vui_notebook.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {
    "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": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b56497c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Audio Signal** : (84231,)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Transcript** : her father is a most remarkable person to say the least"
      ],
      "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/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "from data_generator import vis_train_features, plot_raw_audio\n",
    "from IPython.display import Audio\n",
    "%matplotlib inline\n",
    "\n",
    "# plot audio signal\n",
    "plot_raw_audio(vis_raw_audio)\n",
    "# print length of audio signal\n",
    "display(Markdown('**Shape of Audio Signal** : ' + str(vis_raw_audio.shape)))\n",
    "# print transcript corresponding to audio clip\n",
    "display(Markdown('**Transcript** : ' + str(vis_text)))\n",
    "# play the audio file\n",
    "Audio(vis_audio_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step1'></a>\n",
    "## STEP 1: Acoustic Features for Speech Recognition\n",
    "\n",
    "For this project, you won't use the raw audio waveform as input to your model.  Instead, we provide code that first performs a pre-processing step to convert the raw audio to a feature representation that has historically proven successful for ASR models.  Your acoustic model will accept the feature representation as input.\n",
    "\n",
    "In this project, you will explore two possible feature representations.  _After completing the project_, if you'd like to read more about deep learning architectures that can accept raw audio input, you are encouraged to explore this [research paper](https://pdfs.semanticscholar.org/a566/cd4a8623d661a4931814d9dffc72ecbf63c4.pdf).\n",
    "\n",
    "### Spectrograms\n",
    "\n",
    "The first option for an audio feature representation is the [spectrogram](https://www.youtube.com/watch?v=_FatxGN3vAM).  In order to complete this project, you will **not** need to dig deeply into the details of how a spectrogram is calculated; but, if you are curious, the code for calculating the spectrogram was borrowed from [this repository](https://github.com/baidu-research/ba-dls-deepspeech).  The implementation appears in the `utils.py` file in your repository.\n",
    "\n",
    "The code that we give you returns the spectrogram as a 2D tensor, where the first (_vertical_) dimension indexes time, and the second (_horizontal_) dimension indexes frequency.  To speed the convergence of your algorithm, we have also normalized the spectrogram.  (You can see this quickly in the visualization below by noting that the mean value hovers around zero, and most entries in the tensor assume values close to zero.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b563f6b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Spectrogram** : (381, 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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b54b98ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of MFCC** : (381, 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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#####################################################################\n",
    "# RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK #\n",
    "#####################################################################\n",
    "\n",
    "# allocate 50% of GPU memory (if you like, feel free to change this)\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "import tensorflow as tf \n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 29)          16617     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 16,617\n",
      "Trainable params: 16,617\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_0 = simple_rnn_model(input_dim=161) # change to 13 if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explored in the lesson, you will train the acoustic model with the [CTC loss](http://www.cs.toronto.edu/~graves/icml_2006.pdf) criterion.  Custom loss functions take a bit of hacking in Keras, and so we have implemented the CTC loss function for you, so that you can focus on trying out as many deep learning architectures as possible :).  If you'd like to peek at the implementation details, look at the `add_ctc_loss` function within the `train_utils.py` file in the repository.\n",
    "\n",
    "To train your architecture, you will use the `train_model` function within the `train_utils` module; it has already been imported in one of the above code cells.  The `train_model` function takes three **required** arguments:\n",
    "- `input_to_softmax` - a Keras model instance.\n",
    "- `pickle_path` - the name of the pickle file where the loss history will be saved.\n",
    "- `save_model_path` - the name of the HDF5 file where the model will be saved.\n",
    "\n",
    "If we have already supplied values for `input_to_softmax`, `pickle_path`, and `save_model_path`, please **DO NOT** modify these values. \n",
    "\n",
    "There are several **optional** arguments that allow you to have more control over the training process.  You are welcome to, but not required to, supply your own values for these arguments.\n",
    "- `minibatch_size` - the size of the minibatches that are generated while training the model (default: `20`).\n",
    "- `spectrogram` - Boolean value dictating whether spectrogram (`True`) or MFCC (`False`) features are used for training (default: `True`).\n",
    "- `mfcc_dim` - the size of the feature dimension to use when generating MFCC features (default: `13`).\n",
