CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "virtual-money",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install matplotlib\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import models, layers\n",
    "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "referenced-joyce",
   "metadata": {},
   "source": [
    "We will create Mel Spectrogram Images for each of the music files.These Mel spectrogram images will be used as INput for Our CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "coordinate-median",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_spectrogram(filename,name):\n",
    "    plt.interactive(False)\n",
    "    clip, sample_rate = librosa.load(filename, sr=None,duration=30)\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",
    "    filename  = './' + name + '.jpg'\n",
    "    plt.savefig(filename, dpi=800, bbox_inches='tight',pad_inches=0)\n",
    "    plt.close()    \n",
    "    fig.clf()\n",
    "    plt.close(fig)\n",
    "    plt.close('all')\n",
    "    del filename,name,clip,sample_rate,fig,ax,S\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "anonymous-yield",
   "metadata": {},
   "source": [
    "For each of the music file I create one image file and store it in the images directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "third-sender",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "filesnames=[]\n",
    "genrenames=[]\n",
    "for root, dirs, files in os.walk(path):\n",
    "    for name in files:\n",
    "        filename = os.path.join(root, name)\n",
    "        filesnames.append(filename)\n",
    "        \n",
    "        genrenames.append(filename.split(\"/\")[3])\n",
    "try:\n",
    "    os.mkdir('images')\n",
    "except FileExistsError as e:\n",
    "    None\n",
    "finally:\n",
    "    None\n",
    "for name in set(genrenames):\n",
    "    try:\n",
    "        os.mkdir(\"./images/\"+name)\n",
    "    except FileExistsError as e:\n",
    "        None    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "first-competition",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "clinical-embassy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(filesnames)):\n",
    "    \n",
    "    genre=genrenames[i]\n",
    "    filename=filesnames[i]\n",
    "    endname=filename.split('/')\n",
    "#     print(endname)\n",
    "    endname=endname[-1]\n",
    "#     print(endname)\n",
    "    pathh=\"images/\"+genre+\"/\"+endname\n",
    "    print(pathh)\n",
    "    create_spectrogram(filename,pathh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "sporting-applicant",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "hidden-oriental",
   "metadata": {},
   "source": [
    "### Loading Image Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "incoming-tournament",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 947 files belonging to 10 classes.\n",
      "Using 758 files for training.\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE=8\n",
    "TARGET_SIZEX=223\n",
    "TARGET_SIZEY=217\n",
    "NUM_CLASSES=10\n",
    "image_dataset=image_dataset_from_directory(\n",
    "  \"./images\",\n",
    "  validation_split=0.2,\n",
    "  subset=\"training\",\n",
    "  seed=42,\n",
    "  image_size=(TARGET_SIZEX, TARGET_SIZEY),\n",
    "  batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "affecting-buffalo",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 947 files belonging to 10 classes.\n",
      "Using 189 files for validation.\n"
     ]
    }
   ],
   "source": [
    "val_ds = image_dataset_from_directory(\n",
    "   \"./images\",\n",
    "  validation_split=0.2,\n",
    "  subset=\"validation\",\n",
    "  seed=42,\n",
    "  image_size=(TARGET_SIZEX, TARGET_SIZEY),\n",
    "  batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indonesian-horizontal",
   "metadata": {},
   "source": [
    "## Building CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "strong-linux",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_save = tf.keras.callbacks.ModelCheckpoint('./cnnmodel.h5', \n",
    "                             save_best_only = True, \n",
    "                             save_weights_only = False,\n",
    "                             monitor = 'val_accuracy', \n",
    "                             mode = 'max', verbose = 1)\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor = 'val_accuracy', \n",
    "                           patience = 10 ,mode = 'max', verbose = 1,\n",
    "                           restore_best_weights = True)\n",
