getData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\djaym7\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "import python_speech_features as psf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import librosa.display\n",
    "import os\n",
    "import pandas as pd\n",
    "import Soundnetgetdata as snd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "address=r'C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "FileNames = []\n",
    "labels = ['ANG','DIS','FEA','HAP','NEU','SAD']\n",
    "yLabels = []\n",
    "#address=r'C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/'\n",
    "total_num_people = []\n",
    "for i in range(1,92):\n",
    "    if i <10:\n",
    "        total_num_people.append('100'+str(i))\n",
    "    else:\n",
    "        total_num_people.append('10'+str(i))\n",
    "#create y\n",
    "for i in os.listdir(address):\n",
    "    for label in labels:\n",
    "        if label in i:\n",
    "            FileNames.append((i,label))\n",
    "            #yLabels.append(label)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001_DFA_ANG_XX.wav\n",
      "1001_DFA_DIS_XX.wav\n",
      "1001_DFA_FEA_XX.wav\n",
      "1001_DFA_HAP_XX.wav\n",
      "1001_DFA_NEU_XX.wav\n",
      "1001_DFA_SAD_XX.wav\n",
      "1001_IEO_ANG_HI.wav\n",
      "1001_IEO_ANG_LO.wav\n",
      "1001_IEO_ANG_MD.wav\n",
      "1001_IEO_DIS_HI.wav\n",
      "1001_IEO_DIS_LO.wav\n",
      "1001_IEO_DIS_MD.wav\n",
      "1001_IEO_FEA_HI.wav\n",
      "1001_IEO_FEA_LO.wav\n",
      "1001_IEO_FEA_MD.wav\n",
      "1001_IEO_HAP_HI.wav\n",
      "1001_IEO_HAP_LO.wav\n",
      "1001_IEO_HAP_MD.wav\n",
      "1001_IEO_NEU_XX.wav\n",
      "1001_IEO_SAD_HI.wav\n",
      "1001_IEO_SAD_LO.wav\n",
      "1001_IEO_SAD_MD.wav\n",
      "1001_IOM_ANG_XX.wav\n",
      "1001_IOM_DIS_XX.wav\n",
      "1001_IOM_FEA_XX.wav\n",
      "1001_IOM_HAP_XX.wav\n",
      "1001_IOM_NEU_XX.wav\n",
      "1001_IOM_SAD_XX.wav\n",
      "1001_ITH_ANG_XX.wav\n",
      "1001_ITH_DIS_XX.wav\n",
      "1001_ITH_FEA_XX.wav\n",
      "1001_ITH_HAP_XX.wav\n",
      "1001_ITH_NEU_XX.wav\n",
      "1001_ITH_SAD_XX.wav\n",
      "1001_ITS_ANG_XX.wav\n",
      "1001_ITS_DIS_XX.wav\n",
      "1001_ITS_FEA_XX.wav\n",
      "1001_ITS_HAP_XX.wav\n",
      "1001_ITS_NEU_XX.wav\n",
      "1001_ITS_SAD_XX.wav\n",
      "1001_IWL_ANG_XX.wav\n",
      "1001_IWL_DIS_XX.wav\n",
      "1001_IWL_FEA_XX.wav\n",
      "1001_IWL_HAP_XX.wav\n",
      "1001_IWL_NEU_XX.wav\n",
      "1001_IWL_SAD_XX.wav\n",
      "1001_IWW_ANG_XX.wav\n",
      "1001_IWW_DIS_XX.wav\n",
      "1001_IWW_FEA_XX.wav\n",
      "1001_IWW_HAP_XX.wav\n",
      "1001_IWW_NEU_XX.wav\n",
      "1001_IWW_SAD_XX.wav\n",
      "1001_MTI_ANG_XX.wav\n",
      "1001_MTI_DIS_XX.wav\n",
      "1001_MTI_FEA_XX.wav\n",
      "1001_MTI_HAP_XX.wav\n",
      "1001_MTI_NEU_XX.wav\n",
      "1001_MTI_SAD_XX.wav\n",
      "1001_TAI_ANG_XX.wav\n",
      "1001_TAI_DIS_XX.wav\n",
      "1001_TAI_FEA_XX.wav\n",
      "1001_TAI_HAP_XX.wav\n",
      "1001_TAI_NEU_XX.wav\n",
      "1001_TAI_SAD_XX.wav\n",
      "1001_TIE_ANG_XX.wav\n",
      "1001_TIE_DIS_XX.wav\n",
      "1001_TIE_FEA_XX.wav\n",
      "1001_TIE_HAP_XX.wav\n",
      "1001_TIE_NEU_XX.wav\n",
      "1001_TIE_SAD_XX.wav\n",
      "1001_TSI_ANG_XX.wav\n",
      "1001_TSI_DIS_XX.wav\n",
      "1001_TSI_FEA_XX.wav\n",
      "1001_TSI_HAP_XX.wav\n",
      "1001_TSI_NEU_XX.wav\n",
      "1001_TSI_SAD_XX.wav\n",
      "1001_WSI_ANG_XX.wav\n",
      "1001_WSI_DIS_XX.wav\n",
      "1001_WSI_FEA_XX.wav\n",
      "1001_WSI_HAP_XX.wav\n",
      "1001_WSI_NEU_XX.wav\n",
      "1001_WSI_SAD_XX.wav\n",
      "1002_DFA_ANG_XX.wav\n",
      "1002_DFA_DIS_XX.wav\n",
      "1002_DFA_FEA_XX.wav\n",
      "1002_DFA_HAP_XX.wav\n",
      "1002_DFA_NEU_XX.wav\n",
      "1002_DFA_SAD_XX.wav\n",
      "1002_IEO_ANG_HI.wav\n",
      "1002_IEO_ANG_LO.wav\n",
      "1002_IEO_ANG_MD.wav\n",
      "1002_IEO_DIS_HI.wav\n",
      "1002_IEO_DIS_LO.wav\n",
      "1002_IEO_DIS_MD.wav\n",
      "1002_IEO_FEA_HI.wav\n",
      "1002_IEO_FEA_LO.wav\n",
      "1002_IEO_FEA_MD.wav\n",
      "1002_IEO_HAP_HI.wav\n",
      "1002_IEO_HAP_LO.wav\n",
      "1002_IEO_HAP_MD.wav\n",
      "1002_IEO_NEU_XX.wav\n",
      "1002_IEO_SAD_HI.wav\n",
      "1002_IEO_SAD_LO.wav\n",
      "1002_IEO_SAD_MD.wav\n",
      "1002_IOM_ANG_XX.wav\n",
      "1002_IOM_DIS_XX.wav\n",
      "1002_IOM_FEA_XX.wav\n",
      "1002_IOM_HAP_XX.wav\n",
      "1002_IOM_NEU_XX.wav\n",
      "1002_IOM_SAD_XX.wav\n",
      "1002_ITH_ANG_XX.wav\n",
      "1002_ITH_DIS_XX.wav\n",
      "1002_ITH_FEA_XX.wav\n",
