speech-recognition-neural-network/vui_notebook.ipynb

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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": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2023 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": false
   },
   "outputs": [
    {
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JqXMXMGHlPqOGVEiwfaSUwg/L9ug68/Doj6uxbNeJvGA7Wt3+VeCUl+lh1okn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\ne3C/24f73iDM4SYiIiIiMhFnuImIiIiITMSA2wA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a68c406a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Audio Signal** : (129103,)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Transcript** : mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "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": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2EBAQYJDnFLRKQMaFClD4cdOZCp5AznKxWjGftFwxeYh9yfdZChDHjpsu+y4E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KKKpfJkX2U0BX4Hp5vivSLZBf2WsitVe/HvBVuy1XoGZlFxwIoFAtUcbAp+9Oa4DxzPTLGCwHOG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\n6To6mRNdZUvrkoznUbdFltA2OBZA84fOLiFrACwjc2ZNjO2R4YEsq1eK7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c6WOXdT2vKc\n06WKeqXy6Jqvvbc52m3+vsTmum6uWhJ9UtOmcSymTYzrxtR7AqgFx7aLlcw9L6bSFHwpHEvgS2Z+\n4dJ4KB88MSpjgqWSu4x7qATuSlU5BTz5+7kuBHILLo9FXwTxo0dH8OO60a2hVM3Vg6cO/joz9c6d\nlXIF7WNWRkCUs5ZDheI/msYXd08JfS0e7m991CUwPMQn9EeKsiGgMMwDlvLucVF+/a4rruMz0qtD\n/9XpjNDPSQH3UPcQcRUUXtJSJ8hVvjiv30gxdsB4Hl3GJIvcQ1Y43fLPyHqAqPn37+qO52A+l08c\njvO45QqubUoTA0ENamVeLYJ1T2+c1y0G4v1u6ovnmsXO+q7hAczUI5B1bJooBT9FKcjE0x1zCkWv\nvJdMu7XXHDsVCn3c9VHnXf8+Ko5lbV3wPGuRERae0tqVnzWeWTLjMuHKgy2Go2w0LsM9aSxE3p7T\n/Fu6vjUcQPphrTdt8PuPdPtS47RHDUj/S1qXv7zxYJ9XVfWeB/v/jdnGNrepjm795/q+voAeivqk\nq82VKowK3nIQf8uqyNauvYtsK3cyjJ0ryZcLK22DBGghHa/g+9ZZ+Umc9+nbRWApmyOA8RB3k+Zm\nUg+171wT/QSPnfPl0Nd1qBTEqj+cQWy/gdLwIPD5BCQWHDLIVza65Hedj2UtiA3g529dgz67iqYu\nFq7pUjpL/bS9n+vAJvizKfzzg0K25sMpCuZT0+dC37GxyYybZagEqn3WV6dPh77++fucFy17tT2t\n6Z3MFHd/kEP5R9mhbA51fXltnRPL1Fk6GU3BmwBFf11N7frnWizFgEKv36kITUoh08aCkyJDOP3i\n6GpTGwtdMkpZNrw3edDnT5oc+jPOi25MtXc3s4V9x7aU6Ubf6vx+pbzljM0dMwCuPwW46p0nMHWN\nO4/ev5QK1MJNAl1dUkopIPPPHVefXZBeqj3gntT9Ra8sXXt8d6wIJx8YFdWz50dFVveZ2h52j+au\nQwapOha6GaFQDJ6AcqQLmPPunsvGuvmdlFor29ldtm5Org5pvSX9s9pXG6981ID0f8jdZWO0LZpP\nrnZttYN2ZCw0M/5hVcz2snY+DnEKT/3nkPUBO+rv5W8VXjYzVXivwvdHbR/9Fjcj53v/1dGtoOQ2\nITiU0dWPe6feCPZyIKvvreZgi9L4/ZJpTSBq0OyPKYKjiffWFdF0vtO4+C4yXY6N4O4LV8aglpfv\nGcvq5cqdwNJ3lRWU2dbs/5Fvvyv0X3b0Zzo+u9dTgdHqIbDV2qTSIRsuUNZvPGc1dclQOXpW+t/Q\nNzhx7g2/Cf1iZUnab8dGBLDDcJQvW14Nk+XB5aFtTmk/fwV9KxrmAMQCOZriBU8kIKqB8rPo6yss\nUPVdO6VETKmct90jT7Dm2Oi7nIN8q6EKrhwr86QbplEq1FJin5n3BYy90Ucm05VjHFdKKSngyZvg\ny3nzWioIVkP9Dn1BsPc7pvD5e2O3YqwaKljM3cI3Um7+BsrN4yVRZXPd8Px3naj86HrjOjE7i3uw\noFCsJTZyxGF8XxBv6nDfR+LOsczn2loBXss9oIXDdVNSaFg3S7/YZiwObA6kea371oP00c0p1UGt\nt6V/Vvt64+WPGpD+DwWObozWeHKzSme0d1nfftHHtP9k/F2fzwXUXK0wqgBzWvL9/yc+M5LbDS8T\n/kN2wfP5/L4IkqdNjn6Ni1dMCf37+f4muGjo7nLvcNx1o7uiq4CMqMx5pzzrpYI4KgCag/XZ/NWq\nmIBWtwZZfxnQXXh2gaug38wVuoR0srA82P1z/1ldYy74bERmTz01SlYDuZwHmXDZYOepnr0lvptW\nDdlizZ6+u2NrHIhBtvnc/cfQF8Nnn+k+LfQ1ye6fYmVYMyxpRXCeVYh2GoinqMGOAtXlE+JJpMJV\nq4i8kpPMUvcewvn9BdmFQ3PtSalj+1Xv00O/ZgUQnMlgAlCWfoBUfK+AipPEML+1Yx0zOqa7AaJb\n5u+rrH0nfVk8s3LYdx5899JxXWLKtVqUmHtBuAqbSkm2jGugV7BEqoZrKA4koapCQa6qNEeFgnVZ\nAVQbgjmzt5jEuRAseceP4kLbYjBqiyNz5U88oCLaSflJKd3E2DoW0wX1EnulKp2n038tffeQSkIp\nyDYXTyokYiHnSaW5VOxMxdY9kL17c3FKrb9XAaQf0HJTb7z2zcbLHjV50t36j7q2+Zar04z92yDm\nNnf0afzARSu7o3B3YXmw5IsehR0sVBcAr4igfOTxUfytacUdN3nfeAHZ6PsXI23B+PfzPMt3jb6A\n02bfGPp3pSeG/j0D8eVvvjOCtbnbbRgg6Q5SdxcxS8aGS8WnlNKpoyNVpp+zQFYl4RrcX/TjFuQL\n3rbieQVjpmzsFFimq47rQH9ZQbZ9x9Jnk/V/12D0WntPT2Tin4XftG5Qv8QPSyDsuwvKff/8+T7U\n/ebwmW5EJ6Svhb4WFC0uOw9EvwZ9NHv+jElWecGeuuPYuCfGX8OpCku4YFJ0wxh/H9+3kIvp5/K+\nIF0mC8leK7jDIbtdLyyDJ4OyUVM5zzPxOOCcjGnJPUZXgRjyk7YUcORgz2f/CH0D+rzXCfRVnry3\n5doF+TY/14WDd6XUQJ3VFGgzF3dnCtTdALPFTNMIQPmcGIJSB82uUT3OBLYoKA3fnXVSxWMlNVRw\nXPcoUGP+g0Ne16Z87p/EZ/qY/5E+8mEnYZvMOUGwa2Gr17KuNi9Zx3iXRXw+VaVAy5x7MvcWlp69\nkL7ypKQ8qd0pv+y7prNWpUbRzfJfqRUrjj6a29bNydXBrfZBbqXIPwxFCTBwNj4i/80F1Uwvo68L\nyhvyh+EzBekb6O9HX3ZGhUCFwjqse3P78XFXbD4qAuX+P0Urw+47RFXYNIO3DccdL9Mu27xv5iJi\ncKDM8lNqznOx6RNeihTXz1r3FkG7Ckkt8AyJ4+e1zDlk+jGbg8A2b6bx0x3Fe2nhEJSbYlGFZ1ve\nRSZ9b3zSDaY0jkMlwD0p8+/389RbzrPZlXx337WU5nTP/ph5oiHD6oFu30NT8/H3Y3cF3x/nIUkx\nkxoAyYG1IFd5ozzxXrpkXEu/VGTGQ1dgXArw81B27DyDPfS9X/5+vrvXNguH72ITgAg8ZRFVtgRb\nzjMuHrWx9nreX9YTpePGWe0LzPwRE6t7intAF4lClc2bWZd6LT2B/hT62whsdYvyqAC0LwIIj2Ms\na8WP8t+rkLiOPkVfH3PbefRVRN2zKmelwkyuO59Xhck92CmORZcv94j7U7zi/vf7Wub8fkawNj+Y\nUmtJm0l/YnNatW/rfemf1c5vHN+RSW80GgekdaulK6V0dlVVH/5H7veYBumNXXar0o/bEVSjto6s\n3erlIOcWwZsUjknb4GNqW44B69zsb91bBNWX0dfUpbvM9vQL1RXTmfQ9WFQiqI7Wu300BQy0uCEC\nwyI7suM5w2vwoU3QLLtaCug1veXPbnlB6L9wh1jAQnb3m4MxO8OBPZHiMKuH7PbPLoj3G7VfjA94\nck90KcmZfivT+myOhant9PGWSRcE6yJSSgOoO4vXc+5UqP4yGNHhbj2d87rn1xNUrxqOi9iUiSpn\njo0Kj2NtJVtThepOo0Jj0Zoay8ieu2JsDPnRSuH7Tx1evP7vFV1xDZZqDfwqRRexZ+LPX4oDMXvV\nydSDN2f97EXRj2ENh/Dinhhc6TqYuTKCSasp9gxg9chAxHUzIipXqba5pu3vP0jVTdqKnpj607zm\nNbBUAl8Ay/lz4lhNGorv07OKsQC4fnd6u6DQIYMxg8BI/aIFS7L+ujmoLOneIvOtr7PnWAn4lmIh\nbCo8vk/+vioogmqboFrQ7FipKJeAsAqLY+m7y5yrzOliIkjPxuKOqRF1K+enz4exVBvz2lolC65D\n+dg3/zul1m0RpO/d+mDhAo9c+2Hj2A2C9Eaj0ZXW2Vyek9bRur9NKR1bVdVND/b9h9Ie0yC9pzmj\nmtFqHxYytCtXxIPs/svYsZdxQRe9QNuMLDlm+BafCZKPpC8oF0Tb17xk32dXs7VvCWMZEwuGeT8t\nAeqK2Vkwft944Jo95bbBKF1WL6bSpGOlFcF5cux0RVJgyGioIHl9FSZ/Xwr6zdeG1/YQMovHu+Op\nNoIA4bWLifjbiv39JxRVr+/Ylg7BX9BXWV1M34PNucnX4Yehd8+M/uy1Neq8eegSc7LJZ+Kptxl5\n1w2GNsuQyptWBhUgK1WqvAqsBYshmxQKhLJPhcR21bURARy7W8ywZJyFAcWlCqOao303FSRlQun9\nvF+unBoH4b1UaHSL0kKzCOSpZU1l7a1LY9GZ0ybGLEBzUgxotoCZ7oE+r2lNn0xcialA87GZMBzH\nTWVPZayUjOHFBAs41uOG4zr8W1d0RdTqWMrH/W2SDJSspj6f188D70tB+p9MsYCOBIsWXdeoaU+/\nTmC8+f19N+WJ60LLo4SL7R5AQU4SlCr8fg9AY9zYRWhbLyEJve9my8f+1Ob16Y+te9YfXFs1t6ue\n1fqHyOl/qP248cJOIP3pKaV3V1W1/wP9t6SUUlVVH3qw7z+U9i8F0hcPx0VqRpOae4tgSuB6Ln2B\ndt5KwFHmXGC3mL4Mw9n0XSKaVAUsgqHv0ddUJvgy6LaUA3ZKh2s/3Ew3gjGBo/MoQyHIdx6dC999\nCn3XSeeinfX3z5/fZ/sq9M1p0HCOjcqX814C/aWMRs6rvy9lJTqJ/t70XZf5WLpOnHefXUVyCn33\nJPPYd1IMPN+qqzO4W740gqW+8RR2WhOZ8Cf0RPBl1iALzXSqcHrdhQgUrYJbmXkmDl7flAggDLI/\n+CsxJYkWGkGzcSSC8ou/HVNTzDo6ZpcybarNeAOBcg4izrvopeGzw/bX7yA2LSaCIYOnS0H05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0HegZoBuUINq4DEG82CvfQ5s8yBn17wTSH9PuLuObE6oTW6es78tgvC19IPRdtP+Lr55uEm46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Ejvb4+v/mXA7KzO+HGKIN1LIfOUEHWlydD8PB+6ziRaY7i0YJSYjd9fMRotCtO7TDCM2Uda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\nUZKuhdNPY4ny6WaVc5BGNh1rGuUVY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a68c405c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Spectrogram** : (584, 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": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3bQUxK2morSAmhI4WZ7ezfde6YwDpLg8PPklbQUxXplZ/UZYzquhwbQURy/Le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\nO4LZidziHcEu/lbv7Md47wgmP+ZC7whml3GOdwSzbQvQBTn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ay2q6Vcde5PQC8Wdx506P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\nSqmRyUF6SimllFJKjcz/AycU/tubfRdXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a6a2f0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of MFCC** : (584, 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": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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 = 0.5\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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_0 = simple_rnn_model(input_dim=13) # 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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 119s - loss: 837.7633 - val_loss: 756.5092\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 115s - loss: 779.7239 - val_loss: 758.3753\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 115s - loss: 779.5841 - val_loss: 751.7412\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 117s - loss: 779.3612 - val_loss: 760.3783\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 117s - loss: 779.2541 - val_loss: 758.1526\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 115s - loss: 779.1985 - val_loss: 754.7404\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 115s - loss: 779.3162 - val_loss: 752.1416\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 