    "- `optimizer` - the Keras optimizer used to train the model (default: `SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)`).  \n",
    "- `epochs` - the number of epochs to use to train the model (default: `20`).  If you choose to modify this parameter, make sure that it is *at least* 20.\n",
    "- `verbose` - controls the verbosity of the training output in the `model.fit_generator` method (default: `1`).\n",
    "- `sort_by_duration` - Boolean value dictating whether the training and validation sets are sorted by (increasing) duration before the start of the first epoch (default: `False`).\n",
    "\n",
    "The `train_model` function defaults to using spectrogram features; if you choose to use these features, note that the acoustic model in `simple_rnn_model` should have `input_dim=161`.  Otherwise, if you choose to use MFCC features, the acoustic model should have `input_dim=13`.\n",
    "\n",
    "We have chosen to use `GRU` units in the supplied RNN.  If you would like to experiment with `LSTM` or `SimpleRNN` cells, feel free to do so here.  If you change the `GRU` units to `SimpleRNN` cells in `simple_rnn_model`, you may notice that the loss quickly becomes undefined (`nan`) - you are strongly encouraged to check this for yourself!  This is due to the [exploding gradients problem](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/).  We have already implemented [gradient clipping](https://arxiv.org/pdf/1211.5063.pdf) in your optimizer to help you avoid this issue.\n",
    "\n",
    "__IMPORTANT NOTE:__ If you notice that your gradient has exploded in any of the models below, feel free to explore more with gradient clipping (the `clipnorm` argument in your optimizer) or swap out any `SimpleRNN` cells for `LSTM` or `GRU` cells.  You can also try restarting the kernel to restart the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 232s 2s/step - loss: 859.6049 - val_loss: 758.7569\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 780.3632 - val_loss: 758.1268\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.5015 - val_loss: 761.9109\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.8482 - val_loss: 759.4582\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 778.9798 - val_loss: 757.8212\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.4765 - val_loss: 759.8114\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.6542 - val_loss: 754.2173\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.3123 - val_loss: 764.4062\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 779.1759 - val_loss: 755.8183\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 778.8104 - val_loss: 758.8386\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.0144 - val_loss: 754.8759\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 778.8946 - val_loss: 756.3432\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 778.8350 - val_loss: 755.2811\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.2521 - val_loss: 754.1070\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 239s 2s/step - loss: 779.2537 - val_loss: 759.7075\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 778.9898 - val_loss: 753.6466\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 779.2256 - val_loss: 755.5483\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.5549 - val_loss: 756.2179\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 778.9264 - val_loss: 758.8772\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 779.2065 - val_loss: 756.7019\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_0, \n",
    "            pickle_path='model_0.pickle', \n",
    "            save_model_path='model_0.h5',\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model1'></a>\n",
    "### (IMPLEMENTATION) Model 1: RNN + TimeDistributed Dense\n",
    "\n",
    "Read about the [TimeDistributed](https://keras.io/layers/wrappers/) wrapper and the [BatchNormalization](https://keras.io/layers/normalization/) layer in the Keras documentation.  For your next architecture, you will add [batch normalization](https://arxiv.org/pdf/1510.01378.pdf) to the recurrent layer to reduce training times.  The `TimeDistributed` layer will be used to find more complex patterns in the dataset.  The unrolled snapshot of the architecture is depicted below.\n",
    "\n",
    "<img src=\"images/rnn_model.png\" width=\"60%\">\n",
    "\n",
    "The next figure shows an equivalent, rolled depiction of the RNN that shows the (`TimeDistrbuted`) dense and output layers in greater detail.  \n",
    "\n",
    "<img src=\"images/rnn_model_unrolled.png\" width=\"60%\">\n",
    "\n",
    "Use your research to complete the `rnn_model` function within the `sample_models.py` file.  The function should specify an architecture that satisfies the following requirements:\n",
    "- The first layer of the neural network should be an RNN (`SimpleRNN`, `LSTM`, or `GRU`) that takes the time sequence of audio features as input.  We have added `GRU` units for you, but feel free to change `GRU` to `SimpleRNN` or `LSTM`, if you like!\n",
    "- Whereas the architecture in `simple_rnn_model` treated the RNN output as the final layer of the model, you will use the output of your RNN as a hidden layer.  Use `TimeDistributed` to apply a `Dense` layer to each of the time steps in the RNN output.  Ensure that each `Dense` layer has `output_dim` units.\n",
    "\n",
    "Use the code cell below to load your model into the `model_1` variable.  Use a value for `input_dim` that matches your chosen audio features, and feel free to change the values for `units` and `activation` to tweak the behavior of your recurrent layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 200)         217200    \n",
