SpeakerID_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "-iHkw8iK5WDb",
    "outputId": "a20b5729-2534-493f-e695-c5a16dfc0f78"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting memory_profiler\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9f/fe/1fca7273dd111108f204a686b12a12b6422d405fe4614087aa7d5a66ea87/memory_profiler-0.55.0.tar.gz (40kB)\n",
      "\r",
      "\u001b[K     |████████                        | 10kB 17.6MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████                | 20kB 4.7MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████        | 30kB 6.6MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████████████| 40kB 3.8MB/s \n",
      "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (from memory_profiler) (5.4.8)\n",
      "Building wheels for collected packages: memory-profiler\n",
      "  Building wheel for memory-profiler (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for memory-profiler: filename=memory_profiler-0.55.0-cp36-none-any.whl size=27174 sha256=9b42cc1ec85de2e6c2f5e74b1eca4196cd70fcc719312769dc68c5a82c9ff26a\n",
      "  Stored in directory: /root/.cache/pip/wheels/f0/ff/63/fdbff3f1e1b76ad4eae491dd5b190902906b093e93eb86dd5a\n",
      "Successfully built memory-profiler\n",
      "Installing collected packages: memory-profiler\n",
      "Successfully installed memory-profiler-0.55.0\n"
     ]
    }
   ],
   "source": [
    "#Importing bunch of libraries.\n",
    "!pip install memory_profiler\n",
    "import os\n",
    "import librosa\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential,Model,load_model\n",
    "from keras.layers import Conv2D,MaxPool2D,Dense,GlobalMaxPool2D,Dropout\n",
    "import glob\n",
    "from memory_profiler import memory_usage\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "colab_type": "code",
    "id": "XJZHx7fD6A0G",
    "outputId": "7e0c094f-20e6-4068-d75c-8126e97b4ac4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-11-21 12:22:46--  http://www.openslr.org/resources/12/train-clean-100.tar.gz\n",
      "Resolving www.openslr.org (www.openslr.org)... 46.101.158.64\n",
      "Connecting to www.openslr.org (www.openslr.org)|46.101.158.64|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6387309499 (5.9G) [application/x-gzip]\n",
      "Saving to: ‘train-clean-100.tar.gz’\n",
      "\n",
      "train-clean-100.tar 100%[===================>]   5.95G  27.7MB/s    in 5m 15s  \n",
      "\n",
      "2019-11-21 12:28:02 (19.3 MB/s) - ‘train-clean-100.tar.gz’ saved [6387309499/6387309499]\n",
      "\n",
      "--2019-11-21 12:28:04--  http://www.openslr.org/resources/12/test-clean.tar.gz\n",
      "Resolving www.openslr.org (www.openslr.org)... 46.101.158.64\n",
      "Connecting to www.openslr.org (www.openslr.org)|46.101.158.64|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 346663984 (331M) [application/x-gzip]\n",
      "Saving to: ‘test-clean.tar.gz’\n",
      "\n",
      "test-clean.tar.gz   100%[===================>] 330.60M  33.6MB/s    in 12s     \n",
      "\n",
      "2019-11-21 12:28:16 (28.6 MB/s) - ‘test-clean.tar.gz’ saved [346663984/346663984]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Downloading the data\n",
    "!wget http://www.openslr.org/resources/12/train-clean-100.tar.gz\n",
    "!wget http://www.openslr.org/resources/12/test-clean.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "rMl6GWbQ6j5t",
    "outputId": "57151ace-d615-45bc-a39b-5f901811d0b5"
   },
   "outputs": [],
   "source": [
    "!mkdir train_data_wav\n",
    "!mkdir test_data_wav\n",
    "!tar -C /content/train_data_wav/ -xvf /content/train-clean-100.tar.gz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "6-fbSzlL650I",
    "outputId": "687fa3dc-75a8-45b9-a39e-c87b4ea1605c"
   },
   "outputs": [],
   "source": [
    "!tar -C /content/test_data_wav/ -xvf /content/test-clean.tar.gz\n",
    "!rm /content/test-clean.tar.gz\n",
    "!rm /content/train-clean-100.tar.gz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lY5ECUql8JAd"
   },
   "outputs": [],
   "source": [
    "def create_spectrogram(filepath,save_path):\n",
    "  plt.interactive(False)\n",
    "  clip,sample_rate=librosa.load(filepath,sr=None)\n",
    "  fig=plt.figure(figsize=[0.72,0.72])\n",
    "  ax=fig.add_subplot(111)\n",
    "  ax.axes.get_xaxis().set_visible(False)\n",
    "  ax.axes.get_yaxis().set_visible(False)\n",
    "  ax.set_frame_on(False)\n",
    "  S=librosa.feature.melspectrogram(y=clip,sr=sample_rate)\n",
    "  librosa.display.specshow(librosa.power_to_db(S,ref=np.max))\n",
    "  fig.savefig(save_path,dpi=400,bbox_inches='tight',pad_inches=0)\n",
