Code_SC_Challenge_6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "[20210623]SC6_with_bg2_noise.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mU4p2Wl_b5RW"
      },
      "source": [
        "## Link to Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dI8Ip3gb8ds",
        "outputId": "6f7d1aef-2a5b-456d-919d-49a91357bc4e"
      },
      "source": [
        "# connect to google colab\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtLPLbA0cxBE"
      },
      "source": [
        "## Downloading Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2h7Cxnb-5XHg",
        "outputId": "f95f7da9-e26f-41d5-9c33-c64d9e229554"
      },
      "source": [
        "# install torchaudio\n",
        "!pip install torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torchaudio==0.7.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5d/75/5ce994c76cf7b53ff8c577d7a8221fa0c9dfe9e34c0536c6eaf3e466788a/torchaudio-0.7.0-cp37-cp37m-manylinux1_x86_64.whl (7.6MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6MB 8.0MB/s \n",
            "\u001b[?25hCollecting torch==1.7.0\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu92/torch-1.7.0%2Bcu92-cp37-cp37m-linux_x86_64.whl (577.3MB)\n",
            "\u001b[K     |████████████████████████████████| 577.3MB 27kB/s \n",
            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from torch==1.7.0->torchaudio==0.7.0) (0.16.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.7.0->torchaudio==0.7.0) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.7.0->torchaudio==0.7.0) (1.19.5)\n",
            "Collecting dataclasses\n",
            "  Downloading https://files.pythonhosted.org/packages/26/2f/1095cdc2868052dd1e64520f7c0d5c8c550ad297e944e641dbf1ffbb9a5d/dataclasses-0.6-py3-none-any.whl\n",
            "\u001b[31mERROR: torchvision 0.10.0+cu102 has requirement torch==1.9.0, but you'll have torch 1.7.0+cu92 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: torchtext 0.10.0 has requirement torch==1.9.0, but you'll have torch 1.7.0+cu92 which is incompatible.\u001b[0m\n",
            "Installing collected packages: dataclasses, torch, torchaudio\n",
            "  Found existing installation: torch 1.9.0+cu102\n",
            "    Uninstalling torch-1.9.0+cu102:\n",
            "      Successfully uninstalled torch-1.9.0+cu102\n",
            "Successfully installed dataclasses-0.6 torch-1.7.0+cu92 torchaudio-0.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3e-YTe8ZK0Ak",
        "outputId": "fcad16e3-2c93-4a03-8f1a-7bdd4d8b241e"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# for F1-score & confusion matrix later\n",
        "from sklearn.metrics import classification_report\n",
        "import seaborn as sns\n",
        "\n",
        "# current torch version is 1.7.0+cu101\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import torchaudio\n",
        "from torchaudio.transforms import MelSpectrogram\n",
        "from torchaudio.transforms import TimeMasking, FrequencyMasking\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import IPython.display as ipd\n",
        "\n",
        "import time\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "\n",
        "# check if cuda GPU is available, make sure you're using GPU runtime on Google Colab\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device) # you should output \"cuda\"\n",
        "\n",
        "# COLAB CONFIG\n",
        "# change colab flag to false if train using jupyter notebook\n",
        "COLAB_FLAG = True\n",
        "COLAB_FILEPATH = './drive/MyDrive/TIL2021/competition/SC/' if COLAB_FLAG == True else './'\n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfGD99r-LCVQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9z4qZ8A8HTn"
      },
      "source": [
        "## Speech Classification Dataset\n",
        "We will be providing the base dataset that will be used for the first task of the Speech Classification competition."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eghj9hRaFWUz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b8d3cab-1cde-426a-ead7-9b19ba79120d"
      },
      "source": [
        "# old dataset\n",
        "#!gdown --id 1im5shxcavdoTRNT66mhVdtA_E0ZR8QLl\n",
        "#!unzip s1_train_release.zip\n",
        "\n",
        "# the extra+base dataset\n",
        "# 1st acc\n",
        "base_folder = '/content/drive/MyDrive/TIL2021/competition/SC'\n",
        "training_path = os.path.join(base_folder, \"s6_bg2.zip\") # zip file for training dataset\n",
        "!unzip $training_path"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8Iv0DXlINyH"
      },
      "source": [
        "class CustomSpeechDataset(torch.utils.data.Dataset):\n",
        "  def __init__(self, path, typ='train', transforms=None):\n",
        "\n",
        "    assert typ == 'train' or typ == 'test', 'typ must be either \"train\" or \"test\"'\n",
        "\n",
        "    self.typ = typ\n",
        "    self.transforms = transforms\n",
        "    self.targets = []\n",
        "\n",
        "    if self.typ == 'train':\n",
        "      self.class_names = sorted(os.listdir(path))\n",
        "      num_classes = len(self.class_names)\n",
        "\n",
        "      for class_idx, class_name in enumerate(self.class_names):\n",
        "        class_dirx = os.path.join(path, class_name)\n",
        "        wav_list = os.listdir(class_dirx)\n",
        "\n",
        "        for wav_file in wav_list:\n",
        "          self.targets.append({\n",
        "              'filename': wav_file,\n",
        "              'path': os.path.join(class_dirx, wav_file),\n",
        "              'class': class_name\n",
        "          })\n",
        "\n",
        "    if self.typ == 'test':\n",
        "      wav_list = os.listdir(path)\n",
        "      for wav_file in wav_list:\n",
        "        self.targets.append({\n",
        "            'filename': wav_file,\n",
        "            'path': os.path.join(path, wav_file)\n",
        "        })\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.targets)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    if torch.is_tensor(idx):\n",
        "      idx.tolist()\n",
        "\n",
        "    # sr is the sampling rate\n",
        "    signal, sr = torchaudio.load(self.targets[idx]['path'], normalization=True)\n",
        "    filename = self.targets[idx]['filename']\n",
        "\n",
        "    if self.transforms:\n",
        "      for transform in self.transforms:\n",
        "        signal = transform(signal)\n",
        "\n",
        "    if self.typ == 'train':\n",
        "      clx_name = self.targets[idx]['class']\n",
        "      return filename, signal, sr, clx_name\n",
        "    \n",
        "    elif self.typ == 'test':\n",
        "      return filename, signal, sr\n",
        "\n",
        "# helper function to check the time taken to train an epoch\n",
        "def epoch_time(start_time, end_time):\n",
        "    elapsed_time = end_time - start_time\n",
        "    elapsed_mins = int(elapsed_time / 60)\n",
        "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
        "    return elapsed_mins, elapsed_secs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yCUkL1VKtTH",
        "outputId": "167ce77d-0fab-4d35-ab1f-bc3ff1a3a734"
      },
      "source": [
        "fd = CustomSpeechDataset(path='s6_bg2', typ='train')\n",
        "train_size = int(len(fd)*0.8)\n",
        "valid_size = len(fd) - train_size\n",
        "print(train_size, valid_size)\n",
        "# train test split\n",
        "#train_set, valid_set = torch.utils.data.random_split(fd, [train_size, valid_size])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7560 1890\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8l4yPp15Nyo4",
        "outputId": "3306d8ef-cfa7-4bac-ba00-c12abd8f930c"
      },
      "source": [
        "# turn the custom dataset into a list of data\n",
        "full_dataset = list(fd)\n",
        "full_dataset[:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('train_backward_6128.wav',\n",
              "  tensor([[ 0.0039,  0.0012,  0.0023,  ..., -0.0013, -0.0024, -0.0017]]),\n",
              "  16000,\n",
              "  'backward'),\n",
              " ('train_backward_6183.wav',\n",
              "  tensor([[ 0.0004,  0.0041,  0.0034,  ...,  0.0045, -0.0006, -0.0090]]),\n",
              "  16000,\n",
              "  'backward')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IjMf7ZwZ-dUi",
        "outputId": "5e9a24b0-b9cc-4a05-80f1-4ed161810ae9"
      },
      "source": [
        "full_dataset[699:701]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('train_bird_8435.wav',\n",
              "  tensor([[0.0004, 0.0005, 0.0005,  ..., 0.0004, 0.0004, 0.0004]]),\n",
              "  16000,\n",
              "  'bird'),\n",
              " ('train_cat_7894.wav',\n",
              "  tensor([[-0.0024,  0.0008,  0.0019,  ...,  0.0014,  0.0026, -0.0014]]),\n",
              "  16000,\n",
              "  'cat')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6g3bXJjv9-Qi",
        "outputId": "f0d9ee2d-611e-4864-a3a4-909b3e21b162"
      },
      "source": [
        "full_dataset[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('train_backward_6183.wav',\n",
              " tensor([[ 0.0004,  0.0041,  0.0034,  ...,  0.0045, -0.0006, -0.0090]]),\n",
              " 16000,\n",
              " 'backward')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_DU0FHHSxTZ",
        "outputId": "dfaad6ae-c00d-4333-fb53-59b1fe019b38"
      },
      "source": [
        "len(full_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9450"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3z-2XLXfNzRd",
        "outputId": "6b448f80-b0c0-4800-f4ff-c350efe430d6"
      },
      "source": [
        "labels = fd.class_names\n",
        "print(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['backward', 'bird', 'cat', 'chicken', 'crocodile', 'dinosaur', 'dog', 'down', 'eight', 'elephant', 'falcon', 'five', 'forward', 'four', 'go', 'left', 'nine', 'one', 'right', 'seven', 'six', 'snake', 'stop', 'three', 'two', 'up', 'zero']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uio7QFjHaDoe"
      },
      "source": [
        "labels_to_indices = {}\n",
        "for idx, l in enumerate(labels):\n",
        "  labels_to_indices[l] = idx"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7tPihV3a9K6",
        "outputId": "a1303879-b7b0-42a0-b70e-3fdfcc4526dc"
      },
      "source": [
        "print(labels_to_indices)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'backward': 0, 'bird': 1, 'cat': 2, 'chicken': 3, 'crocodile': 4, 'dinosaur': 5, 'dog': 6, 'down': 7, 'eight': 8, 'elephant': 9, 'falcon': 10, 'five': 11, 'forward': 12, 'four': 13, 'go': 14, 'left': 15, 'nine': 16, 'one': 17, 'right': 18, 'seven': 19, 'six': 20, 'snake': 21, 'stop': 22, 'three': 23, 'two': 24, 'up': 25, 'zero': 26}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luE4_NuYNzUH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ruuplF7CA3pD"