115s - loss: 779.1746 - val_loss: 763.1058\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 117s - loss: 779.4733 - val_loss: 763.4400\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 120s - loss: 779.2467 - val_loss: 761.2202\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 117s - loss: 779.3238 - val_loss: 758.5069\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 117s - loss: 778.9843 - val_loss: 757.9038\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 113s - loss: 779.0450 - val_loss: 754.7018\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 118s - loss: 779.1672 - val_loss: 755.7240\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 118s - loss: 778.9910 - val_loss: 756.8546\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 125s - loss: 779.3150 - val_loss: 771.5823\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 122s - loss: 779.2270 - val_loss: 743.7648\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 114s - loss: 779.5980 - val_loss: 765.3291\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 117s - loss: 779.5590 - val_loss: 755.4187\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 119s - loss: 779.4739 - val_loss: 756.5679\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_0, \n",
    "            pickle_path='model_0.pickle', \n",
    "            save_model_path='model_0.h5',\n",
    "            spectrogram=False) # 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": 10,
   "metadata": {
    "collapsed": false,
    "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 (LSTM)                   (None, None, 200)         289600    \n",
      "_________________________________________________________________\n",
      "bn_rnn_1d (BatchNormalizatio (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, None, 29)          5829      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 296,229\n",
      "Trainable params: 295,829\n",
      "Non-trainable params: 400\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=200,\n",
    "                    activation='relu')"
   ]
  },
  {
   "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 117s - loss: 300.2976 - val_loss: 225.7929\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 117s - loss: 233.6712 - val_loss: 222.8865\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 116s - loss: 233.3889 - val_loss: 222.9376\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 117s - loss: 233.0243 - val_loss: 223.8781\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 119s - loss: 233.1200 - val_loss: 223.6895\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 114s - loss: 233.0251 - val_loss: 221.9075\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 116s - loss: 233.0817 - val_loss: 226.5832\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 123s - loss: 232.9440 - val_loss: 222.6163\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 116s - loss: 232.9801 - val_loss: 222.6744\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 115s - loss: 232.8376 - val_loss: 223.2700\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 115s - loss: 232.9068 - val_loss: 224.0518\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 113s - loss: 232.6422 - val_loss: 222.5316\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 114s - loss: 232.6875 - val_loss: 224.4348\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 117s - loss: 232.8250 - val_loss: 220.8547\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 114s - loss: 233.0361 - val_loss: 226.2597\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 120s - loss: 232.8660 - val_loss: 221.1238\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 123s - loss: 232.8365 - val_loss: 223.1869\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 120s - loss: 232.8119 - val_loss: 221.8838\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 120s - loss: 232.8611 - val_loss: 222.8693\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 114s - loss: 232.8867 - val_loss: 221.4095\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_1, \n",
    "            pickle_path='model_1.pickle', \n",
    "            save_model_path='model_1.h5',\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": 12,
   "metadata": {
    "collapsed": false
   },
   "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, 200)         354400    \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 200)         240600    \n",
      "_________________________________________________________________\n",
      "bn_rnn_1d (BatchNormalizatio (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_2 (TimeDist (None, None, 29)          5829      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 602,429\n",
      "Trainable params: 601,629\n",
      "Non-trainable params: 800\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=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=200)"
   ]
  },
  {
   "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 63s - loss: 249.1161 - val_loss: 279.6265\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 62s - loss: 192.2664 - val_loss: 181.4677\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 60s - loss: 156.6647 - val_loss: 154.3840\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 60s - loss: 140.1063 - val_loss: 144.7106\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 60s - loss: 129.1700 - val_loss: 135.3142\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 60s - loss: 121.0549 - val_loss: 132.8940\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 61s - loss: 114.1555 - val_loss: 131.7514\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 66s - loss: 108.3093 - val_loss: 132.3798\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 65s - loss: 102.7633 - val_loss: 130.5701\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 65s - loss: 98.1989 - val_loss: 130.0429\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 64s - loss: 93.3816 - val_loss: 128.8333\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 63s - loss: 89.0302 - val_loss: 129.7670\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 61s - loss: 85.0271 - val_loss: 130.5819\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 64s - loss: 81.2161 - val_loss: 131.1650\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 64s - loss: 77.4708 - val_loss: 133.3010\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 63s - loss: 73.7549 - val_loss: 138.3304\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 62s - loss: 70.4167 - val_loss: 136.0437\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 62s - loss: 67.1312 - val_loss: 144.0295\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 62s - loss: 64.0253 - val_loss: 144.0692\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 62s - loss: 60.9438 - val_loss: 147.9649\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_2, \n",
    "            pickle_path='model_2.pickle', \n",
    "            save_model_path='model_2.h5', \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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, None, 200)         289600    \n",
      "_________________________________________________________________\n",
      "bt_rnn_1 (BatchNormalization (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, None, 200)         320800    \n",
      "_________________________________________________________________\n",
      "bt_rnn_last_rnn (BatchNormal (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_3 (TimeDist (None, None, 29)          5829      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 617,829\n",
      "Trainable params: 617,029\n",
      "Non-trainable params: 800\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=200,\n",
    "                         recur_layers=2) "
   ]
  },
  {
