Data_to_array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import librosa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import specgram\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = 'Ubuntu'\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['axes.labelsize'] = 11\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 10\n",
    "plt.rcParams['ytick.labelsize'] = 10\n",
    "plt.rcParams['legend.fontsize'] = 11\n",
    "plt.rcParams['figure.titlesize'] = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_sound_files(file_paths):\n",
    "    raw_sounds = []\n",
    "    for fp in file_paths:\n",
    "        X,sr = librosa.load(fp)\n",
    "        raw_sounds.append(X)\n",
    "    return raw_sounds\n",
    "\n",
    "def plot_waves(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        librosa.display.waveplot(np.array(f),sr=22050)\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 1: Waveplot',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_specgram(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        specgram(np.array(f), Fs=22050)\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 2: Spectrogram',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()\n",
    "\n",
    "def plot_log_power_specgram(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        D = librosa.logamplitude(np.abs(librosa.stft(f))**2, ref_power=np.max)\n",
    "        librosa.display.specshow(D,x_axis='time' ,y_axis='log')\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 3: Log power spectrogram',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_feature(file_name):\n",
    "    X, sample_rate = librosa.load(file_name)\n",
    "    stft = np.abs(librosa.stft(X))\n",
    "    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
    "    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
    "    mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n",
    "    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
    "    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n",
    "    return mfccs,chroma,mel,contrast,tonnetz\n",
    "\n",
    "def parse_audio_files(parent_dir,sub_dirs,file_ext='*.wav'):\n",
    "    features, labels = np.empty((0,193)), np.empty(0)\n",
    "    for label, sub_dir in enumerate(sub_dirs):\n",
    "        for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):\n",
    "            mfccs, chroma, mel, contrast,tonnetz = extract_feature(fn)\n",
    "            ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])\n",
    "            features = np.vstack([features,ext_features])\n",
    "            labels = np.append(labels, fn.split('/')[2].split('-')[1])\n",
    "    return np.array(features), np.array(labels, dtype = np.int)\n",
    "\n",
    "def one_hot_encode(labels):\n",
    "    n_labels = len(labels)\n",
    "    n_unique_labels = len(np.unique(labels))\n",
    "    one_hot_encode = np.zeros((n_labels,n_unique_labels))\n",
    "    one_hot_encode[np.arange(n_labels), labels] = 1\n",
    "    return one_hot_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parent_dir = \"Sound-Data\"\n",
    "\n",
    "sub_dirs = [['fold1','fold2','fold3'],\n",
    "            ['fold4','fold5','fold6'],\n",
    "            ['fold7','fold8','fold9'],\n",
    "            ['fold10']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Kevin/anaconda/lib/python3.5/site-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
      "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 is finished\n",
      "3 is finished\n",
      "4 is finished\n",
      "5 is finished\n",
      "6 is finished\n",
      "7 is finished\n",
      "8 is finished\n",
      "9 is finished\n",
      "10 is finished\n"
     ]
    }
   ],
   "source": [
    "for i in range(2, 11):\n",
    "    try: \n",
    "        features, labels = parse_audio_files(parent_dir,['fold' + str(i)])\n",
    "        hot_labels = one_hot_encode(labels)\n",
    "\n",
    "        feature_txt = open(\"feature\" + str(i) + \".npy\", \"wb+\")\n",
    "        labels_txt = open(\"labels\" + str(i) + \".npy\", \"wb+\")\n",
    "        hot_labels_txt = open(\"hot_labels\" + str(i) + \".npy\", \"wb+\")\n",
    "\n",
    "        np.save(feature_txt, features)\n",
    "        np.save(labels_txt, labels)\n",
    "        np.save(hot_labels_txt, hot_labels)\n",
    "\n",
    "        feature_txt.close()\n",
    "        labels_txt.close()\n",
    "        hot_labels_txt.close()\n",
    "        print(i, \"is finished\")\n",
    "    except ParameterError as e: \n",
    "        print(i, \"didn't work\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Kevin/anaconda/lib/python3.5/site-packages/librosa/core/pitch.py:145: UserWarning: Trying to estimate tuning from empty frequency set.\n",
      "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n",
      "/Users/Kevin/anaconda/lib/python3.5/site-packages/librosa/util/utils.py:1646: RuntimeWarning: invalid value encountered in less\n",
      "  if np.any(X < 0) or np.any(X_ref < 0):\n",
      "/Users/Kevin/anaconda/lib/python3.5/site-packages/librosa/util/utils.py:1659: RuntimeWarning: invalid value encountered in less\n",
      "  bad_idx = (Z < np.finfo(dtype).tiny)\n"
     ]
    },
    {
     "ename": "ParameterError",
     "evalue": "Audio buffer is not finite everywhere",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mParameterError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-76074c7f8bc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_audio_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msub_dirs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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      4\u001b[0m \u001b[0mhot_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mone_hot_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-eb3211c25b2b>\u001b[0m in \u001b[0;36mparse_audio_files\u001b[0;34m(parent_dir, sub_dirs, file_ext)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_dir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub_dirs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_ext\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---> 15\u001b[0;31m             \u001b[0mmfccs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchroma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrast\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtonnetz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_feature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m             \u001b[0mext_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmfccs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchroma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcontrast\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtonnetz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mext_features\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-eb3211c25b2b>\u001b[0m in \u001b[0;36mextract_feature\u001b[0;34m(file_name)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mmel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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[0mmelspectrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mcontrast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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[0mspectral_contrast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mtonnetz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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[0mtonnetz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meffects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mharmonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmfccs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchroma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcontrast\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtonnetz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/feature/spectral.py\u001b[0m