      },
      "source": [
        "## Manual split of data to the different classes\n",
        "Since the data is in order, we can do this way"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TrBu1qjDNJh"
      },
      "source": [
        "multiplier = 350\n",
        "\n",
        "backward_list = full_dataset[:1*multiplier]\n",
        "bird_list = full_dataset[1*multiplier:2*multiplier]\n",
        "cat_list = full_dataset[2*multiplier:3*multiplier]\n",
        "chicken_list = full_dataset[3*multiplier:4*multiplier]\n",
        "crocodile_list = full_dataset[4*multiplier:5*multiplier]\n",
        "dinosaur_list = full_dataset[5*multiplier:6*multiplier]\n",
        "dog_list = full_dataset[6*multiplier:7*multiplier]\n",
        "down_list = full_dataset[7*multiplier:8*multiplier]\n",
        "eight_list = full_dataset[8*multiplier:9*multiplier]\n",
        "elephant_list = full_dataset[9*multiplier:10*multiplier]\n",
        "falcon_list = full_dataset[10*multiplier:11*multiplier]\n",
        "five_list = full_dataset[11*multiplier:12*multiplier]\n",
        "forward_list = full_dataset[12*multiplier:13*multiplier]\n",
        "four_list = full_dataset[13*multiplier:14*multiplier]\n",
        "go_list = full_dataset[14*multiplier:15*multiplier]\n",
        "left_list = full_dataset[15*multiplier:16*multiplier]\n",
        "nine_list = full_dataset[16*multiplier:17*multiplier]\n",
        "one_list = full_dataset[17*multiplier:18*multiplier]\n",
        "right_list = full_dataset[18*multiplier:19*multiplier]\n",
        "seven_list = full_dataset[19*multiplier:20*multiplier]\n",
        "six_list = full_dataset[20*multiplier:21*multiplier]\n",
        "snake_list = full_dataset[21*multiplier:22*multiplier]\n",
        "stop_list = full_dataset[22*multiplier:23*multiplier]\n",
        "three_list = full_dataset[23*multiplier:24*multiplier]\n",
        "two_list = full_dataset[24*multiplier:25*multiplier]\n",
        "up_list = full_dataset[25*multiplier:26*multiplier]\n",
        "zero_list = full_dataset[26*multiplier:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yeT04COC80t"
      },
      "source": [
        "# sanity check\n",
        "assert bird_list[0][3] == bird_list[-1][3]\n",
        "assert eight_list[0][3] == eight_list[-1][3]\n",
        "assert falcon_list[0][3] == falcon_list[-1][3]\n",
        "assert five_list[0][3] == five_list[-1][3]\n",
        "assert four_list[0][3] == four_list[-1][3]\n",
        "assert nine_list[0][3] == nine_list[-1][3]\n",
        "assert one_list[0][3] == one_list[-1][3]\n",
        "assert seven_list[0][3] == seven_list[-1][3]\n",
        "assert six_list[0][3] == six_list[-1][3]\n",
        "assert snake_list[0][3] == snake_list[-1][3]\n",
        "assert three_list[0][3] == three_list[-1][3]\n",
        "assert two_list[0][3] == two_list[-1][3]\n",
        "assert zero_list[0][3] == zero_list[-1][3]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAv1fcM2FuFE",
        "outputId": "b77651c9-115b-49cb-dce9-611c4739e6b0"
      },
      "source": [
        "len(eight_list) == len(bird_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9fluL2ZO9cn"
      },
      "source": [
        "## Manual kFold implementation\n",
        "Create kFold data for later part of this notebook"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQSQRW2PViQ2",
        "outputId": "71714cb4-976b-440d-947f-2eaf83196def"
      },
      "source": [
        "# define a class that gets the complement of the list\n",
        "class ComplementList(list):\n",
        "    def __getitem__(self, val):\n",
        "        if type(val) is slice and val.step == 'c':\n",
        "            copy = self[:]\n",
        "            copy[val.start:val.stop] = []\n",
        "            return copy\n",
        "\n",
        "        return super(ComplementList, self).__getitem__(val)\n",
        "\n",
        "# testing the class implementation\n",
        "l = ComplementList([1,2,3,4,5])\n",
        "print(l[2:4:'c']) # [1, 5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 2, 5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UShmVlRGPKWY"
      },
      "source": [
        "# define the k-fold constant\n",
        "K = 5\n",
        "\n",
        "# k is the k-fold constant, fd is the full dataset in list\n",
        "def manual_k_fold(k, fd, fd_length):\n",
        "    val_list = []\n",
        "    train_list = []\n",
        "\n",
        "    # randomise the dataset first\n",
        "    random.shuffle(fd)\n",
        "\n",
        "    # get the combinations of splits for the validation set\n",
        "    for i in tqdm(range(k)):\n",
        "        val_list.append(fd[int((i/k)*fd_length):int(((i+1)/k)*fd_length)])\n",
        "        clist = ComplementList(fd)\n",
        "        train_list.append(clist[int((i/k)*fd_length):int(((i+1)/k)*fd_length):'c'])\n",
        "\n",
        "    return val_list, train_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrguB_4bF28u",
        "outputId": "35db64d3-39df-4dff-e455-e47bdc314da1"
      },
      "source": [
        "# extend the code by doing a separate split first\n",
        "val_list_0, train_list_0 = manual_k_fold(K, bird_list, multiplier)\n",
        "val_list_1, train_list_1 = manual_k_fold(K, eight_list, multiplier)\n",
        "val_list_2, train_list_2 = manual_k_fold(K, falcon_list, multiplier)\n",
        "val_list_3, train_list_3 = manual_k_fold(K, five_list, multiplier)\n",
        "val_list_4, train_list_4 = manual_k_fold(K, four_list, multiplier)\n",
        "val_list_5, train_list_5 = manual_k_fold(K, nine_list, multiplier)\n",
        "val_list_6, train_list_6 = manual_k_fold(K, one_list, multiplier)\n",
        "val_list_7, train_list_7 = manual_k_fold(K, seven_list, multiplier)\n",
        "val_list_8, train_list_8 = manual_k_fold(K, six_list, multiplier)\n",
        "val_list_9, train_list_9 = manual_k_fold(K, snake_list, multiplier)\n",
        "val_list_10, train_list_10 = manual_k_fold(K, three_list, multiplier)\n",
        "val_list_11, train_list_11 = manual_k_fold(K, two_list, multiplier)\n",
        "val_list_12, train_list_12 = manual_k_fold(K, zero_list, multiplier)\n",
        "\n",
        "val_list_13, train_list_13 = manual_k_fold(K, cat_list, multiplier)\n",
        "val_list_14, train_list_14 = manual_k_fold(K, chicken_list, multiplier)\n",
        "val_list_15, train_list_15 = manual_k_fold(K, dog_list, multiplier)\n",
        "val_list_16, train_list_16 = manual_k_fold(K, down_list, multiplier)\n",
        "val_list_17, train_list_17 = manual_k_fold(K, go_list, multiplier)\n",
        "val_list_18, train_list_18 = manual_k_fold(K, left_list, multiplier)\n",
        "val_list_19, train_list_19 = manual_k_fold(K, right_list, multiplier)\n",
        "val_list_20, train_list_20 = manual_k_fold(K, stop_list, multiplier)\n",
        "val_list_21, train_list_21 = manual_k_fold(K, up_list, multiplier)\n",
        "\n",
        "val_list_22, train_list_22 = manual_k_fold(K, backward_list, multiplier)\n",
        "val_list_23, train_list_23 = manual_k_fold(K, forward_list, multiplier)\n",
        "val_list_24, train_list_24 = manual_k_fold(K, elephant_list, multiplier)\n",
        "val_list_25, train_list_25 = manual_k_fold(K, crocodile_list, multiplier)\n",
        "val_list_26, train_list_26 = manual_k_fold(K, dinosaur_list, multiplier)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5/5 [00:00<00:00, 2288.97it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 2493.94it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 2115.98it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 2707.40it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 3177.02it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 1043.51it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 13417.48it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 5639.02it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4920.58it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4064.25it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 3091.32it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 5189.69it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 22333.89it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 2251.85it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 1577.16it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4869.17it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4260.77it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4683.23it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4828.81it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 5417.60it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4042.31it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 4416.92it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 3127.74it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 15318.86it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 6907.62it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 20480.00it/s]\n",
            "100%|██████████| 5/5 [00:00<00:00, 6628.17it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOj2dbIxKXqq"
      },
      "source": [
        "val_list_list = [val_list_0,val_list_1,val_list_2,val_list_3,val_list_4,val_list_5,val_list_6,\n",
        "                 val_list_7,val_list_8,val_list_9,val_list_10,val_list_11,val_list_12,val_list_13,\n",
        "                 val_list_14,val_list_15,val_list_16,val_list_17,val_list_18,val_list_19,val_list_20,\n",
        "                 val_list_21,val_list_22,val_list_23,val_list_24,val_list_25,val_list_26]\n",
        "\n",
        "train_list_list = [train_list_0, train_list_1, train_list_2,train_list_3,train_list_4,train_list_5, train_list_6,\n",
        "                 train_list_7, train_list_8, train_list_9, train_list_10, train_list_11, train_list_12, train_list_13,\n",
        "                 train_list_14, train_list_15, train_list_16, train_list_17, train_list_18, train_list_19, train_list_20,\n",
        "                 train_list_21,train_list_22,train_list_23,train_list_24,train_list_25, train_list_26]            "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6ADAl_0TLaq",
        "outputId": "5e5e4e52-9b6a-4020-9f5f-d403c9734b78"
      },
      "source": [
        "# merge the files \n",
        "train_list = []\n",
        "val_list = []\n",
        "# merge the files into a list\n",
        "for i in tqdm(range(K)):\n",
        "    train_temp = []\n",
        "    val_temp = []\n",
        "    for j in range(len(val_list_list)):\n",
        "        train_temp += train_list_list[j][i]\n",
        "        val_temp += val_list_list[j][i]\n",
        "    train_list.append(train_temp)\n",
        "    val_list.append(val_temp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5/5 [00:00<00:00, 3080.42it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TWsrvlBTK5Y",