   "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 283s - loss: 299.6555 - val_loss: 223.3386\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 263s - loss: 234.0082 - val_loss: 223.4137\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 260s - loss: 233.3815 - val_loss: 222.8910\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 264s - loss: 233.2315 - val_loss: 226.2374\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 261s - loss: 233.0499 - val_loss: 223.1657\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 262s - loss: 232.9850 - val_loss: 222.8622\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 264s - loss: 233.0063 - val_loss: 224.3826\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 261s - loss: 233.0306 - val_loss: 222.3476\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 262s - loss: 232.7922 - val_loss: 222.7110\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 261s - loss: 232.7517 - val_loss: 222.6574\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 262s - loss: 233.0781 - val_loss: 223.4762\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 263s - loss: 232.8227 - val_loss: 222.2776\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 261s - loss: 233.0050 - val_loss: 225.1248\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 260s - loss: 233.0632 - val_loss: 223.2428\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 261s - loss: 232.7974 - val_loss: 221.2334\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 261s - loss: 232.8635 - val_loss: 223.2759\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 262s - loss: 232.9474 - val_loss: 220.7706\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 263s - loss: 232.8470 - val_loss: 223.5131\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 261s - loss: 232.8623 - val_loss: 223.4732\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 262s - loss: 232.8314 - val_loss: 222.8387\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_3, \n",
    "            pickle_path='model_3.pickle', \n",
    "            save_model_path='model_3.h5', \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": 16,
   "metadata": {
    "collapsed": false,
    "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",
      "bidirectional_1 (Bidirection (None, None, 400)         579200    \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 590,829\n",
      "Trainable params: 590,829\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_4 = bidirectional_rnn_model(input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "                                  units=200)"
   ]
  },
  {
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 260s - loss: 385.6249 - val_loss: 228.0993\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 264s - loss: 232.9343 - val_loss: 224.7966\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 265s - loss: 233.0141 - val_loss: 223.8822\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 263s - loss: 232.8795 - val_loss: 221.4249\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 263s - loss: 232.7045 - val_loss: 222.0958\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 264s - loss: 233.0212 - val_loss: 224.5067\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 263s - loss: 232.8923 - val_loss: 224.2478\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 266s - loss: 232.8256 - val_loss: 222.8118\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 265s - loss: 232.8941 - val_loss: 222.3292\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 265s - loss: 232.6294 - val_loss: 224.3733\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 262s - loss: 232.8698 - val_loss: 222.3533\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 265s - loss: 232.8434 - val_loss: 222.2620\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 262s - loss: 232.9374 - val_loss: 222.3328\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 264s - loss: 232.8260 - val_loss: 223.6158\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 267s - loss: 232.9704 - val_loss: 223.5902\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 261s - loss: 232.7302 - val_loss: 221.7624\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 265s - loss: 232.7211 - val_loss: 224.3096\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 266s - loss: 232.5758 - val_loss: 223.4360\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 265s - loss: 233.0511 - val_loss: 221.9609\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 264s - loss: 232.8166 - val_loss: 222.9856\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_4, \n",
    "            pickle_path='model_4.pickle', \n",
    "            save_model_path='model_4.h5', \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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## (Optional) TODO: Try out some more models!\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tu3wSbJ1OJ7NmzeL48eNYLBaeffZZbDYbs2bNwjAMrr/+eubOnYvFYmHp0qVs3boVm83G\nnDlzGDBgwFUdKzosmJWPDmXmux/ziz99ytFzpSy8v39AXslLREREmkdEaBB9uil8NafaU69F5OrF\ndQpj8YMXR3BX/u1zVu88ysPD4oi9Jox1fz/GJ6eK6RBk5e7+1zFxaA+Se16jQbsr5JNg+8EHH+By\nucjKymL79u388pe/xOl0MnXqVIYNG0Z6ejrZ2dnExMSwc+dO1q1bx8mTJ0lNTWX9+vVXfbwQm5UX\nJwwirmMYv9r8GSculPPKdwd7L+4gIiIiIiLSGtQOuEu3HOa3H36O22MyMDaKzPv7862B12lWxL/B\nJ8E2Pj4et9uNx+OhpKQEm83G7t27GTp0KACjRo1i+/btxMfHk5KSgmEYxMTE4Ha7OXfuHB07Xv2F\noAzD4Jk7b6BHxzBmb9jLg6/uYPkjQ4i95qtfwltERERERKQ5xXUK478fHMjUMUmUVrpJ7Gr3d0kB\nzSfzdcPCwjh+/Djjxo0jLS2N733ve5im6R1GDw8Pp7i4mJKSEuz2i29gTftX8VByD377g6Gc/LKc\n+1/ZwccFF77S/kRERERERHwlJrqDQm0z8EmwXbFiBSkpKfzxj3/kf/7nf5g1axZOp9O73OFwEBkZ\nid1ux+Fw1GmPiIj4yscfkdiZDU+MINhqYcKyv/HnA6e/8j5FRERERESkdfJJsI2MjPQG1KioKFwu\nFzfeeCO5ubkAbNu2jeTkZAYPHkxOTg4ej4cTJ07g8Xj+rWnIDbn+2gg2PjmCpGvtPL7y7/xm+5Fm\n2a+IiIiIiIi0Lj75ju0jjzzCnDlzePjhh3E6nUybNo1+/fqRlpbGkiVLSEhIYOzYsVitVpKTk5kw\nYQIej4f09PRmraNrRChZjw/nP7M+Yt7/HuDzL0pJu+dGrLrPloiIiIiISJvhk2AbHh7OSy+9VK99\n1apV9dpSU1NJTU31RRkAdAi28up3bybz9wf5dc4RCs6X8atJg5r1vm4iIiIiIiLiP+0i3VktBmn3\n3EjPTmFkvL+fCcv+xv03dQeg5rZQNWO4NRe4urS9psGo+xSD+usbRnW7UfPcqLusenntW1IZhlF3\nW+9jaq1n1Fq/4bobqrGhemvtqt52tWu6/PL6+wAwzerf3udmned4l5v1t7lk24aOX/MaVrXVPZ9L\nX+/addZ+XU2z6vhVv6tqrNNWq91jVlfrbataz2PWOrdaBXuP1+R7atR6btQ7v5rP0MUXqfbD+q9d\nvce1NzXrv6KGYWC55FiWWjXXPK7bfnEbqHls1Dqnhmu5tJ7G6rp0HfOSD01D53fxs2M23H7xA1df\nA+9N1WPjkufVKze0HhfP3TTBY5rezwnez0n1Z4yq5dT6LF1cv+HP4CXlXnzcwH/fVevUedLo9t7P\nsaf+Z91bc63Pee3aLm3zNPTaXqb2essus7Cx86n7WtT//9+l+615fO7MqcuVKSIiIgGqXQTbGlOG\n96J7dAf+M2s38393wN/liIhIS3OcI8TfNYiIiEiza1fBFmB032vJSxtDeaXHO5LT1AhjYyM/DS2/\ndASQWutcurxmHzUjO7VHaGqvX/sYtetoYBCufq2XOb862zWyn8bWaPDYNDRa3PAI+KWjqnXXqbtN\n7ePVeU28yy59LS8urb8u3ltP1R49NWqNSNYeZb10hL3mce1Ry9qjrjWjck29p429v/U+Q/VeoysY\nmWpgZL8h9Ubkar0+dUYTG6i77jYXl116vHrPr2CqQGMzBxqcidDI5+viCH4jy6n7eTG5+EbVHpXH\n+7h6mVnrv6wG3kOL5eLot0H1SHdjnyXAYqn1ebvkM1izrfdwDfw/oH577fUbHw03TbAYDddXu807\non/piH3t86nV1hgTs/Fllxntbex8GtvmSl6jkydP8MifGj+miIiIBKZ2F2wBQmxWQmxWf5chIiIt\nzFbewd8liIiIiA/45HY/IiIiIiIiIi1FwVZEREREREQCmoKtiIiIiIiIBDQFWxEREREREQlo7fLi\nUSIiIu3Nhg0b2LhxIwAVFRUcPHiQlStXsnDhQqxWKykpKTz11FN4PB4yMjI4dOgQwcHBLFiwgJ49\ne/q5ehERkctTsBUREWkHHnjgAR544AEA5s2bx/jx45k7dy4vv/wyPXr04PHHH+fAgQMUFBRQWVnJ\nmjVr2L17N8899xyvvvqqn6sXERG5PE1FFhERaUf27t3LZ599xje/+U0qKyuJi4vDMAxSUlLYsWMH\neXl5jBw5EoBBgwaxb98+P1csIiLSNAVbERGRdmTZsmU8+eSTlJSUYLfbve3h4eEUFxfXa7darbhc\nLn+UKiIicsU0FVlERKSdKCoq4siRI9xyyy2UlJTgcDi8yxwOB5GRkZSXl9dp93g82Gz654KIiLRu\nGrEVERFpJ3bt2sXw4cMBsNvtBAUFcfToUUzTJCcnh+TkZAYPHsy2bdsA2L17N0lJSf4sWURE5Iro\nT7AiIiLtxJEjR4iNjfU+nzdvHtOnT8ftdpOSksLAgQPp378/27dvZ+LEiZimSWZmph8rFhERuTIK\ntiIiIu3EY489Vuf5oEGDWLt2bZ02i8XC/PnzW7IsERGRr0xTkUVERERERCSgKdiKiIiIiIhIQFOw\nFRERERERkYCmYCsiIiIiIiIBTcFWREREREREApqCrYiIiIiIiAQ0BVsREREREREJaAq2IiIiIiIi\nEtBsvtjphg0b2LhxIwAVFRUcPHiQlStXsnDhQqxWKykpKTz11FN4PB4yMjI4dOgQwcHBLFiwgJ49\ne/qiJBEREREREWmjfBJsH3jgAR544AEA5s2bx/jx45k7dy4vv/wyPXr04PHHH+fAgQMUFBRQWVnJ\nmjVr2L17N8899xyvvvqqL0oSERERERGRNsqnU5H37t3LZ599xje/+U0qKyuJi4vDMAxSUlLYsWMH\neXl5jBw5EoBBgwaxb98+X5YjIiIiIiIibZBPg+2yZct48sknKSkpwW63e9vDw8MpLi6u1261WnG5\nXL4sSURERERERNoYn0xFBigqKuLIkSPccsstlJSU4HA4vMscDgeRkZGUl5fXafd4PNhsPitJRERE\nRERE2iCfjdju2rWL4cOHA2C32wkKCuLo0aOYpklOTg7JyckMHjyYbdu2AbB7926SkpJ8VY6IiIiI\niIi0UT4bHj1y5AixsbHe5/PmzWP69Om43W5SUlIYOHAg/fv3Z/v27UycOBHTNMnMzPRVOSIiIiIi\nItJG+SzYPvbYY3WeDxo0iLVr19Zps1gszJ8/31cliIiIiIiISDvg04tHiYiIiIiIiPiagq2IiIiI\niIgENAVbERERERERCWgKtiIiIiIiIhLQFGxFREREREQkoCnYioiIiIiISEBTsBUREREREZGApmAr\nIiIiIiIiAU3BVkRERERERAKagq2IiIiIiIgENAVbERERERERCWgKtiIiIiIiIhLQFGxFREREREQk\noCnYioiIiIiISECz+bsAERERaRnLli1j8+bNOJ1OJk2axNChQ5k1axaGYXD99dczd+5cLBYLS5cu\nZevWrdhsNubMmcOAAQP8XbqIiMhlacRWRESkHcjNzeWjjz5i9erVrFy5klOnTrFo0SKmTp3KO++8\ng2maZGdns3//fnbu3Mm6detYsmQJ8+bN83fpIiIiTVKwFRERaQdycnJISkriySef5Mc//jG33347\n+/fvZ+jQoQCMGjWKHTt2kJeXR0pKCoZhEBMTg9vt5ty5c36uXkRE5PI0FVlERKQdOH/+PCdOnOC1\n116joKCAJ554AtM0MQwDgPDwcIqLiykpKSE6Otq7XU17x44d/VW6iIhIkxRsRURE2oHo6GgSEhII\nDg4mISGBkJAQTp065V3ucDiIjIzEbrfjcDjqtEdERPijZBERkSumqcgiIiLtwM0338xf//pXTNPk\n9OnTlJWVMXz4cHJzcwHYtm0bycnJDB48mJycHDweDydOnMDj8Wi0VkREWj2N2IqIiLQDd9xxB7t2\n7eLBBx/ENE3S09OJjY0lLS2NJUuWkJCQwNixY7FarSQnJzNhwgQ8Hg/p6en+Ll1ERKRJCrYiIiLt\nxIwZM+q1rVq1ql5bamoqqampLVGSiIhIs9BUZBEREREREQloCrYiIiIiIiIS0Hw2FXnZsmVs3rwZ\np9PJpEmTGDp0KLNmzcIwDK6//nrmzp2LxWJh6dKlbN26FZvNxpw5cxgwYICvShIREREREZE2yCcj\ntrm5uXz00UesXr2alStXcurUKRYtWsTUqVN55513ME2T7Oxs9u/fz86dO1m3bh1Llixh3rx5vihH\nRERERERE2jCfBNucnBySkpJ48skn+fGPf8ztt9/O/v37GTp0KACjRo1ix44d5OXlkZKSgmEYxMTE\n4Ha7OXfunC9KEhERERERkTbKJ1ORz58/z4kTJ3jttdcoKCjgiSeewDRNDMMAIDw8nOLiYkpKSoiO\njvZuV9Ou++WJiIiIiIjIlfJJsI2OjiYhIYHg4GASEhIICQnh1KlT3uUOh4PIyEjsdjsOh6NOe0RE\nhC9KEhERERERkTbKJ1ORb775Zv76179imianT5+mrKyM4cOHk5ubC8C2bdtITk5m8ODB5OTk4PF4\nOHHiBB6PR6O1IiIiIiIiclV8MmJ7xx13sGvXLh588EFM0yQ9PZ3Y2FjS0tJYsmQJCQkJjB07FqvV\nSnJyMhMmTMDj8ZCenu6LckRERERERKQN89ntfmbMmFGvbdWqVfXaUlNTSU1N9VUZIiIiIiIi0sb5\nZCqyiIiIiIiISEtRsBUREREREZGApmArIiIiIiIiAU3BVkRERERERAKagq2IiIiIiIgENAVbERER\nERERCWgKtiIiIiIiIhLQFGxFREREREQkoCnYioiIiIiISEBTsBUREQlgHo/H3yWIiIj4nYKtiIhI\ngPnDH/7Apk2b2LhxI7feeiu//vWv/V2SiIiIXynYioiIBJjly5czYsQI3n//fT744AO2bNni75JE\nRET8yubvAkRE5KtZn1fA2r8fa9Z9fie5B+Nvjm3WfdZWUVHBuHHj2Lx5c4PLc3NzycrK4sUXX2xw\nucfjISMjg0OHDhEcHMyCBQvo2bOnz+ptbUJCQgAIDw8nODgYh8Ph54pERKQ29c0t3zdf1YjtyZMn\nfVWHiIjIFfvLX/5CZWUla9as4ac//SnPPfecv0tqUbGxsYwfP57x48ezdOlSBgwY4O+SRESknfN3\n39zkiO1vf/tbQkNDKSoqYsOGDYwcOZLZs2e3RG0iInIFxt8c69O/4DZkw4YNbNmyhfLycgoLC5ky\nZQrZ2dkcPnyYGTNmUFpayltvvUVwcDC9evVi/vz5VFZWMn36dIqKioiLi/Pu69ChQyxYsACA6Oho\nMjMzmzx+Xl4eI0eOBGDQoEHs27fPNyfaSj333HM4HA7Cw8Pp378/nTt39ndJIiJSi/rmlu+bmwy2\nv/vd71i1ahWPPfYYv//975kyZUpL1CUiIq2cw+Fg+fLlbNq0iRUrVrB27Vpyc3NZsWIF+fn5bNy4\nEbvdTmZmJmvWrAEgKSmJadOmsWfPHnJzcwFIS0sjMzOTxMRE1q1bx5tvvsmIESMue+ySkhLsdrv3\nudVqxeVyYbO1j2/Y7Nq1i7KyMkzT5Nlnn+U///M/+da3vuXvskRExM/ac9/c5FEsFguFhYXevwZf\nuHDB50WJiEjr17dvXwAiIiLo3bs3hmEQFRVFWVkZiYmJ3s5tyJAh5OTkAHj/kjtw4EBvR5efn8+8\nefMAcDqd9OrVq8lj2+32Ot8r9Xg87SbUAjz//PP84he/YN68eaxevZqpU6cq2IqISLvum5s80rBh\nw/jud7/LCy+8QGZmJmPHjm2JukREpJUzDKPR9vz8fEpLSwkLC2Pnzp3Ex8djGAa7d+9mzJgxHDhw\nAJfLBUB8fDyLFy8mJiaGvLw8CgsLmzz24MGD2bJlC3fffTe7d+8mKSmpWc+ttQsJCaFTp07YbDa6\ndOlCRUWFv0sSEZFWoD33zU0G22nTpjFt2jQA+vfvT1BQkM+LEhGRwGW1WklNTWXKlClYLBbi4uKY\nPn06VquV2bNnM2nSJBISErz9SUZGBjNnzsTlcmEYBgsXLuTMmTOXPcY3vvENtm/fzsSJEzFN84q+\n+9OW2O260QymAAAgAElEQVR2vv/97/Pwww/z9ttvExMT4++SRESkFWsPfbNhmqZ5uRX+8Ic/4PF4\nqKys5Pnnn+fRRx/l0UcfbZHiCgoKGD16NNnZ2cTGtuyXr0VEpO1pK/1KZWUlR48eJTExkcOHD9Oz\nZ0+Cg4Nb5Nht5TUUEZHWobn6lSZHbJcvX87rr7/OM888w9atW/nBD37QYsFWRETat4yMDPLz8+u1\nv/HGG4SGhvqhotbh3Llz/OpXvyI/P59evXoxe/ZshUwREWkRrbVvbjLY6ibwIiLiLxkZGf4uoVX6\n+c9/zqRJkxgyZAg7d+7kZz/7GW+99VaT291///3eC4fExsYyYcIEFi5ciNVqJSUlhaeeegqPx0NG\nRgaHDh0iODiYBQsW0LNnT1+fkoiIBIjW2jc3GWxrbgKflpamm8CLiIi0AhUVFYwePRqAMWPG8Jvf\n/OaKtjFNk5UrV3rb7r33Xl5++WV69OjB448/zoEDBygoKKCyspI1a9awe/dunnvuOV599VWfnYuI\niEhzaDLY6ibwIiIirYvb7ebQoUPccMMNHDp0qNGrYNb2ySefUFZWxg9+8ANcLhepqalUVlYSFxcH\nQEpKCjt27KCwsNB764dBgwaxb98+n56LiIhIc2gy2B46dIg5c+Zw+vRpOnfuTGZmJjfeeGOTO9Z0\nJxEREd9IS0tjzpw5FBYW0rVrVxYsWNDkNqGhoTz66KM89NBD/Otf/+KHP/whkZGR3uXh4eEcO3aM\nkpISb/8NVVfSdLlc7eo+wSIiEnia7KUWLFjAwoUL6dOnDwcPHmTevHlkZWVddhtNdxIREfGdvn37\nsn79eu/zv//9701uEx8fT8+ePTEMg/j4eCIiIrhw4YJ3ucPhIDIykvLy8jrX0/B4PAq1IiLS6l1R\nT9WnTx+gqiO9ks5N051ERFrQ7tXw0arm3edN34VBk5p3n7VUVFQwbtw4Nm/e3ODy3NxcsrKyePHF\nFy+7nz179vCLX/yizh9S26PnnnuOd99997LrvPvuu3z66adkZGRw+vRpysrKCAsL4+jRo/To0YOc\nnByeeuopTp06xZYtW7j77rvZvXs3SUlJLXQWIiJtiPrmFu+bm0ypFouFLVu2kJyczK5du67oPnma\n7iQiIr72xhtv8P7779OhQwd/l+J3TdySHoAHH3yQ2bNnM2nSJAzDIDMzE4vFwvTp03G73aSkpDBw\n4ED69+/P9u3bmThxIqZpkpmZ2QJnICIibYE/++YmE2RmZiaLFy/mhRdeoHfv3lf0PR5NdxIRaUGD\nJvn0L7gN2bBhA1u2bKG8vJzCwkKmTJlCdnY2hw8fZsaMGZSWlvLWW28RHBxMr169mD9/PpWVlUyf\nPp2ioiLvDB6oupZDTd8SHR19xUEqLi6Ol19+mRkzZvjkHAPJlVw8Kjg4mBdeeKFe+9q1a+s8t1gs\nzJ8/v9lqExFpl9Q3++QcL6fJFNm9e3d+9atfeZ///ve/JyYm5rLbaLqTiEjb53A4WL58OZs2bWLF\nihWsXbuW3NxcVqxYQX5+Phs3bsRut5OZmcmaNWsASEpKYtq0aezZs4fc3Fyg6kJImZmZJCYmsm7d\nOt58801GjBjR5PHHjh1LQUGBT8+xtZkwYUK9EGuaJv/85z/9VJGIiLQm7blvvurh0eXLl3P33Xdf\ndh1NdxIRafv69u0LQEREBL1798YwDKKioigrKyMxMdH7VZMhQ4aQk5MD4L2uwsCBA70zdPLz85k3\nbx4ATqeTXr16tfCZBI4lS5b4uwQREWnF2nPffNXB9kq+x6PpTiIibV9j018NwyA/P5/S0lLCwsLY\nuXMn8fHxGIbB7t27GTNmDAcOHMDlcgFVX19ZvHgxMTEx5OXlUVhY2JKnEVC6d+/u7xJERKQVa899\n81UH2yv5Ho+IiLRfVquV1NRUpkyZgsViIS4ujunTp2O1Wr2zeRISEggKCgIgIyODmTNn4nK5MAyD\nhQsXcubMGT+fhYiISNvRHvpmw2xkCDYlJaXBDS5cuNBit+UpKChg9OjRZGdnExsb2yLHFBGRtkv9\nylen11BERJpTc/UrjY7Y1sy5FhER8ZeMjAzy8/Prtb/xxhuEhob6oaLW4eDBg6xZs4aKigpv26JF\ni/xYkYiItBettW/WvXVERKTVysjI8HcJrdKsWbP47ne/S7du3fxdioiItDOttW9WsBUREQkwnTt3\n5qGHHvJ3GSIiIq2Ggq2IiEiA6d69O6+//jp9+/b1XtSxsWtjiIiItAdNBts777wTt9t9cQObjeuu\nu47/+q//4mtf+5pPixMREZH6nE4nR44c4ciRI942BVsREWnPmgy2t9xyC3fddRfJycl89NFHrFu3\njvHjx7NgwQJWr17dEjWKiIhILYsWLeLTTz/ls88+Iz4+nr59+/q7JBEREb9qMtgeOXKEESNGADBs\n2DBeeeUVhg8fztKlS31enIiINO39/PfZeHhjs+7z/uvv59u9v92s+6ytoqKCcePGsXnz5gaX5+bm\nkpWVxYsvvtjgcqfTyZw5czh+/DiVlZU88cQTjB492mf1tjYrV67kd7/7HQMGDGD58uWMGzeORx99\n1N9liYhINfXNLd83Nxlsg4ODWb16NTfddBMfffQRwcHB7Nu3r870ZBERkZb0/vvvEx0dzfPPP8+F\nCxe477772lWw/d3vfsfbb7+NzWbD6XQyceJEBVsREfErf/fNTQbbX/ziF7z22mtkZ2eTlJTEf//3\nf/Pxxx+zcOHClqhPRESa8O3e3/bpX3AbsmHDBrZs2UJ5eTmFhYVMmTKF7OxsDh8+zIwZMygtLeWt\nt94iODiYXr16MX/+fCorK5k+fTpFRUXExcV593Xo0CEWLFgAQHR0NJmZmU0e/6677mLs2LEAmKaJ\n1Wr1zYm2UqZpYrNVdeFBQUEEBQX5uSIREalNfXPL981NBttrrrmGxx9/3HsT+LKyMm677TafFyYi\nIq2bw+Fg+fLlbNq0iRUrVrB27Vpyc3NZsWIF+fn5bNy4EbvdTmZmJmvWrAEgKSmJadOmsWfPHnJz\ncwFIS0sjMzOTxMRE1q1bx5tvvun9CkxjwsPDASgpKeHpp59m6tSpvj3ZVubmm2/m6aef5uabbyYv\nL4+bbrrJ3yWJiEgr0J775iaDbUZGBtu2baNr166YpolhGGRlZbVEbSIi0orVXLAoIiKC3r17YxgG\nUVFRlJWVkZiYiN1uB2DIkCHk5OQAMHLkSAAGDhzoHXHMz89n3rx5QNX3c3r16nVFxz958iRPPvkk\nDz/8MN/61rea89RavZkzZ7J161by8/MZP368/uAsIiJA++6bmwy2H3/8MX/5y1+wWCwtUY+IiASI\nmvunNtSen59PaWkpYWFh7Ny5k/j4eAzDYPfu3YwZM4YDBw7gcrkAiI+PZ/HixcTExJCXl0dhYWGT\nxz579iw/+MEPSE9PZ/jw4c16Xq3Zli1buOOOO7x/Zbfb7Zw6dYo1a9YwYcIEP1cnIiL+1p775iaD\nbc+ePamoqKBDhw4tUY+IiAQ4q9VKamoqU6ZMwWKxEBcXx/Tp07FarcyePZtJkyaRkJDg/V5oRkYG\nM2fOxOVyYRgGCxcu5MyZM5c9xmuvvUZRURGvvPIKr7zyCgBvvPEGoaGhPj8/f7pw4QLAFf0DQ0RE\npEZ76JsN0zTNy60wceJE/vWvf9GzZ8+qDVpwKnJBQQGjR48mOzub2NjYFjmmiIi0XW2lX3nllVf4\nyU9+4n3+wgsv8NOf/rRFjt1WXkMREWkdmqtfaXLE9oUXXvi3dy4iIvJVZGRkkJ+fX6+9PYzONmTd\nunW8++675Ofns23bNgA8Hg9Op7PFgq2IiLRvrbVvbjTYrlu3joceeoisrKx6c7WfeeYZnxcmIiKS\nkZHh7xJalXvvvZfhw4ezbNkyfvzjHwNgsVjo1KmTnysTEZH2orX2zY0G227dugGQkJDQYsWIiIhI\n44KDg4mNjSU9PZ19+/bhcrkwTZO8vDzuuecef5cnIiLiN40G25rLPt93333s3bvXex9bERER8a/U\n1FScTidnzpzB7XbTtWtXBVsREWnXmvyObWpqKufOnfOO4BqGwZAhQ3xemIiIiDSspKSEVatW8bOf\n/Yy0tDS+//3v+7skERERv2oy2J49e7bFroIsIiIiTbPZqrrvsrIyQkNDcTqdfq5IRETEv5oMtvHx\n8Zw+fZprr722JeoREZGrdOG99/hy/YZm3WfU+AeIvu++Zt1nbRUVFYwbN47Nmzc3uDw3N5esrCxe\nfPHFBpe73W5+/vOfc+TIEaxWK4sWLSIuLs5n9bY23/jGN1i6dCl9+vThO9/5Dna73d8liYhILeqb\nW75vbjLY5uXlcccdd9CxY0dvW05OTpM7/uKLL3jggQdYvnw5NpuNWbNmYRgG119/PXPnzsVisbB0\n6VK2bt2KzWZjzpw5DBgw4KudjYiItAtbtmwBICsri9zcXBYtWsSrr77q56pazuTJk72Pb7vtNnr1\n6uW/YkRERPB/39xksP3Tn/501Tt1Op2kp6d772O0aNEipk6dyrBhw0hPTyc7O5uYmBh27tzJunXr\nOHnyJKmpqaxfv/7qz0BEpJ2Lvu8+n/4FtyEbNmxgy5YtlJeXU1hYyJQpU8jOzubw4cPMmDGD0tJS\n3nrrLYKDg+nVqxfz58+nsrKS6dOnU1RUVOcvuIcOHWLBggVV5xIdTWZmZpPHHzNmDLfffjsAJ06c\noHPnzj45z9Zm9uzZjS5btGhRC1YiIiKXo7655fvmRoPtK6+8wk9+8hOeeeaZevexfeGFFy6708WL\nFzNx4kRef/11APbv38/QoUMBGDVqFNu3byc+Pp6UlBQMwyAmJga32825c+fqjAyLiEjr5XA4WL58\nOZs2bWLFihWsXbuW3NxcVqxYQX5+Phs3bsRut5OZmcmaNWsASEpKYtq0aezZs4fc3FwA0tLSyMzM\nJDExkXXr1vHmm28yYsSIJo9vs9mYOXMmf/7zn/nVr37l03NtLe6++24AVq9ezU033cTgwYPZu3cv\ne/fu9XNlIiLSGrTnvrnRYPv1r38dgIkTJ17VDjds2EDHjh0ZOXKkN9iapukNx+Hh4RQXF1NSUkJ0\ndLR3u5p2BVsRkcDQt29fACIiIujduzeGYRAVFUVZWRmJiYne730OGTLE+xWWmlvJDRw40HsBpPz8\nfObNmwdUzfi5mmm1ixcvZvr06XznO99h06ZNhIWFNdfptUo1r99vfvMbfvjDHwJw880366rIIiIC\ntO++udFg26dPH6Aqwefk5HhvAn/mzBnv6GtD1q9fj2EYfPjhhxw8eJCZM2dy7tw573KHw0FkZCR2\nux2Hw1GnPSIiojnOSUREWsCls3lqt+fn51NaWkpYWBg7d+4kPj4ewzDYvXs3Y8aM4cCBA7hcLqDq\nIoWLFy8mJiaGvLw8CgsLmzz2e++9x+nTp/nRj35Ehw4dMAwDi8XSrOfXmpWWlvLhhx/Sv39/Pvro\noyu+KrKufyEi0ra15765ye/YPv300/Tq1YtPP/2UkJAQOnTocNn13377be/j733ve2RkZPD888+T\nm5vLsGHD2LZtG7fccgtxcXE8//zzPProo5w6dQqPx6PRWhGRNsBqtZKamsqUKVOwWCzExcUxffp0\nrFYrs2fPZtKkSSQkJBAUFARARkYGM2fOxOVyYRgGCxcu5MyZM5c9xp133sns2bOZPHkyLpeLOXPm\neK/r0B4sXLiQl156iWeffZbevXs3eoXK2nT9CxGR9qs99M2GaZrm5VaYMmUKv/3tb5k9ezYLFy5k\n8uTJrF69+op2XhNsLRYLaWlpOJ1OEhISWLBgAVarlZdffplt27bh8XiYPXs2ycnJdbYvKChg9OjR\