vui_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# voiceAI\n",
    "\n",
    "Another AIND project, this time centered around **Voice User Interfaces** and **Speech Recognition with Neural Networks**!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction  \n",
    "\n",
    "In this notebook, we will build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline!  \n",
    "\n",
    "<img src=\"images/pipeline.png\">\n",
    "\n",
    "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",
    "- **STEP 1: PRE-PROCESSING:** Converts raw audio to one of two feature representations that are commonly used for ASR. \n",
    "- **STEP 2: ACOUSTIC MODEL:** Accept transformed audio features as input and return a probability distribution over all potential transcriptions, picking it's best guess (using a variety of models!)\n",
    "- **STEP 3: PREDICTION:** Lastly, the pipeline takes the output from the acoustic model and returns a predicted transcription for validation.\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<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.\n",
    "\n",
    "[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**. \n",
    "\n",
    "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.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2136 total training examples.\n"
     ]
    }
   ],
   "source": [
    "from data_generator import vis_train_features\n",
    "\n",
    "# extract label and audio features for a single training example\n",
    "vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 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": "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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FmCVbD/pcftHbf+RbtnGv70Go8ejuL/OX//PnTJZiX5gDYaPhs9kb0fy539Fh+BRk7Ins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\n6KmeOZNJ/HJfRyx4ogceuqA+7uni2a5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa477bc0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Audio Signal** : (103966,)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Transcript** : the last two days of the voyage bartley found almost intolerable"
      ],
      "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, *won't* use the raw audio waveform as input to your model. \n",
    "\n",
    "Instead, we will use the provided code to **first perform a pre-processing step that converts the raw audio to a feature representation that has historically proven successful for ASR models**. Your acoustic model will accept *this feature representation* as input.\n",
    "\n",
    "In this project, we 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). \n",
    "\n",
    "In order to complete our 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).  \n",
    "\n",
    "The implementation appears in the `utils.py` file in our repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tE1gy4Dgquwzbkn3pE+qm4RoVAL6LVnl+GxSi7PVqncnPVb4rLSKkJYTAIIEv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8gpiz8fPyDzxJtwgVayFygrgFQ06FW2xSh+NWJKgfYmiGbj3ui6f6I9UmP55qiBYtosmxvCKSM/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xMyYA8gyFqrHw2ckR5WBonvuU1Vk4aDRw33NtacAUWiuet1D9aj47y/ix0gTBDsERowbkmDMB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QruN+9fB0vVSNHZo15QT135/vR41miUBM7GTcZp8NI0znY9L0G9OTaeAZ9Fw9Vn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\n/oSbMxPwqi4obPhSMkHvTpRQuBylNxFOjmrIw5BlK/sP9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa477b7eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Spectrogram** : (470, 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": [
    "The code that we give you returns the spectrogram as a 2D tensor, where:\n",
    "* The first (_vertical_) dimension indexes time\n",
    "* THe second (_horizontal_) dimension indexes frequency.  \n",
    "\n",
    "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 (hello sparse matrices!)"
