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    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install numpy==1.23.1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIqPNnysl1Fo",
        "outputId": "8f7db75d-2bde-46ef-de6c-60d7744a5a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy==1.23.1 in /usr/local/lib/python3.10/dist-packages (1.23.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "7GBSiX3ZPlzz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbf49f24-61ed-4a15-b3e9-1a28a4a56dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import dot\n",
        "from numpy.linalg import norm\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import urllib.request\n",
        "\n",
        "!pip install sentence-transformers\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "import librosa\n",
        "import librosa.display\n",
        "from IPython.display import Audio"
      ],
      "metadata": {
        "id": "Yoz3ugWIPcdM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "txt_data = pd.read_csv(\"/content/drive/MyDrive/BOAZ_MiniProject1/data/df_calculation.csv\", encoding= 'utf-8')\n",
        "# txt_data = pd.read_csv(\"/content/df_calculation.csv\", encoding= 'utf-8')\n",
        "print(f\"txt_data 길이: {len(txt_data)}\")\n",
        "print(f\"txt_data shape: {txt_data.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPVwd0mzPftq",
        "outputId": "67bff3ea-4226-4207-e86d-1d28955a6367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "txt_data 길이: 15463\n",
            "txt_data shape: (15463, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "txt_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "uwT1yCBcQPXh",
        "outputId": "8a8e9bec-899d-4e8e-8a1c-688cc8f38c7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                    wav_id  \\\n",
              "0           0  5f4141e29dd513131eacee2f   \n",
              "1           1  5f4141f59dd513131eacee30   \n",
              "2           2  5f4142119dd513131eacee31   \n",
              "3           3  5f4142279dd513131eacee32   \n",
              "4           4  5f3c9ed98a3c1005aa97c4bd   \n",
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              "                                                text  situation    emotion  \\\n",
              "0                                   헐! 나 이벤트에 당첨 됐어.  happiness  happiness   \n",
              "1        내가 좋아하는 인플루언서가 이벤트를 하더라고. 그래서 그냥 신청 한번 해봤지.  happiness  happiness   \n",
              "2                        한 명 뽑는 거였는데, 그게 바로 내가 된 거야.  happiness  happiness   \n",
              "3  당연히 마음에 드는 선물이니깐, 이벤트에 내가 신청 한번 해본 거지. 비싼 거야. ...  happiness  happiness   \n",
              "4                   에피타이저 정말 좋아해. 그 것도 괜찮은 생각인 것 같애.    neutral  happiness   \n",
              "\n",
              "   label  \n",
              "0      1  \n",
              "1      1  \n",
              "2      1  \n",
              "3      1  \n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "txt_data",
              "summary": "{\n  \"name\": \"txt_data\",\n  \"rows\": 15463,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5634,\n        \"min\": 0,\n        \"max\": 19373,\n        \"num_unique_values\": 15463,\n        \"samples\": [\n          12948,\n          6965,\n          10853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wav_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15463,\n        \"samples\": [\n          \"5fb383f1e3dcec421f8358ad\",\n          \"5f808733111dfd48d40fd8a1\",\n          \"5f791df79e04b149046cc16a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13165,\n        \"samples\": [\n          \"\\uc544\\ub2c8 \\ud63c\\uc790 \\ubb34\\uc2a8 \\ub9d0\\uc744 \\ud558\\ub294 \\uac70\\ub0d0\\uace0 \\ub300\\uccb4. \\ub098\\ub294 \\uc9dc\\uc7a5\\uba74\\uc5d0\\uc11c \\ubc8c\\ub808\\ub098\\uc628 \\uac8c \\uc9dc\\uc99d\\ub09c\\ub2e4\\uace0.\",\n          \"\\uc9d1\\uc5d0\\ub9cc \\uc788\\uc73c\\ub2c8\\uae4c \\ub354 \\ud798\\ub4e0 \\uac83 \\uac19\\uc544. \\ud65c\\ub825\\uc774 \\uc5c6\\uc5b4\\uc84c\\uc5b4.\",\n          \"\\uadf8\\ub807\\uad6c\\ub098. \\ubab0\\ub790\\uc5b4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"situation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"happiness\",\n          \"neutral\",\n          \"disgust\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"emotion\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"sadness\",\n          \"angry\",\n          \"happiness\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "txt_data.rename(columns={'emo_num':'label'}, inplace=True)\n",