    "  plt.close()\n",
    "  fig.clf()\n",
    "  plt.close(fig)\n",
    "  plt.close('all')\n",
    "  del filepath,save_path,clip,sample_rate,fig,ax,S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gM6b8o1H9S-v"
   },
   "outputs": [],
   "source": [
    "#Finding all files in a directory.\n",
    "\n",
    "\n",
    "train_list=[]\n",
    "\n",
    "path = '/content/train_data_wav/LibriSpeech/train-clean-100/'\n",
    "\n",
    "files = []\n",
    "\n",
    "for r, d, f in os.walk(path):\n",
    "    for file in f:\n",
    "        if '.flac' in file:\n",
    "            files.append(os.path.join(r, file))\n",
    "\n",
    "for f in files:\n",
    "    train_list.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D3b0MIfQ_G7R"
   },
   "outputs": [],
   "source": [
    "#Finding all files in a directory.\n",
    "\n",
    "\n",
    "test_list=[]\n",
    "\n",
    "path = '/content/test_data_wav/LibriSpeech/test-clean/'\n",
    "\n",
    "files = []\n",
    "\n",
    "for r, d, f in os.walk(path):\n",
    "    for file in f:\n",
    "        if '.flac' in file:\n",
    "            files.append(os.path.join(r, file))\n",
    "\n",
    "for f in files:\n",
    "    test_list.append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5JHPYR4B_WUf"
   },
   "outputs": [],
   "source": [
    "!mkdir train_images\n",
    "!mkdir test_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "Jy48sjkEACRL",
    "outputId": "bae5aae3-28b7-4a74-f3a6-d8a279c4264c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 475.05 MiB, increment: 0.00 MiB\n",
      "peak memory: 662.65 MiB, increment: -0.01 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "18576"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating spectrograms and storing to their respective directory.\n",
    "\n",
    "\n",
    "%load_ext memory_profiler \n",
    "%memit   #Emit memory\n",
    "\n",
    "#Train_data\n",
    "for filepath in train_list:\n",
    "  filename=filepath.split('/')[-1].split('.')[0]\n",
    "  create_spectrogram(filepath,'/content/train_images/'+filename+'.jpg')\n",
    "\n",
    "gc.collect()\n",
    "\n",
    "#Test data\n",
    "%memit   #Emit memory\n",
    "for filepath in test_list:\n",
    "  filename=filepath.split('/')[-1].split('.')[0]\n",
    "  create_spectrogram(filepath,'/content/test_images/'+filename+'.jpg')\n",
    "\n",
    "gc.collect()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oKdnbhhMCZnl"
   },
   "outputs": [],
   "source": [
    "#Building the batch generator.\n",
    "import cv2\n",
    "def load_img(path):\n",
    "  img=cv2.imread(path)\n",
    "  img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "  img=cv2.resize(img,(150,150))\n",
    "  return img\n",
    "\n",
    "def generate_idxs(X):\n",
    "    idxs1=np.random.randint(0,len(X),1)[0]\n",
    "    return idxs1\n",
    "\n",
    "def same_label_index(X):\n",
    "\n",
    "  while True:\n",
    "    idx1=generate_idxs(X)\n",
    "    idx2=generate_idxs(X)\n",
    "    file1_dir=X[idx1]\n",
    "    file2_dir=X[idx2]\n",
    "    lb1=file1_dir.split(\"/\")[-1].split(\"-\")[0]\n",
    "    lb2=file2_dir.split(\"/\")[-1].split(\"-\")[0]\n",
    "    if lb1==lb2:\n",
    "      break\n",
    "\n",
    "  return idx1,idx2\n",
    "\n",
    "def different_label_index(X):\n",
    "  while True:\n",
    "    idx1=generate_idxs(X)\n",
    "    idx2=generate_idxs(X)\n",
    "    file1_dir=X[idx1]\n",
    "    file2_dir=X[idx2]\n",
    "    lb1=file1_dir.split(\"/\")[-1].split(\"-\")[0]\n",
    "    lb2=file2_dir.split(\"/\")[-1].split(\"-\")[0]\n",
    "    if lb1!=lb2:\n",
    "      break\n",
    "\n",
    "  return idx1,idx2\n",
    "\n",
    "def batch_generator(X,batch_size):\n",
    "  while True:\n",
    "    data=[np.zeros((batch_size,150,150,3)) for i in range(2)]\n",
    "    tar=[np.zeros(batch_size,)]\n",
    "\n",
    "    #Generating same pairs.\n",
    "    for i in range(0,batch_size//2):\n",
    "      idx1,idx2=same_label_index(X)\n",
    "      img1=load_img(X[idx1])\n",
    "      img1=img1/255\n",
    "      img2=load_img(X[idx2])\n",
    "      img2=img2/255\n",
    "\n",
    "      data[0][i,:,:,:]=img1\n",
    "      data[1][i,:,:,:]=img2\n",
    "      tar[0][i]=1\n",
    "\n",
    "    #Generating different pairs.\n",
    "    for k in range(batch_size//2,batch_size):\n",
    "      idx1,idx2=different_label_index(X)\n",
    "      img1=load_img(X[idx1])\n",
    "      img1=img1/255\n",
    "      img2=load_img(X[idx2])\n",
    "      img2=img2/255\n",
    "\n",
    "      data[0][k,:,:,:]=img1\n",
    "      data[1][k,:,:,:]=img2\n",
