2**.  You need only re-execute the code cell below, that is marked with **`RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK`**, before transitioning to the code cells corresponding to **Model 2**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/pjordan/anaconda3/envs/dnn-speech-recognizer/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "#####################################################################\n",
    "# RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK #\n",
    "#####################################################################\n",
    "\n",
    "# allocate 50% of GPU memory (if you like, feel free to change this)\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "import tensorflow as tf \n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.4\n",
    "set_session(tf.Session(config=config))\n",
    "\n",
    "# watch for any changes in the sample_models module, and reload it automatically\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# import NN architectures for speech recognition\n",
    "from sample_models import *\n",
    "# import function for training acoustic model\n",
    "from train_utils import train_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model0'></a>\n",
    "### Model 0: RNN\n",
    "\n",
    "Given their effectiveness in modeling sequential data, the first acoustic model you will use is an RNN.  As shown in the figure below, the RNN we supply to you will take the time sequence of audio features as input.\n",
    "\n",
    "<img src=\"images/simple_rnn.png\" width=\"50%\">\n",
    "\n",
    "At each time step, the speaker pronounces one of 28 possible characters, including each of the 26 letters in the English alphabet, along with a space character (\" \"), and an apostrophe (').\n",
    "\n",
    "The output of the RNN at each time step is a vector of probabilities with 29 entries, where the $i$-th entry encodes the probability that the $i$-th character is spoken in the time sequence.  (The extra 29th character is an empty \"character\" used to pad training examples within batches containing uneven lengths.)  If you would like to peek under the hood at how characters are mapped to indices in the probability vector, look at the `char_map.py` file in the repository.  The figure below shows an equivalent, rolled depiction of the RNN that shows the output layer in greater detail. \n",
    "\n",
    "<img src=\"images/simple_rnn_unrolled.png\" width=\"60%\">\n",
    "\n",
    "The model has already been specified for you in Keras.  To import it, you need only run the code cell below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 29)          16617     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 16,617\n",
      "Trainable params: 16,617\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_0 = simple_rnn_model(input_dim=161) # change to 13 if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explored in the lesson, you will train the acoustic model with the [CTC loss](http://www.cs.toronto.edu/~graves/icml_2006.pdf) criterion.  Custom loss functions take a bit of hacking in Keras, and so we have implemented the CTC loss function for you, so that you can focus on trying out as many deep learning architectures as possible :).  If you'd like to peek at the implementation details, look at the `add_ctc_loss` function within the `train_utils.py` file in the repository.\n",
    "\n",
    "To train your architecture, you will use the `train_model` function within the `train_utils` module; it has already been imported in one of the above code cells.  The `train_model` function takes three **required** arguments:\n",
    "- `input_to_softmax` - a Keras model instance.\n",
    "- `pickle_path` - the name of the pickle file where the loss history will be saved.\n",
    "- `save_model_path` - the name of the HDF5 file where the model will be saved.\n",
    "\n",
    "If we have already supplied values for `input_to_softmax`, `pickle_path`, and `save_model_path`, please **DO NOT** modify these values. \n",
    "\n",
    "There are several **optional** arguments that allow you to have more control over the training process.  You are welcome to, but not required to, supply your own values for these arguments.\n",
    "- `minibatch_size` - the size of the minibatches that are generated while training the model (default: `20`).\n",
    "- `spectrogram` - Boolean value dictating whether spectrogram (`True`) or MFCC (`False`) features are used for training (default: `True`).\n",
    "- `mfcc_dim` - the size of the feature dimension to use when generating MFCC features (default: `13`).\n",
    "- `optimizer` - the Keras optimizer used to train the model (default: `SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)`).  \n",
    "- `epochs` - the number of epochs to use to train the model (default: `20`).  If you choose to modify this parameter, make sure that it is *at least* 20.\n",
    "- `verbose` - controls the verbosity of the training output in the `model.fit_generator` method (default: `1`).\n",
    "- `sort_by_duration` - Boolean value dictating whether the training and validation sets are sorted by (increasing) duration before the start of the first epoch (default: `False`).\n",
    "\n",
    "The `train_model` function defaults to using spectrogram features; if you choose to use these features, note that the acoustic model in `simple_rnn_model` should have `input_dim=161`.  Otherwise, if you choose to use MFCC features, the acoustic model should have `input_dim=13`.\n",
    "\n",
    "We have chosen to use `GRU` units in the supplied RNN.  If you would like to experiment with `LSTM` or `SimpleRNN` cells, feel free to do so here.  If you change the `GRU` units to `SimpleRNN` cells in `simple_rnn_model`, you may notice that the loss quickly becomes undefined (`nan`) - you are strongly encouraged to check this for yourself!  This is due to the [exploding gradients problem](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/).  We have already implemented [gradient clipping](https://arxiv.org/pdf/1211.5063.pdf) in your optimizer to help you avoid this issue.\n",
    "\n",
