final_results_gender_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from matplotlib.pyplot import specgram\n",
    "import keras\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding\n",
    "from keras.layers import LSTM\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers import Input, Flatten, Dropout, Activation\n",
    "from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mylist= os.listdir('RawData/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f01 (10).wav\n"
     ]
    }
   ],
   "source": [
    "print(mylist[1800])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03\n"
     ]
    }
   ],
   "source": [
    "print(mylist[400][6:-16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the audio file's waveform and its spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data, sampling_rate = librosa.load('RawData/f11 (2).wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x281d95e7358>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n85Q4DuUWm0W5JAjvrK/DiaMGKLY/Sk2sKndtS3fGypErLntyORb/eqZq+1d7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\nAAAAAKTc/wPJrS1yTPa8hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x281d75bbb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import librosa\n",
    "import glob \n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "librosa.display.waveplot(data, sr=sampling_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k09eyGeZXmv4UYp6x/BvlRuzfzZkQ0VYDwyzSdflVi9vvVZL/w8tGWH4dNHOm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\nt7x/gfRs0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WM/M14uH4JuTq/goDCUeMSqRIak1Pd4KAl4xLrI4Z1q9F02CGnc88AJ2CJR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UssYl+2WpKEPc8W7B2yWuMynnldyvvo6YJrtyUu5fgZvTNbrLJI50j5mladSz3OfioCnN31yJ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\n3G7+29aQxC2jQNGcxi1l8B5fm++xahLefbjN1nTFlfGcSVKSRY0og9kzsju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ERGldHgrL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x281d758b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy.io.wavfile\n",
    "import numpy as np\n",
    "import sys\n",
    "\n",
    "\n",
    "sr,x = scipy.io.wavfile.read('RawData/f10 (2).wav')\n",
    "\n",
    "## Parameters: 10ms step, 30ms window\n",
    "nstep = int(sr * 0.01)\n",
    "nwin  = int(sr * 0.03)\n",
    "nfft = nwin\n",
    "\n",
    "window = np.hamming(nwin)\n",
    "\n",
    "## will take windows x[n1:n2].  generate\n",
    "## and loop over n2 such that all frames\n",
    "## fit within the waveform\n",
    "nn = range(nwin, len(x), nstep)\n",
    "\n",
    "X = np.zeros( (len(nn), nfft//2) )\n",
    "\n",
    "for i,n in enumerate(nn):\n",
    "    xseg = x[n-nwin:n]\n",
    "    z = np.fft.fft(window * xseg, nfft)\n",
    "    X[i,:] = np.log(np.abs(z[:nfft//2]))\n",
    "\n",
    "plt.imshow(X.T, interpolation='nearest',\n",
    "    origin='lower',\n",
    "    aspect='auto')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feeling_list=[]\n",
    "for item in mylist:\n",
    "    if item[6:-16]=='02' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_calm')\n",
    "    elif item[6:-16]=='02' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_calm')\n",
    "    elif item[6:-16]=='03' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_happy')\n",
    "    elif item[6:-16]=='03' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_happy')\n",
    "    elif item[6:-16]=='04' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_sad')\n",
    "    elif item[6:-16]=='04' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_sad')\n",
    "    elif item[6:-16]=='05' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_angry')\n",
    "    elif item[6:-16]=='05' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_angry')\n",
    "    elif item[6:-16]=='06' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_fearful')\n",
    "    elif item[6:-16]=='06' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_fearful')\n",
    "    elif item[:1]=='a':\n",
    "        feeling_list.append('male_angry')\n",
    "    elif item[:1]=='f':\n",
    "        feeling_list.append('male_fearful')\n",
    "    elif item[:1]=='h':\n",
    "        feeling_list.append('male_happy')\n",
    "    #elif item[:1]=='n':\n",
    "        #feeling_list.append('neutral')\n",
    "    elif item[:2]=='sa':\n",
    "        feeling_list.append('male_sad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = pd.DataFrame(feeling_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0    male_calm\n",
       "1  female_calm\n",
       "2    male_calm\n",
       "3  female_calm\n",
       "4    male_calm\n",
       "5  female_calm\n",
       "6    male_calm\n",
       "7  female_calm\n",
       "8    male_calm\n",
       "9  female_calm"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the features of audio files using librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns=['feature'])\n",
    "bookmark=0\n",
    "for index,y in enumerate(mylist):\n",
    "    if mylist[index][6:-16]!='01' and mylist[index][6:-16]!='07' and mylist[index][6:-16]!='08' and mylist[index][:2]!='su' and mylist[index][:1]!='n' and mylist[index][:1]!='d':\n",