in \u001b[0;36mtonnetz\u001b[0;34m(y, sr, chroma)\u001b[0m\n\u001b[1;32m   1280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1281\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchroma\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1282\u001b[0;31m         \u001b[0mchroma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchroma_cqt\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m     \u001b[0;31m# Generate Transformation matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/feature/spectral.py\u001b[0m in \u001b[0;36mchroma_cqt\u001b[0;34m(y, sr, C, hop_length, fmin, norm, threshold, tuning, n_chroma, n_octaves, window, bins_per_octave, cqt_mode)\u001b[0m\n\u001b[1;32m   1058\u001b[0m                                       \u001b[0mn_bins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_octaves\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbins_per_octave\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m                                       \u001b[0mbins_per_octave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins_per_octave\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m                                       tuning=tuning))\n\u001b[0m\u001b[1;32m   1061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m     \u001b[0;31m# Map to chroma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/core/constantq.py\u001b[0m in \u001b[0;36mcqt\u001b[0;34m(y, sr, hop_length, fmin, n_bins, bins_per_octave, tuning, filter_scale, norm, sparsity, window, scale, pad_mode)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtuning\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m         \u001b[0mtuning\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimate_tuning\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;31m# First thing, get the freqs of the top octave\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/core/pitch.py\u001b[0m in \u001b[0;36mestimate_tuning\u001b[0;34m(y, sr, S, n_fft, resolution, bins_per_octave, **kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m     '''\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mpitch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpiptrack\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[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_fft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;31m# Only count magnitude where frequency is > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/core/pitch.py\u001b[0m in \u001b[0;36mpiptrack\u001b[0;34m(y, sr, S, n_fft, hop_length, fmin, fmax, threshold)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0mhop_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_fft\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m     \u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_spectrogram\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[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_fft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhop_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhop_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m     \u001b[0;31m# Make sure we're dealing with magnitudes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/core/spectrum.py\u001b[0m in \u001b[0;36m_spectrogram\u001b[0;34m(y, S, n_fft, hop_length, power)\u001b[0m\n\u001b[1;32m   1323\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1324\u001b[0m         \u001b[0;31m# Otherwise, compute a magnitude spectrogram from input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1325\u001b[0;31m         \u001b[0mS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_fft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhop_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhop_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mpower\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_fft\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/core/spectrum.py\u001b[0m in \u001b[0;36mstft\u001b[0;34m(y, n_fft, hop_length, win_length, window, center, dtype, pad_mode)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     \u001b[0;31m# Check audio is valid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m     \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;31m# Pad the time series so that frames are centered\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/librosa/util/utils.py\u001b[0m in \u001b[0;36mvalid_audio\u001b[0;34m(y, mono)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\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--> 170\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mParameterError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Audio buffer is not finite everywhere'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mParameterError\u001b[0m: Audio buffer is not finite everywhere"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "\n",
    "features, labels = parse_audio_files(parent_dir,sub_dirs[i])\n",
    "hot_labels = one_hot_encode(labels)\n",
    "\n",
    "feature_txt = open(\"feature\" + str(i) + \".npy\", \"wb+\")\n",
    "labels_txt = open(\"labels\" + str(i) + \".npy\", \"wb+\")\n",
    "hot_labels_txt = open(\"hot_labels\" + str(i) + \".npy\", \"wb+\")\n",
    "\n",
    "np.save(feature_txt, features)\n",
    "np.save(labels_txt, labels)\n",
    "np.save(hot_labels_txt, hot_labels)\n",
    "\n",
    "feature_txt.close()\n",
    "labels_txt.close()\n",
    "hot_labels_txt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = 1\n",
    "features, labels = parse_audio_files(parent_dir,sub_dirs[i])\n",
    "hot_labels = one_hot_encode(labels)\n",
    "\n",
    "feature_txt = open(\"feature\" + str(i) + \".npy\", \"wb+\")\n",
    "labels_txt = open(\"labels\" + str(i) + \".npy\", \"wb+\")\n",
    "hot_labels_txt = open(\"hot_labels\" + str(i) + \".npy\", \"wb+\")\n",
    "\n",
    "np.save(feature_txt, features)\n",
    "np.save(labels_txt, labels)\n",
    "np.save(hot_labels_txt, hot_labels)\n",
    "\n",
    "feature_txt.close()\n",
    "labels_txt.close()\n",
    "hot_labels_txt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i= 2\n",
    "features, labels = parse_audio_files(parent_dir,sub_dirs[i])\n",
    "hot_labels = one_hot_encode(labels)\n",
    "\n",
    "feature_txt = open(\"feature\" + str(i) + \".npy\", \"wb+\")\n",
    "labels_txt = open(\"labels\" + str(i) + \".npy\", \"wb+\")\n",
    "hot_labels_txt = open(\"hot_labels\" + str(i) + \".npy\", \"wb+\")\n",
    "\n",
    "np.save(feature_txt, features)\n",
    "np.save(labels_txt, labels)\n",
    "np.save(hot_labels_txt, hot_labels)\n",
    "\n",
    "feature_txt.close()\n",
    "labels_txt.close()\n",
    "hot_labels_txt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=3\n",
    "features, labels = parse_audio_files(parent_dir,sub_dirs[i])\n",
    "hot_labels = one_hot_encode(labels)\n",
    "\n",
    "feature_txt = open(\"feature\" + str(i) + \".npy\", \"wb+\")\n",
    "labels_txt = open(\"labels\" + str(i) + \".npy\", \"wb+\")\n",
    "hot_labels_txt = open(\"hot_labels\" + str(i) + \".npy\", \"wb+\")\n",
    "\n",
    "np.save(feature_txt, features)\n",
    "np.save(labels_txt, labels)\n",
    "np.save(hot_labels_txt, hot_labels)\n",
    "\n",
    "feature_txt.close()\n",
    "labels_txt.close()\n",
    "hot_labels_txt.close()"
   ]
  }
 ],
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