    "__IMPORTANT NOTE:__ If you notice that your gradient has exploded in any of the models below, feel free to explore more with gradient clipping (the `clipnorm` argument in your optimizer) or swap out any `SimpleRNN` cells for `LSTM` or `GRU` cells.  You can also try restarting the kernel to restart the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 116s - loss: 962.2045 - val_loss: 746.4123\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 111s - loss: 757.1928 - val_loss: 729.0466\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 116s - loss: 753.0298 - val_loss: 730.4964\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 115s - loss: 750.8956 - val_loss: 721.6433\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 115s - loss: 751.6414 - val_loss: 726.6612\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 115s - loss: 750.7420 - val_loss: 727.9034\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 112s - loss: 750.2763 - val_loss: 729.9839\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 116s - loss: 751.5226 - val_loss: 723.4622\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 117s - loss: 750.4366 - val_loss: 721.1129\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 117s - loss: 751.0709 - val_loss: 733.4978\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 116s - loss: 751.7690 - val_loss: 725.5819\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 117s - loss: 750.4331 - val_loss: 728.1983\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 116s - loss: 750.6872 - val_loss: 721.3921\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 117s - loss: 750.7719 - val_loss: 723.6158\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 117s - loss: 749.6198 - val_loss: 728.7696\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 115s - loss: 750.4491 - val_loss: 723.0323\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 116s - loss: 750.8921 - val_loss: 725.6289\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 116s - loss: 750.8845 - val_loss: 725.6971\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 116s - loss: 750.1892 - val_loss: 722.8667\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 117s - loss: 750.7994 - val_loss: 724.6980\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_0, \n",
    "            pickle_path='model_0.pickle', \n",
    "            save_model_path='model_0.h5',\n",
    "            optimizer=SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model1'></a>\n",
    "### (IMPLEMENTATION) Model 1: RNN + TimeDistributed Dense\n",
    "\n",
    "Read about the [TimeDistributed](https://keras.io/layers/wrappers/) wrapper and the [BatchNormalization](https://keras.io/layers/normalization/) layer in the Keras documentation.  For your next architecture, you will add [batch normalization](https://arxiv.org/pdf/1510.01378.pdf) to the recurrent layer to reduce training times.  The `TimeDistributed` layer will be used to find more complex patterns in the dataset.  The unrolled snapshot of the architecture is depicted below.\n",
    "\n",
    "<img src=\"images/rnn_model.png\" width=\"60%\">\n",
    "\n",
    "The next figure shows an equivalent, rolled depiction of the RNN that shows the (`TimeDistrbuted`) dense and output layers in greater detail.  \n",
    "\n",
    "<img src=\"images/rnn_model_unrolled.png\" width=\"60%\">\n",
    "\n",
    "Use your research to complete the `rnn_model` function within the `sample_models.py` file.  The function should specify an architecture that satisfies the following requirements:\n",
    "- The first layer of the neural network should be an RNN (`SimpleRNN`, `LSTM`, or `GRU`) that takes the time sequence of audio features as input.  We have added `GRU` units for you, but feel free to change `GRU` to `SimpleRNN` or `LSTM`, if you like!\n",
    "- Whereas the architecture in `simple_rnn_model` treated the RNN output as the final layer of the model, you will use the output of your RNN as a hidden layer.  Use `TimeDistributed` to apply a `Dense` layer to each of the time steps in the RNN output.  Ensure that each `Dense` layer has `output_dim` units.\n",
    "\n",
    "Use the code cell below to load your model into the `model_1` variable.  Use a value for `input_dim` that matches your chosen audio features, and feel free to change the values for `units` and `activation` to tweak the behavior of your recurrent layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 246)         301104    \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, None, 246)         984       \n",
      "_________________________________________________________________\n",
      "time_distributed_11 (TimeDis (None, None, 29)          7163      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 309,251\n",
      "Trainable params: 308,759\n",
      "Non-trainable params: 492\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_1 = rnn_model(input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "                    units=246,\n",
    "                    activation='relu',\n",
    "                    dropout_rate=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_1.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_1.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 125s - loss: 301.3889 - val_loss: 255.1117\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 126s - loss: 208.7791 - val_loss: 195.5662\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 126s - loss: 188.6020 - val_loss: 184.3830\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 126s - loss: 172.8454 - val_loss: 165.9265\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 126s - loss: 159.9952 - val_loss: 160.3791\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 126s - loss: 151.2288 - val_loss: 150.3075\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 125s - loss: 144.6389 - val_loss: 147.3992\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 126s - loss: 139.3690 - val_loss: 143.2048\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 124s - loss: 134.5651 - val_loss: 140.9699\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 126s - loss: 130.5984 - val_loss: 139.4818\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 