video-captioning.ipynb

{
  "cells": [
    {
      "metadata": {
        "_uuid": "b60eed4a-4184-49f4-b1fb-af2c50754b1e",
        "_cell_guid": "f48ec521-cf45-4fcc-b266-16de6ca56488",
        "trusted": true,
        "id": "2fTSu1-Sp0kl"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import torch\n",
        "import spacy\n",
        "import pandas as pd\n",
        "import torchvision.transforms as T\n",
        "\n",
        "from torchvision.io.video import read_video\n",
        "from tqdm.notebook import tqdm\n",
        "from PIL import Image\n",
        "from torch.utils.data import DataLoader,Dataset\n",
        "from collections import Counter\n",
        "from IPython.display import clear_output"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download ru_core_news_lg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISVhgC6tq3IA",
        "outputId": "64a9d06c-fc05-41fc-f40a-9fac6864f227"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-10-01 02:54:58.721795: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Collecting ru-core-news-lg==3.6.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/ru_core_news_lg-3.6.0/ru_core_news_lg-3.6.0-py3-none-any.whl (513.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m513.4/513.4 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.7.0,>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from ru-core-news-lg==3.6.0) (3.6.1)\n",
            "Collecting pymorphy3>=1.0.0 (from ru-core-news-lg==3.6.0)\n",
            "  Downloading pymorphy3-1.2.1-py3-none-any.whl (55 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.4/55.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting dawg-python>=0.7.1 (from pymorphy3>=1.0.0->ru-core-news-lg==3.6.0)\n",
            "  Downloading DAWG_Python-0.7.2-py2.py3-none-any.whl (11 kB)\n",
            "Collecting docopt-ng>=0.6 (from pymorphy3>=1.0.0->ru-core-news-lg==3.6.0)\n",
            "  Downloading docopt_ng-0.9.0-py3-none-any.whl (16 kB)\n",
            "Collecting pymorphy3-dicts-ru (from pymorphy3>=1.0.0->ru-core-news-lg==3.6.0)\n",
            "  Downloading pymorphy3_dicts_ru-2.4.417150.4580142-py2.py3-none-any.whl (8.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (1.0.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (1.0.9)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2.0.7)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (3.0.8)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2.4.7)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2.0.9)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (0.10.2)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (1.23.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (1.10.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (23.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2023.7.22)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (0.7.10)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (0.1.2)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.7.0,>=3.6.0->ru-core-news-lg==3.6.0) (2.1.3)\n",
            "Installing collected packages: pymorphy3-dicts-ru, dawg-python, docopt-ng, pymorphy3, ru-core-news-lg\n",
            "Successfully installed dawg-python-0.7.2 docopt-ng-0.9.0 pymorphy3-1.2.1 pymorphy3-dicts-ru-2.4.417150.4580142 ru-core-news-lg-3.6.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('ru_core_news_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RR-7N5x4vFSl"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "uhOu_6e6vHLx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8264344b-c272-422f-9dd6-a87e5ee5b68e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp ./drive/MyDrive/rutube_hackathon_novosibirsk.zip ./rutube_hackathon_novosibirsk.zip"
      ],
      "metadata": {
        "id": "pp0axwGfvUVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -FF ./rutube_hackathon_novosibirsk.zip --out ./pleasework.zip\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "_bbaVWk0wjOE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -qq ./pleasework.zip"
      ],
      "metadata": {
        "id": "hX3Huj8IvwUP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spacy_ru = spacy.load(\"ru_core_news_lg\")\n",
        "\n",
        "text = \"Сергей Чуприн любит hi! ОН - КАРТОфЕЛЕЛЮБ!!!\"\n",
        "[token.text.lower() for token in spacy_ru.tokenizer(text)]"
      ],
      "metadata": {
        "id": "NjctJmudqhOs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12ef6411-0067-435d-e967-d3c40da3af38"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['сергей',\n",
              " 'чуприн',\n",
              " 'любит',\n",
              " 'hi',\n",
              " '!',\n",
              " 'он',\n",
              " '-',\n",
              " 'картофелелюб',\n",