   ]
  },
  {
   "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). \n",
    "\n",
    "We are **not** going 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, the difference is in fidelity: for each time window, the MFCC feature yields a single feature vector that characterizes the sound within the window. \n",
    "\n",
    "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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431JFHWFqTHexwVqaM5W9tDVEHLb6TW0FOU9qC8jo+IW2gZzvmKmtIOa8S+1P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\nfpAeAl2wPSSuWVrhKMR0FzNUkWAlGTt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HSLY/j1hHSNaPddYRkvSidcsDsSZ9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa476089d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of MFCC** : (470, 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": [
    "Comparing these side by side, it is clear that this different treatment of **time** can have different pros and cons, especially with regards to understanding sequences vs. understanding a window. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we construct our pipeline, we 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, we will experiment with various neural network architectures for acoustic modeling!\n",
    "\n",
    "All models will be specified in the `sample_models.py` file.  After importing the `sample_models` module, we will just train and visualize our architectures in the notebook.\n",
    "\n",
    "After experimenting with the five simple architectures, we will then compare their performance, from which we will construct an even **deeper architecture that is designed to outperform all of the shallow models**!)\n",
    "\n",
    "For your convenience, we have designed the notebook so that each model can be specified and trained on separate occasions.  That is, say you decide to take a break from the notebook after training **Model 1**.  Then, you need not re-execute all prior code cells in the notebook before training **Model 2**.  You need only re-execute the code cell below, that is marked with **`RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK`**, before transitioning to the code cells corresponding to **Model 2**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "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",
    "\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.  (That extra 29th character is an empty \"character\" used to pad training examples within batches containing uneven lengths; we need matching sequences for our RNNs!)  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. \n",
    "\n",
    "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",
    "In a general sense, each RNN unit casts a guess against the predicted output vector using the input vector, optimizing using backpropogation to improve guess over time. "
   ]
  },
  {
   "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": [
    "### Criteria\n",
    "\n",
    "We will train the acoustic model with the [CTC loss](http://www.cs.toronto.edu/~graves/icml_2006.pdf) criterion; which excels at handling sequences.\n",
    "\n",
    "Custom loss functions take a bit of hacking in Keras, so let's use the pre-implemented the CTC loss function, so that we can focus on trying out as many deep learning architectures as possible :D. Again, 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 224s - loss: 841.9181 - val_loss: 736.3170\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 221s - loss: 758.2415 - val_loss: 730.2314\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 220s - loss: 752.7252 - val_loss: 721.6480\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 217s - loss: 752.0862 - val_loss: 732.1707\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 220s - loss: 751.9443 - val_loss: 724.9035\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 221s - loss: 753.4452 - val_loss: 725.2089\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 221s - loss: 751.5216 - val_loss: 730.7225\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 219s - loss: 752.3161 - val_loss: 719.4578\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 220s - loss: 751.3191 - val_loss: 732.4377\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 220s - loss: 751.8280 - val_loss: 727.7332\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 219s - loss: 750.9857 - val_loss: 727.2653\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 220s - loss: 751.6806 - val_loss: 722.2152\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 218s - loss: 752.6698 - val_loss: 735.1222\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 219s - loss: 751.6500 - val_loss: 721.5078\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 218s - loss: 751.2457 - val_loss: 722.8050\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 221s - loss: 752.1653 - val_loss: 734.7422\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 222s - loss: 751.9123 - val_loss: 731.1000\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 217s - loss: 751.9818 - val_loss: 719.7570\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 218s - loss: 751.9087 - val_loss: 736.3279\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 218s - loss: 752.6974 - val_loss: 727.2684\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": [
    "There are several **optional** arguments that allow you to have more control over the training process.  You are welcome to supply your own values for these arguments!\n",
    "\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",