125s - loss: 127.2223 - val_loss: 134.7147\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 125s - loss: 124.1384 - val_loss: 135.1391\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 127s - loss: 121.4931 - val_loss: 135.6264\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 139s - loss: 119.0370 - val_loss: 132.6101\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 149s - loss: 117.5036 - val_loss: 135.2287\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 149s - loss: 115.1628 - val_loss: 134.6172\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 148s - loss: 114.1567 - val_loss: 133.6147\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 149s - loss: 113.1525 - val_loss: 131.8664\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 151s - loss: 110.8212 - val_loss: 133.1285\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 149s - loss: 109.7723 - val_loss: 133.2252\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD\n",
    "\n",
    "train_model(input_to_softmax=model_1, \n",
    "            pickle_path='model_1.pickle', \n",
    "            save_model_path='model_1.h5',\n",
    "            optimizer=SGD(lr=0.07693823225442271, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model2'></a>\n",
    "### (IMPLEMENTATION) Model 2: CNN + RNN + TimeDistributed Dense\n",
    "\n",
    "The architecture in `cnn_rnn_model` adds an additional level of complexity, by introducing a [1D convolution layer](https://keras.io/layers/convolutional/#conv1d).  \n",
    "\n",
    "<img src=\"images/cnn_rnn_model.png\" width=\"100%\">\n",
    "\n",
    "This layer incorporates many arguments that can be (optionally) tuned when calling the `cnn_rnn_model` module.  We provide sample starting parameters, which you might find useful if you choose to use spectrogram audio features.  \n",
    "\n",
    "If you instead want to use MFCC features, these arguments will have to be tuned.  Note that the current architecture only supports values of `'same'` or `'valid'` for the `conv_border_mode` argument.\n",
    "\n",
    "When tuning the parameters, be careful not to choose settings that make the convolutional layer overly small.  If the temporal length of the CNN layer is shorter than the length of the transcribed text label, your code will throw an error.\n",
    "\n",
    "Before running the code cell below, you must modify the `cnn_rnn_model` function in `sample_models.py`.  Please add batch normalization to the recurrent layer, and provide the same `TimeDistributed` layer as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 185)         149110    \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 185)         740       \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 350)         562800    \n",
      "_________________________________________________________________\n",
      "batch_normalization_18 (Batc (None, None, 350)         1400      \n",
      "_________________________________________________________________\n",
      "time_distributed_18 (TimeDis (None, None, 29)          10179     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 724,229\n",
      "Trainable params: 723,159\n",
      "Non-trainable params: 1,070\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_2 = cnn_rnn_model(input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "                        filters=185,\n",
    "                        kernel_size=5, \n",
    "                        conv_stride=3,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=350,\n",
    "                        dropout_rate=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_2.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_2.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 47s - loss: 258.7976 - val_loss: 215.1476\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 44s - loss: 210.2469 - val_loss: 195.7121\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 44s - loss: 194.4411 - val_loss: 176.9136\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 44s - loss: 184.4350 - val_loss: 164.3036\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 44s - loss: 176.8723 - val_loss: 161.7172\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 45s - loss: 171.1767 - val_loss: 155.6394\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 44s - loss: 166.1970 - val_loss: 150.5580\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 45s - loss: 162.9583 - val_loss: 150.3715\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 45s - loss: 159.4488 - val_loss: 146.7499\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 44s - loss: 156.4711 - val_loss: 143.3999\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 44s - loss: 153.5752 - val_loss: 141.8302\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 44s - loss: 151.9115 - val_loss: 141.0765\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 45s - loss: 149.8154 - val_loss: 140.0649\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 44s - loss: 148.0079 - val_loss: 138.6670\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 45s - loss: 146.1044 - val_loss: 138.5527\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 45s - loss: 144.4150 - val_loss: 135.1045\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 44s - loss: 143.2880 - val_loss: 135.8767\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 45s - loss: 141.8172 - val_loss: 134.6186\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 44s - loss: 140.8268 - val_loss: 130.9444\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 45s - loss: 139.1327 - val_loss: 132.9859\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD\n",
    "\n",
    "train_model(input_to_softmax=model_2, \n",
    "            pickle_path='model_2.pickle', \n",
    "            save_model_path='model_2.h5',\n",
    "            optimizer=SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model3'></a>\n",
    "### (IMPLEMENTATION) Model 3: Deeper RNN + TimeDistributed Dense\n",
    "\n",
