Untitled1.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
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     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/therochvoices/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "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\n",
    "from keras import regularizers\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from keras.utils import np_utils\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = os.listdir('testfiles/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['03-02-06-01-01-01-01.wav',\n",
       " '03-02-05-02-02-02-24.wav',\n",
       " '03-02-04-01-01-02-22.wav',\n",
       " '03-02-04-02-02-02-11.wav',\n",
       " '03-02-03-02-01-02-24.wav']"
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     },
     "execution_count": 30,
     "metadata": {},
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    }
   ],
   "source": [
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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')\n",
    "labels = pd.DataFrame(feeling_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 1, 9, 3])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame(df['feature'].values.tolist())\n",
    "newdf = pd.concat([df3,labels], axis=1)\n",
    "rnewdf = newdf.rename(index=str, columns={\"0\": \"label\"})\n",
    "\n",
    "rnewdf=rnewdf.fillna(0)\n",
    "\n",
    "testfeatures = rnewdf.iloc[:, :-1]\n",
    "testlabel = rnewdf.iloc[:, -1:]\n",
    "\n",
    "\n",
    "\n",
    "X_test = np.array(testfeatures)\n",
    "y_test = np.array(testlabel)\n",
    "\n",
    "y_test = np_utils.to_categorical(lb.fit_transform(y_test))\n",
    "\n",
    "x_testcnn= np.expand_dims(X_test, axis=2)\n",
    "\n",
    "opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)\n",
    "\n",
    "# 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",
    "\n",
    "\n",
    "preds = loaded_model.predict(x_testcnn, \n",
    "                         batch_size=32, \n",
    "                         verbose=1)\n",
    "\n",
    "preds1=preds.argmax(axis=1)\n",
    "abc = preds1.astype(int).flatten()\n",
    "#predictions = (lb.inverse_transform((abc)))\n",
    "#preddf = pd.DataFrame({'predictedvalues': predictions})\n",
    "\n",
    "\n",
    "abc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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