Survey_ASVspoof_svm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac047b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "from collections import deque\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score, classification_report, precision_recall_curve, average_precision_score \n",
    "#from sklearn.cross_validation import cross_val_score\n",
    "import operator\n",
    "from sklearn import metrics\n",
    "import openpyxl\n",
    "from pathlib import Path\n",
    "import glob\n",
    "import pickle \n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import librosa as lr\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from tqdm.notebook import tqdm_notebook\n",
    "import soundfile as sf\n",
    "from tensorflow.errors import *\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa, librosa.display\n",
    "import os, shlex,pandas as pd, subprocess\n",
    "import warnings\n",
    "import time\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2a1e53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing ASVspoof2019 Data features and labels\n",
      "Reading Paths\n"
     ]
    }
   ],
   "source": [
    "print('Preparing ASVspoof2019 Data features and labels')\n",
    "\n",
    "\n",
    "print(\"Reading Paths\")\n",
    "feature='LPCC';\n",
    "\n",
    "train_dataset_path='/scratch/projects/smiles/Multi_Features/Awais_new/'+feature+'/ASVSpoof2019/PA/ASVspoof2019_PA_train/flac/'\n",
    "eval_dataset_path='/scratch/projects/smiles/Multi_Features/Awais_new/'+feature +'/ASVSpoof2019/PA/ASVspoof2019_PA_eval/flac/'\n",
    "dev_dataset_path ='/scratch/projects/smiles/Multi_Features/Awais_new/'+feature +'/ASVSpoof2019/PA/ASVspoof2019_PA_dev/flac/'\n",
    "\n",
    "train_dataset_load = os.listdir(train_dataset_path) \n",
    "train_raw_labels =  open('/scratch/projects/smiles/Multi_Features/Awais_new/ASVspoof2019.PA.cm.train.trn.txt', 'r')\n",
    "\n",
    "dev_dataset_load = os.listdir(dev_dataset_path) \n",
    "dev_raw_labels =    open('/scratch/projects/smiles/Multi_Features/Awais_new/ASVspoof2019.PA.cm.dev.trl.txt', 'r')\n",
    "\n",
    "eval_dataset_load = os.listdir(eval_dataset_path) \n",
    "eval_raw_labels =   open('/scratch/projects/smiles/Multi_Features/Awais_new/ASVspoof2019.PA.cm.eval.trl.txt', 'r')\n",
    "\n",
    "\n",
    "train_names= []\n",
    "train_labels= []\n",
    "\n",
    "eval_names= []\n",
    "eval_labels= []\n",
    "\n",
    "dev_names= []\n",
    "dev_labels= []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d54c968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/scratch/projects/smiles/Multi_Features/Awais_new/LPCC/ASVSpoof2019/PA/ASVspoof2019_PA_train/flac/'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c407ef96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "54000it [00:00, 1010058.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "with open('/scratch/projects/smiles/Multi_Features/Awais_new/ASVspoof2019.PA.cm.train.trn.txt', 'r') as f:\n",
    "    for i,line in enumerate(tqdm(f)): #train_raw_labels\n",
    "        t_label = line.split(' ')\n",
    "        t_name = t_label[1]\n",
    "        #print(i)\n",
    "        #time.sleep(2)\n",
    "        train_names.append(t_name.strip())\n",
    "        t_lab = t_label[4].strip()\n",
    "        if str(t_lab)=='spoof':\n",
    "            train_labels.append(0)\n",
    "        elif str(t_lab)=='bonafide':\n",
    "            train_labels.append(1)  \n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "087546ae",
   "metadata": {},
   "outputs": [
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       " 'PA_T_0000990',\n",
       " 'PA_T_0000991',\n",
       " 'PA_T_0000992',\n",
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       " 'PA_T_0000994',\n",
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       " 'PA_T_0000997',\n",
       " 'PA_T_0000998',\n",
       " 'PA_T_0000999',\n",
       " 'PA_T_0001000',\n",
       " ...]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20a1ddaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading & Creating Features \n",
      "(54000, 60)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                                                                                | 0/54000 [00:00<?, ?it/s]\u001b[A\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (0,0) into shape (60,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m t_features \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_excel(train_dataset_path\u001b[38;5;241m+\u001b[39mtrain_names[i]\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.xlsx\u001b[39m\u001b[38;5;124m'\u001b[39m, engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopenpyxl\u001b[39m\u001b[38;5;124m'\u001b[39m, header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m      8\u001b[0m t_features \u001b[38;5;241m=\u001b[39m t_features\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[0;32m----> 9\u001b[0m train_features_space[i]\u001b[38;5;241m=\u001b[39mt_features\n",