    "        X, sample_rate = librosa.load('RawData/'+y, res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)\n",
    "        sample_rate = np.array(sample_rate)\n",
    "        mfccs = np.mean(librosa.feature.mfcc(y=X, \n",
    "                                            sr=sample_rate, \n",
    "                                            n_mfcc=13),\n",
    "                        axis=0)\n",
    "        feature = mfccs\n",
    "        #[float(i) for i in feature]\n",
    "        #feature1=feature[:135]\n",
    "        df.loc[bookmark] = [feature]\n",
    "        bookmark=bookmark+1        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-70.2677641611, -70.2677641611, -70.267764161...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-65.7076524007, -65.7076524007, -63.114722422...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-65.4824988827, -65.4824988827, -65.482498882...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-64.5284491035, -64.5284491035, -64.528449103...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-62.3643105275, -59.9347251381, -61.869599961...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             feature\n",
       "0  [-70.2677641611, -70.2677641611, -70.267764161...\n",
       "1  [-65.7076524007, -65.7076524007, -63.114722422...\n",
       "2  [-65.4824988827, -65.4824988827, -65.482498882...\n",
       "3  [-64.5284491035, -64.5284491035, -64.528449103...\n",
       "4  [-62.3643105275, -59.9347251381, -61.869599961..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame(df['feature'].values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df3[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newdf = pd.concat([df3,labels], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnewdf = newdf.rename(index=str, columns={\"0\": \"label\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "         208        209        210        211        212        213  \\\n",
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       "\n",
       "         214        215          0    \n",
       "0 -61.116223 -60.357015    male_calm  \n",
       "1 -49.146184 -48.687893  female_calm  \n",
       "2 -41.333382 -40.721261    male_calm  \n",
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       "4 -43.386896 -44.065982    male_calm  \n",
       "\n",
       "[5 rows x 217 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "rnewdf[:5]"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "      <td>-47.580376</td>\n",
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       "      <td>-17.796563</td>\n",
       "      <td>-17.936725</td>\n",
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       "      <td>-17.991303</td>\n",
       "      <td>-18.088496</td>\n",
       "      <td>-19.591927</td>\n",
       "      <td>-19.046431</td>\n",
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       "      <td>-25.198551</td>\n",
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       "      <td>-26.599865</td>\n",
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       "      <td>-26.959255</td>\n",
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       "      <td>-5.119769</td>\n",
       "      <td>-7.367091</td>\n",
       "      <td>-8.972608</td>\n",
       "      <td>-10.649444</td>\n",
       "      <td>-14.163026</td>\n",
       "      <td>-18.706701</td>\n",
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       "      <td>-53.206541</td>\n",
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       "      <td>-51.743066</td>\n",
       "      <td>-53.126957</td>\n",
       "      <td>-53.163642</td>\n",
       "      <td>-53.088024</td>\n",
       "      <td>-54.972413</td>\n",
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       "      <td>-28.217343</td>\n",
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       "      <td>-38.404735</td>\n",
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       "      <td>-57.550879</td>\n",
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       "      <td>-52.575092</td>\n",
       "      <td>-52.733973</td>\n",
       "      <td>-53.780274</td>\n",
       "      <td>-54.433161</td>\n",
       "      <td>-57.540984</td>\n",
       "      <td>-57.138044</td>\n",
       "      <td>-55.263390</td>\n",
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       "      <th>1538</th>\n",
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       "      <td>-41.617890</td>\n",
       "      <td>-44.013965</td>\n",
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       "      <td>-43.273342</td>\n",
       "      <td>-37.321326</td>\n",
       "      <td>-30.551111</td>\n",
       "      <td>-27.834584</td>\n",
       "      <td>-26.978020</td>\n",
       "      <td>-27.281610</td>\n",
       "      <td>-27.756957</td>\n",
       "      <td>-25.164714</td>\n",
       "      <td>-19.921744</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>-62.748082</td>\n",
       "      <td>-62.748239</td>\n",
       "      <td>-62.759160</td>\n",
       "      <td>-62.761158</td>\n",
       "      <td>-62.758553</td>\n",
       "      <td>-62.763573</td>\n",
       "      <td>-62.760713</td>\n",
       "      <td>-62.748239</td>\n",
       "      <td>-62.748239</td>\n",
