Speech-Classification-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Required packages"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "import numpy\r\n",
    "import os\r\n",
    "import librosa\r\n",
    "import librosa.display\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "from python_speech_features import mfcc\r\n",
    "from python_speech_features import logfbank\r\n",
    "import scipy.io.wavfile as wav\r\n",
    "from sklearn.preprocessing import StandardScaler\r\n",
    "from imblearn.over_sampling import SMOTE\r\n",
    "from numpy import array\r\n",
    "from numpy import argmax\r\n",
    "from sklearn.preprocessing import LabelEncoder\r\n",
    "from sklearn.preprocessing import OneHotEncoder\r\n",
    "from sklearn.model_selection import train_test_split\r\n",
    "from tensorflow import keras \r\n",
    "from tensorflow.keras.models import Sequential\r\n",
    "from tensorflow.keras.layers import Dense, Embedding\r\n",
    "from tensorflow.keras.layers import Input, Flatten, Dropout, Activation\r\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D\r\n",
    "from tensorflow.keras.models import Model\r\n",
    "from tensorflow.keras.optimizers import Adam"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## train set extractor"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "def csv_extractor(file_name):\r\n",
    "\r\n",
    "    csv_open = open(file_name,\"r\")\r\n",
    "    feature_names = []\r\n",
    "    labels = []\r\n",
    "\r\n",
    "    for index, wave in enumerate(csv_open):\r\n",
    "        wave = wave.strip(\"\\n\")\r\n",
    "        wave = wave.split(\",\")\r\n",
    "    \r\n",
    "        feature = wave[0]\r\n",
    "        label = wave[1]\r\n",
    "        if index == 0:\r\n",
    "            continue\r\n",
    "    \r\n",
    "        else:\r\n",
    "            feature_names.append(feature)\r\n",
    "            labels.append(label)\r\n",
    "\r\n",
    "    feature_names = numpy.array(feature_names)\r\n",
    "    labels = numpy.array(labels)\r\n",
    "    \r\n",
    "    return feature_names, labels\r\n",
    "csv_train = csv_extractor(\"train.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# test set extractor"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "def csv_extractor_test(file_name):\r\n",
    "\r\n",
    "    csv_open = open(file_name,\"r\")\r\n",
    "    \r\n",
    "    feature_name_test = []\r\n",
    "\r\n",
    "    for index, wave in enumerate(csv_open):\r\n",
    "        wave = wave.strip(\"\\n\")\r\n",
    "\r\n",
    "        feature = wave\r\n",
    "        if index == 0:\r\n",
    "            continue\r\n",
    "    \r\n",
    "        else:\r\n",
    "            feature_name_test.append(feature)\r\n",
    "\r\n",
    "    feature_name_test = numpy.array(feature_name_test)\r\n",
    "    \r\n",
    "    return feature_name_test\r\n",
    "\r\n",
    "csv_test = csv_extractor_test(\"test.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Store train and test in an array "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "feature_names, labels = csv_train\r\n",
    "feature_name_test = csv_test"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## checkpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "print(\"len of train(features) examples:\", feature_names.shape)\r\n",
    "print(\"len of test examples\",feature_name_test.shape)\r\n",
    "print()\r\n",
    "print(\"example of a file name:\", feature_names[-1])\r\n",
    "print(\"example of a label:\", labels[-1])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "len of train(features) examples: (94824,)\n",
      "len of test examples (11005,)\n",
      "\n",
      "example of a file name: 89717a535d5301648bd3ff1e4a3949922a38d22f.wav\n",
      "example of a label: house\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# step 1: Open our Dataset & Explore the structure of wave samples\n",
    "- Okay, now that we have a list with all wave names and labels.\n",
    "<br>\n",
    "- Load wave file and visualize its waveform (using librosa)\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "cwd = os.getcwd()\r\n",
    "my_path = cwd + \"/mlchallenge-oct-2019/wav/wav/\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "data, sampling_rate = librosa.load(my_path+'00000b653e96c0697676f91ec9dfb9fc9b4d085f.wav')\r\n",
