vui_notebook.ipynb

{
 "cells": [
  {
   "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 2136 total training examples.\n"
     ]
    }
   ],
   "source": [
    "from data_generator import vis_train_features\n",
    "\n",
    "# extract label and audio features for a single training example\n",
    "vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code cell visualizes the audio waveform for your chosen example, along with the corresponding transcript.  You also have the option to play the audio in the notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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0Rqr6EoDTAGCUPeRUeEVIOBOnvDlrY/CVAhg30/rrrQS/L0/N/x3e/nVTWG0K1eGs/J6O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\nXMd1t/tSm/Fzj7F6qMf6b3he7i7Wxxquk6Clqro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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc824b13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Audio Signal** : (167691,)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Transcript** : the houses seemed miserable in the extreme especially to an eye accustomed to the smiling neatness of english cottages"
      ],
      "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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5vKkyx31C4txNt+NlzHyA+XPVhTJszKsypnJ5RCGTevV6C55V3t3vGAUksZKQ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I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc821b80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Spectrogram** : (759, 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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z2PP9rRXUPHN0H2sFNf0aPmqtoGYg91grqJnRwf/i11+oZ62g4nbOs1ZQcwOX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\nLEDH0YZr498N6MOL3hFMWrN8/YM5SG+yr/1h4HxVHSciw2H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6wDuCyQ/qL/aOYPZdLveO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\nUpX5P95oNNbU2itWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc819acb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of MFCC** : (759, 13)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from data_generator import plot_mfcc_feature\n",
    "\n",
    "# plot normalized MFCC\n",
    "plot_mfcc_feature(vis_mfcc_feature)\n",
    "# print shape of MFCC\n",
    "display(Markdown('**Shape of MFCC** : ' + str(vis_mfcc_feature.shape)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you construct your pipeline, you will be able to choose to use either spectrogram or MFCC features.  If you would like to see different implementations that make use of MFCCs and/or spectrograms, please check out the links below:\n",
    "- This [repository](https://github.com/baidu-research/ba-dls-deepspeech) uses spectrograms.\n",
    "- This [repository](https://github.com/mozilla/DeepSpeech) uses MFCCs.\n",
    "- This [repository](https://github.com/buriburisuri/speech-to-text-wavenet) also uses MFCCs.\n",
    "- This [repository](https://github.com/pannous/tensorflow-speech-recognition/blob/master/speech_data.py) experiments with raw audio, spectrograms, and MFCCs as features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step2'></a>\n",
    "## STEP 2: Deep Neural Networks for Acoustic Modeling\n",
    "\n",
    "In this section, you will experiment with various neural network architectures for acoustic modeling.  \n",
    "\n",
    "You will begin by training five relatively simple architectures.  **Model 0** is provided for you.  You will write code to implement **Models 1**, **2**, **3**, and **4**.  If you would like to experiment further, you are welcome to create and train more models under the **Models 5+** heading.  \n",
    "\n",
    "All models will be specified in the `sample_models.py` file.  After importing the `sample_models` module, you will train your architectures in the notebook.\n",
    "\n",
    "After experimenting with the five simple architectures, you will have the opportunity to compare their performance.  Based on your findings, you will construct a deeper architecture that is designed to outperform all of the shallow models.\n",
    "\n",
    "For your convenience, we have designed the notebook so that each model can be specified and trained on separate occasions.  That is, say you decide to take a break from the notebook after training **Model 1**.  Then, you need not re-execute all prior code cells in the notebook before training **Model 2**.  You need only re-execute the code cell below, that is marked with **`RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK`**, before transitioning to the code cells corresponding to **Model 2**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 29)          3741      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 3,741\n",
      "Trainable params: 3,741\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_0 = simple_rnn_model(input_dim=13) # change to 13 if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explored in the lesson, you will train the acoustic model with the [CTC loss](http://www.cs.toronto.edu/~graves/icml_2006.pdf) criterion.  Custom loss functions take a bit of hacking in Keras, and so we have implemented the CTC loss function for you, so that you can focus on trying out as many deep learning architectures as possible :).  If you'd like to peek at the implementation details, look at the `add_ctc_loss` function within the `train_utils.py` file in the repository.\n",
    "\n",
    "To train your architecture, you will use the `train_model` function within the `train_utils` module; it has already been imported in one of the above code cells.  The `train_model` function takes three **required** arguments:\n",
    "- `input_to_softmax` - a Keras model instance.\n",
    "- `pickle_path` - the name of the pickle file where the loss history will be saved.\n",
    "- `save_model_path` - the name of the HDF5 file where the model will be saved.\n",
    "\n",
    "If we have already supplied values for `input_to_softmax`, `pickle_path`, and `save_model_path`, please **DO NOT** modify these values. \n",
    "\n",
    "There are several **optional** arguments that allow you to have more control over the training process.  You are welcome to, but not required to, supply your own values for these arguments.\n",
    "- `minibatch_size` - the size of the minibatches that are generated while training the model (default: `20`).\n",
    "- `spectrogram` - Boolean value dictating whether spectrogram (`True`) or MFCC (`False`) features are used for training (default: `True`).\n",
    "- `mfcc_dim` - the size of the feature dimension to use when generating MFCC features (default: `13`).\n",
    "- `optimizer` - the Keras optimizer used to train the model (default: `SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)`).  \n",
    "- `epochs` - the number of epochs to use to train the model (default: `20`).  If you choose to modify this parameter, make sure that it is *at least* 20.\n",
    "- `verbose` - controls the verbosity of the training output in the `model.fit_generator` method (default: `1`).\n",
    "- `sort_by_duration` - Boolean value dictating whether the training and validation sets are sorted by (increasing) duration before the start of the first epoch (default: `False`).\n",
    "\n",
    "The `train_model` function defaults to using spectrogram features; if you choose to use these features, note that the acoustic model in `simple_rnn_model` should have `input_dim=161`.  Otherwise, if you choose to use MFCC features, the acoustic model should have `input_dim=13`.\n",
    "\n",
    "We have chosen to use `GRU` units in the supplied RNN.  If you would like to experiment with `LSTM` or `SimpleRNN` cells, feel free to do so here.  If you change the `GRU` units to `SimpleRNN` cells in `simple_rnn_model`, you may notice that the loss quickly becomes undefined (`nan`) - you are strongly encouraged to check this for yourself!  This is due to the [exploding gradients problem](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/).  We have already implemented [gradient clipping](https://arxiv.org/pdf/1211.5063.pdf) in your optimizer to help you avoid this issue.\n",
    "\n",
    "__IMPORTANT NOTE:__ If you notice that your gradient has exploded in any of the models below, feel free to explore more with gradient clipping (the `clipnorm` argument in your optimizer) or swap out any `SimpleRNN` cells for `LSTM` or `GRU` cells.  You can also try restarting the kernel to restart the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 206s - loss: 801.8416 - val_loss: 726.3580\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 206s - loss: 752.5855 - val_loss: 723.4784\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 201s - loss: 751.2714 - val_loss: 720.7924\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 202s - loss: 752.4263 - val_loss: 732.5443\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 203s - loss: 752.4225 - val_loss: 735.0062\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 201s - loss: 752.6877 - val_loss: 730.1500\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 201s - loss: 752.6008 - val_loss: 718.1197\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 201s - loss: 752.5462 - val_loss: 724.9985\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 202s - loss: 751.5518 - val_loss: 725.4937\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 202s - loss: 752.2558 - val_loss: 721.6915\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 202s - loss: 752.3198 - val_loss: 726.6813\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 202s - loss: 752.5085 - val_loss: 727.5243\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 202s - loss: 752.8079 - val_loss: 726.8957\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 201s - loss: 752.3394 - val_loss: 718.4281\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 202s - loss: 752.8887 - val_loss: 724.4417\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 201s - loss: 751.8528 - val_loss: 723.8319\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 201s - loss: 751.7758 - val_loss: 724.8054\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 200s - loss: 752.5586 - val_loss: 729.1738\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 203s - loss: 752.2861 - val_loss: 728.6537\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 200s - loss: 750.8923 - val_loss: 723.0555\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_0, \n",
    "            pickle_path='model_0.pickle', \n",
    "            save_model_path='model_0.h5',\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model1'></a>\n",
    "### (IMPLEMENTATION) Model 1: RNN + TimeDistributed Dense\n",
    "\n",
    "Read about the [TimeDistributed](https://keras.io/layers/wrappers/) wrapper and the [BatchNormalization](https://keras.io/layers/normalization/) layer in the Keras documentation.  For your next architecture, you will add [batch normalization](https://arxiv.org/pdf/1510.01378.pdf) to the recurrent layer to reduce training times.  The `TimeDistributed` layer will be used to find more complex patterns in the dataset.  The unrolled snapshot of the architecture is depicted below.\n",
    "\n",
    "<img src=\"images/rnn_model.png\" width=\"60%\">\n",
    "\n",
    "The next figure shows an equivalent, rolled depiction of the RNN that shows the (`TimeDistrbuted`) dense and output layers in greater detail.  \n",
    "\n",
    "<img src=\"images/rnn_model_unrolled.png\" width=\"60%\">\n",
    "\n",
    "Use your research to complete the `rnn_model` function within the `sample_models.py` file.  The function should specify an architecture that satisfies the following requirements:\n",
    "- The first layer of the neural network should be an RNN (`SimpleRNN`, `LSTM`, or `GRU`) that takes the time sequence of audio features as input.  We have added `GRU` units for you, but feel free to change `GRU` to `SimpleRNN` or `LSTM`, if you like!\n",