        "new_txt_data = txt_data[['wav_id', 'text', 'label']]\n",
        "new_txt_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xhVQSJOrQaxt",
        "outputId": "f2f8742c-318f-401a-87df-b7bd56029d4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     wav_id  \\\n",
              "0  5f4141e29dd513131eacee2f   \n",
              "1  5f4141f59dd513131eacee30   \n",
              "2  5f4142119dd513131eacee31   \n",
              "3  5f4142279dd513131eacee32   \n",
              "4  5f3c9ed98a3c1005aa97c4bd   \n",
              "\n",
              "                                                text  label  \n",
              "0                                   헐! 나 이벤트에 당첨 됐어.      1  \n",
              "1        내가 좋아하는 인플루언서가 이벤트를 하더라고. 그래서 그냥 신청 한번 해봤지.      1  \n",
              "2                        한 명 뽑는 거였는데, 그게 바로 내가 된 거야.      1  \n",
              "3  당연히 마음에 드는 선물이니깐, 이벤트에 내가 신청 한번 해본 거지. 비싼 거야. ...      1  \n",
              "4                   에피타이저 정말 좋아해. 그 것도 괜찮은 생각인 것 같애.      1  "
            ],
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              "      <th>0</th>\n",
              "      <td>5f4141e29dd513131eacee2f</td>\n",
              "      <td>헐! 나 이벤트에 당첨 됐어.</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5f4141f59dd513131eacee30</td>\n",
              "      <td>내가 좋아하는 인플루언서가 이벤트를 하더라고. 그래서 그냥 신청 한번 해봤지.</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5f4142119dd513131eacee31</td>\n",
              "      <td>한 명 뽑는 거였는데, 그게 바로 내가 된 거야.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5f4142279dd513131eacee32</td>\n",
              "      <td>당연히 마음에 드는 선물이니깐, 이벤트에 내가 신청 한번 해본 거지. 비싼 거야. ...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5f3c9ed98a3c1005aa97c4bd</td>\n",
              "      <td>에피타이저 정말 좋아해. 그 것도 괜찮은 생각인 것 같애.</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
              "    }\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",
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              "    }\n",
              "\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",
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              "\n",
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              "        document.querySelector('#df-7e106ea1-a8a7-457e-853b-37d3000583b8 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-7e106ea1-a8a7-457e-853b-37d3000583b8');\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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df4859fb-4ff8-4681-8706-31a27a7b3230 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_txt_data",
              "summary": "{\n  \"name\": \"new_txt_data\",\n  \"rows\": 15463,\n  \"fields\": [\n    {\n      \"column\": \"wav_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15463,\n        \"samples\": [\n          \"5fb383f1e3dcec421f8358ad\",\n          \"5f808733111dfd48d40fd8a1\",\n          \"5f791df79e04b149046cc16a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13165,\n        \"samples\": [\n          \"\\uc544\\ub2c8 \\ud63c\\uc790 \\ubb34\\uc2a8 \\ub9d0\\uc744 \\ud558\\ub294 \\uac70\\ub0d0\\uace0 \\ub300\\uccb4. \\ub098\\ub294 \\uc9dc\\uc7a5\\uba74\\uc5d0\\uc11c \\ubc8c\\ub808\\ub098\\uc628 \\uac8c \\uc9dc\\uc99d\\ub09c\\ub2e4\\uace0.\",\n          \"\\uc9d1\\uc5d0\\ub9cc \\uc788\\uc73c\\ub2c8\\uae4c \\ub354 \\ud798\\ub4e0 \\uac83 \\uac19\\uc544. \\ud65c\\ub825\\uc774 \\uc5c6\\uc5b4\\uc84c\\uc5b4.\",\n          \"\\uadf8\\ub807\\uad6c\\ub098. \\ubab0\\ub790\\uc5b4.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 음성 데이터 로드"
      ],
      "metadata": {
        "id": "eR1EIKEEQ9Wc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # 데이터 15,000 기준 압축 해제 15분 소요\n",
        "# %cd '/content/drive/MyDrive/BOAZ_MiniProject1'\n",
        "# !zip -FFv '5차년도_2차.zip' --out 'audio_data/5차년도_2차_fixed.zip'\n",