    "\r\n",
    "plt.figure(figsize=(15, 5))\r\n",
    "librosa.display.waveplot(data, sr=sampling_rate)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x13da898ab88>"
      ]
     },
     "metadata": {},
     "execution_count": 27
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# step 2: match feature_names with wave_files and extract MFCC features\n",
    "\n",
    "- all_wave = folder with all wave files\n",
    "- feature_names(numpy array) = name of all wave files\n",
    "- labels(numpy array) = labels of all feature_names\n",
    "- function will return features and labels"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "my_path_examples = []\r\n",
    "for example in os.listdir(my_path):\r\n",
    "    my_path_examples.append(example)\r\n",
    "    \r\n",
    "my_path_examples = numpy.array(my_path_examples)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## checkpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "print(\"len of train(features) examples:\", feature_names.shape)\r\n",
    "print(\"len of train(labels) examples:\", labels.shape)\r\n",
    "print(\"len of train + test examples:\", my_path_examples.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "len of train(features) examples: (94824,)\n",
      "len of train(labels) examples: (94824,)\n",
      "len of train + test examples: (105835,)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# MFCC feature extraction (train set)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "def mfcc_features(my_path,feature_names, labels):\r\n",
    "    features = []\r\n",
    "    all_labels = []\r\n",
    "    \r\n",
    "    for (wave_name,label) in zip(feature_names,labels):\r\n",
    "        \r\n",
    "        for wave in os.listdir(my_path): \r\n",
    "            #if wav and wave_name are equal\r\n",
    "            #extract mfcc features and append them to features\r\n",
    "            #append label to labels\r\n",
    "            if wave == wave_name:\r\n",
    "                (rate,sig) = wav.read(my_path+wave)\r\n",
    "                mfcc_feat = mfcc(sig,rate)\r\n",
    "                \r\n",
    "                features.append(mfcc_feat)\r\n",
    "                all_labels.append(label)\r\n",
    "                 \r\n",
    "    features = numpy.array(features)\r\n",
    "    all_labels = numpy.array(all_labels)\r\n",
    "    \r\n",
    "    return features, all_labels\r\n",
    "\r\n",
    "mfcc_feat = mfcc_features(my_path, feature_names, labels)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\voice\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\ipykernel_launcher.py:18: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# MFCC feature extraction (test set)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "def mfcc_features_test(my_path,feature_names):\r\n",
    "\r\n",
    "    all_features = []\r\n",
    "    all_filename_test = []\r\n",
    "    \r\n",
    "    for wave_name in feature_names:\r\n",
    "        \r\n",
    "        for wave in os.listdir(my_path): \r\n",
    "            #if wav and wave_name are equal\r\n",
    "            #extract mfcc features and append them to features\r\n",
    "            if wave == wave_name: \r\n",
    "                (rate,sig) = wav.read(my_path+wave)\r\n",
    "                mfcc_feat = mfcc(sig,rate)\r\n",
    "                all_filename_test.append(wave)\r\n",
    "                all_features.append(mfcc_feat)\r\n",
    "                 \r\n",
    "    all_features = numpy.array(all_features) \r\n",
    "    all_filename_test = numpy.array(all_filename_test)\r\n",
    "    \r\n",
    "    return all_features, all_filename_test\r\n",
    "\r\n",
    "mfcc_feat_test = mfcc_features_test(my_path, feature_name_test)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\voice\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\ipykernel_launcher.py:17: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## extract features and labels for train and test"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "MFCC_features, MFCC_labels = mfcc_feat\r\n",
