Finetuning_hubert.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "KBEv1UOqmlQa"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "from tqdm import tqdm\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olyW-AI9t_N2"
      },
      "source": [
        "# Download dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "w8P2YyKq-fl1"
      },
      "outputs": [],
      "source": [
        "! pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7sXdVssg-tIQ"
      },
      "outputs": [],
      "source": [
        "! mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "3IHNEGxm-tLD"
      },
      "outputs": [],
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ViMBrfuh-tNc"
      },
      "outputs": [],
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyG_uq01-zlv",
        "outputId": "3764549d-b8d8-4642-96f9-007a52a5ba49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading cremad.zip to /content\n",
            " 97% 439M/451M [00:02<00:00, 161MB/s]\n",
            "100% 451M/451M [00:02<00:00, 210MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d ejlok1/cremad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzZgssEQ-zoE",
        "outputId": "a6cd679e-33b7-4a9c-9d84-15f6c828cd4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading features.zip to /content\n",
            " 90% 33.0M/36.8M [00:00<00:00, 91.1MB/s]\n",
            "100% 36.8M/36.8M [00:00<00:00, 98.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d cracc97/features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjPLiFQL-2Dd",
        "outputId": "269123bd-2efc-44cf-c408-afdcd9dfcd4b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading ravdess-emotional-song-audio.zip to /content\n",
            " 94% 430M/456M [00:01<00:00, 263MB/s]\n",
            "100% 456M/456M [00:02<00:00, 231MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d uwrfkaggler/ravdess-emotional-song-audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tFQwWzP-2Ft",
        "outputId": "b4e954f3-bba5-4688-a5b8-5408819a25eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading ravdess-emotional-speech-audio.zip to /content\n",
            " 98% 422M/429M [00:02<00:00, 203MB/s]\n",
            "100% 429M/429M [00:02<00:00, 211MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d uwrfkaggler/ravdess-emotional-speech-audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MC9EYR98-fpG",
        "outputId": "abe84106-2ff7-42eb-92ad-cb1b7d3de7ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading surrey-audiovisual-expressed-emotion-savee.zip to /content\n",
            " 83% 89.0M/107M [00:00<00:00, 140MB/s]\n",
            "100% 107M/107M [00:00<00:00, 168MB/s] \n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d ejlok1/surrey-audiovisual-expressed-emotion-savee"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tp653YLG-69a",
        "outputId": "c7b889b6-43ac-4a54-aede-7515ba45d392"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading toronto-emotional-speech-set-tess.zip to /content\n",
            " 94% 403M/428M [00:02<00:00, 215MB/s]\n",
            "100% 428M/428M [00:02<00:00, 201MB/s]\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d ejlok1/toronto-emotional-speech-set-tess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wObzE9-6-8rS",
        "outputId": "9efc641b-55e1-47b4-b968-b27bcafb79cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  features.zip\n",
            "  inflating: features/Female_features.csv  \n",
            "  inflating: features/Male_features.csv  \n"
          ]
        }
      ],
      "source": [
        "! unzip features.zip -d features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XG5NrHPH-8tn",
        "outputId": "ce143bef-cb73-4a12-c2aa-05fbfa3509c3"
      },
      "outputs": [],
      "source": [
        "# ! unzip ravdess-emotional-song-audio.zip -d ravdess-emotional-song-audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bwrcEcY-_KF",
        "outputId": "1c82da17-9abb-4f9e-bed7-b27d0e4ebdd3"
      },
      "outputs": [],
      "source": [
        "# ! unzip ravdess-emotional-speech-audio.zip -d ravdess-emotional-speech-audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1XMx4xF-7CZ",
        "outputId": "b02b6be3-fee6-4f3f-dba4-231bc3cfd2b7"
      },
      "outputs": [],
      "source": [
        "# !unzip surrey-audiovisual-expressed-emotion-savee.zip -d surrey-audiovisual-expressed-emotion-savee"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07rDHjYK_oq_",
        "outputId": "22879dab-ebb2-427c-9d83-55f864550bf1"
      },
      "outputs": [],
      "source": [
        "# !unzip toronto-emotional-speech-set-tess.zip -d toronto-emotional-speech-set-tess"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6PJ0xaEue7K"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "023yce6s_rA1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "import librosa \n",
        "import librosa.display\n",
        "\n",
        "from IPython.display import Audio\n",
        "plt.style.use('seaborn-white')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "QbAhPItP_-Da"
      },
      "outputs": [],
      "source": [
        "DATA_FRAMES = False\n",
        "fem_path = '../content/features/Female_features.csv'\n",
        "mal_path = '../content/features/Male_features.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "ZCRQtTbz__QH"
      },
      "outputs": [],
      "source": [
        "TESS = \"../content/toronto-emotional-speech-set-tess/tess toronto emotional speech set data/TESS Toronto emotional speech set data/\" #check\n",
        "RAV = \"../content/ravdess-emotional-speech-audio/audio_speech_actors_01-24\" #check\n",
        "SAVEE = \"../content/surrey-audiovisual-expressed-emotion-savee/ALL\" #check"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "K0_eawmz_-F8"