      "_________________________________________________________________\n",
      "batch_norm (BatchNormalizati (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: 223,829\n",
      "Trainable params: 223,429\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 234s 2s/step - loss: 295.8491 - val_loss: 294.8086\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 228.4506 - val_loss: 210.1133\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 239s 2s/step - loss: 209.1710 - val_loss: 202.1292\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 236s 2s/step - loss: 185.1521 - val_loss: 179.2130\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 235s 2s/step - loss: 168.3744 - val_loss: 165.4834\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 158.2915 - val_loss: 160.1868\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 236s 2s/step - loss: 151.0963 - val_loss: 152.5897\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 146.6885 - val_loss: 152.8548\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 238s 2s/step - loss: 143.5710 - val_loss: 146.6204\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 140.1517 - val_loss: 146.3468\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 236s 2s/step - loss: 137.1004 - val_loss: 147.8210\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 135.8155 - val_loss: 149.0609\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 135.0152 - val_loss: 145.7760\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 234s 2s/step - loss: 132.4415 - val_loss: 146.3474\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 235s 2s/step - loss: 132.0545 - val_loss: 143.0998\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 233s 2s/step - loss: 130.6326 - val_loss: 144.8840\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 131.4601 - val_loss: 145.1476\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 233s 2s/step - loss: 134.4799 - val_loss: 151.5867\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 233s 2s/step - loss: 133.3684 - val_loss: 148.0432\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 237s 2s/step - loss: 131.8492 - val_loss: 146.0668\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": 4,
   "metadata": {},
   "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 (SimpleRNN)              (None, None, 200)         80200     \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: 442,029\n",
      "Trainable params: 441,229\n",
      "Non-trainable params: 800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/keras/layers/recurrent.py:1004: UserWarning: The `implementation` argument in `SimpleRNN` has been deprecated. Please remove it from your layer call.\n",
      "  warnings.warn('The `implementation` argument '\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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 64s 630ms/step - loss: 250.5404 - val_loss: 233.2271\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 62s 616ms/step - loss: 193.9988 - val_loss: 175.9356\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 60s 592ms/step - loss: 163.7432 - val_loss: 155.4128\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 61s 608ms/step - loss: 148.7628 - val_loss: 149.8307\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 61s 604ms/step - loss: 139.1211 - val_loss: 140.8169\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 61s 600ms/step - loss: 131.1584 - val_loss: 139.7926\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 60s 598ms/step - loss: 125.2223 - val_loss: 136.9346\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 61s 601ms/step - loss: 120.2655 - val_loss: 136.6324\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 61s 601ms/step - loss: 116.0521 - val_loss: 132.1330\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 61s 607ms/step - loss: 112.1920 - val_loss: 135.4063\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 61s 601ms/step - loss: 108.7231 - val_loss: 136.7905\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 60s 597ms/step - loss: 105.5008 - val_loss: 130.7621\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 61s 600ms/step - loss: 101.9338 - val_loss: 135.3953\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 60s 598ms/step - loss: 99.1511 - val_loss: 134.7462\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 59s 580ms/step - loss: 96.6171 - val_loss: 134.2346\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 59s 588ms/step - loss: 93.7400 - val_loss: 138.5266\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 59s 587ms/step - loss: 91.0347 - val_loss: 134.2203\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 59s 586ms/step - loss: 88.2612 - val_loss: 136.7045\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 60s 590ms/step - loss: 86.2997 - val_loss: 141.4072\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 60s 598ms/step - loss: 83.7984 - val_loss: 140.9242\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 161)         0         \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, None, 200)         217200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "gru_2 (GRU)                  (None, None, 200)         240600    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (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: 465,229\n",