      "1002_ITH_HAP_XX.wav\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-50b50af20ce8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mid\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m             \u001b[0msound_clip\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msr\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m             \u001b[0mmax_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m110361\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0msound_clip\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msound_clip\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpad_width\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax_size\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msound_clip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'constant'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\librosa\\core\\audio.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(path, sr, mono, offset, duration, dtype, res_type)\u001b[0m\n\u001b[0;32m    158\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    159\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 160\u001b[1;33m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msr_native\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mres_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    162\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\librosa\\core\\audio.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(y, orig_sr, target_sr, res_type, fix, scale, **kwargs)\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[0my_hat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m         \u001b[0my_hat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresampy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig_sr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_sr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mres_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfix\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\resampy\\core.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(x, sr_orig, sr_new, axis, filter, **kwargs)\u001b[0m\n\u001b[0;32m    111\u001b[0m     \u001b[0mx_2d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m     \u001b[0my_2d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 113\u001b[1;33m     \u001b[0mresample_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_ratio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterp_win\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterp_delta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecision\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#for mfccs\n",
    "data=[]\n",
    "\n",
    "for id in total_num_people:\n",
    "    id_data=[]\n",
    "    for fname,label in FileNames:\n",
    "        if id in fname:  \n",
    "            #print(fname)\n",
    "            sound_clip,sr= librosa.load(address+fname)\n",
    "            max_size = 110361\n",
    "            sound_clip=np.pad(sound_clip,pad_width=(0,max_size-len(sound_clip)),mode='constant')\n",
    "            mfccs =psf.base.mfcc(sound_clip,samplerate=sr,winlen=0.01,winstep=0.01) #new lib\n",
    "\n",
    "            id_data.append((mfccs,label)) \n",
    "            \n",
    "    data.append((id_data,id))\n",
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_DFA_ANG_XX.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_DFA_DIS_XX.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_DFA_FEA_XX.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_DFA_HAP_XX.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_DFA_NEU_XX.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_DFA_SAD_XX.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_IEO_ANG_HI.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_IEO_ANG_LO.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_IEO_ANG_MD.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_IEO_DIS_HI.wav\n",
      "1001\n",
      "C:\\Users\\djaym7\\Desktop\\Github\\EmotionRecognition\\AudioWAV/1001_IEO_DIS_LO.wav\n",
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     ]
    }
   ],
   "source": [
    "#forSoundnet\n",
    "data=[]\n",
    "\n",
    "for id in total_num_people:\n",
    "    id_data=[]\n",
    "    for fname,label in FileNames:\n",
    "        if id in fname:  \n",
    "            #print(address+fname)\n",
    "            \n",
    "            audio = snd.load_audio(address+fname)\n",
    "            \n",
    "            print(id)\n",
    "            id_data.append((audio,label)) \n",
    "            \n",
    "    data.append((id_data,id))\n",
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "for id in total_num_people:\n",
    "    X_test_data=[]\n",
    "    y_test_data=[]\n",
    "    X_train_data=[]\n",
    "    y_train_data=[]\n",
    "    for value,vid in data:        \n",
    "        if vid == id:\n",
    "            for m,n in value:\n",
    "                #print(f'Test id {vid} m shape : {np.array(m).shape}  n shape : {np.array(n).shape}')\n",
    "                X_test_data.append(m)\n",
    "                y_test_data.append(n)      \n",
    "        else:\n",
    "            for m,n in value:\n",
    "                \n",
    "                #print(f'Train id {vid} m shape : {np.array(m).shape}  n shape : {np.array(n).shape}')\n",
    "                X_train_data.append(m)\n",
    "                y_train_data.append(n)\n",
    "                \n",
    "    with open(f'X_train_{id}','wb') as f:\n",
    "        pickle.dump(X_train_data,f)\n",
    "    with open(f'y_train_{id}','wb') as f:\n",
    "        pickle.dump(y_train_data,f)\n",
    "    with open(f'X_test_{id}','wb') as f:\n",
    "        pickle.dump(X_test_data,f)\n",
    "    with open(f'y_test_{id}','wb') as f:\n",
    "        pickle.dump(y_test_data,f)\n",
    "    break"
   ]
  }
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