       "      <td>-62.774872</td>\n",
       "      <td>...</td>\n",
       "      <td>-39.684156</td>\n",
       "      <td>-43.283529</td>\n",
       "      <td>-44.693196</td>\n",
       "      <td>-42.968453</td>\n",
       "      <td>-41.411920</td>\n",
       "      <td>-44.474100</td>\n",
       "      <td>-46.436648</td>\n",
       "      <td>-44.232114</td>\n",
       "      <td>-41.150455</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>-25.975306</td>\n",
       "      <td>-22.825102</td>\n",
       "      <td>-21.225165</td>\n",
       "      <td>-20.821093</td>\n",
       "      <td>-19.913892</td>\n",
       "      <td>-20.578139</td>\n",
       "      <td>-22.024481</td>\n",
       "      <td>-20.963704</td>\n",
       "      <td>-14.915607</td>\n",
       "      <td>-11.356564</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male_angry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2          3          4          5    \\\n",
       "1130 -21.316034 -21.405165 -23.427492 -22.760799 -23.077980 -22.940379   \n",
       "1611 -28.023489 -27.012539 -24.843077 -26.073845 -26.605960 -25.415706   \n",
       "606  -47.580376 -47.580376 -47.580376 -47.580376 -47.580376 -47.580376   \n",
       "1356 -24.687263 -23.900377 -22.685773 -23.947191 -25.353984 -25.198551   \n",
       "535  -59.254190 -55.769097 -53.499152 -53.206541 -51.857334 -51.743066   \n",
       "1397 -13.596653 -17.466871 -26.128334 -27.447346 -28.217343 -28.169207   \n",
       "813  -57.705173 -57.728170 -56.672727 -56.152195 -57.413794 -58.473491   \n",
       "1538 -29.934310 -31.293358 -32.880679 -34.448076 -35.698980 -38.608411   \n",
       "111  -62.748082 -62.748239 -62.759160 -62.761158 -62.758553 -62.763573   \n",
       "1042 -25.975306 -22.825102 -21.225165 -20.821093 -19.913892 -20.578139   \n",
       "\n",
       "            6          7          8          9         ...              207  \\\n",
       "1130 -22.521370 -22.318232 -23.623762 -22.484483       ...       -30.090434   \n",
       "1611 -26.137463 -25.882579 -26.144014 -25.486000       ...        -9.060266   \n",
       "606  -47.580376 -47.580376 -47.580376 -47.580376       ...       -17.349710   \n",
       "1356 -26.250432 -26.599865 -27.327227 -26.959255       ...        -2.508218   \n",
       "535  -53.126957 -53.163642 -53.088024 -54.972413       ...       -52.252787   \n",
       "1397 -28.163326 -29.641523 -30.600591 -31.308342       ...       -39.573127   \n",
       "813  -58.040922 -57.550879 -58.224060 -56.276742       ...       -52.148175   \n",
       "1538 -40.439090 -40.267872 -41.617890 -44.013965       ...       -43.273342   \n",
       "111  -62.760713 -62.748239 -62.748239 -62.774872       ...       -39.684156   \n",
       "1042 -22.024481 -20.963704 -14.915607 -11.356564       ...              NaN   \n",
       "\n",
       "            208        209        210        211        212        213  \\\n",
       "1130 -25.397213 -24.566368 -25.313775 -27.188736 -27.189815 -26.741160   \n",
       "1611 -10.795528 -11.224794 -10.564187 -10.653920 -10.423875 -10.612708   \n",
       "606  -17.208624 -17.796563 -17.936725 -17.350310 -17.991303 -18.088496   \n",
       "1356  -2.625317  -3.431428  -5.119769  -7.367091  -8.972608 -10.649444   \n",
       "535  -50.024027 -50.142246 -50.522777 -51.571928 -50.673416 -51.651291   \n",
       "1397 -38.788175 -38.404735 -41.522524        NaN        NaN        NaN   \n",
       "813  -52.575092 -52.733973 -53.780274 -54.433161 -57.540984 -57.138044   \n",
       "1538 -37.321326 -30.551111 -27.834584 -26.978020 -27.281610 -27.756957   \n",
       "111  -43.283529 -44.693196 -42.968453 -41.411920 -44.474100 -46.436648   \n",
       "1042        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            214        215             0    \n",
       "1130 -24.144054 -21.752971      male_angry  \n",
       "1611  -9.730200  -6.192753        male_sad  \n",
       "606  -19.591927 -19.046431      male_angry  \n",
       "1356 -14.163026 -18.706701      male_happy  \n",
       "535  -52.595671 -50.934325      female_sad  \n",
       "1397        NaN        NaN      male_happy  \n",
       "813  -55.263390 -54.655835  female_fearful  \n",
       "1538 -25.164714 -19.921744        male_sad  \n",
       "111  -44.232114 -41.150455     female_calm  \n",
       "1042        NaN        NaN      male_angry  \n",
       "\n",
       "[10 rows x 217 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "rnewdf = shuffle(newdf)\n",
    "rnewdf[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnewdf=rnewdf.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividing the data into test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newdf1 = np.random.rand(len(rnewdf)) < 0.8\n",
    "train = rnewdf[newdf1]\n",
    "test = rnewdf[~newdf1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>5</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
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       "      <th>213</th>\n",
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-62.797142</td>\n",
       "      <td>-65.721075</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>-66.348220</td>\n",
       "      <td>...</td>\n",
