3. modeling_cosine.ipynb

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      "machine_shape": "hm",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hyooz-Kang/Hmm2Song/blob/subeen/3.%20modeling_cosine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. settings**"
      ],
      "metadata": {
        "id": "6eSk6_slmEXY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import librosa\n",
        "import numpy as np\n",
        "import soundfile as sf\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "CFVjBb289KuT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "outputId": "bd78b8b2-bc0f-4644-8d20-aa7d53a7073f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "### GPU 사용 여부 체크\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using {device} for training.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-CCp0cVG5xW",
        "outputId": "94dd8c03-cf10-40a0-d7d6-72e69737d05a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda for training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Preparing Data**\n",
        "- 원곡 음원과 허밍 음원(변형된 음원)의 mel 벡터를 데이터프레임에 매칭\n",
        "  - 각 음원 조각별로 5가지의 변형\n",
        "- train_map 리스트에 각 원본 음원과 허밍 음원의 경로를 튜플로 저장"
      ],
      "metadata": {
        "id": "-tpAPi4ZI-2X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2-1. Load & Map Data**"
      ],
      "metadata": {
        "id": "39Sdgg1qJkUU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### 곡 번호 가져오기\n",
        "\n",
        "input_path = '/content/drive/MyDrive/Hmm2Song/data/song/Split_input/'\n",
        "\n",
        "files = os.listdir(input_path)\n",
        "mp3_files = [f for f in files if f.endswith(\".mp3\")]\n",
        "mp3_files = sorted(mp3_files)\n",
        "\n",
        "# 곡 번호 추출\n",
        "numbers = []\n",
        "for f in mp3_files:\n",
        "  # Split the filename by \"/\"\n",
        "  parts = f.split(\"/\")\n",
        "  # Get the last part of the filename, which is the ID\n",
        "  number = parts[-1].split(\"_\")[0]\n",
        "  numbers.append(number)"
      ],
      "metadata": {
        "id": "U7-eg2hy2-mo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 데이터 파일 경로 설정\n",
        "\n",
        "org_path = '/content/drive/MyDrive/Hmm2Song/data/song/org.npy'\n",
        "fh_path = '/content/drive/MyDrive/Hmm2Song/data/song/fh.npy'\n",
        "fl_path = '/content/drive/MyDrive/Hmm2Song/data/song/fl.npy'\n",
        "mh_path = '/content/drive/MyDrive/Hmm2Song/data/song/mh.npy'\n",
        "ml_path = '/content/drive/MyDrive/Hmm2Song/data/song/ml.npy'\n",
        "n_path = '/content/drive/MyDrive/Hmm2Song/data/song/n.npy'"
      ],
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "### 원곡 & 허밍 데이터 로드\n",
        "org = np.load(org_path, allow_pickle = True)\n",
        "fh = np.load(fh_path, allow_pickle = True)\n",
        "fl = np.load(fl_path, allow_pickle = True)\n",
        "mh = np.load(mh_path, allow_pickle = True)\n",
        "ml = np.load(ml_path, allow_pickle = True)\n",
        "n = np.load(n_path, allow_pickle = True)"
      ],
      "metadata": {
        "id": "ksvEh_fW27UM"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "### 데이터프레임 생성\n",
        "data = {\n",
        "    'song_no': numbers,\n",
        "    'org': org,\n",
        "    'fh': fh,\n",
        "    'fl': fl,\n",
        "    'mh': mh,\n",
        "    'ml': ml,\n",
        "    'n': n\n",
        "}\n",
        "\n",
        "music = pd.DataFrame(data)\n",
        "music.head()"
      ],
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      "outputs": [
        {
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              "  song_no                                                org  \\\n",
              "0     000  [[-53.434498, -53.434498, -53.434498, -53.4344...   \n",
              "1     000  [[-11.306869, -10.583914, -13.366933, -14.5223...   \n",
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              "3     000  [[20.618156, 21.427076, 19.18558, 13.680927, 3...   \n",
              "4     000  [[-16.27028, -21.62627, -47.989334, -50.855732...   \n",
              "\n",
              "                                                  fh  \\\n",
              "0  [[-64.46365, -64.46365, -64.46365, -64.46365, ...   \n",
              "1  [[-33.90235, -39.70997, -57.306282, -57.306282...   \n",
              "2  [[-44.07569, -49.08165, -47.601494, -48.4676, ...   \n",
              "3  [[-30.319366, -31.61372, -34.726913, -40.04141...   \n",
              "4  [[-45.3732, -53.73243, -59.59111, -59.59111, -...   \n",
              "\n",
              "                                                  fl  \\\n",
              "0  [[-53.462494, -53.462494, -53.462494, -53.4624...   \n",
              "1  [[-11.283159, -10.587484, -13.5872135, -14.666...   \n",
              "2  [[-8.294864, -10.802288, -13.778696, -21.03367...   \n",
              "3  [[20.610598, 21.41199, 19.180431, 13.631516, 3...   \n",
              "4  [[-14.381916, -19.772512, -47.65141, -51.05184...   \n",
              "\n",
              "                                                  mh  \\\n",
              "0  [[-55.8885, -55.8885, -55.8885, -55.8885, -55....   \n",
              "1  [[-12.906219, -11.651057, -14.351291, -15.2013...   \n",
              "2  [[-9.173194, -13.967354, -21.154625, -29.20704...   \n",
              "3  [[16.674326, 14.218125, 11.978937, 7.5957184, ...   \n",
              "4  [[-19.572826, -24.678566, -46.37851, -52.57517...   \n",
              "\n",
              "                                                  ml  \\\n",
              "0  [[-55.95763, -55.95763, -55.95763, -55.95763, ...   \n",
              "1  [[-10.752398, -10.82634, -14.578722, -15.97892...   \n",
              "2  [[-6.0015, -9.459395, -13.348994, -20.772518, ...   \n",
              "3  [[23.048532, 24.449047, 21.363308, 15.357315, ...   \n",
              "4  [[-15.229172, -20.558203, -43.36681, -43.05114...   \n",
              "\n",
              "                                                   n  \n",
              "0  [[-9.18961, -8.082821, -10.054024, -9.653147, ...  \n",
              "1  [[-5.086788, -5.206139, -11.650671, -7.4844594...  \n",
              "2  [[-4.316294, -7.043939, -8.572198, -9.052606, ...  \n",
              "3  [[20.633581, 21.456093, 19.215803, 13.742048, ...  \n",
              "4  [[-6.430007, -8.372921, -9.389027, -9.194475, ...  "
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              "      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-eac60d00-13e5-4971-88f9-a3e30270bec1 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": "music",
              "repr_error": "unhashable type: 'numpy.ndarray'"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "각각의 곡에 대해 원곡과 허밍된 음성의 mel 값이 벡터 형태로 넘어온다.\n"
      ],
      "metadata": {
        "id": "NOiEJPDUiuqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(music)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_WfLIAah7rj",
        "outputId": "7e97d735-c5fa-4fc5-ab30-6d2bded390a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22730"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2-2. Train/Test Split**\n",
        "- train의 경우 각 원본 음원과 허밍 음원의 mel 벡터를 쌍으로 매칭해 튜플로 저장\n",
        "- test의 경우 mel 벡터만 저장"
      ],
      "metadata": {
        "id": "WQ2NbFu_J3cM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_train_test(df, random_seed = 42):\n",
        "\n",
        "    np.random.seed(random_seed)\n",
        "\n",
        "    train_maps, test_li = [], []\n",
        "\n",
        "    for index, row in df.iterrows():\n",
        "        test_hum_col = np.random.choice(['fh', 'fl', 'mh', 'ml', 'n'])\n",
        "        test = row[test_hum_col]\n",
        "        test_li.append(test)\n",
        "\n",
        "        train_hum_cols = list(set(['fh', 'fl', 'mh', 'ml', 'n']) - {test_hum_col})\n",
        "        for train_hum_col in train_hum_cols:\n",
        "            train_map = (row['org'], row[train_hum_col])\n",
        "            train_maps.append(train_map)\n",
        "\n",
        "    return train_maps, test_li"
      ],
      "metadata": {
        "id": "XigTY52dGEr0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_map, test = make_train_test(music, random_seed = 42)"
      ],
      "metadata": {