    "Review the code in `rnn_model`, which makes use of a single recurrent layer.  Now, specify an architecture in `deep_rnn_model` that utilizes a variable number `recur_layers` of recurrent layers.  The figure below shows the architecture that should be returned if `recur_layers=2`.  In the figure, the output sequence of the first recurrent layer is used as input for the next recurrent layer.\n",
    "\n",
    "<img src=\"images/deep_rnn_model.png\" width=\"80%\">\n",
    "\n",
    "Feel free to change the supplied values of `units` to whatever you think performs best.  You can change the value of `recur_layers`, as long as your final value is greater than 1. (As a quick check that you have implemented the additional functionality in `deep_rnn_model` correctly, make sure that the architecture that you specify here is identical to `rnn_model` if `recur_layers=1`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/pjordan/anaconda3/envs/dnn-speech-recognizer/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1190: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /home/pjordan/anaconda3/envs/dnn-speech-recognizer/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1154: calling reduce_max (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, None, 290)         393240    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, None, 290)         1160      \n",
      "_________________________________________________________________\n",
      "gru_2 (GRU)                  (None, None, 290)         505470    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, None, 290)         1160      \n",
      "_________________________________________________________________\n",
      "gru_3 (GRU)                  (None, None, 290)         505470    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, None, 290)         1160      \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, None, 29)          8439      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 1,416,099\n",
      "Trainable params: 1,414,359\n",
      "Non-trainable params: 1,740\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_3 = deep_rnn_model(input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "                         units=290,\n",
    "                         recur_layers=3,\n",
    "                         dropout_rate=0.3035064397585259) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_3.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_3.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/pjordan/anaconda3/envs/dnn-speech-recognizer/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1297: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:Variable *= will be deprecated. Use `var.assign(var * other)` if you want assignment to the variable value or `x = x * y` if you want a new python Tensor object.\n",
      "Epoch 1/20\n",
      "106/106 [==============================] - 346s - loss: 306.0022 - val_loss: 230.8003\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 356s - loss: 228.5843 - val_loss: 208.5356\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 359s - loss: 221.6973 - val_loss: 202.8349\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 359s - loss: 214.3285 - val_loss: 193.8858\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 357s - loss: 206.8792 - val_loss: 190.5545\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 354s - loss: 197.0360 - val_loss: 180.5237\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 359s - loss: 186.1461 - val_loss: 173.6953\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 360s - loss: 177.0056 - val_loss: 156.9757\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 354s - loss: 170.2661 - val_loss: 152.6200\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 361s - loss: 166.5991 - val_loss: 152.2965\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 357s - loss: 164.0009 - val_loss: 146.9104\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 358s - loss: 160.5796 - val_loss: 142.4707\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 357s - loss: 157.5561 - val_loss: 142.0041\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 362s - loss: 156.8146 - val_loss: 141.1184\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 360s - loss: 154.9966 - val_loss: 140.2970\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 360s - loss: 152.4878 - val_loss: 138.7927\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 359s - loss: 152.0400 - val_loss: 136.6318\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 359s - loss: 149.7010 - val_loss: 137.6884\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 354s - loss: 148.1568 - val_loss: 130.7026\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 357s - loss: 148.5415 - val_loss: 136.0464\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD\n",
    "\n",
    "train_model(input_to_softmax=model_3, \n",
    "            pickle_path='model_3.pickle', \n",
    "            save_model_path='model_3.h5', \n",
    "            optimizer=SGD(lr=0.0635459438114008, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model4'></a>\n",
    "### (IMPLEMENTATION) Model 4: Bidirectional RNN + TimeDistributed Dense\n",
    "\n",
    "Read about the [Bidirectional](https://keras.io/layers/wrappers/) wrapper in the Keras documentation.  For your next architecture, you will specify an architecture that uses a single bidirectional RNN layer, before a (`TimeDistributed`) dense layer.  The added value of a bidirectional RNN is described well in [this paper](http://www.cs.toronto.edu/~hinton/absps/DRNN_speech.pdf).\n",
    "> One shortcoming of conventional RNNs is that they are only able to make use of previous context. In speech recognition, where whole utterances are transcribed at once, there is no reason not to exploit future context as well.  Bidirectional RNNs (BRNNs) do this by processing the data in both directions with two separate hidden layers which are then fed forwards to the same output layer.\n",
    "\n",
    "<img src=\"images/bidirectional_rnn_model.png\" width=\"80%\">\n",
    "\n",
    "Before running the code cell below, you must complete the `bidirectional_rnn_model` function in `sample_models.py`.  Feel free to use `SimpleRNN`, `LSTM`, or `GRU` units.  When specifying the `Bidirectional` wrapper, use `merge_mode='concat'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "bidirectional_9 (Bidirection (None, None, 500)         618000    \n",
