Voice_Recognition_(singer)_(Augmented).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Voice_Recognition (singer) (Augmented).ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "rpJSwRzAYe-y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# **Voice Recognition of a Singer using CNN**"
      ]
    },
    {
      "metadata": {
        "id": "46mF-k_rTJg_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oubQXxepg9S-",
        "colab_type": "code",
        "outputId": "7809c78f-de09-4bd7-91cf-fbc51b727f79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive') "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yp9lSqyLhklr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('./drive/My Drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YRZejHfLhz3h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "baseDir = '/content/drive/My Drive/Specs/'\n",
        "trainDir = baseDir + 'train'\n",
        "testDir = baseDir + 'test'\n",
        "valDir = baseDir + 'validation'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BLnOLxfWtR8V",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "width = 225\n",
        "height = 150\n",
        "epochs = 20\n",
        "batch_size = 5\n",
        "num_classes = 8"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BZ-CLB5biLsQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "480cd936-8202-4c26-dcb1-bbd999f73550"
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.applications import VGG16\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import layers\n",
        "from keras import models\n",
        "from keras.layers import Conv2D, MaxPool2D, Flatten,Dense, Dropout"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZRlSJhysngn_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Using VGG16 model "
      ]
    },
    {
      "metadata": {
        "id": "q3m4PM2VGwN3",
        "colab_type": "code",
        "outputId": "35b73e85-a5a2-4ec1-bda6-acf9b16a59b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "conv_base = VGG16(weights='imagenet',\n",
        "include_top=False,\n",
        "input_shape=(225, 150, 3))\n",
        "conv_base.trainable = False  #freezing all layers of the vgg16 model"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GROZovGUxiZb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "9d56f3f6-9f93-4991-c07b-7cdf53f619d9"
      },
      "cell_type": "code",
      "source": [
        "#dense layers\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(512, activation='relu',input_dim=7*4*512))\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(layers.Dense(8, activation='softmax'))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sR6wMsa2ydhb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "raQoaVnqyFZW",
        "colab_type": "code",
        "outputId": "46c1b0bb-c708-4507-cacb-addfb0ea0dbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_11 (Dense)             (None, 512)               7340544   \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 8)                 4104      \n",
            "=================================================================\n",
            "Total params: 7,607,304\n",
            "Trainable params: 7,607,304\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CsyaPihL3Jpd",
        "colab_type": "code",
        "outputId": "111427e6-642c-4d08-c22d-9632c699f9e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "cell_type": "code",
      "source": [
        "conv_base.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 225, 150, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 225, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 225, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 75, 64)       0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 75, 128)      73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 75, 128)      147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 9, 512)        0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 9, 512)        2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 9, 512)        2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 9, 512)        2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 4, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 0\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mtuVlMu07TU9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "hnxhHqnUnt8J",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Extracting features from the spectrograms**"
      ]
    },
    {
      "metadata": {
        "id": "xIqNd-bm05QL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Extracting the features and labels as numpy arrays\n",
        "#predict function of conv_base is used to get the features of the image\n",
        "datagen = ImageDataGenerator(rescale=1./255)\n",
        "batch_size = 5\n",
        "def extract_features(directory, sample_count):\n",
        "  features = np.zeros(shape=(sample_count, 7, 4, 512))\n",
        "  labels = np.zeros(shape=(sample_count,8))\n",
        "  generator = datagen.flow_from_directory( directory,\n",
        "  target_size=(225,150),\n",
        "  batch_size=batch_size,\n",
        "  shuffle = False,                               \n",
        "  class_mode='categorical')\n",
        "  print(generator.class_indices)\n",
        "  i = 0\n",
        "  for inputs_batch, labels_batch in generator:\n",
        "    features_batch = conv_base.predict(inputs_batch)\n",
        "    features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
        "    labels[i * batch_size : (i + 1) * batch_size,:] = labels_batch\n",
        "    i += 1\n",
        "    if i * batch_size >= sample_count:\n",
        "        break\n",
        "  return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "358XaMkr2DrZ",
        "colab_type": "code",
        "outputId": "9ac04f5e-7648-4f26-adb0-85de115b9568",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "train_features, train_labels = extract_features(trainDir, 3303)\n",
        "validation_features, validation_labels = extract_features(valDir, 225)\n",
        "test_features, test_labels = extract_features(testDir, 245)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3303 images belonging to 8 classes.\n",
            "{'Arijit Singh': 0, 'Armaan Malik': 1, 'Atif Aslam': 2, 'Honey Singh': 3, 'Raahat Fateh Ali Khan': 4, 'Shreya Ghoshal': 5, 'Sonu Nigam': 6, 'Sunidhi Chauhan': 7}\n",
            "Found 225 images belonging to 8 classes.\n",
            "{'Arijit Singh': 0, 'Armaan Malik': 1, 'Atif Aslam': 2, 'Honey Singh': 3, 'Raahat Fateh Ali Khan': 4, 'Shreya Ghoshal': 5, 'Sonu Nigam': 6, 'Sunidhi Chauhan': 7}\n",
            "Found 245 images belonging to 8 classes.\n",
            "{'Arijit Singh': 0, 'Armaan Malik': 1, 'Atif Aslam': 2, 'Honey Singh': 3, 'Raahat Fateh Ali Khan': 4, 'Shreya Ghoshal': 5, 'Sonu Nigam': 6, 'Sunidhi Chauhan': 7}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MGpuzZVA7HEs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_features = np.reshape(train_features, (3303, 7*4*512))\n",
        "validation_features = np.reshape(validation_features, (225,7*4*512))\n",
        "test_features = np.reshape(test_features, (245,7*4*512))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i7SjU5UV8Uyb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#os.getcwd()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BkdcDnRD6oGA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#np.savetxt('train_features.csv', train_features, fmt='1.8%f', delimiter = ',')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3xr2rFcdn3Ex",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Using callbacks**"
      ]
    },
    {
      "metadata": {
        "id": "zZLaA8hVrhQt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "stop_early = keras.callbacks.EarlyStopping(monitor='val_acc', patience=20, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "a-VwpjnB2mZ5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reduce_lr = keras.callbacks.ReduceLROnPlateau( monitor='val_loss', factor=0.1, patience=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sk79esF_n8ke",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Complilation of the model and fit**"
      ]
    },
    {
      "metadata": {
        "id": "z6J7aM254iY6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import optimizers\n",
        "model.compile(loss='categorical_crossentropy',optimizer=optimizers.RMSprop(lr = 1e-4),metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9Y8UW-tayxcS",
        "colab_type": "code",
        "outputId": "aacf5b70-cc54-47ff-9bc1-1c83b67d9c2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2074
        }
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(train_features, train_labels,\n",
        "epochs=60,\n",
        "batch_size=5,\n",
        "validation_data=(validation_features, validation_labels),\n",
        "                   callbacks=[stop_early,reduce_lr])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3303 samples, validate on 225 samples\n",
            "Epoch 1/60\n",
            "3303/3303 [==============================] - 9s 3ms/step - loss: 2.0637 - acc: 0.1732 - val_loss: 2.0108 - val_acc: 0.2133\n",
            "Epoch 2/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.9040 - acc: 0.2555 - val_loss: 2.0031 - val_acc: 0.2133\n",