        "outputId": "02cd245b-4c99-4fc0-afba-d176e36fc0c0"
      },
      "source": [
        "len(val_temp), len(train_temp), len(train_list), len(val_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1890, 7560, 5, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MgNtkviSPIO"
      },
      "source": [
        "#val_list, train_list = manual_k_fold(K, full_dataset, len(full_dataset))\n",
        "#len(train_list[0]) , len(val_list[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1V5L5E2YvsO"
      },
      "source": [
        "# an example\n",
        "# [fold][item set][particular file]\n",
        "#print(val_list[0][0][0]) \n",
        "#print(val_list[1][0][0]) \n",
        "#print(val_list[2][0][0]) \n",
        "#print(val_list[3][0][0]) \n",
        "#print(val_list[4][0][0]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AY7xPu03WIwn"
      },
      "source": [
        "# shuffle the data to let the model generalise better\n",
        "for i in range(K):\n",
        "    random.shuffle(train_list[i])\n",
        "    random.shuffle(val_list[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1CjVExfBXI93",
        "outputId": "6b242e85-833a-4e55-da72-4739fdf03077"
      },
      "source": [
        "# random check\n",
        "train_list[0][1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('train_four_2975.wav',\n",
              " tensor([[0.0009, 0.0053, 0.0012,  ..., 0.0064, 0.0018, 0.0036]]),\n",
              " 16000,\n",
              " 'four')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvKpNpNxR1mI"
      },
      "source": [
        "## Get the data distribution of each class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCmbFj0GZNIW"
      },
      "source": [
        "def train_data_distribution(data):\n",
        "    train_filename_list = []\n",
        "    train_label_list = []\n",
        "    for i in range(len(data)):\n",
        "        train_filename_list.append(data[i][0])\n",
        "        train_label_list.append(data[i][3])\n",
        "\n",
        "    # make to dataframe\n",
        "    train_tuple = list(zip(train_filename_list, train_label_list))\n",
        "    train_df = pd.DataFrame(train_tuple, columns=['train_filename', 'train_label'])\n",
        "\n",
        "    return train_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BEzfzvQXQrE",
        "outputId": "28993fba-054b-41df-c230-bf2485d3d338"
      },
      "source": [
        "for i in range(K):\n",
        "    print(f\"Train Set {i+1}\")\n",
        "    train_dist = train_data_distribution(train_list[i])\n",
        "    #print(train_dist.head(3))\n",
        "    # find the count of each label to check distribution of the labels\n",
        "    print(train_dist[\"train_label\"].value_counts())\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Set 1\n",
            "elephant     280\n",
            "five         280\n",
            "one          280\n",
            "up           280\n",
            "six          280\n",
            "falcon       280\n",
            "down         280\n",
            "go           280\n",
            "bird         280\n",
            "seven        280\n",
            "forward      280\n",
            "chicken      280\n",
            "backward     280\n",
            "cat          280\n",
            "eight        280\n",
            "zero         280\n",
            "four         280\n",
            "dog          280\n",
            "snake        280\n",
            "right        280\n",
            "stop         280\n",
            "dinosaur     280\n",
            "left         280\n",
            "nine         280\n",
            "two          280\n",
            "crocodile    280\n",
            "three        280\n",
            "Name: train_label, dtype: int64\n",
            "\n",
            "Train Set 2\n",
            "elephant     280\n",
            "five         280\n",
            "one          280\n",
            "up           280\n",
            "six          280\n",
            "falcon       280\n",
            "down         280\n",
            "go           280\n",
            "bird         280\n",
            "seven        280\n",
            "forward      280\n",
            "chicken      280\n",
            "backward     280\n",
            "cat          280\n",
            "eight        280\n",
            "zero         280\n",
            "four         280\n",
            "dog          280\n",
            "snake        280\n",
            "right        280\n",
            "stop         280\n",
            "dinosaur     280\n",
            "left         280\n",
            "nine         280\n",
            "two          280\n",
            "crocodile    280\n",
            "three        280\n",
            "Name: train_label, dtype: int64\n",
            "\n",
            "Train Set 3\n",
            "elephant     280\n",
            "five         280\n",
            "one          280\n",
            "up           280\n",
            "six          280\n",
            "falcon       280\n",
            "down         280\n",
            "go           280\n",
            "bird         280\n",
            "seven        280\n",
            "forward      280\n",
            "chicken      280\n",
            "backward     280\n",
            "cat          280\n",
            "eight        280\n",
            "zero         280\n",
            "four         280\n",
            "dog          280\n",
            "snake        280\n",
            "right        280\n",
            "stop         280\n",
            "dinosaur     280\n",
            "left         280\n",
            "nine         280\n",
            "two          280\n",
            "crocodile    280\n",
            "three        280\n",
            "Name: train_label, dtype: int64\n",
            "\n",
            "Train Set 4\n",
            "elephant     280\n",
            "five         280\n",
            "one          280\n",
            "up           280\n",
            "six          280\n",
            "falcon       280\n",
            "down         280\n",
            "go           280\n",
            "bird         280\n",
            "seven        280\n",
            "forward      280\n",
            "chicken      280\n",
            "backward     280\n",
            "cat          280\n",
            "eight        280\n",
            "zero         280\n",
            "four         280\n",
            "dog          280\n",
            "snake        280\n",
            "right        280\n",
            "stop         280\n",
            "dinosaur     280\n",
            "left         280\n",
            "nine         280\n",
            "two          280\n",
            "crocodile    280\n",
            "three        280\n",
            "Name: train_label, dtype: int64\n",
            "\n",
            "Train Set 5\n",
            "elephant     280\n",
            "five         280\n",
            "one          280\n",
            "up           280\n",
            "six          280\n",
            "falcon       280\n",
            "down         280\n",
            "go           280\n",
            "bird         280\n",
            "seven        280\n",
            "forward      280\n",
            "chicken      280\n",
            "backward     280\n",
            "cat          280\n",
            "eight        280\n",
            "zero         280\n",
            "four         280\n",
            "dog          280\n",
            "snake        280\n",
            "right        280\n",
            "stop         280\n",
            "dinosaur     280\n",
            "left         280\n",
            "nine         280\n",
            "two          280\n",
            "crocodile    280\n",
            "three        280\n",
            "Name: train_label, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7b_13ZKWBBd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrrtzO7KBiq4"
      },
      "source": [
        "## Look at one example from the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMh48YVpbapr",
        "outputId": "705caf84-392e-4db3-edae-777f8055f093"
      },
      "source": [
        "train_list[0][1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('train_four_2975.wav',\n",
              " tensor([[0.0009, 0.0053, 0.0012,  ..., 0.0064, 0.0018, 0.0036]]),\n",
              " 16000,\n",
              " 'four')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgwfKIIl_P5k"
      },
      "source": [
        "filename, waveform, sample_rate, label_id = train_list[0][10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "WmL1bsGlALvM",
        "outputId": "182149d8-5a65-483e-eb3c-15aacb412a6f"
      },
      "source": [
        "print(\"Shape of waveform: {}\".format(waveform.size()))\n",
        "print(\"Sample rate of waveform: {}\".format(sample_rate))\n",
        "\n",
        "# Let's plot the waveform using matplotlib\n",
        "# We observe that the main audio activity happens at the later end of the clip\n",
        "plt.plot(waveform.t().numpy());"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of waveform: torch.Size([1, 16000])\n",
            "Sample rate of waveform: 16000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 61
        },
        "id": "PBhtVJs5Ao2s",
        "outputId": "09f71b23-66c7-4a43-c0a6-7592e36c511c"
      },
      "source": [
        "# let's play the audio clip and hear it for ourselves!\n",
        "ipd.Audio(waveform.numpy(), rate=sample_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 61
        },
        "id": "kd6WT8mwHEEY",
        "outputId": "7c555d13-3141-472f-eba5-90101e12a22e"
      },
      "source": [
        "# import background sound first\n",
        "import librosa\n",
        "\n",
        "bg_files = f'{COLAB_FILEPATH}/noise/noise_cut.wav'\n",
        "bg, sr = librosa.load(bg_files, sr=16000)\n",
        "#print(bg_files,'|', bg.shape[0], bg.max(), bg.min())\n",
        "#ipd.Audio(bg, rate=sr)\n",
        "# merge the two sound\n",
        "start_ = np.random.randint(bg.shape[0]-16000)\n",
        "bg_slice = bg[start_ : start_+16000]\n",
        "wav_with_bg = waveform.numpy() * np.random.uniform(0.8, 1.2) + \\\n",
        "              bg_slice * np.random.uniform(0, 0.07)\n",
        "ipd.Audio(wav_with_bg, rate=sr) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-WdQtviI5p8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9NEmiilKjHr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tz-eN4RZTvyW"
      },
      "source": [
        "## Constant Sample Lengths\n",
        "In order to insert our features into a model, we have to ensure that the features are of the same size. Below, we see that the sample length varies across the audio clips.\n",
        "\n",
        "Let's pad the audio clips to a maximum sample length of 16000. (16000 sample length is equal to 1 second at 16,000 Hz sampling rate)\n",