       "      <td>-52.630403</td>\n",
       "      <td>-49.383379</td>\n",
       "      <td>-49.429265</td>\n",
       "      <td>-52.419689</td>\n",
       "      <td>-53.660921</td>\n",
       "      <td>-53.482833</td>\n",
       "      <td>-52.205515</td>\n",
       "      <td>-53.841911</td>\n",
       "      <td>-54.661535</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>...</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.659748</td>\n",
       "      <td>-66.092053</td>\n",
       "      <td>-66.427894</td>\n",
       "      <td>-66.730016</td>\n",
       "      <td>-66.561697</td>\n",
       "      <td>-65.294555</td>\n",
       "      <td>-65.872494</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>-53.769216</td>\n",
       "      <td>-55.061158</td>\n",
       "      <td>-53.305227</td>\n",
       "      <td>-51.120282</td>\n",
       "      <td>-52.921001</td>\n",
       "      <td>-52.180352</td>\n",
       "      <td>-53.490261</td>\n",
       "      <td>-54.648841</td>\n",
       "      <td>-52.985473</td>\n",
       "      <td>-51.930319</td>\n",
       "      <td>...</td>\n",
       "      <td>-48.560605</td>\n",
       "      <td>-47.684605</td>\n",
       "      <td>-46.392758</td>\n",
       "      <td>-47.169001</td>\n",
       "      <td>-48.063584</td>\n",
       "      <td>-49.027493</td>\n",
       "      <td>-50.796035</td>\n",
       "      <td>-50.996393</td>\n",
       "      <td>-49.011706</td>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>0.141154</td>\n",
       "      <td>-1.890112</td>\n",
       "      <td>-6.834041</td>\n",
       "      <td>-7.146915</td>\n",
       "      <td>-7.428035</td>\n",
       "      <td>-7.331070</td>\n",
       "      <td>-7.330199</td>\n",
       "      <td>-7.143712</td>\n",
       "      <td>-7.353791</td>\n",
       "      <td>-7.521930</td>\n",
       "      <td>...</td>\n",
       "      <td>-21.726595</td>\n",
       "      <td>-21.101817</td>\n",
       "      <td>-20.511847</td>\n",
       "      <td>-19.783785</td>\n",
       "      <td>-22.081602</td>\n",
       "      <td>-21.137747</td>\n",
       "      <td>-19.667655</td>\n",
       "      <td>-20.545305</td>\n",
       "      <td>-22.456955</td>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>-43.826744</td>\n",
       "      <td>-44.310900</td>\n",
       "      <td>-44.328308</td>\n",
       "      <td>-44.203774</td>\n",
       "      <td>-44.118380</td>\n",
       "      <td>-44.075670</td>\n",
       "      <td>-43.239178</td>\n",
       "      <td>-43.239971</td>\n",
       "      <td>-43.038306</td>\n",
       "      <td>-42.939907</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.941555</td>\n",
       "      <td>-25.617258</td>\n",
       "      <td>-26.698866</td>\n",
       "      <td>-25.630834</td>\n",
       "      <td>-28.005305</td>\n",
       "      <td>-30.681028</td>\n",
       "      <td>-32.177225</td>\n",
       "      <td>-31.636646</td>\n",
       "      <td>-30.872800</td>\n",
       "      <td>male_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>-28.520928</td>\n",
       "      <td>-26.120322</td>\n",
       "      <td>-26.568178</td>\n",
       "      <td>-26.995864</td>\n",
       "      <td>-26.450248</td>\n",
       "      <td>-26.345966</td>\n",
       "      <td>-26.882970</td>\n",
       "      <td>-25.830752</td>\n",
       "      <td>-26.015952</td>\n",
       "      <td>-27.689731</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>-43.612680</td>\n",
       "      <td>...</td>\n",
       "      <td>-42.085371</td>\n",
       "      <td>-42.376162</td>\n",
       "      <td>-43.340164</td>\n",
       "      <td>-42.947368</td>\n",
       "      <td>-43.227011</td>\n",
       "      <td>-43.231191</td>\n",
       "      <td>-44.128215</td>\n",
       "      <td>-43.663723</td>\n",
       "      <td>-42.887394</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>-39.991394</td>\n",
       "      <td>-35.741445</td>\n",
       "      <td>-28.598387</td>\n",
       "      <td>-26.013478</td>\n",
       "      <td>-26.699656</td>\n",
       "      <td>-23.846577</td>\n",
       "      <td>-22.048639</td>\n",
       "      <td>-22.696152</td>\n",
       "      <td>-24.532903</td>\n",
       "      <td>-30.425296</td>\n",
       "      <td>...</td>\n",
       "      <td>-42.750622</td>\n",
       "      <td>-42.401878</td>\n",
       "      <td>-42.229751</td>\n",
       "      <td>-41.913388</td>\n",
       "      <td>-40.750282</td>\n",
       "      <td>-39.964052</td>\n",
       "      <td>-40.102125</td>\n",
       "      <td>-38.991461</td>\n",
       "      <td>-36.121244</td>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>-46.424902</td>\n",
       "      <td>-46.442376</td>\n",
       "      <td>-47.522544</td>\n",
       "      <td>-47.161764</td>\n",
       "      <td>-47.574394</td>\n",
       "      <td>-49.036715</td>\n",
       "      <td>-49.656684</td>\n",
       "      <td>-50.018894</td>\n",
       "      <td>-49.297414</td>\n",
       "      <td>-48.125211</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.007094</td>\n",
       "      <td>-47.213200</td>\n",
       "      <td>-46.885091</td>\n",
       "      <td>-47.983125</td>\n",
       "      <td>-50.044078</td>\n",