      "_________________________________________________________________\n",
      "time_distributed_28 (TimeDis (None, None, 29)          14529     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 632,529\n",
      "Trainable params: 632,529\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_4 = bidirectional_rnn_model(\n",
    "    input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "    units=250,\n",
    "    dropout_rate=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_4.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_4.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 205s - loss: 275.6266 - val_loss: 226.8717\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 205s - loss: 213.2997 - val_loss: 201.3109\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 204s - loss: 200.7651 - val_loss: 186.7573\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 205s - loss: 193.3435 - val_loss: 182.8960\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 204s - loss: 187.6618 - val_loss: 173.7006\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 204s - loss: 182.4469 - val_loss: 177.4735\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 204s - loss: 177.6839 - val_loss: 169.6660\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 204s - loss: 173.7626 - val_loss: 169.5262\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 205s - loss: 169.5368 - val_loss: 162.4727\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 204s - loss: 166.1426 - val_loss: 161.0329\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 205s - loss: 162.1614 - val_loss: 159.1479\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 205s - loss: 159.1850 - val_loss: 154.9204\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 204s - loss: 156.0412 - val_loss: 149.9123\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 204s - loss: 153.1229 - val_loss: 151.7496\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 204s - loss: 150.3786 - val_loss: 147.4174\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 205s - loss: 148.0250 - val_loss: 148.3927\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 203s - loss: 145.1999 - val_loss: 142.2009\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 205s - loss: 143.4235 - val_loss: 142.9599\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 205s - loss: 140.9955 - val_loss: 141.4220\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 204s - loss: 139.4114 - val_loss: 138.3626\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_4, \n",
    "            pickle_path='model_4.pickle', \n",
    "            save_model_path='model_4.h5',             \n",
    "            optimizer=SGD(lr=0.06, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model5'></a>\n",
    "### (OPTIONAL IMPLEMENTATION) Models 5+\n",
    "\n",
    "If you would like to try out more architectures than the ones above, please use the code cell below.  Please continue to follow the same convention for saving the models; for the $i$-th sample model, please save the loss at **`model_i.pickle`** and saving the trained model at **`model_i.h5`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "lambda_8 (Lambda)            (None, None, 161, 1)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, None, 161, 50)     6100      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, None, 32, 50)      0         \n",
      "_________________________________________________________________\n",
      "time_distributed_11 (TimeDis (None, None, 1600)        0         \n",
      "_________________________________________________________________\n",
      "gru_8 (GRU)                  (None, None, 200)         1080600   \n",
      "_________________________________________________________________\n",
      "time_distributed_12 (TimeDis (None, None, 29)          5829      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 1,092,529\n",
      "Trainable params: 1,092,529\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_5 = cnn2d_rnn_model(\n",
    "    input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "    filters=50,\n",
    "    kernel_size=(11,11), \n",
    "    conv_stride=1,\n",
    "    conv_border_mode='same',\n",
    "    pool_size=(1,5),\n",
    "    units=200,\n",
    "    dropout_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 137s - loss: 285.0588 - val_loss: 228.7582\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 129s - loss: 230.2834 - val_loss: 213.1584\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 126s - loss: 213.9887 - val_loss: 194.7103\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 126s - loss: 197.2486 - val_loss: 179.5294\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 126s - loss: 180.5510 - val_loss: 166.0413\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 125s - loss: 166.6758 - val_loss: 153.4104\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 125s - loss: 157.2719 - val_loss: 144.9292\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 126s - loss: 150.0972 - val_loss: 142.1533\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 125s - loss: 143.9420 - val_loss: 138.1702\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 124s - loss: 138.9901 - val_loss: 132.9487\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 125s - loss: 135.0339 - val_loss: 131.0782\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 125s - loss: 131.4873 - val_loss: 129.4672\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 124s - loss: 128.0020 - val_loss: 128.9729\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 124s - loss: 125.3787 - val_loss: 126.8662\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 124s - loss: 122.5167 - val_loss: 122.6902\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 124s - loss: 119.9851 - val_loss: 123.2564\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 125s - loss: 117.8803 - val_loss: 121.1491\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 124s - loss: 115.6461 - val_loss: 121.6285\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 124s - loss: 113.2564 - val_loss: 119.9097\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 125s - loss: 111.4150 - val_loss: 118.3596\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD\n",