        "We will pad audio clips which are less than 1 second in length, with parts of itself."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeIumefFiZkl",
        "outputId": "cd006efa-0e9b-45f7-9972-e2509f85798a"
      },
      "source": [
        "# checking the first fold\n",
        "audio_lens = []\n",
        "for i in range(len(train_list[0])):\n",
        "  audio_lens.append(train_list[0][i][1].size(1))\n",
        "\n",
        "print('Max Sample Length:', max(audio_lens))\n",
        "print('Min Sample Length:', min(audio_lens))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Max Sample Length: 16000\n",
            "Min Sample Length: 16000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtVgHsiLKkyq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cAj98Z3HJeKq"
      },
      "source": [
        "## Fixed Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkWLB5gTI62L"
      },
      "source": [
        "# data augmentation -> have to fix this two, as it is a global implementation\n",
        "# create multiple notebooks for different values of these two hyperparameters\n",
        "#TIME_MASK_PARAM = 5\n",
        "#FREQ_MASK_PARAM = 5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSqYkandKk8S"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3_rz0d4Khpn"
      },
      "source": [
        "## Data Augmentation\n",
        "\n",
        "Since the min and the max length is the same and hence no need to pad the audio, else can run PadAudio and the shorter ones will be repeated with its own audio\n",
        "\n",
        "We will do a simple data augmentation process in order to increase the variations in our dataset.\n",
        "\n",
        "In the audio domain, the augmentation technique known as [SpecAugment](https://arxiv.org/abs/1904.08779) is often used. It makes use of 3 steps:\n",
        "- Time Warp (warps the spectrogram to the left or right) 2nd row\n",
        "- Frequency Masking (randomly masks a range of frequencies) 3rd row\n",
        "- Time Masking (randomly masks a range of time) 4th row\n",
        "\n",
        "![specaugment pic](https://drive.google.com/uc?export=view&id=1C085-PlXVhjzh4kzCy869VHRGwC3aDHJ)\n",
        "\n",
        "As Time Warp is computationally intensive and does not contribute significant improvement in results, we shall simply use Frequency and Time Masking in this example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLJ4Shh6kN6q"
      },
      "source": [
        "# pad the audio to the same length\n",
        "class PadAudio(torch.nn.Module):\n",
        "    def __init__(self, req_length = 16000):\n",
        "        super().__init__()\n",
        "        self.req_length = req_length\n",
        "\n",
        "    def forward(self, waveform):\n",
        "        while waveform.size(1) < self.req_length:\n",
        "        # example if audio length is 15800 and max is 16000, the remaining 200 samples will be concatenated\n",
        "        # with the FIRST 200 samples of the waveform itself again (repetition)\n",
        "            waveform = torch.cat((waveform, waveform[:, :self.req_length - waveform.size(1)]), axis=1)\n",
        "        return waveform\n",
        "\n",
        "# Log-Mel Transform here to get ready to train with our RNN model later on\n",
        "class LogMelTransform(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, log_offset = 1e-6):\n",
        "        super().__init__()\n",
        "        self.log_offset = log_offset\n",
        "\n",
        "    def forward(self, melspectrogram):\n",
        "        return torch.log(melspectrogram + self.log_offset)\n",
        "\n",
        "class GaussianNoise(nn.Module):\n",
        "    \"\"\"Gaussian noise regularizer.\n",
        "\n",
        "    Args:\n",
        "        sigma (float, optional): relative standard deviation used to generate the\n",
        "            noise. Relative means that it will be multiplied by the magnitude of\n",
        "            the value your are adding the noise to. This means that sigma can be\n",
        "            the same regardless of the scale of the vector.\n",
        "        is_relative_detach (bool, optional): whether to detach the variable before\n",
        "            computing the scale of the noise. If `False` then the scale of the noise\n",
        "            won't be seen as a constant but something to optimize: this will bias the\n",
        "            network to generate vectors with smaller values.\n",
        "    \"\"\"\n",
        "    def __init__(self, sigma=0.1, is_relative_detach=True):\n",
        "        super().__init__()\n",
        "        self.sigma = sigma\n",
        "        self.is_relative_detach = is_relative_detach\n",
        "        self.register_buffer('noise', torch.tensor(0))\n",
        "\n",
        "    def forward(self, x):\n",
        "        if self.training and self.sigma != 0:\n",
        "            scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x\n",
        "            sampled_noise = self.noise.expand(*x.size()).float().normal_() * scale\n",
        "            x = x + sampled_noise\n",
        "        return x \n",
        "\n",
        "# define a transformation sequence\n",
        "def transform_sequence(time_mask, freq_mask, sigma=0.1):\n",
        "    transformations = []\n",
        "    transformations.append(PadAudio())\n",
        "    transformations.append(MelSpectrogram(sample_rate = 16000, n_mels = 128))\n",
        "    transformations.append(LogMelTransform())\n",
        "    #eval_transformations = transformations.copy()\n",
        "    # Noise Injection\n",
        "    transformations.append(GaussianNoise(sigma=sigma, is_relative_detach=True))\n",
        "\n",
        "    # for challenge 4/6\n",
        "    eval_transformations = transformations.copy()\n",
        "    # a maximum of 5 time steps will be masked\n",
        "    transformations.append(TimeMasking(time_mask_param = time_mask)) \n",
        "    # maximum of 3 freq channels will be masked\n",
        "    transformations.append(FrequencyMasking(freq_mask_param = freq_mask))\n",
        "    return transformations, eval_transformations"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aph6914ESZeN"
      },
      "source": [
        "TM1, FM1, SIGMA1 = 5, 9, 0.7\n",
        "TM2, FM2, SIGMA2 = 5, 5, 1.2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5pXE6rqoFBo",
        "outputId": "ae3240f8-d1ea-48e0-d636-d40d3c3001ba"
      },
      "source": [
        "transformations, eval_transformations = transform_sequence(time_mask=5, freq_mask=9, sigma=0.7)\n",
        "print(transformations)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[PadAudio(), MelSpectrogram(\n",
            "  (spectrogram): Spectrogram()\n",
            "  (mel_scale): MelScale()\n",
            "), LogMelTransform(), GaussianNoise(), TimeMasking(), FrequencyMasking()]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8bQy8HUCkR3",
        "outputId": "3753f921-34a6-410a-fcf2-c37fa3f019ab"
      },
      "source": [
        "transformations2, _ = transform_sequence(time_mask=5, freq_mask=5, sigma=1.2)\n",
        "print(transformations)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[PadAudio(), MelSpectrogram(\n",
            "  (spectrogram): Spectrogram()\n",
            "  (mel_scale): MelScale()\n",
            "), LogMelTransform(), GaussianNoise(), TimeMasking(), FrequencyMasking()]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idUWTY5k8_mt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9hhlhDDMEos"
      },
      "source": [
        "## Data Loaders\n",
        "\n",
        "Let's now set up our data loaders so that we can streamline the batch loading of data for our model training later on. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEJ5I9rNN34D"
      },
      "source": [
        "'''\n",
        "# to add background noise to the file\n",
        "import librosa\n",
        "\n",
        "# import background sound first\n",
        "NOISE_PATH = f'{COLAB_FILEPATH}noise/'\n",
        "bg_files = os.listdir(NOISE_PATH)\n",
        "'''\n",
        "\n",
        "# Fixed parameters\n",
        "NUM_WORKERS = 4\n",
        "PIN_MEMORY = True if device == 'cuda' else False\n",
        "\n",
        "\n",
        "def train_collate_fn(batch):\n",
        "\n",
        "    # A data tuple has the form:\n",
        "    # filename, waveform, sample_rate, label\n",
        "\n",
        "    tensors, targets, filenames = [], [], []\n",
        "\n",
        "    # Gather in lists, and encode labels as indices\n",
        "    for filename, waveform, sample_rate, label in batch:\n",
        "\n",
        "        # duplicate one copy of plain data\n",
        "        waveform_org = waveform.clone()\n",
        "      \n",
        "        # apply transformations\n",
        "        for transform in transformations:\n",
        "            #print(f'{type(waveform)}-1')\n",
        "            waveform = transform(waveform)\n",
        "            #print(type(waveform))\n",
        "        \n",
        "        waveform = waveform.squeeze().T\n",
        "        tensors += [waveform]\n",
        "        targets += [labels_to_indices[label]]\n",
        "        filenames += [filename]\n",
        "\n",
        "        # append the original waveform\n",
        "        for transform2 in transformations2:\n",
        "            waveform_org = transform2(waveform_org)\n",
        "\n",
        "        waveform_org = waveform_org.squeeze().T\n",
        "        tensors += [waveform_org]\n",
        "        targets += [labels_to_indices[label]]\n",
        "        filenames += [filename]\n",
        "\n",
        "    # Group the list of tensors into a batched tensor\n",
        "    tensors = torch.stack(tensors)\n",
        "    targets = torch.LongTensor(targets)\n",
        "\n",
        "    return (tensors, targets, filenames)\n",
        "\n",
        "def eval_collate_fn(batch):\n",
        "\n",
        "    # A data tuple has the form:\n",
        "    # filename, waveform, sample_rate, label\n",
        "\n",
        "    tensors, targets, filenames = [], [], []\n",
        "\n",
        "    # Gather in lists, and encode labels as indices\n",
        "    for filename, waveform, sample_rate, label in batch:\n",
        "\n",
        "        # apply transformations\n",
        "        for transform in eval_transformations:\n",
        "            waveform = transform(waveform)\n",
        "        waveform = waveform.squeeze().T\n",
        "        tensors += [waveform]\n",
        "        targets += [labels_to_indices[label]]\n",
        "        filenames += [filename]\n",
        "\n",
        "    # Group the list of tensors into a batched tensor\n",
        "    tensors = torch.stack(tensors)\n",
        "    targets = torch.LongTensor(targets)\n",
        "    filenames += [filename]\n",
        "\n",