              " '!',\n",
              " '!',\n",
              " '!']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_csv = pd.read_csv(\"./rutube_hackathon_novosibirsk/train/train.csv\")"
      ],
      "metadata": {
        "id": "w3BRATY2txVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_csv[\"len\"] = train_csv[\"description\"].apply(lambda x: len(x.split()))"
      ],
      "metadata": {
        "id": "yGB1Yge2yB97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_csv.len.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqfIN1r5orsc",
        "outputId": "4a09e23e-2edb-406c-c77d-006ae0315c97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "321"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "NoPrKUpfpX4g",
        "outputId": "10dd3a6c-a815-4e6e-c25b-6d4d1ddefc2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    video_name stt_name category_name  \\\n",
              "0        0.mp4    0.txt   Развлечения   \n",
              "1        1.mp4    1.txt    Спорт/Игры   \n",
              "2        2.mp4    2.txt         Блоги   \n",
              "3        3.mp4    3.txt   Путешествия   \n",
              "4        4.mp4    4.txt     Искусство   \n",
              "..         ...      ...           ...   \n",
              "495    495.mp4  495.txt          Юмор   \n",
              "496    496.mp4  496.txt     Лайфстайл   \n",
              "497    497.mp4  497.txt   Развлечения   \n",
              "498    498.mp4  498.txt   Развлечения   \n",
              "499    499.mp4  499.txt         Блоги   \n",
              "\n",
              "                                                 title  \\\n",
              "0                                 Правильная цена I #3   \n",
              "1                       Три лошадиные силы | Выпуск №2   \n",
              "2    Хашлама | Выпуск 4 | Силиконовый ПРЕСС Давы | ...   \n",
              "3                     Прогулка по стране - Владивосток   \n",
              "4        Артмеханика. Выпуск 3. Татуировки + Mika Vino   \n",
              "..                                                 ...   \n",
              "495  Тот самый мент I Выпуск 3I Мента забирают в армию   \n",
              "496  Сколько Стоит День? Выпуск 7. Сколько тратит в...   \n",
              "497                               Правильная цена I #4   \n",
              "498                                  Тру ДЕТЕКТОР I #6   \n",
              "499                        Какова Меркурия? | Выпуск 3   \n",
              "\n",
              "                                           description  len  \n",
              "0    С вами Макс Климток и это шоу Правильная цена!...   53  \n",
              "1    В этом новом выпуске нас ждут не менее новые и...   46  \n",
              "2    Привет, это Султан и Авет! Мы опять хаваем вку...   58  \n",
              "3    Прогулка по Владивостоку. Самому большому горо...   42  \n",
              "4    Были ли татуировки на теле Николая II? Почему ...   62  \n",
              "..                                                 ...  ...  \n",
              "495  В начале рабочего дня Максиму Константиновичу ...   56  \n",
              "496  Сегодня мы провели целый день с певицей, актри...   68  \n",
              "497  С вами Макс Климток и это шоу Правильная цена!...   54  \n",
              "498  Вы смотрите шоу с детектором лжи «Тру ДЕТЕКТОР...   93  \n",
              "499  Действительно ли овны самые конфликтные? А бли...   42  \n",
              "\n",
              "[500 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c750707e-68d2-44ba-9b96-20a2c3c8b1be\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video_name</th>\n",
              "      <th>stt_name</th>\n",
              "      <th>category_name</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.mp4</td>\n",
              "      <td>0.txt</td>\n",
              "      <td>Развлечения</td>\n",
              "      <td>Правильная цена I #3</td>\n",
              "      <td>С вами Макс Климток и это шоу Правильная цена!...</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.mp4</td>\n",
              "      <td>1.txt</td>\n",
              "      <td>Спорт/Игры</td>\n",
              "      <td>Три лошадиные силы | Выпуск №2</td>\n",
              "      <td>В этом новом выпуске нас ждут не менее новые и...</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.mp4</td>\n",
              "      <td>2.txt</td>\n",
              "      <td>Блоги</td>\n",
              "      <td>Хашлама | Выпуск 4 | Силиконовый ПРЕСС Давы | ...</td>\n",
              "      <td>Привет, это Султан и Авет! Мы опять хаваем вку...</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.mp4</td>\n",
              "      <td>3.txt</td>\n",