        "id": "iJG31oukM2uA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_map[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZE-8IlrNFjZ",
        "outputId": "e8b74d05-c461-4a1e-84f5-d95e1c2fafb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-53.434498 , -53.434498 , -53.434498 , ..., -11.491163 ,\n",
              "         -19.790825 ,  -4.282523 ],\n",
              "        [-53.434498 , -53.434498 , -53.434498 , ...,  -6.7589192,\n",
              "         -10.0783825,  -3.128749 ],\n",
              "        [-53.434498 , -53.434498 , -53.434498 , ...,  -6.1969037,\n",
              "          -9.043031 ,   1.9364727],\n",
              "        ...,\n",
              "        [-53.434498 , -53.434498 , -53.434498 , ..., -53.434498 ,\n",
              "         -53.434498 , -33.53729  ],\n",
              "        [-53.434498 , -53.434498 , -53.434498 , ..., -53.434498 ,\n",
              "         -53.434498 , -33.758835 ],\n",
              "        [-53.434498 , -53.434498 , -53.434498 , ..., -53.434498 ,\n",
              "         -53.434498 , -35.193287 ]], dtype=float32),\n",
              " array([[-55.8885   , -55.8885   , -55.8885   , ..., -16.854303 ,\n",
              "         -18.92465  , -10.810755 ],\n",
              "        [-55.8885   , -55.8885   , -55.8885   , ..., -15.1059   ,\n",
              "         -19.543844 , -11.441732 ],\n",
              "        [-55.8885   , -55.8885   , -55.8885   , ...,  -7.560642 ,\n",
              "         -10.973306 ,  -5.6860375],\n",
              "        ...,\n",
              "        [-55.8885   , -55.8885   , -55.8885   , ..., -55.8885   ,\n",
              "         -53.303276 , -44.510006 ],\n",
              "        [-55.8885   , -55.8885   , -55.8885   , ..., -55.8885   ,\n",
              "         -55.8885   , -47.985565 ],\n",
              "        [-55.8885   , -55.8885   , -55.8885   , ..., -55.8885   ,\n",
              "         -55.8885   , -54.24843  ]], dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_map[0][0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7drXcL9AwYVL",
        "outputId": "43564199-edf5-4836-cb7d-f11fd6390c9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 431)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbOQANQNNIrn",
        "outputId": "00440d8d-42e0-4bef-90a0-d1c9159ec9ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-55.95763  , -55.95763  , -55.95763  , ..., -13.875435 ,\n",
              "        -15.662851 ,  -5.4593253],\n",
              "       [-55.95763  , -55.95763  , -55.95763  , ...,  -7.1582246,\n",
              "        -11.020405 ,  -2.7339075],\n",
              "       [-55.95763  , -55.95763  , -55.95763  , ...,   6.5668344,\n",
              "          6.6708145,   6.1347055],\n",
              "       ...,\n",
              "       [-55.95763  , -55.95763  , -55.95763  , ..., -55.95763  ,\n",
              "        -52.45276  , -37.819366 ],\n",
              "       [-55.95763  , -55.95763  , -55.95763  , ..., -55.95763  ,\n",
              "        -53.005585 , -37.424305 ],\n",
              "       [-55.95763  , -55.95763  , -55.95763  , ..., -55.95763  ,\n",
              "        -55.95763  , -40.89798  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWNhMpllw6m4",
        "outputId": "4b762921-b7d9-446a-9dc1-6d406829c65e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 431)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Dataset**\n",
        "- 데이터셋을 정의하고 로드하는 과정\n",
        "- Triplet Loss를 계산하기 위한 triplet dataset 생성\n",
        "- PyTorch의 `Dataset` 클래스를 상속받아 트리플렛 데이터셋을 구현\n",
        "- 원본 음원, 변형된 음원, 부정적 예제로 이루어진 트리플렛 쌍을 제공\n",
        "  - 각 샘플은 anchor(허밍), positive(원곡), negative(다른곡)의 세 부분으로 구성됨"
      ],
      "metadata": {
        "id": "csXwQpI_XN6a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn.functional as F"
      ],
      "metadata": {
        "id": "4MdFkhHHBaUO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 스펙트로그램 불러오기\n",
        "## 128: 주파수, 431: 시간 스텝 수\n",
        "\n",
        "def load_spectrogram(spectrogram):\n",
        "  if not isinstance(spectrogram, torch.Tensor):\n",
        "    spectrogram = torch.tensor(spectrogram)\n",
        "  if spectrogram.shape != (128, 431): # 길이가 짧게 잘린 음원에 패딩을 0으로 넣어 모양을 맞춰줌\n",
        "    pad = (0, 431 - spectrogram.shape[1])\n",
        "    spectrogram = F.pad(spectrogram, pad, \"constant\", 0)\n",
        "\n",
        "  return spectrogram"
      ],
      "metadata": {
        "id": "gD1tuLtEE9Fx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MusicTripletDataset(Dataset):\n",
        "\n",
        "  ### 클래스를 초기화하고 데이터셋의 정보를 설정\n",
        "  def __init__(self, music_files):\n",
        "    self.music_files = music_files # (원곡, 허밍)\n",
        "\n",
        "  ### 데이터셋의 전체 길이를 반환\n",
        "  def __len__(self):\n",
        "    return len(self.music_files)\n",
        "\n",
        "  ### 트리플렛 데이터셋 구성\n",
        "  def __getitem__(self, idx):\n",
        "    ## 원곡(Positive)과 허밍(Anchor) 데이터 쌍 구성\n",
        "    positive_file, anchor_file = self.music_files[idx]\n",
        "    # 스펙트로그램 정보 가져오기\n",
        "    positive = load_spectrogram(positive_file)\n",
        "    anchor = load_spectrogram(anchor_file)\n",
        "\n",
        "    ## 부정(negative) 예제 생성\n",
        "    # positive가 아닌 것은 모두 부정 예제 -> 임의의 곡을 선택하여 부정 예제로 학습시킴\n",
        "    negative_idx = np.random.choice([i for i in range(len(self.music_files)) if i != idx])\n",
        "    negative_file = self.music_files[negative_idx][0] # 다른 곡(-> 원곡) 선택\n",
        "    negative = load_spectrogram(negative_file)\n",
        "\n",
        "    return anchor, positive, negative"
      ],
      "metadata": {
        "id": "I3p52ZNNkk_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 데이터 준비\n",
        "train = MusicTripletDataset(train_map)\n",
        "test = [load_spectrogram(spec) for spec in test]\n",
        "\n",
        "# 미니배치 단위로 데이터를 효율적으로 불러오기\n",
        "batch_size = 64\n",
        "trainloader = DataLoader(train, batch_size = batch_size, shuffle = True)"
      ],
      "metadata": {
        "id": "TTg3ZdP_kvtB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 데이터 shape 확인\n",
        "anchor, positive, negative = next(iter(trainloader))\n",
        "\n",
        "print('Anchor shape:', anchor.shape)\n",
        "print('Positive shape:', positive.shape)\n",
        "print('Negative shape:', negative.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUTHJh8_GG1E",
        "outputId": "37d4d2df-7704-4bad-d9fa-0e3cc3004265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Anchor shape: torch.Size([64, 128, 431])\n",
            "Positive shape: torch.Size([64, 128, 431])\n",
            "Negative shape: torch.Size([64, 128, 431])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "batch_size, height(= 주파수), width(= time step)"
      ],
      "metadata": {
        "id": "w2vdbUoQG3HM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Modeling**\n",
        "- 신경망 모델을 정의하고 훈련하는 부분\n",
        "  - Triplet Loss를 활용한 학습을 위한 모델 구성 부분\n",
        "- Triplet Loss는 세 가지 샘플(Anchor, Positive, Negative) 간의 거리 관계를 학습\n",
        "  - Anchor(허밍)와 Positive(원곡) 간의 거리를 최소화하고, Anchor(허밍)와 Negative(다른곡) 간의 거리를 최대화하는 방식으로 모델을 훈련"
      ],
      "metadata": {
        "id": "fPpdYqjLYAM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "rxhV8Pz7LZL4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 학습을 위한 모델 구성 부분\n",
        "## 음원 스펙트로그램에 적합한 합성곱 층과 풀링 층, 그리고 완전 연결 층으로 구성\n",
        "\n",
        "class ConvSubnet(nn.Module):\n",
        "  def __init__(self, embedding_dims = 128):\n",
        "    super(ConvSubnet, self).__init__()\n",
        "    ## Convolutional Layer\n",
        "    self.conv1 = nn.Conv2d(1, 64, kernel_size = 3, padding = 1)\n",
        "    self.conv2 = nn.Conv2d(64, 128, kernel_size = 3, padding = 1)\n",
        "    self.conv3 = nn.Conv2d(128, 256, kernel_size = 3, padding = 1)\n",
        "    ## MaxPooling Layer\n",
        "    self.pool = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
        "    ## Fully Connected Layer\n",
        "    # maxpooling을 3번 거치면서 height(128 => 64 -> 32 -> 16), width(431 => 215, 107, 53)\n",
        "    self.fc = nn.Linear(848, embedding_dims)  # 16 * 53 = 848\n",
        "\n",
        "  def forward(self, x):\n",
        "    ## 데이터 차원 조정 -> [batch, channel, height, width]\n",
