urbansound8k-dataprep-model.ipynb

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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import librosa \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import keras.models as models\n",
    "import keras.layers as layers\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "import IPython.display as idp\n",
    "from keras.models import load_model\n",
    "import cv2\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30008a30",
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    "tags": []
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   "outputs": [],
   "source": [
    "audio_dataset_path = 'extracted/'\n",
    "df = pd.read_csv('extracted/UrbanSound8K.csv')"
   ]
  },
  {
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       "      <th>end</th>\n",
       "      <th>salience</th>\n",
       "      <th>fold</th>\n",
       "      <th>classID</th>\n",
       "      <th>class</th>\n",
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       "      slice_file_name    fsID  start        end  salience  fold  classID  \\\n",
       "0    100032-3-0-0.wav  100032    0.0   0.317551         1     5        3   \n",
       "1  100263-2-0-117.wav  100263   58.5  62.500000         1     5        2   \n",
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       "4  100263-2-0-137.wav  100263   68.5  72.500000         1     5        2   \n",
       "\n",
       "              class  \n",
       "0          dog_bark  \n",
       "1  children_playing  \n",
       "2  children_playing  \n",
       "3  children_playing  \n",
       "4  children_playing  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "606c95b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes are:  ['dog_bark' 'children_playing' 'car_horn' 'air_conditioner' 'street_music'\n",
      " 'gun_shot' 'siren' 'engine_idling' 'jackhammer' 'drilling']\n",
      "# of Classes are:  10\n"
     ]
    }
   ],
   "source": [
    "classes = df['class'].unique()\n",
    "print(\"Classes are: \",classes)\n",
    "print(\"# of Classes are: \",classes.shape[0])"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "up_width = 173\n",
    "up_height = 40\n",
    "for data in df.iterrows():\n",
    "    file_name = os.path.join(os.path.abspath(audio_dataset_path),'fold'+str(data[1][5])+'/',str(data[1][0]))\n",
    "    raw , sr = librosa.load(file_name, res_type='kaiser_fast')\n",
    "    X_ = librosa.feature.mfcc(y=raw, sr=sr, n_mfcc=40)\n",
    "    up_points = (up_width, up_height)\n",
    "    X_ = cv2.resize(X_, up_points, interpolation= cv2.INTER_LINEAR)\n",
    "    X.append(X_)\n",
    "    y.append(data[1][7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5ae8ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a53d94b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8732, 40, 173)\n",
      "(8732,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2d8ac53",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(le.fit_transform(y))\n",
    "X = X.reshape(X.shape[0], X.shape[1], X.shape[2], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba915817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Shape is:  (8732, 40, 173, 1)\n",
      "y Shape is:  (8732, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"X Shape is: \", X.shape)\n",
    "print(\"y Shape is: \", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc428ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X , y ,test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28deecb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUTSHAPE = (up_height,up_width,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a608b5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 38, 171, 32)       320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 19, 86, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 17, 84, 128)       36992     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 9, 42, 128)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 9, 42, 128)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 7, 40, 128)        147584    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 20, 128)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 4, 20, 128)        0         \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 128)              0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               33024     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 220,490\n",
      "Trainable params: 220,490\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model =  models.Sequential([\n",
    "    \n",
    "                          layers.Conv2D(32 , (3,3),activation = 'relu',padding='valid', input_shape = INPUTSHAPE),  \n",
    "                          layers.MaxPooling2D(2, padding='same'),\n",
    "                          layers.Conv2D(128, (3,3), activation='relu',padding='valid'),\n",
    "                          layers.MaxPooling2D(2, padding='same'),\n",
    "                          layers.Dropout(0.3),\n",
    "                          layers.Conv2D(128, (3,3), activation='relu',padding='valid'),\n",
    "                          layers.MaxPooling2D(2, padding='same'),\n",
    "                          layers.Dropout(0.3),\n",
    "                          layers.GlobalAveragePooling2D(),\n",
    "                          layers.Dense(256 , activation = 'relu'),\n",
    "                          layers.Dense(10 , activation = 'softmax')\n",
    "])\n",
    "opt = keras.optimizers.Adam(learning_rate = 0.001)\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer = opt, metrics = 'acc')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d9329fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "874/874 [==============================] - 11s 6ms/step - loss: 1.3614 - acc: 0.5178 - val_loss: 0.9672 - val_acc: 0.6669\n",
      "Epoch 2/15\n",
      "874/874 [==============================] - 5s 6ms/step - loss: 0.8341 - acc: 0.7124 - val_loss: 0.8197 - val_acc: 0.7149\n",
      "Epoch 3/15\n",
      "874/874 [==============================] - 5s 6ms/step - loss: 0.6382 - acc: 0.7861 - val_loss: 0.5395 - val_acc: 0.8191\n",
      "Epoch 4/15\n",