    "      tar[0][k]=0\n",
    "    np.delete(data[0],np.where(~data[0].any(axis=1))[0], axis=0)\n",
    "    np.delete(data[1],np.where(~data[1].any(axis=1))[0], axis=0)\n",
    "    yield data,tar\n",
    "\n",
    "\n",
    "\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "vPD88uvOzkOm",
    "outputId": "c588b448-01c3-4f01-dfb2-bc4232b1a087"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4af818de10>"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAD8CAYAAAB6iWHJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n57IrDjt27DiXXXHYsWPHufwP/GwTBbdbhZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "img_test=cv2.imread('/content/train_images/587-54108-0035.jpg')\n",
    "img_test=cv2.cvtColor(img_test,cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(img_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WA78dkWMLrtn"
   },
   "outputs": [],
   "source": [
    "#Getting training images_list.\n",
    "\n",
    "\n",
    "X_train=[]\n",
    "temp_file_list_= os.listdir('/content/train_images/')\n",
    "for filename in temp_file_list_:\n",
    "  X_train.append(str('/content/train_images/'+filename))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "asPFKEvXNQK-"
   },
   "outputs": [],
   "source": [
    "#Getting test images_list.\n",
    "\n",
    "\n",
    "\n",
    "X_test=[]\n",
    "temp_file_list_= os.listdir('/content/test_images/')\n",
    "for filename in temp_file_list_:\n",
    "  X_test.append(str('/content/test_images/'+filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7irbl443Jkq4"
   },
   "outputs": [],
   "source": [
    "#Build the model\n",
    "from keras.layers import Lambda,Input\n",
    "import keras.backend as K\n",
    "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "\n",
    "def cosine_distance(vests):\n",
    "    x, y = vests\n",
    "    x = K.l2_normalize(x, axis=-1)\n",
    "    y = K.l2_normalize(y, axis=-1)\n",
    "    return -K.mean(x * y, axis=-1, keepdims=True)\n",
    "\n",
    "def get_encoder(input_size):\n",
    "  model=Sequential()\n",
    "  model.add(Conv2D(32,(3,3),input_shape=(150,150,3),activation='relu'))\n",
    "  model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "  model.add(MaxPool2D(2,2))\n",
    "  model.add(Dropout(0.2))\n",
    "\n",
    "  model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "  model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "  model.add(MaxPool2D(2,2))\n",
    "  model.add(Dropout(0.2))\n",
    "\n",
    "  model.add(Conv2D(128,(3,3),activation='relu'))\n",
    "  model.add(Conv2D(128,(3,3),activation='relu'))\n",
    "  model.add(MaxPool2D(2,2))\n",
    "  model.add(Dropout(0.2))\n",
    "\n",
    "  model.add(GlobalMaxPool2D())\n",
    "\n",
    "  return model\n",
    "\n",
    "def get_siamese_network(encoder,input_size):\n",
    "  input1=Input(input_size)\n",
    "  input2=Input(input_size)\n",
    "\n",
    "  encoder_l=encoder(input1)\n",
    "  encoder_r=encoder(input2)\n",
    "  \n",
    "  L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))\n",
    "  L1_distance = L1_layer([encoder_l, encoder_r])\n",
    "\n",
    "  output=Dense(1,activation='sigmoid')(L1_distance)\n",
    "  siam_model=Model(inputs=[input1,input2],outputs=output)\n",
    "  return siam_model\n",
    "\n",
    "encoder=get_encoder((150,150,3))\n",
    "siamese_net=get_siamese_network(encoder,(150,150,3))\n",
    "siamese_net.compile(loss='binary_crossentropy',optimizer='adam')\n",
    "\n",
    "\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "zj1T8ymuNUdb",
    "outputId": "52a7a7a5-c298-46d8-ad46-2702d4e30304"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "150/150 [==============================] - 33s 219ms/step - loss: 0.6830 - val_loss: 0.6933\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.69331, saving model to SpeakerID_best.hdf5\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - 25s 169ms/step - loss: 0.6616 - val_loss: 0.6705\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.69331 to 0.67052, saving model to SpeakerID_best.hdf5\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.6235 - val_loss: 0.6709\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.67052\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - 27s 178ms/step - loss: 0.5982 - val_loss: 0.5963\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.67052 to 0.59628, saving model to SpeakerID_best.hdf5\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - 27s 179ms/step - loss: 0.5804 - val_loss: 0.6089\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.59628\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.5692 - val_loss: 