        "    x = x.unsqueeze(1)\n",
        "    ## Convolutional Layer를 통과시켜 특징을 추출하고 활성화 함수를 적용\n",
        "    x = self.pool(F.relu(self.conv1(x)))\n",
        "    x = self.pool(F.relu(self.conv2(x)))\n",
        "    x = self.pool(F.relu(self.conv3(x)))\n",
        "    ## 데이터를 1차원으로 펼침\n",
        "    x = x.view(-1, 848)\n",
        "    ## FC Layer를 통과시켜 임베딩 생성\n",
        "    x = self.fc(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "0EGGZLUC5CCt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "### Loss 시각화 함수\n",
        "def plot_loss_curve(loss_curve):\n",
        "  plt.plot(loss_curve, label='Training Loss')\n",
        "  plt.title('Training Loss Curve')\n",
        "  plt.xlabel('Iteration')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "EMawGXkVOJmE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### TripletMarginLoss 및 모델 초기화\n",
        "\n",
        "triplet_loss = nn.TripletMarginWithDistanceLoss(distance_function = nn.CosineSimilarity()) # 코사인 유사도로 거리 측정\n",
        "model = ConvSubnet().to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)"
      ],
      "metadata": {
        "id": "T6QA1COKeL-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## 모델 가중치 저장\n",
        "now = datetime.now().strftime('%Y%m%d_%H%M%S')\n",
        "model_dir = f'/content/drive/MyDrive/Hmm2Song/차수빈/model/{now}/'\n",
        "os.mkdir(model_dir)"
      ],
      "metadata": {
        "id": "0upCDvOLebX1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 학습 loop 최적화\n",
        "\n",
        "num_epochs = 5\n",
        "checkpoint = 1\n",
        "log_steps = 50\n",
        "loss_curve = []  # Loss 저장을 위한 리스트\n",
        "\n",
        "# 모델을 학습 모드로 설정\n",
        "model.train()\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  print(f'========== Epoch {epoch + 1} ==========')\n",
        "  for step, (anchor, positive, negative) in enumerate(trainloader, 1):\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    anchor, positive, negative = anchor.to(device), positive.to(device), negative.to(device)\n",
        "    embedded_anchor, embedded_positive, embedded_negative = model(anchor), model(positive), model(negative)\n",
        "\n",
        "    loss = triplet_loss(embedded_anchor, embedded_positive, embedded_negative)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    loss_curve.append(loss.item())\n",
        "\n",
        "    if step % log_steps == 0:\n",
        "      print(f'Epoch {epoch + 1}, Step {step}, Loss: {loss.item()}')\n",
        "\n",
        "  # 에폭이 끝날 때마다 모델 저장\n",
        "  if (epoch + 1) % checkpoint == 0:\n",
        "      torch.save(model.state_dict(), os.path.join(model_dir, f'model_weights_{epoch + 1}.pth'))\n",
        "\n",
        "  print(f'Epoch {epoch + 1}, Loss: {loss.item()}')\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGeDi3viUz2e",
        "outputId": "ee526179-cd2e-435e-d34b-b5ea3ad3f982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========== Epoch 1 ==========\n",
            "Epoch 1, Step 50, Loss: 1.000361680984497\n",
            "Epoch 1, Step 100, Loss: 1.0001509189605713\n",
            "Epoch 1, Step 150, Loss: 1.0001165866851807\n",
            "Epoch 1, Step 200, Loss: 1.000097393989563\n",
            "Epoch 1, Step 250, Loss: 1.0000646114349365\n",
            "Epoch 1, Step 300, Loss: 1.0000734329223633\n",
            "Epoch 1, Step 350, Loss: 1.0000474452972412\n",
            "Epoch 1, Step 400, Loss: 1.0000462532043457\n",
            "Epoch 1, Step 450, Loss: 1.000036597251892\n",
            "Epoch 1, Step 500, Loss: 1.0000317096710205\n",
            "Epoch 1, Step 550, Loss: 1.0000249147415161\n",
            "Epoch 1, Step 600, Loss: 1.0000276565551758\n",
            "Epoch 1, Step 650, Loss: 1.000024437904358\n",
            "Epoch 1, Step 700, Loss: 1.0000183582305908\n",
            "Epoch 1, Step 750, Loss: 1.0000152587890625\n",
            "Epoch 1, Step 800, Loss: 1.0000157356262207\n",
            "Epoch 1, Step 850, Loss: 1.0000113248825073\n",
            "Epoch 1, Step 900, Loss: 1.0000122785568237\n",
            "Epoch 1, Step 950, Loss: 1.0000110864639282\n",
            "Epoch 1, Step 1000, Loss: 1.000009298324585\n",
            "Epoch 1, Step 1050, Loss: 1.000009536743164\n",
            "Epoch 1, Step 1100, Loss: 1.000006914138794\n",
            "Epoch 1, Step 1150, Loss: 1.0000064373016357\n",
            "Epoch 1, Step 1200, Loss: 1.000006079673767\n",
            "Epoch 1, Step 1250, Loss: 1.0000061988830566\n",
            "Epoch 1, Step 1300, Loss: 1.000006079673767\n",
            "Epoch 1, Step 1350, Loss: 1.0000059604644775\n",
            "Epoch 1, Step 1400, Loss: 1.0000053644180298\n",
            "Epoch 1, Loss: 1.000003695487976\n",
            "\n",
            "========== Epoch 2 ==========\n",
            "Epoch 2, Step 50, Loss: 1.0000041723251343\n",
            "Epoch 2, Step 100, Loss: 1.0000035762786865\n",
            "Epoch 2, Step 150, Loss: 1.0000032186508179\n",
            "Epoch 2, Step 200, Loss: 1.000003695487976\n",
            "Epoch 2, Step 250, Loss: 1.0000033378601074\n",
            "Epoch 2, Step 300, Loss: 1.000003457069397\n",
            "Epoch 2, Step 350, Loss: 1.0000025033950806\n",
            "Epoch 2, Step 400, Loss: 1.0000032186508179\n",
            "Epoch 2, Step 450, Loss: 1.0000030994415283\n",
            "Epoch 2, Step 500, Loss: 1.000002145767212\n",
            "Epoch 2, Step 550, Loss: 1.0000025033950806\n",
            "Epoch 2, Step 600, Loss: 1.0000025033950806\n",
            "Epoch 2, Step 650, Loss: 1.000002384185791\n",
            "Epoch 2, Step 700, Loss: 1.000002145767212\n",
            "Epoch 2, Step 750, Loss: 1.0000019073486328\n",
            "Epoch 2, Step 800, Loss: 1.0000020265579224\n",
            "Epoch 2, Step 850, Loss: 1.000002145767212\n",
            "Epoch 2, Step 900, Loss: 1.0000019073486328\n",
            "Epoch 2, Step 950, Loss: 1.0000017881393433\n",
            "Epoch 2, Step 1000, Loss: 1.0000011920928955\n",
            "Epoch 2, Step 1050, Loss: 1.0000016689300537\n",
            "Epoch 2, Step 1100, Loss: 1.0000014305114746\n",
            "Epoch 2, Step 1150, Loss: 1.0000019073486328\n",
            "Epoch 2, Step 1200, Loss: 1.0000014305114746\n",
            "Epoch 2, Step 1250, Loss: 1.0000011920928955\n",
            "Epoch 2, Step 1300, Loss: 1.0000011920928955\n",
            "Epoch 2, Step 1350, Loss: 1.000001311302185\n",
            "Epoch 2, Step 1400, Loss: 1.0000014305114746\n",
            "Epoch 2, Loss: 1.000001311302185\n",
            "\n",
            "========== Epoch 3 ==========\n",
            "Epoch 3, Step 50, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 100, Loss: 1.000001072883606\n",
            "Epoch 3, Step 150, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 200, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 250, Loss: 1.000001072883606\n",
            "Epoch 3, Step 300, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 350, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 400, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 450, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 500, Loss: 1.0000009536743164\n",
            "Epoch 3, Step 550, Loss: 1.0000009536743164\n",
            "Epoch 3, Step 600, Loss: 1.0000011920928955\n",
            "Epoch 3, Step 650, Loss: 1.0000009536743164\n",
            "Epoch 3, Step 700, Loss: 1.0000008344650269\n",
            "Epoch 3, Step 750, Loss: 1.0000008344650269\n",
            "Epoch 3, Step 800, Loss: 1.000001072883606\n",
            "Epoch 3, Step 850, Loss: 1.0000009536743164\n",
            "Epoch 3, Step 900, Loss: 1.0000007152557373\n",
            "Epoch 3, Step 950, Loss: 1.0000005960464478\n",
            "Epoch 3, Step 1000, Loss: 1.0000008344650269\n",
            "Epoch 3, Step 1050, Loss: 1.0000007152557373\n",
            "Epoch 3, Step 1100, Loss: 1.0000005960464478\n",
            "Epoch 3, Step 1150, Loss: 1.0000007152557373\n",
            "Epoch 3, Step 1200, Loss: 1.0000004768371582\n",
            "Epoch 3, Step 1250, Loss: 1.0000007152557373\n",
            "Epoch 3, Step 1300, Loss: 1.0000004768371582\n",
            "Epoch 3, Step 1350, Loss: 1.0000008344650269\n",
            "Epoch 3, Step 1400, Loss: 1.0000008344650269\n",
            "Epoch 3, Loss: 1.0000003576278687\n",