    "MFCC_features_test, MFCC_labels_test = mfcc_feat_test"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## checkpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "print(\"shape of extracted mfcc features from test set:\", MFCC_features_test.shape)\r\n",
    "print(\"shape of extracted mfcc features:\", MFCC_features[0].shape)\r\n",
    "print(\"shape of extracted mfcc labels:\", MFCC_labels.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "shape of extracted mfcc features from test set: (11005,)\n",
      "shape of extracted mfcc features: (99, 13)\n",
      "shape of extracted mfcc labels: (94824,)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# zero pad function"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "def zero_pad(features):\r\n",
    "    \r\n",
    "    new_spoken_train = []\r\n",
    "    max_shape = 0\r\n",
    "    for x in features:\r\n",
    "        all_shapes = x.shape[0]\r\n",
    "        if all_shapes > max_shape:\r\n",
    "            max_shape = all_shapes\r\n",
    "\r\n",
    "    for x in features:\r\n",
    "        difference = max_shape-x.shape[0]\r\n",
    "        zero_padded = numpy.pad(x,((0,difference),(0,0)), \"constant\")\r\n",
    "        new_spoken_train.append(zero_padded)\r\n",
    "\r\n",
    "    new_spoken_train = numpy.array(new_spoken_train)\r\n",
    "    \r\n",
    "    return new_spoken_train"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## zero pad MFCC features train set"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "new_spoken_train = zero_pad(MFCC_features)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## zero pad MFCC features test set"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "new_spoken_test = zero_pad(MFCC_features_test)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# checkpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "print(new_spoken_train.shape)\r\n",
    "print(new_spoken_test.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(94824, 99, 13)\n",
      "(11005, 99, 13)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Apply standardization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "new_spoken_train = numpy.reshape(new_spoken_train,(new_spoken_train.shape[0],1287))\r\n",
    "new_spoken_test = numpy.reshape(new_spoken_test,(new_spoken_test.shape[0],1287))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "scaler = StandardScaler()\r\n",
    "scaler.fit(new_spoken_train)\r\n",
    "new_spoken_train = scaler.fit_transform(new_spoken_train)\r\n",
    "new_spoken_test = scaler.transform(new_spoken_test)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Apply oversampling"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "ros = SMOTE(random_state=42)\r\n",
    "new_mfcc_features,  mfcc_labels = ros.fit_resample(new_spoken_train, MFCC_labels)\r\n",
    "\r\n",
    "dictionary_2 = {}\r\n",
    "for i in mfcc_labels:\r\n",
    "    dictionary_2[i] = dictionary_2.get(i,0) + 1\r\n",
    "\r\n",
    "print(dictionary_2)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'one': 3634, 'down': 3634, 'dog': 3634, 'three': 3634, 'forward': 3634, 'up': 3634, 'yes': 3634, 'stop': 3634, 'follow': 3634, 'seven': 3634, 'left': 3634, 'wow': 3634, 'eight': 3634, 'two': 3634, 'right': 3634, 'learn': 3634, 'no': 3634, 'happy': 3634, 'nine': 3634, 'zero': 3634, 'cat': 3634, 'visual': 3634, 'off': 3634, 'sheila': 3634, 'marvin': 3634, 'tree': 3634, 'four': 3634, 'bird': 3634, 'bed': 3634, 'on': 3634, 'go': 3634, 'five': 3634, 'six': 3634, 'backward': 3634, 'house': 3634}\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# reshape "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "new_mfcc_features = numpy.reshape(new_mfcc_features,(new_mfcc_features.shape[0],99,13))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# data overview"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "print(new_spoken_test.shape)\r\n",
    "print(new_mfcc_features.shape)\r\n",
    "print(mfcc_labels.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(11005, 1287)\n",
      "(127190, 99, 13)\n",
      "(127190,)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# reshape"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "new_spoken_test = numpy.reshape(new_spoken_test,(new_spoken_test.shape[0],1287))\r\n",