    "\n",
    "model = Sequential([\n",
    "  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(TARGET_SIZEX, TARGET_SIZEY, 3)),\n",
    "  layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
    "  layers.MaxPooling2D(),\n",
    "  layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
    "  layers.MaxPooling2D(),\n",
    "  layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "  layers.MaxPooling2D(),\n",
    "  layers.Dropout(0.3),\n",
    "  layers.Flatten(),\n",
    "  layers.Dense(128, activation='relu'),\n",
    "  layers.Dense(NUM_CLASSES,activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "prescription-rover",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "rescaling_2 (Rescaling)      (None, 223, 217, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 223, 217, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 111, 108, 16)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 111, 108, 32)      4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 55, 54, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 55, 54, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 27, 27, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 27, 27, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 46656)             0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               5972096   \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 5,996,970\n",
      "Trainable params: 5,996,970\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=Adam(lr = 0.001),\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "molecular-mother",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "stupid-charter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "95/95 - 20s - loss: 2.3117 - accuracy: 0.2164 - val_loss: 2.0901 - val_accuracy: 0.2804\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.28042, saving model to ./cnnmodel.h5\n",
      "Epoch 2/40\n",
      "95/95 - 19s - loss: 1.8828 - accuracy: 0.3166 - val_loss: 1.8282 - val_accuracy: 0.3122\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.28042 to 0.31217, saving model to ./cnnmodel.h5\n",
      "Epoch 3/40\n",
      "95/95 - 19s - loss: 1.7092 - accuracy: 0.3892 - val_loss: 1.7629 - val_accuracy: 0.3545\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.31217 to 0.35450, saving model to ./cnnmodel.h5\n",
      "Epoch 4/40\n",
      "95/95 - 19s - loss: 1.4867 - accuracy: 0.4749 - val_loss: 1.6843 - val_accuracy: 0.3862\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.35450 to 0.38624, saving model to ./cnnmodel.h5\n",
      "Epoch 5/40\n",
      "95/95 - 19s - loss: 1.2328 - accuracy: 0.5409 - val_loss: 1.5681 - val_accuracy: 0.4180\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.38624 to 0.41799, saving model to ./cnnmodel.h5\n",
      "Epoch 6/40\n",
      "95/95 - 19s - loss: 1.0219 - accuracy: 0.6240 - val_loss: 1.4671 - val_accuracy: 0.4709\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.41799 to 0.47090, saving model to ./cnnmodel.h5\n",
      "Epoch 7/40\n",
      "95/95 - 19s - loss: 0.7651 - accuracy: 0.7084 - val_loss: 1.4695 - val_accuracy: 0.5185\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.47090 to 0.51852, saving model to ./cnnmodel.h5\n",
      "Epoch 8/40\n",
      "95/95 - 19s - loss: 0.4976 - accuracy: 0.8298 - val_loss: 1.6984 - val_accuracy: 0.5608\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.51852 to 0.56085, saving model to ./cnnmodel.h5\n",
      "Epoch 9/40\n",
      "95/95 - 19s - loss: 0.3211 - accuracy: 0.8852 - val_loss: 1.5601 - val_accuracy: 0.5926\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.56085 to 0.59259, saving model to ./cnnmodel.h5\n",
      "Epoch 10/40\n",
      "95/95 - 19s - loss: 0.1341 - accuracy: 0.9591 - val_loss: 1.6435 - val_accuracy: 0.5979\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.59259 to 0.59788, saving model to ./cnnmodel.h5\n",
      "Epoch 11/40\n",
      "95/95 - 19s - loss: 0.0641 - accuracy: 0.9868 - val_loss: 1.9780 - val_accuracy: 0.6085\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.59788 to 0.60847, saving model to ./cnnmodel.h5\n",
      "Epoch 12/40\n",
      "95/95 - 19s - loss: 0.0381 - accuracy: 0.9934 - val_loss: 1.8571 - val_accuracy: 0.6296\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.60847 to 0.62963, saving model to ./cnnmodel.h5\n",
      "Epoch 13/40\n",
      "95/95 - 19s - loss: 0.0496 - accuracy: 0.9855 - val_loss: 2.8794 - val_accuracy: 0.6085\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.62963\n",