    "- Whereas the architecture in `simple_rnn_model` treated the RNN output as the final layer of the model, you will use the output of your RNN as a hidden layer.  Use `TimeDistributed` to apply a `Dense` layer to each of the time steps in the RNN output.  Ensure that each `Dense` layer has `output_dim` units.\n",
    "\n",
    "Use the code cell below to load your model into the `model_1` variable.  Use a value for `input_dim` that matches your chosen audio features, and feel free to change the values for `units` and `activation` to tweak the behavior of your recurrent layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 200)         128400    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (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: 135,029\n",
      "Trainable params: 134,629\n",
      "Non-trainable params: 400\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_1 = rnn_model(input_dim=13, # 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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 205s - loss: 305.7423 - val_loss: 296.6979\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 205s - loss: 215.3288 - val_loss: 202.1194\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 204s - loss: 193.3995 - val_loss: 196.2064\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 205s - loss: 176.6753 - val_loss: 171.7307\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 204s - loss: 163.1130 - val_loss: 164.3029\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 203s - loss: 153.5710 - val_loss: 153.6830\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 202s - loss: 145.8884 - val_loss: 144.8432\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 202s - loss: 139.2378 - val_loss: 143.0991\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 204s - loss: 134.9006 - val_loss: 140.4479\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 203s - loss: 130.7768 - val_loss: 134.3652\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 204s - loss: 127.2326 - val_loss: 135.2690\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 203s - loss: 124.3319 - val_loss: 133.3378\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 203s - loss: 121.5862 - val_loss: 132.2160\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 203s - loss: 118.9877 - val_loss: 128.8496\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 204s - loss: 116.8712 - val_loss: 127.7373\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 202s - loss: 114.8243 - val_loss: 125.5805\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 203s - loss: 114.5587 - val_loss: 136.9372\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 203s - loss: 111.7114 - val_loss: 125.8040\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 203s - loss: 109.7198 - val_loss: 123.2213\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 204s - loss: 108.2947 - val_loss: 124.7556\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_1, \n",
    "            pickle_path='model_1.pickle', \n",
    "            save_model_path='model_1.h5',\n",
    "            spectrogram=False) # 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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 200)         28800     \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "rnn (SimpleRNN)              (None, None, 200)         80200     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, None, 29)          5829      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 116,429\n",
      "Trainable params: 115,629\n",
      "Non-trainable params: 800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_2 = cnn_rnn_model(input_dim=13, # 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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 70s - loss: 240.2165 - val_loss: 203.6262\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 65s - loss: 177.4611 - val_loss: 176.0184\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 65s - loss: 152.6354 - val_loss: 149.5574\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 64s - loss: 140.9491 - val_loss: 142.8858\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 65s - loss: 133.5081 - val_loss: 135.2553\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 65s - loss: 127.3908 - val_loss: 139.7807\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 65s - loss: 123.3225 - val_loss: 130.3152\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 65s - loss: 119.7367 - val_loss: 130.0715\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 65s - loss: 117.1816 - val_loss: 130.3894\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 65s - loss: 114.6226 - val_loss: 126.3842\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 65s - loss: 111.9511 - val_loss: 125.5697\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 65s - loss: 110.2151 - val_loss: 127.1003\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 65s - loss: 108.5706 - val_loss: 124.9251\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 66s - loss: 107.1163 - val_loss: 124.8984\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 66s - loss: 105.3432 - val_loss: 126.2865\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 65s - loss: 104.0693 - val_loss: 126.3311\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 65s - loss: 102.6673 - val_loss: 124.4281\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 66s - loss: 101.4796 - val_loss: 123.9835\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 64s - loss: 100.4146 - val_loss: 123.9160\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 65s - loss: 99.7422 - val_loss: 126.2153\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_2, \n",
    "            pickle_path='model_2.pickle', \n",
    "            save_model_path='model_2.h5', \n",
    "            spectrogram=False) # 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, None, 200)         128400    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "gru_2 (GRU)                  (None, None, 200)         240600    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_5 (TimeDist (None, None, 29)          5829      \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 376,429\n",
      "Trainable params: 375,629\n",
      "Non-trainable params: 800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_3 = deep_rnn_model(input_dim=13, # 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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 360s - loss: 276.5376 - val_loss: 257.6094\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 360s - loss: 220.4237 - val_loss: 208.3890\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 358s - loss: 183.6579 - val_loss: 186.3643\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 360s - loss: 161.8461 - val_loss: 161.7141\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 359s - loss: 149.8463 - val_loss: 150.6101\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 359s - loss: 141.0458 - val_loss: 144.2440\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 358s - loss: 134.4142 - val_loss: 137.8501\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 359s - loss: 129.5609 - val_loss: 139.7161\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 356s - loss: 125.6802 - val_loss: 132.3824\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 355s - loss: 121.7066 - val_loss: 130.3933\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 359s - loss: 117.5087 - val_loss: 128.2021\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 359s - loss: 115.1510 - val_loss: 127.4145\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 358s - loss: 112.2906 - val_loss: 124.0645\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 360s - loss: 109.5372 - val_loss: 125.5536\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 360s - loss: 107.8101 - val_loss: 123.3970\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 360s - loss: 105.4077 - val_loss: 121.6854\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 358s - loss: 103.5051 - val_loss: 122.6611\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 360s - loss: 101.9603 - val_loss: 121.6676\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 358s - loss: 100.0538 - val_loss: 120.1273\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 360s - loss: 98.8588 - val_loss: 118.7999\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_3, \n",
    "            pickle_path='model_3.pickle', \n",
    "            save_model_path='model_3.h5', \n",
    "            spectrogram=False) # 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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, None, 400)         342400    \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, None, 29)          11629     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 29)          0         \n",
      "=================================================================\n",
      "Total params: 354,029\n",
      "Trainable params: 354,029\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_4 = bidirectional_rnn_model(input_dim=13, # 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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 403s - loss: 293.7316 - val_loss: 208.4598\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 398s - loss: 202.4459 - val_loss: 181.7270\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 395s - loss: 180.4353 - val_loss: 165.8111\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 393s - loss: 165.8232 - val_loss: 156.9870\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 395s - loss: 153.5176 - val_loss: 150.8199\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 394s - loss: 144.4471 - val_loss: 145.5029\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 393s - loss: 137.1976 - val_loss: 140.9786\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 392s - loss: 130.6399 - val_loss: 138.6262\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 394s - loss: 125.1285 - val_loss: 135.3748\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 393s - loss: 120.4325 - val_loss: 133.7241\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 391s - loss: 115.9021 - val_loss: 132.5403\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 393s - loss: 112.3208 - val_loss: 132.6953\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 393s - loss: 108.6977 - val_loss: 130.9604\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 388s - loss: 105.2290 - val_loss: 133.1560\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 392s - loss: 102.1709 - val_loss: 131.5377\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 394s - loss: 99.2968 - val_loss: 135.6388\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 394s - loss: 95.8915 - val_loss: 131.8034\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 394s - loss: 93.3064 - val_loss: 135.1880\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 395s - loss: 90.5941 - val_loss: 135.5908\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 395s - loss: 87.9544 - val_loss: 135.5592\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_4, \n",
    "            pickle_path='model_4.pickle', \n",
    "            save_model_path='model_4.h5', \n",
    "            spectrogram=False) # 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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aind2/anaconda3/envs/aind-vui/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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XWepmS2srLFu2jIyMDD7//HNSU1O59dZbefjhh9m4cWNHxCciIt/BXzKP8Mb7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcac2911da0>"
      ]
     },
     "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",
    "- Model 0(blue) is the worst one. It basically learned nothing. I can explain this with the fact, that data is not presented right for this model - each item should be a sequence. It doesn't take time series into account and that is why it performs so bad.\n",
    "\n",