            "\n",
            "========== Epoch 4 ==========\n",
            "Epoch 4, Step 50, Loss: 1.0000004768371582\n",
            "Epoch 4, Step 100, Loss: 1.0000008344650269\n",
            "Epoch 4, Step 150, Loss: 1.0000234842300415\n",
            "Epoch 4, Step 200, Loss: 1.0000061988830566\n",
            "Epoch 4, Step 250, Loss: 1.0000028610229492\n",
            "Epoch 4, Step 300, Loss: 1.0000019073486328\n",
            "Epoch 4, Step 350, Loss: 1.0000011920928955\n",
            "Epoch 4, Step 400, Loss: 1.0000011920928955\n",
            "Epoch 4, Step 450, Loss: 1.0000009536743164\n",
            "Epoch 4, Step 500, Loss: 1.0000008344650269\n",
            "Epoch 4, Step 550, Loss: 1.0000007152557373\n",
            "Epoch 4, Step 600, Loss: 1.0000005960464478\n",
            "Epoch 4, Step 650, Loss: 1.0000007152557373\n",
            "Epoch 4, Step 700, Loss: 1.0000004768371582\n",
            "Epoch 4, Step 750, Loss: 1.0000003576278687\n",
            "Epoch 4, Step 800, Loss: 1.0000007152557373\n",
            "Epoch 4, Step 850, Loss: 1.0000003576278687\n",
            "Epoch 4, Step 900, Loss: 1.0000004768371582\n",
            "Epoch 4, Step 950, Loss: 1.000000238418579\n",
            "Epoch 4, Step 1000, Loss: 1.0000004768371582\n",
            "Epoch 4, Step 1050, Loss: 1.0000004768371582\n",
            "Epoch 4, Step 1100, Loss: 1.0000003576278687\n",
            "Epoch 4, Step 1150, Loss: 1.0000005960464478\n",
            "Epoch 4, Step 1200, Loss: 1.0000003576278687\n",
            "Epoch 4, Step 1250, Loss: 1.000000238418579\n",
            "Epoch 4, Step 1300, Loss: 1.000000238418579\n",
            "Epoch 4, Step 1350, Loss: 1.000000238418579\n",
            "Epoch 4, Step 1400, Loss: 1.000000238418579\n",
            "Epoch 4, Loss: 1.0000003576278687\n",
            "\n",
            "========== Epoch 5 ==========\n",
            "Epoch 5, Step 50, Loss: 1.000000238418579\n",
            "Epoch 5, Step 100, Loss: 1.000000238418579\n",
            "Epoch 5, Step 150, Loss: 1.000000238418579\n",
            "Epoch 5, Step 200, Loss: 1.0000003576278687\n",
            "Epoch 5, Step 250, Loss: 1.000000238418579\n",
            "Epoch 5, Step 300, Loss: 1.0000004768371582\n",
            "Epoch 5, Step 350, Loss: 1.000000238418579\n",
            "Epoch 5, Step 400, Loss: 1.000000238418579\n",
            "Epoch 5, Step 450, Loss: 1.0\n",
            "Epoch 5, Step 500, Loss: 1.0000003576278687\n",
            "Epoch 5, Step 550, Loss: 1.000000238418579\n",
            "Epoch 5, Step 600, Loss: 1.0\n",
            "Epoch 5, Step 650, Loss: 1.0\n",
            "Epoch 5, Step 700, Loss: 1.0000004768371582\n",
            "Epoch 5, Step 750, Loss: 1.0000007152557373\n",
            "Epoch 5, Step 800, Loss: 1.0000014305114746\n",
            "Epoch 5, Step 850, Loss: 1.0000003576278687\n",
            "Epoch 5, Step 900, Loss: 1.0\n",
            "Epoch 5, Step 950, Loss: 1.0000008344650269\n",
            "Epoch 5, Step 1000, Loss: 1.000000238418579\n",
            "Epoch 5, Step 1050, Loss: 0.9999999403953552\n",
            "Epoch 5, Step 1100, Loss: 1.0000007152557373\n",
            "Epoch 5, Step 1150, Loss: 1.0\n",
            "Epoch 5, Step 1200, Loss: 0.9999990463256836\n",
            "Epoch 5, Step 1250, Loss: 1.0000128746032715\n",
            "Epoch 5, Step 1300, Loss: 1.0000073909759521\n",
            "Epoch 5, Step 1350, Loss: 1.0000054836273193\n",
            "Epoch 5, Step 1400, Loss: 1.0000033378601074\n",
            "Epoch 5, Loss: 1.0000030994415283\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### loss 시각화\n",
        "\n",
        "plot_loss_curve(loss_curve)"
      ],
      "metadata": {
        "id": "Zt0sK7LJRLSe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "cc88b35d-339a-4e89-e5fc-5a75d91ad888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Inference**\n",
        "- 정확도는 모르겠고 추론이란 걸 한 번 해보아요 (oﾟvﾟ)ノ"
      ],
      "metadata": {
        "id": "LCPlzZTBaES0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 로딩\n",
        "\n",
        "model_path = '/content/drive/MyDrive/Hmm2Song/차수빈/model/euron_modeling_5.pth'\n",
        "model = ConvSubnet().to(device)\n",
        "model.load_state_dict(torch.load(model_path))\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "bjbpo7uHV1nD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57299f90-8f8e-465d-f0da-fbed486eb940"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ConvSubnet(\n",
              "  (conv1): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (conv2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (conv3): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (fc): Linear(in_features=848, out_features=128, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# info.csv 로딩\n",
        "song_info = pd.read_csv('/content/drive/MyDrive/Hmm2Song/data/info2.csv')\n",
        "song_info.head()"
      ],
      "metadata": {
        "id": "xvS2Gqf8JKLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "848e016f-1335-40ec-9e66-a2dcc0337861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id           Title            Singer  \\\n",
              "0   0           To. X      태연 (TAEYEON)   \n",
              "1   1      Either Way         IVE (아이브)   \n",
              "2   2       DIE 4 YOU              DEAN   \n",
              "3   3  Must Have Love  SG 워너비, 브라운아이드걸스   \n",
              "4   4  Still With You                정국   \n",
              "\n",
              "                                               Lyric     genre  \\\n",
              "0  처음 본 널 기억해 We skipped the small talk 바로 다음 단계였...  RnB_Soul   \n",
              "1  누가 내 말투가 재수없대 잘난 척만 한대 또 누구는 내가 너무 착하대 바보같을 정도...  RnB_Soul   \n",
              "2  I don’t wanna go Till everything comes crashin...  RnB_Soul   \n",
              "3  함께 있단 이유로 행복했었던 우리들의 겨울날의 소중한 기억들 좋은 날엔 언제나 네가...  RnB_Soul   \n",
              "4  날 스치는 그대의 옅은 그 목소리 내 이름을 한 번만 더 불러주세요 얼어버린 노을 ...  RnB_Soul   \n",
              "\n",
              "                                                path gender  \n",
              "0  /content/drive/MyDrive/Hmm2Song/data/song/Full...     여성  \n",
              "1  /content/drive/MyDrive/Hmm2Song/data/song/Full...     여성  \n",
              "2  /content/drive/MyDrive/Hmm2Song/data/song/Full...     남성  \n",
              "3  /content/drive/MyDrive/Hmm2Song/data/song/Full...     혼성  \n",
              "4  /content/drive/MyDrive/Hmm2Song/data/song/Full...     남성  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b02f6f00-0a7c-4b26-b260-ef757cccbcc6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Title</th>\n",
              "      <th>Singer</th>\n",
              "      <th>Lyric</th>\n",
              "      <th>genre</th>\n",
              "      <th>path</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>To. X</td>\n",
              "      <td>태연 (TAEYEON)</td>\n",
              "      <td>처음 본 널 기억해 We skipped the small talk 바로 다음 단계였...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>여성</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Either Way</td>\n",
              "      <td>IVE (아이브)</td>\n",
              "      <td>누가 내 말투가 재수없대 잘난 척만 한대 또 누구는 내가 너무 착하대 바보같을 정도...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>여성</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>DIE 4 YOU</td>\n",
              "      <td>DEAN</td>\n",
              "      <td>I don’t wanna go Till everything comes crashin...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>남성</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Must Have Love</td>\n",
              "      <td>SG 워너비, 브라운아이드걸스</td>\n",
              "      <td>함께 있단 이유로 행복했었던 우리들의 겨울날의 소중한 기억들 좋은 날엔 언제나 네가...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>혼성</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Still With You</td>\n",
              "      <td>정국</td>\n",