      "Epoch 14/40\n",
      "95/95 - 19s - loss: 0.1071 - accuracy: 0.9631 - val_loss: 1.9850 - val_accuracy: 0.5767\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.62963\n",
      "Epoch 15/40\n",
      "95/95 - 19s - loss: 0.0433 - accuracy: 0.9934 - val_loss: 1.6814 - val_accuracy: 0.6614\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.62963 to 0.66138, saving model to ./cnnmodel.h5\n",
      "Epoch 16/40\n",
      "95/95 - 19s - loss: 0.0158 - accuracy: 0.9974 - val_loss: 1.7991 - val_accuracy: 0.6614\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.66138\n",
      "Epoch 17/40\n",
      "95/95 - 20s - loss: 0.0222 - accuracy: 0.9947 - val_loss: 2.0182 - val_accuracy: 0.6455\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.66138\n",
      "Epoch 18/40\n",
      "95/95 - 19s - loss: 0.0246 - accuracy: 0.9960 - val_loss: 1.9611 - val_accuracy: 0.6455\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.66138\n",
      "Epoch 19/40\n",
      "95/95 - 19s - loss: 0.0136 - accuracy: 0.9974 - val_loss: 1.9021 - val_accuracy: 0.6455\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.66138\n",
      "Epoch 20/40\n",
      "95/95 - 19s - loss: 0.0179 - accuracy: 0.9974 - val_loss: 2.0836 - val_accuracy: 0.6667\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.66138 to 0.66667, saving model to ./cnnmodel.h5\n",
      "Epoch 21/40\n",
      "95/95 - 19s - loss: 0.0155 - accuracy: 0.9974 - val_loss: 2.0297 - val_accuracy: 0.6349\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.66667\n",
      "Epoch 22/40\n",
      "95/95 - 19s - loss: 0.0083 - accuracy: 0.9974 - val_loss: 1.9615 - val_accuracy: 0.6667\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.66667\n",
      "Epoch 23/40\n",
      "95/95 - 19s - loss: 0.0100 - accuracy: 0.9974 - val_loss: 1.9484 - val_accuracy: 0.6561\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.66667\n",
      "Epoch 24/40\n",
      "95/95 - 19s - loss: 0.0107 - accuracy: 0.9974 - val_loss: 1.8337 - val_accuracy: 0.6825\n",
      "\n",
      "Epoch 00024: val_accuracy improved from 0.66667 to 0.68254, saving model to ./cnnmodel.h5\n",
      "Epoch 25/40\n",
      "95/95 - 19s - loss: 0.0077 - accuracy: 0.9974 - val_loss: 1.8526 - val_accuracy: 0.6825\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.68254\n",
      "Epoch 26/40\n",
      "95/95 - 19s - loss: 0.0064 - accuracy: 0.9974 - val_loss: 1.8546 - val_accuracy: 0.6667\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.68254\n",
      "Epoch 27/40\n",
      "95/95 - 19s - loss: 0.0072 - accuracy: 0.9974 - val_loss: 1.8231 - val_accuracy: 0.6614\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.68254\n",
      "Epoch 28/40\n",
      "95/95 - 19s - loss: 0.0051 - accuracy: 0.9974 - val_loss: 1.9009 - val_accuracy: 0.6878\n",
      "\n",
      "Epoch 00028: val_accuracy improved from 0.68254 to 0.68783, saving model to ./cnnmodel.h5\n",
      "Epoch 29/40\n",
      "95/95 - 19s - loss: 0.0046 - accuracy: 0.9974 - val_loss: 1.9349 - val_accuracy: 0.6825\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.68783\n",
      "Epoch 30/40\n",
      "95/95 - 19s - loss: 0.0054 - accuracy: 0.9974 - val_loss: 1.9488 - val_accuracy: 0.6455\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.68783\n",
      "Epoch 31/40\n",
      "95/95 - 19s - loss: 0.0065 - accuracy: 0.9974 - val_loss: 1.9620 - val_accuracy: 0.6825\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.68783\n",
      "Epoch 32/40\n",
      "95/95 - 19s - loss: 0.0080 - accuracy: 0.9974 - val_loss: 1.7923 - val_accuracy: 0.6772\n",
      "\n",
      "Epoch 00032: val_accuracy did not improve from 0.68783\n",
      "Epoch 33/40\n",
      "95/95 - 19s - loss: 0.0039 - accuracy: 0.9974 - val_loss: 1.8740 - val_accuracy: 0.6614\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.68783\n",
      "Epoch 34/40\n",
      "95/95 - 19s - loss: 0.0049 - accuracy: 0.9974 - val_loss: 1.8926 - val_accuracy: 0.6667\n",
      "\n",
      "Epoch 00034: val_accuracy did not improve from 0.68783\n",
      "Epoch 35/40\n",
      "95/95 - 19s - loss: 0.0043 - accuracy: 0.9974 - val_loss: 1.8986 - val_accuracy: 0.6720\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.68783\n",
      "Epoch 36/40\n",
      "95/95 - 19s - loss: 0.0045 - accuracy: 0.9974 - val_loss: 1.9578 - val_accuracy: 0.6667\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.68783\n",
      "Epoch 37/40\n",
      "95/95 - 19s - loss: 0.0036 - accuracy: 0.9974 - val_loss: 2.0425 - val_accuracy: 0.6825\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.68783\n",