      "Trainable params: 464,429\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 407s 4s/step - loss: 284.8065 - val_loss: 244.4049\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 407s 4s/step - loss: 229.4671 - val_loss: 220.6311\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 411s 4s/step - loss: 207.2745 - val_loss: 204.4370\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 418s 4s/step - loss: 184.1160 - val_loss: 178.8500\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 417s 4s/step - loss: 163.4508 - val_loss: 169.1353\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 414s 4s/step - loss: 151.1032 - val_loss: 155.7954\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 422s 4s/step - loss: 142.1479 - val_loss: 151.8248\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 135.2372 - val_loss: 145.2850\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 413s 4s/step - loss: 129.6277 - val_loss: 140.4895\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 125.4067 - val_loss: 138.7370\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 414s 4s/step - loss: 121.1443 - val_loss: 138.0635\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 118.0006 - val_loss: 136.8916\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 412s 4s/step - loss: 114.9364 - val_loss: 134.2726\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 112.8227 - val_loss: 134.4753\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 415s 4s/step - loss: 110.2985 - val_loss: 136.4915\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 108.0236 - val_loss: 132.7650\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 418s 4s/step - loss: 106.6662 - val_loss: 138.0395\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 417s 4s/step - loss: 104.8606 - val_loss: 137.3473\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 103.0032 - val_loss: 132.6905\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 416s 4s/step - loss: 101.7726 - val_loss: 135.7886\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": 8,
   "metadata": {},
   "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)         434400    \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 446,029\n",
      "Trainable params: 446,029\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 401s 4s/step - loss: 383.1113 - val_loss: 398.2219\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 325.0597 - val_loss: 334.6058\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 408s 4s/step - loss: 316.6573 - val_loss: 334.9690\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 409s 4s/step - loss: 287.2159 - val_loss: 226.6723\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 404s 4s/step - loss: 228.4199 - val_loss: 209.7777\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 405s 4s/step - loss: 211.0869 - val_loss: 199.0211\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 408s 4s/step - loss: 198.6625 - val_loss: 193.6194\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 412s 4s/step - loss: 189.0110 - val_loss: 182.1961\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 413s 4s/step - loss: 181.4364 - val_loss: 178.2792\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 415s 4s/step - loss: 174.8142 - val_loss: 172.6791\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 408s 4s/step - loss: 168.1890 - val_loss: 169.5705\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 410s 4s/step - loss: 162.2758 - val_loss: 164.3447\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 156.9233 - val_loss: 162.9594\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 408s 4s/step - loss: 151.9036 - val_loss: 157.2814\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 409s 4s/step - loss: 147.4605 - val_loss: 157.0400\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 143.4933 - val_loss: 155.8897\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 139.2931 - val_loss: 153.3390\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 409s 4s/step - loss: 135.6642 - val_loss: 149.5801\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 413s 4s/step - loss: 132.0340 - val_loss: 153.7826\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 413s 4s/step - loss: 128.6329 - val_loss: 146.9931\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0cd36339e8>"
      ]
     },
     "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:__ We note that __Model 0__ does not perform as well as other models with the key difference being the absence of a time distributed layer. This layer is critical to understanding the context of the words being spoken, and the ability of the network to learn what is more likely given the letters adjacent to something.\n",
    "\n",
    "__Model 1__ performs is simply an addition of time distributed layer to Model 0 and performs well for the reasons stated above. However, we note that the performance of Model 1 flattens out rather quickly and more training does not reduce error after epoch 8. This can be attributed to a limited number of parameters in the model that are effectively trained earlier than other models.\n",
    "\n",
    "__Model 3__ is a deep variant of Model 1 and performs similar to model 1 to a great extent except it appears to beat out model 1 on both training and validation datasets (albeit marginally) as we approach more epochs. This can be attributed to the deep layer of the model and we would expect the model to perform better with more training.\n",
    "\n",
    "__Model 4__ is a bidirectional model that surprisingly does not perform as well as Model 1, and it appears to do a little overfitting as evidenced by the diverging performance on the training and validation datasets. However, we believe there are merits to a bidirectional approach with appropriate structure as the dataset suggests that future (next letter) is highly relevant to what is being said at a given moment.\n",