            "Epoch 3/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.7743 - acc: 0.3221 - val_loss: 1.9439 - val_acc: 0.2756\n",
            "Epoch 4/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.6662 - acc: 0.3812 - val_loss: 1.9189 - val_acc: 0.2933\n",
            "Epoch 5/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.5910 - acc: 0.4057 - val_loss: 1.7972 - val_acc: 0.3378\n",
            "Epoch 6/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.5091 - acc: 0.4284 - val_loss: 1.7986 - val_acc: 0.3333\n",
            "Epoch 7/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.4351 - acc: 0.4668 - val_loss: 2.0480 - val_acc: 0.2711\n",
            "Epoch 8/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.3809 - acc: 0.4917 - val_loss: 1.8940 - val_acc: 0.3289\n",
            "Epoch 9/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.3287 - acc: 0.5107 - val_loss: 1.6953 - val_acc: 0.3778\n",
            "Epoch 10/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.2743 - acc: 0.5362 - val_loss: 1.6096 - val_acc: 0.4489\n",
            "Epoch 11/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.2224 - acc: 0.5619 - val_loss: 1.7807 - val_acc: 0.3600\n",
            "Epoch 12/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.1870 - acc: 0.5662 - val_loss: 1.5275 - val_acc: 0.4844\n",
            "Epoch 13/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.1499 - acc: 0.5828 - val_loss: 1.6152 - val_acc: 0.4133\n",
            "Epoch 14/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.1191 - acc: 0.5931 - val_loss: 1.6206 - val_acc: 0.4267\n",
            "Epoch 15/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.0684 - acc: 0.6110 - val_loss: 1.8896 - val_acc: 0.3867\n",
            "Epoch 16/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.0410 - acc: 0.6331 - val_loss: 1.6340 - val_acc: 0.4089\n",
            "Epoch 17/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 1.0078 - acc: 0.6315 - val_loss: 1.7124 - val_acc: 0.4578\n",
            "Epoch 18/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.9757 - acc: 0.6591 - val_loss: 1.7544 - val_acc: 0.4400\n",
            "Epoch 19/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.9465 - acc: 0.6512 - val_loss: 1.8760 - val_acc: 0.4133\n",
            "Epoch 20/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.9151 - acc: 0.6739 - val_loss: 1.8099 - val_acc: 0.4356\n",
            "Epoch 21/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.8912 - acc: 0.6891 - val_loss: 1.9943 - val_acc: 0.4133\n",
            "Epoch 22/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.8716 - acc: 0.6876 - val_loss: 1.6300 - val_acc: 0.4889\n",
            "Epoch 23/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.6485 - acc: 0.7814 - val_loss: 1.5900 - val_acc: 0.5022\n",
            "Epoch 24/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.6316 - acc: 0.7847 - val_loss: 1.5929 - val_acc: 0.5111\n",
            "Epoch 25/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.6232 - acc: 0.7887 - val_loss: 1.6310 - val_acc: 0.5200\n",
            "Epoch 26/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.6191 - acc: 0.7962 - val_loss: 1.5947 - val_acc: 0.5244\n",
            "Epoch 27/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.6128 - acc: 0.7935 - val_loss: 1.6162 - val_acc: 0.5200\n",
            "Epoch 28/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.6055 - acc: 0.7987 - val_loss: 1.6060 - val_acc: 0.5067\n",
            "Epoch 29/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5981 - acc: 0.7993 - val_loss: 1.6278 - val_acc: 0.5111\n",
            "Epoch 30/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5958 - acc: 0.8050 - val_loss: 1.6285 - val_acc: 0.5244\n",
            "Epoch 31/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5927 - acc: 0.7932 - val_loss: 1.5997 - val_acc: 0.5156\n",
            "Epoch 32/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5838 - acc: 0.8093 - val_loss: 1.6355 - val_acc: 0.5333\n",
            "Epoch 33/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5678 - acc: 0.8150 - val_loss: 1.6040 - val_acc: 0.5244\n",
            "Epoch 34/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5653 - acc: 0.8162 - val_loss: 1.6086 - val_acc: 0.5289\n",
            "Epoch 35/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5620 - acc: 0.8144 - val_loss: 1.6064 - val_acc: 0.5244\n",
            "Epoch 36/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5638 - acc: 0.8150 - val_loss: 1.6040 - val_acc: 0.5289\n",
            "Epoch 37/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5615 - acc: 0.8153 - val_loss: 1.6091 - val_acc: 0.5333\n",
            "Epoch 38/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5636 - acc: 0.8171 - val_loss: 1.6046 - val_acc: 0.5333\n",
            "Epoch 39/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5572 - acc: 0.8208 - val_loss: 1.6123 - val_acc: 0.5333\n",
            "Epoch 40/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5612 - acc: 0.8202 - val_loss: 1.6150 - val_acc: 0.5378\n",
            "Epoch 41/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5572 - acc: 0.8123 - val_loss: 1.6070 - val_acc: 0.5378\n",
            "Epoch 42/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5570 - acc: 0.8211 - val_loss: 1.6069 - val_acc: 0.5333\n",
            "Epoch 43/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5576 - acc: 0.8180 - val_loss: 1.6071 - val_acc: 0.5333\n",
            "Epoch 44/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5530 - acc: 0.8180 - val_loss: 1.6072 - val_acc: 0.5333\n",
            "Epoch 45/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5536 - acc: 0.8208 - val_loss: 1.6078 - val_acc: 0.5333\n",
            "Epoch 46/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5588 - acc: 0.8186 - val_loss: 1.6072 - val_acc: 0.5378\n",
            "Epoch 47/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5562 - acc: 0.8186 - val_loss: 1.6074 - val_acc: 0.5289\n",
            "Epoch 48/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5593 - acc: 0.8199 - val_loss: 1.6079 - val_acc: 0.5333\n",
            "Epoch 49/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5542 - acc: 0.8208 - val_loss: 1.6070 - val_acc: 0.5333\n",
            "Epoch 50/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5550 - acc: 0.8159 - val_loss: 1.6078 - val_acc: 0.5289\n",
            "Epoch 51/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5521 - acc: 0.8232 - val_loss: 1.6083 - val_acc: 0.5333\n",
            "Epoch 52/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5583 - acc: 0.8205 - val_loss: 1.6076 - val_acc: 0.5333\n",
            "Epoch 53/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5573 - acc: 0.8193 - val_loss: 1.6076 - val_acc: 0.5333\n",
            "Epoch 54/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5508 - acc: 0.8214 - val_loss: 1.6077 - val_acc: 0.5333\n",
            "Epoch 55/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5526 - acc: 0.8202 - val_loss: 1.6076 - val_acc: 0.5333\n",
            "Epoch 56/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5562 - acc: 0.8183 - val_loss: 1.6077 - val_acc: 0.5333\n",
            "Epoch 57/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5514 - acc: 0.8208 - val_loss: 1.6077 - val_acc: 0.5333\n",
            "Epoch 58/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5560 - acc: 0.8211 - val_loss: 1.6077 - val_acc: 0.5333\n",
            "Epoch 59/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5554 - acc: 0.8223 - val_loss: 1.6077 - val_acc: 0.5333\n",
            "Epoch 60/60\n",
            "3303/3303 [==============================] - 7s 2ms/step - loss: 0.5562 - acc: 0.8208 - val_loss: 1.6077 - val_acc: 0.5333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yk03CBUriqif",
        "colab_type": "code",
        "outputId": "958e10cb-b4e8-46eb-db01-172d6b423a9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(test_features,test_labels, batch_size = 5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "245/245 [==============================] - 0s 535us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8265200265664228, 0.46938776544162203]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "id": "Grl4uypx-zzu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save('voice_rec.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OuMDC4TNRxud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "53f9ff86-4ccc-423d-d21f-b8f388813002"
      },
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "model = load_model('voice_rec.h5')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CxL_btlJisLp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_predictions = model.predict(test_features,batch_size=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mL4tLKfjixMz",
        "colab_type": "code",
        "outputId": "fbf13385-9a30-4570-86e5-38a059455ca5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "test_predictions.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(245, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "u5ejiHIR6rc-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "CDLPQYYT0k08",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = []\n",