    "__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": "markdown",
   "metadata": {},
   "source": [
    "We have also 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model1'></a>\n",
    "### Model 1: RNN + TimeDistributed Dense\n",
    "\n",
    "<img src=\"images/rnn_model.png\" width=\"60%\">\n",
    "\n",
    "For our next next architecture, let's use the [TimeDistributed](https://keras.io/layers/wrappers/) wrapper and the [BatchNormalization](https://keras.io/layers/normalization/) layer from keras:\n",
    "* Time Distribution via the `TimeDistributed` layer will allow us to find more complex patterns in the dataset; consider the unrolled snapshot above.\n",
    "* [Batch normalization](https://arxiv.org/pdf/1510.01378.pdf) in our recurrent layer helps reduce training times.  \n",
    "If we zoom on just the ouput; we can see the (`TimeDistrbuted`) dense and output layers in greater detail.  \n",
    "\n",
    "<img src=\"images/rnn_model_unrolled.png\" width=\"60%\">\n",
    "\n",
    "Let's build a model in `sample_model.py` that impements this!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_normalization_1 (Batch (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": [
    "- The first layer of the neural network uses an RNN (`SimpleRNN`, `LSTM`, or `GRU`) that takes the time sequence of audio features as input.\n",
    "\n",
    "- The hidden layer in `simple_rnn_model` treats the RNN output as the input to the final layer of the model, and we use `TimeDistributed` in this hidden step to apply a `Dense` connection layer to each of the time steps in the RNN output. \n",
    "\n",
    "- The output layer ensures that each `Dense` layer has `output_dim` units, but is essentially plugged straight into the RNN's probability predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also execute the code cell below to train the neural network you specified in `input_to_softmax`. \n",
    "\n",
    "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 too](https://wiki.python.org/moin/UsingPickle), using `model_1.pickle`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 225s - loss: 367.9386 - val_loss: 337.5201\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 229s - loss: 227.6331 - val_loss: 265.0209\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 228s - loss: 198.8542 - val_loss: 186.8425\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 227s - loss: 181.7907 - val_loss: 171.4935\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 228s - loss: 168.1042 - val_loss: 168.4838\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 230s - loss: 157.4330 - val_loss: 156.9629\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 227s - loss: 150.3916 - val_loss: 155.1179\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 228s - loss: 145.8944 - val_loss: 157.8674\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 227s - loss: 142.7567 - val_loss: 148.8004\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 227s - loss: 140.0274 - val_loss: 147.1977\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 226s - loss: 137.8551 - val_loss: 147.9762\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 225s - loss: 136.7871 - val_loss: 149.9069\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 227s - loss: 136.5525 - val_loss: 148.2636\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 225s - loss: 134.7058 - val_loss: 144.3187\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 226s - loss: 133.3694 - val_loss: 145.0704\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 227s - loss: 133.3552 - val_loss: 140.7199\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 229s - loss: 137.3734 - val_loss: 146.2730\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 228s - loss: 132.8446 - val_loss: 140.5503\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 226s - loss: 133.1923 - val_loss: 147.7639\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 226s - loss: 137.8584 - val_loss: 142.0406\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\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model2'></a>\n",
    "### 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": 10,
   "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",
      "batch_normalization_2 (Batch (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"
     ]
    }
   ],
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 61s - loss: 229.0710 - val_loss: 185.6440\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 59s - loss: 166.7276 - val_loss: 154.8832\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 58s - loss: 146.7739 - val_loss: 144.8785\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 57s - loss: 135.9777 - val_loss: 140.5358\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 57s - loss: 128.2075 - val_loss: 135.6018\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 57s - loss: 122.8382 - val_loss: 135.9319\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 58s - loss: 117.2671 - val_loss: 132.8129\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 58s - loss: 113.1700 - val_loss: 133.8851\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 58s - loss: 109.2029 - val_loss: 130.3848\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 57s - loss: 105.9890 - val_loss: 134.8307\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 56s - loss: 102.6300 - val_loss: 131.7514\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 56s - loss: 99.5150 - val_loss: 131.6749\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 58s - loss: 96.6035 - val_loss: 130.2747\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 58s - loss: 93.8159 - val_loss: 132.7941\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 57s - loss: 91.3080 - val_loss: 132.7317\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 57s - loss: 88.7120 - val_loss: 132.5699\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 57s - loss: 86.4573 - val_loss: 135.4340\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 57s - loss: 84.6415 - val_loss: 136.5684\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 58s - loss: 81.9793 - val_loss: 137.6616\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 57s - loss: 80.1717 - val_loss: 141.2940\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\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model3'></a>\n",