      "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (0,0) into shape (60,)"
     ]
    }
   ],
   "source": [
    "print(\" Reading & Creating Features \")\n",
    "\n",
    "train_features_space = np.zeros((len(train_names), 60), dtype='float')\n",
    "print(train_features_space.shape)\n",
    "\n",
    "for i in tqdm(range(len(train_names))): #len(names)\n",
    "    t_features = pd.read_excel(train_dataset_path+train_names[i]+'.xlsx', engine='openpyxl', header=None)\n",
    "    t_features = t_features.to_numpy()\n",
    "    train_features_space[i]=t_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5057141b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\" Preparing Development Data \")  \n",
    "for j in dev_raw_labels:\n",
    "    d_label = j.split(' ')\n",
    "    d_name = d_label[1]\n",
    "    dev_names.append(d_name.strip())\n",
    "    dev_lab = d_label[4].strip()\n",
    "    if str(dev_lab)=='spoof':\n",
    "        dev_labels.append(0)\n",
    "    elif str(dev_lab)=='bonafide':\n",
    "        dev_labels.append(1)  \n",
    "        \n",
    "dev_features_space = np.zeros((len(dev_names), 60), dtype='float')\n",
    "print(dev_features_space.shape)\n",
    "\n",
    "\n",
    "for j in range(len(dev_names)): #len(names)\n",
    "    d_features = pd.read_excel(dev_dataset_path+dev_names[j]+'.xlsx', engine='openpyxl', header=None)\n",
    "    d_features = d_features.to_numpy()\n",
    "    dev_features_space[j]=d_features \n",
    "    \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22fef1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\" Preparing Evaluation Data \")  \n",
    "for k in eval_raw_labels:\n",
    "    e_label = k.split(' ')\n",
    "    e_name = e_label[1]\n",
    "    eval_names.append(e_name.strip())\n",
    "    eval_lab = e_label[4].strip()\n",
    "    if str(eval_lab)=='spoof':\n",
    "        eval_labels.append(0)\n",
    "    elif str(eval_lab)=='bonafide':\n",
    "        eval_labels.append(1)\n",
    "        \n",
    "eval_features_space = np.zeros((len(eval_names), 60), dtype='float')\n",
    "print(eval_features_space.shape)        \n",
    "        \n",
    "for k in range(len(eval_names)): #len(names)\n",
    "    e_features = pd.read_excel(eval_dataset_path+eval_names[j]+'.xlsx', engine='openpyxl', header=None)\n",
    "    e_features = e_features.to_numpy()\n",
    "    eval_features_space[k]=e_features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "651f97d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_labels = np.array(train_labels)\n",
    "Devlopment_labels = np.array(dev_labels)\n",
    "Evaluation_labels = np.array(eval_labels)\n",
    "training_features = np.array(train_features_space)\n",
    "Devlopment_features= np.array(dev_features_space)\n",
    "Evaluation_features = np.array(eval_features_space)\n",
    " \n",
    "print(\" Setting Classifiers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95e9483f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = object\n",
    "svclassifier = SVC(kernel='poly')\n",
    "svclassifier.fit(X_train, y_train)\n",
    "g.fit(X_train, y_train)\n",
    "pred = g.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64aa0203",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('classification Report')\n",
    "print(classification_report(y_test, pred))\n",
    "accuracy=accuracy_score(y_test, pred)\n",
    "precision, recall, fscore, support = score(y_test, pred)\n",
    "print('accuracy: {}'.format(accuracy))\n",
    "print('precision: {}'.format(precision))\n",
    "print('recall: {}'.format(recall))\n",
    "print('fscore: {}'.format(fscore))\n",
    "print('support: {}'.format(support))\n",
    "print('Done!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5833464d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3ea5843",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e408b93c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eadac199",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94745911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b842f1f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf257a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69d16040",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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