        "for i in test_predictions:\n",
        "  max = -1\n",
        "  pos=-1\n",
        "  for j in range(8):\n",
        "    if i[j] > max:\n",
        "      max=i[j]\n",
        "      pos = j\n",
        "  y_pred.append(pos)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i9Jg513yyf3p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_test = []\n",
        "for i in test_labels:\n",
        "  max = -1\n",
        "  pos=-1\n",
        "  for j in range(8):\n",
        "    if i[j] > max:\n",
        "      max=i[j]\n",
        "      pos = j\n",
        "  y_test.append(pos)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nS2SldKpMLKl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_train = []\n",
        "for i in train_labels:\n",
        "  max = -1\n",
        "  pos=-1\n",
        "  for j in range(8):\n",
        "    if i[j] > max:\n",
        "      max=i[j]\n",
        "      pos = j\n",
        "  y_train.append(pos)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "inQheBD-lcye",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = np.array(y_pred)\n",
        "y_test = np.array(y_test)\n",
        "y_train = np.array(y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Xrn_eNexxmTi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q5d2wceBoGT1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Confusion Matrix**"
      ]
    },
    {
      "metadata": {
        "id": "jgvMUMJeyW3x",
        "colab_type": "code",
        "outputId": "f2a6dc51-31ec-4734-c34b-62f8966d7724",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,y_pred))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[10  7  2  3  1  1  4  2]\n",
            " [ 5 17  3  0  0  3  2  0]\n",
            " [ 4  2 15  0  1  2  0  1]\n",
            " [ 5  0  0 14  3  1  2  5]\n",
            " [ 4  1  6  0 17  2  3  2]\n",
            " [ 0  3  0  0  1 20  2  4]\n",
            " [ 4  6  0  1  9  0  9  6]\n",
            " [ 4  3  0  1  3  4  2 13]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wMcZnkt9yskD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "singer_labels = { 0:'Arijit Singh',  1:'Armaan Malik',  2:'Atif Aslam' , 3:'Honey Singh', 4:'Raahat Fateh Ali Khan', 5:'Shreya Ghoshal', 6:'Sonu Nigam', 7:'Sunidhi Chauhan'}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ouxkNQFRoOkV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Trying XGBoost and LGBoost**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "pCreFbLny4-X",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from xgboost import XGBClassifier\n",
        "import lightgbm as lgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "h15AmkA6y4mH",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lg = lgb.LGBMClassifier(n_threads=-1,n_estimators=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "3fdfe21b-61d4-41d7-9f02-8d3c747114d3",
        "id": "2al5CdlQy4Ji",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "lg.fit(train_features,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
              "        importance_type='split', learning_rate=0.1, max_depth=-1,\n",
              "        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "        n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
              "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
              "        subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "5SYPGJquYGp1",
        "colab_type": "code",
        "outputId": "62d3cfe8-3d1d-4c27-940b-0ce86b1a29bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "lg.score(test_features,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3673469387755102"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "q3ym9k8xLiqZ",
        "colab_type": "code",
        "outputId": "fe0b31a5-e4f6-46ed-ddce-ffafecc6d96f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "clf = XGBClassifier(n_threads=-1,n_estimators=100)\n",
        "clf.fit(train_features,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
              "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
              "       n_jobs=1, n_threads=-1, nthread=None, objective='multi:softprob',\n",
              "       random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "       seed=None, silent=True, subsample=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "dys5ESH7frqf",
        "colab_type": "code",
        "outputId": "12e7e4ad-0d4d-4d43-d86d-d3b17bafa305",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "clf.score(test_features,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3795918367346939"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "-LHmbW0dMaRB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "#save model\n",
        " \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qSQ4oEf3yXPq",
        "colab_type": "code",
        "outputId": "b2587aa2-ca2e-4f66-a748-9f0ad9c21dc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "joblib.dump(clf, 'XGB model2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['XGB model2']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "Q1I70bkYMmc5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#load model\n",
        "clf = joblib.load('XGB model2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "koLB42s1Ls2h",
        "colab_type": "code",
        "outputId": "d26e18d4-7ca1-4778-9599-eca71fd21152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "y_pred_xg = clf.predict(test_features)\n",
        "y_pred_xg"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 4, 3, 1, 2, 0, 2, 0, 5, 0, 0, 6, 1, 5, 0, 0, 4, 2, 1, 0, 0, 1,\n",
              "       6, 1, 7, 7, 1, 4, 4, 4, 1, 0, 6, 6, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       6, 5, 2, 1, 1, 1, 0, 1, 4, 0, 7, 1, 1, 1, 1, 0, 2, 5, 2, 2, 4, 5,\n",
              "       0, 1, 1, 0, 0, 6, 0, 2, 2, 4, 7, 2, 6, 0, 2, 2, 2, 0, 2, 3, 6, 5,\n",
              "       5, 7, 5, 3, 3, 7, 7, 7, 7, 7, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "       3, 4, 4, 4, 3, 5, 6, 4, 1, 1, 5, 0, 1, 2, 0, 4, 2, 0, 4, 1, 6, 4,\n",
              "       1, 5, 4, 2, 5, 4, 4, 6, 4, 6, 4, 6, 7, 7, 2, 6, 4, 5, 5, 5, 5, 5,\n",
              "       6, 7, 7, 1, 5, 5, 5, 5, 2, 5, 1, 0, 2, 4, 5, 5, 5, 5, 5, 4, 5, 7,\n",
              "       6, 5, 5, 5, 7, 0, 4, 4, 4, 4, 1, 4, 4, 4, 2, 7, 7, 1, 7, 4, 0, 6,\n",
              "       1, 1, 1, 1, 0, 1, 6, 6, 6, 6, 6, 1, 0, 0, 1, 0, 0, 0, 1, 2, 1, 1,\n",
              "       7, 2, 6, 1, 7, 5, 0, 0, 5, 4, 4, 0, 7, 7, 6, 7, 4, 4, 5, 2, 7, 3,\n",
              "       7, 7, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "1Mr_iMDTCdXC",
        "colab_type": "code",
        "outputId": "b6db9993-a81b-40bb-acd4-6039529a9f80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 7, 1, 1, 1, 0, 2, 0, 6, 0, 0, 6, 6, 5, 0, 0, 4, 0, 1, 0, 2, 1,\n",
              "       6, 0, 1, 7, 3, 3, 3, 1, 1, 1, 1, 0, 0, 2, 0, 1, 1, 1, 1, 1, 0, 5,\n",
              "       6, 5, 2, 1, 1, 1, 1, 1, 6, 5, 2, 1, 1, 1, 1, 0, 2, 0, 2, 2, 2, 5,\n",
              "       1, 2, 1, 0, 2, 2, 0, 2, 2, 5, 7, 2, 2, 2, 2, 2, 0, 2, 4, 3, 0, 4,\n",
              "       6, 4, 6, 5, 7, 7, 7, 7, 3, 7, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3,\n",
              "       3, 0, 0, 0, 0, 7, 4, 4, 0, 0, 5, 4, 1, 0, 0, 4, 2, 2, 4, 4, 4, 4,\n",
              "       2, 2, 4, 4, 4, 7, 4, 4, 4, 6, 4, 6, 4, 2, 2, 6, 4, 5, 5, 5, 6, 7,\n",
              "       6, 7, 7, 5, 5, 5, 7, 5, 1, 5, 1, 5, 1, 4, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "       5, 5, 5, 5, 7, 4, 4, 4, 4, 4, 1, 7, 6, 3, 7, 0, 7, 4, 7, 4, 6, 6,\n",
              "       4, 1, 1, 1, 6, 1, 6, 6, 6, 6, 6, 7, 0, 0, 4, 1, 0, 6, 4, 7, 1, 1,\n",
              "       5, 5, 5, 0, 7, 1, 6, 7, 0, 7, 4, 0, 0, 7, 7, 7, 3, 7, 5, 7, 7, 4,\n",
              "       7, 7, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "metadata": {
        "id": "O4__vhehgE9z",
        "colab_type": "code",
        "outputId": "cbeb71ec-8e5c-469d-8d30-71d242e39d27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3,\n",
              "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "       3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
              "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5,\n",
              "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
              "       5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
              "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7,\n",
              "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
              "       7, 7, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "x_frldbj2qQG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from collections import Counter"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LGv4djUdMHM-",
        "colab_type": "code",
        "outputId": "be246e86-7d48-4a1e-c21d-84d20307eded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "dirr = os.getcwd() + '/Songs_Dataset/test/'\n",
        "dirr"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/Songs_Dataset/test/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "VJn6rPcre6zA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "folders = os.listdir(dirr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_lQhYPRse_ry",
        "colab_type": "code",