              "      <td>날 스치는 그대의 옅은 그 목소리 내 이름을 한 번만 더 불러주세요 얼어버린 노을 ...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>남성</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b02f6f00-0a7c-4b26-b260-ef757cccbcc6')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "      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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b02f6f00-0a7c-4b26-b260-ef757cccbcc6 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-b02f6f00-0a7c-4b26-b260-ef757cccbcc6');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ebf44334-ef92-463b-87fc-d4185d24244d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ebf44334-ef92-463b-87fc-d4185d24244d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</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",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --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-ebf44334-ef92-463b-87fc-d4185d24244d 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": "song_info",
              "summary": "{\n  \"name\": \"song_info\",\n  \"rows\": 821,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 237,\n        \"min\": 0,\n        \"max\": 820,\n        \"samples\": [\n          609,\n          174,\n          67\n        ],\n        \"num_unique_values\": 821,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Too Sad to Dance\",\n          \"\\ub108\\ub97c \\ub9cc\\ub098\",\n          \"\\ucc2c\\ub780\\ud55c \\ud558\\ub8e8\"\n        ],\n        \"num_unique_values\": 805,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Singer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"\\ucc2c\\uc5f4 (CHANYEOL), \\ud380\\uce58 (Punch)\",\n          \"\\uc774\\ubb34\\uc9c4\",\n          \"\\uae40\\ud544\"\n        ],\n        \"num_unique_values\": 343,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lyric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"\\ub2c8\\uac00 \\ub0a0 \\uc2eb\\uc5b4\\ud574 \\ud558\\ub294 \\uac78 \\uc54c\\uc544 \\ub098\\ub294 \\uc11c\\uc6b4\\ud574 \\uadf8\\ub7f0 \\ub0a0 \\uc65c \\ub108\\ub294 \\ubabb \\uc774\\ud574\\ud574 You don't understand \\ub09c \\ub108\\ub97c \\uc88b\\uc544\\ud55c\\ub2e4\\uace0 \\ub0b4\\uac00 \\ubb58 \\uc798\\ubabb\\ud588\\ub294\\ub370 \\ub0b4\\uac8c \\uc65c \\uadf8\\ub7ec\\ub294\\ub370 \\uadf8\\ub7f4\\uc218\\ub85d \\ub09c \\ub418\\uac8c \\uc12d\\uc12d\\ud574 Oh I'm so sad \\uadf8\\ub7ec\\ub2c8\\uae4c \\uc2ac\\uc2ac Let me come into your \\ub9c8\\uc74c \\uc911\\uc694\\ud55c \\uac74 \\ub9c8\\uc74c \\uacb0\\ucf54 \\ub124 \\uc5bc\\uad74\\ub9cc \\ubcf4\\uace0 \\uc88b\\uc544\\ud558\\ub294 \\uac70 \\uc544\\ub2c8 \\uc544\\ub2c8\\uc57c \\ub0a0 \\ubbf8\\uc6cc\\ud558\\ub294 \\ub108\\uc758 \\ub0a0\\uc774 \\uc120 \\ub9d0\\ud22c\\uae4c\\uc9c0\\ub3c4 \\uc0ac\\ub791\\ud558\\uac8c \\ub41c \\uac70 \\uc774\\uac8c \\ub0b4 \\ub9d8\\uc774\\uc57c \\ub108\\uc5d0 \\ub300\\ud55c \\uac74 \\uc0ac\\uc18c\\ud55c \\uac83\\ub3c4 \\uae30\\uc5b5\\ud574 \\ub09c \\ub9c8\\ub2c8\\uc544 \\uc544\\ubb34\\ub9ac \\ub098\\uc090 \\uad74\\uc5b4\\ub3c4 \\ub10c \\ub0b4\\uac8c \\uc774 \\uc21c\\uac04 \\ub9cc\\ud654\\uc57c \\uc21c\\uc815\\ub9cc\\ud654\\uc57c \\uc8fc\\uc778\\uacf5\\uc740 \\ub9e8\\ub0a0 \\ub9e8\\ub0a0 \\uc774\\ub807\\uac8c \\ubc24\\ub9c8\\ub2e4 \\uae30\\ub3c4\\ud574 Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love Give Love Give Love \\uc798\\ubabb\\ud55c \\uac83\\ub3c4 \\uc5c6\\ub294\\ub370 \\uc65c \\ubb34\\uc791\\uc815 \\uc2eb\\uc5b4\\ud558\\uace0 \\ubcf4\\ub294 \\ub108 Why can't you understand me \\ub09c \\ub108\\ub97c \\uc88b\\uc544\\ud55c\\ub2e4\\uace0 \\ub124\\uac00 \\ub0a0 \\uc2eb\\uc5b4\\ud574 \\ud558\\ub294 \\uac78 \\uc548 \\ud6c4 \\ubd80\\ud130 \\uc0d8\\uc19f\\ub358 \\uc758\\uc695\\uc774 \\ub2e4 \\uc2dc\\ub4e4\\uace0 \\uc124\\ub808\\ub358 \\ub0b4 \\ub9d8\\ub3c4 \\ub05d\\uc774\\uaca0\\uad6c\\ub098 \\ud588\\ub294\\ub370 \\ub610\\ub2e4\\uc2dc \\uc2ac\\uae08\\uc2ac\\uae08 \\ub2e4\\uac00\\uac00\\ub3c4 \\ub420\\uae4c \\ubc14\\ub77c\\ubcf4\\ub294 \\uac83\\uc870\\ucc28 \\uc2eb\\uc5b4\\ud560\\uae4c \\ubd10 \\ub09c \\ubab0\\ub798 \\ub4a4\\uc5d0\\uc11c \\uae0d\\uae0d\\uc804\\uc804\\ud574 \\uc810\\uc810 \\ud574\\uac00 \\uc9c0\\uba74 \\ub2ec\\ube5b \\uc815\\uc804\\uc5d0 \\uc6a9\\uae30\\uac00 \\ub098 \\ub0b4 \\ub9d8\\uc744 \\uc804\\ubd80 \\uc804\\ud574 \\ud558\\uc9c0\\ub9cc \\uadf8\\ub140\\uc640 \\ub0b4 \\uc0ac\\uc774 \\uac70\\ub9ac \\ub108\\ubb34 \\uba40\\uc5b4 \\uc8fc\\uace0 \\ub610 \\uc8fc\\ub294 \\uc0ac\\ub791\\uc774 \\uae38\\ubc14\\ub2e5\\uc5d0 \\ub2e4 \\ubc84\\ub824\\uc838 \\ub099\\uc5fd\\ucc98\\ub7fc \\uc313\\uc774\\ub124 \\ubd04\\uc774 \\ub418\\uba74 \\ud759\\uc73c\\ub85c \\ub0a8\\uc544 \\ud639\\uc2dc \\uae30\\ub300\\ud574 \\uc2f9\\uc774 \\ub420\\uae4c Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love Give Love Give Love Give Love Give Love Give Love\",\n          \"\\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\ubab8\\uc744 \\ub2f9\\uaca8\\uc11c \\ub2e8 \\ud55c \\ubc88\\uc5d0 \\ub2ec\\ube5b\\uc744 \\ub0b4 \\ub208\\uc5d0 \\ub2f4\\uc558\\uc5b4 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\ud63c\\uc790 \\ub450\\uc9c0 \\uc54a\\uc744\\uac8c \\uc800 \\ub2ec\\uc774 \\uc678\\ub86d\\uc9c0 \\uc54a\\uac8c \\ud574 \\uc918 \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 \\uc774\\ub7f0 \\uac00\\uc0ac \\ud55c \\ub9c8\\ub514\\uac00 \\ub110 \\uc704\\ub85c\\ud55c\\ub2e4\\uba74 \\ub098 \\ud39c\\uc744 \\uc7a1\\uc744\\uac8c \\uc790\\uadf9\\uc801\\uc778 \\uac83\\ubcf4\\ub2e4 \\uc9c4\\uc2ec\\ub9cc\\uc73c\\ub85c \\ub9d0\\ud560 \\uc218 \\uc788\\uc5b4 All day \\uace0\\uc591\\uc774\\ubcf4\\ub2e4 \\ub10c \\uac15\\uc544\\uc9c0\\uac19\\uc774 \\ub0a0 \\uae30\\ub2e4\\ub838\\uc9c0 \\ud558\\ub8e8 \\uc885\\uc77c \\ubb50\\uac00 \\uadf8\\ub9ac \\uc2ac\\ud390\\uc9c0 \\uc774 \\ubcc4\\uc5d0\\uc11c \\ub124\\uac00 \\uac00\\uc7a5 \\ud2b9\\ubcc4\\ud574 \\ub108\\ub294 \\uadf8\\ub7f0 \\uac70 \\uc804\\ud600 \\ubab0\\ub790\\uc9c0 You want some more \\uc65c \\uc790\\uafb8\\ub9cc \\ub110 \\uac00\\ub450\\ub824 \\ud558\\ub294 \\uac74\\uc9c0 You want some more Sky is nothing to believe \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 \\uc788\\uc796\\uc544 \\ub09c \\ub124\\uac00 \\uc544\\uc8fc \\uac00\\ub054\\uc529 \\ubb34\\uac81\\uac8c \\ud560 \\ub54c\\ub9c8\\ub2e4 \\uc6c0\\uce6b \\ub110 \\uc704\\ub85c\\ud560 \\uc190\\uc774 \\ub09c \\uc5c6\\ub124 \\uc9c0\\uae08 \\ub09c \\uac08 \\uae38\\uc744 \\uc783\\uc740 \\ubcc4 \\ub0a0 \\ub2f9\\uae30\\ub294 \\ud798\\uc740 \\ub108 \\ub10c \\ubcc4\\uc744 \\uc783\\uc740 \\ud558\\ub298\\ucc98\\ub7fc \\ub5a0\\ub3cc\\uace0 \\ub2e4\\ub140 \\uc6b0\\ub9ac\\uac00 \\uc9c0\\ub09c \\uae38\\uc774 \\ubcc4\\uc790\\ub9ac\\ucc98\\ub7fc \\ub098\\uc640 \\ub108 \\uadf8 \\uae38\\uc744 \\ub530\\ub77c\\uc11c \\ub0a0 \\uae30\\uc5b5\\ud574\\uc918 You want some more \\uc65c \\uc790\\uafb8\\ub9cc \\ub110 \\uac00\\ub450\\ub824 \\ud558\\ub294 \\uac74\\uc9c0 You want some more Sky is nothing to believe \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 Girl I don\\u2019t know why I\\u2019m leaving Girl I don\\u2019t know why \\ucc98\\uc74c\\ubd80\\ud130 \\ub2e4\\uc2dc \\uc54c\\ub824 \\uc918 Girl I don\\u2019t know why I\\u2019m leaving Girl I don\\u2019t know why \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 \\uaf2d \\ub2ee\\uc740 \\ub108\\ub97c \\ucc3e\\uc544 \\ub098\\uc130\\uc9c0 \\ub108\\ub97c \\ub2f9\\uaca8 \\ubcf4\\ub824 \\ud574\\ub3c4 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918\",\n          \"\\uc5b8\\uc81c\\ubd80\\ud130\\uc778\\uc9c0 \\uadf8\\ub300\\ub97c \\ubcf4\\uba74 \\uc6b4\\uba85\\uc774\\ub77c\\uace0 \\ub290\\uaf08\\ub358 \\uac78\\uae4c \\ubc24\\ud558\\ub298\\uc758 \\ubcc4\\uc774 \\ube5b\\ub09c \\uac83\\ucc98\\ub7fc \\uc624\\ub7ab\\ub3d9\\uc548 \\ub0b4 \\uacc1\\uc5d0 \\uc788\\uc5b4\\uc694 \\uadf8\\ub300\\ub77c\\ub294 \\uc2dc\\uac00 \\ub09c \\ub5a0\\uc624\\ub97c \\ub54c\\ub9c8\\ub2e4 \\uc678\\uc6cc\\ub450\\uace0 \\uc2f6\\uc5b4 \\uadf8\\ub308 \\uae30\\uc5b5\\ud560 \\uc218 \\uc788\\uac8c \\uc2ac\\ud508 \\ubc24\\uc774 \\uc624\\uba74 \\ub0b4\\uac00 \\uadf8\\ub300\\ub97c \\uc9c0\\ucf1c\\uc904\\uac8c \\ub0b4 \\ub9c8\\uc74c \\ub4e4\\ub824\\uc624\\ub098\\uc694 \\uc78a\\uc9c0 \\ub9d0\\uc544\\uc694 \\uc9c0\\ub098\\uac00\\ub294 \\uacc4\\uc808 \\uc18d\\uc5d0 \\ub0b4 \\ub9c8\\uc74c\\uc740 \\ubcc0\\ud558\\uc9c0 \\uc54a\\ub294\\ub2e8 \\uac78 \\uc544\\ub098\\uc694 \\uadf8\\uc800 \\ubc14\\ub77c\\ubcf4\\ub294 \\ub208\\ube5b \\uadf8 \\ud558\\ub098\\ub85c \\uc138\\uc0c1\\uc744 \\ub2e4 \\uac00\\uc9c4 \\uac83 \\uac19\\uc740\\ub370 \\uadf8\\ub300\\ub77c\\ub294 \\uc2dc\\uac00 \\ub09c \\ub5a0\\uc624\\ub97c \\ub54c\\ub9c8\\ub2e4 \\uc678\\uc6cc\\ub450\\uace0 \\uc2f6\\uc5b4 \\uadf8\\ub308 \\uae30\\uc5b5\\ud560 \\uc218 \\uc788\\uac8c \\uc2ac\\ud508 \\ubc24\\uc774 \\uc624\\uba74 \\ub0b4\\uac00 \\uadf8\\ub300\\ub97c \\uc9c0\\ucf1c\\uc904\\uac8c \\ub0b4 \\ub9c8\\uc74c \\ub4e4\\ub824\\uc624\\ub098\\uc694 \\uc78a\\uc9c0 \\ub9d0\\uc544\\uc694 \\uaf43\\uc774 \\ud53c\\uace0 \\uc9c0\\ub294 \\ub0a0\\uc774 \\uc640\\ub3c4 \\uc774\\uac83 \\ud558\\ub098\\ub9cc \\uae30\\uc5b5\\ud574\\uc918\\uc694 \\uadf8\\ub308 \\ud5a5\\ud55c \\ub9c8\\uc74c\\uc744 \\uc5b8\\uc820\\uac00\\ub294 \\uc6b0\\ub9ac \\uba40\\uc5b4\\uc9c8\\uc9c0 \\ubab0\\ub77c\\ub3c4 \\ub098\\ub294 \\uadf8\\ub300\\ub77c\\uba74 \\uae30\\ub2e4\\ub9b4 \\uc218 \\uc788\\uc744 \\ud150\\ub370 \\uc2dc\\uac04\\uc774 \\ud758\\ub7ec\\ub3c4 \\ub0b4\\uac00 \\uc774\\uacf3\\uc5d0 \\uc11c \\uc788\\uc744\\uac8c \\uadf8\\ub300 \\ub9dd\\uc124\\uc774\\uc9c0 \\ub9d0\\uc544\\uc694 \\uadf8\\ub54c\\uac00 \\uc624\\uba74\"\n        ],\n        \"num_unique_values\": 816,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"RnB_Soul\",\n          \"RnB_Soul, Indie\",\n          \"Indie\"\n        ],\n        \"num_unique_values\": 17,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"/content/drive/MyDrive/Hmm2Song/data/song/Full_input/609.mp3\",\n          \"/content/drive/MyDrive/Hmm2Song/data/song/Full_input/174.mp3\",\n          \"/content/drive/MyDrive/Hmm2Song/data/song/Full_input/067.mp3\"\n        ],\n        \"num_unique_values\": 821,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"\\uc5ec\\uc131\",\n          \"\\ub0a8\\uc131\",\n          \"\\ud63c\\uc131\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 데이터형 변경\n",
        "\n",
        "# id 컬럼을 object 형으로 변경\n",
        "song_info['id'] = song_info['id'].astype('object')\n",
        "\n",
        "# id 컬럼을 3자리로 맞춤\n",
        "song_info['id'] = song_info['id'].apply(lambda x: str(x).zfill(3))"
      ],
      "metadata": {
        "id": "uAdS9zj7RdHm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "song_info.head()"
      ],
      "metadata": {
        "id": "FiuK4J5lSDRM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "df7059ed-f1ed-4634-e916-402b57484a93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id           Title            Singer  \\\n",
              "0  000           To. X      태연 (TAEYEON)   \n",
              "1  001      Either Way         IVE (아이브)   \n",
              "2  002       DIE 4 YOU              DEAN   \n",
              "3  003  Must Have Love  SG 워너비, 브라운아이드걸스   \n",
              "4  004  Still With You                정국   \n",
              "\n",
              "                                               Lyric     genre  \\\n",
              "0  처음 본 널 기억해 We skipped the small talk 바로 다음 단계였...  RnB_Soul   \n",
              "1  누가 내 말투가 재수없대 잘난 척만 한대 또 누구는 내가 너무 착하대 바보같을 정도...  RnB_Soul   \n",
              "2  I don’t wanna go Till everything comes crashin...  RnB_Soul   \n",
              "3  함께 있단 이유로 행복했었던 우리들의 겨울날의 소중한 기억들 좋은 날엔 언제나 네가...  RnB_Soul   \n",
              "4  날 스치는 그대의 옅은 그 목소리 내 이름을 한 번만 더 불러주세요 얼어버린 노을 ...  RnB_Soul   \n",
              "\n",
              "                                                path gender  \n",
              "0  /content/drive/MyDrive/Hmm2Song/data/song/Full...     여성  \n",
              "1  /content/drive/MyDrive/Hmm2Song/data/song/Full...     여성  \n",
              "2  /content/drive/MyDrive/Hmm2Song/data/song/Full...     남성  \n",
              "3  /content/drive/MyDrive/Hmm2Song/data/song/Full...     혼성  \n",
              "4  /content/drive/MyDrive/Hmm2Song/data/song/Full...     남성  "
            ],
            "text/html": [
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              "      <th>id</th>\n",
              "      <th>Title</th>\n",
              "      <th>Singer</th>\n",
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              "      <td>000</td>\n",
              "      <td>To. X</td>\n",
              "      <td>태연 (TAEYEON)</td>\n",
              "      <td>처음 본 널 기억해 We skipped the small talk 바로 다음 단계였...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>여성</td>\n",
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              "      <td>IVE (아이브)</td>\n",
              "      <td>누가 내 말투가 재수없대 잘난 척만 한대 또 누구는 내가 너무 착하대 바보같을 정도...</td>\n",
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              "      <td>RnB_Soul</td>\n",
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              "      <td>Must Have Love</td>\n",
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              "      <td>함께 있단 이유로 행복했었던 우리들의 겨울날의 소중한 기억들 좋은 날엔 언제나 네가...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>혼성</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>004</td>\n",
              "      <td>Still With You</td>\n",
              "      <td>정국</td>\n",
              "      <td>날 스치는 그대의 옅은 그 목소리 내 이름을 한 번만 더 불러주세요 얼어버린 노을 ...</td>\n",
              "      <td>RnB_Soul</td>\n",
              "      <td>/content/drive/MyDrive/Hmm2Song/data/song/Full...</td>\n",
              "      <td>남성</td>\n",
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              "      display:flex;\n",
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              "\n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "        const element = document.querySelector('#df-569eea08-2046-46bc-91fd-6a990dc4fb23');\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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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    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-bacce819-8f28-414a-b334-8decf7b7ad7e 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": "song_info",
              "summary": "{\n  \"name\": \"song_info\",\n  \"rows\": 821,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"609\",\n          \"174\",\n          \"067\"\n        ],\n        \"num_unique_values\": 821,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Too Sad to Dance\",\n          \"\\ub108\\ub97c \\ub9cc\\ub098\",\n          \"\\ucc2c\\ub780\\ud55c \\ud558\\ub8e8\"\n        ],\n        \"num_unique_values\": 805,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Singer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"\\ucc2c\\uc5f4 (CHANYEOL), \\ud380\\uce58 (Punch)\",\n          \"\\uc774\\ubb34\\uc9c4\",\n          \"\\uae40\\ud544\"\n        ],\n        \"num_unique_values\": 343,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lyric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"\\ub2c8\\uac00 \\ub0a0 \\uc2eb\\uc5b4\\ud574 \\ud558\\ub294 \\uac78 \\uc54c\\uc544 \\ub098\\ub294 \\uc11c\\uc6b4\\ud574 \\uadf8\\ub7f0 \\ub0a0 \\uc65c \\ub108\\ub294 \\ubabb \\uc774\\ud574\\ud574 You don't understand \\ub09c \\ub108\\ub97c \\uc88b\\uc544\\ud55c\\ub2e4\\uace0 \\ub0b4\\uac00 \\ubb58 \\uc798\\ubabb\\ud588\\ub294\\ub370 \\ub0b4\\uac8c \\uc65c \\uadf8\\ub7ec\\ub294\\ub370 \\uadf8\\ub7f4\\uc218\\ub85d \\ub09c \\ub418\\uac8c \\uc12d\\uc12d\\ud574 Oh I'm so sad \\uadf8\\ub7ec\\ub2c8\\uae4c \\uc2ac\\uc2ac Let me come into your \\ub9c8\\uc74c \\uc911\\uc694\\ud55c \\uac74 \\ub9c8\\uc74c \\uacb0\\ucf54 \\ub124 \\uc5bc\\uad74\\ub9cc \\ubcf4\\uace0 \\uc88b\\uc544\\ud558\\ub294 \\uac70 \\uc544\\ub2c8 \\uc544\\ub2c8\\uc57c \\ub0a0 \\ubbf8\\uc6cc\\ud558\\ub294 \\ub108\\uc758 \\ub0a0\\uc774 \\uc120 \\ub9d0\\ud22c\\uae4c\\uc9c0\\ub3c4 \\uc0ac\\ub791\\ud558\\uac8c \\ub41c \\uac70 \\uc774\\uac8c \\ub0b4 \\ub9d8\\uc774\\uc57c \\ub108\\uc5d0 \\ub300\\ud55c \\uac74 \\uc0ac\\uc18c\\ud55c \\uac83\\ub3c4 \\uae30\\uc5b5\\ud574 \\ub09c \\ub9c8\\ub2c8\\uc544 \\uc544\\ubb34\\ub9ac \\ub098\\uc090 \\uad74\\uc5b4\\ub3c4 \\ub10c \\ub0b4\\uac8c \\uc774 \\uc21c\\uac04 \\ub9cc\\ud654\\uc57c \\uc21c\\uc815\\ub9cc\\ud654\\uc57c \\uc8fc\\uc778\\uacf5\\uc740 \\ub9e8\\ub0a0 \\ub9e8\\ub0a0 \\uc774\\ub807\\uac8c \\ubc24\\ub9c8\\ub2e4 \\uae30\\ub3c4\\ud574 Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love Give Love Give Love \\uc798\\ubabb\\ud55c \\uac83\\ub3c4 \\uc5c6\\ub294\\ub370 \\uc65c \\ubb34\\uc791\\uc815 \\uc2eb\\uc5b4\\ud558\\uace0 \\ubcf4\\ub294 \\ub108 Why can't you understand me \\ub09c \\ub108\\ub97c \\uc88b\\uc544\\ud55c\\ub2e4\\uace0 \\ub124\\uac00 \\ub0a0 \\uc2eb\\uc5b4\\ud574 \\ud558\\ub294 \\uac78 \\uc548 \\ud6c4 \\ubd80\\ud130 \\uc0d8\\uc19f\\ub358 \\uc758\\uc695\\uc774 \\ub2e4 \\uc2dc\\ub4e4\\uace0 \\uc124\\ub808\\ub358 \\ub0b4 \\ub9d8\\ub3c4 \\ub05d\\uc774\\uaca0\\uad6c\\ub098 \\ud588\\ub294\\ub370 \\ub610\\ub2e4\\uc2dc \\uc2ac\\uae08\\uc2ac\\uae08 \\ub2e4\\uac00\\uac00\\ub3c4 \\ub420\\uae4c \\ubc14\\ub77c\\ubcf4\\ub294 \\uac83\\uc870\\ucc28 \\uc2eb\\uc5b4\\ud560\\uae4c \\ubd10 \\ub09c \\ubab0\\ub798 \\ub4a4\\uc5d0\\uc11c \\uae0d\\uae0d\\uc804\\uc804\\ud574 \\uc810\\uc810 \\ud574\\uac00 \\uc9c0\\uba74 \\ub2ec\\ube5b \\uc815\\uc804\\uc5d0 \\uc6a9\\uae30\\uac00 \\ub098 \\ub0b4 \\ub9d8\\uc744 \\uc804\\ubd80 \\uc804\\ud574 \\ud558\\uc9c0\\ub9cc \\uadf8\\ub140\\uc640 \\ub0b4 \\uc0ac\\uc774 \\uac70\\ub9ac \\ub108\\ubb34 \\uba40\\uc5b4 \\uc8fc\\uace0 \\ub610 \\uc8fc\\ub294 \\uc0ac\\ub791\\uc774 \\uae38\\ubc14\\ub2e5\\uc5d0 \\ub2e4 \\ubc84\\ub824\\uc838 \\ub099\\uc5fd\\ucc98\\ub7fc \\uc313\\uc774\\ub124 \\ubd04\\uc774 \\ub418\\uba74 \\ud759\\uc73c\\ub85c \\ub0a8\\uc544 \\ud639\\uc2dc \\uae30\\ub300\\ud574 \\uc2f9\\uc774 \\ub420\\uae4c Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love \\uc0ac\\ub791\\uc744 \\uc880 \\uc8fc\\uc138\\uc694 Give Love \\uc0ac\\ub791\\uc774 \\ubaa8\\uc790\\ub77c\\uc694 \\ub9e4\\uc77c\\ub9e4\\uc77c \\uc790\\ub77c\\ub294 \\uc0ac\\ub791\\uc744 \\uadf8\\ub140\\uc5d0\\uac8c \\uc8fc\\ub294\\ub370\\ub3c4 \\ubc1b\\uc9c8 \\uc54a\\uc73c\\ub2c8 Give Love Give Love Give Love Give Love Give Love Give Love\",\n          \"\\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\ubab8\\uc744 \\ub2f9\\uaca8\\uc11c \\ub2e8 \\ud55c \\ubc88\\uc5d0 \\ub2ec\\ube5b\\uc744 \\ub0b4 \\ub208\\uc5d0 \\ub2f4\\uc558\\uc5b4 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\ud63c\\uc790 \\ub450\\uc9c0 \\uc54a\\uc744\\uac8c \\uc800 \\ub2ec\\uc774 \\uc678\\ub86d\\uc9c0 \\uc54a\\uac8c \\ud574 \\uc918 \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 \\uc774\\ub7f0 \\uac00\\uc0ac \\ud55c \\ub9c8\\ub514\\uac00 \\ub110 \\uc704\\ub85c\\ud55c\\ub2e4\\uba74 \\ub098 \\ud39c\\uc744 \\uc7a1\\uc744\\uac8c \\uc790\\uadf9\\uc801\\uc778 \\uac83\\ubcf4\\ub2e4 \\uc9c4\\uc2ec\\ub9cc\\uc73c\\ub85c \\ub9d0\\ud560 \\uc218 \\uc788\\uc5b4 All day \\uace0\\uc591\\uc774\\ubcf4\\ub2e4 \\ub10c \\uac15\\uc544\\uc9c0\\uac19\\uc774 \\ub0a0 \\uae30\\ub2e4\\ub838\\uc9c0 \\ud558\\ub8e8 \\uc885\\uc77c \\ubb50\\uac00 \\uadf8\\ub9ac \\uc2ac\\ud390\\uc9c0 \\uc774 \\ubcc4\\uc5d0\\uc11c \\ub124\\uac00 \\uac00\\uc7a5 \\ud2b9\\ubcc4\\ud574 \\ub108\\ub294 \\uadf8\\ub7f0 \\uac70 \\uc804\\ud600 \\ubab0\\ub790\\uc9c0 You want some more \\uc65c \\uc790\\uafb8\\ub9cc \\ub110 \\uac00\\ub450\\ub824 \\ud558\\ub294 \\uac74\\uc9c0 You want some more Sky is nothing to believe \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 \\uc788\\uc796\\uc544 \\ub09c \\ub124\\uac00 \\uc544\\uc8fc \\uac00\\ub054\\uc529 \\ubb34\\uac81\\uac8c \\ud560 \\ub54c\\ub9c8\\ub2e4 \\uc6c0\\uce6b \\ub110 \\uc704\\ub85c\\ud560 \\uc190\\uc774 \\ub09c \\uc5c6\\ub124 \\uc9c0\\uae08 \\ub09c \\uac08 \\uae38\\uc744 \\uc783\\uc740 \\ubcc4 \\ub0a0 \\ub2f9\\uae30\\ub294 \\ud798\\uc740 \\ub108 \\ub10c \\ubcc4\\uc744 \\uc783\\uc740 \\ud558\\ub298\\ucc98\\ub7fc \\ub5a0\\ub3cc\\uace0 \\ub2e4\\ub140 \\uc6b0\\ub9ac\\uac00 \\uc9c0\\ub09c \\uae38\\uc774 \\ubcc4\\uc790\\ub9ac\\ucc98\\ub7fc \\ub098\\uc640 \\ub108 \\uadf8 \\uae38\\uc744 \\ub530\\ub77c\\uc11c \\ub0a0 \\uae30\\uc5b5\\ud574\\uc918 You want some more \\uc65c \\uc790\\uafb8\\ub9cc \\ub110 \\uac00\\ub450\\ub824 \\ud558\\ub294 \\uac74\\uc9c0 You want some more Sky is nothing to believe \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 Girl I don\\u2019t know why I\\u2019m leaving Girl I don\\u2019t know why \\ucc98\\uc74c\\ubd80\\ud130 \\ub2e4\\uc2dc \\uc54c\\ub824 \\uc918 Girl I don\\u2019t know why I\\u2019m leaving Girl I don\\u2019t know why \\ub108\\ub294 \\ubcc4\\uc744 \\ubcf4\\uc790\\uba70 \\ub0b4 \\uc190\\uc744 \\ub04c\\uc5b4\\uc11c \\uc800\\ub141\\ub178\\uc744\\uc774 \\uc9c4 \\uc625\\uc0c1\\uc5d0 \\uac78\\ud130\\uc549\\uc544 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918 \\uaf2d \\ub2ee\\uc740 \\ub108\\ub97c \\ucc3e\\uc544 \\ub098\\uc130\\uc9c0 \\ub108\\ub97c \\ub2f9\\uaca8 \\ubcf4\\ub824 \\ud574\\ub3c4 Every time I look up in the sky \\uadfc\\ub370 \\ub2e8 \\ud55c \\uac1c\\ub3c4 \\uc5c6\\ub294 Star \\uad1c\\ucc2e\\uc544 \\ub124\\uac00 \\ub0b4 \\uc6b0\\uc8fc\\uace0 \\ubc1d\\uac8c \\ube5b\\ub098 \\uc918\",\n          \"\\uc5b8\\uc81c\\ubd80\\ud130\\uc778\\uc9c0 \\uadf8\\ub300\\ub97c \\ubcf4\\uba74 \\uc6b4\\uba85\\uc774\\ub77c\\uace0 \\ub290\\uaf08\\ub358 \\uac78\\uae4c \\ubc24\\ud558\\ub298\\uc758 \\ubcc4\\uc774 \\ube5b\\ub09c \\uac83\\ucc98\\ub7fc \\uc624\\ub7ab\\ub3d9\\uc548 \\ub0b4 \\uacc1\\uc5d0 \\uc788\\uc5b4\\uc694 \\uadf8\\ub300\\ub77c\\ub294 \\uc2dc\\uac00 \\ub09c \\ub5a0\\uc624\\ub97c \\ub54c\\ub9c8\\ub2e4 \\uc678\\uc6cc\\ub450\\uace0 \\uc2f6\\uc5b4 \\uadf8\\ub308 \\uae30\\uc5b5\\ud560 \\uc218 \\uc788\\uac8c \\uc2ac\\ud508 \\ubc24\\uc774 \\uc624\\uba74 \\ub0b4\\uac00 \\uadf8\\ub300\\ub97c \\uc9c0\\ucf1c\\uc904\\uac8c \\ub0b4 \\ub9c8\\uc74c \\ub4e4\\ub824\\uc624\\ub098\\uc694 \\uc78a\\uc9c0 \\ub9d0\\uc544\\uc694 \\uc9c0\\ub098\\uac00\\ub294 \\uacc4\\uc808 \\uc18d\\uc5d0 \\ub0b4 \\ub9c8\\uc74c\\uc740 \\ubcc0\\ud558\\uc9c0 \\uc54a\\ub294\\ub2e8 \\uac78 \\uc544\\ub098\\uc694 \\uadf8\\uc800 \\ubc14\\ub77c\\ubcf4\\ub294 \\ub208\\ube5b \\uadf8 \\ud558\\ub098\\ub85c \\uc138\\uc0c1\\uc744 \\ub2e4 \\uac00\\uc9c4 \\uac83 \\uac19\\uc740\\ub370 \\uadf8\\ub300\\ub77c\\ub294 \\uc2dc\\uac00 \\ub09c \\ub5a0\\uc624\\ub97c \\ub54c\\ub9c8\\ub2e4 \\uc678\\uc6cc\\ub450\\uace0 \\uc2f6\\uc5b4 \\uadf8\\ub308 \\uae30\\uc5b5\\ud560 \\uc218 \\uc788\\uac8c \\uc2ac\\ud508 \\ubc24\\uc774 \\uc624\\uba74 \\ub0b4\\uac00 \\uadf8\\ub300\\ub97c \\uc9c0\\ucf1c\\uc904\\uac8c \\ub0b4 \\ub9c8\\uc74c \\ub4e4\\ub824\\uc624\\ub098\\uc694 \\uc78a\\uc9c0 \\ub9d0\\uc544\\uc694 \\uaf43\\uc774 \\ud53c\\uace0 \\uc9c0\\ub294 \\ub0a0\\uc774 \\uc640\\ub3c4 \\uc774\\uac83 \\ud558\\ub098\\ub9cc \\uae30\\uc5b5\\ud574\\uc918\\uc694 \\uadf8\\ub308 \\ud5a5\\ud55c \\ub9c8\\uc74c\\uc744 \\uc5b8\\uc820\\uac00\\ub294 \\uc6b0\\ub9ac \\uba40\\uc5b4\\uc9c8\\uc9c0 \\ubab0\\ub77c\\ub3c4 \\ub098\\ub294 \\uadf8\\ub300\\ub77c\\uba74 \\uae30\\ub2e4\\ub9b4 \\uc218 \\uc788\\uc744 \\ud150\\ub370 \\uc2dc\\uac04\\uc774 \\ud758\\ub7ec\\ub3c4 \\ub0b4\\uac00 \\uc774\\uacf3\\uc5d0 \\uc11c \\uc788\\uc744\\uac8c \\uadf8\\ub300 \\ub9dd\\uc124\\uc774\\uc9c0 \\ub9d0\\uc544\\uc694 \\uadf8\\ub54c\\uac00 \\uc624\\uba74\"\n        ],\n        \"num_unique_values\": 816,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"RnB_Soul\",\n          \"RnB_Soul, Indie\",\n          \"Indie\"\n        ],\n        \"num_unique_values\": 17,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"/content/drive/MyDrive/Hmm2Song/data/song/Full_input/609.mp3\",\n          \"/content/drive/MyDrive/Hmm2Song/data/song/Full_input/174.mp3\",\n          \"/content/drive/MyDrive/Hmm2Song/data/song/Full_input/067.mp3\"\n        ],\n        \"num_unique_values\": 821,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"\\uc5ec\\uc131\",\n          \"\\ub0a8\\uc131\",\n          \"\\ud63c\\uc131\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 원곡 음원 임베딩\n",
        "\n",
        "music_emb = music[['song_no', 'org']]\n",
        "music_emb['org_emb'] = music_emb['org'].apply(lambda x: load_spectrogram(x))\n",
        "music_emb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "vwYz3Ri5Cm7n",
        "outputId": "1028bf2c-5dcd-47e3-99e1-579b8a3c271d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-c0985b14037a>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  music_emb['org_emb'] = music_emb['org'].apply(lambda x: load_spectrogram(x))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  song_no                                                org  \\\n",
              "0     000  [[-53.434498, -53.434498, -53.434498, -53.4344...   \n",
              "1     000  [[-11.306869, -10.583914, -13.366933, -14.5223...   \n",
              "2     000  [[-7.9065266, -10.674957, -13.891659, -20.9580...   \n",
              "3     000  [[20.618156, 21.427076, 19.18558, 13.680927, 3...   \n",
              "4     000  [[-16.27028, -21.62627, -47.989334, -50.855732...   \n",
              "\n",
              "                                             org_emb  \n",
              "0  [[tensor(-53.4345), tensor(-53.4345), tensor(-...  \n",
              "1  [[tensor(-11.3069), tensor(-10.5839), tensor(-...  \n",
              "2  [[tensor(-7.9065), tensor(-10.6750), tensor(-1...  \n",
              "3  [[tensor(20.6182), tensor(21.4271), tensor(19....  \n",
              "4  [[tensor(-16.2703), tensor(-21.6263), tensor(-...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c3029a2-02f5-45b4-82f1-e152162ddf95\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song_no</th>\n",