    "\n",
    "__Model 2__ is the best performing model that makes use of convolutional layer. This success can be attributed to two factors. One, much like a bidirectional model a CNN has ability to be aware of its adjacent letters. Two, because CNN works by considering local adjacencies, this can reduce the noise from letters spoken far away (as parts of other words, for example). This makes CNNs perform better in our dataset. We do believe however that it is possible that with larger dataset where entire sentence contexts can be learned, other models such a bidirectional models can perform better since context from other words can have meaning.\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": 2,
   "metadata": {},
   "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",
      "bidirectional_1 (Bidirection (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 1,572,429\n",
      "Trainable params: 1,570,429\n",
      "Non-trainable params: 2,000\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_end = final_model(input_dim = 161, \n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units = 200, \n",
    "                        recur_layers=2,\n",
    "                        dropout_rate=.2,\n",
    "                        activation = 'relu',\n",
    "                        cell=GRU,\n",
    "                        bidirection = True)"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "101/101 [==============================] - 403s 4s/step - loss: 253.2241 - val_loss: 249.8109\n",
      "Epoch 2/20\n",
      "101/101 [==============================] - 410s 4s/step - loss: 201.0202 - val_loss: 174.6379\n",
      "Epoch 3/20\n",
      "101/101 [==============================] - 407s 4s/step - loss: 168.0427 - val_loss: 155.6197\n",
      "Epoch 4/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 148.8415 - val_loss: 140.4590\n",
      "Epoch 5/20\n",
      "101/101 [==============================] - 402s 4s/step - loss: 136.6415 - val_loss: 134.3201\n",
      "Epoch 6/20\n",
      "101/101 [==============================] - 403s 4s/step - loss: 126.7283 - val_loss: 129.0414\n",
      "Epoch 7/20\n",
      "101/101 [==============================] - 403s 4s/step - loss: 119.0529 - val_loss: 123.1765\n",
      "Epoch 8/20\n",
      "101/101 [==============================] - 408s 4s/step - loss: 113.0067 - val_loss: 119.1822\n",
      "Epoch 9/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 106.3380 - val_loss: 116.1106\n",
      "Epoch 10/20\n",
      "101/101 [==============================] - 405s 4s/step - loss: 101.2636 - val_loss: 114.5466\n",
      "Epoch 11/20\n",
      "101/101 [==============================] - 412s 4s/step - loss: 96.5268 - val_loss: 113.6257\n",
      "Epoch 12/20\n",
      "101/101 [==============================] - 405s 4s/step - loss: 92.1767 - val_loss: 113.2371\n",
      "Epoch 13/20\n",
      "101/101 [==============================] - 409s 4s/step - loss: 88.2993 - val_loss: 110.9334\n",
      "Epoch 14/20\n",
      "101/101 [==============================] - 405s 4s/step - loss: 84.5719 - val_loss: 109.8104\n",
      "Epoch 15/20\n",
      "101/101 [==============================] - 405s 4s/step - loss: 80.8088 - val_loss: 106.7746\n",
      "Epoch 16/20\n",
      "101/101 [==============================] - 404s 4s/step - loss: 77.4786 - val_loss: 108.5473\n",
      "Epoch 17/20\n",
      "101/101 [==============================] - 406s 4s/step - loss: 74.3689 - val_loss: 107.3548\n",
      "Epoch 18/20\n",
      "101/101 [==============================] - 407s 4s/step - loss: 71.7364 - val_loss: 110.5282\n",
      "Epoch 19/20\n",
      "101/101 [==============================] - 410s 4s/step - loss: 69.0493 - val_loss: 108.1944\n",
      "Epoch 20/20\n",
      "101/101 [==============================] - 411s 4s/step - loss: 66.7368 - val_loss: 108.6718\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_end, \n",
    "            pickle_path='model_end.pickle', \n",
    "            save_model_path='model_end.h5', \n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ Describe your final model architecture and your reasoning at each step.  \n",
    "\n",
    "__Answer:__ As noted earlier, each of the structural elements of the neural network tested earlier deliver benefits in the ability to identify the letters by exploiting the context and structure of the data. Our model has the following basic elements (outputs are normalized after each layer): 1 CNN Layer; 2 Bidirectional RNN layers; 1 Time distributed layer. Our model was built with the ability to test various activation functions. Moreover, while the bidirectional layer performs roughly the same as a plain RNN layer in our tests earlier, we built the ability to test the same with an argument as we expect the outcome can be different with sufficient data and training and when combined with a CNN layer. \n",
    "\n",
    "To avoid overfitting, we also trained with a small dropout factor. This is because we have increased the number of parameters in this model and can expect some overfitting. Even with the dropout, we see that the model starts to overfit after epoch 8-10 or so. \n",
    "\n",
    "Ideally, we would like to test the model variations and dropout layers (and hence we have built the capability in the model using arguments). However, we were limited in computing power and were satisfied with the performance of our final model exceeding all other models tested above."