      "Epoch 38/40\n",
      "95/95 - 19s - loss: 0.1482 - accuracy: 0.9433 - val_loss: 2.4187 - val_accuracy: 0.5132\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.68783\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00038: early stopping\n"
     ]
    }
   ],
   "source": [
    "\n",
    "x=model.fit(\n",
    "  image_dataset,\n",
    "  validation_data=val_ds,\n",
    "  epochs=40,\n",
    "  verbose=2,\n",
    "callbacks=[model_save,early_stop]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hybrid-immigration",
   "metadata": {},
   "source": [
    "We got the best accuracy at epoch 28 and model was saved at this state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "geological-mileage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist(history):\n",
    "    acc = history.history['accuracy']\n",
    "    val_acc = history.history['val_accuracy']\n",
    "\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(acc, label='Training Accuracy')\n",
    "    plt.plot(val_acc, label='Validation Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.title('Training and Validation Accuracy')\n",
    "    plt.grid()\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(loss, label='Training Loss')\n",
    "    plt.plot(val_loss, label='Validation Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "plot_hist(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "supported-egypt",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = []\n",
    "x =[]\n",
    "for images , label in val_ds.take(100) :\n",
    "    for i in range(len(images)):\n",
    "\n",
    "        x.append(images[i])\n",
    "        y.append(label[i]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "matched-messaging",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 1s 44ms/step - loss: 1.9009 - accuracy: 0.6878\n",
      "Accuracy -  0.6878306865692139\n"
     ]
    }
   ],
   "source": [
    "loss,acc=model.evaluate(val_ds)\n",
    "print(\"Accuracy - \",acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "global-lying",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=val_ds.class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "surgical-indie",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "elect-matthew",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_spectrogram(filename,name):\n",
    "    plt.interactive(False)\n",
    "    clip, sample_rate = librosa.load(filename, sr=None,duration=30)\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",
    "    filename  = './' + name + '.jpg'\n",
    "    plt.savefig(filename, dpi=800, bbox_inches='tight',pad_inches=0)\n",
    "    plt.close()    \n",
    "    fig.clf()\n",
    "    plt.close(fig)\n",
    "    plt.close('all')\n",
    "    del filename,name,clip,sample_rate,fig,ax,S\n",
    "\n",
    "\n",
    "def predict_single(path):\n",
    "    create_spectrogram(path,\"temp\")\n",
    "    image = tf.keras.preprocessing.image.load_img(path=\"./temp.jpg\",target_size=(TARGET_SIZEX, TARGET_SIZEY))\n",
    "    input_arr = tf.keras.preprocessing.image.img_to_array(image)\n",
    "    input_arr = np.array([input_arr])  # Convert single image to a batch.\n",
    "    predictions = model.predict(input_arr)\n",
    "#     print(predictions[0])\n",
    "    samp={}\n",
    "    \n",
    "    for i in range(10):\n",
    "        samp[labels[i]]=predictions[0][i]\n",
    "    return list({k: v for k, v in sorted(samp.items(), key=lambda item: item[1],reverse=True)}.keys())[:3]\n",
    "#     return list(sorted(samp,reverse=True))\n",
    "# model.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "spectacular-disclosure",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model('./cnnmodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "mediterranean-consultancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['blues', 'jazz', 'reggae']"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_single(\"./sample/bashful.wav\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "likely-today",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['blues', 'country', 'rock']"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_single(\"./sample/sinceseventeen.wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "competitive-spell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hiphop', 'reggae', 'metal']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_single(\"./sample/mytribe.wav\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}