    "new_spoken_train = numpy.reshape(new_mfcc_features,(new_mfcc_features.shape[0],1287))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# One-hot encode Labels"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "source": [
    "def one_hot_encoder(labels):\r\n",
    "    values = array(labels)\r\n",
    "    # integer encode\r\n",
    "    label_encoder = LabelEncoder()\r\n",
    "    integer_encoded = label_encoder.fit_transform(values)\r\n",
    "    # binary encode\r\n",
    "    onehot_encoder = OneHotEncoder(sparse=False)\r\n",
    "    integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\r\n",
    "    onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\r\n",
    "\r\n",
    "    return onehot_encoded\r\n",
    "\r\n",
    "one_hot_encoded_labels = one_hot_encoder(mfcc_labels)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# import train_test_split and split data in train & val"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_spoken_train, \r\n",
    "                                                    one_hot_encoded_labels, \r\n",
    "                                                    test_size=0.33, \r\n",
    "                                                    random_state=42, \r\n",
    "                                                    stratify = one_hot_encoded_labels, \r\n",
    "                                                    shuffle= True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## checkpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "source": [
    "print(X_train.shape)\r\n",
    "print(X_test.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(85217, 1287)\n",
      "(41973, 1287)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## reshape"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "X_train = numpy.reshape(X_train,(X_train.shape[0],99,13))\r\n",
    "X_test = numpy.reshape(X_test,(X_test.shape[0],99,13))\r\n",
    "print(X_train.shape)\r\n",
    "print(X_test.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(85217, 99, 13)\n",
      "(41973, 99, 13)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## checkpoint"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "source": [
    "print(\"X_train shape:\", X_train.shape)\r\n",
    "print(\"y_train shape:\",y_train.shape)\r\n",
    "print(\"X_test shape:\",X_test.shape)\r\n",
    "print(\"y_test shape:\",y_test.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train shape: (85217, 99, 13)\n",
      "y_train shape: (85217, 35)\n",
      "X_test shape: (41973, 99, 13)\n",
      "y_test shape: (41973, 35)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Build classification model: 1D CNN"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "source": [
    "model = Sequential()\r\n",
    "model.add(Conv1D(128, 2, activation = \"relu\", input_shape=(99,13)))\r\n",
    "model.add(MaxPooling1D(pool_size=(2)))\r\n",
    "model.add(Conv1D(256, 2, activation = \"relu\"))\r\n",
    "model.add(MaxPooling1D(pool_size=(2)))\r\n",
    "model.add(Dropout(0.3))\r\n",
    "model.add(Conv1D(356, 2, activation = \"relu\"))\r\n",
    "model.add(MaxPooling1D(pool_size=(2)))\r\n",
    "model.add(Conv1D(456, 2, activation = \"relu\"))\r\n",
    "model.add(MaxPooling1D(pool_size=(2)))\r\n",
    "model.add(Dropout(0.3))\r\n",
    "model.add(Flatten())\r\n",
    "model.add(Dense(1048))\r\n",
    "model.add(Dense(35, activation='softmax'))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "model.summary()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 98, 128)           3456      \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 49, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 48, 256)           65792     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 24, 256)           0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 24, 256)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 23, 356)           182628    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 11, 356)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 10, 456)           325128    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 5, 456)            0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 456)            0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2280)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1048)              2390488   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 35)                36715     \n",