        "# !unzip -qq 'audio_data/5차년도_2차_fixed.zip' -d 'audio_data/'"
      ],
      "metadata": {
        "id": "Ek8ymEI8Q_nH",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "source": [
        "audio_path = '/content/drive/MyDrive/BOAZ_MiniProject1/audio_data'\n",
        "wav_list = txt_data['wav_id']+'.wav'\n",
        "wav_list_tmp = txt_data['wav_id']+'.wav'\n",
        "\n",
        "wav_list_tmp_id = txt_data['wav_id']\n",
        "\n",
        "wav_list_tmp_label, wav_list_tmp_sentence = [], []\n",
        "for x in wav_list_tmp_id:\n",
        "    # Check if wav_id exists in the DataFrame before accessing the label\n",
        "    if x in new_txt_data['wav_id'].values:\n",
        "        wav_list_tmp_label.append(new_txt_data[new_txt_data['wav_id'] == x]['label'].values[0])\n",
        "        wav_list_tmp_sentence.append(new_txt_data[new_txt_data['wav_id'] == x]['text'].values[0])\n",
        "    else:\n",
        "        print(f\"Warning: wav_id '{x}' not found in new_txt_data\")\n",
        "        # Handle missing data, e.g., append empty strings or NaN to all lists\n",
        "        wav_list_tmp_id.remove(x) # Remove the id from the list as well\n",
        "        wav_list_tmp_label.append(None)  # Or any placeholder\n",
        "        wav_list_tmp_sentence.append(None)\n",
        "\n",
        "wav_df = pd.DataFrame(\n",
        "    {'wav_id': wav_list_tmp_id,\n",
        "     'final_label': wav_list_tmp_label,\n",
        "     'sentence': wav_list_tmp_sentence\n",
        "    })"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "pI61BXeGxqEF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 라벨 분포 확인\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "sns.countplot(x = wav_df['final_label'], palette = \"husl\",  ax = ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "mBhKBEpKXrvf",
        "outputId": "3eff6d5f-ab25-4643-a4c1-d3b956cb70c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-61d308f4f34f>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x = wav_df['final_label'], palette = \"husl\",  ax = ax)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### (2) 음성 데이터 feature vector 추출"
      ],
      "metadata": {
        "id": "6O5UcI8TH0Zn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qmeYes_qHQxE"
      },
      "outputs": [],
      "source": [
        "def noise(data):\n",
        "    noise_amp = 0.035*np.random.uniform()*np.amax(data)\n",
        "    data = data + noise_amp*np.random.normal(size=data.shape[0])\n",
        "    return data\n",
        "\n",
        "def stretch(data, rate=0.8):\n",
        "    return librosa.effects.time_stretch(data, rate)\n",
        "\n",
        "def shift(data):\n",
        "    shift_range = int(np.random.uniform(low=-5, high = 5)*1000)\n",
        "    return np.roll(data, shift_range)\n",
        "\n",
        "def pitch(data, sampling_rate, pitch_factor=0.7):\n",
        "    return librosa.effects.pitch_shift(data, sampling_rate, pitch_factor)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_features(data, sample_rate):\n",
        "    # ZCR\n",
        "    result = np.array([])\n",
        "    zcr = np.mean(librosa.feature.zero_crossing_rate(y=data).T, axis=0)\n",
        "    result=np.hstack((result, zcr)) # stacking horizontally\n",
        "\n",
        "    # Chroma_stft\n",
        "    stft = np.abs(librosa.stft(data))\n",
        "    chroma_stft = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)\n",
        "    result = np.hstack((result, chroma_stft)) # stacking horizontally\n",
        "\n",
        "    # MFCC\n",
        "    mfcc = np.mean(librosa.feature.mfcc(y=data, sr=sample_rate).T, axis=0)\n",
        "    result = np.hstack((result, mfcc)) # stacking horizontally\n",
        "\n",
        "    # Root Mean Square Value\n",
        "    rms = np.mean(librosa.feature.rms(y=data).T, axis=0)\n",
        "    result = np.hstack((result, rms)) # stacking horizontally\n",
        "\n",
        "    # MelSpectogram\n",
        "    mel = np.mean(librosa.feature.melspectrogram(y=data, sr=sample_rate).T, axis=0)\n",