    "- Model 1(green) uses TimeDistributed Dense and BatchNormalization. TimeDistributed Dense fix the problem of Model 0 with data presentation. Also, BatchNormalization makes network learn faster. It explains why this model performs much better than the Model 0.\n",
    "\n",
    "-  Model 2(red) uses additional convolutional layer and TimeDistributed Dense. It shows one of the best performance from among all models. I think this is because convolutional layer capture 'pattern' in audio and as the result it generalizes the different voices better. Also, this model trained faster, than other models. Its because it uses SimpleRNN, not heavy LSTM or GRU layers. But unfortunately, it has small loss, but not so small validation loss. Models 1 and 3 have smaller validation losses, which mean that they are more accurate.\n",
    "\n",
    "-  Model 3(purple) uses Deeper RNN and TimeDistributedDense. As expected, it performs better, than simpler Model 1 with only one RNN layer. But it is also almost two times slower, than the Model 1. Eventually, it overperform also Model 2 - it has a bit smaller validation loss and this is the smallest validation loss among all models.\n",
    "\n",
    "- Model 4(yellow) uses Bidirectional RNN and TimeDistributed Dense. Validation loss of the Model 4 was very close to the validation loss of the Model 2 at the beginning. But after 10 epochs validation loss of the Model 4 stays the same, but training loss became smaller. It shows that Model 4 is probably overfitted. Also, it is almost 6 times slower, than the Model 2"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "the_input (InputLayer)           (None, None, 13)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_conv (Conv1D)              (None, None, 128)     1792        the_input[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "bn_input (BatchNormalization)    (None, None, 128)     512         input_conv[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_0 (Conv1D)       (None, None, 128)     114816      bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_0 (Conv1D)         (None, None, 128)     114816      bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)            (None, None, 128)     0           bn_filter_1_0[0][0]              \n",
      "                                                                   bn_gate_1_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_0 (Conv1D)          (None, None, 128)     16512       multiply_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_1 (Add)                      (None, None, 128)     0           bn_out_1_0[0][0]                 \n",
      "                                                                   bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_0 (Conv1D)       (None, None, 128)     114816      add_1[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_0 (Conv1D)         (None, None, 128)     114816      add_1[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_2 (Multiply)            (None, None, 128)     0           bn_filter_2_0[0][0]              \n",
      "                                                                   bn_gate_2_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_0 (Conv1D)          (None, None, 128)     16512       multiply_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_2 (Add)                      (None, None, 128)     0           bn_out_2_0[0][0]                 \n",
      "                                                                   add_1[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_0 (Conv1D)       (None, None, 128)     114816      add_2[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_0 (Conv1D)         (None, None, 128)     114816      add_2[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_3 (Multiply)            (None, None, 128)     0           bn_filter_3_0[0][0]              \n",
      "                                                                   bn_gate_3_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_0 (Conv1D)          (None, None, 128)     16512       multiply_3[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_4 (Add)                      (None, None, 128)     0           bn_out_3_0[0][0]                 \n",
      "                                                                   add_2[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_0 (Conv1D)       (None, None, 128)     114816      add_4[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_0 (Conv1D)         (None, None, 128)     114816      add_4[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_4 (Multiply)            (None, None, 128)     0           bn_filter_4_0[0][0]              \n",
      "                                                                   bn_gate_4_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_0 (Conv1D)          (None, None, 128)     16512       multiply_4[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_6 (Add)                      (None, None, 128)     0           bn_out_4_0[0][0]                 \n",
      "                                                                   add_4[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_0 (Conv1D)       (None, None, 128)     114816      add_6[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_0 (Conv1D)         (None, None, 128)     114816      add_6[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_5 (Multiply)            (None, None, 128)     0           bn_filter_8_0[0][0]              \n",
      "                                                                   bn_gate_8_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_0 (Conv1D)          (None, None, 128)     16512       multiply_5[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_8 (Add)                      (None, None, 128)     0           bn_out_8_0[0][0]                 \n",
      "                                                                   add_6[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_0 (Conv1D)      (None, None, 128)     114816      add_8[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_0 (Conv1D)        (None, None, 128)     114816      add_8[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_0 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_0[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_0 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_0[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "multiply_6 (Multiply)            (None, None, 128)     0           bn_filter_16_0[0][0]             \n",
      "                                                                   bn_gate_16_0[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_0 (Conv1D)         (None, None, 128)     16512       multiply_6[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_0 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_10 (Add)                     (None, None, 128)     0           bn_out_16_0[0][0]                \n",
      "                                                                   add_8[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_1 (Conv1D)       (None, None, 128)     114816      add_10[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_1 (Conv1D)         (None, None, 128)     114816      add_10[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_7 (Multiply)            (None, None, 128)     0           bn_filter_1_1[0][0]              \n",
      "                                                                   bn_gate_1_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_1 (Conv1D)          (None, None, 128)     16512       multiply_7[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_12 (Add)                     (None, None, 128)     0           bn_out_1_1[0][0]                 \n",
      "                                                                   add_10[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_1 (Conv1D)       (None, None, 128)     114816      add_12[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_1 (Conv1D)         (None, None, 128)     114816      add_12[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_8 (Multiply)            (None, None, 128)     0           bn_filter_2_1[0][0]              \n",
      "                                                                   bn_gate_2_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_1 (Conv1D)          (None, None, 128)     16512       multiply_8[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_14 (Add)                     (None, None, 128)     0           bn_out_2_1[0][0]                 \n",
      "                                                                   add_12[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_1 (Conv1D)       (None, None, 128)     114816      add_14[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_1 (Conv1D)         (None, None, 128)     114816      add_14[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_9 (Multiply)            (None, None, 128)     0           bn_filter_3_1[0][0]              \n",
      "                                                                   bn_gate_3_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_1 (Conv1D)          (None, None, 128)     16512       multiply_9[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_16 (Add)                     (None, None, 128)     0           bn_out_3_1[0][0]                 \n",
      "                                                                   add_14[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_1 (Conv1D)       (None, None, 128)     114816      add_16[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_1 (Conv1D)         (None, None, 128)     114816      add_16[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_10 (Multiply)           (None, None, 128)     0           bn_filter_4_1[0][0]              \n",
      "                                                                   bn_gate_4_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_1 (Conv1D)          (None, None, 128)     16512       multiply_10[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_18 (Add)                     (None, None, 128)     0           bn_out_4_1[0][0]                 \n",
      "                                                                   add_16[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_1 (Conv1D)       (None, None, 128)     114816      add_18[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_1 (Conv1D)         (None, None, 128)     114816      add_18[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_11 (Multiply)           (None, None, 128)     0           bn_filter_8_1[0][0]              \n",
      "                                                                   bn_gate_8_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_1 (Conv1D)          (None, None, 128)     16512       multiply_11[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_20 (Add)                     (None, None, 128)     0           bn_out_8_1[0][0]                 \n",
      "                                                                   add_18[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_1 (Conv1D)      (None, None, 128)     114816      add_20[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_1 (Conv1D)        (None, None, 128)     114816      add_20[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_1 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_1[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_1 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_1[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "multiply_12 (Multiply)           (None, None, 128)     0           bn_filter_16_1[0][0]             \n",
      "                                                                   bn_gate_16_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_1 (Conv1D)         (None, None, 128)     16512       multiply_12[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_1 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_22 (Add)                     (None, None, 128)     0           bn_out_16_1[0][0]                \n",