nZGdnExsb+++fpYiICIHfr7hcLmw2G5WVlfWWBQcHX3bbBQsWcNttt/H666+TkZHBI488wrZt2zAM\ng7/85S/e61+Ul5fz+OOPA3DfffexfPnyOn94DvTXUEREWpfm6leaHLGFqnsalZWVYRgGpaWlV7zz\nlStXeh+vWrWq3vLU1FRSU1Ob3E9Zcf0OXERE2r6MjAzy8/Prtb/xxhvtaoS2xsyZM3nhhRe46667\nvNPNaq5jkZ2d3eh2uv6FiIg0l9baNzcZbCdPnsxbb73Frbfeym233cbNN9/cEnXVcSj3NNf3TWjx\n44qIiH9lZGT4u4RWpeauBJs3b76q7XT9CxERaS6ttW9uMthWVFR4pySNGzfOeyWtlvTpzlO4HnZj\nC7a2+LFFRERaiwkTJjR6YZCsrKxGt9P1L0REpK1rMtiuXbuWb3/72wB+CbUAFQ4Xn+46zY23xvjl\n+CIiIq3BkiVLmm1fM2fOJC0tjSVLlpCQkMDYsWOxWq0kJyczYcIEPB4P6enpzXY8ERERX2oy2FZW\nVnLfffcRHx/vvVxzzVSolhJ9XRh7so/Rd8R1jf6lWkREpK3r3r07AJ9//jn/93//573Nz5kzZ5g/\nf/4V7aO5rn8hIiLSmjQZbKdPn94SdVzWjSOuY9/vL1Bw8Dw9btSUKBERad9mzpzJHXfcwT/+8Q+6\ndu16VRd2FBERaYsaDbZTp07ll7/8JUOHDm3JehoUP6AL+Tml7Nl8TMFWROQSn/ztJAe3n2zWffa9\n9Tr63HJds+6ztoqKCsaNG9foRZByc3PJyspq8v6sX3zxBQ888ADLly+nd+/evii1VQoNDeVHP/oR\n//rXv1i0aBEPP/ywv0sSEZFa1De3fN9saWxB7asl+pvFZtD/tu58vu8Lzp9yNL2BiIi0eU6nk/T0\n9HZ52x/TNCksLKS0tJTS0lK+/PJLf5ckIiLi17650RHbY8eONXqRimeeecZnBTXmayO7k/eHz/l4\ncwG3PXxDix9fRKS16nOLb/+C25ANGzawZcsWysvLKSwsZMqUKWRnZ3P48GFmzJhBaWkpb731FsHB\nwfTq1Yv58+dTWVnJ9OnTKSoqIi4uzruvQ4cOsWDBAgCio6PJzMy8ohoWL17MxIkTvfdlbU+eeuop\n/vKXv/Dtb3+bMWPGcO+99/q7JBERqUV9c8v3zY0G29DQUOLj41uylssKiwwmaei1fPK3kwy7N4HQ\n8CB/lyQi0q45HA6WL1/Opk2bWLFiBWvXriU3N5cVK1aQn5/Pxo0bsdvtZGZmsmbNGgCSkpKYNm0a\ne/bsITc3F4C0tDQyMzNJTExk3bp1vPnmm4wYMeKyx96wYQMdO3Zk5MiR7SrYvvvuu9xzzz0MGTKE\nIUOGADB69Gg/VyUiIq1Fe+6bGw22nTt35v7772/JWpo0cHQPDu44yYGcEwwe29Pf5YiItGt9+/YF\nICIigt69e2MYBlFRUZSVlZGYmOi9RdyQIUPIyckBYOTIkQAMHDgQm62qC8rPz2fevHlA1RSmXr16\nNXns9evXYxgGH374IQcPHmTmzJm8+uqrdOnSpblPs1U5dOgQy5Yt49Zbb2XixIn06dPH3yWJiEgr\n0p775kaDbb9+/VqkgKvRqbud2D7X8PGWAgaO6YHV2uhXhEVExMcau/2aYRjk5+dTWlpKWFgYO3fu\nJD4+HsMw2L17N2PGjOHAgQO4XC4A4uPjWbx4MTExMeTl5VFYWNjksd9++23v4+9973tkZGS0+VAL\n8LOf/YwZM2aQnZ3NkiVLKCoqYvz48dxzzz106NDB3+WJiIiftee+udFgO3PmzBYr4moMHN2DTf/v\nY/L/cYakId38XY6IiFzCarWSmprKlClTsFgsxMXFMX36dKxWK7Nnz2bSpEkkJCQQFFT1lZKMjAxm\nzpyJy+XCMAwWLlzImTNn/HwWrVdQUBB33XUXd911F6dPn2blypXcfvvt3uljIiIil2oPfbNhmqbp\n7yIaU1BQwOjRo8nOziY2NhYA02PyzrxcgkOtPDgrudG/SoiIiFyqoX4lEFVUVPDnP/+Z9957D4fD\nwfjx43nwwQdb5Nht5TUUEZHWobn6lUZHbFsrw2Iw4I5YtmV9yql/FnFd7yh/lyQiIj6SkZFBfn5+\nvfY33nijXd7mJzc3l/fee4/c3FxGjx7NjBkzSEpK8ndZIiLSjrTWvjnggi3ADbd0I/f9f7In+5iC\nrYhIG5aRkeHvElqVpUuX8p3vfId58+YRHBzs73JERKQdaq19c0AG2+BQGzemxLD7z0cp+qKMyE66\nYIaIiLR9K1eu9HcJIiIirVLAXla4/+2xYBjs3Xrc36WIiIiIiIiIHwVssI3oGErvwV04kHOCynKX\nv8sRERERERERPwnYYAtVt/6pLHPxyYcn/V2KiIiIiIiI+ElAB9tu8VFcGx/Jns0FmJ5We9ciERER\nERER8aGADrZQNWpbVFjGv/ae9XcpIiIiIiIi4gcBH2x739QF+zUh7Nl8zN+liIiIiIiIiB8EfLC1\nWC30vyOW44cuUHis2N/liIiIiIiISAsL+GALcOOtMdiCLXysUVsREREREZF2x+aLnbrdbn7+859z\n5MgRrFYrixYtwjRNZs2ahWEYXH/99cydOxeLxcLSpUvZunUrNpuNOXPmMGDAgKs+Xmh4EH2HX8f+\n7Se45b7ehEeF+OCsREREREREpDXySbDdsmULAFlZWeTm5nqD7dSpUxk2bBjp6elkZ2cTExPDzp07\nWbduHSdPniQ1NZX169f/W8cc8PUe7P3gOPu2HWfYtxKa83RERERERESkFfPJVOQxY8bw7LPPAnDi\nxAk6d+7M/v37GTp0KACjRo1ix44d5OXlkZKSgmEYxMTE4Ha7OXfuXL39eT7f0+Qxo68No1f/Tuzf\ndhyX0928JyQiIiIiIiKtls++Y2uz2Zg5cybPPvssY8eOxTRNDMMAIDw8nOLiYkpKSrDb7d5tatov\ndfyZZ3Ad+rDJYw4c3YOyYief7jzdfCciIiIiIiIirZpPLx61ePFi/vjHP5KWlkZFRYW33eFwEBkZ\nid1ux+Fw1GmPiIiot5/KYjj6yCO4Dm6/7PG633ANnbrb+XjzMUzTbL4TERERERERkVbLJ8H2vffe\nY9myZQB06NABwzDo168fubm5AGzbto3k5GQGDx5MTk4OHo+HEydO4PF46NixY739xaTNoLLYwueP\n/ADn/r82elzDMBg4OpYvjjsoOHTeF6cmIiIiIiIirYxPgu2dd97JgQMHmDx5Mo8++ihz5swhPT2d\nl19+mQkTJuB0Ohk7diz9+vUjOTmZCRMmkJqaSnp6eoP76zByLHEvPYer1MLR7z+G8+OtjR77+iHX\n0iEiiI+zdesfERERERGR9sAnV0UOCwvjpZdeqte+atWqem2pqamkpqY2vc/R99JjqY1jT03n80d/\nRM/XXyHoptH11rMFWek3qju7Nv2LC6dLib427N87CREREREREQkIPv2ObXMLu+2bxL36Iu4KC58/\n9hMq//7HBtfrd1ssFpvBns0atRUREREREWnrAirYAnRIuYu411/G47Lw+eNPU7nz9/XWCYsMJmnI\ntXzy4UnKHU4/VCkiIiIiIiItJeCCLUCHW8YQ9+YrmB4Ln/9oGhUf/q7eOgNH98BV6eFAzgk/VCgi\nItK6uN1uZs+ezcSJE5k8eTJHjx7l888/Z9KkSTz88MPMnTsXj8cDwNKlS3nwwQeZOHEiH3/8sZ8r\nFxERaVpABluA0CF30HP5MkwsfP7EdMpz3quzvHNsBN1viGbv1gLcbo+fqhQREWkdtmzZAkBWVhZP\nP/00ixYtYtGiRUydOpV33nkH0zTJzs5m//797Ny5k3Xr1rFkyRLmzZvn58pFRESaFrDBFiBk8Ch6\nrvg1htXg6JOzKP9gfZ3lA0fHUXK+gn9+VOinCkVERFqHMWPG8OyzzwJw4sQJOnfuzP79+xk6dCgA\no0aNYseOHeTl5ZGSkoJhGMTExOB2uzl37pw/SxcREWlSQAdbgJCBI+j51goMm8HRp39G2ea13mW9\n+nUiqksH9ujWPyIiIthsNmbOnMmzzz7L2LFjMU0TwzAACA8Pp7i4mJKSEux2u3ebmnYREZHWLOCD\nLUBwv2H0XLUSS7DB0anplP1pNQCGxWDA13tw+kgRp/75pZ+rFBER8b/Fixfzxz/+kbS0NCoqKrzt\nDoeDyMhI7HY7DoejTntERIQ/ShUREblibSLYAgT3Tabnqnewhlo4+tN5lP5hJQB9hncjuINNo7Yi\nItKuvffeeyxbtgyADh06YBgG/fr1Izc3F4Bt27aRnJzM4MGDycnJwePxcOLECTweDx07dvRn6SIi\nIk2y+buA5hR0w030fDuLo9+dyLH/WkgPt4uwe77PjSkx7Mk+RvG5ciI6hvq7TBERkRZ35513Mnv2\nbCZPnozL5WLOnDn07t2btLQ0lixZQkJCAmPHjsVqtZKcnMyECRPweDykp6f7u3QREZEmtalgCxB0\n/QDistZx9OGHODpzMT1cbgbc8V32ZB9j75YCRoxP9HeJIiIiLS4sLIyXXnqpXvuqVavqtaWmppKa\nmtoSZYmIiDSLgJiKXOIsuar1g+K/Rs/V6wmOsnLsZ7/A2PIWCYO6cGD7CSrLXT6qUkRERERERPwh\nIILttC3TOFZ8dd+RtfXqS9zajQRfY6Ug7UWS2ElFqYtDfzvloypFRERERETEHwIi2J4vP8/kTZP5\nx+l/XNV2ttgkeq75H0I626j45X/TOaKUPZuPYXpMH1UqIiLSPlR6Kv1dgoiIiFdABNuXvv4SkSGR\nPPanx/jf/P+9qm2t3ROJW/M+oV1sXJu7mi/PlPH5vi98VKmIiEj7sPHwRn+XICIi4hUQwba7vTtv\n3/02g7oOYk7OHF7+6GU8pueKt7del0Dc2v/f3r3HR1HdjR//nJnZW7K5cIeIAcKlCogFEbEqtqUW\nL6jV4g/BghVa+VELUivWK2jheazFYh+hVBRb+/NSC9a2ttU+fR6qYquigIKiVkEEgyCXJCTZZG8z\n5/fH7E52k4CACZuQ7/v1mtfMnDk7e+aye+Y7Zy5/pdR8i0C0kvWP/rMVSyuEEEIc/57895Psq9+X\n680iZvkAACAASURBVGIIIYQQQDt6KnJRoIjlX1vOglcX8OCmB9levZ2FZy0kaB3e63vMHn3ou/Kv\n9Jv5M94LXsJfv3kHhk4AoJRyMynAG87op9JV5rg3nB5PT09/owK0m650w3h6PpnZMgdVw+eyP5/O\no5t8VqEbfa3OmuaWTWd8l/aWz/1k5hzS36DQKiNNZ66XjDzZS9OsrDKnvx9Q+iDpWSna2xRHQnnr\n8JCZUms5Y/tnDmRv0OxxBWjV9CuyZqWzhjN3Hfe7M7arSudX2empVG9V6dRctbtu0G4OjXLTGueD\n1LbTqWnpOWasADK3baPFztgHvP08eyGazoeGMpK57XW6PGRL5dPpsunM+WRnVpkL2EjDesv8Tt1o\nWlPpHOnlaroXNoxm7e+H2DGbbH/V5NNNxlVqaQ+a/0jvoGi8oht/XmX2my5106XTDZu6UYb0+m34\nP0nvd9n/FdpbM43zNUpLD+tmyqJ00/Wb8anM8jT5naHZV1fTZMnE0Ykn4/zizV8w/8z5uS6KEEII\n0X4CWwCf6eOuL91Fv6J+3Lf+Pj6p/YT7v3o/XUNdD+vzRrcT+dLPb2D7gg1s7zrmEDkbH0wffNrB\njhZ1o/HGB8FNAkLVBhvPmzuQdtyWcuVFUaklaRKtAKpxiJbRby5QgObXQ2vdEi23Wre9dXA4V2Lk\n6rfSqGwq8+xBo5MFDSdL2uDvuq3STurEhZPxn5K5gzb8lzaEtI3WN3zmOq+O7wdWtVixO7KL+1/M\n0x88zaSTJjGo06BcF0cIIUQH164CWwClFNcMvYbSwlJueekWJv91MkvHLj3sSjXUu5Rpy0tbuZSf\nn04HilnHzrrpcXS65a5J3ox5ZOTXqfk0tEC76zSr8S1rvFG+Y0x/VqtTc0mNPtN0OgdrkEuNqCbT\ntE4F8umD7dSwSh9kp8bdfI1bVXEfWJbeflktlxptpwImRzfk1463DZVSYBgoQ4EyUOnh9LhS7jig\nDKNh2xkKlR5pvD0zytzQ1JsuY/PpoMFx+9px3EDPcdxpjkbjoDC870tf0aAMI7VaG5r7DrovNZve\n+ORQ03Gd2qdJtUrrzOnpFsF0dp1ad5Aqr2porc64CqO5MmbtBxld5u+qmZI37BcZzcLa21Ea9qGs\n/SO9bjPnmTHcpHzNnVw6qKb/JZn/Iw37aMZ/S3rfgNR+kCqz91/kpPY/1bBKU/umapymQCkjfQFI\nxlUzh9j+n7lI2evqYL/FneXlPDL+8GYpDu2qk6/i+bXPs+j1RTx43oM5qSOEEEKItHYX2KaNLR3L\nI+c/wqzVs5jy7BQWnbuIMb0P1QrbvmReHp1xKJuj0uROkwOlZlZB06SWX0/qEPNsv1ul+ZK3x+Vp\nXObWWgaVEQQfyfe1x3Xa3h1snZuhwDEtx/GswF/AzFNncs/r97CmfA3nnnhuroskhBCiA2vX18kN\n7jKYJy56gj6FfZj1j1k8/u7jTVv4hBBCCNEqJp40kb6Ffbl33b0knESuiyOEEKIDa9eBLUCP/B48\ncv4jfLn3l/nJaz/hP9b+B0knmetiCSGEEMc9n+HjhyN/yEfVH7Hy3ytzXRwhhBAdWLsPbAHyfHnc\n95X7uGboNfzu37/jutXXUROXJ18KIYQQre3c3udyRq8zWPbmMg7EDuS6OEIIITqo4yKwBTCUwQ2n\n3cBdX7qL13a9xpRnp1BeU57rYgkhhBDHNaUUc0fOpTZRywMbH8h1cYQQQnRQx01gm3b5wMtZft5y\n9tbv5apnr+LNPW/mukhCCCHEce0Lnb/AZQMu48n3nuSjAx/lujhCCCE6oBYPbBOJBHPnzmXy5MlM\nmDCB1atXs337diZNmsTkyZOZP38+TupdqEuXLmXChAlceeWVbNq0qcXKMKrXKB6/8HHCvjDT/nsa\nf/nwLy02byGEEEI09f3h3ydgBfjZ+p/luihCCCE6oBYPbJ955hmKi4t54oknWLFiBQsWLODuu+9m\nzpw5PPHEE2itWb16NZs3b+a1115j1apVLF68mLvuuqtFy9G3qC+PX/g4p3Y7lVteuoVfvPkLeWKy\nEEII0Uq6hrrynVO+wwsfv8DaXWtzXRwhhBAdTIsHtueffz7XX389AFprTNNk8+bNjBo1CoAxY8bw\n8ssvs379es4++2yUUpSUlGDbNhUVFS1aluJgMQ+e9yDfGPANHtj4AD9a8yOiyWiLfocQQgghXFMG\nT6Ekv4Sfvv5TbMfOdXGEEEJ0IC0e2Obn5xMOh6mtrWX27NnMmTMHrTVKKW96TU0NtbW1hMPhrM/V\n1LT8k4x9po8ff+nHzBkxh+c+eo7pf5/Ovvp9Lf49QgghREcXMAP84LQf8H7l+/xxyx9zXRwhhBAd\nSKs8PGrXrl1MnTqVSy+9lIsvvhjDaPiaSCRCYWEh4XCYSCSSlV5QUNAaxUEpxfRTpnPfl+/j/Yr3\nueqvV7Fu97pW+S4hhBCiIxvXdxxf7PZFlryxhEgi8tkfEEIIIVpAiwe2+/btY9q0acydO5cJEyYA\nMHjwYNaude+3WbNmDSNHjmTEiBH885//xHEcPvnkExzHoXPnzi1dnCxf6/M1HrngERwcrvnva5j5\nvzN5r+K9Vv1OIYQQoiNRSnHT6TexP7qfFW+tyHVxhBBCdBBWS8/wgQceoLq6mmXLlrFs2TIAbrvt\nNhYuXMjixYspKytj3LhxmKbJyJEjmThxIo7jMG/evJYuSrOGdBnCn7/xZ55870keeushrvjzFVzQ\n9wK+P/z7lBaWHpMyCCGEEMezU7qdwkVlF/H/Nv8/rhh0BSXhklwXSQghxHFO6Tb8qODy8nLGjh3L\n6tWr6d27d4vPvzpezSNvP8Jj7z5Gwk5w+cDLmXHqDLrndW/x7xJCCJF7rV2vdASHuw53R3Zz8R8u\n5isnfoWfnvvTY1hCIYQQ7UlL1c0t3mLbnhT6C5k9YjaTT57M8o3LeeqDp3hm6zNMPnky04ZOoyhQ\nlOsiCiGEEG3Sqp+so0fXcvwhC3/IIpDqu8Mm/pDF1b7Z/G3Ds7wYfJ3BJSdl5TEMletFOGw6Hseu\nrcWpqcGurkHHohh5eRjhcEPn9+e6mK1Da4jVQN0+iKS66AEIFEBeZwh1auisQK5LKwQ4DtTth9rd\n7r6b1xXC3SFYBKr9/O+II9ehA9u0rqGu3Db6NqYOmcqyN5fx67d/zar3VzFt6DSuOvkqQlYo10UU\nQggh2pSS+ucp2NeTuAoTJUy1ziOuQ8SdILZ2Dy8UJ3AB3+Xtf9fwNq9nfd5naXwBRSBg4AuY+IMm\npqkwTIVhGKj0sGmkOndYpdMso2Ga1dBXpoFpGRiGQhkK0wTTiWMm6jBitZjxWoy6aoy6alRdNUQO\nYNekgtbaWpyaCHZtBCdSh11bh1NXh44lPnN9KMvEyAu6XSiAmR7OC2KEgu54fsgdzg9ihEIY+UGM\nvBBmKl0F88AXBCsIviDKFwRfCHxBtBUk6Vgkk5pk3CGR6ifjDomYTSJuk4xlDrt9J5m6ME877gG/\n40AyBvEIxOvdLlGX6kfRiSjEo6hkFJ2IQTKe+pwGjdtHY5LENBKYKt1PYPnADFiYAT++PD9mXggr\nLx+rIIxVWIhV1BmzoBNWQTFGfjFmuDNGQWd3OXPAtp3UOrSxE+66s2MJkvEEybhNMpogmbBJxpLu\neKqzkzaGAZalsXxgmRrLdNxx08GyHEyVxCKJibuOLBXHcNxOOUl0IgF2Ep2Ig51AJ5Jgmih/EBUI\nogIhlD+ECoZQgTwIuH3lD4Hpd/cRyw9mwD2hYJiHvdw6mcSpqcCprsSpqsCpqcKprsKpqcGprcap\nrcGJ1Lq/h0gdTl2929XHcKIxnGgCZZmoQAAjEESFQqhQHkYoD5VXgJFXgMoLo4Lusmh/EO3zgz+I\n9gXQlh9tpfs+HNMPpoX2+TF8PpTfjzKbeQyQnYRoJdRVQn2FG7zWV0JdRcZ4lZum3deNKRwslcBU\ncSwTzHARVrgIs7ALZrgzqqCHG/SGu0M4NZzfHQLh7HWmNToWw6mvR8fjKL8fIxRCBQLem1+arGet\ncZKaRMwmHks2/D5jSe93mojaJOrjJOuj2LaDYVkYpoWyrNR/nvL+y7z/QW88NZzqG4ZC4UA8BvEo\nxKIQj2OYYBrK7ZvK/Z+1VPMnF5tLam75DAMMA2WaGX3T3W5e30AZhrtfN+pjGAddb5+HBLYZTiw4\nkbvPuZtrhl7Dkg1L+K8N/8Xj7z7O/x32f7l80OX4DF+uiyiEEEK0CT3++d/0OMiBiaNMklaooTND\nJK1gVpptpoeDJKw86s0g2jDRysRRBlqZaGWkOrPp+BEcyDcVTnUlKCeBacexnBimE8M0YvgKolgF\nMSxi+FQUS0XxGVH8RhSfGcUyYji2iWMb2I6JY1s4TmrYMXAcE+0Y2LUmTrWJ1u40SJffQas6NFFQ\n1ak0hWP4sc0AtunHzhh2zAC2eWStoUrbGHYcQycBhRveKrdToL3hIJpgajg7n1bKPVJUR/Cs0Xiq\nO+QbHOPAnlSHuw100j0o1xqFRmkHt5Q6leagtIZU3013Gqan82ud6rv53PXqc9en4XPXcaqv1dHt\nQ8qxj2D/86W6VCOJdjCcBKaTwLATmE4cw0lg2PHUMqS+gzqgDsi4Y1CT2nypraQapimlG4ISldkw\nqUBrtLtqId3P+u02DGvygXwvXRm6YbfJcycpQ+Fg4GC5nbJwEiZOwkTXWDiGhaNMtLIOsp4coD7V\nHa0AUJLqjsCn2aOGE8fUCQydwHS2Yzhb3O3hJN3OTqDsBEYy0bDdnCSOYaV+nwFsXxDHCmBbqXHD\nT9LwY6uj38eOCe00LKdOojKGDSc1nhpuGLcxnCQavP/r9H+yk/pfzhrP+L92VHb+zPQD0coWWSQJ\nbJsxqNMgloxdwht73uDn63/OwrUL+c07v+G6L17HBf0uwDiSP3ghhBDiONTjlpspSb/N4CAHySiF\ndpLc/8YSIslPufGkqfjtJMqOQiIKyQgkKyBZB8kY2nG8VkGt3RZC7biBS2YaWuPYGq0VjjZwHJ0K\nHhVaGzhauQ2U2sA2fehAPk4gD8efh+3Pw7ZC2FYQ2whiGwGS+Egk8kkkC0gmDRIJg0QS6hKKRAKv\nc+yMxTRT3SEYhrtqlJExjBuEpIM3L0DTDqaysVSSAAkslcQkgkUVpo5jEcfScSxdj+nEsHQMy4li\n6ihmsh7LSXV2HaZdj3KiKMdGKYUK5qFC+ahQGJVX6Hb5xW5X0BmjoAuqoCsqv9BtLUu3mvn9KJ8f\n5fe5aT4/2nIPHe24jV0XJRGpJxmJkqyLkqyPkayLYkfjJOtjbutnNJEaj2LX12NH67FjCex4Aidu\nk7QdnKSDbafDWHeFaaVwTwSkA24jNc0dRqnsaSoVjKPQygBMNAqlwCSOqeswSWKQxNRJTDvVx3Zb\nn0liKjuVlh623RZpbLdTNibuNtNYOOmTDobbTwfNbhDtx1E+ksqHo3zYysJWPmxtucNYONoiqQPY\n2iDpGKnA021V1xkt7Fprd1jrVHr6t6BTaQ3jOvX78IZxA1EMhVLuFQ8q1ZKWbl3DdFvclGm5rWqW\n5bbAmZbbwqbMhp049fs2TYVh2O76JIlBApMYhnbXteHEMOw6jGQdRjyCSridkYi448kYhmODY2M4\nNsq23d+/o3AcBY6JYwTQBND40fhwtAnawLFB29rtEkmcRALicbcVvBGNgWP43GDb9Kf6ARx/EO0P\n4fiCOJYfbVo4pg9HmTg+C0cZ2IRIqjBOeg9QFrby4+DDIIHlRLG0e0LMcmL47RrMpHuizLTjGMk4\nZjKOkYhhxKMYiRim3ahz4t6we2JDNXNSz8AxLVTAh/Jb4LdQfhN8JsoywTLAUmC5rbeYgKHAAG2A\no9wg21E+bPw4pIcblsfB3SdtLBx8qb6JowNuujZT4ya2NlO/NAcDGwMbhe0GvdhukIyNoWPusG4I\nklVGAK10EkM7KG1TET3kmbDDJoHtIQzvPpxHzn+Ef+78J/+14b+4+aWb+fXbv2b2iNmcc8I5rdKE\nLoQQQrQHBV/7GkWH+ZCPC0aVMO2/p1E21GDGqbNauWStx7YdkjGbZMJBqfQlgW7goAyFoTLGj/Nj\nBDNkQMhHqEtBrosi2qNE1L1XO1rl9uur3CC7oCeEe7r3bBtH1pCkHQcdj7uXC8di6GgUnUy6l0wH\ng6hgECMQAJ/v6H6fyThE9rqdYYIVSt0qkOqs4CHv4dXJJE596rLuugi6vh6nrs5Ni7it80Z+vtvl\n5WUNH+qS53bLsd3bIuwY5Tu2wyVrPvcsJbD9DEopzul9DmedcBZ/2/Y3lr65lOtWX8eI7iO4fsT1\njOgxItdFFEIIIdq003ueztjSsTz89sNcNvCydvv2AdM0MPMM5BFJQnxOPvc+cgp6tNgslWG4wWuw\nle7ZtvxQdILbHQVlWZgFBZgFcjIIcE8O+POAPMiPtMwsW2QuHYChDC4su5A/feNP3DH6DnbU7ODq\nv13N91d/n39X/DvXxRNCCCHatBtOu4GEk2DpG0tzXRQhhBDHIQlsj5DP8PF/vvB/+Otlf+X6Edez\nYc8GrvjzFdz80s28X/k+bfi1wEIIIUTOlBaWctVJV/HHLX/k3f3v5ro4QgghjjMS2B6lPF8e3znl\nOzx3+XNMGzqN1dtX881nvsmXV36ZG164gSfefYL3K9/HyXjCnRBCCNGRXXvqtRQHilm0bpGcCBZC\nCNGi5B7bz6koUMSc0+bwrcHf4qXyl1j36TrW7V7H/2z/H2/6ad1PY2TPkYzsMZJBnQZhfq5XFAgh\nhBBHJpFIcOutt7Jz507i8TgzZ85kwIAB3HzzzSilGDhwIPPnz8cwDJYuXcoLL7yAZVnceuutDBs2\nrMXKUegv5Htf/B7/sfY/+MfH/2Bs6dgWm7cQQoiOTQLbFtI11JXLBl7GZQMvA2Bn7U7W7V7nBbr/\n+PgfABT4CxjRfQSn9zydkT1G8oXOX8AyZDMIIYRoPc888wzFxcUsWrSIqqoqvvGNb3DSSScxZ84c\nzjjjDObNm8fq1aspKSnhtddeY9WqVezatYtZs2bx+9//vkXLMmHQBJ5870kWr1vMmBPG4DPlHfFC\nCCE+P4moWskJ4RM4YcAJXDrgUgB2R3Z7Qe76T9fzYvmLAOT78hnefTgje4xkZM+RDO4yGJ8hlbwQ\nQoiWc/755zNu3DgAtNaYpsnmzZsZNWoUAGPGjOFf//oX/fr14+yzz0YpRUlJCbZtU1FRQef0+2pb\ngGVY3Hj6jcz835k88d4TXD3k6habtxBCiI5LAttjpGd+T8aXjWd82XgA9tbt9QLddZ+u4+c7fw5A\nyAplBbpDuwyVs9lCCCE+l/z8fABqa2uZPXs2c+bM4Z577vHei5ifn09NTQ21tbUUFxdnfa6mpqZF\nA1uAs084m7NKzmL5xuVc0v8SOgU7tej8hRBCdDwS2OZIt7xuXNDvAi7odwEA++v3s/7T9az7dB2v\n736d+9+4HwC/4advUV/6F/WnX3E/+hf1p39xf0oLSiXgFUIIcdh27drFddddx+TJk7n44otZtGiR\nNy0SiVBYWEg4HCYSiWSlF7TSOxdvHHkjE/48gWVvLuO20be1yncIIYToOCSwbSO6hLrw9b5f5+t9\nvw5AZbSSDZ9uYOPejWw9sJVN+zbxt4/+hsZ9iqSpTEoLSykrKqOsqIz+xf0pKyqjb1FfQlYol4si\nhBCijdm3bx/Tpk1j3rx5nHnmmQAMHjyYtWvXcsYZZ7BmzRpGjx5NaWkpixYtYvr06ezevRvHcVq8\ntTZtQKcBTBg0gVXvr2LSSZMoKy5rle8RQgjRMUhg20Z1CnZibJ+xjO3T8MTI+mQ9Hx34iK0HtvJh\n1Yd8eOBDtlZt5YWPX8DWNgAKxQnhEygrLqN/UX/Kisu84DfsD+dqcYQQQuTQAw88QHV1NcuWLWPZ\nsmUA3HbbbSxcuJDFixdTVlbGuHHjME2TkSNHMnHiRBzHYd68ea1aru998Xs8++Gz3LvuXpZ9bVmr\nfpcQQojjmwS27UjICnFyl5M5ucvJWelxO86O6h3ZAe+BrbzyySsknISXr0deD691t3dBb0ryS+gV\n7kWv/F4U+gu9e62EEEIcX26//XZuv/32JumPPfZYk7RZs2Yxa9asY1EsOgc7c+2wa/nZ+p/x8s6X\n+dIJXzom3yuEEOL4I4HtccBv+hnQaQADOg3ISk86ScpryvnwQEPr7ocHPuT3H/ye+mR9Vt58Xz69\n8ntREi7x+unAtyS/hC6hLhjKOJaLJYQQogOYfPJkVr6/kkXrFrGq1yp5BZ4QQoijIrXHccwyLPoW\n9aVvUV++yle9dK01+6P72VW7i08inzTpv/HpG9QkarLm5TN89Mrv5QW6Xj+V1jO/p7ymSAghxBHz\nm35uOO0GfvDCD5j5vzP53he/x/Duw3NdLCGEEO2MBLYdkFKKrqGudA115ZRupzSbpyZew67IrmaD\n35d2vsS++n3Z80TRLdSNbnnd6J7Xne553ekWahhOd3LJsxBCiMbGlo5l7si5PPz2w0x9biqjeo5i\nxrAZnN7zdKkzhBBCHBYJbEWzCvwFFPgLGNRpULPTY3aM3ZHdfFL7idtPBb376vfxcc3HbNizgQOx\nA00+FzADXsDbLa8b3ULd6JHXo0lAnOfLa+1FFEII0UYopZg6ZCoTBk3gqfef4pHNjzD979MZ3n04\n1w67lrNKzpIAVwghxCFJYCuOSsAM0KewD30K+xw0T8yOsbduL3vq9rCnfk/DcN0e9tbv5b2K91hT\nt6bJ/b4AYV/YC367BLvQOdiZLqFUPzXeOeQOB61gay6qEEKIYyTPl8fUIVOZeNJEnv7gaX719q+Y\n+b8zGdplKNcOu5Yvn/hlCXCFEEI0SwJb0WoCZoDeBb3pXdD7oHm01kQSEfbUpwLeRsHvnro9vLXv\nLSqiFUQSkWbnkWflZQW+6a5LqEuToLgoUCQPwRJCiDYuYAaYdNIkJgycwDNbn2HFWyuY/fxsBnUa\nxLXDruW8PufJf7kQQogsEtiKnFJKEfaHCfvDlBWVHTJvfbKeymglFdEKKqIV7K/fz/7ofm+4IlrB\nztqdvLXvLSqjld67fTOZyqRTsBOdgp3oHOjsDWeOpwPjTsFOEggLIUQO+Uwf3xz0TS4dcCnPbnuW\nhzY9xI0v3khZURnfHfZdzu97vjxFWQghBNCKge3GjRu59957efTRR9m+fTs333wzSikGDhzI/Pnz\nMQyDpUuX8sILL2BZFrfeeivDhg1rreKI40DIChEKhygJl3xmXkc7HIgdOGgQXBmtpDJWybsV71IR\nraAmXtPsfAxlUBwoplMgIwDOCHwbB8hF/iJ8pjwdWgghWpJlWFzS/xIu6ncR/7P9f1i+aTm3vHQL\nv3zzl3znlO8wvv94eTK/EEJ0cK0S2D700EM888wzhEIhAO6++27mzJnDGWecwbx581i9ejUlJSW8\n9tprrFq1il27djFr1ix+//vft0ZxRAdkKMMLNvvT/zPzJ5wEVdEqKqIVVMYqqahP9aMVbhCcain+\noPIDKmOVzT4YKy1khSj0F1IYKKTIX5Q93Ey/0F9IUaCIsC+MaZgtuRqEEOK4Yhom5/c7n6/3/TrP\n73ie5ZuWM+/leTyw8QGmnzKdbwz4Bn7Tn+tiCiGEyIFWCWxLS0tZsmQJN910EwCbN29m1KhRAIwZ\nM4Z//etf9OvXj7PPPhulFCUlJdi2TUVFBZ07d26NIglxSD7D5z6lOa/bYeVPOkmqYlUNQW+sgqpo\nFdXxag7EDmT1d9TsoHp/NTXxmmYflJWmcC/LTge66X6Rv8jtpzt/EcXB4oYAOVAkLRVCiA7FUAZj\n+4zlq6Vf5aWdL7F843IWvLqA5RuXc83Qa/jmoG8SskK5LqYQQohjqFUC23HjxlFeXu6Na629pxjm\n5+dTU1NDbW0txcXFXp50ugS2oj2wDMt7F/CRiNvxrKC3OlbNgfiBrH7m9N2R3d54c/cMp+X78psE\nwMWBYi84Lg4UZ00r9BfKZdNCiHZPKcWY3mM454RzeHXXqyzftJx7Xr+Hh956iG8P+TYTvzBRXh8n\nhBAdxDF54oJhNDx8JxKJUFhYSDgcJhKJZKUXFBQci+IIkTN+039UAbHWmtpELQdiBzgQP8CBaKof\nO0BVrMpNjzWk7Y7s9sYd7Rx0vkEzSKG/kAJ/gXdZdIG/oGk/NS3dFfgLyPfly2s3hBBtglKKM0vO\n5MySM1m3ex0PbnqQxesX8/DbD3NRv4s4qfNJDCgeQP/i/hLoCiHEceqYBLaDBw9m7dq1nHHGGaxZ\ns4bRo0dTWlrKokWLmD59Ort378ZxHGmtFeIglFIU+Aso8BfQm4O/PqkxRzteQFwdq6YqVkVVrIqa\neI3XYlyTqPFaivfU7WFL1Raq49XUxmvR6IPO21CGF/ymg90CfwFhX9jt+8MU+FJ9f0Gzw/I0UyFE\nSxvZcyQje45k496NrNi0gqc/eJqoHQXcWz5OCJ/AwE4DGVA8gEGdBjGw00BKC0vllg4hhGjnjslR\n5Y9+9CPuuOMOFi9eTFlZGePGjcM0TUaOHMnEiRNxHId58+Ydi6II0aEYyvACT47wggjbsalN1HpB\ncFYwnB7O6GriNeyp20NNvIbaRO0h7ydOC1khL8hNB8KNg+J0oJzvy/cC57Av7E2Ty6mFEM05tdup\nLBm7BNux2Vm7kw8qP+D9qvfZUrmFD6o+YE35Gu8WD5/ho19RPwYUD2Bgp4EMLB7IwE4D6ZXfS65M\nEUKIVqK1pi5Zx566PS0yv1YLbHv37s3KlSsB6NevH4899liTPLNmzWLWrFmtVQQhxOdgGqZ3T+7R\nSDgJIvEINfEaahI11MZrqUnUuIFvarg2XusFwjXxGqpiVZTXlrufideQcBKf+T0BM+AFvfm+fC8g\nbpyW70/1M9NT/TwrT55ILcRxyjRMSgtLKS0sZWyfsV56zI6x7cA2Pqj8gA+qPmBL5RY27NnAxXQr\nbQAAE/NJREFUs9ue9fLk+/K9YDfdwjugeACdgp1ysShCCNEmOdpxGz1Sz4xpfItc5oNVM9OrY9Uk\ndRJddfArBI+EXAcohGgVPsNHcbCY4mDxZ2c+iJgdozZeS20i1cWb9iOJCDWJGjeITtQQSUTYXr2d\nSCLi5TvUJdVpeVae1wqcbhHO9+UT9mUHyZmtxY3H83x5GMr4zO8SQuRewAxwUueTOKnzSVnp1fFq\ntlZtdQPeVND794/+zlPxp7w8XUNd6V/cnwHFA7yuf3F/CvzyrBAhRG5ordlXv4/t1dvZUbODHdU7\n2F23u+mzVg5ySNTcsVKzaVoTSUbcIDYVpFbHqg95rJX5kNPCQCED8wZmve3DrrL5KT89ouVtjgS2\nQog2K2AGCIQCdAl1Oep5pC9zSQe5NXE3+K1N1GYFv82Nfxr51AuWI4nIZ38Z7p93ZitxZgCc78vP\nCp7T0xsHydKCLETuFPoLGd59OMO7D/fStNbsrd/rXcb8fuX7bK3aytMfPJ1120WPvB5ekJsZ8MoD\nq4QQLUFrzf7ofnZU7/AC2O3V2/m45mN2VO+gLlnn5bWURY/8Hs0+z0Rx+LdYNHc7Rr6VT1GgiN4F\nvZt9LWU6gE2/mvKznmFQXl4uga0QQnwWpZQXbPagx1HPx9EOdYm67BbjdEAcj2S1KmcFzoladkd2\ne+OHGyDnWXleK3CeL89dBiu/Ydxyl+lwxkNWSFqShfgclFJ0z+tO97zufOmEL3npjnb4pPYTtlRt\nYUvVFrZWbWVL1RZef+914k7cy1eSX8KATtkBb1lRGUErmIvFEUIcJkc7VMWq2Fe/j/31+9kf3U9l\ntBJDGeRZbv0atIJZ/ZAZIuQLETTd8SM9Ua21pjJW6QWvmS2wO2p2ZB1HmMrkhPAJlBaWclqP0ygt\ncG+76FPQh17hXh3uIZ0da2mFEOIoGcrwHnJF/tHPp7kAOTMQzmxRro3XUpesI5KIUJeoY3fdbi84\nrkvUeU96/SwKRcgKNQS+Vp43nBkse+lWXlZA3Vy6PEFWCPd/oXdBb3oX9ObLJ37ZS7cdm/LacrZU\nZgS8B7bw8icvk3SSgPu77F3QO6tlt3Ows3tgbIXcg2ZfyBuXk1NCtAzbsamKVbE/ut8LWCuiFV7g\n2jiITT9k7mj5DJ8X+OZZeQ2BsJkdENcl6thes52Pqz+mJlHjfd5UJiXhEkoLSxnefbj7zICCUvoU\nusGr1McNJLAVQohjqKUCZHArZy/wTdZRl2gIgiNJt58eTqdnTttbt5cdyR1Znz+c+5EB/IbfC3y9\noDkV+KYPytPBcjotZIWy0jL7IStEwAzIE2jFccE0TPoU9qFPYZ+sB1YlnAQfV3/stfCmg97MJzQf\nTPogOKvLCHzTv6PG04NmEMuw8Bk+LMM69LDy4TNT48rKGj7cVidHO9jadvuOnT2e6qe7rHTHwTIs\n9xYUK0DADOA3/XLQLgC3FTPuxIkmo25nu/36ZL037PVTXb1dT12irknAWhmrbHrfKW691iXUhS7B\nLvTM78mQrkPoEuzipqXSu4S60DnQGY12vztVhnQXtaPNpycbpdv1RJIR9kf3e+kBM0Cfwj4MKxtG\nn8I+bstrYR9KwiXyOzhMEtgKIUQ7ZRqm9/7gluBoh2gymtVKnBn0ZqVnBs6JiBs8JyPsrd/rVeRH\n0qoMeJd2HSoYbhw0H2p6elheCSXaCp/ho6y4jLLiMr7O1730uB1nR/UODsQPZB0M1ydSv6VkXXZ6\nOi1Rz966vU3ypFuFW5JCeUGwaZhorZsNWFuaqUz8pt8NeM2GgDdoBhvSrUDW9Mw8PtOHQmEqE6Wy\n+4YyvE6hMA3Ty5s1rXF+DC/oT6+TzH5zaS393AStNUmdxHZskk4SW7v99LDt2CS1O+5ox8ubnpaZ\nJ30SIitPZr6DDKe3fea+0PikxaHSGn824SS8wC9mx7xgNR34He6J10xBM0jnYGe6hLpQkl/CKV1P\nyQpS0/2uoa6EfeEjOrnaCXk6elsjga0QQgggFVimLjXuGuraIvO0Hds76K5L1GUNZ/bTgXDj6fXJ\neiqjlexM7mxIS9ST1Id/4G4ZlhfsWtVS7Ym2x2/6GdBpQIvNL+EkvN9U3I6TdJIknITXTw+nO29c\nJ0nYDcON86f7tmN7QV5mwGcaJgYZw42nq6Zp3rhhYDs2MTtGzI4Rt93WOW/YjhK34970dHokGaEy\nVummJWNZ+Q/nlXHHSvrEwKECYiArSPX6GUFqZoDZFjQ+adB4ux4sPXM8neYzfAStIN3yuhE0g1mX\n7AatVJcxHDJDzaanr24ImAF5EGMHIzW8EEKIVmMaZsOl1y0oYScOGiynD+ibm76PfXzIhy1aFiHa\nGp/hw+f3UegvzHVRcirdCuhop9lWxXSnOfg0r8PBcVJ97biBvp3wTgYkdMI7KdDcyYCstIzPeZ93\nEl6rcebl36YyvUvF08PNpaWHTcPEUg3jhmHgUz7vREPmfDP7lrK8ExJZeTLzNRpOt2YL0VZIYCuE\nEKLd8Zk+ikz3lQJHory8nOd5vpVKJYRoSwxlEDADuS6GEOIYkUfsCSGEEEIIIYRo1ySwFUIIIYQQ\nQgjRrklgK4QQQgghhBCiXZPAVgghhBBCCCFEuyaBrRBCCCGEEEKIdk0CWyGEEEIIIYQQ7ZoEtkII\nIUQHsXHjRqZMmQLA9u3bmTRpEpMnT2b+/Pk4jgPA0qVLmTBhAldeeSWbNm3KZXGFEEKIwyaBrRBC\nCNEBPPTQQ9x+++3EYjEA7r77bubMmcMTTzyB1prVq1ezefNmXnvtNVatWsXixYu56667clxqIYQQ\n4vBIYCuEEEJ0AKWlpSxZssQb37x5M6NGjQJgzJgxvPzyy6xfv56zzz4bpRQlJSXYtk1FRUWuiiyE\nEEIcNivXBTgU27YB2L17d45LIoQQ4niQrk/S9UtHMm7cOMrLy71xrTVKKQDy8/OpqamhtraW4uJi\nL086vXPnzl6a1M1CCCFaUkvVzW06sN27dy8AV111VY5LIoQQ4niyd+9e+vTpk+ti5JRhNFy0FYlE\nKCwsJBwOE4lEstILCgqyPid1sxBCiNbweevmNh3YDh06lMcff5xu3bphmmauiyOEEKKds22bvXv3\nMnTo0FwXJecGDx7M2rVrOeOMM1izZg2jR4+mtLSURYsWMX36dHbv3o3jOFmttSB1sxBCiJbVUnWz\n0lrrFiqTEEIIIdqw8vJybrjhBlauXMm2bdu44447SCQSlJWVsXDhQkzTZMmSJaxZswbHcbjlllsY\nOXJkrosthBBCfCYJbIUQQgghhBBCtGvyVGQhhBBCCCGEEO1am77H9lhKJBLceuut7Ny5k3g8zsyZ\nMxk7dqw3/ZFHHmHVqlXevUZ33XUXZWVluSruYbvssssIh8MA9O7dm7vvvtubtnLlSp588kksy2Lm\nzJl85StfyVUxj8jTTz/NH/7wBwBisRjvvvsu//rXvygsLARg4cKFbNiwgfz8fACWLVvW5OEnbcnG\njRu59957efTRR9m+fTs333wzSikGDhzI/Pnzsx7wEo1GmTt3Lvv37yc/P5977rmnyf1vbUHmMr37\n7rssWLAA0zTx+/3cc889dO3aNSv/ofbTtiRzud555x1mzJhB3759AZg0aRIXXnihl7c9bqsf/OAH\n7Nu3D4CdO3dy6qmnct9993l5tdaMGTPGW+YvfvGL/PCHP8xFsQ+quf/yAQMGHBe/q45I6mapm3NF\n6mapm3NJ6uaj3FZaaK21fuqpp/TChQu11lpXVlbqc889N2v6D3/4Q/3WW2/loGRHLxqN6ksvvbTZ\naXv27NHjx4/XsVhMV1dXe8PtzZ133qmffPLJrLQrr7xS79+/P0clOjIPPvigHj9+vL7iiiu01lrP\nmDFDv/rqq1prre+44w7997//PSv/r371K33//fdrrbX+y1/+ohcsWHBsC3wYGi/TVVddpd955x2t\ntda//e1v9X/+539m5T/UftqWNF6ulStX6ocffvig+dvjtkqrqqrSl1xyif7000+z0j/66CM9Y8aM\nY1nEI9bcf/nx8LvqqKRulro5F6Rulro5l6Rudh3NtpJLkVPOP/98rr/+esA989H4SY+bN2/mwQcf\nZNKkSSxfvjwXRTxi7733HvX19UybNo2pU6fy5ptvetM2bdrE8OHD8fv9FBQUUFpaynvvvZfD0h65\nt956iy1btjBx4kQvzXEctm/fzrx587jyyit56qmncljCz1ZaWsqSJUu88c2bNzNq1CgAxowZw8sv\nv5yVf/369Zxzzjne9FdeeeXYFfYwNV6mxYsXc/LJJwPuU+8CgUBW/kPtp21J4+V6++23eeGFF7jq\nqqu49dZbqa2tzcrfHrdV2pIlS/jWt75F9+7ds9I3b97Mp59+ypQpU/jud7/Lhx9+eKyKetia+y8/\nHn5XHZXUzVI354LUzVI355LUza6j2VYS2Kbk5+cTDoepra1l9uzZzJkzJ2v6RRddxJ133slvfvMb\n1q9fz/PPP5+jkh6+YDDI9OnTefjhh7nrrru48cYbSSaTANTW1mZdApSfn9/kx9/WLV++nOuuuy4r\nra6ujm9961ssWrSIFStW8MQTT7Tpg4Jx48ZhWQ13BGitUUoB7japqanJyp+53Zqb3hY0Xqb0H/CG\nDRt47LHH+Pa3v52V/1D7aVvSeLmGDRvGTTfdxOOPP86JJ57IL37xi6z87XFbAezfv59XXnmFyy+/\nvEn+bt26ce211/Loo48yY8YM5s6de6yKetia+y8/Hn5XHZXUzVI354LUzVI355LUza6j2VYS2GbY\ntWsXU6dO5dJLL+Xiiy/20rXWXH311XTu3Bm/38+5557LO++8k8OSHp5+/fpxySWXoJSiX79+FBcX\ns3fvXgDC4TCRSMTLG4lE2vS9Lo1VV1ezbds2Ro8enZUeCoWYOnUqoVCIcDjM6NGj23Tl2VjmvQWR\nSMS7Nyktc7s1N72tevbZZ5k/fz4PPvhgk/sjDrWftmXnnXee97618847r8l/QnvdVn/7298YP358\ns+8nHTp0qHd/48iRI9mzZw+6DT5Yv/F/+fH6u+oopG6WujnXjtf/EKmb28+2krr58LaVBLYp+/bt\nY9q0acydO5cJEyZkTautrWX8+PFEIhG01qxdu/Zzv0D4WHjqqaf4yU9+AsCnn35KbW0t3bp1A9wz\nWuvXrycWi1FTU8PWrVsZNGhQLot7RF5//XXOPPPMJukfffQRkyZNwrZtEokEGzZsYMiQITko4dEZ\nPHgwa9euBWDNmjVN3h85YsQIXnzxRW/6aaeddszLeKT+9Kc/8dhjj/Hoo49y4oknNpl+qP20LZs+\nfTqbNm0C4JVXXmmyn7XHbQXusowZM6bZaUuXLuU3v/kN4F6m1qtXL+9sa1vR3H/58fi76iikbpa6\nuS04Hv9DpG5uP9sKpG4+3G0l77FNWbhwIc8991zW0xSvuOIK6uvrmThxIn/84x959NFH8fv9nHnm\nmcyePTuHpT088XicW265hU8++QSlFDfeeCMbN26ktLSUsWPHsnLlSn73u9+htWbGjBmMGzcu10U+\nbCtWrMCyLO/SmV//+tfecq1YsYLnnnsOn8/HpZdeyqRJk3Jb2M9QXl7ODTfcwMqVK9m2bRt33HEH\niUSCsrIyFi5ciGmaTJs2jQceeADbtvnRj37E3r178fl8/OxnP2uTFU16mX77299y5pln0qtXL+9M\n2+mnn87s2bO56aabmDNnDl27dm2yn44YMSLHS9C8zG21efNmFixYgM/no2vXrixYsIBwONxut9XK\nlSsB99LO3/72t1lnRtPLVF9fz9y5c6mrq8M0TebNm0f//v1zVfRmNfdfftttt7Fw4cJ2/7vqiKRu\nlro5V6Rulro5l6RuPrptJYGtEEIIIYQQQoh2TS5FFkIIIYQQQgjRrklgK4QQQgghhBCiXZPAVggh\nhBBCCCFEuyaBrRBCCCGEEEKIdk0CWyGEEEIIIYQQ7ZqV6wIIIZpau3Ytc+bMYcCAAV5ap06duP/+\n+z/XfG+++WYuvPDCg74LTQghhBDNk7pZiLZNAlsh2qjRo0dz33335boYQgghhEiRulmItksCWyHa\nkSlTptCvXz+2bduG1pr77ruPbt268ZOf/IT169cDMH78eK6++mo++ugjbr/9dhKJBMFg0KuIf/e7\n37FixQpqa2u58847GTZsWC4XSQghhGjXpG4Wom2QwFaINurVV19lypQp3vi5554LwIgRI/jxj3/M\n448/zvLlyznrrLMoLy9n5cqVJJNJJk+ezOjRo/n5z3/Otddey5gxY3j22Wd55513ABgyZAjf+973\nePrpp3n66ael8hRCCCEOk9TNQrRdEtgK0UY1d7nTiy++yOjRowG3Ev3HP/5Bz549GTlyJEopfD4f\np556Klu3bmXbtm0MHz4cgAsvvBCAv/zlLwwZMgSArl27Eo1Gj+ESCSGEEO2b1M1CtF3yVGQh2pm3\n334bgA0bNjBgwAD69+/vXeqUSCR444036NOnD/379+ett94C4JlnnuHRRx8FQCmVm4ILIYQQxymp\nm4XIPWmxFaKNany5E0A0GuUPf/gDjzzyCKFQiJ/+9Kd06tSJ1157jYkTJ5JIJDj//PMZMmQIN910\nE/PmzeOXv/wlwWCQRYsWsXnz5hwtjRBCCNH+Sd0sRNultNY614UQQhyeKVOmcOedd9K/f/9cF0UI\nIYQQSN0sRFshlyILIYQQQgghhGjXpMVWCCGEEEIIIUS7Ji22QgghhBBCCCHaNQlshRBCCCGEEEK0\naxLYCiGEEEIIIYRo1ySwFUIIIYQQQgjRrklgK4QQQgghhBCiXfv/QwPfWW/RSyQAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a5e99bf60>"
      ]
     },
     "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:__ Interesting results overall. All models except model_0 were tested on the Spectogram features not MFCC. By looking to the left graph (training loss) we can see that all models are behaving in the similar way. At the very beginning of the training each model starts by decreasing loss dramaticly and suddenly at the mid of 3rd epoch it keeps its loss value without decreasing any further.\n",
    "\n",
    "If we take into account both graphs (Training and validation losses) the best model is CNN model (2nd model, green lines). That's why we provide the most information to the RNN part of the network. CNN is there to find the best features for each input and use that to 'help' rnn in further training. \n",
    "\n",
    "Analyse of each model:\n",
    "\n",
    "**Model 0**: Given model - It was the only model trained on MFCC features. This model was the worst of them all according to the Training and validation accuracy. This model is very shellow as well and for the complicated task such as speech recognition we should be using deeper models. \n",
    "\n",
    "**Model 1**: Fully connected layer after rnn: This model is a small upgrade on the model 0, in the model 0 we use outputs from RNN and from those we are calculating loss and probs for the spoken words. This is not good in practices as we have seen. The idea of the **Model 1** is to add a fully connected layer between RNN part and output probs. In this way we are getting more information which we use to get better predictions. This add-on really helped in the matter of decreasing loss. As we can see on the left graph, model imediatelly started with loss of about 300. But this model didn't improve over time at both training loss and validation loss.\n",