      "874/874 [==============================] - 5s 6ms/step - loss: 0.4830 - acc: 0.8364 - val_loss: 0.3952 - val_acc: 0.8683\n",
      "Epoch 5/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.4188 - acc: 0.8574 - val_loss: 0.4031 - val_acc: 0.8695\n",
      "Epoch 6/15\n",
      "874/874 [==============================] - 5s 6ms/step - loss: 0.3503 - acc: 0.8789 - val_loss: 0.3886 - val_acc: 0.8666\n",
      "Epoch 7/15\n",
      "874/874 [==============================] - 5s 6ms/step - loss: 0.3217 - acc: 0.8968 - val_loss: 0.5114 - val_acc: 0.8340\n",
      "Epoch 8/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.2911 - acc: 0.8995 - val_loss: 0.5820 - val_acc: 0.8243\n",
      "Epoch 9/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.2554 - acc: 0.9108 - val_loss: 0.3488 - val_acc: 0.8930\n",
      "Epoch 10/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.2168 - acc: 0.9281 - val_loss: 0.3246 - val_acc: 0.9050\n",
      "Epoch 11/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.2075 - acc: 0.9314 - val_loss: 0.3597 - val_acc: 0.9044\n",
      "Epoch 12/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.2156 - acc: 0.9296 - val_loss: 0.3259 - val_acc: 0.8987\n",
      "Epoch 13/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.1784 - acc: 0.9410 - val_loss: 0.2873 - val_acc: 0.9181\n",
      "Epoch 14/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.1712 - acc: 0.9409 - val_loss: 0.4620 - val_acc: 0.8706\n",
      "Epoch 15/15\n",
      "874/874 [==============================] - 5s 5ms/step - loss: 0.1921 - acc: 0.9379 - val_loss: 0.3111 - val_acc: 0.9136\n"
     ]
    }
   ],
   "source": [
    "batch_size = 8\n",
    "callback = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss', min_delta=0, patience=8, verbose=0, mode='auto',\n",
    "    baseline=None, restore_best_weights=False)\n",
    "\n",
    "history = model.fit(X_train,y_train ,\n",
    "            validation_data=(X_test,y_test),\n",
    "            epochs=15,\n",
    "            callbacks = [callback],batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0378f9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a97725a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5af4ccfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_saved = keras.models.load_model('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f8be377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 - 1s - loss: 0.3111 - acc: 0.9136 - 665ms/epoch - 12ms/step\n",
      "Restored model, accuracy: 91.36%\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model_saved.evaluate(X_test, y_test, verbose=2)\n",
    "print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b4f09536",
   "metadata": {},
   "outputs": [],
   "source": [
    "def audio_to_result(filename):\n",
    "    raw , sr = librosa.load(filename, res_type='kaiser_fast')\n",
    "    X_image = librosa.feature.mfcc(y=raw, sr=sr, n_mfcc=40)\n",
    "    up_width = 173\n",
    "    up_height = 40\n",
    "    up_points = (up_width, up_height)\n",
    "    X_image = cv2.resize(X_image, up_points, interpolation= cv2.INTER_LINEAR)\n",
    "    print(X_image.shape)\n",
    "    X_image = X_image.reshape(1,X_image.shape[0],X_image.shape[1],1)\n",
    "    pred = model_saved.predict(X_image)\n",
    "    prediction = np.argmax(pred, axis=-1)\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "932be576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 173)\n",
      "1/1 [==============================] - 0s 97ms/step\n"
     ]
    }
   ],
   "source": [
    "pred = audio_to_result('extracted/fold3/103199-4-1-0.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c1e1a57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drilling\n"
     ]
    }
   ],
   "source": [
    "pred_class = le.inverse_transform(pred)\n",
    "print(pred_class[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29cfb419",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('classes.npy', le.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86dc7b5",
   "metadata": {},
   "source": [
    "## Model Evaluation ✅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "608087d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "Y_pred = [np.argmax(i) for i in Y_pred]\n",
    "y_test = [np.argmax(i) for i in y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e17858dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.94       203\n",
      "           1       0.94      0.92      0.93        86\n",
      "           2       0.86      0.90      0.88       183\n",
      "           3       0.93      0.88      0.90       201\n",
      "           4       0.94      0.80      0.87       206\n",
      "           5       0.94      0.98      0.96       193\n",
      "           6       0.92      0.96      0.94        72\n",
      "           7       0.89      0.92      0.91       208\n",
      "           8       0.94      0.98      0.96       165\n",
      "           9       0.88      0.89      0.89       230\n",
      "\n",
      "    accuracy                           0.91      1747\n",
      "   macro avg       0.92      0.92      0.92      1747\n",
      "weighted avg       0.91      0.91      0.91      1747\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ec8c489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"dark\")\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title(\"CONFUSION MATRIX FOR UltraSound8K PREDICTION\")\n",
    "cm = confusion_matrix([int(x) for x in y_test],Y_pred)\n",
    "sns.heatmap(cm, annot=True, cmap=\"cool\", fmt='g', cbar=False)\n",
    "plt.xlabel(\"ACTUAL VALUES\")\n",
    "plt.ylabel(\"PREDICTED VALUES\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('tensorflow')",
   "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": "3.10.4"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 1099.37774,
   "end_time": "2021-09-12T12:12:22.722567",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-09-12T11:54:03.344827",
   "version": "2.3.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "08f78287c10dac1b18917047464f8c5b747b3620da3b1ada6b5c6a151f80ca41"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}