Domestic Audio Classification (Actual).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 1356\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(seed)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(seed)\n",
    "\n",
    "import sklearn\n",
    "import cv2\n",
    "import random\n",
    "import math\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "from itertools import chain\n",
    "from collections import Counter\n",
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# constants\n",
    "img_folder = 'audio'\n",
    "img_name = ['_pressure.png', '_spec1.png', '_spec2.png', '_spec3.png', '.wav']\n",
    "\n",
    "ismap = {0:'absence', 1:'cooking', 2:'dishwashing', 3:'eating', 4:'other', 5:'social activity', 6:'vacuum cleaning', 7:'watching tv', 8:'working'}\n",
    "\n",
    "im_size = 64\n",
    "im_size_flat = im_size * im_size\n",
    "n_labels = 9\n",
    "n_channels = 1 # grayscale\n",
    "sd = np.sqrt(2) / np.sqrt(im_size_flat)\n",
    "\n",
    "y_length = 160000\n",
    "hop_length = 40000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import optimizers\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "\n",
    "afs, labels, _ = zip(*[line.rstrip('\\n').split('\\t') for line in open('meta.txt').readlines()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ims(links):\n",
    "    return np.array([(1 - cv2.imread(link, 0)/255).reshape((im_size, im_size, 1)) for link in links])\n",
    "def get_wav(links):\n",
    "    temp = []\n",
    "    for link in links:\n",
    "#         y, sr = librosa.load(link, sr = None, mono = True)\n",
    "#         temp.append(librosa.feature.mfcc(y=y,sr=sr).T)\n",
    "        temp.append(mfccs[link])\n",
    "    return np.array(temp)\n",
    "def get_wav2(links):\n",
    "    temp = []\n",
    "    for link in links:\n",
    "#         y, sr = librosa.load(link, sr = None, mono = True)\n",
    "#         temp.append(librosa.feature.mfcc(y=y,sr=sr).T)\n",
    "        temp.append(mfccs2[link])\n",
    "    return np.array(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_loader(files, batch_size, file_type = 0):\n",
    "    L = len(files)\n",
    "    while True:\n",
    "        batch_start = 0\n",
    "        batch_end = batch_size\n",
    "        \n",
    "        while batch_start < L:\n",
    "            lim = min(L, batch_end)\n",
    "            if file_type == 0:\n",
    "                X = get_ims([f[0] for f in files[batch_start:lim]])\n",
    "                Y = np.array([f[1] for f in files[batch_start:lim]])\n",
    "                yield (X, Y)\n",
    "            elif file_type == 1:\n",
    "                # wav files\n",
    "                X = get_wav([f[0] for f in files[batch_start:lim]])\n",
    "                Y = np.array([f[1] for f in files[batch_start:lim]])\n",
    "                yield (X, Y)\n",
    "            batch_start += batch_size\n",
    "            batch_end += batch_size\n",
    "            \n",
    "def data_loader2(files, batch_size, file_type = 0):\n",
    "    L = len(files)\n",
    "    while True:\n",
    "        batch_start = 0\n",
    "        batch_end = batch_size\n",
    "        \n",
    "        while batch_start < L:\n",
    "            lim = min(L, batch_end)\n",
    "            if file_type == 0:\n",
    "                X = get_ims(files[batch_start:lim])\n",
    "                yield X\n",
    "            elif file_type == 1:\n",
    "                # wav files\n",
    "                X = get_wav2(files[batch_start:lim])\n",
    "                yield X\n",
    "            batch_start += batch_size\n",
    "            batch_end += batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred(sr, ys, model1, model2, model3):        \n",
    "    weights = [0.3, 0.4, 0.3]\n",
    "    \n",
    "#     model1 = load_model('models/model_2D_small_3.hdf5')\n",
    "#     model2 = load_model('models/model_1D_norm.hdf5')\n",
    "#     model3 = load_model('models/model_LSTM_norm.hdf5')\n",
    "    \n",
    "    preds = []\n",
    "    \n",
    "    for y in ys:\n",
    "#         print(\"Generating MFCCs\")\n",
    "        y_feat_1d = get_1d_np(y, sr)\n",
    "#         print(\"Generating 2D spectrogram\")\n",
    "        y_feat_2d = get_2d_np(y, sr)\n",
    "        \n",
    "        pred = weights[0] * model1.predict(np.array([y_feat_2d,]))\n",
    "        pred = weights[1] * model2.predict(np.array([y_feat_1d,]))\n",
    "        pred += weights[2] * model3.predict(np.array([y_feat_1d,]))\n",
    "        \n",
    "        preds.append(pred)\n",
    "    return preds\n",
    "        \n",
    "#     afs = [line.rstrip('\\n') for line in open('eval/eval/meta.txt').readlines()]\n",
    "#     tlbatch = int(math.ceil(len(afs)/50))\n",
    "    \n",
    "#     model1_eval_data = ['eval/eval/src/' + af[6:-4] + '_spec3.png' for af in afs]\n",
    "#     model23_eval_data = afs\n",
    "    \n",
    "#     preds = weights[0] * model1.predict_generator(data_loader2(model1_eval_data, 50, 0),\n",
    "#                                                   steps = tlbatch,\n",
    "#                                                   verbose = 1)\n",
    "    \n",
    "#     preds += weights[1] * model2.predict_generator(data_loader2(model23_eval_data, 50, 1),\n",
    "#                                                    steps = tlbatch,\n",
    "#                                                    verbose = 1)\n",
    "    \n",
    "#     preds += weights[2] * model3.predict_generator(data_loader2(model23_eval_data, 50, 1),\n",
    "#                                                    steps = tlbatch,\n",
    "#                                                    verbose = 1)\n",
    "    \n",
    "#     predsT = np.argmax(preds, axis = 1)\n",
    "#     return predsT\n",
    "#     res = {af:ismap[pred] for af,pred in zip(afs,preds)}\n",
    "#     return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y, sr = librosa.load(y=y, sr=None, mono=True)\n",
    "# y.shape = (160000, ) # 10sec\n",
    "\n",
    "def get_1d_np(y, sr):\n",
    "    return librosa.feature.mfcc(y = y, sr = sr).T\n",
    "    \n",
    "def get_2d_np(y, sr):\n",
    "    fig = plt.figure()\n",
    "    ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
    "    ax.set_axis_off()\n",
    "    fig.add_axes(ax)\n",
    "    fig.set_size_inches(1, 1)\n",
    "    D = librosa.power_to_db(np.abs(librosa.stft(y))**2, ref=np.max)\n",
    "    librosa.display.specshow(D, cmap='gray_r', y_axis='log')\n",