      },
      "outputs": [],
      "source": [
        "dir_list = os.listdir(SAVEE)\n",
        "\n",
        "emotion=[]\n",
        "path = []\n",
        "for i in dir_list:\n",
        "    if i[-8:-6]=='_a':\n",
        "        emotion.append('angry')\n",
        "    elif i[-8:-6]=='_d':\n",
        "        emotion.append('disgust')\n",
        "    elif i[-8:-6]=='_f':\n",
        "        emotion.append('fear')\n",
        "    elif i[-8:-6]=='_h':\n",
        "        emotion.append('happy')\n",
        "    elif i[-8:-6]=='_n':\n",
        "        emotion.append('neutral')\n",
        "    elif i[-8:-6]=='sa':\n",
        "        emotion.append('sad')\n",
        "    elif i[-8:-6]=='su':\n",
        "        emotion.append('surprise')\n",
        "    else:\n",
        "        emotion.append('unknown') \n",
        "    path.append(SAVEE + i)\n",
        "\n",
        "SAVEE_df = pd.DataFrame(emotion, columns = ['emotion'])\n",
        "a = SAVEE_df.replace({'neutral':0, 'surprise':1, 'happy':2, 'sad':3, 'angry':4, 'fear':5, 'disgust':6})\n",
        "SAVEE_df = pd.concat([SAVEE_df, pd.DataFrame(path, columns = ['path'])], axis = 1)\n",
        "SAVEE_df['labels'] = a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "80CgEv0o_-IK"
      },
      "outputs": [],
      "source": [
        "path = []\n",
        "emotion = []\n",
        "dir_list = os.listdir(TESS)\n",
        "\n",
        "for i in dir_list:\n",
        "    fname = os.listdir(TESS + i)   \n",
        "    for f in fname:\n",
        "        if i == 'OAF_angry' or i == 'YAF_angry':\n",
        "            emotion.append('angry')\n",
        "        elif i == 'OAF_disgust' or i == 'YAF_disgust':\n",
        "            emotion.append('disgust')\n",
        "        elif i == 'OAF_Fear' or i == 'YAF_fear':\n",
        "            emotion.append('fear')\n",
        "        elif i == 'OAF_happy' or i == 'YAF_happy':\n",
        "            emotion.append('happy')\n",
        "        elif i == 'OAF_neutral' or i == 'YAF_neutral':\n",
        "            emotion.append('neutral')                                \n",
        "        elif i == 'OAF_Pleasant_surprise' or i == 'YAF_pleasant_surprised':\n",
        "            emotion.append('surprise')               \n",
        "        elif i == 'OAF_Sad' or i == 'YAF_sad':\n",
        "            emotion.append('sad')\n",
        "        else:\n",
        "            emotion.append('unknown')\n",
        "        path.append(TESS + i + \"/\" + f)\n",
        "\n",
        "TESS_df = pd.DataFrame(emotion, columns = ['emotion'])\n",
        "a = TESS_df.replace({'neutral':0, 'surprise':1, 'happy':2, 'sad':3, 'angry':4, 'fear':5, 'disgust':6})\n",
        "TESS_df = pd.concat([TESS_df,pd.DataFrame(path, columns = ['path'])],axis=1)\n",
        "TESS_df['labels'] = a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "EGIB2JhsA99-"
      },
      "outputs": [],
      "source": [
        "dir = os.listdir(RAV)\n",
        "\n",
        "males = []\n",
        "females = [] \n",
        "        \n",
        "for actor in dir:\n",
        "    #i = actor   \n",
        "    files = os.listdir(RAV + '/' +actor)\n",
        "        \n",
        "    for file in files: \n",
        "        part = file.split('.')[0]\n",
        "        part = part.split(\"-\")           \n",
        "            \n",
        "        temp = int(part[6])        \n",
        "                \n",
        "        if part[2] == '01':\n",
        "            emotion = 'neutral'\n",
        "        elif part[2] == '02':\n",
        "            emotion = 'calm'\n",
        "        elif part[2] == '03':\n",
        "            emotion = 'happy'\n",
        "        elif part[2] == '04':\n",
        "            emotion = 'sad'\n",
        "        elif part[2] == '05':\n",
        "            emotion = 'angry'\n",
        "        elif part[2] == '06':\n",
        "            emotion = 'fear'\n",
        "        elif part[2] == '07':\n",
        "            emotion = 'disgust'\n",
        "        elif part[2] == '08':\n",
        "            emotion = 'surprise'\n",
        "        else:\n",
        "            emotion = 'unknown'\n",
        "            \n",
        "        if temp%2 == 0:\n",
        "            path = (RAV + actor + '/' + file)\n",
        "            females.append([emotion, path]) \n",
        "        else:\n",
        "            path = (RAV + actor + '/' + file)\n",
        "             #emotion = 'male_'+emotion\n",
        "            males.append([emotion, path])   \n",
        "    \n",
        "   \n",
        "RavFemales_df = pd.DataFrame(females)\n",
        "RavFemales_df.columns = ['emotion', 'path']\n",
        "a = RavFemales_df.replace({'neutral':0, 'surprise':1, 'happy':2, 'sad':3, 'angry':4, 'fear':5, 'disgust':6, 'calm':7})\n",
        "RavFemales_df['labels'] = a['emotion']\n",
        "\n",
        "RavMales_df = pd.DataFrame(males)\n",
        "RavMales_df.columns = ['emotion', 'path']\n",
        "a = RavMales_df.replace({'neutral':0, 'surprise':1, 'happy':2, 'sad':3, 'angry':4, 'fear':5, 'disgust':6, 'calm':7})\n",
        "RavMales_df['labels'] = a['emotion']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Dj9ag1b1A-E3"
      },
      "outputs": [],
      "source": [
        "labels_f = []\n",
        "labels_m = []\n",
        "path_f = []\n",
        "path_m = []\n",
        "emotion_f = []\n",
        "emotion_m = []\n",
        "\n",
        "for i in range(len(SAVEE_df)):\n",
        "  path_m.append(SAVEE_df['path'][i])\n",
        "  labels_m.append(SAVEE_df['labels'][i])\n",
        "  emotion_m.append(SAVEE_df['emotion'][i])\n",
        "\n",
        "for i in range(len(RavMales_df)):\n",
        "  path_m.append(RavMales_df['path'][i])\n",