    "### 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": 12,
   "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_1 (GRU)                  (None, None, 200)         217200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "rnn_2 (GRU)                  (None, None, 200)         240600    \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 385s - loss: 256.8632 - val_loss: 218.4778\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 389s - loss: 220.9016 - val_loss: 214.5276\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 389s - loss: 212.0365 - val_loss: 201.1642\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 391s - loss: 193.2432 - val_loss: 190.5782\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 387s - loss: 162.4471 - val_loss: 153.9289\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 390s - loss: 144.7503 - val_loss: 145.6886\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 389s - loss: 135.3470 - val_loss: 138.2848\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 392s - loss: 128.9875 - val_loss: 140.4947\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 389s - loss: 123.8634 - val_loss: 130.7851\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 386s - loss: 119.6977 - val_loss: 130.6172\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 392s - loss: 116.4428 - val_loss: 130.5726\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 393s - loss: 112.9637 - val_loss: 129.1481\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 390s - loss: 110.7214 - val_loss: 122.8933\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 390s - loss: 107.7262 - val_loss: 123.2316\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 389s - loss: 105.6695 - val_loss: 127.3088\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 387s - loss: 103.5727 - val_loss: 126.1109\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 385s - loss: 101.4479 - val_loss: 120.3132\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 385s - loss: 100.7053 - val_loss: 125.8227\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 385s - loss: 99.8055 - val_loss: 122.9432\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 391s - loss: 97.7769 - val_loss: 120.1020\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",
    "### 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": 14,
   "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",
      "batch_normalization_5 (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: 447,629\n",
      "Trainable params: 446,829\n",
      "Non-trainable params: 800\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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 377s - loss: 309.1368 - val_loss: 274.6563\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 385s - loss: 213.9504 - val_loss: 189.8949\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 385s - loss: 184.9473 - val_loss: 175.1875\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 378s - loss: 168.9086 - val_loss: 167.5569\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 384s - loss: 157.2887 - val_loss: 153.2339\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 386s - loss: 148.0640 - val_loss: 149.4466\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 384s - loss: 140.8739 - val_loss: 143.0215\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 379s - loss: 135.7555 - val_loss: 141.9769\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 382s - loss: 130.8454 - val_loss: 139.7064\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 385s - loss: 126.9327 - val_loss: 141.0884\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 384s - loss: 122.9366 - val_loss: 139.9223\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 386s - loss: 119.7802 - val_loss: 138.2814\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 383s - loss: 116.4796 - val_loss: 134.3777\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 383s - loss: 113.4044 - val_loss: 132.4903\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 388s - loss: 110.5372 - val_loss: 132.6841\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 387s - loss: 108.3411 - val_loss: 133.8261\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 383s - loss: 105.4478 - val_loss: 131.3938\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 387s - loss: 103.3623 - val_loss: 132.4524\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 384s - loss: 100.7206 - val_loss: 131.9914\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 379s - loss: 98.5222 - val_loss: 134.2463\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",
    "### Model 5: Deep Bidirectional RNN + TimeDistributed\n",
    "\n",