       "      <td>-48.948539</td>\n",
       "      <td>-47.469149</td>\n",
       "      <td>-47.738534</td>\n",
       "      <td>-46.882835</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>-59.961592</td>\n",
       "      <td>-57.833689</td>\n",
       "      <td>-59.973265</td>\n",
       "      <td>-61.480152</td>\n",
       "      <td>-61.480152</td>\n",
       "      <td>-61.480152</td>\n",
       "      <td>-61.480152</td>\n",
       "      <td>-61.480152</td>\n",
       "      <td>-60.477495</td>\n",
       "      <td>-57.440553</td>\n",
       "      <td>...</td>\n",
       "      <td>-42.841297</td>\n",
       "      <td>-46.036947</td>\n",
       "      <td>-47.290371</td>\n",
       "      <td>-48.049756</td>\n",
       "      <td>-52.795936</td>\n",
       "      <td>-51.500848</td>\n",
       "      <td>-49.947344</td>\n",
       "      <td>-51.618375</td>\n",
       "      <td>-53.168421</td>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2          3          4          5    \\\n",
       "384  -66.348220 -66.348220 -66.348220 -66.348220 -66.348220 -62.797142   \n",
       "595  -66.730016 -66.730016 -66.730016 -66.730016 -66.730016 -66.730016   \n",
       "419  -53.769216 -55.061158 -53.305227 -51.120282 -52.921001 -52.180352   \n",
       "1318   0.141154  -1.890112  -6.834041  -7.146915  -7.428035  -7.331070   \n",
       "680  -43.826744 -44.310900 -44.328308 -44.203774 -44.118380 -44.075670   \n",
       "1344 -28.520928 -26.120322 -26.568178 -26.995864 -26.450248 -26.345966   \n",
       "723  -43.612680 -43.612680 -43.612680 -43.612680 -43.612680 -43.612680   \n",
       "1265 -39.991394 -35.741445 -28.598387 -26.013478 -26.699656 -23.846577   \n",
       "607  -46.424902 -46.442376 -47.522544 -47.161764 -47.574394 -49.036715   \n",
       "543  -59.961592 -57.833689 -59.973265 -61.480152 -61.480152 -61.480152   \n",
       "\n",
       "            6          7          8          9        ...             207  \\\n",
       "384  -65.721075 -66.348220 -66.348220 -66.348220      ...      -52.630403   \n",
       "595  -66.730016 -66.730016 -66.730016 -66.730016      ...      -66.730016   \n",
       "419  -53.490261 -54.648841 -52.985473 -51.930319      ...      -48.560605   \n",
       "1318  -7.330199  -7.143712  -7.353791  -7.521930      ...      -21.726595   \n",
       "680  -43.239178 -43.239971 -43.038306 -42.939907      ...      -20.941555   \n",
       "1344 -26.882970 -25.830752 -26.015952 -27.689731      ...        0.000000   \n",
       "723  -43.612680 -43.612680 -43.612680 -43.612680      ...      -42.085371   \n",
       "1265 -22.048639 -22.696152 -24.532903 -30.425296      ...      -42.750622   \n",
       "607  -49.656684 -50.018894 -49.297414 -48.125211      ...      -47.007094   \n",
       "543  -61.480152 -61.480152 -60.477495 -57.440553      ...      -42.841297   \n",
       "\n",
       "            208        209        210        211        212        213  \\\n",
       "384  -49.383379 -49.429265 -52.419689 -53.660921 -53.482833 -52.205515   \n",
       "595  -66.730016 -66.659748 -66.092053 -66.427894 -66.730016 -66.561697   \n",
       "419  -47.684605 -46.392758 -47.169001 -48.063584 -49.027493 -50.796035   \n",
       "1318 -21.101817 -20.511847 -19.783785 -22.081602 -21.137747 -19.667655   \n",
       "680  -25.617258 -26.698866 -25.630834 -28.005305 -30.681028 -32.177225   \n",
       "1344   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "723  -42.376162 -43.340164 -42.947368 -43.227011 -43.231191 -44.128215   \n",
       "1265 -42.401878 -42.229751 -41.913388 -40.750282 -39.964052 -40.102125   \n",
       "607  -47.213200 -46.885091 -47.983125 -50.044078 -48.948539 -47.469149   \n",
       "543  -46.036947 -47.290371 -48.049756 -52.795936 -51.500848 -49.947344   \n",
       "\n",
       "            214        215           0    \n",
       "384  -53.841911 -54.661535      male_sad  \n",
       "595  -65.294555 -65.872494  female_angry  \n",
       "419  -50.996393 -49.011706    female_sad  \n",
       "1318 -20.545305 -22.456955  male_fearful  \n",
       "680  -31.636646 -30.872800    male_angry  \n",
       "1344   0.000000   0.000000    male_happy  \n",
       "723  -43.663723 -42.887394  female_angry  \n",
       "1265 -38.991461 -36.121244  male_fearful  \n",
       "607  -47.738534 -46.882835  female_angry  \n",
       "543  -51.618375 -53.168421    female_sad  \n",
       "\n",
       "[10 rows x 217 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[250:260]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainfeatures = train.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainlabel = train.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testfeatures = test.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testlabel = test.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "X_train = np.array(trainfeatures)\n",
    "y_train = np.array(trainlabel)\n",
    "X_test = np.array(testfeatures)\n",
    "y_test = np.array(testlabel)\n",
    "\n",
    "lb = LabelEncoder()\n",
    "\n",
    "y_train = np_utils.to_categorical(lb.fit_transform(y_train))\n",