      "                                                                   add_20[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_2 (Conv1D)       (None, None, 128)     114816      add_22[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_2 (Conv1D)         (None, None, 128)     114816      add_22[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_13 (Multiply)           (None, None, 128)     0           bn_filter_1_2[0][0]              \n",
      "                                                                   bn_gate_1_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_2 (Conv1D)          (None, None, 128)     16512       multiply_13[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_24 (Add)                     (None, None, 128)     0           bn_out_1_2[0][0]                 \n",
      "                                                                   add_22[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_2 (Conv1D)       (None, None, 128)     114816      add_24[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_2 (Conv1D)         (None, None, 128)     114816      add_24[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_14 (Multiply)           (None, None, 128)     0           bn_filter_2_2[0][0]              \n",
      "                                                                   bn_gate_2_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_2 (Conv1D)          (None, None, 128)     16512       multiply_14[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_26 (Add)                     (None, None, 128)     0           bn_out_2_2[0][0]                 \n",
      "                                                                   add_24[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_2 (Conv1D)       (None, None, 128)     114816      add_26[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_2 (Conv1D)         (None, None, 128)     114816      add_26[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_15 (Multiply)           (None, None, 128)     0           bn_filter_3_2[0][0]              \n",
      "                                                                   bn_gate_3_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_2 (Conv1D)          (None, None, 128)     16512       multiply_15[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_28 (Add)                     (None, None, 128)     0           bn_out_3_2[0][0]                 \n",
      "                                                                   add_26[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_2 (Conv1D)       (None, None, 128)     114816      add_28[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_2 (Conv1D)         (None, None, 128)     114816      add_28[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_3 (Add)                      (None, None, 128)     0           bn_out_1_0[0][0]                 \n",
      "                                                                   bn_out_2_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_5 (Add)                      (None, None, 128)     0           add_3[0][0]                      \n",
      "                                                                   bn_out_3_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_16 (Multiply)           (None, None, 128)     0           bn_filter_4_2[0][0]              \n",
      "                                                                   bn_gate_4_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_7 (Add)                      (None, None, 128)     0           add_5[0][0]                      \n",
      "                                                                   bn_out_4_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_2 (Conv1D)          (None, None, 128)     16512       multiply_16[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_9 (Add)                      (None, None, 128)     0           add_7[0][0]                      \n",
      "                                                                   bn_out_8_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_11 (Add)                     (None, None, 128)     0           add_9[0][0]                      \n",
      "                                                                   bn_out_16_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_30 (Add)                     (None, None, 128)     0           bn_out_4_2[0][0]                 \n",
      "                                                                   add_28[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_13 (Add)                     (None, None, 128)     0           add_11[0][0]                     \n",
      "                                                                   bn_out_1_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_2 (Conv1D)       (None, None, 128)     114816      add_30[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_2 (Conv1D)         (None, None, 128)     114816      add_30[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_15 (Add)                     (None, None, 128)     0           add_13[0][0]                     \n",
      "                                                                   bn_out_2_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_17 (Add)                     (None, None, 128)     0           add_15[0][0]                     \n",
      "                                                                   bn_out_3_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_17 (Multiply)           (None, None, 128)     0           bn_filter_8_2[0][0]              \n",
      "                                                                   bn_gate_8_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_19 (Add)                     (None, None, 128)     0           add_17[0][0]                     \n",
      "                                                                   bn_out_4_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_2 (Conv1D)          (None, None, 128)     16512       multiply_17[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_21 (Add)                     (None, None, 128)     0           add_19[0][0]                     \n",
      "                                                                   bn_out_8_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_23 (Add)                     (None, None, 128)     0           add_21[0][0]                     \n",
      "                                                                   bn_out_16_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_32 (Add)                     (None, None, 128)     0           bn_out_8_2[0][0]                 \n",
      "                                                                   add_30[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_25 (Add)                     (None, None, 128)     0           add_23[0][0]                     \n",
      "                                                                   bn_out_1_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_2 (Conv1D)      (None, None, 128)     114816      add_32[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_2 (Conv1D)        (None, None, 128)     114816      add_32[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_27 (Add)                     (None, None, 128)     0           add_25[0][0]                     \n",
      "                                                                   bn_out_2_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_2 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_2[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_2 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_2[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "add_29 (Add)                     (None, None, 128)     0           add_27[0][0]                     \n",
      "                                                                   bn_out_3_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_18 (Multiply)           (None, None, 128)     0           bn_filter_16_2[0][0]             \n",
      "                                                                   bn_gate_16_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "add_31 (Add)                     (None, None, 128)     0           add_29[0][0]                     \n",
      "                                                                   bn_out_4_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_2 (Conv1D)         (None, None, 128)     16512       multiply_18[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_33 (Add)                     (None, None, 128)     0           add_31[0][0]                     \n",
      "                                                                   bn_out_8_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_2 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_35 (Add)                     (None, None, 128)     0           add_33[0][0]                     \n",
      "                                                                   bn_out_16_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "output_conv_1 (Conv1D)           (None, None, 128)     16512       add_35[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "output_bn_1 (BatchNormalization) (None, None, 128)     512         output_conv_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "output_conv_2 (Conv1D)           (None, None, 29)      3741        output_bn_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "softmax (Activation)             (None, None, 29)      0           output_conv_2[0][0]              \n",
      "====================================================================================================\n",
      "Total params: 4,481,309\n",
      "Trainable params: 4,466,973\n",
      "Non-trainable params: 14,336\n",
      "____________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# specify the model\n",
    "model_end = final_model(input_dim=13, filters=128, kernel_size=7)"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "106/106 [==============================] - 219s - loss: 309.1311 - val_loss: 882.8010\n",
      "Epoch 2/20\n",
      "106/106 [==============================] - 146s - loss: 222.7799 - val_loss: 251.6762\n",
      "Epoch 3/20\n",
      "106/106 [==============================] - 143s - loss: 209.3624 - val_loss: 210.4086\n",
      "Epoch 4/20\n",
      "106/106 [==============================] - 142s - loss: 204.4223 - val_loss: 203.1264\n",
      "Epoch 5/20\n",
      "106/106 [==============================] - 142s - loss: 201.8195 - val_loss: 196.3605\n",
      "Epoch 6/20\n",
      "106/106 [==============================] - 139s - loss: 198.5667 - val_loss: 206.2955\n",
      "Epoch 7/20\n",
      "106/106 [==============================] - 139s - loss: 198.1979 - val_loss: 192.6005\n",
      "Epoch 8/20\n",
      "106/106 [==============================] - 139s - loss: 196.6575 - val_loss: 190.8611\n",
      "Epoch 9/20\n",
      "106/106 [==============================] - 139s - loss: 195.4842 - val_loss: 186.4075\n",
      "Epoch 10/20\n",
      "106/106 [==============================] - 138s - loss: 194.2797 - val_loss: 187.9668\n",
      "Epoch 11/20\n",
      "106/106 [==============================] - 137s - loss: 192.9799 - val_loss: 183.9527\n",
      "Epoch 12/20\n",
      "106/106 [==============================] - 138s - loss: 191.8731 - val_loss: 184.3945\n",
      "Epoch 13/20\n",
      "106/106 [==============================] - 138s - loss: 189.8951 - val_loss: 183.4989\n",
      "Epoch 14/20\n",
      "106/106 [==============================] - 140s - loss: 187.8709 - val_loss: 181.0756\n",
      "Epoch 15/20\n",
      "106/106 [==============================] - 137s - loss: 185.8430 - val_loss: 180.3078\n",
      "Epoch 16/20\n",
      "106/106 [==============================] - 137s - loss: 182.3477 - val_loss: 173.9857\n",
      "Epoch 17/20\n",
      "106/106 [==============================] - 137s - loss: 180.1227 - val_loss: 178.9986\n",
      "Epoch 18/20\n",
      "106/106 [==============================] - 137s - loss: 175.5823 - val_loss: 170.8191\n",