        "    result = np.hstack((result, mel)) # stacking horizontally\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "s0QbLMltHs4n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import librosa\n",
        "\n",
        "def noise(data):\n",
        "    noise_amp = 0.005 * np.random.uniform() * np.amax(data)\n",
        "    return data + noise_amp * np.random.normal(size=data.shape)\n",
        "\n",
        "def stretch(data, rate=0.8):\n",
        "    return librosa.effects.time_stretch(y=data, rate=rate)\n",
        "\n",
        "def pitch(data, sampling_rate, pitch_factor=0.7):\n",
        "    return librosa.effects.pitch_shift(y=data, sr=sampling_rate, n_steps=pitch_factor)\n",
        "\n",
        "def extract_features(data, sample_rate):\n",
        "    # Using Mel spectrogram instead of MFCC\n",
        "    mel_spectrogram = librosa.feature.melspectrogram(y=data, sr=sample_rate, n_mels=13)\n",
        "    mel_spectrogram_db = librosa.power_to_db(mel_spectrogram, ref=np.max)\n",
        "    mel_mean = mel_spectrogram_db.mean(axis=1)\n",
        "    return mel_mean\n",
        "\n",
        "def get_features(path):\n",
        "    data, sample_rate = librosa.load(path, duration=2.5, offset=0.0)\n",
        "\n",
        "    # without augmentation\n",
        "    res1 = extract_features(data, sample_rate)\n",
        "    result = np.array(res1)\n",
        "\n",
        "    # data with noise\n",
        "    noise_data = noise(data)\n",
        "    res2 = extract_features(noise_data, sample_rate)\n",
        "    result = np.concatenate((result, res2), axis=0)\n",
        "\n",
        "    # data with stretching and pitching\n",
        "    new_data = stretch(data)\n",
        "    data_stretch_pitch = pitch(new_data, sample_rate)\n",
        "    res3 = extract_features(data_stretch_pitch, sample_rate)\n",
        "    result = np.concatenate((result, res3), axis=0)\n",
        "\n",
        "    return result\n",
        "\n",
        "X_audio, Y = [], []\n",
        "for path, label in zip(wav_df['wav_id'], wav_df['final_label']):\n",
        "    audio_features = get_features(audio_path+'/'+path+'.wav')\n",
        "    X_audio.append(audio_features)\n",
        "    Y.append(label)\n",
        "\n",
        "audio_features_df = pd.DataFrame(X_audio)\n",
        "final_df = pd.concat([audio_features_df, wav_df[['wav_id', 'final_label', 'sentence']]], axis=1)\n",
        "final_df.head(3)\n"
      ],
      "metadata": {
        "id": "guwo1Yg-sLGp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "a60e2937-0186-4905-b92a-d2433da01e2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                     wav_id  final_label  \\\n",
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              "\n",
              "                                      sentence  \n",
              "0                             헐! 나 이벤트에 당첨 됐어.  \n",
              "1  내가 좋아하는 인플루언서가 이벤트를 하더라고. 그래서 그냥 신청 한번 해봤지.  \n",
              "2                  한 명 뽑는 거였는데, 그게 바로 내가 된 거야.  \n",
              "\n",
              "[3 rows x 42 columns]"
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              "      <td>5f4141e29dd513131eacee2f</td>\n",
              "      <td>1</td>\n",
              "      <td>헐! 나 이벤트에 당첨 됐어.</td>\n",
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              "      <td>내가 좋아하는 인플루언서가 이벤트를 하더라고. 그래서 그냥 신청 한번 해봤지.</td>\n",
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              "      <td>-49.742268</td>\n",
              "      <td>-52.676640</td>\n",
              "      <td>-53.806244</td>\n",
              "      <td>-54.051060</td>\n",
              "      <td>-57.111019</td>\n",
              "      <td>-59.885712</td>\n",
              "      <td>-60.253399</td>\n",
              "      <td>-66.104111</td>\n",
              "      <td>...</td>\n",
              "      <td>-55.871662</td>\n",
              "      <td>-59.654465</td>\n",
              "      <td>-59.753307</td>\n",
              "      <td>-64.566338</td>\n",
              "      <td>-69.171631</td>\n",
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              "      <td>-67.324554</td>\n",
              "      <td>5f4142119dd513131eacee31</td>\n",
              "      <td>1</td>\n",