    "y_test = np_utils.to_categorical(lb.fit_transform(y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  1.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1378, 216)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing dimension for CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "x_traincnn =np.expand_dims(X_train, axis=2)\n",
    "x_testcnn= np.expand_dims(X_test, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(256, 5,padding='same',\n",
    "                 input_shape=(216,1)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv1D(128, 5,padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(MaxPooling1D(pool_size=(8)))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "#model.add(Conv1D(128, 5,padding='same',))\n",
    "#model.add(Activation('relu'))\n",
    "#model.add(Conv1D(128, 5,padding='same',))\n",
    "#model.add(Activation('relu'))\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_9 (Conv1D)            (None, 216, 256)          1536      \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 216, 256)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_10 (Conv1D)           (None, 216, 128)          163968    \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 216, 128)          0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 216, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 27, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_11 (Conv1D)           (None, 27, 128)           82048     \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 27, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_12 (Conv1D)           (None, 27, 128)           82048     \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 27, 128)           0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 3456)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                34570     \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 364,170\n",
      "Trainable params: 364,170\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removed the whole training part for avoiding unnecessary long epochs list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnnhistory=model.fit(x_traincnn, y_train, batch_size=16, epochs=700, validation_data=(x_testcnn, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KYrpBQD1OO65UI+P1m9dEZBpwJpBijOlbxXkBngMmAjnANcaYI46h1KRwbD5Z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\npVQpTQpKKaVKaVJQSilVSpOCUkqpUv8PuSaaKnHKeAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x281d75be780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cnnhistory.history['loss'])\n",
    "plt.plot(cnnhistory.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved trained model at C:\\Users\\mites\\Documents\\Cognitive\\Final Exam\\saved_models\\Emotion_Voice_Detection_Model.h5 \n"
     ]
    }
   ],
   "source": [
    "model_name = 'Emotion_Voice_Detection_Model.h5'\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "# Save model and weights\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "model_path = os.path.join(save_dir, model_name)\n",
    "model.save(model_path)\n",
    "print('Saved trained model at %s ' % model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n",
      "acc: 72.73%\n"
     ]
    }
   ],
   "source": [
    "# loading json and creating model\n",
    "from keras.models import model_from_json\n",
    "json_file = open('model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"saved_models/Emotion_Voice_Detection_Model.h5\")\n",
    "print(\"Loaded model from disk\")\n",
    " \n",
    "# evaluate loaded model on test data\n",
    "loaded_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "score = loaded_model.evaluate(x_testcnn, y_test, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting emotions on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "319/319 [==============================] - 0s     \n"
     ]
    }
   ],
   "source": [
    "preds = loaded_model.predict(x_testcnn, \n",
    "                         batch_size=32, \n",
    "                         verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.49815641e-12,   1.18043589e-10,   1.13181663e-19, ...,\n",
       "          1.80016723e-05,   7.36836637e-06,   1.14132257e-04],\n",
       "       [  3.87504338e-16,   5.73074694e-23,   1.31673211e-14, ...,\n",
       "          1.75147681e-04,   1.85760673e-05,   9.99805748e-01],\n",
       "       [  8.39285008e-07,   3.43896300e-11,   5.38965035e-03, ...,\n",
       "          9.93317604e-01,   1.99900052e-04,   1.05243188e-03],\n",
       "       ..., \n",
       "       [  3.49616457e-04,   1.94651744e-04,   6.65218568e-06, ...,\n",
       "          1.67340226e-02,   6.44345134e-02,   9.10043001e-01],\n",
       "       [  4.23396705e-06,   1.59581254e-11,   6.03030126e-12, ...,\n",
       "          6.36715861e-03,   9.64888096e-01,   2.34207995e-02],\n",
       "       [  3.69524572e-31,   0.00000000e+00,   0.00000000e+00, ...,\n",
       "          5.24333927e-07,   9.99998808e-01,   6.36476927e-10]], dtype=float32)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds1=preds.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 7, 1, 7, 8, 1, 2, 5, 8, 7, 1, 9, 9, 4, 0, 5, 0, 5, 8, 4, 1, 5,\n",
       "       9, 7, 5, 7, 1, 7, 5, 8, 1, 8, 9, 2, 2, 1, 8, 6, 0, 5, 9, 1, 0, 7, 5,\n",
       "       5, 7, 7, 0, 0, 7, 0, 0, 6, 5, 3, 7, 5, 8, 5, 4, 8, 8, 9, 7, 2, 8, 6,\n",