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from data_generator import AudioGenerator\n",
    "from keras import backend as K\n",
    "from utils import int_sequence_to_text\n",
    "from IPython.display import Audio\n",
    "\n",
    "def get_predictions(index, partition, input_to_softmax, model_path):\n",
    "    \"\"\" Print a model's decoded predictions\n",
    "    Params:\n",
    "        index (int): The example you would like to visualize\n",
    "        partition (str): One of 'train' or 'validation'\n",
    "        input_to_softmax (Model): The acoustic model\n",
    "        model_path (str): Path to saved acoustic model's weights\n",
    "    \"\"\"\n",
    "    # load the train and test data\n",
    "    data_gen = AudioGenerator()\n",
    "    data_gen.load_train_data()\n",
    "    data_gen.load_validation_data()\n",
    "    \n",
    "    # obtain the true transcription and the audio features \n",
    "    if partition == 'validation':\n",
    "        transcr = data_gen.valid_texts[index]\n",
    "        audio_path = data_gen.valid_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    elif partition == 'train':\n",
    "        transcr = data_gen.train_texts[index]\n",
    "        audio_path = data_gen.train_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    else:\n",
    "        raise Exception('Invalid partition!  Must be \"train\" or \"validation\"')\n",
    "        \n",
    "    # obtain and decode the acoustic model's predictions\n",
    "    input_to_softmax.load_weights(model_path)\n",
    "    prediction = input_to_softmax.predict(np.expand_dims(data_point, axis=0))\n",
    "    output_length = [input_to_softmax.output_length(data_point.shape[0])] \n",
    "    pred_ints = (K.eval(K.ctc_decode(\n",
    "                prediction, output_length)[0][0])+1).flatten().tolist()\n",
    "    \n",
    "    # play the audio file, and display the true and predicted transcriptions\n",
    "    print('-'*80)\n",
    "    Audio(audio_path)\n",
    "    print('True transcription:\\n' + '\\n' + transcr)\n",
    "    print('-'*80)\n",
    "    print('Predicted transcription:\\n' + '\\n' + ''.join(int_sequence_to_text(pred_ints)))\n",
    "    print('-'*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the code cell below to obtain the transcription predicted by your final model for the first example in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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",
      "bidirectional_5 (Bidirection (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_6 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_3 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 1,572,429\n",
      "Trainable params: 1,570,429\n",
      "Non-trainable params: 2,000\n",
      "_________________________________________________________________\n",
      "None\n",
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "her father is a most remarkable person to say the least\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "her fother s a mosst remarkubl porson to say the lacet\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='train',\n",
    "                input_to_softmax=final_model(input_dim = 161, \n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units = 200, \n",
    "                        recur_layers=2,\n",
    "                        dropout_rate=.2,\n",
    "                        activation = 'relu',\n",
    "                        cell=GRU,\n",
    "                        bidirection = True), \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": 8,
   "metadata": {},
   "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",
      "bidirectional_7 (Bidirection (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_8 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 1,572,429\n",
      "Trainable params: 1,570,429\n",
      "Non-trainable params: 2,000\n",
      "_________________________________________________________________\n",
      "None\n",
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "the bogus legislature numbered thirty six members\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "the bi is rdeslature noberg thirty sex memvers\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='validation',\n",
    "                input_to_softmax=final_model(input_dim = 161, \n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units = 200, \n",
    "                        recur_layers=2,\n",
    "                        dropout_rate=.2,\n",
    "                        activation = 'relu',\n",
    "                        cell=GRU,\n",
    "                        bidirection = True), \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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