              "      <td>한 명 뽑는 거였는데, 그게 바로 내가 된 거야.</td>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_df"
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = pd.read_csv(\"/content/drive/MyDrive/BOAZ_MiniProject1/data/audio_features.csv\", encoding= 'utf-8')"
      ],
      "metadata": {
        "id": "1N5QmAg3CFJ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 텍스트 embedding vector 추출"
      ],
      "metadata": {
        "id": "ISHY52_nH4mh"
      }
    },
    {
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import librosa\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n",
        "import keras\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout, BatchNormalization\n",
        "# Instead of importing np_utils, just use to_categorical directly\n",
        "from keras.utils import to_categorical\n",
        "from keras.callbacks import ModelCheckpoint"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "nerQURYOcpfI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "source": [
        "class text_embedding():\n",
        "  def __init__(self, model_name):\n",
        "    self.model_name = model_name\n",
        "\n",
        "  def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "  def transform(self, X):\n",
        "        embedding_model = SentenceTransformer(self.model_name, device=device) # Use the device variable here\n",
        "        embedding_vec = embedding_model.encode(X['sentence'])\n",
        "        X_val = np.concatenate((X.drop(['final_label', 'wav_id', 'sentence'], axis = 1), embedding_vec), axis = 1)\n",
        "        return X_val"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "qdXWwb2o5FPQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "# KoBERT 전용 토크나이저 로드\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"skt/kobert-base-v1\")\n",
        "\n",
        "def text_embedding(model_name):\n",
        "    if model_name == 'skt/kobert-base-v1':\n",
        "        # KoBERT 모델용 임베딩 함수 구현\n",
        "        def transform(dataframe):\n",
        "            # 텍스트 데이터를 KoBERT 전용 토크나이저로 변환\n",
        "            tokenized_data = dataframe['sentence'].apply(lambda x: tokenizer(x, return_tensors=\"pt\", padding=True, truncation=True, max_length=128))\n",
        "            return tokenized_data\n",
        "        return transform\n",
        "    else:\n",
        "        # 다른 모델용 임베딩 함수 (기존 코드)\n",
        "        return SomeOtherEmbeddingFunction(model_name)"
      ],
      "metadata": {
        "id": "ZaEKw2hRH-ZA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 모델링"
      ],
      "metadata": {
        "id": "QVGjnVg5H_XL"
      }
    },
    {
      "source": [
        "def custom_model(x_train):\n",
        "  model=Sequential()\n",
        "  model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))\n",
        "  model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "\n",
        "  model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
        "  model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "\n",
        "  model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
        "  model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "  model.add(Dropout(0.2))\n",
        "\n",
        "  model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
        "  model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
        "\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(units=32, activation='relu'))\n",
        "  model.add(Dropout(0.3))\n",
        "\n",
        "  # Change the number of units in the final Dense layer to match the number of classes\n",
        "  model.add(Dense(units=4, activation='softmax'))  # 4 classes instead of 6\n",
        "  model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])\n",
        "\n",
        "  #model.summary()\n",
        "  return model\n",
        "\n",
        "# Define rlrp outside the function so it's accessible in the global scope\n",
        "rlrp = ReduceLROnPlateau(monitor='loss', factor=0.4, verbose=0, patience=3, min_lr=1e-6) #learning rate 조절\n",
        "\n",
        "scaler = StandardScaler()\n",