      "Epoch 19/20\n",
      "106/106 [==============================] - 137s - loss: 168.1681 - val_loss: 164.4018\n",
      "Epoch 20/20\n",
      "106/106 [==============================] - 137s - loss: 164.0151 - val_loss: 159.7677\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_end, \n",
    "            pickle_path='model_end.pickle', \n",
    "            save_model_path='model_end.h5', \n",
    "            spectrogram=False) # 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 a final model I decided to choose one, that include CNN layers. It's because it is fuster to train such models compares to deep RNN models. For me this is advantage, because I could tune more hyperparameters and try more different approaches. In total I trained 15 models and this one is the best. Unfortunaltelly, it did not overperform Model 1, 2 and 3. I tried to implement architecture that is similar to DeepMind's [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499) and its [speech-to-text](https://github.com/buriburisuri/speech-to-text-wavenet) implementation. I uses a Keras, that is why I had to adopt code a bit. As in speech-to-text repo I used up to 16 rates in dilated layers. I started with a simple convolutional layer with a size = 1. After that I used residual blocks, very similar to one in the WaveNet paper (Chapter 2.4) stacked many times (18 in general). Also in each block I used tanh activation for filter layers and sigmoid for gate layers.\n",
    "I made my own changes to the arcitecture - I finished all with single Convolutional layer with relu activation function to simplify model. Tuning amound of filters and kernel size didn't lead to good results, so I keeped values from the speech-to-text implementation.\n",
    "\n",
    "Final models'performance was not so good as expected. It was more than 2 times faster to train than Models 3 and 4. But its validation loss is 9%  bigger, than in Model 3(with the smallest loss). Moreover it tend to overfit - training losses are much smaller, than validation losses."
   ]
  },
  {
   "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": 10,
   "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( spectrogram=False)\n",
    "    data_gen.load_train_data()\n",
    "    data_gen.load_validation_data()\n",
    "    \n",
    "    # obtain the true transcription and the audio features \n",
    "    if partition == 'validation':\n",
    "        transcr = data_gen.valid_texts[index]\n",
    "        audio_path = data_gen.valid_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    elif partition == 'train':\n",
    "        transcr = data_gen.train_texts[index]\n",
    "        audio_path = data_gen.train_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    else:\n",
    "        raise Exception('Invalid partition!  Must be \"train\" or \"validation\"')\n",
    "        \n",
    "    # obtain and decode the acoustic model's predictions\n",
    "    input_to_softmax.load_weights(model_path)\n",
    "    prediction = input_to_softmax.predict(np.expand_dims(data_point, axis=0))\n",
    "    output_length = [input_to_softmax.output_length(data_point.shape[0])] \n",
    "    pred_ints = (K.eval(K.ctc_decode(\n",
    "                prediction, output_length)[0][0])+1).flatten().tolist()\n",
    "    \n",
    "    # play the audio file, and display the true and predicted transcriptions\n",
    "    print('-'*80)\n",
    "    Audio(audio_path)\n",
    "    print('True transcription:\\n' + '\\n' + transcr)\n",
    "    print('-'*80)\n",
    "    print('Predicted transcription:\\n' + '\\n' + ''.join(int_sequence_to_text(pred_ints)))\n",
    "    print('-'*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the code cell below to obtain the transcription predicted by your final model for the first example in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "the_input (InputLayer)           (None, None, 13)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_conv (Conv1D)              (None, None, 128)     1792        the_input[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "bn_input (BatchNormalization)    (None, None, 128)     512         input_conv[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_0 (Conv1D)       (None, None, 128)     114816      bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_0 (Conv1D)         (None, None, 128)     114816      bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_37 (Multiply)           (None, None, 128)     0           bn_filter_1_0[0][0]              \n",
      "                                                                   bn_gate_1_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_0 (Conv1D)          (None, None, 128)     16512       multiply_37[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_71 (Add)                     (None, None, 128)     0           bn_out_1_0[0][0]                 \n",
      "                                                                   bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_0 (Conv1D)       (None, None, 128)     114816      add_71[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_0 (Conv1D)         (None, None, 128)     114816      add_71[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_38 (Multiply)           (None, None, 128)     0           bn_filter_2_0[0][0]              \n",
      "                                                                   bn_gate_2_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_0 (Conv1D)          (None, None, 128)     16512       multiply_38[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_72 (Add)                     (None, None, 128)     0           bn_out_2_0[0][0]                 \n",
      "                                                                   add_71[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_0 (Conv1D)       (None, None, 128)     114816      add_72[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_0 (Conv1D)         (None, None, 128)     114816      add_72[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_39 (Multiply)           (None, None, 128)     0           bn_filter_3_0[0][0]              \n",
      "                                                                   bn_gate_3_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_0 (Conv1D)          (None, None, 128)     16512       multiply_39[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_74 (Add)                     (None, None, 128)     0           bn_out_3_0[0][0]                 \n",
      "                                                                   add_72[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_0 (Conv1D)       (None, None, 128)     114816      add_74[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_0 (Conv1D)         (None, None, 128)     114816      add_74[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_40 (Multiply)           (None, None, 128)     0           bn_filter_4_0[0][0]              \n",
      "                                                                   bn_gate_4_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_0 (Conv1D)          (None, None, 128)     16512       multiply_40[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_76 (Add)                     (None, None, 128)     0           bn_out_4_0[0][0]                 \n",
      "                                                                   add_74[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_0 (Conv1D)       (None, None, 128)     114816      add_76[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_0 (Conv1D)         (None, None, 128)     114816      add_76[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_41 (Multiply)           (None, None, 128)     0           bn_filter_8_0[0][0]              \n",
      "                                                                   bn_gate_8_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_0 (Conv1D)          (None, None, 128)     16512       multiply_41[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_78 (Add)                     (None, None, 128)     0           bn_out_8_0[0][0]                 \n",
      "                                                                   add_76[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_0 (Conv1D)      (None, None, 128)     114816      add_78[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_0 (Conv1D)        (None, None, 128)     114816      add_78[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_0 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_0[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_0 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_0[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "multiply_42 (Multiply)           (None, None, 128)     0           bn_filter_16_0[0][0]             \n",
      "                                                                   bn_gate_16_0[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_0 (Conv1D)         (None, None, 128)     16512       multiply_42[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_0 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_80 (Add)                     (None, None, 128)     0           bn_out_16_0[0][0]                \n",
      "                                                                   add_78[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_1 (Conv1D)       (None, None, 128)     114816      add_80[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_1 (Conv1D)         (None, None, 128)     114816      add_80[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_43 (Multiply)           (None, None, 128)     0           bn_filter_1_1[0][0]              \n",
      "                                                                   bn_gate_1_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_1 (Conv1D)          (None, None, 128)     16512       multiply_43[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_82 (Add)                     (None, None, 128)     0           bn_out_1_1[0][0]                 \n",
      "                                                                   add_80[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_1 (Conv1D)       (None, None, 128)     114816      add_82[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_1 (Conv1D)         (None, None, 128)     114816      add_82[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_44 (Multiply)           (None, None, 128)     0           bn_filter_2_1[0][0]              \n",
      "                                                                   bn_gate_2_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_1 (Conv1D)          (None, None, 128)     16512       multiply_44[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_84 (Add)                     (None, None, 128)     0           bn_out_2_1[0][0]                 \n",
      "                                                                   add_82[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_1 (Conv1D)       (None, None, 128)     114816      add_84[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_1 (Conv1D)         (None, None, 128)     114816      add_84[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_45 (Multiply)           (None, None, 128)     0           bn_filter_3_1[0][0]              \n",
      "                                                                   bn_gate_3_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_1 (Conv1D)          (None, None, 128)     16512       multiply_45[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_86 (Add)                     (None, None, 128)     0           bn_out_3_1[0][0]                 \n",
      "                                                                   add_84[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_1 (Conv1D)       (None, None, 128)     114816      add_86[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_1 (Conv1D)         (None, None, 128)     114816      add_86[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_46 (Multiply)           (None, None, 128)     0           bn_filter_4_1[0][0]              \n",