    "Bonus architectures, from the kind folks at Udacity!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": 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",
      "bidirectional_2 (Bidirection (None, None, 400)         434400    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_3 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_4 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_5 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 1,893,229\n",
      "Trainable params: 1,890,829\n",
      "Non-trainable params: 2,400\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/20\n",
      "106/106 [==============================] - 1061s - loss: 273.2050 - val_loss: 257.1091\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 1064s - loss: 207.0537 - val_loss: 186.3370\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 1069s - loss: 165.2725 - val_loss: 160.0458\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 1081s - loss: 142.9217 - val_loss: 142.1774\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 1083s - loss: 129.2055 - val_loss: 147.5106\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 1083s - loss: 118.4962 - val_loss: 131.3726\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 1082s - loss: 109.6594 - val_loss: 124.8024\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 1074s - loss: 102.1803 - val_loss: 123.3295\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 1065s - loss: 95.2571 - val_loss: 121.9507\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 1080s - loss: 89.0488 - val_loss: 118.4347\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 1079s - loss: 83.0019 - val_loss: 118.7419\n",
      "Epoch 12/20\n",
      " 75/106 [====================>.........] - ETA: 252s - loss: 76.6052"
     ]
    }
   ],
   "source": [
    "model_5 = deep_bidirectional_rnn_model(input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "                         units=200,\n",
    "                         recur_layers=3,\n",
    "                         RNN_type=GRU)\n",
    "\n",
    "train_model(input_to_softmax=model_5, \n",
    "            pickle_path='model_5.pickle', \n",
    "            save_model_path='model_5.h5', \n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 6: Deep Bidirectional RNN + TimeDistributed with Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 54/106 [==============>...............] - ETA: 434s - loss: 139.8122"
     ]
    }
   ],
   "source": [
    "model_6 = deep_bidirectional_rnn_model_with_dropout(input_dim=161, # change to 13 if you would like to use MFCC features\n",
    "                         units=200,\n",
    "                         recur_layers=3,\n",
    "                         RNN_type=GRU)\n",
    "\n",
    "train_model(input_to_softmax=model_6, \n",
    "            pickle_path='model_6.pickle', \n",
    "            save_model_path='model_6.h5', \n",
    "            spectrogram=True,\n",
    "            epochs=30) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 7: CNN + RNN + TimeDistributed with Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_7 = 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)\n",
    "train_model(input_to_softmax=model_7, \n",
    "            pickle_path='model_7.pickle', \n",
    "            save_model_path='model_7.h5',\n",
    "            spectrogram=True) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='compare'></a>\n",
    "### Compare the Models\n",
    "\n",
    "Execute the code cell below to evaluate the performance of the drafted deep learning models.  The training and validation loss are plotted for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VfPB1PsfOlONjNfC2WvDxsjivrRa8vZzL6pf71N32rlvXt25dby/nMp8G63pZjHYVapsr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\n6AqCIAiCIAiCIAgnFP8/FUOXigBDm14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f133f604c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from glob import glob\n",
    "import numpy as np\n",
    "import _pickle as pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_style(style='white')\n",
    "\n",
    "# obtain the paths for the saved model history\n",
    "all_pickles = sorted(glob(\"results/*.pickle\"))\n",
    "# extract the name of each model\n",
    "model_names = [item[8:-7] for item in all_pickles]\n",
    "# extract the loss history for each model\n",
    "valid_loss = [pickle.load( open( i, \"rb\" ) )['val_loss'] for i in all_pickles]\n",
    "train_loss = [pickle.load( open( i, \"rb\" ) )['loss'] for i in all_pickles]\n",
    "# save the number of epochs used to train each model\n",
    "num_epochs = [len(valid_loss[i]) for i in range(len(valid_loss))]\n",
    "\n",
    "fig = plt.figure(figsize=(16,5))\n",
    "\n",
    "# plot the training loss vs. epoch for each model\n",
    "ax1 = fig.add_subplot(121)\n",
    "for i in range(len(all_pickles)):\n",
    "    ax1.plot(np.linspace(1, num_epochs[i], num_epochs[i]), \n",
    "            train_loss[i], label=model_names[i])\n",
    "# clean up the plot\n",
    "ax1.legend()  \n",
    "ax1.set_xlim([1, max(num_epochs)])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss')\n",
    "\n",
    "# plot the validation loss vs. epoch for each model\n",
    "ax2 = fig.add_subplot(122)\n",
    "for i in range(len(all_pickles)):\n",
    "    ax2.plot(np.linspace(1, num_epochs[i], num_epochs[i]), \n",
    "            valid_loss[i], label=model_names[i])\n",
    "# clean up the plot\n",
    "ax2.legend()  \n",
    "ax2.set_xlim([1, max(num_epochs)])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 1:__ Use the plot above to analyze the performance of each of the attempted architectures.  Which performs best?  Provide an explanation regarding why you think some models perform better than others. \n",