        "    return (tensors, targets, filenames)\n",
        "\n",
        "def loader(train_set, valid_set, batch_size=64, num_workers=NUM_WORKERS, pin_mem=PIN_MEMORY):\n",
        "    # implementing the loader function\n",
        "    train_loader = torch.utils.data.DataLoader(\n",
        "        train_set,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=True,\n",
        "        drop_last=False,\n",
        "        collate_fn=train_collate_fn,\n",
        "        num_workers=num_workers,\n",
        "        pin_memory=pin_mem,\n",
        "    )\n",
        "\n",
        "    valid_loader = torch.utils.data.DataLoader(\n",
        "        valid_set,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=False,\n",
        "        drop_last=False,\n",
        "        collate_fn=eval_collate_fn,\n",
        "        num_workers=num_workers,\n",
        "        pin_memory=pin_mem,\n",
        "    )\n",
        "\n",
        "    return train_loader, valid_loader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OWoxpWcM3D4"
      },
      "source": [
        "## Setting up and defining the Model and training loop\n",
        "\n",
        "In this speech classification example, we will make use of a Long-Short-Term Memory Recurrent Neural Network (LSTM-RNN)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQWw_iedO1wa"
      },
      "source": [
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, num_classes, device, classes=None, drop_flag=False, drop=0.5):\n",
        "        super(RNN, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)\n",
        "        \n",
        "        self.drop_flag = drop_flag\n",
        "        self.dropout = torch.nn.Dropout(drop)\n",
        "\n",
        "        # hidden_size * 2 cuz bidirectional\n",
        "        self.fc = nn.Linear(hidden_size*2, num_classes)\n",
        "        self.device = device\n",
        "        self.classes = classes\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Set initial hidden and cell states\n",
        "        batch_size = x.size(0)\n",
        "        # bidirectional so need to multiply the layers by 2 for initial state\n",
        "        h0 = torch.zeros(self.num_layers*2, batch_size, self.hidden_size).to(self.device) \n",
        "\n",
        "        # only applicable to LSTM because LSTM got 2 gates but GRU only has one!!!\n",
        "        #c0 = torch.zeros(self.num_layers*2, batch_size, self.hidden_size).to(self.device) \n",
        "        \n",
        "        # Forward propagate GRU\n",
        "        out, _ = self.gru(x, h0.detach())  # shape = (batch_size, seq_length, hidden_size)\n",
        "        # lstm implementation\n",
        "        #out, _ = self.gru(x, (h0, c0))  # shape = (batch_size, seq_length, hidden_size)\n",
        "        \n",
        "        if self.drop_flag:\n",
        "            out = self.dropout(out)\n",
        "\n",
        "        # Decode the hidden state of the last time step\n",
        "        out = self.fc(out[:, -1, :])\n",
        "        return out\n",
        "\n",
        "    def predict(self, x):\n",
        "        '''Predict one label from one sample's features'''\n",
        "        # x: feature from a sample, LxN\n",
        "        #   L is length of sequency\n",
        "        #   N is feature dimension\n",
        "        x = torch.tensor(x[np.newaxis, :], dtype=torch.float32)\n",
        "        x = x.to(self.device)\n",
        "        outputs = self.forward(x)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        predicted_index = predicted.item()\n",
        "        return predicted_index\n",
        "\n",
        "\n",
        "'''# initialize the model class\n",
        "model = RNN(input_size=128, \n",
        "            hidden_size=HIDDEN_SIZE, \n",
        "            num_layers=NUM_LAYERS, \n",
        "            num_classes=len(labels), \n",
        "            device=device, \n",
        "            classes=labels).to(device)\n",
        "print(model)'''\n",
        "\n",
        "# define a function to train the model\n",
        "def fit(model, train_loader, valid_loader, num_epochs, fold_num, optim_flag='adam'):\n",
        "    # fixed configuration\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    \n",
        "    # choice of optimizers\n",
        "    if optim_flag == 'adam':\n",
        "        optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "    elif optim_flag == 'sgd99':\n",
        "        optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.99)\n",
        "    elif optim_flag == 'sgd90':\n",
        "        optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)\n",
        "    elif optim_flag == 'adamw':\n",
        "        optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) # included a weight decay of 1e-02 for adam\n",
        "    elif optim_flag == 'adagrad':\n",
        "        optimizer = torch.optim.Adagrad(model.parameters(), lr=1e-2)\n",
        "    elif optim_flag == 'adadelta':\n",
        "        optimizer = torch.optim.Adadelta(model.parameters(), lr=1)\n",
        "    elif optim_flag == 'adamax':\n",
        "        optimizer = torch.optim.Adamax(model.parameters(), lr=2e-3)\n",
        "    else:\n",
        "        return \"No such optimizer, try again\"\n",
        "\n",
        "    # set the LR scheduler\n",
        "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, \n",
        "                                                           mode='min', \n",
        "                                                           factor=0.05, \n",
        "                                                           patience=3, \n",
        "                                                           min_lr=1e-5,\n",
        "                                                           verbose=True)\n",
        "    \n",
        "    # fixed parameters to perform early stopping\n",
        "    n_epochs_stop = 5\n",
        "    epochs_no_improve = 0\n",
        "    early_stop = False\n",
        "    min_val_loss = np.inf\n",
        "    threshold = 0.0001\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # declare the fold number\n",
        "    print(f'---------- Fold Number: {fold_num} ----------')\n",
        "\n",
        "    # start the training loop\n",
        "    for epoch in range(1,num_epochs+1):\n",
        "\n",
        "        start_time = time.time()\n",
        "        # training steps\n",
        "        model.train()\n",
        "        count_correct, count_total = 0, 0\n",
        "        for idx, (features, targets, filenames) in enumerate(train_loader):\n",
        "\n",
        "            features = features.to(device)\n",
        "            targets = targets.to(device)\n",
        "\n",
        "            # forward pass\n",
        "            outputs = model(features)\n",
        "            loss = criterion(outputs, targets)\n",
        "\n",
        "            # backward pass\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # training results\n",
        "            _, argmax = torch.max(outputs, 1)\n",
        "            count_correct += (targets == argmax.squeeze()).sum().item()\n",
        "            count_total += targets.size(0)\n",
        "\n",
        "        train_acc = count_correct / count_total\n",
        "    \n",
        "        # evaluation steps\n",
        "        model.eval()\n",
        "        count_correct, count_total = 0, 0\n",
        "        val_pred_list, val_filename_list = [], []\n",
        "        \n",
        "        with torch.no_grad():\n",
        "            for idx, (features, targets, filenames) in enumerate(valid_loader):\n",
        "\n",
        "                features = features.to(device)\n",
        "                targets = targets.to(device)\n",
        "\n",
        "                # forward pass\n",
        "                val_outputs = model(features)\n",
        "                val_loss = criterion(val_outputs, targets)\n",
        "\n",
        "                # validation results\n",
        "                _, argmax = torch.max(val_outputs, 1)\n",
        "                count_correct += (targets == argmax.squeeze()).sum().item()\n",
        "                count_total += targets.size(0)\n",
        "                val_pred_list += argmax.cpu().tolist()\n",
        "                val_filename_list += filenames\n",
        "        end_time = time.time()\n",
        "\n",
        "        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
        "\n",
        "        # print results\n",
        "        valid_acc = count_correct / count_total\n",
        "        if epoch<=3 or epoch%5==0:\n",
        "            print('Epoch [{}/{}]: Train loss = {:.4f}, Train accuracy = {:.2f}, Valid loss = {:.4f}, Valid accuracy = {:.2f}' \n",
        "                    .format(epoch, num_epochs, loss.item(), 100*train_acc, val_loss.item(), 100*valid_acc))\n",
        "            print(f'Time taken for Epoch {epoch}: {epoch_mins}m {epoch_secs}s')\n",
        "        \n",
        "        # add the scheduler code\n",
        "        scheduler.step(val_loss)\n",
        "\n",
        "        # --- EARLY STOPPING CONDITION ---\n",
        "        if val_loss.item() < min_val_loss+threshold: # give some threshold to make the learning less strict\n",
        "            # resets the no improve counter\n",
        "            epochs_no_improve = 0\n",
        "            min_val_loss = val_loss.item()\n",
        "        else: # val_loss no improvement\n",
        "            epochs_no_improve +=1\n",
        "            print(f\"Validation Loss did not improve count: {epochs_no_improve}\")\n",
        "            # set this line as min now\n",
        "            min_val_loss = val_loss.item()\n",
        "        if epoch > 5 and epochs_no_improve == n_epochs_stop:\n",
        "            print(\"Training stopped due to early stopping!\")\n",
        "            break\n",
        "        else:\n",
        "            continue\n",
        "\n",
        "    return val_loss.item(), 100*valid_acc, val_pred_list, val_filename_list, loss.item(), 100*train_acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RE-MEbXJpQ4_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sU3jeazRso7f"
      },
      "source": [
        "## Configuration for a trained model\n",
        "- Declare hyperparameters\n",
        "- Training the model\n",
        "- Check and save the best fold\n",
        "- F1-score analysis\n",
        "- Confusion Matrix\n",
        "- Load model back to try it on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--pLGTt47tP7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flfTsw0Dgg6u"
      },
      "source": [
        "## Helper Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pSHSeEo-ILu"
      },
      "source": [
        "# Can add the additional data in this function and pass as parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofQtnFR87rhB"