    "\n",
    "**Model 2**: CNN model: In this model we have added CNN layer at the beginning (before RNN layer). This way we are analysing Spectogram or MFCC features with convolutional kernels. With this strategy we are able to get only important features and give them to the RNN part of the network. This strategy showed the best results of all models tested. Training loss kept decreasing over time and also validation loss decreased a lot. This results helped me in decideing what layers/architecture to use in the Final model.\n",
    "\n",
    "**Model 3**: Deep RNN Network: This model is made to have many RNN layers. In the testing (above) I have used 2 RNN layers + fully connected layer after the last RNN layer. This model showed the same training results as our Model 1. At the beginning of the training process it started to decrease loss and after 2.5 epochs it just stoped and kept that amount of loss until the end of the training. Validation loss stayed constant through out whole training process.\n",
    "\n",
    "**Model 4**: Bidirectional RNN layers: This model uses Bidir_Rnn layers, which is logical use in this use case. With using Bidir_rnn we are able to analyse input signal from the beginning to the end and from the end to the beginning. This model showed similar results to models 1 and 3. \n"
   ]
  },
  {
   "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": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "layer_1_conv (Conv1D)        (None, None, 200)         28800     \n",
      "_________________________________________________________________\n",
      "conv_batch_norm (BatchNormal (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "rnn_1 (GRU)                  (None, None, 250)         338250    \n",
      "_________________________________________________________________\n",
      "bt_rnn_1 (BatchNormalization (None, None, 250)         1000      \n",
      "_________________________________________________________________\n",
      "final_layer_of_rnn (GRU)     (None, None, 250)         375750    \n",
      "_________________________________________________________________\n",
      "bt_rnn_final (BatchNormaliza (None, None, 250)         1000      \n",
      "_________________________________________________________________\n",
      "time_distributed_28 (TimeDis (None, None, 29)          7279      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 752,879\n",
      "Trainable params: 751,479\n",
      "Non-trainable params: 1,400\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# specify the model\n",
    "model_end = final_model(input_dim=13,\n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=250,\n",
    "                        activation='relu',\n",
    "                        cell=GRU,\n",
    "                        dropout_rate=1,\n",
    "                        number_of_layers=2)"
   ]
  },
  {
   "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": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 145s - loss: 246.7676 - val_loss: 215.9699\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 144s - loss: 187.5169 - val_loss: 179.8064\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 152s - loss: 151.9236 - val_loss: 146.9361\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 152s - loss: 133.9369 - val_loss: 135.4676\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 148s - loss: 122.4069 - val_loss: 134.8893\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 148s - loss: 113.4147 - val_loss: 128.9967\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 142s - loss: 106.1782 - val_loss: 128.1952\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 152s - loss: 99.3979 - val_loss: 126.2738\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 155s - loss: 93.1851 - val_loss: 126.5522\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 148s - loss: 87.5127 - val_loss: 124.9496\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 150s - loss: 81.7004 - val_loss: 125.9966\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 145s - loss: 76.3927 - val_loss: 125.7233\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 151s - loss: 71.1534 - val_loss: 129.8055\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 150s - loss: 66.3418 - val_loss: 134.3553\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 150s - loss: 61.6126 - val_loss: 137.9712\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 150s - loss: 57.3928 - val_loss: 137.4264\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 147s - loss: 53.5605 - val_loss: 142.9806\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 148s - loss: 49.3938 - val_loss: 147.0497\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 150s - loss: 46.2359 - val_loss: 153.1326\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 150s - loss: 43.3314 - val_loss: 157.6372\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import RMSprop\n",
    "train_model(input_to_softmax=model_end, \n",
    "            pickle_path='model_end.pickle',\n",
    "            save_model_path='model_end.h5', \n",
    "            spectrogram=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ Describe your final model architecture and your reasoning at each step.  \n",
    "\n",
    "__Answer:__ While creating the Final model I have tested many different architectures. Firstly I have started with Deep Bidirectional RNN, I have used 2 and than 3 Bidir layers. But this architecture didn't work well for me. It stayed on 500 loss through whole training. My second attempt was to add CNN to that architecture and that improved the model a bit - it got to about 230 loss (Both training and validation loss).\n",
    "\n",
    "My third attempt was to add dilate param into Conv layer and because of that my conv_strides = 1. This didn't improve the network.\n",
    "\n",
    "The next step was to create just deep rnn with 5 GRU layers with dropout of 40%. This network was really hard to train and it did improve but for really small amount (from 760 loss to 710 in 20 epochs).\n",
    "\n",
    "Then I have changed SGD optimizer to RMSprop optimizer with learning rate to 0.02 (same as we use in SGD). This move didn't show any improvements overall.\n",
    "\n",
    "I have looked at the results of the tested architectures (above, Models 1-4) and started to put a few parts together to get this architecture that I am using now.\n",
    "\n",
    "I have used Conv layer from Model 2, and structure from Deep RNN model. Combined them together, tried the same model with and without dropout. It seemed to work better without dropout so I trained it without dropout at all.\n",
    "\n",
    "**Training of the final model analysis**: The model was training nicely. It kept decreasing Training and validation loss over time. But there is one thing. If we look at the validation loss we can see that it stoped decreasing and started to increase. This means that the network had started to overfit to the training set. \n",
    "\n",
    "There are a few things we can do to prevent this from happening. Early-stopping, we could have stoped training of the network after the 12. epoch. \n",
    "\n",
    "Regularization techniques such Dropout would help too. But dropout with the same setup of parameters didn't show good results, so tweaking other parameters too would help in the case of using dropout."
   ]
  },
  {
   "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": 79,
   "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(spectrogram=False)\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": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "mister quilter is the apoustle of the midtle classes and wear glad to weltem his gospbe\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": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "stuff it into you his belly counselled him\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "stof itin to yu his pely comntiat  im\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!"
   ]
  }
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