0.6246\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.59628\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - 27s 182ms/step - loss: 0.5680 - val_loss: 0.6062\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.59628\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - 27s 182ms/step - loss: 0.5769 - val_loss: 0.6140\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.59628\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - 27s 182ms/step - loss: 0.5586 - val_loss: 0.5869\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.59628 to 0.58690, saving model to SpeakerID_best.hdf5\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - 27s 179ms/step - loss: 0.5475 - val_loss: 0.5966\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.58690\n",
      "Epoch 11/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.5638 - val_loss: 0.5856\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.58690 to 0.58555, saving model to SpeakerID_best.hdf5\n",
      "Epoch 12/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.5403 - val_loss: 0.5625\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.58555 to 0.56253, saving model to SpeakerID_best.hdf5\n",
      "Epoch 13/50\n",
      "150/150 [==============================] - 28s 184ms/step - loss: 0.5571 - val_loss: 0.6250\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.56253\n",
      "Epoch 14/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.5420 - val_loss: 0.5905\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.56253\n",
      "Epoch 15/50\n",
      "150/150 [==============================] - 27s 183ms/step - loss: 0.5426 - val_loss: 0.5948\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.56253\n",
      "Epoch 16/50\n",
      "150/150 [==============================] - 27s 183ms/step - loss: 0.5495 - val_loss: 0.5908\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.56253\n",
      "Epoch 17/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.5403 - val_loss: 0.5791\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.56253\n",
      "Epoch 18/50\n",
      "150/150 [==============================] - 28s 186ms/step - loss: 0.5385 - val_loss: 0.5679\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.56253\n",
      "Epoch 19/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.5341 - val_loss: 0.5332\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.56253 to 0.53324, saving model to SpeakerID_best.hdf5\n",
      "Epoch 20/50\n",
      "150/150 [==============================] - 27s 178ms/step - loss: 0.5461 - val_loss: 0.7501\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.53324\n",
      "Epoch 21/50\n",
      "150/150 [==============================] - 28s 184ms/step - loss: 0.6554 - val_loss: 0.5691\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.53324\n",
      "Epoch 22/50\n",
      "150/150 [==============================] - 28s 185ms/step - loss: 0.5612 - val_loss: 0.5531\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.53324\n",
      "Epoch 23/50\n",
      "150/150 [==============================] - 27s 177ms/step - loss: 0.5301 - val_loss: 0.5991\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.53324\n",
      "Epoch 24/50\n",
      "150/150 [==============================] - 28s 184ms/step - loss: 0.5085 - val_loss: 0.5527\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.53324\n",
      "Epoch 25/50\n",
      "150/150 [==============================] - 27s 178ms/step - loss: 0.5067 - val_loss: 0.5099\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.53324 to 0.50994, saving model to SpeakerID_best.hdf5\n",
      "Epoch 26/50\n",
      "150/150 [==============================] - 27s 183ms/step - loss: 0.5110 - val_loss: 0.5962\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.50994\n",
      "Epoch 27/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.5198 - val_loss: 0.5444\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.50994\n",
      "Epoch 28/50\n",
      "150/150 [==============================] - 28s 184ms/step - loss: 0.5138 - val_loss: 0.5032\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.50994 to 0.50317, saving model to SpeakerID_best.hdf5\n",
      "Epoch 29/50\n",
      "150/150 [==============================] - 27s 182ms/step - loss: 0.4870 - val_loss: 0.5281\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.50317\n",
      "Epoch 30/50\n",
      "150/150 [==============================] - 27s 179ms/step - loss: 0.4902 - val_loss: 0.5667\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.50317\n",