    "\n",
    "__Answer:__\n",
    "\n",
    "\n",
    "| Model   | Description                                           | Lowest Validation Loss | \n",
    "| ------- | ----------------------------------------------------- | ---------------------- | \n",
    "| Model 0 | RNN                                                   | 752.6974 | 727.2684\n",
    "| Model 1 | RNN + TimeDistributed                                 | 137.8584| 142.0406\n",
    "| Model 2 | CNN + RNN + TimeDistributed                           | 80.1717 | 141.2940\n",
    "| Model 3 | Deep RNN + TimeDistributed                            | 97.7769 | 120.1020\n",
    "| Model 4 | Bidirectional RNN + TimeDistributed                   | 98.5222 | 134.2463               |\n",
    "| Model 5 | Deep Bidirectional RNN + TimeDistributed              | N/A                    |N/A |\n",
    "| Model 6 | Deep Bidirectional RNN + TimeDistributed with Dropout | N/A                    |N/A |\n",
    "| Model 7 | CNN + RNN + TimeDistributed with Dropout              | N/A                    |N/A |\n",
    "\n",
    "    \n",
    "By looking at the Training Loss on the right, we can see that all models except model 0 behaving in the similar way; converging to some sort of optima, switching dramatically around the 3rd epoch. The best model appears to be model_2 over model_4 by a hair, but the unfinished Model 5 seems potentially superior.\n",
    "\n",
    "**Model 0:** A basic RNN (model_0) is the worst of them all, and may as well be gibberish. \n",
    "\n",
    "**Model 1:** A remarkable improvement in performance, through the simple addition of a `TimeDistributed(Dense())` layer which, when tied to every unit in the RNN sequence and allowed to softmax (and therefore be backpropogated), which can be seen in the doubling of trainable parameters (464K vs 233K), and therefore trainable space of the model. Problem is, it converges quickly onto a pretty bad loss. We need more noise or feature space to get deeper curves.  \n",
    "\n",
    "**Model 2:** This time, we slap on a CNN layer at the beginning (before RNN layer), and given what each CNN recognizes in its Spectogram (!), we wrap it into an RNN sequence, densely connect all that to activations, and see what we get. It's importnat to note that this is not a pre-trained CNN looking for image vectors, but rather a simple direct convolution; a **zooming in** on a tiny piece of the spectrogram, filtering and convolving all of its neighbours for features that match over time, and then relating those sequences with the expected sequences from our data. This makes it the most performant model with regard to training and validation loss.\n",
    "\n",
    "**Model 3**: If we stack a series of RNN's together, we get a structure reminiscent of [our last project](http://github.com/vvkmnn/touristAI). Surprisingly, the replacement of CNN feature \"zooming\" by stacks of bidirectional RNNs sequences should be way way off, but it turns out it actually sort of works. My hypothesis is this has to do with elongating time space for sequences to interact with each other, as opposed to increasing resolution space and zooming on sequences; essentially higher resolution for positive signals using either medium. \n",
    "\n",
    "**Model 4**: Reversing some of these sequences appers to have little effects; actually performing worse in the while. Would've been nice to compare to 5 - 8.\n",
    "\n",
    "The absence of models 5, 7, and 7 are evidence of Amazon's EC2 pricing, and how annoying it is to keep a Jupyter ssh tunnel open while Tensorflow does its thing. I concede."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='final'></a>\n",
    "### 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": 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_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",
      "bidirectional_3 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (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: 2,295,229\n",
      "Trainable params: 2,292,429\n",
      "Non-trainable params: 2,800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# specify the model\n",
    "model_end = final_model(input_dim=161, \n",
    "                        filters=200, \n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid', \n",
    "                        recur_layers=3, \n",
    "                        RNN_type=GRU, \n",
    "                        dropout=0.2, \n",
    "                        units=200, \n",
    "                        output_dim=29) # 200 Filters of 11x11, \n",
    "                                       # with a stride of 2, with \"valid\" / no padding\n",
    "                                       # Using 3 RNN Bidirecional Chains with GRU Units\n",
