Breathing test calls.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FOR VISUALIZING THE FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/furqan/.local/lib/python3.8/site-packages/librosa/core/audio.py:162: UserWarning: PySoundFile failed. Trying audioread instead.\n",
      "  warnings.warn(\"PySoundFile failed. Trying audioread instead.\")\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './breath/pos_new/baat_cleaned0.wav'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/core/audio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(path, sr, mono, offset, duration, dtype, res_type)\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0msf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSoundFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msf_desc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m             \u001b[0msr_native\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msf_desc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamplerate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, samplerate, channels, subtype, endian, format, closefd)\u001b[0m\n\u001b[1;32m    628\u001b[0m                                          format, subtype, endian)\n\u001b[0;32m--> 629\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosefd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missuperset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'r+'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseekable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, file, mode_int, closefd)\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid file: {0!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m         _error_check(_snd.sf_error(file_ptr),\n\u001b[0m\u001b[1;32m   1184\u001b[0m                      \"Error opening {0!r}: \".format(self.name))\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36m_error_check\u001b[0;34m(err, prefix)\u001b[0m\n\u001b[1;32m   1356\u001b[0m         \u001b[0merr_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_snd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msf_error_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1357\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_ffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'replace'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Error opening './breath/pos_new/baat_cleaned0.wav': System error.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-36d52c68ce52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Zero crossing rate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mzc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchroma_stft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_chroma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4096\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/core/audio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(path, sr, mono, offset, duration, dtype, res_type)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPurePath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PySoundFile failed. Trying audioread instead.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr_native\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__audioread_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/core/audio.py\u001b[0m in \u001b[0;36m__audioread_load\u001b[0;34m(path, offset, duration, dtype)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0maudioread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudio_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m         \u001b[0msr_native\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamplerate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0mn_channels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/audioread/__init__.py\u001b[0m in \u001b[0;36maudio_open\u001b[0;34m(path, backends)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mBackendClass\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbackends\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mBackendClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/audioread/rawread.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \"\"\"\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './breath/pos_new/baat_cleaned0.wav'"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import librosa\n",
    "import math\n",
    "import json\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "\n",
    "path = \"./breath/pos_new/baat_cleaned0.wav\"\n",
    "sr = 22050\n",
    "\n",
    "\n",
    "# Zero crossing rate\n",
    "y, sr = librosa.load(path, sr = sr)\n",
    "zc = librosa.feature.chroma_stft(y=y, sr=sr,n_chroma=12, n_fft=4096)\n",
    "\n",
    "mfcc = librosa.feature.mfcc(y,\n",
    "                            sr =sr,\n",
    "                            n_fft = 2048,\n",
    "                            n_mfcc = 13,\n",
    "                            hop_length = 512\n",
    "                            )\n",
    "                    \n",
    "chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)\n",
    "\n",
    "pitches, magnitudes = librosa.piptrack(y=y, sr=sr)\n",
    "rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.1)\n",
    "\n",
    "features = np.concatenate((mfcc, chroma_cq, pitches, rolloff), axis = 0)\n",
    "\n",
    "features = features.T\n",
    "# zc = librosa.feature.zero_crossing_rate(y)\n",
    "\n",
    "# print(spectral_entropy(zc, 100, method='welch', normalize=True))\n",
    "\n",
    "# librosa.display.waveplot(zc[0], sr)\n",