        "outputId": "07905288-2b77-41a5-c55b-0b2777058112",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "cell_type": "code",
      "source": [
        "test_songs = {}\n",
        "for folder in folders:\n",
        "  songs = os.listdir(dirr+folder)\n",
        "  #print(folder)\n",
        "  for i in range(len(songs)):\n",
        "    songs[i] = songs[i].split('-')[0]\n",
        "  test_songs[folder] = songs\n",
        "  #print(songs)\n",
        "test_songs"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Arijit Singh': ['Lambiyaan Si Judaiyaan Song ',\n",
              "  'Nashe Si Chadh Gayi Song ',\n",
              "  'Enna Sona Song ',\n",
              "  'Phir Le Aya Dil (Reprise) Song ',\n",
              "  'Baaton Ko Teri Song ',\n",
              "  'Yeh Fitoor Mera Song '],\n",
              " 'Armaan Malik': ['Kaun Tujhe & Kuch Toh Hai Mashup Song ',\n",
              "  'Jaadugari Song ',\n",
              "  'Khwaishein (Film Version) Song ',\n",
              "  'Main Hoon Hero Tera (Sad Version) Song ',\n",
              "  'Tum Jo Mile Song ',\n",
              "  'Rendu Kallu Song ',\n",
              "  'Sariyaagi (From '],\n",
              " 'Atif Aslam': ['Mori Araj Suno (From ',\n",
              "  'Jal Pari Song ',\n",
              "  'Khair Mangda Song ',\n",
              "  'Dil Kare Song ',\n",
              "  'Darasal Song '],\n",
              " 'Honey Singh': ['One Bottle Down Song ',\n",
              "  'Khalaara Song ',\n",
              "  'Superman Song ',\n",
              "  'Rani Song ',\n",
              "  'Love Dose Song ',\n",
              "  'Urvashi Song '],\n",
              " 'Raahat Fateh Ali Khan': ['Naino Nay Tere Song ',\n",
              "  'Tum Kon Piya Song ',\n",
              "  'Tinka Tinka Dil Mera Song ',\n",
              "  'Saiyaan Radio ',\n",
              "  'Fakeera Song ',\n",
              "  'Lag Ja Gale Song ',\n",
              "  'Tera Mera Sath Ho Song '],\n",
              " 'Shreya Ghoshal': ['Sunn Raha Hai (Female) Song ',\n",
              "  'Yeh Kya Hua Song ',\n",
              "  'Agar Tum Mil Jao',\n",
              "  'Sunn Raha Hai',\n",
              "  'Daata Tu Song ',\n",
              "  'Saans (Reprise) Song ',\n",
              "  'Tum Bin Song '],\n",
              " 'Sonu Nigam': ['Jaane Nahin Denge Tujhe Song ',\n",
              "  'Aye Zindagi Song ',\n",
              "  'Anisuthide Song ',\n",
              "  'Bijuria Song ',\n",
              "  'Zindagi Is Tarah Song ',\n",
              "  'Gamanisu Song ',\n",
              "  'Deewane Hoke Hum Song '],\n",
              " 'Sunidhi Chauhan': ['Ganaraj Tu Song ',\n",
              "  'Dil Mein Jaagi Dhadkan Aise (Sur (The Melody Of Life) _ Soundtrack Version) Song ',\n",
              "  'Aaja Nachle Song ',\n",
              "  'Piku Song ',\n",
              "  'Jind Mahi Song ',\n",
              "  'Aye Dil Song ',\n",
              "  'Aisa Jadoo (Remix) Song ']}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "3385kMJLORqy",
        "colab_type": "code",
        "outputId": "f96e0c56-4241-493d-c59b-b08be65ef1cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "all_singers={}\n",
        "for singers in singer_labels.values():\n",
        "    all_singers[singers] = {}\n",
        "    for singers2 in singer_labels.values():\n",
        "      all_singers[singers][singers2] = 0\n",
        "print(all_singers)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Arijit Singh': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Armaan Malik': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Atif Aslam': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Honey Singh': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Raahat Fateh Ali Khan': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Shreya Ghoshal': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Sonu Nigam': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Sunidhi Chauhan': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gmhkuly8pDQ7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FQDOjRUXofFl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## **Results**"
      ]
    },
    {
      "metadata": {
        "id": "k29-Nu0e3T_9",
        "colab_type": "code",
        "outputId": "d0575f5f-784f-420c-9f9e-62288827b282",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8874
        }
      },
      "cell_type": "code",
      "source": [
        "ctr=0\n",
        "unsure = 0\n",
        "correct = 0\n",
        "count = 0\n",
        "prev_singer = 0\n",
        "for i in range(49):\n",
        "  count_pred = Counter(y_pred[ctr:ctr+5]).most_common(5)\n",
        "\n",
        "  actual_singer,num = Counter(y_test[ctr:ctr+5]).most_common(1)[0]\n",
        "  if(actual_singer!=prev_singer):\n",
        "    prev_singer = actual_singer\n",
        "    count = 0\n",
        "  \n",
        "  #print(count_pred)\n",
        "  pred_singers = []\n",
        "  for pred in count_pred:\n",
        "    pred_singers.append([singer_labels[pred[0]],pred[1]/5])\n",
        "  #print(pred_singers)\n",
        "  print('Song:',test_songs[singer_labels[actual_singer]][count])\n",
        "  count+=1\n",
        "  print('Actual Singer:', singer_labels[actual_singer])\n",
        "  print('Predictions:-')\n",
        "  max_conf_singers = []\n",
        "  tie = 0\n",
        "  maxi = 0\n",
        "  cnt=0\n",
        "  for singer in pred_singers:\n",
        "      if(cnt==0):\n",
        "        predicted = singer[0]\n",
        "      cnt+=1\n",
        "      print('Singer:', singer[0])\n",
        "      print('Confidence:', singer[1])\n",
        "      if(singer[1]>=maxi):\n",
        "        if singer[1]>0.2:\n",
        "          maxi = singer[1]\n",
        "          max_conf_singers.append(singer[0])\n",
        "      if(singer[1]==0.4):\n",
        "        tie+=1\n",
        "      \n",
        "  #print(max_conf_singer)\n",
        "  if(len(max_conf_singers)!=0):\n",
        "    for  max_conf_singer in  max_conf_singers:\n",
        "        if max_conf_singer == singer_labels[actual_singer] :\n",
        "          correct+=1\n",
        " # print(pred_singers,actual_singer)\n",
        "  for singer in pred_singers:\n",
        "    #print(singer[0])\n",
        "    if singer[0] == singer_labels[actual_singer]:\n",
        "      unsure+=1\n",
        "      break\n",
        "      \n",
        "  print('Predicted Singer:\\033[0m', predicted)\n",
        "  ctr+=5\n",
        "  all_singers[singer_labels[actual_singer]][predicted]+=1\n",
        "  \n",
        "  print()\n",
        "print('Correct majority vote songs:', correct)\n",
        "print('Accuracy: %0.3f' % (100*correct/49))\n",
        "print('Correct + Unsure count:', unsure)\n",
        "print('Correct + Unsure accuracy: %0.3f' % (100*unsure/49))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Song: Lambiyaan Si Judaiyaan Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Nashe Si Chadh Gayi Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Enna Sona Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Phir Le Aya Dil (Reprise) Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.6\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Baaton Ko Teri Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Yeh Fitoor Mera Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.6\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Honey Singh\n",
            "\n",
            "Song: Kaun Tujhe & Kuch Toh Hai Mashup Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Jaadugari Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Khwaishein (Film Version) Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Main Hoon Hero Tera (Sad Version) Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Tum Jo Mile Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Rendu Kallu Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.8\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Mori Araj Suno (From \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.8\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Atif Aslam\n",
            "\n",
            "Song: Jal Pari Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Khair Mangda Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.8\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Atif Aslam\n",
            "\n",
            "Song: Dil Kare Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.6\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Atif Aslam\n",
            "\n",
            "Song: Darasal Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Atif Aslam\n",
            "\n",
            "Song: One Bottle Down Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Khalaara Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.6\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Sunidhi Chauhan\n",
            "\n",
            "Song: Superman Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.6\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Honey Singh\n",
            "\n",
            "Song: Rani Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 1.0\n",
            "Predicted Singer:\u001b[0m Honey Singh\n",
            "\n",
            "Song: Love Dose Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.8\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Honey Singh\n",