      "Epoch 31/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.4947 - val_loss: 0.5957\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.50317\n",
      "Epoch 32/50\n",
      "150/150 [==============================] - 28s 183ms/step - loss: 0.4881 - val_loss: 0.6019\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.50317\n",
      "Epoch 33/50\n",
      "150/150 [==============================] - 27s 183ms/step - loss: 0.4702 - val_loss: 0.5221\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.50317\n",
      "Epoch 34/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.5006 - val_loss: 0.5408\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.50317\n",
      "Epoch 35/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.4830 - val_loss: 0.4828\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.50317 to 0.48277, saving model to SpeakerID_best.hdf5\n",
      "Epoch 36/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.4745 - val_loss: 0.5586\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.48277\n",
      "Epoch 37/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.5018 - val_loss: 0.5025\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.48277\n",
      "Epoch 38/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.4850 - val_loss: 0.5488\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.48277\n",
      "Epoch 39/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.4957 - val_loss: 0.6000\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.48277\n",
      "Epoch 40/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.4585 - val_loss: 0.5503\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.48277\n",
      "Epoch 41/50\n",
      "150/150 [==============================] - 27s 180ms/step - loss: 0.4612 - val_loss: 0.5441\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.48277\n",
      "Epoch 42/50\n",
      "150/150 [==============================] - 27s 177ms/step - loss: 0.4637 - val_loss: 0.5489\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.48277\n",
      "Epoch 43/50\n",
      "150/150 [==============================] - 27s 178ms/step - loss: 0.4620 - val_loss: 0.5261\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.48277\n",
      "Epoch 44/50\n",
      "150/150 [==============================] - 26s 175ms/step - loss: 0.4610 - val_loss: 0.5471\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.48277\n",
      "Epoch 45/50\n",
      "150/150 [==============================] - 27s 181ms/step - loss: 0.4629 - val_loss: 0.4827\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.48277 to 0.48267, saving model to SpeakerID_best.hdf5\n",
      "Epoch 00045: early stopping\n"
     ]
    }
   ],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, min_delta=0.0001) \n",
    "mc = ModelCheckpoint('SpeakerID_best.hdf5', monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
    "history=siamese_net.fit_generator(batch_generator(X_train,10),steps_per_epoch=150,epochs=50,validation_data=batch_generator(X_test,10),\n",
    "                            validation_steps=20,callbacks=[es,mc],shuffle=True,use_multiprocessing=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "LUKm1Uur4-nt",
    "outputId": "4351959a-b2db-4de9-a011-1c9caf913482"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.38627076]]\n",
      "[[0.04046698]]\n"
     ]
    }
   ],
   "source": [
    "#Same pair test.\n",
    "idx1,idx2=same_label_index(X_test)\n",
    "X1=[np.zeros((1,150,150,3)) for i in range(2)]\n",
    "\n",
    "img1=load_img(X_test[idx1])\n",
    "img1=np.array(img1)\n",
    "img1=img1/255\n",
    "img2=load_img(X_test[idx2])\n",
    "img2=np.array(img2)\n",
    "img2=img2/255\n",
    "\n",
    "X1[0][0,:,:,:]=img1\n",
    "X1[1][0,:,:,:]=img2\n",
    "\n",
    "#Different pair.\n",
    "idx1,idx2=different_label_index(X_test)\n",
    "X2=[np.zeros((1,150,150,3)) for i in range(2)]\n",
    "\n",
    "img1=load_img(X_test[idx1])\n",
    "img1=np.array(img1)\n",
    "img1=img1/255\n",
    "img2=load_img(X_test[idx2])\n",
    "img2=np.array(img2)\n",
    "img2=img2/255\n",
    "\n",
    "\n",
    "X2[0][0,:,:,:]=img1\n",
    "X2[1][0,:,:,:]=img2\n",
    "\n",
    "model=load_model('/content/SpeakerID_best.hdf5')\n",
    "print(model.predict(X1))\n",
    "print(model.predict(X2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Clp-eoR5P3M5"
   },
   "outputs": [],
   "source": [
    "from google.colab import files\n",
    "files.download('/content/SpeakerID_best.hdf5')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "SpeakerID CNN.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "TensorFlow-GPU",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}