        "encoder = OneHotEncoder()"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "lVRqfBw-fOA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install onnxruntime==1.12.0\n",
        "!pip install 'git+https://github.com/SKTBrain/KoBERT.git#egg=kobert_tokenizer&subdirectory=kobert_hf'"
      ],
      "metadata": {
        "id": "aJ2ihXwhCFny",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32dfd8fe-dbae-4e18-834b-cee50a8a85eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting onnxruntime==1.12.0\n",
            "  Downloading onnxruntime-1.12.0-cp310-cp310-manylinux_2_27_x86_64.whl.metadata (3.6 kB)\n",
            "Collecting coloredlogs (from onnxruntime==1.12.0)\n",
            "  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime==1.12.0) (24.3.25)\n",
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            "Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime==1.12.0)\n",
            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl.metadata (9.2 kB)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime==1.12.0) (1.3.0)\n",
            "Downloading onnxruntime-1.12.0-cp310-cp310-manylinux_2_27_x86_64.whl (4.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: humanfriendly, coloredlogs, onnxruntime\n",
            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.12.0\n",
            "Collecting kobert_tokenizer\n",
            "  Cloning https://github.com/SKTBrain/KoBERT.git to /tmp/pip-install-v_5z5_wy/kobert-tokenizer_f72eeb9f3ecd498f895d547c9a1dab16\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/SKTBrain/KoBERT.git /tmp/pip-install-v_5z5_wy/kobert-tokenizer_f72eeb9f3ecd498f895d547c9a1dab16\n",
            "  Resolved https://github.com/SKTBrain/KoBERT.git to commit 47a69af87928fc24e20f571fe10c3cc9dd9af9a3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: kobert_tokenizer\n",
            "  Building wheel for kobert_tokenizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert_tokenizer: filename=kobert_tokenizer-0.1-py3-none-any.whl size=4632 sha256=5c97cf807124483d72ca28b7c1def434901e2544f6b646b6366275eac11597b1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_tquutka/wheels/e9/1a/3f/a864970e8a169c176befa3c4a1e07aa612f69195907a4045fe\n",
            "Successfully built kobert_tokenizer\n",
            "Installing collected packages: kobert_tokenizer\n",
            "Successfully installed kobert_tokenizer-0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install sentence_transformers"
      ],
      "metadata": {
        "id": "BT7S2Epc3A5r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "658edc4a-85a0-4a0b-a6fc-83f0353457fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertModel, BertTokenizer\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "\n",
        "class text_embedding:\n",
        "  def __init__(self, model_name):\n",
        "    self.model_name = model_name\n",
        "\n",
        "  def transform(self, X):\n",
        "    if self.model_name == 'skt/kobert-base-v1':\n",
        "        # Use KoBERTTokenizer for skt/kobert-base-v1\n",
        "        tokenizer = BertTokenizer.from_pretrained('skt/kobert-base-v1')\n",
        "        model = BertModel.from_pretrained('skt/kobert-base-v1')\n",
        "        # Tokenize the sentences\n",
        "        inputs = tokenizer(X['sentence'].tolist(), return_tensors='pt', padding=True, truncation=True)\n",
        "        # Get the embeddings\n",
        "        outputs = model(**inputs)\n",
        "        embedding_vec = outputs.last_hidden_state[:, 0, :].detach().numpy()\n",
        "        X_val = np.concatenate((X.drop(['final_label', 'wav_id', 'sentence'], axis=1), embedding_vec), axis=1)\n",
        "    else:\n",
        "        # For other models, use SentenceTransformer\n",
        "        embedding_model = SentenceTransformer(self.model_name)\n",
        "        embedding_vec = embedding_model.encode(X['sentence'])\n",
        "        X_val = np.concatenate((X.drop(['final_label', 'wav_id', 'sentence'], axis=1), embedding_vec), axis=1)\n",
        "    return X_val"
      ],
      "metadata": {
        "id": "JR7sWw99KObv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# 필수 패키지 설치\n",
        "!pip install scikit-learn\n",
        "\n",