        "  labels_m.append(RavMales_df['labels'][i])\n",
        "  emotion_m.append(RavMales_df['emotion'][i])\n",
        "\n",
        "\n",
        "for i in range(len(TESS_df)):\n",
        "  path_f.append(TESS_df['path'][i])\n",
        "  labels_f.append(TESS_df['labels'][i])\n",
        "  emotion_f.append(TESS_df['emotion'][i])\n",
        "\n",
        "for i in range(len(RavFemales_df)):\n",
        "  path_f.append(RavFemales_df['path'][i])\n",
        "  labels_f.append(RavFemales_df['labels'][i])\n",
        "  emotion_f.append(RavFemales_df['emotion'][i])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "f9K_eGO3D0Bm"
      },
      "outputs": [],
      "source": [
        "d_f = {'labels': labels_f, 'path': path_f, 'emotion' : emotion_f}\n",
        "Females = pd.DataFrame(data=d_f)\n",
        "\n",
        "d_m = {'labels': labels_m, 'path': path_m,  'emotion' : emotion_m}\n",
        "Males = pd.DataFrame(data=d_m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "7lJca1L_Et2s"
      },
      "outputs": [],
      "source": [
        "Nogender = pd.concat([Females, Males], ignore_index=True, sort=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "nl_KlZUEA-He"
      },
      "outputs": [],
      "source": [
        "Nogender = Nogender.sample(frac=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "n2pfL8TyA-Jz"
      },
      "outputs": [],
      "source": [
        "Nogender = Nogender.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "TzAwjG56BzO8"
      },
      "outputs": [],
      "source": [
        "Nogender = Nogender[(Nogender[\"emotion\"]==\"neutral\") | (Nogender[\"emotion\"]==\"happy\") | (Nogender[\"emotion\"]==\"sad\") | (Nogender[\"emotion\"]==\"angry\")]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-qTJ0B35PWOY",
        "outputId": "09e3942c-7642-4534-86c2-abd307523e52"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>path</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>../content/toronto-emotional-speech-set-tess/t...</td>\n",
              "      <td>happy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>happy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4712</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4713</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/toronto-emotional-speech-set-tess/t...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4715</th>\n",
              "      <td>2</td>\n",
              "      <td>../content/toronto-emotional-speech-set-tess/t...</td>\n",
              "      <td>happy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4716</th>\n",
              "      <td>0</td>\n",
              "      <td>../content/surrey-audiovisual-expressed-emotio...</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4717</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/toronto-emotional-speech-set-tess/t...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2572 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d650ef39-f22d-4a48-bdae-5928c1a00f7b')\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d650ef39-f22d-4a48-bdae-5928c1a00f7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      labels                                               path  emotion\n",
              "0          2  ../content/toronto-emotional-speech-set-tess/t...    happy\n",
              "1          0  ../content/ravdess-emotional-speech-audio/audi...  neutral\n",
              "3          3  ../content/ravdess-emotional-speech-audio/audi...      sad\n",
              "5          2  ../content/ravdess-emotional-speech-audio/audi...    happy\n",
              "6          3  ../content/ravdess-emotional-speech-audio/audi...      sad\n",
              "...      ...                                                ...      ...\n",
              "4712       3  ../content/ravdess-emotional-speech-audio/audi...      sad\n",
              "4713       3  ../content/toronto-emotional-speech-set-tess/t...      sad\n",
              "4715       2  ../content/toronto-emotional-speech-set-tess/t...    happy\n",
              "4716       0  ../content/surrey-audiovisual-expressed-emotio...  neutral\n",
              "4717       3  ../content/toronto-emotional-speech-set-tess/t...      sad\n",
              "\n",
              "[2572 rows x 3 columns]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Nogender"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "TRkPKe2zvxbh"
      },
      "outputs": [],
      "source": [
        "train_df = Nogender.sample(frac=0.8)\n",
        "test_df = Nogender.drop(train_df.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "UUXy41QqGr-P"
      },
      "outputs": [],
      "source": [
        "train_df = train_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNJROo3Z03ZV",
        "outputId": "24a6362a-b2c2-4721-cdd5-992ca81ff38d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "happy      526\n",
              "angry      519\n",
              "sad        516\n",
              "neutral    497\n",
              "Name: emotion, dtype: int64"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df[\"emotion\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6FDlEhm0_yi"
      },
      "source": [
        "# Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HO1jfdmR1NSe",
        "outputId": "9ebae6f0-5b11-4760-ff84-e48335cf0b2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.21.3-py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 4.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
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            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 80.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