       "       1, 5, 6, 8, 6, 5, 3, 4, 8, 8, 9, 9, 0, 8, 9, 4, 5, 0, 0, 5, 5, 7, 9,\n",
       "       4, 7, 8, 6, 9, 5, 6, 8, 1, 7, 0, 8, 8, 7, 3, 2, 7, 8, 7, 9, 7, 9, 5,\n",
       "       7, 8, 6, 0, 1, 6, 9, 1, 5, 8, 7, 1, 8, 6, 9, 3, 7, 7, 4, 6, 5, 8, 8,\n",
       "       1, 0, 5, 0, 7, 6, 5, 7, 4, 9, 2, 5, 4, 7, 5, 6, 8, 5, 5, 4, 8, 8, 2,\n",
       "       7, 5, 7, 6, 9, 8, 9, 9, 0, 2, 5, 7, 8, 3, 0, 4, 6, 6, 9, 9, 9, 3, 8,\n",
       "       1, 7, 2, 5, 8, 1, 8, 4, 4, 8, 5, 2, 9, 9, 5, 6, 0, 5, 9, 0, 8, 7, 4,\n",
       "       6, 6, 3, 9, 5, 9, 6, 1, 7, 5, 7, 8, 4, 9, 2, 5, 6, 6, 4, 2, 1, 8, 2,\n",
       "       7, 5, 7, 3, 5, 6, 8, 7, 1, 6, 0, 0, 4, 7, 4, 4, 4, 4, 4, 0, 8, 1, 6,\n",
       "       7, 4, 8, 7, 8, 2, 9, 6, 2, 7, 8, 3, 9, 9, 7, 2, 5, 7, 2, 9, 5, 5, 7,\n",
       "       8, 5, 6, 8, 1, 2, 5, 9, 4, 5, 5, 6, 7, 8, 7, 0, 9, 5, 9, 5, 9, 7, 8,\n",
       "       1, 2, 8, 7, 7, 5, 7, 8, 7, 7, 4, 5, 8, 5, 0, 0, 5, 9, 8, 8], dtype=int64)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abc = preds1.astype(int).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = (lb.inverse_transform((abc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>male_sad</td>\n",
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       "      <td>male_fearful</td>\n",
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       "      <td>male_happy</td>\n",
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       "</table>\n",
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      "text/plain": [
       "  predictedvalues\n",
       "0       male_calm\n",
       "1        male_sad\n",
       "2    male_fearful\n",
       "3     female_calm\n",
       "4    male_fearful\n",
       "5      male_happy\n",
       "6     female_calm\n",
       "7  female_fearful\n",
       "8      male_angry\n",
       "9      male_happy"
      ]
     },
     "execution_count": 119,
     "metadata": {},
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   ],
   "source": [
    "preddf = pd.DataFrame({'predictedvalues': predictions})\n",
    "preddf[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "actual=y_test.argmax(axis=1)\n",
    "abc123 = actual.astype(int).flatten()\n",
    "actualvalues = (lb.inverse_transform((abc123)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <td>male_calm</td>\n",
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       "      <td>male_sad</td>\n",
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       "      <td>male_fearful</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male_fearful</td>\n",
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       "      <th>5</th>\n",
       "      <td>male_happy</td>\n",
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       "      <th>6</th>\n",
       "      <td>female_calm</td>\n",
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       "      <th>7</th>\n",
       "      <td>female_angry</td>\n",
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       "      <th>8</th>\n",
       "      <td>male_angry</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>male_happy</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "     actualvalues\n",
       "0       male_calm\n",
       "1        male_sad\n",
       "2    male_fearful\n",
       "3  female_fearful\n",
       "4    male_fearful\n",
       "5      male_happy\n",
       "6     female_calm\n",
       "7    female_angry\n",
       "8      male_angry\n",
       "9      male_happy"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actualdf = pd.DataFrame({'actualvalues': actualvalues})\n",
    "actualdf[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finaldf = actualdf.join(preddf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual v/s Predicted emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>171</th>\n",
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       "      <th>172</th>\n",
       "      <td>male_fearful</td>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>male_happy</td>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>female_happy</td>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>female_angry</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>female_angry</td>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>male_angry</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actualvalues predictedvalues\n",
       "170  female_fearful  female_fearful\n",
       "171      male_angry      male_angry\n",
       "172    male_fearful    male_fearful\n",
       "173      male_happy      male_happy\n",
       "174    female_happy    female_happy\n",
       "175    female_angry    female_angry\n",
       "176    female_angry      female_sad\n",
       "177        male_sad       male_calm\n",
       "178      male_angry       male_calm\n",
       "179        male_sad        male_sad"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf[170:180]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>female_angry</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_calm</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_fearful</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_happy</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_sad</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_angry</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_calm</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_fearful</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_happy</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_sad</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                predictedvalues\n",