    "\n",
    "train_model(input_to_softmax=model_5, \n",
    "            pickle_path='model_5.pickle', \n",
    "            save_model_path='model_5.h5', \n",
    "            optimizer=SGD(lr=0.06, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='compare'></a>\n",
    "### Compare the Models\n",
    "\n",
    "Execute the code cell below to evaluate the performance of the drafted deep learning models.  The training and validation loss are plotted for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from glob import glob\n",
    "import numpy as np\n",
    "import _pickle as pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_style(style='white')\n",
    "\n",
    "# obtain the paths for the saved model history\n",
    "all_pickles = sorted(glob(\"results/*.pickle\"))\n",
    "# extract the name of each model\n",
    "model_names = [item[8:-7] for item in all_pickles]\n",
    "# extract the loss history for each model\n",
    "valid_loss = [pickle.load( open( i, \"rb\" ) )['val_loss'] for i in all_pickles]\n",
    "train_loss = [pickle.load( open( i, \"rb\" ) )['loss'] for i in all_pickles]\n",
    "# save the number of epochs used to train each model\n",
    "num_epochs = [len(valid_loss[i]) for i in range(len(valid_loss))]\n",
    "\n",
    "fig = plt.figure(figsize=(16,5))\n",
    "\n",
    "# plot the training loss vs. epoch for each model\n",
    "ax1 = fig.add_subplot(121)\n",
    "for i in range(len(all_pickles)):\n",
    "    ax1.plot(np.linspace(1, num_epochs[i], num_epochs[i]), \n",
    "            train_loss[i], label=model_names[i])\n",
    "# clean up the plot\n",
    "ax1.legend()  \n",
    "ax1.set_xlim([1, max(num_epochs)])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss')\n",
    "\n",
    "# plot the validation loss vs. epoch for each model\n",
    "ax2 = fig.add_subplot(122)\n",
    "for i in range(len(all_pickles)):\n",
    "    ax2.plot(np.linspace(1, num_epochs[i], num_epochs[i]), \n",
    "            valid_loss[i], label=model_names[i])\n",
    "# clean up the plot\n",
    "ax2.legend()  \n",
    "ax2.set_xlim([1, max(num_epochs)])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Question 1:__ Use the plot above to analyze the performance of each of the attempted architectures.  Which performs best?  Provide an explanation regarding why you think some models perform better than others. \n",
    "\n",
    "__Answer:__\n",
    "\n",
    "The following table gives the model performance in ascending order of (best) validation loss.\n",
    "\n",
    "| Rank | Model | Description | Best Loss | \n",
    "| -- | -- | -- | -- |\n",
    "| 1 | 5| 2D CNN + RNN + TimeDistributed Dense | 118.3596 |\n",
    "| 2 | 3 | Deeper RNN + TimeDistributed Dense | 130.7026 | \n",
    "| 3 | 2 | CNN + RNN + TimeDistributed Dense | 130.9444 | \n",
    "| 4 | 1 | RNN + TimeDistributed Dense | 131.8664 | \n",
    "| 5 | 4 | Bidirectional RNN + TimeDistributed Dense | 138.3626 |\n",
    "| 6 | 0 | RNN |  721.1129 | \n",
    "\n",
    "\n",
    "All of the time distributed models perform well, indicating that the time series gives valuable signal (as expected).  The models that preprocessed the input with CNNs performed well, but we prone to overfitting.  The network with the two-dimensional convolutional layer performed best, indicating that the convolutional layer can produce features beyond what a time series model alone infer.  In particular, the frequency dimension has informative patterns that can be mined.  Deeper recurrent layers do not seem to add much to performance, as evidenced by the model 3 to model 1 comparison, within the 20-epoch evaluation.  Models 3 and 4, with sufficient dropout rates, do seem like they are not prone to overfitting and may perform better with more epochs than the models with convolutional layers.  The latter two models both use recurrent layers that are less prone to gradient explosions, possibly why they take longer to train.\n",
    "\n",
    "The final model combines the best convolutional layer with the bidirectional RNN with time distributed dense layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='final'></a>\n",
    "### (IMPLEMENTATION) Final Model\n",
    "\n",
    "Now that you've tried out many sample models, use what you've learned to draft your own architecture!  While your final acoustic model should not be identical to any of the architectures explored above, you are welcome to merely combine the explored layers above into a deeper architecture.  It is **NOT** necessary to include new layer types that were not explored in the notebook.\n",
    "\n",
    "However, if you would like some ideas for even more layer types, check out these ideas for some additional, optional extensions to your model:\n",
    "\n",
    "- If you notice your model is overfitting to the training dataset, consider adding **dropout**!  To add dropout to [recurrent layers](https://faroit.github.io/keras-docs/1.0.2/layers/recurrent/), pay special attention to the `dropout_W` and `dropout_U` arguments.  This [paper](http://arxiv.org/abs/1512.05287) may also provide some interesting theoretical background.\n",
    "- If you choose to include a convolutional layer in your model, you may get better results by working with **dilated convolutions**.  If you choose to use dilated convolutions, make sure that you are able to accurately calculate the length of the acoustic model's output in the `model.output_length` lambda function.  You can read more about dilated convolutions in Google's [WaveNet paper](https://arxiv.org/abs/1609.03499).  For an example of a speech-to-text system that makes use of dilated convolutions, check out this GitHub [repository](https://github.com/buriburisuri/speech-to-text-wavenet).  You can work with dilated convolutions [in Keras](https://keras.io/layers/convolutional/) by paying special attention to the `padding` argument when you specify a convolutional layer.\n",