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            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.9.1 tokenizers-0.12.1 transformers-4.21.3\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "H6f8Xujx1cvO",
        "outputId": "69c3ec37-5d5a-496e-dc82-7b9332b3170b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.4.0-py3-none-any.whl (365 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (2022.8.2)\n",
            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.64.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.3.5)\n",
            "Requirement already satisfied: dill<0.3.6 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.5.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n",
            "Collecting multiprocess\n",
            "  Downloading multiprocess-0.70.13-py37-none-any.whl (115 kB)\n",
            "\u001b[K     |████████████████████████████████| 115 kB 75.9 MB/s \n",
            "\u001b[?25hCollecting xxhash\n",
            "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 83.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.3)\n",
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            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (0.13.0)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 84.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed datasets-2.4.0 multiprocess-0.70.13 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6YpfiTAxBO91",
        "outputId": "64c64b00-24fe-4b3b-8af9-9137ee6853aa"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
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              "      <th></th>\n",
              "      <th>labels</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
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              "      <td>happy</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <td>happy</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2053</th>\n",
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              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2054</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/toronto-emotional-speech-set-tess/t...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2055</th>\n",
              "      <td>2</td>\n",
              "      <td>../content/toronto-emotional-speech-set-tess/t...</td>\n",
              "      <td>happy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2056</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2057</th>\n",
              "      <td>3</td>\n",
              "      <td>../content/ravdess-emotional-speech-audio/audi...</td>\n",
              "      <td>sad</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2058 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f2ccb52-1685-419f-b2c3-ae3b2c5ba9f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f2ccb52-1685-419f-b2c3-ae3b2c5ba9f9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f2ccb52-1685-419f-b2c3-ae3b2c5ba9f9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      labels                                               path  emotion\n",
              "0          4  ../content/toronto-emotional-speech-set-tess/t...    angry\n",
              "1          4  ../content/toronto-emotional-speech-set-tess/t...    angry\n",
              "2          2  ../content/ravdess-emotional-speech-audio/audi...    happy\n",
              "3          4  ../content/toronto-emotional-speech-set-tess/t...    angry\n",
              "4          2  ../content/toronto-emotional-speech-set-tess/t...    happy\n",
              "...      ...                                                ...      ...\n",
              "2053       0  ../content/toronto-emotional-speech-set-tess/t...  neutral\n",
              "2054       3  ../content/toronto-emotional-speech-set-tess/t...      sad\n",
              "2055       2  ../content/toronto-emotional-speech-set-tess/t...    happy\n",
              "2056       3  ../content/ravdess-emotional-speech-audio/audi...      sad\n",
              "2057       3  ../content/ravdess-emotional-speech-audio/audi...      sad\n",
              "\n",
              "[2058 rows x 3 columns]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ATZbvTJBFK1",
        "outputId": "627a6e4d-aa7e-4af6-da55-3c25ea22f321"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/librosa/core/audio.py:165: UserWarning: PySoundFile failed. Trying audioread instead.\n",
            "  warnings.warn(\"PySoundFile failed. Trying audioread instead.\")\n",
            "/usr/local/lib/python3.7/dist-packages/librosa/core/audio.py:165: UserWarning: PySoundFile failed. Trying audioread instead.\n",
            "  warnings.warn(\"PySoundFile failed. Trying audioread instead.\")\n"
          ]
        }
      ],
      "source": [
        "from transformers import Wav2Vec2FeatureExtractor\n",
        "from datasets import Dataset\n",
        "import librosa\n",
        "\n",
        "train_speech = []\n",
        "index_list = []\n",
        "for i in range(len(train_df[\"path\"])):\n",
        "  try:\n",
        "    speech, _ = librosa.load(train_df[\"path\"][i], sr=16000, mono=True)\n",
        "    train_speech.append(speech)\n",
        "  except:\n",
        "    train_speech.append([])\n",
        "    index_list.append(i)\n",
        "train_df[\"speech\"] = train_speech\n",
        "train_df.drop(train_df.index[index_list],inplace=True)\n",
        "\n",
        "test_speech = []\n",
        "index_list = []\n",
        "for i in range(len(test_df[\"path\"])):\n",
        "  try:\n",
        "    speech, _ = librosa.load(test_df[\"path\"][i], sr=16000, mono=True)\n",
        "    test_speech.append(speech)\n",
        "  except:\n",
        "    test_speech.append([])\n",
        "    index_list.append(i)\n",
        "test_df[\"speech\"] = test_speech\n",