            "\n",
            "Song: Urvashi Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.8\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Naino Nay Tere Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Tum Kon Piya Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Tinka Tinka Dil Mera Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Saiyaan Radio \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Fakeera Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.8\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Lag Ja Gale Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.6\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Tera Mera Sath Ho Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Atif Aslam\n",
            "\n",
            "Song: Sunn Raha Hai (Female) Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Yeh Kya Hua Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.6\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Agar Tum Mil Jao\n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.4\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Sunn Raha Hai\n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.6\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Daata Tu Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 1.0\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Saans (Reprise) Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 1.0\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Jaane Nahin Denge Tujhe Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.8\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Aye Zindagi Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Anisuthide Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Sunidhi Chauhan\n",
            "\n",
            "Song: Bijuria Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Raahat Fateh Ali Khan\n",
            "\n",
            "Song: Zindagi Is Tarah Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Gamanisu Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.8\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Sonu Nigam\n",
            "\n",
            "Song: Deewane Hoke Hum Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.6\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Ganaraj Tu Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Armaan Malik\n",
            "\n",
            "Song: Dil Mein Jaagi Dhadkan Aise (Sur (The Melody Of Life) _ Soundtrack Version) Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Shreya Ghoshal\n",
            "\n",
            "Song: Aaja Nachle Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Sunidhi Chauhan\n",
            "\n",
            "Song: Piku Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Arijit Singh\n",
            "\n",
            "Song: Jind Mahi Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.6\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Sunidhi Chauhan\n",
            "\n",
            "Song: Aye Dil Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.8\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Predicted Singer:\u001b[0m Sunidhi Chauhan\n",
            "\n",
            "Correct majority vote songs: 33\n",
            "Accuracy: 67.347\n",
            "Correct + Unsure count: 44\n",
            "Correct + Unsure accuracy: 89.796\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "baKa6eGSPiQC",
        "colab_type": "code",
        "outputId": "88532b3f-6799-489f-9904-314babd208ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "print(all_singers)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Arijit Singh': {'Arijit Singh': 3, 'Armaan Malik': 2, 'Atif Aslam': 0, 'Honey Singh': 1, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Armaan Malik': {'Arijit Singh': 0, 'Armaan Malik': 6, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Atif Aslam': {'Arijit Singh': 0, 'Armaan Malik': 1, 'Atif Aslam': 4, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Honey Singh': {'Arijit Singh': 1, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 3, 'Raahat Fateh Ali Khan': 1, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 1}, 'Raahat Fateh Ali Khan': {'Arijit Singh': 1, 'Armaan Malik': 0, 'Atif Aslam': 1, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 5, 'Shreya Ghoshal': 0, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Shreya Ghoshal': {'Arijit Singh': 0, 'Armaan Malik': 0, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 6, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 0}, 'Sonu Nigam': {'Arijit Singh': 1, 'Armaan Malik': 1, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 3, 'Shreya Ghoshal': 0, 'Sonu Nigam': 1, 'Sunidhi Chauhan': 1}, 'Sunidhi Chauhan': {'Arijit Singh': 1, 'Armaan Malik': 1, 'Atif Aslam': 0, 'Honey Singh': 0, 'Raahat Fateh Ali Khan': 0, 'Shreya Ghoshal': 1, 'Sonu Nigam': 0, 'Sunidhi Chauhan': 3}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pomUX0ChZNZx",
        "colab_type": "code",
        "outputId": "275b5934-8ff8-4852-fc4f-9b4d0b23c424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Arijit Singh',\n",
              " 'Armaan Malik',\n",
              " 'Atif Aslam',\n",
              " 'Honey Singh',\n",
              " 'Raahat Fateh Ali Khan',\n",
              " 'Shreya Ghoshal',\n",
              " 'Sonu Nigam',\n",
              " 'Sunidhi Chauhan']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "metadata": {
        "id": "hdrnyQxKWFIM",
        "colab_type": "code",
        "outputId": "57e68427-d0f7-482c-f640-35a876f47011",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2873
        }
      },
      "cell_type": "code",
      "source": [
        "for singer in all_singers.keys():\n",
        "  fig, ax = plt.subplots()\n",
        "  fig.subplots_adjust(bottom=0.1)\n",
        "  ax.set_xticklabels([i.split()[0] for i in list(singer_labels.values())]) \n",
        "  plt.bar(all_singers[singer].keys(), all_singers[singer].values())\n",
        "  print('Actual Singer:', singer)\n",
        "  plt.ylabel('Number of songs predicted')\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Arijit Singh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T0/EPAAC4h+GWd926dTV16lRXZAEAAJVguOV933336ciRI67IAgAAKsFwy3vz5s1asmSJ\nAgIC5OXlJbvdLpvNps8//9wF8QAAwLUMy/vtt992RQ4AAFBJhuXdqFEjrVmzRnv27JEk3X///Xr8\n8cdNDwYAAMpnWN6JiYk6e/asOnbsKLvdrr///e/69ttvNX78eFfkAwAA1zAs70OHDmnZsmWO6djY\nWA0aNKhSCz948KBGjBihp59+WrGxsWXm9ejRQ40aNXJ87SwpKYkrlAEAUAmG5V1UVKTS0lJ5eFw5\nML2kpEQlJSWGC75w4YImTZqkzp07O71PSkqKfHx8biAuAAAwLO9u3bqpf//+6tChgyRp+/btio6O\nNlxwrVq1lJKSopSUlFtPCQAAHAzLe8SIEXr44Ye1a9cu2Ww2JSQkqG3btsYL9vKSl1fFi584caJO\nnjypBx54QKNHj5bNZqt8cgAAblOG5Z2XlycfHx/FxcXpiy++0ObNmxUSEqKgoKBbWvHIkSPVtWtX\n+fn5KT4+XhkZGYqKinJ6/4CAuvLyuv1OyxoU5OvuCFXK3eNx9/qrGuOp3hhP9Wbl8RiW95gxYxQX\nFydvb2/NmDFDAwcO1GuvvaYFCxbc0or79u3r+Dk8PFwHDx6ssLxzcy/c0vqsKju7Zl0z3Z3jCQry\nrVHPJ+Op3hhP9WaV8Th7g2F4etSLFy+qS5cuSk9P1+DBgzV48GAVFRXdUpj8/Hw988wzKiwslCTt\n2LFDzZs3v6VlAgBwuzDc8r548aLOnTunjIwMzZs3T3a7XXl5eYYL3rNnj6ZPn66TJ0/Ky8tLGRkZ\n6tGjh0JDQxUREaHw8HDFxMSodu3aatWqVYVb3QAA4F8My7t3797q1auXnnrqKYWEhGjOnDnq2LGj\n4YLbtGmj1NRUp/Pj4uIUFxd3Y2kBAIBxeV9bskOHDlX9+vVNDQUAAJwz/Mz7WhQ3AADudcPlDQAA\n3Mtpeb///vuSpL/97W8uCwMAAIw5/cz77bffVlFRkf7617+We+az/v37mxoMAACUz2l5v/zyy9q0\naZPy8/O1c+fO6+ZT3gAAuIfT8u7Vq5d69eqljIwMRUZGujITAACogOFXxe6//36NGzdO3333nWw2\nm+6//36NGjVKgYGBrsgHAACuYXi0+cSJE9W6dWvNmjVLSUlJ+vWvf61x48a5IhsAAChHpU6POnjw\nYMd0ixYt9Nlnn5kaCgAAOFepC5NkZWU5pk+fPu24oAgAAHA9wy3vESNGqF+/fgoKCpLdbte5c+c0\nefJkV2QDAADlMCzv7t27a/369Tp27JgkKSwsTLVr1zY7FwAAcMKwvCWpTp06uueee8zOAgAAKoFz\nmwMAYDGG5W23212RAwAAVJJheQ8dOtQVOQAAQCUZfuZ97733avbs2WrXrp28vb0dt3fu3NnUYAAA\noHyG5b1v3z5J0jfffOO4zWazUd4AALiJYXmnpqZKuvLZd3mXBgUAAK5l+Jn3/v371a9fPz322GOS\npLlz52rXrl2mBwMAAOUzLO+EhARNmTJFQUFBkqTo6GhNnTrV9GAAAKB8huXt5eVV5gQtYWFh8vKq\n1LldAACACSpV3pmZmY7Puzdt2sR3vwEAcCPDTehXXnlFI0aM0NGjR9W+fXuFhoZq+vTprsgGAADK\nYVjeLVu21Jo1a3Tu3DnVqlVL9erVc0UuAADghGF5Hz58WMnJyTp8+LBsNptatGih559/Xr/+9a9d\nkQ8AAFzDsLxffvllDRo0SCNHjpQk7dy5U2PGjNH7779vejgAAHA9w/L28fFR//79HdNNmzZVRkaG\nqaEAAIBzTo82Ly0tVWlpqTp37qxPPvlEBQUF+vnnn7V+/Xp16NDBlRkBAMAvON3ybtWqlWw2W7lf\nC/Py8tJ///d/mxoMAACUz2l579+/35U5AABAJRl+5n3mzBllZGQoPz+/zFb4888/b2owAABQPsMz\nrA0bNkz79u1TUVGRiouLHf8AAIB7GG55+/v7cyESAACqEcPyjoiI0Mcff6x27drJ09PTcfudd95p\najAAAFA+w/I+cOCA1qxZI39/f8dtNptNn3/+uZm5AACAE4blvWvXLu3YsUO1atVyRR4AAGDA8IC1\nNm3a6PLly67IAgAAKqFSXxXr0aOHmjZtWuYz77S0NFODAQCA8hmW962cSe3gwYMaMWKEnn76acXG\nxpaZt3XrVs2aNUuenp4KDw9XfHz8Ta8HAIDbieFu85KSknL/Gblw4YImTZqkzp07lzs/MTFRycnJ\nWrFihb788ksdPnz4xtMDAHAbMtzynjdvnuPnoqIiHT58WO3bt3daylfVqlVLKSkpSklJuW5eZmam\n/Pz8FBISIknq1q2btm3bpmbNmt1ofgAAbjuG5Z2amlpm+uzZs5o5c6bxgr285OVV/uKzs7MVGBjo\nmA4MDFRmZqbhMgEAQCXK+1oNGjTQ999/b0aWCgUE1JWXl6fxHWuYoCBfd0eoUu4ejzvX33v0R25b\nd2WtmdnHret3999HVWM81ZuVx2NY3mPGjJHNZnNM//jjj/LwMPyovELBwcHKyclxTJ85c0bBwcEV\nPiY398ItrdOqsrPz3R2hSrlzPEFBvjXu+axq/H6qDuOp3qwyHmdvMAzL++GHH3b8bLPZVK9ePXXp\n0uWWwoSGhqqgoEAnTpxQo0aNtHHjRiUlJd3SMgEAuF0YlvcTTzxxUwves2ePpk+frpMnT8rLy0sZ\nGRnq0aOHQkNDFRERoddff12jR4+WJEVHRyssLOym1gMAwO3GaXn36NGjzO5yu90um82mwsJC5eTk\naN++fRUuuE2bNtcd7PZLHTp00MqVK28iMgAAtzen5f3ZZ59dd9v69es1c+ZMPfnkk6aGAgAAzlXq\naPNjx44pMTFR3t7eWrBggZo0aWJ2LgAA4ESF5X3hwgXNnTtXmzZt0pgxY9StWzdX5QIAAE44/c7X\n2rVr1a9fP/n5+Wn16tUUNwAA1YTTLe8//OEPuvvuu7V582Zt2bLFcfvVA9eWLl3qkoAAAKAsp+W9\nYcMGV+YAAACV5LS8Gzdu7MocAACgkm7tPKcAAMDlKG8AACyG8gYAwGIobwAALIbyBgDAYihvAAAs\nhvIGAMBiKG8AACyG8gYAwGIobwAALIbyBgDAYihvAAAshvIGAMBiKG8AACyG8gYAwGIobwAALIby\nBgDAYihvAAAshvIGAMBiKG8AACyG8gYAwGIobwAALIbyBgDAYihvAAAshvIGAMBiKG8AACyG8gYA\nwGIobwAALIbyBgDAYihvAAAshvIGAMBiKG8AACyG8gYAwGK8zFz4lClTtGvXLtlsNo0bN05t27Z1\nzOvRo4caNWokT09PSVJSUpIaNmxoZhwAAGoE08r766+/1vHjx7Vy5UodOXJE48aN08qVK8vcJyUl\nRT4+PmZFAACgRjJtt/m2bdvUs2dPSVLTpk2Vl5engoICs1YHAMBtw7TyzsnJUUBAgGM6MDBQ2dnZ\nZe4zceJEDRw4UElJSbLb7WZFAQCgRjH1M+9furacR44cqa5du8rPz0/x8fHKyMhQVFSU08cHBNSV\nl5en2TGrnaAgX3dHqFLuHo+711/dufv5cff6qxrjqd6sPB7Tyjs4OFg5OTmO6aysLAUFBTmm+/bt\n6/g5PDxcBw8erLC8c3MvmBO0msvOznd3hCrlzvEEBfnWuOezqvH7qTqMp3qzynicvcEwbbd5ly5d\nlJGRIUnau3evgoODVa9ePUlSfn6+nnnmGRUWFkqSduzYoebNm5sVBQCAGsW0Le/27durdevWGjBg\ngGw2myZOnKgPPvhAvr6+ioiIUHh4uGJiYlS7dm21atWqwq1uAADwL6Z+5v2HP/yhzPQ999zj+Dku\nLk5xcXFmrh4AgBqJM6wBAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZD\neQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kD\nAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBg\nMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGU\nNwAAFmNqeU+ZMkUxMTEaMGCAdu/eXWbe1q1b1b9/f8XExGju3LlmxgAAoEYxrby//vprHT9+XCtX\nrtTkyZM1efLkMvMTExOVnJysFStW6Msvv9Thw4fNigIAQI1iWnlv27ZNPXv2lCQ1bdpUeXl5Kigo\nkCRlZmbKz89PISEh8vDwULdu3bRt2zazogAAUKOYVt45OTkKCAhwTAcGBio7O1uSlJ2drcDAwHLn\nAQCAinm5akV2u/2WHh8U5FtFSa5YM7NPlS6vOqiJY6pqVf13dCP4/Rhz5+/HDIynerPyeEzb8g4O\nDlZOTo5jOisrS0FBQeXOO3PmjIKDg82KAgBAjWJaeXfp0kUZGRmSpL179yo4OFj16tWTJIWGhqqg\noEAnTpxQcXGxNm7cqC5dupgVBQCAGsVmv9X92RVISkrSN998I5vNpokTJ+p///d/5evrq4iICO3Y\nsUNJSUmSpF69eumZZ54xKwYAADWKqeUNAACqHmdYAwDAYihvAAAshvK+QWvXrlXr1q117ty56+Z9\n8MEH+vTTT7Vv3z699dZbkqTnnntOkrR//34dPXrUpVmlivPWZNeO+5fP/+9//3tdunRJR44cUWRk\npFJTU90ZVZJ04sQJ9evXr8xtycnJWrZsmZsS3ZwTJ06oXbt2GjJkiIYMGaKYmBhNmDBBJSUlN7Sc\njh07Vvq+GzZsUGFh4Y1GrbS0tDT99re/VWxsrPr376+tW7dq7Nix2rhxo2nrdKXyxmcVt5p98uTJ\nyszMLHPbwYMHNWTIEElSjx499PPPP5eZ/8UXX2j58uW3FrwKuOx73jXF2rVr1aRJE2VkZGjgwIFl\n5v3yP997771XkvT2229Lkj799FO1adNGYWFhrgurivPWZNeO+5fP/5/+9CdJ0nfffafw8HDHCxVV\nIywsrMwborFjx2rNmjXq27evKetbsmSJOnXqpFq1alX5sk+cOKH33ntPq1atkre3t44dO6bx48cr\nNDS0ytflDs7G9/DDD7s7mqGqyP7aa6/d8HrDw8Nv+DFmoLxvwPnz57V7925NmTJFCxcu1MCBAzVk\nyBA1b95ckhQQEKCAgAA1b95caWlpeuutt9SxY0ctXbpU7777rgIDA9WgQQO1bdu22uTNzc3V8ePH\ndeLECb344ot6//33dfLkSaWkpCgkJESvvPKKzpw5owsXLuiFF17QI488oq1bt2r27Nny9vZW/fr1\n9ec//1n//Oc/lZaWJpvNpu+//16RkZF6/vnnXTJOo3G3b9++zPM/atQopaWlaf78+bp48aJCQ0MV\nFxfnlqyV8de//lXr1q2TJD366KN69tlnNXbsWAUHB2vv3r06deqUkpKS1Lp1a6WlpWnNmjXy8PBQ\nz549FRcXp8jISH300Ufy8fHRzp079c4772jOnDkuy9+2bVsdP35cU6dO1e7du3X58mUNHDhQTz31\nlPbv36833nhDXl5e8vDw0OzZs+Xv7y9Jmj17tr788kv5+/tr/vz5ysrK0pgxYyRJxcXFmj59uv7x\nj3/o22+/1bBhw7RkyZIqL/CCggJdvnxZRUVF8vb21t13361ly5Zp7Nix2r59u5YtW6Yff/xRSUlJ\nql+/vsaMGaO6desqNjZWvr6+mjVrlry8vBQSEqJJkybp5ZdfVkxMjDp37qzCwkJFR0crPT293NeZ\nKzgb34EDB5SQkCAPDw/5+Pho2rRpOnDgQLmv8SFDhmjChAlq0aKFli1bptzcXL3wwgtuy15enoce\neqjC7PXr19eLL76oWrVqqWXLlmXWk5aWpk2bNqmkpEQLFy7UJ598okOHDumVV14xfYwVYbf5DUhP\nT1f37t3VtWtXHTt2TGfOnJEkNW/eXH/84x+dPq5ly5bq2rWrXnrpJZcVt1S5vHl5eVq0aJGioqL0\n4YcfOn7esGGD8vLy9Jvf/EbLli3T7NmzlZyc7HhMUlKSli1bpnr16mnLli2SpN27d2vatGl69913\n3bor+tpx+/v7X/f8169fX88++6yio6OrTXEfPXrUsbt5yJAhWr16tex2u1avXq20tDSlpaXp73//\nu3744QdJUmFhoRYtWqShQ4dik0VkAAAHUklEQVTqww8/VGZmptLT07VixQqlpaXpk08+0ZkzZxQR\nEaHPPvtM0pVdzI8//rjLxlRUVKQNGzaoWbNmaty4sVasWKHly5dr9uzZkqSzZ89qwoQJSk1NVfv2\n7bVmzRpJV/7GIiMj9d577ykvL08HDhxQVlaW4uPjlZqaqieffFLLly9X3759FRQUpJSUFFO2vO+5\n5x61bdtWjz76qMaOHat169apuLhYkmSz2RzP/+rVqyVJ+/btU1JSkh555BElJiZq3rx5Wrp0qRo0\naKD09HT16dPH8UZs27ZtCg8Pd/o6cwVn45s8ebJefvllpaamqkOHDlq6dKmk6vMaryi7MxVlX7p0\nqaKjo5WamnrdCcOubozdeeed+uqrr0wZy81gy/sGrF27ViNGjJCnp6eioqIcL0JXFvKNqEzef//3\nf5ckx9nvJOnf/u3fdP78edWvX1/fffedVq5cKQ8PD50/f17SlXPRjx8/XiUlJcrMzFSnTp3k4+Oj\nVq1a6Y477nDhCMvnbNzV3bW7m5OTk5WXl6f77rtPXl5XXqrt27fX/v37JUkPPvigJKlRo0bavXu3\nvvvuOx0/flxDhw6VJP388886efKk+vTpo9mzZ6t37976+uuv9eKLL5o6jqtvQiTpwIED+t3vfqf/\n+I//UHJysgYMGCBvb2/l5uZKkho0aKCkpCRdunRJWVlZ6t27tySpXr16uueeeyRJDRs2VH5+vpo0\naeK4GuFPP/2k1q1bmzqOq2bMmKEjR45o8+bNWrhwoVasWKE777xTDzzwgCPfrl27JElNmjRRQECA\ncnJydPz4cccW6IULFxQQEKDo6Gi9+eabjjc1TzzxhNPXmauUN74jR47ovvvuk3Tl+IM5c+aoY8eO\n1eY1flV52Z19+7mi7EeOHFFUVJSkK+PdvHmzY94vf8/5+flVPIKbR3lX0unTp7Vr1y5NmzZNNptN\nly5dkq+vr+644w55e3u7O951Kpv3ailc+7PdbtfatWuVl5en5cuX6/z58+rfv78kady4cVqwYIGa\nNm2qhISEch/vLs7G3axZM3dHuyk2m63Mf0ZFRUXy8Liyw8zT09Nxu91ul7e3t7p3717md3JVTk6O\ndu/erebNm6t27dqmZv7lm5CRI0cqLCxMX3/9tb766iulpqbK29tb7dq1k3TlgKFhw4YpPDxcixYt\n0oULF64b29XxvfXWW/rNb36jgQMHKj09XZ9//rmp47i63sLCQjVt2lRNmzbVkCFD9Nhjj12X8erv\n6Opry9vbW8HBweVunXbp0kXbtm3ToUOH1K5dO61evbrc15krOBvf2bNnHff55d+c0Wu8oi3fquYs\ne8OGDcvNU1F2u93uGGNpaWmZeeX9nqsDdptX0tq1azV48GB9/PHH+uijj5Senq68vDzHLkwjNpvt\nho+4vRW3mleScnNzFRoaKg8PD3366aeOI3oLCgoUEhKin376Sdu3b1dRUZFZw7hhzsZ98uRJlz7/\nVcXPz0/ffvutiouLVVxcrF27djkOhrxW69attX37dl28eFF2u12JiYm6dOmSJOmxxx5TQkKCY8vW\nVcaMGaOkpCSdPn1ajRo1kre3tzZs2KCSkhIVFhbq/Pnzuuuuu1RYWKhNmzZV+LeUm5uru+66S3a7\nXRs2bHDc18zX1qpVqzRhwgTHf9r5+fkqLS1VgwYNKnycn5+fJOnw4cOSpNTUVMcekz59+uitt97S\nQw895BhXea8zV3A2vk6dOumf//ynJGnHjh1q06aN02XUq1fPcVXIf/zjH+aH/n/OsteqVeuG84SF\nhWnPnj2SpO3bt5sTuIq5f1PJIv7nf/5H06dPd0zbbDb17dtX8+bNq9TjH3zwQSUmJsrHx0edO3c2\nK6bDreaVrpy29rnnntO3336rJ598Uo0aNdKcOXM0aNAgDRw4UHfffbd+97vfKTk5WS+99JIZw7hh\nzsbt4eHheP6tJiYmRrGxsbLb7XrqqafUuHHjcu935513aujQoRo8eLA8PT3Vs2dP1alTR5IUHR2t\nxYsXq1OnTq6MriZNmigyMlJ79uzR8ePHFRsbq549e6p79+56/fXXFRsbq/j4eDVp0kRDhgxRQkKC\noqOjy11WTEyMJk2apMaNGzsONNqyZYseeughDRo0SEuXLi1zqeGq0K9fP33//fd66qmnVLduXRUX\nF2v8+PFKT083fOzkyZP16quvOrbCY2JiJElt2rRRXl6e442Us9eZKw74dDa+0NBQvfHGG7LZbPLz\n89PUqVO1d+/ecpcRExOjhIQE/epXv9Jdd91lemaj7JJuOM/QoUM1atQoffrpp2rRooWZsasMp0c1\nwZYtW/T+++87vpIEuNvVbxGMHDnS3VFue0ePHtUbb7yhJUuWuDsKLIwt7yqWmZmpSZMmafjw4e6O\nAkiSxo8fr8zMTM2dO9fdUW57K1as0Hvvvadp06a5Owosji1vAAAshgPWAACwGMobAACLobwBALAY\nyhsAAIuhvAEAsBjKGwAAi/k/XW3XQZDMQT0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Armaan Malik\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6zlOnTqlVq1ZKTk5Wz5491bNnT+Xm5lou9Pvvv9c777wjSTp27JhycnIUFhZ2EbEBAPBN\nJSrnjIwMLV26VG3btpXT6VRWVpbljLt166aMjAz16NFDffv21QsvvOA64hsAALhnuVm7U6dOateu\nnR555BFVqVJFU6dOVbNmzSxnfO2112rSpElXJCQAAL7Espx79+6t3r17u6ZjY2NVrlw5W0MBAODL\nLno7M8UMAIC92AkMAIBh3Jbzxx9/LEn66KOPPBYGAAAUs8/5rbfeUm5urt57770iTxzStWtXW4MB\nAOCr3JbzsGHDtHr1amVnZ2vDhg1/up9yBgDAHm7LuV27dmrXrp2WLl2q9u3bezITAAA+zfKrVHfc\ncYdGjBihLVu2yOFw6I477tDgwYNVoUIFT+QDAMDnWB6t/eKLL6p+/fp6/fXXNXHiRN10000aMWKE\nJ7IBAOCTLNecT506pZ49e7qm69atqy+++MLWUAAA+LISnVs7LS3NNX3kyBGdPXvW1lAAAPgyyzXn\n/v37q0uXLoqIiJDT6VRGRobGjh3riWwAAPgky3Ju27atVqxYof3790uSatWqpWuuucbuXAAA+CzL\ncpbOXWHq1ltvtTsLAAAQ59YGAMA4luXsdDo9kQMAAJxnWc6xsbGeyAEAAM6z3Odcr149TZ48WY0a\nNVJgYKDr9hYtWtgaDAAAX2VZztu3b5ckff/9967bHA4H5QwAgE0syzkhIUHSuX3PRV06EgAAXFmW\n+5x37NihLl26qGPHjpKkadOmadOmTbYHAwDAV1mWc1xcnF555RVFRERIkqKiojRu3DjbgwEA4Kss\nyzkgIKDQCUhq1aqlgIASnbsEAABcghKVc2pqqmt/8+rVq/nuMwAANrJcBX722WfVv39/7du3T40b\nN1b16tU1YcIET2QDAMAnWZbzLbfcoqSkJGVkZCgoKEjBwcGeyAUAgM+yLOc9e/YoPj5ee/bskcPh\nUN26dfXkk0/qpptu8kQ+AAB8jmU5Dxs2TD169NDAgQMlSRs2bNDQoUP18ccf2x4OAABfZFnOZcuW\nVdeuXV3TtWvX1tKlS20NBQCAL3N7tHZBQYEKCgrUokULLVu2TCdPntRvv/2mFStWqEmTJp7MCACA\nT3G75nzbbbfJ4XAU+bWpgIAAPf7447YGAwDAV7kt5x07dngyBwAAOM9yn/PRo0e1dOlSZWdnF1qL\nfvLJJ20NBgCAr7I8Q1ifPn20fft25ebmKi8vz/UPAADYw3LNOTQ0lAtdAADgQZblfP/992vx4sVq\n1KiR/P39XbdXrVrV1mAAAPgqy3LeuXOnkpKSFBoa6rrN4XBo1apVduYCAMBnWZbzpk2b9N133yko\nKMgTeQAA8HmWB4Q1aNBAZ86c8UQWAACgEn6V6t5771Xt2rUL7XNOTEy0NRgAAL7Kspwv50xgr776\nqjZs2KC8vDz169dP7dq1u+R5AQDgKyzLOT8//5Jm/M0332j37t2aP3++MjMz9dBDD1HOAACUgGU5\nv/nmm66fc3NztWfPHjVu3FgtWrQo9nlNmjRRw4YNJUnlypXTqVOnlJ+fX2jTOAAA+DPLck5ISCg0\nffz4cU2aNMlyxv7+/ipTpowkacGCBWrdujXFDABACViW8x+Fh4frp59+KvHjV6xYoQULFuidd94p\n9nFhYWUUEGB+eUdEhHg7gtG8/fp4e/lXGuMxG+Mx29U8HstyHjp0qBwOh2v6l19+kZ+f5TewJElr\n1qzR9OnTNXPmTIWEFP8iZWbmlGie3hQREaL09GxvxzCaN1+f0vb+MB6zMR6zXQ3jKe6PB8tybtmy\npetnh8Oh4OBgtWrVynKh2dnZevXVVzV79uxCZxcDAADFsyznhx566JJm/NlnnykzM1ODBw923TZh\nwgTOyQ0AgAW35XzvvfcW2pztdDrlcDh09uxZHTt2TNu3by92xtHR0YqOjr5ySQEA8BFuy/mLL774\n020rVqzQpEmT9PDDD9saCgAAX1aio7X379+vMWPGKDAwUDNmzFCNGjXszgUAgM8qtpxzcnI0bdo0\nrV69WkOHDlWbNm08lQsAAJ/l9jtRS5YsUZcuXVS+fHktXLiQYgYAwEPcrjk/88wzuvHGG7VmzRqt\nXbvWdfuFA8PmzJnjkYAAAPgat+WckpLiyRwAAOA8t+VcrVo1T+YAAADnlew8nAAAwGMoZwAADEM5\nAwBgGMoZAADDUM4AABiGcgYAwDCUMwAAhqGcAQAwDOUMAIBhKGcAAAxDOQMAYBjKGQAAw1DOAAAY\nhnIGAMAwlDMAAIahnAEAMAzlDACAYShnAAAMQzkDAGAYyhkAAMNQzgAAGIZyBgDAMJQzAACGoZwB\nADAM5QwAgGEoZwAADEM5AwBgGMoZAADDUM4AABiGcgYAwDCUMwAAhqGcAQAwDOUMAIBhbC3nXbt2\nKTIyUnPnzrVzMQAAlCq2lXNOTo5Gjx6tFi1a2LUIAABKJdvKOSgoSG+//bYqVqxo1yIAACiVAmyb\ncUCAAgJsmz0AAKWWMe0ZFlZGAQH+3o5hKSIixNsRjObt18fby7/SGI/ZGI/ZrubxGFPOmZk53o5g\nKSIiROnp2d6OYTRvvj6l7f1hPGZjPGa7GsZT3B8PfJUKAADD2LbmvHXrVk2YMEGHDh1SQECAli5d\nqvj4eIWGhtq1SAAASgXbyrlBgwZKSEiwa/YAAJRabNYGAMAwlDMAAIahnAEAMAzlDACAYShnAAAM\nQzkDAGAYyhkAAMNQzgAAGIZyBgDAMJQzAACGoZwBADAM5QwAgGEoZwAADEM5AwBgGMoZAADDUM4A\nABiGcgYAwDCUMwAAhqGcAQAwDOUMAIBhKGcAAAxDOQMAYBjKGQAAw1DOAAAYhnIGAMAwlDMAAIah\nnAEAMAzlDACAYShnAAAMQzkDAGAYyhkAAMNQzgAAGIZyBgDAMJQzAACGoZwBADAM5QwAgGEoZwAA\nDEM5AwBgGMoZAADDUM4AABgmwM6Zv/LKK9q0aZMcDodGjBihhg0b2rk4AABKBdvK+dtvv9WBAwc0\nf/587d27VyNGjND8+fPtWhwAAKWGbZu1161bp8jISElS7dq1lZWVpZMnT9q1OAAASg3byvnYsWMK\nCwtzTVeoUEHp6el2LQ4AgFLD1n3Ov+d0Oou9PyIixENJLo+3ciZN6uyV5V5trpb/RyXFeMzGeMx2\nNY/HtjXnihUr6tixY67ptLQ0RURE2LU4AABKDdvKuVWrVlq6dKkkadu2bapYsaKCg4PtWhwAAKWG\nbZu1GzdurPr166tbt25yOBx68cUX7VoUAAClisNptTMYAAB4FGcIAwDAMJQzAACGoZx/Z8mSJapf\nv74yMjL+dN8nn3yi5cuXa/v27ZoyZYok