      "                                                                   bn_gate_4_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_1 (Conv1D)          (None, None, 128)     16512       multiply_46[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_88 (Add)                     (None, None, 128)     0           bn_out_4_1[0][0]                 \n",
      "                                                                   add_86[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_1 (Conv1D)       (None, None, 128)     114816      add_88[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_1 (Conv1D)         (None, None, 128)     114816      add_88[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_47 (Multiply)           (None, None, 128)     0           bn_filter_8_1[0][0]              \n",
      "                                                                   bn_gate_8_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_1 (Conv1D)          (None, None, 128)     16512       multiply_47[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_90 (Add)                     (None, None, 128)     0           bn_out_8_1[0][0]                 \n",
      "                                                                   add_88[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_1 (Conv1D)      (None, None, 128)     114816      add_90[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_1 (Conv1D)        (None, None, 128)     114816      add_90[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_1 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_1[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_1 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_1[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "multiply_48 (Multiply)           (None, None, 128)     0           bn_filter_16_1[0][0]             \n",
      "                                                                   bn_gate_16_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_1 (Conv1D)         (None, None, 128)     16512       multiply_48[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_1 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_92 (Add)                     (None, None, 128)     0           bn_out_16_1[0][0]                \n",
      "                                                                   add_90[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_2 (Conv1D)       (None, None, 128)     114816      add_92[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_2 (Conv1D)         (None, None, 128)     114816      add_92[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_49 (Multiply)           (None, None, 128)     0           bn_filter_1_2[0][0]              \n",
      "                                                                   bn_gate_1_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_2 (Conv1D)          (None, None, 128)     16512       multiply_49[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_94 (Add)                     (None, None, 128)     0           bn_out_1_2[0][0]                 \n",
      "                                                                   add_92[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_2 (Conv1D)       (None, None, 128)     114816      add_94[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_2 (Conv1D)         (None, None, 128)     114816      add_94[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_50 (Multiply)           (None, None, 128)     0           bn_filter_2_2[0][0]              \n",
      "                                                                   bn_gate_2_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_2 (Conv1D)          (None, None, 128)     16512       multiply_50[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_96 (Add)                     (None, None, 128)     0           bn_out_2_2[0][0]                 \n",
      "                                                                   add_94[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_2 (Conv1D)       (None, None, 128)     114816      add_96[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_2 (Conv1D)         (None, None, 128)     114816      add_96[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_51 (Multiply)           (None, None, 128)     0           bn_filter_3_2[0][0]              \n",
      "                                                                   bn_gate_3_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_2 (Conv1D)          (None, None, 128)     16512       multiply_51[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_98 (Add)                     (None, None, 128)     0           bn_out_3_2[0][0]                 \n",
      "                                                                   add_96[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_2 (Conv1D)       (None, None, 128)     114816      add_98[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_2 (Conv1D)         (None, None, 128)     114816      add_98[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_73 (Add)                     (None, None, 128)     0           bn_out_1_0[0][0]                 \n",
      "                                                                   bn_out_2_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_75 (Add)                     (None, None, 128)     0           add_73[0][0]                     \n",
      "                                                                   bn_out_3_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_52 (Multiply)           (None, None, 128)     0           bn_filter_4_2[0][0]              \n",
      "                                                                   bn_gate_4_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_77 (Add)                     (None, None, 128)     0           add_75[0][0]                     \n",
      "                                                                   bn_out_4_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_2 (Conv1D)          (None, None, 128)     16512       multiply_52[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_79 (Add)                     (None, None, 128)     0           add_77[0][0]                     \n",
      "                                                                   bn_out_8_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_81 (Add)                     (None, None, 128)     0           add_79[0][0]                     \n",
      "                                                                   bn_out_16_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_100 (Add)                    (None, None, 128)     0           bn_out_4_2[0][0]                 \n",
      "                                                                   add_98[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "add_83 (Add)                     (None, None, 128)     0           add_81[0][0]                     \n",
      "                                                                   bn_out_1_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_2 (Conv1D)       (None, None, 128)     114816      add_100[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_2 (Conv1D)         (None, None, 128)     114816      add_100[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_85 (Add)                     (None, None, 128)     0           add_83[0][0]                     \n",
      "                                                                   bn_out_2_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_87 (Add)                     (None, None, 128)     0           add_85[0][0]                     \n",
      "                                                                   bn_out_3_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_53 (Multiply)           (None, None, 128)     0           bn_filter_8_2[0][0]              \n",
      "                                                                   bn_gate_8_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_89 (Add)                     (None, None, 128)     0           add_87[0][0]                     \n",
      "                                                                   bn_out_4_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_2 (Conv1D)          (None, None, 128)     16512       multiply_53[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_91 (Add)                     (None, None, 128)     0           add_89[0][0]                     \n",
      "                                                                   bn_out_8_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_93 (Add)                     (None, None, 128)     0           add_91[0][0]                     \n",
      "                                                                   bn_out_16_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_102 (Add)                    (None, None, 128)     0           bn_out_8_2[0][0]                 \n",
      "                                                                   add_100[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_95 (Add)                     (None, None, 128)     0           add_93[0][0]                     \n",
      "                                                                   bn_out_1_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_2 (Conv1D)      (None, None, 128)     114816      add_102[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_2 (Conv1D)        (None, None, 128)     114816      add_102[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_97 (Add)                     (None, None, 128)     0           add_95[0][0]                     \n",
      "                                                                   bn_out_2_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_2 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_2[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_2 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_2[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "add_99 (Add)                     (None, None, 128)     0           add_97[0][0]                     \n",
      "                                                                   bn_out_3_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_54 (Multiply)           (None, None, 128)     0           bn_filter_16_2[0][0]             \n",
      "                                                                   bn_gate_16_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "add_101 (Add)                    (None, None, 128)     0           add_99[0][0]                     \n",
      "                                                                   bn_out_4_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_2 (Conv1D)         (None, None, 128)     16512       multiply_54[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_103 (Add)                    (None, None, 128)     0           add_101[0][0]                    \n",
      "                                                                   bn_out_8_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_2 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_105 (Add)                    (None, None, 128)     0           add_103[0][0]                    \n",
      "                                                                   bn_out_16_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "output_conv_1 (Conv1D)           (None, None, 128)     16512       add_105[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "output_bn_1 (BatchNormalization) (None, None, 128)     512         output_conv_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "output_conv_2 (Conv1D)           (None, None, 29)      3741        output_bn_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "softmax (Activation)             (None, None, 29)      0           output_conv_2[0][0]              \n",
      "====================================================================================================\n",