        "test_df.drop(test_df.index[index_list],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bf6e853d25c74a4c808c10d7f44b5173",
            "03f55ebaeb84449999ecd57d523401fb",
            "6841a324da1648e383796726136e8847",
            "66f9492267ea41e48b0ed8d9b1978873",
            "c32170c27b4e4b7b9559f135761c340e",
            "5542db6da5b04c0c868e6acd8564948f",
            "60a162d5830742beb2c05a0c1ccd774e",
            "439ca3465baa4d91941e0c7e25573d41",
            "9efaa5021a324d589c8cd62b1fcf618b",
            "0cd18b620b6c4616a454858137990513",
            "73417719d900468c857de6711e462abc"
          ]
        },
        "id": "4mBgYrDORsmm",
        "outputId": "affe2018-f43a-4e73-c31a-90c701cf8878"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf6e853d25c74a4c808c10d7f44b5173",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading preprocessor_config.json:   0%|          | 0.00/212 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_data = Dataset.from_pandas(train_df)\n",
        "test_data = Dataset.from_pandas(test_df)\n",
        "\n",
        "feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(\"superb/hubert-large-superb-er\")\n",
        "\n",
        "train_encodings = feature_extractor(list(train_data[\"speech\"]), sampling_rate=16000, padding=True, return_tensors=\"pt\")\n",
        "test_encodings = feature_extractor(list(test_data[\"speech\"]), sampling_rate=16000, padding=True, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeWBN3CR1tvb"
      },
      "source": [
        "# Turn data into a Dataset object"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "JeWDh1Lm1WvX"
      },
      "outputs": [],
      "source": [
        "class EmotionDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        pattern = {0:0, 2:1, 3:3, 4:2}\n",
        "        self.labels = [pattern[x] for x in labels]\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: val[idx] for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "    \n",
        "\n",
        "train_dataset = EmotionDataset(train_encodings, list(train_data[\"labels\"]))\n",
        "test_dataset = EmotionDataset(test_encodings, list(test_data[\"labels\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xFbNOOG3x8-"
      },
      "source": [
        "# Loading the Model and Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "98ff53bfcec940c98a86c83b8dcba918",
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            "341d223d87b24ba2a124b93362b38beb",
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            "8bd284182a6c4979bfc17e0eccc2494a",
            "0b397e5a3c624252bf8662dd62d234f8",
            "2f71faac006e4478b772c047b62842eb",
            "d2737e6bab20459f92a977e7c7c5f75f",
            "5006b18fdcba4b54a39c31d9c0e7f209",
            "0049e141ec5f4442a3c9975ca77b62b2",
            "8653507b1934404aafa296fb1425ee2c",
            "93a3eb07eaa441719d0b6e9b7068b883",
            "8ee4e4db2c6e4c4aa6248ed8e6d4e8b5",
            "afa566c1e15a4711b83623273f33bdc6",
            "2a2c304daa5a4b029d7e50863c4a01d6",
            "eddd485638ae40bf946c5ef591742bee",
            "5024f6dc50164fc0a88ebd045d9b09c0"
          ]
        },
        "id": "RbPsGHQg3vi3",
        "outputId": "6636f897-b9b6-4f54-a04b-85b79fe5f1c4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98ff53bfcec940c98a86c83b8dcba918",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/1.79k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f71faac006e4478b772c047b62842eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.18G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import HubertForSequenceClassification\n",
        "from torch.optim import AdamW\n",
        "\n",
        "# Loading the model\n",
        "model = HubertForSequenceClassification.from_pretrained(\"superb/hubert-large-superb-er\")\n",
        "\n",
        "# Loading the optimizer\n",
        "optim = AdamW(model.parameters(), lr=1e-5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfzV-yuo37XJ"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "-wtPtv2w35gd"
      },
      "outputs": [],
      "source": [
        "# Prediction function\n",
        "def predict(outputs):\n",
        "    probabilities = torch.softmax(outputs[\"logits\"], dim=1)\n",
        "    predictions = torch.argmax(probabilities, dim=1)\n",
        "    return predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzxkoadU42na",
        "outputId": "7e09f8bb-cec3-42e9-8a19-69f54dfee27d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 636/636 [1:16:58<00:00,  7.26s/it, Loss=0.005, Accuracy=0.884]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 2/3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 636/636 [1:13:39<00:00,  6.95s/it, Loss=0.000, Accuracy=0.982]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 3/3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 636/636 [1:13:30<00:00,  6.93s/it, Loss=0.003, Accuracy=0.995]\n"
          ]
        }
      ],
      "source": [
        "# Training\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Set the number of epoch\n",
        "epoch = 3\n",
        "\n",
        "# Start training\n",
        "model.train()\n",
        "\n",
        "train_loss = list()\n",
        "train_accuracies = list()\n",
        "for epoch_i in range(epoch):\n",
        "    print('Epoch %s/%s' % (epoch_i + 1, epoch))\n",
        "    time.sleep(0.3)\n",
        "\n",
        "    # Get training data by DataLoader\n",
        "    train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)\n",
        "    \n",
        "    correct = 0\n",
        "    count = 0\n",
        "    epoch_loss = list()\n",
        "    \n",
        "    pbar = tqdm(train_loader)\n",
        "    for batch in pbar:\n",
        "        #print(batch)\n",
        "        optim.zero_grad()\n",
        "        input_ids = batch['input_values']\n",