6YknnpAk7dixQ/v27fNo1guKy1ya/XHcv38Pnn76aZ0+\nfVp79+5V+/btlZCQ4M2oOnjwoLp06VLotvj4eM2dO9dLiS7dwYMH1ahRI8XExCgmJkbR0dEaNWqU\n8vPzL2o+zZo1K/FjU1JSdPbs2YuNWmKJiYl69NFH1atXL3Xt2lVff/21hg8frpUrV9q2TE8qanxX\ni8vNPnbsWKWmpha6bdeuXYqJiZEk3Xvvvfrtt98K3f/ll19q3rx5lxf8CvDY95yvBkuWLFGNGjW0\ndOlSde/evdB9v//lWq9ePUnSW2+9JUlavny5GjRooFq1anku7HnFZS7N/jju378H//nPfyRJW7Zs\nUevWrV0fRFwZtWrVKvQHz/Dhw5WUlKQHH3zQluXNnj1bzZs3V1BQ0BWf98GDB/Xhhx9qwYIFCgwM\n1P79+zVy5EhVr179ii/LG9yNr2XLlt6OZulKZH/++ecvermtW7e+6OfYgXI+78SJE9q8ebNeeeUV\nzZw5U927d1dMTIzq1KkjSQoLC1NYWJjq1KmjxMRETZkyRc2aNdOcOXP0wQcfqEKFCgoPD/foxT1K\nkjkzM1MHDhzQwYMHNWjQIH388cc6dOiQ3n77bVWpUkXPPvusjh49qpycHD311FO655579PXXX2vy\n5MkKDAxUuXLl9MYbb+jHH39UYmKiHA6HfvrpJ7Vv315PPvmkx8Za3LgbN25c6D0YPHiwEhMTNX36\ndJ06dUrVq1dX7969vZLVynvvvafPPvtMknTfffepb9++Gj58uCpWrKht27bp8OHDmjhxourXr6/E\nxEQlJSXJz89PkZGR6t27t9q3b69PP/1UZcuW1YYNG/Tuu+9q6tSpHh1Dw4YNdeDAAY0bN06bN2/W\nmTNn1L17dz3yyCPasWOHXn75ZQUEBMjPz0+TJ09WaGioJGny5Mn66quvFBoaqunTpystLU1Dhw6V\nJOXl5WnChAn64YcftHHjRvXp00ezZ8++4gV98uRJnTlzRrm5uQoMDNSNN96ouXPnavjw4Vq/fr3m\nzp2rX375RRMnTlS5cuU0dOhQlSlTRr169VJISIhef/11BQQEqEqVKho9erSGDRum6OhotWjRQmfP\nnlVUVJSSk5OL/Jx5grvx7dy5U3FxcfLz81PZsmU1fvx47dy5s8jPeExMjEaNGqW6detq7ty5yszM\n1FNPPeW17EXladq0abHZy5Urp0GDBikoKEi33HJLoeUkJiZq9erVys/P18yZM7Vs2TLt3r1bzz77\nrO1jLA6btc9LTk5W27Ztdffdd2v//v06evSoJKlOnTp64YUX3D7vlltu0d13360hQ4Z4/KpbJcmc\nlZWlWbNmqUOHDlq0aJHr55SUFGVlZemuu+7S3LlzNXnyZMXHx7ueM3HiRM2dO1fBwcFau3atJGnz\n5s0aP368PvjgA69uKv7juENDQ//0HpQrV059+/ZVVFSUEcW8b98+16bgmJgYLVy4UE6nUwsXLlRi\nYqISExP1+eef6+eff5YknT17VrNmzVJsbKwWLVqk1NRUJScn6/3331diYqKWLVumo0eP6v7779cX\nX3wh6dzm3wceeMCj48rNzVVKSopuvvlmVatWTe+//77mzZunyZMnS5KOHz+uUaNGKSEhQY0bN1ZS\nUpKkc//H2rdvrw8//FBZWVnauXOn0tLSNGDAACUkJOjhhx/WvHnz9OCDDyoiIkJvv/22LWvOt956\nqxo2bKj77rtPw4cP12effaa8vDxJksPhcL0HCxculCRt375dEydO1D333KMxY8bozTff1Jw5cxQe\nHq7k5GR17tzZ9cfWunXr1Lp1a7efM09wN76xY8dq2LBhSkhIUJMmTTRnzhxJ5nzGi8vuTnHZ58yZ\no6ioKCUkJKhixYqF7ruwwlW1alV98803tozlUrDmfN6SJUvUv39/+fv7q0OHDq4PmMmXuSxJ5r/8\n5S+SVOjsbNdff71OnDihcuXKacuWLZo/f778/Px04sQJSefOgz5y5Ejl5+crNTVVzZs3V9myZXXb\nbbfpuuuu8+AIi+Zu3Cb746bg+Ph4ZWVl6fbbb1dAwLmPYePGjbVjxw5J0p133ilJqly5sjZv3qwt\nW7bowIEDio2NlST99ttvOnTokDp37qzJkyerU6dO+vbbbzVo0CDbx3LhDw1J2rlzpx577DH97W9/\nU3x8vLp166bAwEBlZmZKksLDwzVx4kSdPn1aaWlp6tSpkyQpODhYt956qySpUqVKys7OVo0aNTRm\nzBjFx8fr119/Vf369W0fiyS9+uqr2rt3r9asWaOZM2fq/fffV9WqVfXXv/7VlW/Tpk2SpBo1aigs\nLEzHjh3TgQMHXGuQOTk5CgsLU1RUlF577TXXHy0PPfSQ28+ZpxQ1vr179+r222+XdG7//9SpU9Ws\nWTNjPuMXFJXd3bd/i8u+d+9edejQQdK58a5Zs8Z13+/f5+zs7Cs8gktHOUs6cuSINm3apPHjx8vh\ncOj06dMKCQnRddddp8DAQG/HK1JJM1/4xf/Hn51Op5YsWaKsrCzNmzdPJ06cUNeuXSVJI0aM0IwZ\nM1S7dm3FxcUV+XxvcTfum2++2dvRLprD4Sj0iyY3N1d+fuc2Zvn7+7tudzqdCgwMVNu2bQu9Hxcc\nO3ZMmzdvVp06dXTNNdfYnvv3f2gMHDhQtWrV0rfffqtvvvlGCQkJCgwMVKNGjSSdOyCnT58+at26\ntWbNmqWcnJw/jU86N8YpU6borrvuUvfu3ZWcnKxVq1bZPhan06mzZ8+qdu3aql27tmJiYtSxY8c/\nZbzwPl34bAUGBqpixYpFrl22atVK69at0+7du9WoUSMtXLiwyM+ZJ7gb3/Hjx12P+f3/O6vPeHFr\nrleau+yVKlUqMk9x2Z1Op2swxugkAAADXklEQVSMBQUFhe4r6n02AZu1dW5NrGfPnlq8eLE+/fRT\nJScnKysry7WJ0YrD4bjoo1Uv1+VmlqTMzExVr15dfn5+Wr58ueuI2JMnT6pKlSr69ddftX79euXm\n5to1jIvmbtyHDh3y+HtwucqXL6+NGzcqLy9PeXl52rRpk+tgwz+qX7++1q9fr1OnTsnpdGrMmDE6\nffq0JKljx46Ki4tzrZV60tChQzVx4kQdOXJElStXVmBgoFJSUpSfn6+zZ8/qxIkTqlmzps6ePavV\nq1cX+38pMzNTNWvWlNPpVEpKiuuxdn6+FixYoFGjRrl+KWdnZ6ugoEDh4eHFPq98+fKSpD179kiS\nEhISXFs9OnfurClTpqhp06aucRX1OfMEd+Nr3ry5fvzxR0nSd999pwYNGridR3BwsOuKgj/88IP9\noc9zlz0oKOii89SqVUtbt26VJK1fv96ewFeY91eFDPC///1PEyZMcE07HA49+OCDevPNN0v0/Dvv\nvFNjxoxR2bJl1aJFC7tiFnK5mSWpXbt2euKJJ7Rx40Y9/PDDqly5sqZOnaoePXqoe/fuuvHGG/XY\nY48pPj5eQ4YMsWMYF83duP38/FzvwdUkOjpavXr1ktPp1COPPKJq1aoV+biqVasqNjZWPXv2lL+/\nvyIjI3XttddKkqKiovTOO++oefPmnowu6dxm3vbt22vr1q06cOCAevXqpcjISLVt21YvvfSSevXq\npQEDBqhGjRqKiYlRXFycoqKiipxXdHS0Ro8erWrVqrkO5Fm7dq2aNm2qHj16aM6cOapQocIVzd+l\nSxf99NNPeuSRR1SmTBnl5eVp5MiRSk5Otnzu2LFj9dxzz7nWoqOjoyVJDRo0UFZWluuPJXefM08c\nUOlufNWrV9fLL78sh8Oh8uXLa9y4cdq2bVuR84iOjlZcXJxuuOEG1axZ0/bMVtklXXSe2NhYDR48\nWMuXL1fdunXtjH3FcPrOi7R27Vp9/PHHrq/rAN524Qj8gQMHejsKdG6f/Msvv6zZs2d7OwquYqw5\nX4TU1FSNHj1a/fr183YUQJI0cuRIpaamatq0ad6OAknvv/++PvzwQ40fP97bUXCVY80ZAADDcEAY\nAACGoZwBADAM5QwAgGEoZwAADEM5AwBgGMoZAADD/B9LO6AJPNeM0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Atif Aslam\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RaWlp6tChg+x2u3JyclyRDQAAFMOwvLt06aJOnTqpdevWCgkJ0YwZM9SqVStXZAMAAMUw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\nQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Honey Singh\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0dPwDAADuYbjnXbduXU2bNs0VWQAAQCUY7nnfd999OnLkiCuyAACASjDc805PT9eyZcsU\nEBAgLy8v2e122Ww2ff755y6IBwAArmVY3m+//bYrcgAAgEoyLO+QkBCtW7dOe/fulSTdf//9evzx\nx00PBgAAymdY3gkJCTp79qw6dOggu92uv//97/r22281fvx4V+QDAADXMCzvQ4cOacWKFY7pmJgY\nDR48uFILz8jI0IgRI/T0008rJiamzLzu3bsrJCTE8bWzxMRErlAGAEAlGJZ3UVGRSktL5eFx5cD0\nkpISlZSUGC74woULmjx5sjp16uT0PosWLZKPj88NxAUAAIbl3bVrVw0YMEDt27eXJO3YsUNRUVGG\nC65Vq5YWLVqkRYsW3XpKAADgYFjeI0aM0MMPP6zdu3fLZrNp0qRJatOmjfGCvbzk5VXx4idOnKgT\nJ07ogQce0OjRo2Wz2SqfHACA25Rheefl5cnHx0dxcXH64osvlJ6ertDQUAUFBd3SikeOHKkuXbrI\nz89P8fHxSktLU+/evZ3ePyCgrry8qv9pWYOCfN0doUoxntuLu7ePu9df1RhP9Wbl8RiW95gxYxQX\nFydvb2/NnDlTgwYN0muvvaaFCxfe0or79evn+DkiIkIZGRkVlndu7oVbWp8rBAX5Kju75lzjnPHc\nfty5fWra74fxVG9WGY+zFxiGp0e9ePGiOnfurNTUVA0ZMkRDhgxRUVHRLYXJz8/XM888o8LCQknS\nzp071axZs1taJgAAtwvDPe+LFy/q3LlzSktL0/z582W325WXl2e44L1792rGjBk6ceKEvLy8lJaW\npu7duyssLEyRkZGKiIhQdHS0ateurZYtW1a41w0AAP7FsLz79Omjnj176qmnnlJoaKjmzp2rDh06\nGC64devWSk5Odjo/Li5OcXFxN5YWAAAYl/e1JTt06FDVr1/f1FAAAMA5w8+8r0VxAwDgXjdc3gAA\nwL2clvf7778vSfrb3/7msjAAAMCY08+83377bRUVFemvf/1ruWc+GzBggKnBAABA+ZyW98svv6wt\nW7YoPz9fu3btum4+5Q0AgHs4Le+ePXuqZ8+eSktLU69evVyZCQAAVMDwq2L333+/xo0bp++++042\nm03333+/Ro0apcDAQFfkAwAA1zA82nzixIlq1aqVZs+ercTERP3617/WuHHjXJENAACUo1KnRx0y\nZIhjunnz5vrss89MDQUAAJyr1IVJzpw545g+deqU44IiAADA9Qz3vEeMGKH+/fsrKChIdrtd586d\n05QpU1yRDQAAlMOwvLt166aNGzfq2LFjkqTw8HDVrl3b7FwAAMAJw/KWpDp16uiee+4xOwsAAKgE\nzm0OAIDFGJa33W53RQ4AAFBJhuU9dOhQV+QAAACVZPiZ97333qs5c+aobdu28vb2dtzeqVMnU4MB\nAIDyGZb3/v37JUnffPON4zabzUZ5AwDgJoblnZycLOnKZ9/lXRoUAAC4luFn3gcOHFD//v312GOP\nSZLmzZun3bt3mx4MAACUz7C8J02apKlTpyooKEiSFBUVpWnTppkeDAAAlM+wvL28vMqcoCU8PFxe\nXpU6twsAADBBpco7KyvL8Xn3li1b+O43AABuZLgL/corr2jEiBE6evSo2rVrp7CwMM2YMcMV2QAA\nQDkMy7tFixZat26dzp07p1q1aqlevXquyAUAAJwwLO/Dhw8rKSlJhw8fls1mU/PmzfX888/r17/+\ntSvyAQCAaxiW98svv6zBgwdr5MiRkqRdu3ZpzJgxev/9900PBwAArmdY3j4+PhowYIBjukmTJkpL\nSzM1FAAAcM7p0ealpaUqLS1Vp06d9Mknn6igoEA///yzNm7cqPbt27syIwAA+AWne94tW7aUzWYr\n92thXl5e+u///m9TgwEAgPI5Le8DBw64MgcAAKgkw8+8T58+rbS0NOXn55fZC3/++edNDQYAAMpn\neIa1YcOGaf/+/SoqKlJxcbHjHwAAcA/DPW9/f38uRAIAQDViWN6RkZH6+OOP1bZtW3l6ejpuv/PO\nO00NBgAAymdY3gcPHtS6devk7+/vuM1ms+nzzz83MxcAAHDCsLx3796tnTt3qlatWq7IAwAADBge\nsNa6dWtdvnzZFVkAAEAlVOqrYt27d1eTJk3KfOadkpJiajAAAFA+w/K+lTOpZWRkaMSIEXr66acV\nExNTZt62bds0e/ZseXp6KiIiQvHx8Te9HgAAbieGb5uXlJSU+8/IhQsXNHnyZHXq1Knc+QkJCUpK\nStKqVav05Zdf6vDhwzeeHgCA25Dhnvf8+fMdPxcVFenw4cNq166d01K+qlatWlq0aJEWLVp03bys\nrCz5+fkpNDRUktS1a1dt375dTZs2vdH8AADcdgzLOzk5ucz02bNnNWvWLOMFe3nJy6v8xWdnZysw\nMNAxHRgYqKysLMNlAgCASpT3tRo0aKDvv//ejCwVCgioKy8vT+M7VlKf0R9V2bLMtG5WX7euPyjI\n163rr2o1bTxV7Ua2jxWeQzx/qpY7x8PfW1mG5T1mzBjZbDbH9I8//igPD8OPyisUHBysnJwcx/Tp\n06cVHBxc4WNycy/c0jqtKjs7323rDgrydev6q1pNG48Zatr24flTdWraeMxgxvZx9oLJsLwffvhh\nx882m0316tVT586dbylMWFiYCgoKdPz4cYWEhGjz5s1KTEy8pWUCAHC7MCzvJ5544qYWvHfvXs2Y\nMUMnTpyQl5eX0tLS1L17d4WFhSkyMlKvv/66Ro8eLUmKiopSeHj4Ta0HAIDbjdPy7t69e5m3y+12\nu2w2mwoLC5WTk6P9+/dXuODWrVtfd7DbL7Vv316rV6++icgAANzenJb3Z599dt1tGzdu1KxZs/Tk\nk0+aGgoAADhXqaPNjx07poSEBHl7e2vhwoVq3Lix2bkAAIATFZb3hQsXNG/ePG3ZskVjxoxR165d\nXZULAAA44fQ7X+vXr1f//v3l5+entWvXUtwAAFQTTve8//CHP+juu+9Wenq6tm7d6rj96oFry5cv\nd0lAAABQltPy3rRpkytzAACASnJa3o0aNXJlDgAAUEm3dp5TAADgcpQ3AAAWQ3kDAGAxlDcAABZD\neQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kD\nAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBg\nMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxXmYufOrUqdq9e7ds\nNpvGjRunNm3aOOZ1795dISEh8vT0lCQlJiaqYcOGZsYBAKBGMK28v/76a2VmZmr16tU6cuSIxo0b\np9WrV5e5z6JFi+Tj42NWBAAAaiTT3jbfvn27evToIUlq0qSJ8vLyVFBQYNbqAAC4bZhW3jk5OQoI\nCHBMBwYGKjs7u8x9Jk6cqEGDBikxMVF2u92sKAAA1Cimfub9S9eW88iRI9WlSxf5+fkpPj5eaWlp\n6t27t9PHBwTUlZeXp9kxq52gIN/bev1VraaNp6rVtO3j7vG4e/1VraaNp6q5cvuYVt7BwcHKyclx\nTJ85c0ZBQUGO6X79+jl+joiIUEZGRoXlnZt7wZyg1Vx2dr7b1h0U5OvW9Ve1mjYeM9S07cPzp+rU\ntPGYwYzt4+wFgWlvm3fu3FlpaWmSpH379ik4OFj16tWTJOXn5+uZZ55RYWGhJGnnzp1q1qyZWVEA\nAKhRTNvzbteunVq1aqWBAwfKZrNp4sSJ+uCDD+Tr66vIyEhFREQoOjpatWvXVsuWLSvc6wYAAP9i\n6mfef/jDH8pM33PPPY6f4+LiFBcXZ+bqAQCokTjDGgAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZD\neQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kD\nAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBg\nMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGUNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxlDcAABZDeQMAYDGU\nNwAAFkN5AwBgMZQ3AAAWQ3kDAGAxppb31KlTFR0drYEDB2rPnj1l5m3btk0DBgxQdHS05s2bZ2YM\nAABqFNPK++uvv1ZmZqZWr16tKVOmaMqUKWXmJyQkKCkpSatWrdKXX36pw4cPmxUFAIAaxbTy3r59\nu3r06CFJatKkifLy8lRQUCBJysrKkp+fn0JDQ+Xh4aGuXbtq+/btZkUBAKBGMa28c3JyFBAQ4JgO\nDAxUdna2JCk7O1uBgYHlzgMAABXzctWK7Hb7LT0+KMi3ipJcsW5W3ypdXk1V1dvd3dw5npr2N1fT\nxmMGnj9Vh7+3skzb8w4ODlZOTo5j+syZMwoKCip33unTpxUcHGxWFAAAahTTyrtz585KS0uTJO3b\nt0/BwcGqV6+eJCksLEwFBQU6fvy4iouLtXnzZnXu3NmsKAAA1Cg2+62+n12BxMREffPNN7LZbJo4\ncaL+93//V76+voqMjNTOnTuVmJgoSerZs6eeeeYZs2IAAFCjmFreAACg6nGGNQAALIbyBgDAYijv\nG7R+/Xq1atVK586du27eBx98oE8//VT79+/XW2+9JUl67rnnJEkHDhzQ0aNHXZpVqjhvTXbtuH+5\n/X//+9/r0qVLOnLkiHr16qXk5GR3RpUkHT9+XP379y9zW1JSklasWOGmRDfn+PHjatu2rWJjYxUb\nG6vo6GhNmDBBJSU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            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Raahat Fateh Ali Khan\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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evHmW7zN3yGp8Y8eO1dChQxUTE6MGDRpowYIFksx5j2eXPSvZZV+wYIFCQ0MV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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Shreya Ghoshal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//lmSdPbsWc2dO1exsbFaunSpUlNTlZycrHfeeUeJiYlauXKljh49qnvuuUef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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Sonu Nigam\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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48KA8PDzk6enp+AcAANzDcM+7bt26mjZtmiuyAACASjDc877vvvt09OhRV2QBAACVYLjn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\njh0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Actual Singer: Sunidhi Chauhan\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAIBKMNzzvu+++3T48GFXZAEAAJVguOedkZGhpUuXys/PTx4eHrLb7bLZbPr8889dEA8A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\nRekAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "M64UOW3k4idH",