    "                                       # Over 200 Units, with 20% Dropout, in Sequences of 29"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 515s - loss: 251.6511 - val_loss: 231.2747\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 517s - loss: 224.0173 - val_loss: 205.9121\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 517s - loss: 208.3266 - val_loss: 179.7048\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 517s - loss: 178.5236 - val_loss: 155.6232\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 510s - loss: 157.4867 - val_loss: 138.5522\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 515s - loss: 142.6672 - val_loss: 131.4548\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 518s - loss: 132.5957 - val_loss: 125.0108\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 512s - loss: 124.9007 - val_loss: 117.8693\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 507s - loss: 118.7908 - val_loss: 114.7536\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 509s - loss: 112.8262 - val_loss: 111.7047\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 506s - loss: 108.1205 - val_loss: 108.5606\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 512s - loss: 104.0067 - val_loss: 106.1705\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 502s - loss: 100.3817 - val_loss: 104.7133\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 513s - loss: 96.9186 - val_loss: 103.7046\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 503s - loss: 93.7232 - val_loss: 98.3857\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 511s - loss: 90.6794 - val_loss: 100.1045\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 510s - loss: 88.1375 - val_loss: 99.0563\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 512s - loss: 85.2925 - val_loss: 95.9856\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 509s - loss: 83.0669 - val_loss: 97.0832\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 517s - loss: 80.7469 - val_loss: 97.0541\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:__\n",
    "\n",
    "The model consists of 9 layers, depending on how many recursive RNN chains are used (3 here). It borrows heavily from the highly successful [Model 2](#model2).\n",
    "\n",
    "1) **Input**: An input vector of *BatchID, 161* (Spectograms)\n",
    "\n",
    "`Input(shape=(None, input_dim))`\n",
    "\n",
    "2) **Convolution**: N filters, using specified kernel size, stride, and padding, mapping over the image and activating via ReLU\n",
    "\n",
    "`Conv1D(filters, kernel_size, \n",
    "                     strides=conv_stride, \n",
    "                     padding=conv_border_mode,\n",
    "                     activation='relu',\n",
    "                     name='conv1d')(input)`\n",
    "                     \n",
    "3) **Batch Normalization**: Noise reduction layer, normalizing outliers\n",
    "\n",
    "`BatchNormalization(prev)`\n",
    "\n",
    "4-6) **RNN Layers** - A chain of bidirectional RNNs to improve temporal comprehension, returning in sequences, with dropout. \n",
    "\n",
    "`for i in range(recur_layers): \n",
    "rnn = Bidirectional(RNN_type(units, \n",
    "activation='relu',\n",
    "    return_sequences=True, \n",
    "    implementation=2, \n",
    "    recurrent_dropout=dropout, \n",
    "    dropout=dropout, \n",
    "    name='bidir_rnn_{}'.format(i + 1)))(prev)` \n",
    "    \n",
    "7) **Batch Normalization**: Another Noise reduction layer, normalizing outliers\n",
    "\n",
    "`BatchNormalization(prev)` \n",
    "\n",
    "8) **Dense Activation**: Relate every sequence in every RNN densely with the next layer.\n",
    "\n",
    "`TimeDistributed(Dense(prev))` \n",
    "\n",
    "9) **Final Activation**: Pass each densely connected unit to Softmax activation for probability prediction.\n",
    "\n",
    "`Activation('softmax')(prev)`\n",
    "\n",
    "Then we specify inputs and outputs, pad the uneven sequences, use our pickled hyperparameters, and find predictions!"
   ]
  },
  {
   "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": 11,
   "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": 15,
   "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",
      "bidirectional_9 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (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: 2,295,229\n",
      "Trainable params: 2,292,429\n",
      "Non-trainable params: 2,800\n",
      "_________________________________________________________________\n",
      "None\n",
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "the last two days of the voyage bartley found almost intolerable\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "the las to das of the voage barttly foned omost in telerble\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",
    "                        recur_layers=3, \n",
    "                        RNN_type=GRU, \n",
    "                        dropout=0.2, \n",
    "                        units=200, \n",
    "                        output_dim=29), \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": 16,
   "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_10 (Bidirectio (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_11 (Bidirectio (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "bidirectional_12 (Bidirectio (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (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: 2,295,229\n",
      "Trainable params: 2,292,429\n",
      "Non-trainable params: 2,800\n",
      "_________________________________________________________________\n",
      "None\n",
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "out in the woods stood a nice little fir tree\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "ondein the wod stood an nice  the hol firtr\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",
    "                        recur_layers=3, \n",
    "                        RNN_type=GRU, \n",
    "                        dropout=0.2, \n",
    "                        units=200, \n",
    "                        output_dim=29), \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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