        "# 전처리 및 모델 학습 코드\n",
        "pre_trained_models = [\n",
        "    'klue/roberta-base',\n",
        "    'monologg/koelectra-base-discriminator'\n",
        "]\n",
        "\n",
        "Y = final_df['final_label'].values\n",
        "Y = encoder.fit_transform(np.array(Y).reshape(-1, 1)).toarray()\n",
        "\n",
        "# 결과 저장 디렉토리 설정\n",
        "save_dir = '/content/drive/MyDrive/BOAZ_MiniProject1/model_results'\n",
        "os.makedirs(save_dir, exist_ok=True)\n",
        "\n",
        "def sanitize_filename(filename):\n",
        "    return filename.replace('/', '_').replace(':', '_')\n",
        "\n",
        "for model_name in pre_trained_models:\n",
        "    # 텍스트 임베딩 변환\n",
        "    txt_embed = text_embedding(model_name=model_name)\n",
        "    X = txt_embed.transform(final_df)\n",
        "\n",
        "    # 데이터 분할\n",
        "    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42, shuffle=True)\n",
        "    scaler = StandardScaler()\n",
        "    x_train = scaler.fit_transform(x_train)\n",
        "    x_test = scaler.transform(x_test)\n",
        "\n",
        "    # 차원 확장\n",
        "    x_train = np.expand_dims(x_train, axis=2)\n",
        "    x_test = np.expand_dims(x_test, axis=2)\n",
        "\n",
        "    # 모델 생성 및 학습\n",
        "    model = custom_model(x_train)\n",
        "    history = model.fit(x_train, y_train, batch_size=16, epochs=10, validation_data=(x_test, y_test), callbacks=[rlrp])\n",
        "\n",
        "    # 모델 평가\n",
        "    test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
        "    print(\"Pre-trained Model: \", model_name)\n",
        "    print(\"Test Accuracy: \", test_acc)\n",
        "\n",
        "    # 예측 및 평가\n",
        "    y_pred = model.predict(x_test)\n",
        "    y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "    y_test_classes = np.argmax(y_test, axis=1)\n",
        "    f1 = f1_score(y_test_classes, y_pred_classes, average='weighted')\n",
        "\n",
        "    # 결과 출력\n",
        "    print(\"Pre-trained Model: \", model_name)\n",
        "    print(\"Test Accuracy: \", test_acc)\n",
        "    print(\"F1 Score: \", f1)\n",
        "\n",
        "    # 파일 이름 sanitize\n",
        "    sanitized_model_name = sanitize_filename(model_name)\n",
        "\n",
        "    # 파일 저장\n",
        "    np.save(os.path.join(save_dir, f'{sanitized_model_name}_predictions.npy'), y_pred_classes)\n",
        "    np.save(os.path.join(save_dir, f'{sanitized_model_name}_true_labels.npy'), y_test_classes)\n",
        "    np.save(os.path.join(save_dir, f'{sanitized_model_name}_probabilities.npy'), y_pred)\n",
        "\n",
        "    print(f\"Saved prediction results for {model_name}\")\n",
        "    print(\"-----------------------------\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "outputId": "3e04dc61-299e-4658-9eab-6c9cdc1bd42d"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.1)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name klue/roberta-base. Creating a new one with mean pooling.\n",
            "Some weights of RobertaModel were not initialized from the model checkpoint at klue/roberta-base and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m428s\u001b[0m 550ms/step - accuracy: 0.3519 - loss: 1.3578 - val_accuracy: 0.4504 - val_loss: 1.2420 - learning_rate: 0.0010\n",
            "Epoch 2/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m385s\u001b[0m 497ms/step - accuracy: 0.4460 - loss: 1.2342 - val_accuracy: 0.5011 - val_loss: 1.1436 - learning_rate: 0.0010\n",
            "Epoch 3/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m381s\u001b[0m 492ms/step - accuracy: 0.5000 - loss: 1.1267 - val_accuracy: 0.5639 - val_loss: 1.0422 - learning_rate: 0.0010\n",
            "Epoch 4/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m386s\u001b[0m 499ms/step - accuracy: 0.5256 - loss: 1.0921 - val_accuracy: 0.5671 - val_loss: 1.0265 - learning_rate: 0.0010\n",
            "Epoch 5/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m440s\u001b[0m 496ms/step - accuracy: 0.5403 - loss: 1.0618 - val_accuracy: 0.5917 - val_loss: 0.9937 - learning_rate: 0.0010\n",