       "actualvalues                   \n",
       "female_angry                 21\n",
       "female_calm                  20\n",
       "female_fearful               19\n",
       "female_happy                 17\n",
       "female_sad                   20\n",
       "male_angry                   55\n",
       "male_calm                    25\n",
       "male_fearful                 55\n",
       "male_happy                   49\n",
       "male_sad                     38"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf.groupby('actualvalues').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actualvalues</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predictedvalues</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female_angry</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_calm</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_fearful</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_happy</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_sad</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_angry</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_calm</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_fearful</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_happy</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_sad</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 actualvalues\n",
       "predictedvalues              \n",
       "female_angry               25\n",
       "female_calm                22\n",
       "female_fearful             19\n",
       "female_happy                9\n",
       "female_sad                 25\n",
       "male_angry                 51\n",
       "male_calm                  29\n",
       "male_fearful               51\n",
       "male_happy                 50\n",
       "male_sad                   38"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf.groupby('predictedvalues').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finaldf.to_csv('Predictions.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Live Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The file 'output10.wav' in the next cell is the file that was recorded live using the code in AudioRecoreder notebook found in the repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data, sampling_rate = librosa.load('output10.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x23b43824048>"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RGYcZOyLom0no7Es9X6+9dwB/XrpbNaMilTl2BveC8z1jd88LQzNbtpcgKkv0Ska/xJGh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\nhsjObUI2Uy/5OJ8sm6GYU0cNQ01ZEXNMKpR1253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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b453ee550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import librosa\n",
    "import glob \n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "librosa.display.waveplot(data, sr=sampling_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#livedf= pd.DataFrame(columns=['feature'])\n",
    "X, sample_rate = librosa.load('output10.wav', res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)\n",
    "sample_rate = np.array(sample_rate)\n",
    "mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13),axis=0)\n",
    "featurelive = mfccs\n",
    "livedf2 = featurelive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "livedf2= pd.DataFrame(data=livedf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "livedf2 = livedf2.stack().to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0 -24.43919 -25.210171 -24.740646 -22.311913 -22.579805 -22.31466 -21.552436  \n",
       "\n",
       "[1 rows x 216 columns]"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "livedf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "twodim= np.expand_dims(livedf2, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s\n"
     ]
    }
   ],
   "source": [
    "livepreds = loaded_model.predict(twodim, \n",
    "                         batch_size=32, \n",
    "                         verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.24052530e-22,   0.00000000e+00,   3.62402176e-26,\n",
       "          1.30680162e-36,   4.47264152e-28,   1.00000000e+00,\n",
       "          1.80208343e-30,   2.76873961e-27,   3.62227194e-23,\n",
       "          1.67396652e-11]], dtype=float32)"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "livepreds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "livepreds1=livepreds.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "liveabc = livepreds1.astype(int).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male_angry'], dtype=object)"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "livepredictions = (lb.inverse_transform((liveabc)))\n",
    "livepredictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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