    "- If your model makes use of convolutional layers, why not also experiment with adding **max pooling**?  Check out [this paper](https://arxiv.org/pdf/1701.02720.pdf) for example architecture that makes use of max pooling in an acoustic model.\n",
    "- So far, you have experimented with a single bidirectional RNN layer.  Consider stacking the bidirectional layers, to produce a [deep bidirectional RNN](https://www.cs.toronto.edu/~graves/asru_2013.pdf)!\n",
    "\n",
    "All models that you specify in this repository should have `output_length` defined as an attribute.  This attribute is a lambda function that maps the (temporal) length of the input acoustic features to the (temporal) length of the output softmax layer.  This function is used in the computation of CTC loss; to see this, look at the `add_ctc_loss` function in `train_utils.py`.  To see where the `output_length` attribute is defined for the models in the code, take a look at the `sample_models.py` file.  You will notice this line of code within most models:\n",
    "```\n",
    "model.output_length = lambda x: x\n",
    "```\n",
    "The acoustic model that incorporates a convolutional layer (`cnn_rnn_model`) has a line that is a bit different:\n",
    "```\n",
    "model.output_length = lambda x: cnn_output_length(\n",
    "        x, kernel_size, conv_border_mode, conv_stride)\n",
    "```\n",
    "\n",
    "In the case of models that use purely recurrent layers, the lambda function is the identity function, as the recurrent layers do not modify the (temporal) length of their input tensors.  However, convolutional layers are more complicated and require a specialized function (`cnn_output_length` in `sample_models.py`) to determine the temporal length of their output.\n",
    "\n",
    "You will have to add the `output_length` attribute to your final model before running the code cell below.  Feel free to use the `cnn_output_length` function, if it suits your model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/pjordan/anaconda3/envs/dnn-speech-recognizer/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1208: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "lambda_17 (Lambda)           (None, None, 161, 1)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, None, 161, 50)     6100      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, None, 32, 50)      0         \n",
      "_________________________________________________________________\n",
      "time_distributed_19 (TimeDis (None, None, 1600)        0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, None, 400)         2161200   \n",
      "_________________________________________________________________\n",
      "batch_normalization_19 (Batc (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_20 (TimeDis (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 2,180,529\n",
      "Trainable params: 2,179,729\n",
      "Non-trainable params: 800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# specify the model\n",
    "model_end = final_model(\n",
    "    input_dim=161,\n",
    "    filters=50,\n",
    "    kernel_size=(11,11),\n",
    "    conv_stride=1,\n",
    "    conv_border_mode='same',\n",
    "    pool_size=(1,5),\n",
    "    units=200,\n",
    "    recur_layers=1,\n",
    "    dropout_rate=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_end.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_end.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 248s - loss: 335.9858 - val_loss: 255.5860\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 240s - loss: 242.4996 - val_loss: 238.2656\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 239s - loss: 222.3218 - val_loss: 197.3325\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 241s - loss: 200.9018 - val_loss: 185.4125\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 239s - loss: 187.2262 - val_loss: 171.6594\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 236s - loss: 175.8966 - val_loss: 157.7085\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 241s - loss: 167.2219 - val_loss: 154.9972\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 236s - loss: 161.3043 - val_loss: 151.5892\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 237s - loss: 156.4006 - val_loss: 145.9787\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 238s - loss: 152.1061 - val_loss: 141.4593\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 238s - loss: 148.1522 - val_loss: 139.1478\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 238s - loss: 145.1965 - val_loss: 136.7189\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 239s - loss: 142.2492 - val_loss: 134.3185\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 237s - loss: 140.1469 - val_loss: 131.4872\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 238s - loss: 137.7255 - val_loss: 129.9063\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 236s - loss: 135.6871 - val_loss: 129.2205\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 239s - loss: 133.5798 - val_loss: 127.0699\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 239s - loss: 131.8900 - val_loss: 125.6219\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 237s - loss: 130.2181 - val_loss: 126.2355\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 237s - loss: 128.9155 - val_loss: 126.0262\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD\n",
    "\n",
    "train_model(input_to_softmax=model_end, \n",
    "            pickle_path='model_end.pickle', \n",
    "            save_model_path='model_end.h5',\n",
    "            optimizer=SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=1),\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ Describe your final model architecture and your reasoning at each step.  \n",
    "\n",
    "__Answer:__\n",
    "\n",
    "The final architecture included a two-dimensional convolutional layer followed by a max-pooling layer.  The output of the max pooling layer fed into a bi-directional GRU layer, which outputted to a time-distributed dense layer.  In total, the network has 2,179,729.\n",
    "\n",