      "Total params: 4,481,309\n",
      "Trainable params: 4,466,973\n",
      "Non-trainable params: 14,336\n",
      "____________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "the houses seemed miserable in the extreme especially to an eye accustomed to the smiling neatness of english cottages\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "e onis shie nas sero olin e srin  fassle to e ma aan to is in e es so e was ai to\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='train',\n",
    "                input_to_softmax=final_model(input_dim=13, filters=128, kernel_size=7), \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": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "the_input (InputLayer)           (None, None, 13)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_conv (Conv1D)              (None, None, 128)     1792        the_input[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "bn_input (BatchNormalization)    (None, None, 128)     512         input_conv[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_0 (Conv1D)       (None, None, 128)     114816      bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_0 (Conv1D)         (None, None, 128)     114816      bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_73 (Multiply)           (None, None, 128)     0           bn_filter_1_0[0][0]              \n",
      "                                                                   bn_gate_1_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_0 (Conv1D)          (None, None, 128)     16512       multiply_73[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_141 (Add)                    (None, None, 128)     0           bn_out_1_0[0][0]                 \n",
      "                                                                   bn_input[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_0 (Conv1D)       (None, None, 128)     114816      add_141[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_0 (Conv1D)         (None, None, 128)     114816      add_141[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_74 (Multiply)           (None, None, 128)     0           bn_filter_2_0[0][0]              \n",
      "                                                                   bn_gate_2_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_0 (Conv1D)          (None, None, 128)     16512       multiply_74[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_142 (Add)                    (None, None, 128)     0           bn_out_2_0[0][0]                 \n",
      "                                                                   add_141[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_0 (Conv1D)       (None, None, 128)     114816      add_142[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_0 (Conv1D)         (None, None, 128)     114816      add_142[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_75 (Multiply)           (None, None, 128)     0           bn_filter_3_0[0][0]              \n",
      "                                                                   bn_gate_3_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_0 (Conv1D)          (None, None, 128)     16512       multiply_75[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_144 (Add)                    (None, None, 128)     0           bn_out_3_0[0][0]                 \n",
      "                                                                   add_142[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_0 (Conv1D)       (None, None, 128)     114816      add_144[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_0 (Conv1D)         (None, None, 128)     114816      add_144[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_76 (Multiply)           (None, None, 128)     0           bn_filter_4_0[0][0]              \n",
      "                                                                   bn_gate_4_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_0 (Conv1D)          (None, None, 128)     16512       multiply_76[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_146 (Add)                    (None, None, 128)     0           bn_out_4_0[0][0]                 \n",
      "                                                                   add_144[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_0 (Conv1D)       (None, None, 128)     114816      add_146[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_0 (Conv1D)         (None, None, 128)     114816      add_146[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_0 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_0[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_0 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_77 (Multiply)           (None, None, 128)     0           bn_filter_8_0[0][0]              \n",
      "                                                                   bn_gate_8_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_0 (Conv1D)          (None, None, 128)     16512       multiply_77[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_0 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_0[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_148 (Add)                    (None, None, 128)     0           bn_out_8_0[0][0]                 \n",
      "                                                                   add_146[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_0 (Conv1D)      (None, None, 128)     114816      add_148[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_0 (Conv1D)        (None, None, 128)     114816      add_148[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_0 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_0[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_0 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_0[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "multiply_78 (Multiply)           (None, None, 128)     0           bn_filter_16_0[0][0]             \n",
      "                                                                   bn_gate_16_0[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_0 (Conv1D)         (None, None, 128)     16512       multiply_78[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_0 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_0[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_150 (Add)                    (None, None, 128)     0           bn_out_16_0[0][0]                \n",
      "                                                                   add_148[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_1 (Conv1D)       (None, None, 128)     114816      add_150[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_1 (Conv1D)         (None, None, 128)     114816      add_150[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_79 (Multiply)           (None, None, 128)     0           bn_filter_1_1[0][0]              \n",
      "                                                                   bn_gate_1_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_1 (Conv1D)          (None, None, 128)     16512       multiply_79[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_152 (Add)                    (None, None, 128)     0           bn_out_1_1[0][0]                 \n",
      "                                                                   add_150[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_1 (Conv1D)       (None, None, 128)     114816      add_152[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_1 (Conv1D)         (None, None, 128)     114816      add_152[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_80 (Multiply)           (None, None, 128)     0           bn_filter_2_1[0][0]              \n",
      "                                                                   bn_gate_2_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_1 (Conv1D)          (None, None, 128)     16512       multiply_80[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_154 (Add)                    (None, None, 128)     0           bn_out_2_1[0][0]                 \n",
      "                                                                   add_152[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_1 (Conv1D)       (None, None, 128)     114816      add_154[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_1 (Conv1D)         (None, None, 128)     114816      add_154[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_81 (Multiply)           (None, None, 128)     0           bn_filter_3_1[0][0]              \n",
      "                                                                   bn_gate_3_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_1 (Conv1D)          (None, None, 128)     16512       multiply_81[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_156 (Add)                    (None, None, 128)     0           bn_out_3_1[0][0]                 \n",
      "                                                                   add_154[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_1 (Conv1D)       (None, None, 128)     114816      add_156[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_1 (Conv1D)         (None, None, 128)     114816      add_156[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_82 (Multiply)           (None, None, 128)     0           bn_filter_4_1[0][0]              \n",
      "                                                                   bn_gate_4_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_1 (Conv1D)          (None, None, 128)     16512       multiply_82[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_158 (Add)                    (None, None, 128)     0           bn_out_4_1[0][0]                 \n",
      "                                                                   add_156[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_1 (Conv1D)       (None, None, 128)     114816      add_158[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_1 (Conv1D)         (None, None, 128)     114816      add_158[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_1 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_1 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_83 (Multiply)           (None, None, 128)     0           bn_filter_8_1[0][0]              \n",
      "                                                                   bn_gate_8_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_1 (Conv1D)          (None, None, 128)     16512       multiply_83[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_1 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_160 (Add)                    (None, None, 128)     0           bn_out_8_1[0][0]                 \n",
      "                                                                   add_158[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_1 (Conv1D)      (None, None, 128)     114816      add_160[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_1 (Conv1D)        (None, None, 128)     114816      add_160[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_1 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_1[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_1 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_1[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "multiply_84 (Multiply)           (None, None, 128)     0           bn_filter_16_1[0][0]             \n",
      "                                                                   bn_gate_16_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_1 (Conv1D)         (None, None, 128)     16512       multiply_84[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_1 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_162 (Add)                    (None, None, 128)     0           bn_out_16_1[0][0]                \n",