      },
      "source": [
        "# ----- TRAINING THE MODEL -----\n",
        "def train_helper(num_folds, train_list, val_list, batch_size, hidden_size, num_layers, epochs, optim_flag='adam',drop_flag=False, drop=0.5):\n",
        "\n",
        "    # data loader for all the k-fold datasets\n",
        "    train_loader_list = []\n",
        "    valid_loader_list = []\n",
        "\n",
        "    for i in range(num_folds):\n",
        "        train_loader, valid_loader = loader(train_list[i], val_list[i], batch_size)\n",
        "        train_loader_list.append(train_loader)\n",
        "        valid_loader_list.append(valid_loader)\n",
        "\n",
        "    # initialise list to store the kth folds results\n",
        "    loss_list = [] # validation\n",
        "    acc_list = [] # validation\n",
        "    val_pred_array = []\n",
        "    val_filename_array = []\n",
        "    model_state_list = []\n",
        "    train_loss_list = [] # train\n",
        "    train_acc_list = [] # train\n",
        "\n",
        "    # 5-fold loop\n",
        "    #for i in range(num_folds):\n",
        "    for i in range(1): # due to time constraint just run one\n",
        "        # intialise the model class\n",
        "        model = RNN(input_size=128, \n",
        "                    hidden_size=hidden_size, \n",
        "                    num_layers=num_layers, \n",
        "                    num_classes=len(labels), \n",
        "                    device=device, \n",
        "                    classes=labels,\n",
        "                    drop_flag=drop_flag, drop=drop).to(device)\n",
        "        print(model)\n",
        "        print()\n",
        "\n",
        "        # fit the model for training\n",
        "        val_loss, val_acc, val_pred_list, val_filename_list, train_loss, train_acc = fit(model=model, \n",
        "                                                                                         train_loader=train_loader_list[i], \n",
        "                                                                                         valid_loader=valid_loader_list[i], \n",
        "                                                                                         num_epochs=epochs, \n",
        "                                                                                         fold_num=i+1, \n",
        "                                                                                         optim_flag=optim_flag)\n",
        "        loss_list.append(val_loss)\n",
        "        acc_list.append(val_acc)\n",
        "        val_pred_array.append(val_pred_list)\n",
        "        val_filename_array.append(val_filename_list)\n",
        "        model_state_list.append(model)\n",
        "        train_loss_list.append(train_loss)\n",
        "        train_acc_list.append(train_acc)\n",
        "\n",
        "    return loss_list, acc_list, val_pred_array, val_filename_array, model_state_list, train_loss_list, train_acc_list\n",
        "\n",
        "# ----- CHECK BEST FOLD BASED ON VALIDATION ACCURACY AND SAVE BEST FOLD MODEL -----\n",
        "def save_best_fold(loss_list, acc_list, model_state_list, train_loss_list, train_acc_list, filename):\n",
        "    # ----- CHECK THE BEST FOLD MODEL BASED ON MODEL VALIDATION ACCURACY -----\n",
        "    print(f'Train Losses: {train_loss_list}')\n",
        "    print(f'Train Accuracies: {train_acc_list}')\n",
        "    print(f'Validation Losses: {loss_list}')\n",
        "    print(f'Validation Accuracies: {acc_list}')\n",
        "    best_fold_index = acc_list.index(max(acc_list))\n",
        "    print(f'Best fold: {best_fold_index+1}')\n",
        "\n",
        "    # ----- SAVE THE BEST FOLD MODEL -----\n",
        "    torch.save(model_state_list[best_fold_index].state_dict(), f'{COLAB_FILEPATH}model/model-{filename}.pt')  \n",
        "    print(f'Model Saved for Fold {best_fold_index+1}')\n",
        "    return best_fold_index\n",
        "\n",
        "# ----- F1 & CONFUSION MATRIX -----\n",
        "def metrics_helper(val_list, val_filename_array, val_pred_array, best_fold_index, filename):\n",
        "    # ----- F1-Score Analysis -----\n",
        "    # get the pred and filename\n",
        "    val_tuple = list(zip(val_filename_array[best_fold_index], val_pred_array[best_fold_index]))\n",
        "    val_df = pd.DataFrame(val_tuple, columns=['val_filename', 'val_label_pred_num'])\n",
        "    val_df['val_label_pred'] = val_df['val_label_pred_num'].apply(lambda x: labels[x])\n",
        "    #val_df.head()\n",
        "\n",
        "    # make a combined data frame by iteratively appending the values to a list\n",
        "    val_filename_list_final = []\n",
        "    val_true_list = []\n",
        "    for i in range(len(val_list[best_fold_index])):\n",
        "        val_filename_list_final.append(val_list[best_fold_index][i][0])\n",
        "        val_true_list.append(val_list[best_fold_index][i][3])\n",
        "\n",
        "    # make to dataframe\n",
        "    val_tuple_final = list(zip(val_filename_list_final, val_true_list))\n",
        "    #print(result_tuple)\n",
        "    val_df_final = pd.DataFrame(val_tuple_final, columns=['val_filename', 'val_label_true'])\n",
        "    #val_df_final.head()\n",
        "    # append the predicted column into the final dataframe\n",
        "    val_df_final['val_label_pred'] = val_df['val_label_pred']\n",
        "    #val_df_final.head(3)\n",
        "\n",
        "    # save the table to csv\n",
        "    val_df_final.to_csv(f\"{COLAB_FILEPATH}f1-table/f1-{filename}.csv\", index=False)\n",
        "\n",
        "    # import the csv back again\n",
        "    #val_df_final = pd.read_csv(f\"{COLAB_FILEPATH}f1-table/f1-{filename}.csv\")\n",
        "    #val_df_final.head()\n",
        "\n",
        "    # simple check if length of data are equal\n",
        "    assert len(val_df_final[\"val_label_true\"]) == len(val_df_final[\"val_label_pred\"])\n",
        "\n",
        "    # get the multi-class F1-score to see the distribution of the predictions\n",
        "    print(classification_report(list(val_df_final['val_label_true']), list(val_df_final['val_label_pred']), target_names=labels, digits=4))\n",
        "    print()\n",
        "\n",
        "    # confusion matrix\n",
        "    cm_sns = pd.crosstab(val_df_final['val_label_true'], val_df_final['val_label_pred'], rownames=['Actual'], colnames=['Predicted'])\n",
        "    plt.subplots(figsize=(12,10))\n",
        "    sns.heatmap(cm_sns, annot=True, fmt=\"d\", cmap='YlGnBu_r')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9E0IfzL7s4H"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbfFR11g7s6x"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcNJZCfl2hsm"
      },
      "source": [
        "## CONFIGURATION 1\n",
        "- Hidden Size: 256\n",
        "- Number of layers: 4\n",
        "- Batch Size: 32\n",
        "- Optimizer: Adagrad\n",
        "- Epoch: 30\n",
        "- Dropout: 0.3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5DWuJW02Sbl",
        "outputId": "301de6c7-33a0-406d-a978-64c9577c0ed6"
      },
      "source": [
        "# ----- DECLARE HYPERPARAMETERS -----\n",
        "HIDDEN_SIZE, NUM_LAYERS, BATCH_SIZE, OPTIM_NAME, EPOCHS, DROP_FLAG, DROPOUT = 256, 4, 32, 'adagrad', 30, True, 0.3\n",
        "DR = f'_drop-{DROPOUT}' if DROP_FLAG == True else ''\n",
        "\n",
        "# ----- EXPORTED FILENAME -----\n",
        "FILENAME = f'SC6_fullest_bg2_noise_HS-{HIDDEN_SIZE}_NL-{NUM_LAYERS}_BS-{BATCH_SIZE}_OP-{OPTIM_NAME}_EP-{EPOCHS}{DR}_TFS1-{TM1}-{FM1}-{SIGMA1}_TFS2-{TM2}-{FM2}-{SIGMA2}'\n",
        "print(f'Filename: {FILENAME}\\n')\n",
        "\n",
        "# ----- TRAINING THE K-FOLD MODEL -----\n",
        "loss_list, acc_list, val_pred_array, val_filename_array, model_state_list, train_loss_list, train_acc_list = train_helper(num_folds=K, \n",
        "                                                                                                                          train_list=train_list, \n",
        "                                                                                                                          val_list=val_list, \n",
        "                                                                                                                          batch_size=BATCH_SIZE, \n",
        "                                                                                                                          hidden_size=HIDDEN_SIZE, \n",
        "                                                                                                                          num_layers=NUM_LAYERS, \n",
        "                                                                                                                          epochs=EPOCHS, \n",
        "                                                                                                                          optim_flag=OPTIM_NAME,\n",
        "                                                                                                                          drop_flag=DROP_FLAG, \n",
        "                                                                                                                          drop=DROPOUT)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filename: SC6_fullest_bg2_noise_HS-256_NL-4_BS-32_OP-adagrad_EP-30_drop-0.3_TFS1-5-9-0.7_TFS2-5-5-1.2\n",
            "\n",
            "RNN(\n",
            "  (gru): GRU(128, 256, num_layers=4, batch_first=True, bidirectional=True)\n",
            "  (dropout): Dropout(p=0.3, inplace=False)\n",
            "  (fc): Linear(in_features=512, out_features=27, bias=True)\n",
            ")\n",
            "\n",
            "---------- Fold Number: 1 ----------\n",
            "Epoch [1/30]: Train loss = 3.2269, Train accuracy = 3.61, Valid loss = 3.0440, Valid accuracy = 3.70\n",
            "Time taken for Epoch 1: 0m 25s\n",
            "Epoch [2/30]: Train loss = 3.4987, Train accuracy = 3.96, Valid loss = 3.5724, Valid accuracy = 3.97\n",
            "Time taken for Epoch 2: 0m 25s\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [3/30]: Train loss = 2.8982, Train accuracy = 5.50, Valid loss = 3.0899, Valid accuracy = 6.93\n",
            "Time taken for Epoch 3: 0m 25s\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [5/30]: Train loss = 1.7971, Train accuracy = 34.44, Valid loss = 4.5353, Valid accuracy = 37.30\n",
            "Time taken for Epoch 5: 0m 25s\n",
            "Epoch     5: reducing learning rate of group 0 to 5.0000e-04.\n",
            "Validation Loss did not improve count: 2\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 2\n",
            "Epoch [10/30]: Train loss = 1.0455, Train accuracy = 62.68, Valid loss = 2.5013, Valid accuracy = 64.97\n",