    "The 2D convolutional and max pooling layers are used to transform the time and frequency matrix into a time and feature matrix input, hopefully producing meaningful distilations of common waveforms.  As in the base models in the previous section, the bidirectional GRU allows more flexibility by processing in both directions in time.  The latter does not appear to add much improvement over a GRU with comparable parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step3'></a>\n",
    "## STEP 3: Obtain Predictions\n",
    "\n",
    "We have written a function for you to decode the predictions of your acoustic model.  To use the function, please execute the code cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from data_generator import AudioGenerator\n",
    "from keras import backend as K\n",
    "from utils import int_sequence_to_text\n",
    "from IPython.display import Audio\n",
    "\n",
    "def get_predictions(index, partition, input_to_softmax, model_path):\n",
    "    \"\"\" Print a model's decoded predictions\n",
    "    Params:\n",
    "        index (int): The example you would like to visualize\n",
    "        partition (str): One of 'train' or 'validation'\n",
    "        input_to_softmax (Model): The acoustic model\n",
    "        model_path (str): Path to saved acoustic model's weights\n",
    "    \"\"\"\n",
    "    # load the train and test data\n",
    "    data_gen = AudioGenerator()\n",
    "    data_gen.load_train_data()\n",
    "    data_gen.load_validation_data()\n",
    "    \n",
    "    # obtain the true transcription and the audio features \n",
    "    if partition == 'validation':\n",
    "        transcr = data_gen.valid_texts[index]\n",
    "        audio_path = data_gen.valid_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    elif partition == 'train':\n",
    "        transcr = data_gen.train_texts[index]\n",
    "        audio_path = data_gen.train_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    else:\n",
    "        raise Exception('Invalid partition!  Must be \"train\" or \"validation\"')\n",
    "        \n",
    "    # obtain and decode the acoustic model's predictions\n",
    "    input_to_softmax.load_weights(model_path)\n",
    "    prediction = input_to_softmax.predict(np.expand_dims(data_point, axis=0))\n",
    "    output_length = [input_to_softmax.output_length(data_point.shape[0])] \n",
    "    pred_ints = (K.eval(K.ctc_decode(\n",
    "                prediction, output_length)[0][0])+1).flatten().tolist()\n",
    "    \n",
    "    # play the audio file, and display the true and predicted transcriptions\n",
    "    print('-'*80)\n",
    "    Audio(audio_path)\n",
    "    print('True transcription:\\n' + '\\n' + transcr)\n",
    "    print('-'*80)\n",
    "    print('Predicted transcription:\\n' + '\\n' + ''.join(int_sequence_to_text(pred_ints)))\n",
    "    print('-'*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the code cell below to obtain the transcription predicted by your final model for the first example in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "he was young no spear had touched him no poison lurked in his wine\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "he was o  no sperhd thtm no pis on mork din iso\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='train',\n",
    "                input_to_softmax=model_end, \n",
    "                model_path='results/model_end.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the next code cell to visualize the model's prediction for the first example in the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "o life of this our spring\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      " bo f an dhes rbrn\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='validation',\n",
    "                input_to_softmax=model_end, \n",
    "                model_path='results/model_end.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One standard way to improve the results of the decoder is to incorporate a language model.  We won't pursue this in the notebook, but you are welcome to do so as an _optional extension_. \n",
    "\n",
    "If you are interested in creating models that provide improved transcriptions, you are encouraged to download [more data](http://www.openslr.org/12/) and train bigger, deeper models.  But beware - the model will likely take a long while to train.  For instance, training this [state-of-the-art](https://arxiv.org/pdf/1512.02595v1.pdf) model would take 3-6 weeks on a single GPU!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[NbConvertApp] Converting notebook vui_notebook.ipynb to html',\n",
       " '[NbConvertApp] Writing 944945 bytes to vui_notebook.html']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!!python -m nbconvert *.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['updating: vui_notebook.ipynb (deflated 37%)',\n",
       " 'updating: report.html (deflated 52%)',\n",
       " 'updating: sample_models.py (deflated 82%)',\n",
       " 'updating: results/model_0.h5 (deflated 17%)',\n",
       " 'updating: results/model_0.pickle (deflated 30%)',\n",
       " 'updating: results/model_1.h5 (deflated 8%)',\n",
       " 'updating: results/model_1.pickle (deflated 28%)',\n",
       " 'updating: results/model_2.h5 (deflated 7%)',\n",
       " 'updating: results/model_2.pickle (deflated 29%)',\n",
       " 'updating: results/model_3.h5 (deflated 7%)',\n",
       " 'updating: results/model_3.pickle (deflated 28%)',\n",
       " 'updating: results/model_4.h5 (deflated 7%)',\n",
       " 'updating: results/model_4.pickle (deflated 29%)',\n",
       " 'updating: results/model_5.h5 (deflated 7%)',\n",
       " 'updating: results/model_5.pickle (deflated 28%)',\n",
       " 'updating: results/model_end.h5 (deflated 7%)',\n",
       " 'updating: results/model_end.pickle (deflated 27%)']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!!zip submission.zip vui_notebook.ipynb report.html sample_models.py results/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "conda-env-dnn-speech-recognizer-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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