    "    plt.savefig('tmp/tmp.png', dpi=64)\n",
    "    plt.close()\n",
    "    return np.array((1 - cv2.imread('tmp/tmp.png', 0)/255).reshape((im_size, im_size, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_audio(audio_file, hop_length = hop_length):\n",
    "    y, sr = librosa.load(audio_file, sr=None, mono=True)\n",
    "    if len(y) < y_length:\n",
    "        return (sr, [np.pad(y, (0, y_length - len(y)), 'constant', constant_values = 0)])\n",
    "    elif len(y) > y_length:\n",
    "        final_start = len(y)//hop_length * hop_length\n",
    "        l_final = y_length - (len(y) - final_start)\n",
    "        b = [0] * l_final\n",
    "        tmp = [(list(y)+list(b))[i:y_length+i] for i in range(0, len(y), hop_length)]\n",
    "        return (sr, [np.array(ar) for ar in tmp if len(ar) == y_length])\n",
    "    else:\n",
    "        return (sr, [y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = load_model('models/model_2D_small_3.hdf5')\n",
    "model2 = load_model('models/model_1D_norm.hdf5')\n",
    "model3 = load_model('models/model_LSTM_norm.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def avgpreds(preds):\n",
    "    return [ismap[p] for p in np.argmax(np.array([sum(i) for i in zip(*preds)]), axis = 1)][0]\n",
    "def indivpreds(preds):\n",
    "    data = [ismap[p[0]] for p in np.argmax(preds, axis = 2)]\n",
    "    return max(data, key = Counter(data).get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cooking1.wav\n",
      "1\n",
      "cooking\n",
      "cooking\n",
      "\n",
      "cooking2.wav\n",
      "56\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "cooking3.wav\n",
      "74\n",
      "absence\n",
      "absence\n",
      "\n",
      "cooking4_1.wav\n",
      "20\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "dishwashing1.wav\n",
      "23\n",
      "other\n",
      "other\n",
      "\n",
      "dishwashing2_1.wav\n",
      "29\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "dishwashing2_2.wav\n",
      "27\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "dishwashing3_1.wav\n",
      "11\n",
      "social activity\n",
      "absence\n",
      "\n",
      "dishwashing3_2.wav\n",
      "13\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "dishwashing4_1.wav\n",
      "23\n",
      "vacuum cleaning\n",
      "vacuum cleaning\n",
      "\n",
      "dishwashing4_2.wav\n",
      "25\n",
      "vacuum cleaning\n",
      "vacuum cleaning\n",
      "\n",
      "eating1_1.wav\n",
      "13\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "eating2_1.wav\n",
      "10\n",
      "working\n",
      "working\n",
      "\n",
      "eating3_1.wav\n",
      "13\n",
      "other\n",
      "dishwashing\n",
      "\n",
      "eating4_1.wav\n",
      "12\n",
      "working\n",
      "working\n",
      "\n",
      "eating4_2.wav\n",
      "14\n",
      "working\n",
      "working\n",
      "\n",
      "vacuum1_1.wav\n",
      "12\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "vacuum1_2.wav\n",
      "13\n",
      "other\n",
      "other\n",
      "\n",
      "vacuum1_3.wav\n",
      "15\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "vacuum2.wav\n",
      "22\n",
      "vacuum cleaning\n",
      "vacuum cleaning\n",
      "\n",
      "vacuum3.wav\n",
      "13\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "vacuum4_1.wav\n",
      "15\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "vacuum4_2.wav\n",
      "18\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "vacuum4_3.wav\n",
      "15\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "working1_1.wav\n",
      "20\n",
      "absence\n",
      "absence\n",
      "\n",
      "working1_2.wav\n",
      "20\n",
      "absence\n",
      "absence\n",
      "\n",
      "working1_3.wav\n",
      "15\n",
      "social activity\n",
      "absence\n",
      "\n",
      "working2.wav\n",
      "14\n",
      "other\n",
      "other\n",
      "\n",
      "working3.wav\n",
      "11\n",
      "absence\n",
      "absence\n",
      "\n",
      "working4.wav\n",
      "9\n",
      "other\n",
      "eating\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "for fn in os.listdir(\"audio\"):\n",
    "    print(fn)\n",
    "    sr, ys = make_audio(\"audio/\" + fn)\n",
    "    print(len(ys))\n",
    "    preds = pred(sr, ys, model1, model2, model3)\n",
    "    print(avgpreds(preds))\n",
    "    print(indivpreds(preds))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cooking1.wav\n",
      "1\n",
      "cooking\n",
      "cooking\n",
      "\n",
      "cooking2.wav\n",
      "56\n",
      "social activity\n",
      "social activity\n",
      "\n",
      "cooking3.wav\n",
      "74\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-126-55be28b69ed4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0msr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_audio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"unedited audio/\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpred\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mavgpreds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindivpreds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-119-f2e754454541>\u001b[0m in \u001b[0;36mpred\u001b[1;34m(sr, ys, model1, model2, model3)\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[0my_feat_1d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_1d_np\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m#         print(\"Generating 2D spectrogram\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0my_feat_2d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_2d_np\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmodel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my_feat_2d\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-44-f363a12fc3eb>\u001b[0m in \u001b[0;36mget_2d_np\u001b[1;34m(y, sr)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_2d_np\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m     \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.