            "Epoch 6/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m382s\u001b[0m 494ms/step - accuracy: 0.5415 - loss: 1.0453 - val_accuracy: 0.5988 - val_loss: 0.9716 - learning_rate: 0.0010\n",
            "Epoch 7/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m440s\u001b[0m 492ms/step - accuracy: 0.5626 - loss: 1.0096 - val_accuracy: 0.6140 - val_loss: 0.9566 - learning_rate: 0.0010\n",
            "Epoch 8/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m387s\u001b[0m 498ms/step - accuracy: 0.5710 - loss: 0.9944 - val_accuracy: 0.6117 - val_loss: 0.9648 - learning_rate: 0.0010\n",
            "Epoch 9/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m380s\u001b[0m 491ms/step - accuracy: 0.5838 - loss: 0.9691 - val_accuracy: 0.6146 - val_loss: 0.9556 - learning_rate: 0.0010\n",
            "Epoch 10/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m383s\u001b[0m 494ms/step - accuracy: 0.5874 - loss: 0.9582 - val_accuracy: 0.6117 - val_loss: 0.9456 - learning_rate: 0.0010\n",
            "Pre-trained Model:  klue/roberta-base\n",
            "Test Accuracy:  0.6117038726806641\n",
            "\u001b[1m97/97\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 242ms/step\n",
            "Pre-trained Model:  klue/roberta-base\n",
            "Test Accuracy:  0.6117038726806641\n",
            "F1 Score:  0.5977136346400203\n",
            "Saved prediction results for klue/roberta-base\n",
            "-----------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name monologg/koelectra-base-discriminator. Creating a new one with mean pooling.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m394s\u001b[0m 507ms/step - accuracy: 0.3631 - loss: 1.3568 - val_accuracy: 0.3679 - val_loss: 1.3424 - learning_rate: 0.0010\n",
            "Epoch 2/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m440s\u001b[0m 505ms/step - accuracy: 0.3670 - loss: 1.3397 - val_accuracy: 0.3679 - val_loss: 1.3337 - learning_rate: 0.0010\n",
            "Epoch 3/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m456s\u001b[0m 523ms/step - accuracy: 0.3635 - loss: 1.3356 - val_accuracy: 0.3679 - val_loss: 1.3332 - learning_rate: 0.0010\n",
            "Epoch 4/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m425s\u001b[0m 502ms/step - accuracy: 0.3609 - loss: 1.3391 - val_accuracy: 0.3679 - val_loss: 1.3331 - learning_rate: 0.0010\n",
            "Epoch 5/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m443s\u001b[0m 504ms/step - accuracy: 0.3600 - loss: 1.3393 - val_accuracy: 0.3679 - val_loss: 1.3333 - learning_rate: 0.0010\n",
            "Epoch 6/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m460s\u001b[0m 527ms/step - accuracy: 0.3594 - loss: 1.3383 - val_accuracy: 0.3679 - val_loss: 1.3332 - learning_rate: 0.0010\n",
            "Epoch 7/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m389s\u001b[0m 503ms/step - accuracy: 0.3661 - loss: 1.3316 - val_accuracy: 0.3679 - val_loss: 1.3333 - learning_rate: 0.0010\n",
            "Epoch 8/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m441s\u001b[0m 502ms/step - accuracy: 0.3689 - loss: 1.3349 - val_accuracy: 0.3679 - val_loss: 1.3332 - learning_rate: 4.0000e-04\n",
            "Epoch 9/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m406s\u001b[0m 524ms/step - accuracy: 0.3637 - loss: 1.3353 - val_accuracy: 0.3679 - val_loss: 1.3332 - learning_rate: 4.0000e-04\n",
            "Epoch 10/10\n",
            "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m426s\u001b[0m 503ms/step - accuracy: 0.3647 - loss: 1.3356 - val_accuracy: 0.3679 - val_loss: 1.3332 - learning_rate: 4.0000e-04\n",
            "Pre-trained Model:  monologg/koelectra-base-discriminator\n",
            "Test Accuracy:  0.36792758107185364\n",
            "\u001b[1m97/97\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 236ms/step\n",
            "Pre-trained Model:  monologg/koelectra-base-discriminator\n",
            "Test Accuracy:  0.36792758107185364\n",
            "F1 Score:  0.1979208623126626\n",
            "Saved prediction results for monologg/koelectra-base-discriminator\n",
            "-----------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kpgLr8WEdVky"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}