            "Time taken for Epoch 10: 0m 24s\n",
            "Epoch    11: reducing learning rate of group 0 to 2.5000e-05.\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [15/30]: Train loss = 1.1529, Train accuracy = 63.99, Valid loss = 2.5644, Valid accuracy = 66.77\n",
            "Time taken for Epoch 15: 0m 24s\n",
            "Epoch    15: reducing learning rate of group 0 to 1.0000e-05.\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [20/30]: Train loss = 1.0761, Train accuracy = 64.31, Valid loss = 2.9952, Valid accuracy = 66.08\n",
            "Time taken for Epoch 20: 0m 24s\n",
            "Validation Loss did not improve count: 2\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 2\n",
            "Epoch [25/30]: Train loss = 1.2812, Train accuracy = 64.87, Valid loss = 2.7241, Valid accuracy = 66.08\n",
            "Time taken for Epoch 25: 0m 24s\n",
            "Validation Loss did not improve count: 3\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [30/30]: Train loss = 0.7182, Train accuracy = 64.72, Valid loss = 2.5912, Valid accuracy = 66.88\n",
            "Time taken for Epoch 30: 0m 24s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKYxo-oE2Sd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ef822ac0-e5d4-468e-b274-dbb758d5c830"
      },
      "source": [
        "# ----- SAVE THE BEST FOLD MODEL -----\n",
        "best_fold_index = save_best_fold(loss_list, acc_list, model_state_list, train_loss_list, train_acc_list, FILENAME)\n",
        "\n",
        "# ----- GET THE F1 SCORE AND CONFUSION MATRIX -----\n",
        "metrics_helper(val_list, val_filename_array, val_pred_array, best_fold_index, FILENAME)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Losses: [0.718244731426239]\n",
            "Train Accuracies: [64.72222222222223]\n",
            "Validation Losses: [2.5911784172058105]\n",
            "Validation Accuracies: [66.87830687830689]\n",
            "Best fold: 1\n",
            "Model Saved for Fold 1\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    backward     0.7500    0.7714    0.7606        70\n",
            "        bird     0.6250    0.7143    0.6667        70\n",
            "         cat     0.6232    0.6143    0.6187        70\n",
            "     chicken     0.8333    0.7857    0.8088        70\n",
            "   crocodile     0.7808    0.8143    0.7972        70\n",
            "    dinosaur     0.7966    0.6714    0.7287        70\n",
            "         dog     0.5165    0.6714    0.5839        70\n",
            "        down     0.4667    0.3000    0.3652        70\n",
            "       eight     0.6892    0.7286    0.7083        70\n",
            "    elephant     0.6571    0.6571    0.6571        70\n",
            "      falcon     0.7385    0.6857    0.7111        70\n",
            "        five     0.6988    0.8286    0.7582        70\n",
            "     forward     0.5287    0.6571    0.5860        70\n",
            "        four     0.5714    0.6857    0.6234        70\n",
            "          go     0.5854    0.3429    0.4324        70\n",
            "        left     0.7121    0.6714    0.6912        70\n",
            "        nine     0.5763    0.4857    0.5271        70\n",
            "         one     0.5938    0.5429    0.5672        70\n",
            "       right     0.6769    0.6286    0.6519        70\n",
            "       seven     0.7963    0.6143    0.6935        70\n",
            "         six     0.7051    0.7857    0.7432        70\n",
            "       snake     0.5970    0.5714    0.5839        70\n",
            "        stop     0.7246    0.7143    0.7194        70\n",
            "       three     0.6944    0.7143    0.7042        70\n",
            "         two     0.6087    0.8000    0.6914        70\n",
            "          up     0.6923    0.7714    0.7297        70\n",
            "        zero     0.8657    0.8286    0.8467        70\n",
            "\n",
            "    accuracy                         0.6688      1890\n",
            "   macro avg     0.6705    0.6688    0.6650      1890\n",
            "weighted avg     0.6705    0.6688    0.6650      1890\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MY8lXI0l2SgQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pYOF2uwX6Ft"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxeBDbCWKyCc"
      },
      "source": [
        "## CONFIGURATION 2\n",
        "- Hidden Size: 256\n",
        "- Number of layers: 4\n",
        "- Batch Size: 32\n",
        "- Optimizer: Adam\n",
        "- Epoch: 30\n",
        "- Dropout: 0.3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cg9RI68l8WY_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1721604-56ec-4ee4-a15f-f90486f907b6"
      },
      "source": [
        "# ----- DECLARE HYPERPARAMETERS -----\n",
        "HIDDEN_SIZE, NUM_LAYERS, BATCH_SIZE, OPTIM_NAME, EPOCHS, DROP_FLAG, DROPOUT = 256, 4, 32, 'adam', 30, True, 0.3\n",
        "DR = f'_drop-{DROPOUT}' if DROP_FLAG == True else ''\n",
        "\n",
        "# ----- EXPORTED FILENAME -----\n",
        "FILENAME = f'SC6_fullest_bg2_noise_HS-{HIDDEN_SIZE}_NL-{NUM_LAYERS}_BS-{BATCH_SIZE}_OP-{OPTIM_NAME}_EP-{EPOCHS}{DR}_TFS1-{TM1}-{FM1}-{SIGMA1}_TFS2-{TM2}-{FM2}-{SIGMA2}'\n",
        "print(f'Filename: {FILENAME}\\n')\n",
        "\n",
        "# ----- TRAINING THE K-FOLD MODEL -----\n",
        "loss_list, acc_list, val_pred_array, val_filename_array, model_state_list, train_loss_list, train_acc_list = train_helper(num_folds=K, \n",
        "                                                                                                                          train_list=train_list, \n",
        "                                                                                                                          val_list=val_list, \n",
        "                                                                                                                          batch_size=BATCH_SIZE, \n",
        "                                                                                                                          hidden_size=HIDDEN_SIZE, \n",
        "                                                                                                                          num_layers=NUM_LAYERS, \n",
        "                                                                                                                          epochs=EPOCHS, \n",
        "                                                                                                                          optim_flag=OPTIM_NAME,\n",
        "                                                                                                                          drop_flag=DROP_FLAG, \n",
        "                                                                                                                          drop=DROPOUT)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filename: SC6_fullest_bg2_noise_HS-256_NL-4_BS-32_OP-adam_EP-30_drop-0.3_TFS1-5-9-0.7_TFS2-5-5-1.2\n",
            "\n",
            "RNN(\n",
            "  (gru): GRU(128, 256, num_layers=4, batch_first=True, bidirectional=True)\n",
            "  (dropout): Dropout(p=0.3, inplace=False)\n",
            "  (fc): Linear(in_features=512, out_features=27, bias=True)\n",
            ")\n",
            "\n",
            "---------- Fold Number: 1 ----------\n",
            "Epoch [1/30]: Train loss = 2.3140, Train accuracy = 11.38, Valid loss = 2.9391, Valid accuracy = 28.04\n",
            "Time taken for Epoch 1: 0m 25s\n",
            "Epoch [2/30]: Train loss = 0.8193, Train accuracy = 44.45, Valid loss = 1.5892, Valid accuracy = 62.12\n",
            "Time taken for Epoch 2: 0m 25s\n",
            "Epoch [3/30]: Train loss = 0.9933, Train accuracy = 65.19, Valid loss = 2.4173, Valid accuracy = 71.01\n",
            "Time taken for Epoch 3: 0m 25s\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [5/30]: Train loss = 0.8821, Train accuracy = 76.57, Valid loss = 2.2832, Valid accuracy = 78.73\n",
            "Time taken for Epoch 5: 0m 25s\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 2\n",
            "Epoch [10/30]: Train loss = 1.2049, Train accuracy = 84.50, Valid loss = 1.7084, Valid accuracy = 80.90\n",
            "Time taken for Epoch 10: 0m 24s\n",
            "Validation Loss did not improve count: 3\n",
            "Epoch    11: reducing learning rate of group 0 to 5.0000e-05.\n",
            "Validation Loss did not improve count: 4\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [15/30]: Train loss = 0.0573, Train accuracy = 91.87, Valid loss = 0.6347, Valid accuracy = 85.66\n",
            "Time taken for Epoch 15: 0m 24s\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch    17: reducing learning rate of group 0 to 1.0000e-05.\n",
            "Validation Loss did not improve count: 2\n",
            "Validation Loss did not improve count: 1\n",
            "Epoch [20/30]: Train loss = 0.1454, Train accuracy = 93.00, Valid loss = 0.6822, Valid accuracy = 86.61\n",
            "Time taken for Epoch 20: 0m 24s\n",
            "Validation Loss did not improve count: 2\n",
            "Validation Loss did not improve count: 3\n",
            "Validation Loss did not improve count: 4\n",
            "Epoch [25/30]: Train loss = 0.0787, Train accuracy = 93.02, Valid loss = 0.2852, Valid accuracy = 86.83\n",
            "Time taken for Epoch 25: 0m 24s\n",
            "Validation Loss did not improve count: 1\n",
            "Validation Loss did not improve count: 2\n",
            "Epoch [30/30]: Train loss = 0.1303, Train accuracy = 93.32, Valid loss = 1.1303, Valid accuracy = 86.19\n",
            "Time taken for Epoch 30: 0m 24s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXR1ag3Yc2fW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7e01b84e-214e-4ecc-b771-e4fbe37baaf2"
      },
      "source": [
        "# ----- SAVE THE BEST FOLD MODEL -----\n",
        "best_fold_index = save_best_fold(loss_list, acc_list, model_state_list, train_loss_list, train_acc_list, FILENAME)\n",
        "\n",
        "# ----- GET THE F1 SCORE AND CONFUSION MATRIX -----\n",
        "metrics_helper(val_list, val_filename_array, val_pred_array, best_fold_index, FILENAME)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Losses: [0.13028936088085175]\n",
            "Train Accuracies: [93.32010582010582]\n",
            "Validation Losses: [1.130340337753296]\n",
            "Validation Accuracies: [86.19047619047619]\n",
            "Best fold: 1\n",
            "Model Saved for Fold 1\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    backward     0.8182    0.9000    0.8571        70\n",
            "        bird     0.8382    0.8143    0.8261        70\n",
            "         cat     0.8841    0.8714    0.8777        70\n",
            "     chicken     0.9853    0.9571    0.9710        70\n",