              "      <th>org</th>\n",
              "      <th>org_emb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>000</td>\n",
              "      <td>[[-53.434498, -53.434498, -53.434498, -53.4344...</td>\n",
              "      <td>[[tensor(-53.4345), tensor(-53.4345), tensor(-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000</td>\n",
              "      <td>[[-11.306869, -10.583914, -13.366933, -14.5223...</td>\n",
              "      <td>[[tensor(-11.3069), tensor(-10.5839), tensor(-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000</td>\n",
              "      <td>[[-7.9065266, -10.674957, -13.891659, -20.9580...</td>\n",
              "      <td>[[tensor(-7.9065), tensor(-10.6750), tensor(-1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>000</td>\n",
              "      <td>[[20.618156, 21.427076, 19.18558, 13.680927, 3...</td>\n",
              "      <td>[[tensor(20.6182), tensor(21.4271), tensor(19....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000</td>\n",
              "      <td>[[-16.27028, -21.62627, -47.989334, -50.855732...</td>\n",
              "      <td>[[tensor(-16.2703), tensor(-21.6263), tensor(-...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c3029a2-02f5-45b4-82f1-e152162ddf95')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "      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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2c3029a2-02f5-45b4-82f1-e152162ddf95 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-2c3029a2-02f5-45b4-82f1-e152162ddf95');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9b4b2cc-26ad-4f61-bffa-964e5638e254\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9b4b2cc-26ad-4f61-bffa-964e5638e254')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</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",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --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-f9b4b2cc-26ad-4f61-bffa-964e5638e254 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": "music_emb",
              "repr_error": "unhashable type: 'numpy.ndarray'"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 추론\n",
        "\n",
        "def infer_with_filtering(model, test_mel, music_df, info_df, user_genre, user_gender):\n",
        "    with torch.no_grad():\n",
        "        # 테스트 데이터에 대한 mel 벡터를 모델에 전달하여 임베딩 얻기\n",
        "        test_embedding = model(test_mel.unsqueeze(0).to(device))\n",
        "\n",
        "        # 필터링을 위한 데이터프레임 생성\n",
        "        filtered_info_df = info_df[(info_df['genre'] == user_genre) & (info_df['gender'] == user_gender)]\n",
        "\n",
        "        if filtered_info_df.empty:  # 필터링된 데이터가 없을 경우\n",
        "            return None\n",
        "\n",
        "        # 필터링된 데이터프레임의 id와 song_no를 일치시켜서 필터링된 음악 정보를 가져옴\n",
        "        filtered_music_df = music_df[music_df['song_no'].isin(filtered_info_df['id'])].reset_index(drop=True)\n",
        "\n",
        "        # 모든 원곡의 mel 벡터에 대해 임베딩을 계산하고 거리 측정\n",
        "        distances = []\n",
        "        for _, row in filtered_music_df.iterrows():\n",
        "            org_embedding = model(row['org_emb'].unsqueeze(0).to(device))\n",
        "            cdist = torch.nn.CosineSimilarity()\n",
        "            dist = cdist(test_embedding, org_embedding).cpu().numpy()\n",
        "            distances.append(dist)\n",
        "\n",
        "        if not distances:  # 필터링된 데이터가 없을 경우\n",
        "            return None\n",
        "\n",
        "        # 거리가 가장 가까운 원곡의 정보 얻기\n",
        "        closest_idx = min(range(len(distances)), key=lambda i: distances[i][0])\n",
        "        closest_song_no = filtered_music_df.loc[closest_idx, 'song_no']\n",
        "\n",
        "        # 해당 원곡의 정보 얻기\n",
        "        song_info = info_df.loc[info_df['id'] == closest_song_no].iloc[0]\n",
        "\n",
        "    return song_info"
      ],
      "metadata": {
        "id": "OqRqKnMvJmn6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iAo0C5RchDw",
        "outputId": "945f7650-ca3a-4772-c5d2-2414e2609d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-55.9576, -55.9576, -55.9576,  ..., -13.8754, -15.6629,  -5.4593],\n",
              "        [-55.9576, -55.9576, -55.9576,  ...,  -7.1582, -11.0204,  -2.7339],\n",
              "        [-55.9576, -55.9576, -55.9576,  ...,   6.5668,   6.6708,   6.1347],\n",
              "        ...,\n",
              "        [-55.9576, -55.9576, -55.9576,  ..., -55.9576, -52.4528, -37.8194],\n",
              "        [-55.9576, -55.9576, -55.9576,  ..., -55.9576, -53.0056, -37.4243],\n",
              "        [-55.9576, -55.9576, -55.9576,  ..., -55.9576, -55.9576, -40.8980]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예시로 첫 번째 허밍된 음성을 사용\n",
        "test_mel = test[1] # 태연 - To.X\n",
        "\n",
        "user_genre = input(\"장르를 입력하세요: \")\n",
        "user_gender = input(\"성별을 입력하세요: \")\n",
        "\n",
        "## 추론 결과 얻기\n",
        "inferred_info = infer_with_filtering(model, test_mel, music_emb, song_info, user_genre, user_gender)\n",
        "\n",
        "if inferred_info.any():\n",
        "    print(\"== 가장 유사한 원곡 정보 ==\")\n",
        "    print(\"곡 번호:\", inferred_info['id'])\n",
        "    print(\"제목:\", inferred_info['Title'])\n",
        "    print(\"가수:\", inferred_info['Singer'])\n",
        "    print(\"장르:\", inferred_info['genre'])\n",
        "    print(\"가수 성별:\", inferred_info['gender'])\n",
        "    print(\"가사:\", inferred_info['Lyric'])\n",
        "else:\n",
        "    print(\"입력한 장르와 성별에 해당하는 음악이 없습니다.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKlYQBJNczqE",
        "outputId": "9c4c5d6b-656c-47f1-f4da-e5551caedcf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "장르를 입력하세요: RnB_Soul\n",
            "성별을 입력하세요: 여성\n",
            "== 가장 유사한 원곡 정보 ==\n",
            "곡 번호: 091\n",
            "제목: 아이(I) (Feat. DAWN)\n",
            "가수: 박봄 (Park Bom)\n",
            "장르: RnB_Soul\n",
            "가수 성별: 여성\n",
            "가사: lonely lonely lonely lonely  초라해 보여 가시밭길 속에 넌 위험해  어쩌다 마주친 슬픔, 쓸쓸함에 너무 무거워진 발걸음  날이 점점 어두워져 해가 지나봐 너는 왜 아직 그 자리에  비라도 내릴까 봐 걱정돼 이제 내 손을 잡아 눈물은 멈추지 않아도 돼  저길을 잃은 아이 (I) 혼자 외롭게 울고 있는 아이 (I) 꼭 나인 것 같아  갇혀진 아이 (I) 지쳐 쓰러져 울고 있는 아이 (I) 네 손을 건네줘  (제발) lonely lonely (내게) lonely lonely (와줘) lonely lonely 거울 속 비춰진 그 아이 (I)  lonely lonely (올 순) lonely lonely (없어?) lonely lonely eh  너무 위태롭게 느껴지는 걸 금방 꺼질 듯 흔들리는 촛불처럼  그저 그 자리에 내 손잡아 줄 누군갈 기다려 떠나지 못해 홀로 남겨진 채  얼어붙은 맘을 녹이질 못해 차갑게 식어버린 바람만 탓해 비를 품은 구름 뒤에 숨어 헝클어져 버린 나를 혼자 달래곤 해  슬픈 결말은 없을 거라 믿던 해맑던 아이는 이미 빛을 잃어 낯선 세상 속을 혼자 걷고 있어  붉어진 눈시울에 홀로 애써 입꼬리만 웃고 있어  시간이 점점 흘러가면 사라질까 봐 두려워 매일 그 자리에  괜찮다 말해줄 너를 기다리며 눈물을 멈출 수가 없는걸  저길을 잃은 아이 (I) 혼자 외롭게 울고 있는 아이 (I) 꼭 나인 것 같아  갇혀진 아이 (I) 지쳐 쓰러져 울고 있는 아이 (I) 네 손을 건네줘  슬픈 영화 속 주인공처럼 늦봄에 남겨진 들꽃들처럼 yeah 가엾은 아이의 눈물방울 거울 속에서 비치는데  저길을 잃은 아이 (I) 혼자 외롭게 울고 있는 아이 (I) 꼭 나인 것 같아  갇혀진 아이 (I) 지쳐 쓰러져 울고 있는 아이 (I) 네 손을 건네줘  (제발) lonely lonely (내게) lonely lonely (와줘) lonely lonely 거울 속 비춰진 그 아이 (I)  lonely lonely (올 순) lonely lonely (없어?) lonely lonely eh\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test Accuracy\n",
        "correct = 0  # 맞는 곡의 개수\n",
        "\n",
        "for i in range(1000):\n",
        "    test_mel = test[i]\n",
        "    answer_id = numbers[i]\n",
        "\n",
        "    user_genre = song_info.loc[song_info['id'] == answer_id, 'genre'].values[0]\n",
        "    user_gender = song_info.loc[song_info['id'] == answer_id, 'gender'].values[0]\n",
        "\n",
        "    inferred_info = infer_with_filtering(model, test_mel, music_emb, song_info, user_genre, user_gender)\n",
        "\n",
        "    if inferred_info.any():\n",
        "      inferred_id = inferred_info['id']\n",
        "\n",
        "      if inferred_id == answer_id:\n",
        "        correct += 1\n",
        "\n",
        "accuracy = (correct / 1000) * 100\n",
        "print(f\"정확도: {round(accuracy, 2)}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVUj5X6X8L2Q",
        "outputId": "6afa5dd6-b939-4c59-d285-558f3fde76a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "정확도: 7.3%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "정확도가 별로인 것을 확인할 수 있다.."
      ],
      "metadata": {
        "id": "Tfb3o2znM1e_"
      }
    }
  ]
}