        "        attention_mask = batch['attention_mask']\n",
        "        labels = batch['labels']\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs['loss']\n",
        "        loss.backward()\n",
        "        optim.step()\n",
        "        \n",
        "        # make predictions\n",
        "        predictions = predict(outputs)\n",
        "\n",
        "        # count accuracy\n",
        "        correct += predictions.eq(labels).sum().item()\n",
        "        count += len(labels)\n",
        "        accuracy = correct * 1.0 / count\n",
        "\n",
        "        # show progress along with metrics\n",
        "        pbar.set_postfix({\n",
        "            'Loss': '{:.3f}'.format(loss.item()),\n",
        "            'Accuracy': '{:.3f}'.format(accuracy)\n",
        "        })\n",
        "        \n",
        "        # record the loss for each batch\n",
        "        epoch_loss.append(loss.item())\n",
        "        \n",
        "    pbar.close()\n",
        "    \n",
        "    # record the loss and accuracy for each epoch\n",
        "    train_loss += epoch_loss\n",
        "    train_accuracies.append(accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "dcs66yfo9ZXy",
        "outputId": "91e33272-fbf0-48d0-9e5f-695a0cd28927"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Q0tPT6dWrF0OHDiUxMZG3336bZ555hk8++YSPPvqIdu3aMW3aNKysrCgsLCQlJYWmTZuye/fuCrPOClGX5RUWcyYtt9xwTtnjM5pcCotLda9rbG2J2lGJt6sTakclapWSNo5KmjdqgLnZg91X+0EYXSOobTp27AhAkyZN+Prrr1m5ciWFhYU0bNjwttfemAyqadOmlU7BXX791atXSUhIwMPDA4B+/fqxcuXKCq/Py8vj999/5+OPP0apVGJpacmxY8d0TcXKygorKyuCg4M5cuTIbc/dzfeytbXl6NGjhIaGYmZmRlZWFgBnz56lXbt2ACxcuBCA5557jm3btjFo0CCUSiVNmjS5u/+IQtQCWq2WtKvXOH3LUE5Cag4Xswt0rzNTwEONrVE7WtPnUcfrO3xrWjdRYm9tacBvcGdG1wiGP+Fy37/e9aFevXoArF27FicnJxYtWsTRo0d1O8fyys8DUlm849b1Wq1Wd9enG/e5LS8yMpKSkhLdjK2ZmZn89NNPDBo0iNLS0gqvNTMzu+25WxUVFd32vX788Ueys7MJCQkhKyuLESNG6LZ3q2effZa33nqLBg0a6G5sLkRtU1hcyoWMXE6n5lYYyjmTmsPVa8W611lbmqNWKenWurFuKKeNSknLxg2pb1G3psk3ukZQW2VmZtK2bVugbAddfqd6v1q2bEl8fDwDBgzg119/vW39jz/+yMKFC+nbty9QFip5+eWXefPNNzl79iy5ublYWFjw2muv8dVXX9323KpVq1AoFOTn5wNw/PjxSr+Xi4sLZmZm7Ny5k8LCsnFPtVpNXFwcnTp1wt/fnwkTJqBWq7Gzs2Pr1q2VTk8uRE3Kzi+6bSgnIS2HC+l5FJc7W9vU1oo2KiXD3J1Rq5Rlv/AdlTjZ1q/0B1hdJI2ghjz//PNMnz6d7du3M3r0aH788Uc2b978QNscOnQob7zxBmPGjOHJJ5+s8Cs8MzOTv//+m6eeekr3nIuLCy1atOD48eNMnTqVcePGATB27FgaNmx423MKhYIXX3yRUaNGoVarK0wRfkP//v15/fXX+fPPPxk+fDhNmzZl6dKlzJw5k7lz5wLQuXNn3dVK3t7eREdHyxxSokaUlmpJzsq/eWVOuR2/Juea7nWW5mY83KQhbZ1sGOTWDLXqxslaJcr6xr+blCkm6rDk5GTOnDlD7969OXz4MEFBQaxatcrQZf2j6dOnM3ToULp3727oUoQRKSgquXmyttwY/hlNDgVFN4c87RrUo831E7Q3dvZqRyUu9g2wMDfefG1V+07jb3VGzMbGhjVr1uiuvpk5c6aBK7qza9euMWbMGB577DFpAuK+aLVa0nMLdb/oy1+OmZyVz42ftAoFtLBviNrRmifVjcsN51jjYG1pNMM51UkaQR1ma2t725VCtVX9+vXZuHGjocsQdUBxSSkXMvJuGcop2/ln5988t9agnjmtHa1xb2nPyCda0EZV9iv/4cbWWNWrWydrDU0agRDCIK4WFFUYzjl9/Zf++fRcikpujlirbOqjdlQypFMz3VCOWqWkma0VZga89t6YSCMQQuiNVqvlUnZBpVfnXL5y82SthZmChxo3RO2oxKuDk+5SzNaO1tha1TPgNzAN0giEEA+soKiE8+m3T5SWkJZDXmGJ7nU2Vha0USnp/YijbtxerVLS0qEh9Yz4ZG1tJ41ACHHXbkyUdvqWnX1iJROlqVVKfB5uoRvOaaNS0kQpJ2trI2kEQogKSkq1JGXe+HVf8ZLM8hOl1bcwo1UTax5ztuOFzjfCVmVTKTSwlJO1dYk0AiFMVO61Ys5qbl6Zc/r6jv9sesWJ0pooLWntqMTbtaluKKc2TJQmqo80AiGMmFarJfXqtdvG7W+dKM3cTEFLh7Jr759u53jz6hxHaxo1rJ0TpYnqI41ACCNQ6URp16/SySk3UZqyvgVqR2u6t26sG8pRO9bNidJE9ZFGIEQdkp1XdH0a5BzdGP6ZtBzOZ+RRUu5sbTM7K9SOSoa7O5cFra5fe6+yMZ6J0kT1kUYgRC1zLxOltWpiTbtmNgzueDNs1crR2iQmShPVR/62CGEg+YUlnNXkXj9Je3MM/+wtE6U1aliPNo5K+rVTVZgorYVDQzlZK6qFNAIh9Eir1aLJKawwlFPZRGlmCmjhUJas7dWmsW4oR+2oxKGW3tVKGA9pBEJUk8tXCjiSlH3L5Zg5XCm4ebK2QT1z1CprnnjInlFdWuhuYygTpQlD0msjmDdvHnFxcSgUCvz9/XX3uYWyu3QFBwdjaWnJ4MGD8fPzIzc3l+nTp5OdnU1RURFTpkyhd+/e+ixRiGoRezYDv5Uxuuvvb0yU9nxnZ92192pHJU1lojRRC+mtEcTGxnL+/HlCQ0NJSEjA39+f0NBQAEpLSwkICCA8PJxGjRoxadIkPD09iYyMpFWrVrz77rtcvnyZV155he3bt+urRCGqxenUHCb99wAu9g1YPLITbVRKmShN1Cl6m+Vp3759eHp6AmX3r83OziYnJwcou42ira0tDg4OmJmZ0b17d/bu3Yu9vT1ZWVkAXLlyBXt7e32VJ0S1SLt6jbGrY6lnrmDtOA/cW9pLExB1jt4agUajqbAjd3BwIC0tTfc4NzeXc+fOUVRURExMDBqNhsGDB3Px4kW8vLzw8/Nj+vTp+ipPiAeWV1jMhLX7Sc8pZNXYrrRwaGjokoS4LzV2srj8rZEVCgWBgYH4+/tjY2Oju4fm1q1bad68OStXruTEiRP4+/sTFhZWUyUKcdeKS0p5K+Qw8cnZrHi5Cx1dGhm6JCHum96OCFQqFRqNRrecmpqKo6OjbtnDw4OQkBCWL1+OjY0Nzs7OHDp0iF69egHQrl07UlNTKSkpuW3bQhiSVqtl7g/HiDqRysfPu9GvvZOhSxLigeitEfTs2ZOIiAgAjh07hkqlQqlU6tZPnDiR9PR08vLyiI6OpkePHjz00EPExcUBkJycjLW1NebmckmdqF2W/3qG9X9c4LU+avy6P2TocoR4YHobGnJ3d8fV1RVfX18UCgVz5swhLCwMGxsbvLy8GDVqFOPHj0ehUDB58mQcHBzw8fHB398fPz8/iouLmTt3rr7KE+K+fB93kcBtJxjSqTnTvNsauhwhqoVCW37wvpZLSkqiX79+REVF6c4rCFFTYs6kM2ZlLJ1bNmLdBA+ZrVPUGVXtO+UmoULchdOpV5n03wO0cGjAf8Y8IU1AGBVpBEJUIfVqAWNX78fSwpw14zzkRi3C6EgjEOIf5BUWM2HNgetZgS6SFRBGSRqBEHdwIytw7GI2S196XLICwmjJ7KNCVEKr1TLn+7KswCcvSFZAGDc5IhCiEst+OcOGmAu8/rRkBYTxk0YgxC22/pnMgu0neK5Tc97vL1kBYfykEQhRzh9n0nn/f0fo1sqBRSM7yr0DhEmQRiDEdadTrzL5vwdo2bgh/xnTRbICwmRIIxCCsqzAK6vKsgKrx3bFrqHcU0CYDmkEwuTlXitm/Jr9ZOQWslruKyBMkDQCYdKKS0p569vD/HXxCl+NfpzHXOwMXZIQNU5yBMJk3cgK7DqRyqdD3ejbTrICwjTJEYEwWeWzAqO7SVZAmC5pBMIkSVZAiJukEQiTI1kBISqSRiBMyqnLkhUQ4lbSCITJSL1Sdl+B+vXMWTNOsgJC3CCNQJiE3GvFjF+7n8y8Qla90hUXe8kKCHGDNAJh9IpLSnkz5FBZVuAld8kKCHELyREIo6bVapm99RjRf6cxb+hjPNNOZeiShKh15IhAGLXgXxL4NvYCbzyt5qVuLQ1djhC1kjQCYbS2/pnMwu1/83zn5rwnWQEh7kgagTBK+xLSee9/cXRr5cDCEZIVEOKfSCMQRufk5atMXneAhxtbS1ZAiLsgjUAYldQrBYxbvR+reuaslqyAEHdFGoEwGrnXihm3piwrsHqsZAWEuFt6bQTz5s3Dx8cHX19fjhw5UmFdZGQkw4cP58UXX2T9+vW657///nuee+45hg0bxu7du/VZnjAixSWlTAk5xImUq3w12h03Z8kKCHG39JYjiI2N5fz584SGhpKQkIC/vz+hoaEAlJaWEhAQQHh4OI0aNWLSpEl4enpSv359vvrqKzZv3kxeXh5BQUE8/fTT+ipRGImyrEA8u29kBdpKVkCIe6G3RrBv3z48PT0BUKvVZGdnk5OTg1KpJDMzE1tbWxwcHADo3r07e/fuxcrKih49eqBUKlEqlQQEBOirPGFEvt6dwLexiUx5RrICQtwPvQ0NaTQa7O3tdcsODg6kpaXpHufm5nLu3DmKioqIiYlBo9GQlJREQUEBr732Gi+99BL79u3TV3nCSGw5nMyiCMkKCPEgamyKCa1Wq3usUCgIDAzE398fGxsbXFxcdOuysrJYunQpFy9e5OWXXyY6OhqFQq4BF7fbm6Dh/U1xdG9dlhWQvydC3B+9HRGoVCo0Go1uOTU1FUdHR92yh4cHISEhLF++HBsbG5ydnWncuDGPP/44FhYWtGzZEmtrazIyMvRVoqjDTl6+yqvrDvJwY2uW+0lWQIgHobdG0LNnTyIiIgA4duwYKpUKpVKpWz9x4kTS09PJy8sjOjqaHj160KtXL/744w9KS0vJzMwkLy+vwvCSEACXJSsgRLXS29CQu7s7rq6u+Pr6olAomDNnDmFhYdjY2ODl5cWoUaMYP348CoWCyZMn604ce3t7M2rUKABmzZqFmZlEHcRNOdeKGX89K7Dx1R6SFRCiGii05Qfva7mkpCT69etHVFRUhfMKwjQUl5QyYe0Bfjut4ZtXushlokLcpar2nfJzW9QJWq2WWVvi+eVkGp+84CZNQIhqJI1A1Alf707gu/2JvPlMG170kKyAENVJGoGo9cIPJ7Eo4m+GPu7Mu/0fNXQ5QhgdaQSiVtuboGHapiP0aN2YBcMlKyCEPkgjELVW+azAsjFPYGkhf12F0Af5lyVqpctXChi7KpYG9cxZM94DuwaSFRBCX2psigkh7taNrEBWfhEbX+2Bc6MGhi5JCKMmjUDUKkUlpUzZUHZfgW9e6SL3FRCiBsjQkKg1tFots69nBT6VrIAQNUYagag1voo+zXf7E3mrbxt8JSsgRI2RRiBqhfDDSSzecZJhjzvzf16SFRCiJkkjEAa39/TNrECgZAWEqHFVNoK33nqLHTt2UFhYWBP1CBPzd8pVXl1/kFZNJCsghKFU+a9u3LhxxMXFMXr0aPz9/eX2kaLalN1XoCwrsHqcZAWEMJQqLx91d3fH3d0dgKNHj/Lxxx9z+fJl3f0EGjaU+eDFvcu5Vsy41fvJzi8iVLICQhhUlY0gPz+fXbt28fPPP6PRaBg0aBCDBg3i999/Z8qUKaxevbom6hRGpKiklDc2HOLvy1dZKVkBIQyuykbw3HPP4eXlxdSpU2nbtq3u+WHDhnH48GG9FieMz42swK8n0wgc9hhPS1ZACIOr8hxBeHg4TzzxhK4JbNmyhby8PAACAgL0W50wOkt3SVZAiNqmykbw3nvvkZSUpFu+du0a7777rl6LEsZp88EkluyUrIAQtU2VjeDq1au88sorumUfHx9ycnL0WpQwPr+f1jB98xGeVEtWQIjapspzBEqlkvXr1+Pu7k5paSl//PEHNjY2NVGbMBJ/p1zltXUHUTsqJSsgRC1U5b/IxYsXo9Fo+OKLL1i6dCmFhYUsXLiwJmoTRiAlu4Cxq2NpWN+c1eO6YmslWQEhapsqjwhsbGyYNGkS2dnZABQWFjJ16lRWrVql9+JE3ZZzrZhxa/ZzJb+Ija/1oLlkBYSolapsBEuXLiU8PJysrCyaN2/OxYsX8fHxqYnaRB12Iytw8vJVVo3timtzyQoIUVtVOTS0Z88eoqKi6NChAz/88AP//e9/MTc3r4naRB2l1WqZFV6WFZg31I0+jzoauiQhxD+oshEoFAq0Wi0lJSUUFBTg6urKwYMHa6I2UUct3XWa0AOJTO3bBp+ukhUQorarcmjI29ubtWvXMmTIEJ5//nkaN25MgwYy1isqVz4r8C/JCghRJ1TZCLp160aHDh0A6NOnD5mZmbRv3/6uNj5v3jzi4uJQKBT4+/vTsWNH3brIyEiCg4OxtLRk8ODB+Pn56dYVFBTw7G8mYawAABSvSURBVLPP8sYbbzBs2LB7/U7CQCQrIETdVOXQUGBgIMXFxQA0b94cV1dXzMyqvg48NjaW8+fPExoayqeffsqnn36qW1daWkpAQAArVqxgw4YNREdHk5KSolsfHByMnZ2cXKxLTqRckayAEHVUlUcEDRo0oH///rRr14569W5eA/7ll1/+4/v27duHp6cnAGq1muzsbHJyclAqlWRmZmJra4uDgwMA3bt3Z+/evQwbNoyEhAROnz7N008//QBfS9SklOwCxq3eL1kBIeqoKhvBhAkT7mvDGo0GV1dX3bKDgwNpaWkolUocHBzIzc3l3LlzODs7ExMTg4eHBwALFixg9uzZbNmy5b4+V9SsqwVFjFuzn6sFxWx8VbICQtRFVTaC2NjYSp+/seO+W1qtVvdYoVAQGBiIv78/NjY2uLi4AGUzm3bu3JkWLVrc07aFYZTPCqwe25UOzW0NXZIQ4j5U2Qjs7e11j4uKijh06BBOTk5VblilUqHRaHTLqampODrevJ7cw8ODkJAQAJYsWYKzszM7d+4kMTGR3bt3k5KSgqWlJU2bNuXJJ5+8py8l9E+r1TIz/Ch7TmlYOLwjT0lWQIg6q8pGMHr06ArLY8eO5bXXXqtywz179iQoKAhfX1+OHTuGSqVCqVTq1k+cOJEFCxbQoEEDoqOjGTduHIMHD9atDwoKwtnZWZpALRW06zQbDyQxtd8jjOoqR3BC1GVVNoLTp09XWE5LS+Ps2bNVbtjd3R1XV1d8fX1RKBTMmTOHsLAwbGxs8PLy0t3zWKFQMHnyZN2JY1H7bTqYxGc7TzLM3Zl/eT5i6HKEEA9IoS0/eF+JMWPG3HyxQoGNjQ2jRo2iT58+ei/uVklJSfTr14+oqCjdeQVRs34/reGVVbF0a+3A6rEecpmoEHVAVfvOKo8I1q1bx8WLF2nevDkACQkJqNXq6q9U1HrlswLBfpIVEMJYVPkvedGiRfz73//WLa9atUruR2CCLmXnM3aVZAWEMEZVNoLDhw8TGBioW/7000+Ji4vTa1GidrlaUMS41fvJuVbM6rEekhUQwshU2QhKS0s5deqUbvnIkSNUcVpBGJEbWYHTqTl8PdpdsgJCGKEqzxF8+OGHzJ07l3PnzqFQKGjTpg1z586tgdKEoWm1WvzDrmcFRkhWQAhjVWUj6NChA4sWLZKTxSbo31Gn+d/BJN7u9wijukhWQAhjJSeLRaU2HUzi88iTDHd34R3JCghh1ORksbjNb6c0zNh8hF5tmjB/2GNyXwEhjJycLBYVHL90hdfWH6SNSsnXfu6SFRDCBNzzyWK1Wk3Pnj1rojZRwy5l5zNu9X6U9S0kKyCECany516HDh347LPPmDRpEs2aNePSpUt3dYcyUbeUzwqsGtuVZnaSFRDCVNzxiCArK4uIiAh+/PFHzp8/T//+/bl69So7duyoyfpEDSgqKeX19WVZgVVyXwEhTM4dG0GvXr1o2bIl06dPp3fv3piZmfHCCy/UZG2iBmi1Wj4IO8pvpzUskqyAECbpjmM8gYGBtGzZkpkzZzJnzhz27dtXk3WJGvJl1Ck2HUziHc9HGClZASFM0h0bwbPPPsuyZcv46aefcHNz4+uvv+bMmTMsWLDgtnsUiLrpfwcS+SLyFCOecOHtfpIVEMJUVXnW187ODh8fH9atW8fOnTtp0qQJ06ZNq4nahB7tOZXGB2FH6f2IZAWEMHX3dPmPk5MTEyZMICwsTF/1iBpw/NIVXl9/qCwrMNqdeuZyFZgQpkz2ACbm1qyAjWQFhDB5VQbKhPG4Ui4r8L/XekhWQAgByBGBySgsLuWN61mBYD932jeTrIAQoowcEZiAW7MCvR+RrIAQ4iY5IjABX0SeYvMhyQoIISonjcDIbTyQyJdRpxgpWQEhxB1IIzBiv55Mw/96VmCeZAWEEHcgjcBI/XXxCm9skKyAEKJqsncwQpey8xm/Zj82VhasGechWQEhxD/S61VD8+bNIy4uDoVCgb+/Px07dtSti4yMJDg4GEtLSwYPHoyfnx8ACxcu5ODBgxQXF/Pqq6/Sv39/fZZodG5kBXKvFfO/13vQ1M7K0CUJIWo5vTWC2NhYzp8/T2hoKAkJCfj7+xMaGgqU3f4yICCA8PBwGjVqxKRJk/D09OTcuXOcOnWK0NBQMjMzGTp0qDSCe1A+K7BmnAftmkpWQAhRNb01gn379uHp6QmAWq0mOzubnJwclEolmZmZ2Nra4uDgAED37t3Zu3cvzz//vO6owdbWlvz8fEpKSjA3N9dXmUajfFZg8chO9HqkiaFLEkLUEXo7R6DRaLC3t9ctOzg4kJaWpnucm5vLuXPnKCoqIiYmBo1Gg7m5OQ0bNgRg06ZNPPXUU9IE7tLn17MC//J8lBFPuBi6HCFEHVJjyWKtVqt7rFAoCAwMxN/fHxsbG1xcKu64IiMj2bRpE6tWraqp8uq0jfsT+ff1rMDUfm0MXY4Qoo7RWyNQqVRoNBrdcmpqKo6ON6c28PDwICQkBIAlS5bg7OwMwJ49e1i2bBnffPMNNjY2+irPaPxyMo0PwiUrIIS4f3obGurZsycREREAHDt2DJVKhVKp1K2fOHEi6enp5OXlER0dTY8ePbh69SoLFy5k+fLlNGrUSF+lGY1jF7N5Y/1BHnWykayAEOK+6e2IwN3dHVdXV3x9fVEoFMyZM4ewsDBsbGzw8vJi1KhRjB8/HoVCweTJk3FwcNBdLfTOO+/otrNgwQKaN2+urzLrrItZZVkB2wb1WD1W7isghLh/Cm35wftaLikpiX79+hEVFXXbeQVTcqWgiJHB+7iYlc//Xu8hl4kKIf5RVftOmYa6jiksLuX19QdJSMth7XjJCgghHpw0gjpEq9UyI+wIv59OZ8nITvRsI1kBIcSDk7OLdcjnkacIO5TM/3k9ynDJCgghqok0gjriRlZgVBcX3uorWQEhRPWRRlAHlM8KfDpUsgJCiOoljaCWk6yAEELfZK9SiyVn5TNutWQFhBD6JY2glsrOL2Lc6ljyC0tYPa6r3FdACKE3cvloLXQjK3BWk8taua+AEELPpBHUMlqtlhmbj7A3IZ3PRnXiSckKCCH0TIaGapnPd54k7HAy73o9yjB3yQoIIfRPGkEtErr/Av/edRqfLi14U7ICQogaIo2glvjlZBr+4fE89agjnwx1k6yAEKLGSCOoBW5kBdpKVkAIYQCyxzGwG1kBuwb1WD2uK8r6cv5eCFGzZK9jQOWzAptefxInW8kKCCFqnjQCAyksLuW1dTezAm2byv2ZhRCGIY3AALRaLdM3H2HfGckKCCEMT84RGMBnO08SfjiZ9/pLVkAIYXjSCGrYd7EXCNp1Gt+uLZjyjGQFhBCGJ42gBu3+O5WZW+Lp86gjAS9IVkAIUTtII6gh8cnZTNlwiLZONnwlWQEhRC0ie6MakJyVz/g1khUQQtROskfSM11WoKiEzZIVEELUQnJEoEfXikt4dd0BzmpyWe73BI86SVZACFH7yBGBnmi1WqZvOsIfZzL43EeyAkKI2kuOCPRkyY6TbPnzIu/1f5Shj0tWQAhRe+m1EcybNw8fHx98fX05cuRIhXWRkZEMHz6cF198kfXr19/Ve+qKb2MvsDRasgJCiLpBb0NDsbGxnD9/ntDQUBISEvD39yc0NBSA0tJSAgICCA8Pp1GjRkyaNAlPT08uXLhwx/fUFdF/pzLrelbgE8kKCCHqAL01gn379uHp6QmAWq0mOzubnJwclEolmZmZ2Nra4uDgAED37t3Zu3cviYmJd3xPXXAjK9CuaVlWwEKyAkKIOkBveyqNRoO9vb1u2cHBgbS0NN3j3Nxczp07R1FRETExMWg0mn98T22XlJnHuDX7sW9oyaqxkhUQQtQdNba30mq1uscKhYLAwED8/f2xsbHBxaXyk6nl31OblWUF9lNQVMKGid0kKyCEqFP01ghUKhUajUa3nJqaiqOjo27Zw8ODkJAQAJYsWYKzszPXrl37x/fURjeyAufSc1k73kOyAkKIOkdvQ0M9e/YkIiICgGPHjqFSqSqM9U+cOJH09HTy8vKIjo6mR48eVb6ntimfFVg0ohNPqiUrIISoe/R2RODu7o6rqyu+vr4oFArmzJlDWFgYNjY2eHl5MWrUKMaPH49CoWDy5Mk4ODjg4OBw23tqs8U7/mbLnxd537stLzzubOhyhBDivii0dWUgHkhKSqJfv35ERUXd8bxCTQmJuYB/+FFe9GjBvKGPyWWiQohaq6p9p1zfeB+iT6Qye+v1+wo8L1kBIUTdJo3gHsUnZzMlRLICQgjjIXuxe1A+K7BasgJCCCMhe7K7lJ1XxNjrWYGQid1QSVZACGEk5IjgLlwrLuHV9Qc4n57Lf8Z04RHJCgghjIgcEVRBq9Uy7XpW4EvfzvRQNzZ0SUIIUa3kiKAKi3f8zdbrWYHnO0tWQAhhfKQR/IOQmAt8FZ3Aix4teeNptaHLEUIIvZBGcAfRJ1KZteUoz7R1JOB5V8kKCCGMljSCShxNKssKdGhuy9KXJCsghDBusoe7RWJGHuPXXr+vwCtdsZasgBDCyMlerpzsvCLGrdnPNckKCCFMiDSC664VlzB53QEupOfx3wkekhUQQpgMaQRAaamW9/93hJizZVmB7q0lKyCEMB1yjoCyrMD3cReZNkCyAkII02PyjWBDzHm+3p3AS91a8nofyQoIIUyPSTeC6BOpzN4SzzNtHfn4OckKCCFMk8k2AskKCCFEGZPc+yVm3LyvwKqxkhUQQpg2k9sDlt1XIJbC4hK+ndQNlY1kBYQQps2kGsG14hImrTtAYka+ZAWEEOI6k2kEWm1ZViBWsgJCCFGByZwjyMgtZFv8JaYPaCdZASGEKMdkjggaK+tz+MP+csN5IYS4hckcEQDSBIQQohIm1QiEEELcTq8/kefNm0dcXBwKhQJ/f386duyoW7dhwwa+//57zMzMcHNzY+bMmVy+fBl/f38KCwspLS3lgw8+wM3NTZ8lCiGEydNbI4iNjeX8+fOEhoaSkJCAv78/oaGhAOTk5LBy5Up27NiBhYUF48eP588//yQiIgIvLy98fX05dOgQn3/+OStXrtRXiUIIIdDj0NC+ffvw9PQEQK1Wk52dTU5ODgD16tWjXr165OXlUVxcTH5+PnZ2dtjb25OVlQXAlStXsLe311d5QgghrtPbEYFGo8HV1VW37ODgQFpaGkqlkvr16zNlyhQ8PT2pX78+gwcPplWrVowdO5YRI0awZcsWcnJy+Pbbb/VVnhBCiOtq7GSxVqvVPc7JyWH58uVs376dqKgo4uLiOHHiBN988w0DBw5k+/btBAQEsGDBgpoqTwghTJbejghUKhUajUa3nJqaiqOjIwAJCQm0aNECBwcHALp06UJ8fDyHDh3inXfeAaBnz5589NFHFbZZUlICQEpKir7KFkIIo3Njn3ljH3orvTWCnj17EhQUhK+vL8eOHUOlUqFUKgFwdnYmISGBgoICrKysiI+Pp0+fPjz00EPExcXh5ubGkSNHeOihhypsMy0tDYDRo0frq2whhDBaaWlpt+1XARTa8mM21Wzx4sUcOHAAhULBnDlz+Ouvv7CxscHLy4vvvvuOsLAwzM3Nefzxx5k2bRqpqanMnDmTgoICAGbOnEm7du102ysoKCA+Ph5HR0fMzc31VbYQQhiVkpIS0tLScHNzw8rq9hmX9doIhBBC1H6SLBZCCBNnMo3g5MmTeHp6sn79ekOXIozQwoUL8fHxYfjw4ezYscPQ5Qgjkp+fz9tvv42fnx8jR44kOjq62j/DJGZhy8vLIyAggB49ehi6FGGE/vjjD06dOkVoaCiZmZkMHTqU/v37G7osYSSio6Nxc3Nj0qRJJCcnM378eJ555plq/QyTaASWlpasWLGCFStWGLoUYYS6du2qm0fL1taW/Px8SkpK5IIGUS0GDRqke3zp0iWcnJyq/TNMohFYWFhgYWESX1UYgLm5OQ0bNgRg06ZNPPXUU9IERLXz9fUlJSWFZcuWVfu2Ze8oRDWJjIxk06ZNrFq1ytClCCP03Xffcfz4cd5//32+//57FApFtW3bZE4WC6FPe/bsYdmyZaxYsQIbGxtDlyOMSHx8PJcuXQKgffv2lJSUkJGRUa2fIY1AiAd09epVFi5cyPLly2nUqJGhyxFG5sCBA7qjTI1GQ15eXrXPzGwSgbL4+HgWLFhAcnIyFhYWODk5ERQUJP9oRbUIDQ0lKCiIVq1a6Z5bsGABzZs3N2BVwlgUFBQwc+ZMLl26REFBAW+++SZ9+/at1s8wiUYghBDizmRoSAghTJw0AiGEMHHSCIQQwsRJIxBCCBMnjUAIIUycJIuFqERSUhJDhgzBzc2twvMPetlxUFAQ9vb2+Pn5PWiJQlQbaQRC3EGrVq1Yt26docsQQu+kEQhxD2bMmEHDhg05c+YMmZmZzJ8/nw4dOrB27Vp+/vlnAPr168fkyZNJTk5mxowZlJSU0Lx5cxYsWACU3Rvj1Vdf5dy5c8ycOZOnnnrKkF9JCDlHIMS9Ki4uZs2aNbz99tt89dVXJCYmEh4ezoYNG9iwYQPbtm3jwoULfP7554wdO5aQkBBUKhXx8fEAZGVlsXz5cmbNmsV3331n4G8jhBwRCHFHZ8+eZcyYMbrlG1NIPPnkkwB07tyZxYsXc/z4cTp16qSb6tzd3Z0TJ07w119/MXPmTACmTZsGwK+//oq7uzsATk5OXL16tca+jxB3Io1AiDuo7BzBjBkzKC0t1S0rFAoUCgXlZ2opKirCzMwMc3NzKpvBRe6NIWobGRoS4h4dPHgQgMOHD6NWq2nfvj1//vknxcXFFBcXExcXR/v27XFzc+OPP/4A4Msvv2Tv3r2GLFuIO5KfJkLcwa1DQwBWVlZYWFjw6quvcunSJRYtWoSLiws+Pj74+fmh1WoZOXIkzs7OTJ06lQ8++ICQkBCaNWvGm2++qWsiQtQmMvuoEPdgxowZeHt7V/vNw4UwJBkaEkIIEydHBEIIYeLkiEAIIUycNAIhhDBx0giEEMLESSMQQggTJ41ACCFMnDQCIYQwcf8P9DiTP9eoSE8AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Plot Iteration vs Training Loss\n",
        "plt.plot(train_loss, label=\"Training Loss\")\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Iteration vs Training Loss\")  \n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot Epoch vs Training Accuracy\n",
        "acc_X = np.arange(len(train_accuracies))+1                          \n",
        "plt.plot(acc_X, train_accuracies,\"-\", label=\"Training Accuracy\")\n",
        "plt.xticks(acc_X)\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Epoch vs Training Accuracy\")  \n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvFB9-wEmSxj"
      },
      "source": [
        "# Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bX-Fok5tmOaX",
        "outputId": "49c974c6-d7a5-425a-a420-6562f84b6450"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 329/329 [05:13<00:00,  1.05it/s, loss=0.000, accuracy=0.997]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The final accuracy on the test dataset: 99.696%\n"
          ]
        }
      ],
      "source": [
        "# Testing\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Get test data by DataLoader\n",
        "test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)\n",
        "\n",
        "# Start testing\n",
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "    \n",
        "    correct = 0\n",
        "    count = 0\n",
        "    record = {\"labels\":list(), \"predictions\":list()}\n",
        "    \n",
        "    pbar = tqdm(test_loader)\n",
        "    for batch in pbar:\n",
        "        input_ids = batch['input_values']\n",
        "        attention_mask = batch['attention_mask']\n",
        "        labels = batch['labels']\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs['loss']\n",
        "        \n",
        "        # make predictions\n",
        "        predictions = predict(outputs)\n",
        "\n",
        "        # count accuracy\n",
        "        correct += predictions.eq(labels).sum().item()\n",
        "        count += len(labels)\n",
        "        accuracy = correct * 1.0 / count\n",
        "\n",
        "        # show progress along with metrics\n",
        "        pbar.set_postfix({\n",
        "            'loss': '{:.3f}'.format(loss.item()),\n",
        "            'accuracy': '{:.3f}'.format(accuracy)\n",
        "        })\n",
        "    \n",
        "        # record the results\n",
        "        record[\"labels\"] += labels.cpu().numpy().tolist()\n",
        "        record[\"predictions\"] += predictions.cpu().numpy().tolist()\n",
        "        \n",
        "    pbar.close()\n",
        "    \n",
        "time.sleep(0.3)\n",
        "print(\"The final accuracy on the test dataset: %s%%\" % round(accuracy*100,4))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
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