            "   crocodile     0.9231    0.8571    0.8889        70\n",
            "    dinosaur     0.9028    0.9286    0.9155        70\n",
            "         dog     0.8261    0.8143    0.8201        70\n",
            "        down     0.8000    0.7429    0.7704        70\n",
            "       eight     0.8784    0.9286    0.9028        70\n",
            "    elephant     0.8429    0.8429    0.8429        70\n",
            "      falcon     0.8451    0.8571    0.8511        70\n",
            "        five     0.9000    0.9000    0.9000        70\n",
            "     forward     0.8378    0.8857    0.8611        70\n",
            "        four     0.8676    0.8429    0.8551        70\n",
            "          go     0.7125    0.8143    0.7600        70\n",
            "        left     0.9206    0.8286    0.8722        70\n",
            "        nine     0.8000    0.8000    0.8000        70\n",
            "         one     0.8873    0.9000    0.8936        70\n",
            "       right     0.7971    0.7857    0.7914        70\n",
            "       seven     0.9254    0.8857    0.9051        70\n",
            "         six     0.9286    0.9286    0.9286        70\n",
            "       snake     0.8356    0.8714    0.8531        70\n",
            "        stop     0.9194    0.8143    0.8636        70\n",
            "       three     0.8857    0.8857    0.8857        70\n",
            "         two     0.8209    0.7857    0.8029        70\n",
            "          up     0.8182    0.9000    0.8571        70\n",
            "        zero     0.9155    0.9286    0.9220        70\n",
            "\n",
            "    accuracy                         0.8619      1890\n",
            "   macro avg     0.8636    0.8619    0.8620      1890\n",
            "weighted avg     0.8636    0.8619    0.8620      1890\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7opTcn7fONBQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zln2nLB-vftw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rUGlO1lFRQQd"
      },
      "source": [
        "## Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vlMBqKgVUjD"
      },
      "source": [
        "# THIS TEST SET MIGHT BE WRONG\n",
        "#!gdown --id 1AvP49xengGjnFTG209AgvAGj-by8WGSi\n",
        "#!unzip -q -o s1_test.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdidyxyQSVj8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "il6d_Uq3QAwu"
      },
      "source": [
        "# THIS TEST SET IS CORRECT\n",
        "!unzip -q -o './drive/My Drive/TIL2021/competition/SC/challenge_6_test_dataset.zip' -d \"./content/\" "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJPXndbLVSRT"
      },
      "source": [
        "# Initialise dataset object for test set\n",
        "#test_set = CustomSpeechDataset(path='s1_test', typ='test')\n",
        "test_set = CustomSpeechDataset(path='./content/challenge_6_test_dataset', typ='test')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XDyltnQyClo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8Kq9OYixyFM"
      },
      "source": [
        "## Load back any model that was saved earlier\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "653SKO3EmzDM",
        "outputId": "c5ce3ee6-0ad8-4a33-ff4d-4db46ed2d193"
      },
      "source": [
        "# ----- MODEL CONFIGURATION -----\n",
        "\n",
        "HIDDEN_SIZE = 256 #256\n",
        "NUM_LAYERS = 4 #2\n",
        "BATCH_SIZE = 32 #64\n",
        "OPTIM_NAME = 'adam' #'adam'\n",
        "EPOCHS = 30 #30\n",
        "DROP_FLAG = True #False\n",
        "DROPOUT = 0.3 #0.5\n",
        "\n",
        "DR = f'_drop-{DROPOUT}' if DROP_FLAG == True else ''\n",
        "\n",
        "# ----- EXPORTED FILENAME -----\n",
        "FILENAME = f'SC6_fullest_bg2_noise_HS-{HIDDEN_SIZE}_NL-{NUM_LAYERS}_BS-{BATCH_SIZE}_OP-{OPTIM_NAME}_EP-{EPOCHS}{DR}_TFS1-{TM1}-{FM1}-{SIGMA1}_TFS2-{TM2}-{FM2}-{SIGMA2}'\n",
        "\n",
        "\n",
        "# ----- LOAD BACK THE MODEL -----\n",
        "model = RNN(input_size=128, \n",
        "            hidden_size=HIDDEN_SIZE, \n",
        "            num_layers=NUM_LAYERS, \n",
        "            num_classes=len(labels), \n",
        "            device=device, \n",
        "            classes=labels, \n",
        "            drop_flag=DROP_FLAG, \n",
        "            drop=DROPOUT).to(device)\n",
        "\n",
        "print(model.load_state_dict(torch.load(f'{COLAB_FILEPATH}model/model-{FILENAME}.pt')))\n",
        "print(f'Loaded filepath: {COLAB_FILEPATH}model/model-{FILENAME}.pt')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<All keys matched successfully>\n",
            "Loaded filepath: ./drive/MyDrive/TIL2021/competition/SC/model/model-SC6_fullest_bg2_noise_HS-256_NL-4_BS-32_OP-adam_EP-30_drop-0.3_TFS1-5-9-0.7_TFS2-5-5-1.2.pt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxT_qOcYSDSv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0O4zKCS0k_1"
      },
      "source": [
        "## Test the model with the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Cqwt6wCV9lg"
      },
      "source": [
        "# define test collate function and set up test loader\n",
        "\n",
        "def test_collate_fn(batch):\n",
        "\n",
        "    # A data tuple has the form:\n",
        "    # filename, waveform, sample_rate\n",
        "\n",
        "    tensors, filenames = [], []\n",
        "\n",
        "    # Gather in lists\n",
        "    for filename, waveform, sample_rate in batch:\n",
        "        # apply transformations\n",
        "        for transform in eval_transformations:\n",
        "            waveform = transform(waveform)\n",
        "        waveform = waveform.squeeze().T\n",
        "        tensors += [waveform]\n",
        "        filenames += [filename]\n",
        "\n",
        "    # Group the list of tensors into a batched tensor\n",
        "    tensors = torch.stack(tensors)\n",
        "\n",
        "    return (tensors, filenames)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    test_set,\n",
        "    batch_size=64,\n",
        "    shuffle=False,\n",
        "    drop_last=False,\n",
        "    collate_fn=test_collate_fn,\n",
        "    num_workers=NUM_WORKERS,\n",
        "    pin_memory=PIN_MEMORY,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wU8vzrvgRUhT"
      },
      "source": [
        "# pass test set through the RNN model\n",
        "model.eval()\n",
        "pred_list, filename_list = [], []\n",
        "with torch.no_grad():\n",
        "  for idx, (features, filenames) in enumerate(test_loader):\n",
        "\n",
        "    features = features.to(device)\n",
        "\n",
        "    # forward pass\n",
        "    outputs = model(features)\n",
        "\n",
        "    # test results\n",
        "    _, argmax = torch.max(outputs, 1)\n",
        "    pred_list += argmax.cpu().tolist()\n",
        "    filename_list += filenames"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCGZZvqKjss3",
        "outputId": "d9c59b64-4ccc-464c-9954-776e6e05a9ab"
      },
      "source": [
        "print(pred_list[:5]) \n",
        "print(filename_list[:5])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[21, 25, 25, 7, 7]\n",
            "['challenge_6_test_1666.wav', 'challenge_6_test_2495.wav', 'challenge_6_test_1521.wav', 'challenge_6_test_0300.wav', 'challenge_6_test_2211.wav']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZeDgtTI0F6s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-y0yc-W0GAo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeGHrPzIa9_7"
      },
      "source": [
        "## Submission of Results\n",
        "Submission csv file should contain only 2 columns for filename and label, in that order. The file should be sorted by filename, and exclude headers. \n",
        "\n",
        "Refer to **sample_submission.csv** for an example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGPpF_F7QX98"
      },
      "source": [
        "result_tuple = list(zip(filename_list, pred_list))\n",
        "#print(result_tuple)\n",
        "submission = pd.DataFrame(result_tuple, columns=['filename', 'pred'])\n",
        "submission = submission.sort_values('filename').reset_index(drop=True)\n",
        "submission['label'] = submission['pred'].apply(lambda x: labels[x])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZXO5uUiKZAqZ",
        "outputId": "78d95e70-716f-4e77-8417-93b58485dceb"
      },
      "source": [
        "submission[['filename', 'label']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>challenge_6_test_0000.wav</td>\n",
              "      <td>seven</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>challenge_6_test_0001.wav</td>\n",
              "      <td>two</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>challenge_6_test_0002.wav</td>\n",
              "      <td>six</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>challenge_6_test_0003.wav</td>\n",
              "      <td>dog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>challenge_6_test_0004.wav</td>\n",
              "      <td>chicken</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    filename    label\n",
              "0  challenge_6_test_0000.wav    seven\n",
              "1  challenge_6_test_0001.wav      two\n",
              "2  challenge_6_test_0002.wav      six\n",
              "3  challenge_6_test_0003.wav      dog\n",
              "4  challenge_6_test_0004.wav  chicken"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeIWUZvEktws",
        "outputId": "b3aa6b76-074b-4a06-fd2b-7651535a9ef1"
      },
      "source": [
        "submission[\"label\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "six          169\n",
              "seven        159\n",
              "go           136\n",
              "crocodile    136\n",
              "down         132\n",
              "bird         116\n",
              "left         101\n",
              "four         101\n",
              "two           96\n",
              "right         94\n",
              "backward      92\n",
              "nine          92\n",
              "eight         91\n",
              "elephant      89\n",
              "cat           89\n",
              "three         89\n",
              "falcon        88\n",
              "zero          88\n",
              "dog           87\n",
              "forward       85\n",
              "one           82\n",
              "stop          78\n",
              "snake         77\n",
              "five          75\n",
              "up            73\n",
              "dinosaur      72\n",
              "chicken       70\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9V77BPcVkMQR"
      },
      "source": [
        "submission[['filename', 'label']].to_csv(f'{COLAB_FILEPATH}submission/submission-{FILENAME}-1.csv', header=None, index=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0ALKrPAkh-t"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}