      "=================================================================\n",
      "Total params: 3,004,207\n",
      "Trainable params: 3,004,207\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## train model"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "model.compile(loss= \"categorical_crossentropy\",\r\n",
    "              optimizer= Adam(lr=0.0001),\r\n",
    "              metrics=['accuracy'])\r\n",
    "\r\n",
    "cnnhistory= model.fit(X_train, y_train,batch_size= 64, epochs= 60 ,validation_data=(X_test, y_test))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/60\n",
      "1332/1332 [==============================] - 45s 34ms/step - loss: 1.6497 - accuracy: 0.5425 - val_loss: 0.8740 - val_accuracy: 0.7595\n",
      "Epoch 2/60\n",
      "1332/1332 [==============================] - 48s 36ms/step - loss: 0.8069 - accuracy: 0.7680 - val_loss: 0.6180 - val_accuracy: 0.8250\n",
      "Epoch 3/60\n",
      "1332/1332 [==============================] - 48s 36ms/step - loss: 0.6305 - accuracy: 0.8166 - val_loss: 0.5081 - val_accuracy: 0.8557\n",
      "Epoch 4/60\n",
      "1332/1332 [==============================] - 47s 35ms/step - loss: 0.5401 - accuracy: 0.8413 - val_loss: 0.4726 - val_accuracy: 0.8637\n",
      "Epoch 5/60\n",
      "1332/1332 [==============================] - 47s 35ms/step - loss: 0.4818 - accuracy: 0.8588 - val_loss: 0.4201 - val_accuracy: 0.8784\n",
      "Epoch 6/60\n",
      "1332/1332 [==============================] - 46s 35ms/step - loss: 0.4392 - accuracy: 0.8703 - val_loss: 0.3984 - val_accuracy: 0.8850\n",
      "Epoch 7/60\n",
      "1332/1332 [==============================] - 46s 35ms/step - loss: 0.4030 - accuracy: 0.8800 - val_loss: 0.3682 - val_accuracy: 0.8943\n",
      "Epoch 8/60\n",
      "1332/1332 [==============================] - 49s 37ms/step - loss: 0.3793 - accuracy: 0.8877 - val_loss: 0.3542 - val_accuracy: 0.8988\n",
      "Epoch 9/60\n",
      "1332/1332 [==============================] - 45s 34ms/step - loss: 0.3493 - accuracy: 0.8955 - val_loss: 0.3382 - val_accuracy: 0.9049\n",
      "Epoch 10/60\n",
      "1332/1332 [==============================] - 44s 33ms/step - loss: 0.3294 - accuracy: 0.9006 - val_loss: 0.3316 - val_accuracy: 0.9056\n",
      "Epoch 11/60\n",
      "1332/1332 [==============================] - 44s 33ms/step - loss: 0.3104 - accuracy: 0.9061 - val_loss: 0.3357 - val_accuracy: 0.9051\n",
      "Epoch 12/60\n",
      "1332/1332 [==============================] - 43s 33ms/step - loss: 0.2899 - accuracy: 0.9114 - val_loss: 0.3176 - val_accuracy: 0.9100\n",
      "Epoch 13/60\n",
      "1332/1332 [==============================] - 45s 33ms/step - loss: 0.2777 - accuracy: 0.9154 - val_loss: 0.3076 - val_accuracy: 0.9141\n",
      "Epoch 14/60\n",
      "1332/1332 [==============================] - 43s 32ms/step - loss: 0.2651 - accuracy: 0.9186 - val_loss: 0.3031 - val_accuracy: 0.9152\n",
      "Epoch 15/60\n",
      "1332/1332 [==============================] - 45s 34ms/step - loss: 0.2525 - accuracy: 0.9223 - val_loss: 0.2985 - val_accuracy: 0.9178\n",
      "Epoch 16/60\n",
      "1332/1332 [==============================] - 41s 31ms/step - loss: 0.2401 - accuracy: 0.9267 - val_loss: 0.2903 - val_accuracy: 0.9193\n",
      "Epoch 17/60\n",
      "1332/1332 [==============================] - 43s 33ms/step - loss: 0.2296 - accuracy: 0.9295 - val_loss: 0.2905 - val_accuracy: 0.9219\n",
      "Epoch 18/60\n",
      "1332/1332 [==============================] - 42s 32ms/step - loss: 0.2182 - accuracy: 0.9320 - val_loss: 0.2739 - val_accuracy: 0.9262\n",
      "Epoch 19/60\n",
      "1332/1332 [==============================] - 48s 36ms/step - loss: 0.2071 - accuracy: 0.9355 - val_loss: 0.2879 - val_accuracy: 0.9233\n",
      "Epoch 20/60\n",
      "1332/1332 [==============================] - 46s 35ms/step - loss: 0.2034 - accuracy: 0.9368 - val_loss: 0.2740 - val_accuracy: 0.9266\n",
      "Epoch 21/60\n",
      "1332/1332 [==============================] - 47s 35ms/step - loss: 0.1918 - accuracy: 0.9397 - val_loss: 0.2767 - val_accuracy: 0.9280\n",
      "Epoch 22/60\n",