\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[0m\u001b[0;32m     10\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, rect, facecolor, frameon, sharex, sharey, label, xscale, yscale, axisbg, **kwargs)\u001b[0m\n\u001b[0;32m    527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m         \u001b[1;31m# this call may differ for non-sep axes, e.g., polar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 529\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    530\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0maxisbg\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfacecolor\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[0;32m    531\u001b[0m             raise TypeError('Both axisbg and facecolor are not None. '\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_init_axis\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    620\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_init_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    621\u001b[0m         \u001b[1;34m\"move this out of __init__ because non-separable axes don't use it\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 622\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mXAxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    623\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'bottom'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'top'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, axes, pickradius)\u001b[0m\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    676\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 677\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_scale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'linear'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    678\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    679\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_label_coords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_set_scale\u001b[1;34m(self, value, **kwargs)\u001b[0m\n\u001b[0;32m    708\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_scale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    709\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_scale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmscale\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 710\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_scale\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_default_locators_and_formatters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    711\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misDefault_majloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\scale.py\u001b[0m in \u001b[0;36mset_default_locators_and_formatters\u001b[1;34m(self, axis)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[0mlinear\u001b[0m \u001b[0mscaling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m         \"\"\"\n\u001b[1;32m---> 74\u001b[1;33m         \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAutoLocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m         \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mScalarFormatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_minor_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNullLocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2322\u001b[0m             \u001b[0mnbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'auto'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2323\u001b[0m             \u001b[0msteps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2324\u001b[1;33m         \u001b[0mMaxNLocator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2325\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1672\u001b[0m                     \"Keywords are required for all arguments except 'nbins'\")\n\u001b[0;32m   1673\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefault_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1674\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1676\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36mset_params\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m   1727\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1728\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_steps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1729\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extended_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_staircase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_steps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1730\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'integer'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1731\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_integer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m_staircase\u001b[1;34m(steps)\u001b[0m\n\u001b[0;32m   1699\u001b[0m         \u001b[1;31m# than necessary.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1700\u001b[0m         \u001b[0mflights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0msteps\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[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0msteps\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-> 1701\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mflights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1703\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\core\\shape_base.py\u001b[0m in \u001b[0;36mhstack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m     \"\"\"\n\u001b[1;32m--> 283\u001b[1;33m     \u001b[0marrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    284\u001b[0m     \u001b[1;31m# As a special case, dimension 0 of 1-dimensional arrays is \"horizontal\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0marrs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\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[1;32mc:\\users\\jerem_000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\numpy\\core\\shape_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m     \"\"\"\n\u001b[1;32m--> 283\u001b[1;33m     \u001b[0marrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    284\u001b[0m     \u001b[1;31m# As a special case, dimension 0 of 1-dimensional arrays is \"horizontal\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0marrs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\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[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9e96a6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "for fn in os.listdir(\"unedited audio\"):\n",
    "    print(fn)\n",
    "    sr, ys = make_audio(\"unedited audio/\" + fn)\n",
    "    print(len(ys))\n",
    "    preds = pred(sr, ys, model1, model2, model3)\n",
    "    print(avgpreds(preds))\n",
    "    print(indivpreds(preds))\n",
    "    print()"
   ]
  }
 ],
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