      "                                                                   add_160[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_1_2 (Conv1D)       (None, None, 128)     114816      add_162[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_1_2 (Conv1D)         (None, None, 128)     114816      add_162[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_1_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_1_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_1_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_1_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_85 (Multiply)           (None, None, 128)     0           bn_filter_1_2[0][0]              \n",
      "                                                                   bn_gate_1_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_1_2 (Conv1D)          (None, None, 128)     16512       multiply_85[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_1_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_1_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_164 (Add)                    (None, None, 128)     0           bn_out_1_2[0][0]                 \n",
      "                                                                   add_162[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_2_2 (Conv1D)       (None, None, 128)     114816      add_164[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_2_2 (Conv1D)         (None, None, 128)     114816      add_164[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_2_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_2_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_2_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_2_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_86 (Multiply)           (None, None, 128)     0           bn_filter_2_2[0][0]              \n",
      "                                                                   bn_gate_2_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_2_2 (Conv1D)          (None, None, 128)     16512       multiply_86[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_2_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_2_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_166 (Add)                    (None, None, 128)     0           bn_out_2_2[0][0]                 \n",
      "                                                                   add_164[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_3_2 (Conv1D)       (None, None, 128)     114816      add_166[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_3_2 (Conv1D)         (None, None, 128)     114816      add_166[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_3_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_3_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_3_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_3_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "multiply_87 (Multiply)           (None, None, 128)     0           bn_filter_3_2[0][0]              \n",
      "                                                                   bn_gate_3_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_3_2 (Conv1D)          (None, None, 128)     16512       multiply_87[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_3_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_3_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_168 (Add)                    (None, None, 128)     0           bn_out_3_2[0][0]                 \n",
      "                                                                   add_166[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_4_2 (Conv1D)       (None, None, 128)     114816      add_168[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_4_2 (Conv1D)         (None, None, 128)     114816      add_168[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_143 (Add)                    (None, None, 128)     0           bn_out_1_0[0][0]                 \n",
      "                                                                   bn_out_2_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_4_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_4_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_4_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_4_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_145 (Add)                    (None, None, 128)     0           add_143[0][0]                    \n",
      "                                                                   bn_out_3_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_88 (Multiply)           (None, None, 128)     0           bn_filter_4_2[0][0]              \n",
      "                                                                   bn_gate_4_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_147 (Add)                    (None, None, 128)     0           add_145[0][0]                    \n",
      "                                                                   bn_out_4_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_4_2 (Conv1D)          (None, None, 128)     16512       multiply_88[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_149 (Add)                    (None, None, 128)     0           add_147[0][0]                    \n",
      "                                                                   bn_out_8_0[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_4_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_4_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_151 (Add)                    (None, None, 128)     0           add_149[0][0]                    \n",
      "                                                                   bn_out_16_0[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_170 (Add)                    (None, None, 128)     0           bn_out_4_2[0][0]                 \n",
      "                                                                   add_168[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_153 (Add)                    (None, None, 128)     0           add_151[0][0]                    \n",
      "                                                                   bn_out_1_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_8_2 (Conv1D)       (None, None, 128)     114816      add_170[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_8_2 (Conv1D)         (None, None, 128)     114816      add_170[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_155 (Add)                    (None, None, 128)     0           add_153[0][0]                    \n",
      "                                                                   bn_out_2_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_8_2 (BatchNormalizatio (None, None, 128)     512         conv1d_filter_8_2[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_8_2 (BatchNormalization) (None, None, 128)     512         conv1d_gate_8_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_157 (Add)                    (None, None, 128)     0           add_155[0][0]                    \n",
      "                                                                   bn_out_3_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_89 (Multiply)           (None, None, 128)     0           bn_filter_8_2[0][0]              \n",
      "                                                                   bn_gate_8_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_159 (Add)                    (None, None, 128)     0           add_157[0][0]                    \n",
      "                                                                   bn_out_4_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_8_2 (Conv1D)          (None, None, 128)     16512       multiply_89[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_161 (Add)                    (None, None, 128)     0           add_159[0][0]                    \n",
      "                                                                   bn_out_8_1[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_8_2 (BatchNormalization)  (None, None, 128)     512         conv1d_out_8_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "add_163 (Add)                    (None, None, 128)     0           add_161[0][0]                    \n",
      "                                                                   bn_out_16_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_172 (Add)                    (None, None, 128)     0           bn_out_8_2[0][0]                 \n",
      "                                                                   add_170[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_165 (Add)                    (None, None, 128)     0           add_163[0][0]                    \n",
      "                                                                   bn_out_1_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_filter_16_2 (Conv1D)      (None, None, 128)     114816      add_172[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_gate_16_2 (Conv1D)        (None, None, 128)     114816      add_172[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "add_167 (Add)                    (None, None, 128)     0           add_165[0][0]                    \n",
      "                                                                   bn_out_2_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_filter_16_2 (BatchNormalizati (None, None, 128)     512         conv1d_filter_16_2[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "bn_gate_16_2 (BatchNormalization (None, None, 128)     512         conv1d_gate_16_2[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "add_169 (Add)                    (None, None, 128)     0           add_167[0][0]                    \n",
      "                                                                   bn_out_3_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "multiply_90 (Multiply)           (None, None, 128)     0           bn_filter_16_2[0][0]             \n",
      "                                                                   bn_gate_16_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "add_171 (Add)                    (None, None, 128)     0           add_169[0][0]                    \n",
      "                                                                   bn_out_4_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_out_16_2 (Conv1D)         (None, None, 128)     16512       multiply_90[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "add_173 (Add)                    (None, None, 128)     0           add_171[0][0]                    \n",
      "                                                                   bn_out_8_2[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "bn_out_16_2 (BatchNormalization) (None, None, 128)     512         conv1d_out_16_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "add_175 (Add)                    (None, None, 128)     0           add_173[0][0]                    \n",
      "                                                                   bn_out_16_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "output_conv_1 (Conv1D)           (None, None, 128)     16512       add_175[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "output_bn_1 (BatchNormalization) (None, None, 128)     512         output_conv_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "output_conv_2 (Conv1D)           (None, None, 29)      3741        output_bn_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "softmax (Activation)             (None, None, 29)      0           output_conv_2[0][0]              \n",
      "====================================================================================================\n",
      "Total params: 4,481,309\n",
      "Trainable params: 4,466,973\n",
      "Non-trainable params: 14,336\n",
      "____________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "the bogus legislature numbered thirty six members\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      " bo is o e litor o er terees eers\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='validation',\n",
    "                input_to_softmax=final_model(input_dim=13, filters=128, kernel_size=7), \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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