      "1332/1332 [==============================] - 44s 33ms/step - loss: 0.1853 - accuracy: 0.9413 - val_loss: 0.2693 - val_accuracy: 0.9281\n",
      "Epoch 23/60\n",
      "1332/1332 [==============================] - 48s 36ms/step - loss: 0.1781 - accuracy: 0.9443 - val_loss: 0.2724 - val_accuracy: 0.9297\n",
      "Epoch 24/60\n",
      "1332/1332 [==============================] - 44s 33ms/step - loss: 0.1729 - accuracy: 0.9457 - val_loss: 0.2765 - val_accuracy: 0.9291\n",
      "Epoch 25/60\n",
      "1332/1332 [==============================] - 41s 31ms/step - loss: 0.1651 - accuracy: 0.9487 - val_loss: 0.2713 - val_accuracy: 0.9298\n",
      "Epoch 26/60\n",
      "1332/1332 [==============================] - 50s 37ms/step - loss: 0.1604 - accuracy: 0.9498 - val_loss: 0.2653 - val_accuracy: 0.9322\n",
      "Epoch 27/60\n",
      "1332/1332 [==============================] - 122s 91ms/step - loss: 0.1554 - accuracy: 0.9520 - val_loss: 0.2672 - val_accuracy: 0.9328\n",
      "Epoch 28/60\n",
      "1332/1332 [==============================] - 46s 35ms/step - loss: 0.1505 - accuracy: 0.9524 - val_loss: 0.2660 - val_accuracy: 0.9324\n",
      "Epoch 29/60\n",
      "1332/1332 [==============================] - 45s 34ms/step - loss: 0.1442 - accuracy: 0.9538 - val_loss: 0.2643 - val_accuracy: 0.9332\n",
      "Epoch 30/60\n",
      "1332/1332 [==============================] - 65s 49ms/step - loss: 0.1382 - accuracy: 0.9562 - val_loss: 0.2638 - val_accuracy: 0.9352\n",
      "Epoch 31/60\n",
      "1332/1332 [==============================] - 81s 61ms/step - loss: 0.1365 - accuracy: 0.9568 - val_loss: 0.2620 - val_accuracy: 0.9352\n",
      "Epoch 32/60\n",
      "1332/1332 [==============================] - 46s 34ms/step - loss: 0.1301 - accuracy: 0.9585 - val_loss: 0.2690 - val_accuracy: 0.9342\n",
      "Epoch 33/60\n",
      "1332/1332 [==============================] - 46s 34ms/step - loss: 0.1310 - accuracy: 0.9584 - val_loss: 0.2629 - val_accuracy: 0.9364\n",
      "Epoch 34/60\n",
      "1332/1332 [==============================] - 63s 48ms/step - loss: 0.1276 - accuracy: 0.9592 - val_loss: 0.2675 - val_accuracy: 0.9352\n",
      "Epoch 35/60\n",
      "1332/1332 [==============================] - 109s 82ms/step - loss: 0.1198 - accuracy: 0.9614 - val_loss: 0.2653 - val_accuracy: 0.9374\n",
      "Epoch 36/60\n",
      "1332/1332 [==============================] - 49s 37ms/step - loss: 0.1171 - accuracy: 0.9627 - val_loss: 0.2661 - val_accuracy: 0.9383\n",
      "Epoch 37/60\n",
      "1332/1332 [==============================] - 87s 66ms/step - loss: 0.1181 - accuracy: 0.9621 - val_loss: 0.2655 - val_accuracy: 0.9375\n",
      "Epoch 38/60\n",
      "1332/1332 [==============================] - 83s 63ms/step - loss: 0.1111 - accuracy: 0.9644 - val_loss: 0.2592 - val_accuracy: 0.9390\n",
      "Epoch 39/60\n",
      "1332/1332 [==============================] - 44s 33ms/step - loss: 0.1053 - accuracy: 0.9665 - val_loss: 0.2723 - val_accuracy: 0.9376\n",
      "Epoch 40/60\n",
      "1332/1332 [==============================] - 140s 105ms/step - loss: 0.1099 - accuracy: 0.9655 - val_loss: 0.2742 - val_accuracy: 0.9389\n",
      "Epoch 41/60\n",
      "1332/1332 [==============================] - 42s 32ms/step - loss: 0.1030 - accuracy: 0.9672 - val_loss: 0.2675 - val_accuracy: 0.9399\n",
      "Epoch 42/60\n",
      "1332/1332 [==============================] - 115s 86ms/step - loss: 0.1028 - accuracy: 0.9673 - val_loss: 0.2671 - val_accuracy: 0.9392\n",
      "Epoch 43/60\n",
      "1332/1332 [==============================] - 61s 46ms/step - loss: 0.0969 - accuracy: 0.9688 - val_loss: 0.2840 - val_accuracy: 0.9385\n",
      "Epoch 44/60\n",
      "1332/1332 [==============================] - 82s 62ms/step - loss: 0.0977 - accuracy: 0.9690 - val_loss: 0.2700 - val_accuracy: 0.9400\n",
      "Epoch 45/60\n",
      "1332/1332 [==============================] - 3394s 3s/step - loss: 0.0946 - accuracy: 0.9690 - val_loss: 0.2686 - val_accuracy: 0.9402\n",
      "Epoch 46/60\n",
      "1332/1332 [==============================] - 31s 23ms/step - loss: 0.0918 - accuracy: 0.9707 - val_loss: 0.2705 - val_accuracy: 0.9395\n",
      "Epoch 47/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0901 - accuracy: 0.9708 - val_loss: 0.2822 - val_accuracy: 0.9389\n",