    "\n",
    "print(mfcc.shape)\n",
    "print(chroma_cq.shape)\n",
    "print(pitches.shape)\n",
    "print(rolloff.shape)\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for plotting features\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 4))\n",
    "librosa.display.specshow(features, y_axis='chroma', x_axis='time')\n",
    "plt.colorbar()\n",
    "plt.title('Chromagram')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing cough data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Processing breath/neg_new\n",
      "breath/neg_new/neg-0420-046-breathe-m-22.wav5.wav, segment : 0\n",
      "breath/neg_new/neg-0421-081-breathe-f-544.wav, segment : 0\n",
      "breath/neg_new/neg-0420-067-breathe-f-52.wav0.wav, segment : 0\n",
      "breath/neg_new/neg-0420-045-breathe-f-29.wav1.wav, segment : 0\n",
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      "breath/neg_new/neg-0420-046-breathe-m-22.wav3.wav, segment : 0\n",
      "\n",
      " Processing breath/pos_new\n",
      "breath/pos_new/pos-0426-128-breathe-m-374.wav, segment : 0\n",
      "breath/pos_new/pos-0420-053-breathe-m-29.wav4.wav, segment : 0\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "breath/pos_new/vlow0.wav, segment : 0\n"
     ]
    }
   ],
   "source": [
    "# preprocessing testing\n",
    "\n",
    "import breath_preprocess\n",
    "\n",
    "DATASET_PATH = \"breath\"\n",
    "JSON_PATH = \"breath.json\"\n",
    "DURATION = 2  # measured in seconds.\n",
    "num_segments = 1\n",
    "\n",
    "breath_preprocess.save_features(DATASET_PATH, JSON_PATH, num_segments = num_segments, duration = DURATION )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training cough model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(370, 91437)\n",
      "Model is running .....\n",
      "Testing Accuracy: 0.5\n",
      "\n",
      "Printing Classification report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.82      0.64        11\n",
      "           1       0.33      0.11      0.17         9\n",
      "\n",
      "    accuracy                           0.50        20\n",
      "   macro avg       0.43      0.46      0.40        20\n",
      "weighted avg       0.44      0.50      0.43        20\n",
      "\n",
      "\n",
      "ROC curve value is:  0.43434343434343436\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[9 2]\n",
      " [8 1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training the cough model\n",
    "\n",
    "import breath_model as cm\n",
    "\n",
    "\n",
    "# Calling random forest class here:\n",
    "DATASET_PATH = \"breath.json\"\n",
    "save_model_in = \"breath_model.pkl\"\n",
    "test_size = 20\n",
    "ran_state = 10\n",
    "rf_maxdepth = 8\n",
    "rf_ran_state = 170\n",
    "rf_estimators = 81\n",
    "\n",
    "ff = cm.RF_Model(DATASET_PATH, save_model_in, test_size, ran_state, rf_maxdepth, rf_ran_state, rf_estimators)\n",
    "mod = ff.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/furqan/Desktop/python_work/tensorflow_env/lib/python3.8/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: \u001b[1mAn import was requested from a module that has moved location.\n",
      "Import requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.\u001b[0m\n",
      "  from numba.decorators import jit as optional_jit\n",
      "/home/furqan/Desktop/python_work/tensorflow_env/lib/python3.8/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: \u001b[1mAn import was requested from a module that has moved location.\n",
      "Import of 'jit' requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.\u001b[0m\n",
      "  from numba.decorators import jit as optional_jit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covid Negative with Percentage : 0.7890695008500819\n"
     ]
    }
   ],
   "source": [
    "# doing prediction\n",
    "\n",
    "import numpy as np\n",
    "import cough_prediction as CP\n",
    "\n",
    "file_path = \"./reliefme_cough/Positive/pos-1.wav\"\n",
    "saved_model = 'cough_model.pkl'\n",
    "\n",
    "CP.predict(file_path, saved_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing prediction part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results are :  [[0.24451724 0.75548276]\n",
      " [0.20873151 0.79126849]\n",
      " [0.20570737 0.79429263]]\n",
      "class neg:  [0.7554827569577766, 0.7912684941692575, 0.7942926337211653]\n",
      "class pos:  []\n"
     ]
    }
   ],
   "source": [
    "# doing prediction 2\n",
    " \n",
    "import numpy as np\n",
    "import cough as CP\n",
    "\n",
    "file_path = \"./reliefme_cough/Negative/neg-0423-118-cough-m-t-56.wav\"\n",
    "saved_model = 'cough_model.pkl'\n",
    "\n",
    "gg = CP.predict(file_path, saved_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note \n",
    "\n",
    "# why dont we just make many segments of our audio sample and then classiy all those segments al together and then take a mean\n",
    "# of all those samples and give 1 output so lets say if we have noise in the cough so normally when we have multiple segments \n",
    "# so some of segents will be containing noise and some of them will be containng meaningfull iformation and so when u do \n",
    "# classification on all those segments so most prbbaly the part which is conating cough will always predict right and the one \n",
    "# containng nouse will not get predicted well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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