        "colab_type": "code",
        "outputId": "91d53160-370c-4ad2-8e3c-0ceea6bc7aa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6647
        }
      },
      "cell_type": "code",
      "source": [
        "ctr=0\n",
        "correct = 0\n",
        "count = 0\n",
        "prev_singer = 0\n",
        "for i in range(49):\n",
        "  count_pred = Counter(y_pred_xg[ctr:ctr+5]).most_common(2)\n",
        "\n",
        "  actual_singer,num = Counter(y_test[ctr:ctr+5]).most_common(1)[0]\n",
        "  if(actual_singer!=prev_singer):\n",
        "    prev_singer = actual_singer\n",
        "    count = 0\n",
        "  \n",
        "  #print(count_pred)\n",
        "  pred_singers = []\n",
        "  for pred in count_pred:\n",
        "    pred_singers.append([singer_labels[pred[0]],pred[1]/5])\n",
        "  #print(pred_singers)\n",
        "  print('Song:',test_songs[singer_labels[actual_singer]][count])\n",
        "  count+=1\n",
        "  print('Actual Singer:', singer_labels[actual_singer])\n",
        "  print('Predictions:-')\n",
        "  max_conf_singers = []\n",
        "  tie = 0\n",
        "  maxi = 0\n",
        "  for singer in pred_singers:\n",
        "      \n",
        "      print('Singer:', singer[0])\n",
        "      print('Confidence:', singer[1])\n",
        "      if(singer[1]>=maxi):\n",
        "        if singer[1]>0.2:\n",
        "          maxi = singer[1]\n",
        "          max_conf_singers.append(singer[0])\n",
        "      if(singer[1]==0.4):\n",
        "        tie+=1\n",
        "      \n",
        "  #print(max_conf_singer)\n",
        "  if(len(max_conf_singers)!=0):\n",
        "    for  max_conf_singer in  max_conf_singers:\n",
        "        if max_conf_singer == singer_labels[actual_singer] :\n",
        "          correct+=1\n",
        "      \n",
        "      \n",
        "  \n",
        "  ctr+=5\n",
        "  print()\n",
        "print('Correct majority vote songs:', correct)\n",
        "print('Accuracy:', correct/49)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Song: Lambiyaan Si Judaiyaan Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Nashe Si Chadh Gayi Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Enna Sona Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Phir Le Aya Dil (Reprise) Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Baaton Ko Teri Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Yeh Fitoor Mera Song \n",
            "Actual Singer: Arijit Singh\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.6\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Kaun Tujhe & Kuch Toh Hai Mashup Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Jaadugari Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.8\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Khwaishein (Film Version) Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Main Hoon Hero Tera (Sad Version) Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Tum Jo Mile Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Rendu Kallu Song \n",
            "Actual Singer: Armaan Malik\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.8\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Mori Araj Suno (From \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.6\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Jal Pari Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Khair Mangda Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Dil Kare Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Darasal Song \n",
            "Actual Singer: Atif Aslam\n",
            "Predictions:-\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.8\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: One Bottle Down Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.4\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Khalaara Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.4\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Superman Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.6\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Rani Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 1.0\n",
            "\n",
            "Song: Love Dose Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Honey Singh\n",
            "Confidence: 1.0\n",
            "\n",
            "Song: Urvashi Song \n",
            "Actual Singer: Honey Singh\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.6\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Naino Nay Tere Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Tum Kon Piya Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Tinka Tinka Dil Mera Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Saiyaan Radio \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Fakeera Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Lag Ja Gale Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Tera Mera Sath Ho Song \n",
            "Actual Singer: Raahat Fateh Ali Khan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Sunn Raha Hai (Female) Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.8\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Yeh Kya Hua Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Agar Tum Mil Jao\n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Sunn Raha Hai\n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.4\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Daata Tu Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.8\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Saans (Reprise) Song \n",
            "Actual Singer: Shreya Ghoshal\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.6\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Jaane Nahin Denge Tujhe Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.6\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Aye Zindagi Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.8\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Anisuthide Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.6\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Bijuria Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.4\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Zindagi Is Tarah Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Gamanisu Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Sonu Nigam\n",
            "Confidence: 0.8\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Deewane Hoke Hum Song \n",
            "Actual Singer: Sonu Nigam\n",
            "Predictions:-\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.8\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Ganaraj Tu Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Armaan Malik\n",
            "Confidence: 0.6\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Dil Mein Jaagi Dhadkan Aise (Sur (The Melody Of Life) _ Soundtrack Version) Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Singer: Atif Aslam\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Aaja Nachle Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Shreya Ghoshal\n",
            "Confidence: 0.4\n",
            "Singer: Arijit Singh\n",
            "Confidence: 0.4\n",
            "\n",
            "Song: Piku Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.4\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Jind Mahi Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Raahat Fateh Ali Khan\n",
            "Confidence: 0.4\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.2\n",
            "\n",
            "Song: Aye Dil Song \n",
            "Actual Singer: Sunidhi Chauhan\n",
            "Predictions:-\n",
            "Singer: Sunidhi Chauhan\n",
            "Confidence: 0.6\n",
            "Singer: Honey Singh\n",
            "Confidence: 0.2\n",
            "\n",
            "Correct majority vote songs: 28\n",
            "Accuracy: 0.5714285714285714\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "u60VgDiEfJ97",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}