      "Epoch 48/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0900 - accuracy: 0.9711 - val_loss: 0.2738 - val_accuracy: 0.9398\n",
      "Epoch 49/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0866 - accuracy: 0.9719 - val_loss: 0.2666 - val_accuracy: 0.9405\n",
      "Epoch 50/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0828 - accuracy: 0.9734 - val_loss: 0.2696 - val_accuracy: 0.9401\n",
      "Epoch 51/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0819 - accuracy: 0.9739 - val_loss: 0.2737 - val_accuracy: 0.9416\n",
      "Epoch 52/60\n",
      "1332/1332 [==============================] - 32s 24ms/step - loss: 0.0838 - accuracy: 0.9730 - val_loss: 0.2727 - val_accuracy: 0.9409\n",
      "Epoch 53/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0775 - accuracy: 0.9752 - val_loss: 0.2724 - val_accuracy: 0.9417\n",
      "Epoch 54/60\n",
      "1332/1332 [==============================] - 32s 24ms/step - loss: 0.0782 - accuracy: 0.9743 - val_loss: 0.2762 - val_accuracy: 0.9417\n",
      "Epoch 55/60\n",
      "1332/1332 [==============================] - 32s 24ms/step - loss: 0.0783 - accuracy: 0.9751 - val_loss: 0.2706 - val_accuracy: 0.9435\n",
      "Epoch 56/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0753 - accuracy: 0.9758 - val_loss: 0.2786 - val_accuracy: 0.9426\n",
      "Epoch 57/60\n",
      "1332/1332 [==============================] - 31s 24ms/step - loss: 0.0753 - accuracy: 0.9760 - val_loss: 0.2843 - val_accuracy: 0.9413\n",
      "Epoch 58/60\n",
      "1332/1332 [==============================] - 32s 24ms/step - loss: 0.0760 - accuracy: 0.9761 - val_loss: 0.2778 - val_accuracy: 0.9437\n",
      "Epoch 59/60\n",
      "1332/1332 [==============================] - 32s 24ms/step - loss: 0.0698 - accuracy: 0.9774 - val_loss: 0.2808 - val_accuracy: 0.9429\n",
      "Epoch 60/60\n",
      "1332/1332 [==============================] - 32s 24ms/step - loss: 0.0702 - accuracy: 0.9774 - val_loss: 0.2884 - val_accuracy: 0.9418\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## result after training"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "source": [
    "result = model.evaluate(X_test, y_test)\r\n",
    "print(\"validation accuracy:\", result[1])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "   1/1312 [..............................] - ETA: 0s - loss: 0.1795 - accuracy: 0.8750WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0050s vs `on_test_batch_end` time: 0.0085s). Check your callbacks.\n",
      "1312/1312 [==============================] - 6s 5ms/step - loss: 0.2884 - accuracy: 0.9418\n",
      "validation accuracy: 0.9417721033096313\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# plot loss and accuracy"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "source": [
    "plt.plot(cnnhistory.history['loss'])\r\n",
    "plt.plot(cnnhistory.history['val_loss'])\r\n",
    "plt.title('model loss')\r\n",
    "plt.ylabel('loss')\r\n",
    "plt.xlabel('epoch')\r\n",
    "plt.legend(['train', 'test'], loc='upper left')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "source": [
    "plt.plot(cnnhistory.history['accuracy'])\r\n",
    "plt.plot(cnnhistory.history['val_accuracy'])\r\n",
    "plt.title('model accuracy')\r\n",
    "plt.ylabel('acc')\r\n",
    "plt.xlabel('epoch')\r\n",
    "plt.legend(['train', 'test'], loc='upper left')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# CNN prediction output"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "source": [
    "new_spoken_test = numpy.reshape(new_spoken_test,(11005,99,13))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "source": [
    "file_open = open(\"result.csv\",\"a+\")\r\n",
    "for index in range(11005):\r\n",
    "    file_name = MFCC_labels_test[index]\r\n",
    "    predicted_word = label_encoder.inverse_transform([argmax(model.predict(new_spoken_test)[index, :])])\r\n",
    "    predicted_word = \"'\".join(predicted_word)\r\n",
    "    print(predicted_word)\r\n",
    "    file_open.write(file_name+\",\"+predicted_word+\"\\n\")\r\n",
    "    \r\n",
    "file_open.close()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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    {
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