              "      <td>Путешествия</td>\n",
              "      <td>Прогулка по стране - Владивосток</td>\n",
              "      <td>Прогулка по Владивостоку. Самому большому горо...</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.mp4</td>\n",
              "      <td>4.txt</td>\n",
              "      <td>Искусство</td>\n",
              "      <td>Артмеханика. Выпуск 3. Татуировки + Mika Vino</td>\n",
              "      <td>Были ли татуировки на теле Николая II? Почему ...</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>495.mp4</td>\n",
              "      <td>495.txt</td>\n",
              "      <td>Юмор</td>\n",
              "      <td>Тот самый мент I Выпуск 3I Мента забирают в армию</td>\n",
              "      <td>В начале рабочего дня Максиму Константиновичу ...</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>496.mp4</td>\n",
              "      <td>496.txt</td>\n",
              "      <td>Лайфстайл</td>\n",
              "      <td>Сколько Стоит День? Выпуск 7. Сколько тратит в...</td>\n",
              "      <td>Сегодня мы провели целый день с певицей, актри...</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>497.mp4</td>\n",
              "      <td>497.txt</td>\n",
              "      <td>Развлечения</td>\n",
              "      <td>Правильная цена I #4</td>\n",
              "      <td>С вами Макс Климток и это шоу Правильная цена!...</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>498.mp4</td>\n",
              "      <td>498.txt</td>\n",
              "      <td>Развлечения</td>\n",
              "      <td>Тру ДЕТЕКТОР I #6</td>\n",
              "      <td>Вы смотрите шоу с детектором лжи «Тру ДЕТЕКТОР...</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>499.mp4</td>\n",
              "      <td>499.txt</td>\n",
              "      <td>Блоги</td>\n",
              "      <td>Какова Меркурия? | Выпуск 3</td>\n",
              "      <td>Действительно ли овны самые конфликтные? А бли...</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c750707e-68d2-44ba-9b96-20a2c3c8b1be')\"\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",
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              "  </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-c750707e-68d2-44ba-9b96-20a2c3c8b1be 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-c750707e-68d2-44ba-9b96-20a2c3c8b1be');\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-91c83852-4286-4858-89f1-83f1c5e58e97\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-91c83852-4286-4858-89f1-83f1c5e58e97')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    <g>\n",
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              "    </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",
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              "      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-91c83852-4286-4858-89f1-83f1c5e58e97 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Vocabulary:\n",
        "    def __init__(self,freq_threshold):\n",
        "        self.itos = {0:\"<PAD>\", 1:\"<SOS>\", 2:\"<EOS>\", 3:\"<UNK>\"}\n",
        "\n",
        "        self.stoi = {v:k for k,v in self.itos.items()}\n",
        "\n",
        "        self.freq_threshold = freq_threshold\n",
        "\n",
        "    def __len__(self): return len(self.itos)\n",
        "\n",
        "    @staticmethod\n",
        "    def tokenize(text):\n",
        "        return [token.text.lower() for token in spacy_ru.tokenizer(text)]\n",
        "\n",
        "    @staticmethod\n",
        "    def del_timestamps(texts):\n",
        "        output = \"\"\n",
        "        for text in texts.split(\"\\n\"):\n",
        "          output += \"\".join(text.split(\"]  \")[1:]) + \" \"\n",
        "        return \" \".join(text)\n",
        "\n",
        "    def build_vocab(self, sentence_list, texts_list):\n",
        "      frequencies = Counter()\n",
        "      idx = 4\n",
        "\n",
        "      for sentence in tqdm(sentence_list):\n",
        "          for word in self.tokenize(sentence):\n",
        "              frequencies[word] += 1\n",
        "\n",
        "              if frequencies[word] == self.freq_threshold:\n",
        "                  self.stoi[word] = idx\n",
        "                  self.itos[idx] = word\n",
        "                  idx += 1\n",
        "\n",
        "      for text in tqdm(texts_list):\n",
        "          text = self.del_timestamps(text)\n",
        "          for word in self.tokenize(text):\n",
        "              frequencies[word] += 1\n",
        "\n",
        "              if frequencies[word] == self.freq_threshold:\n",
        "                  self.stoi[word] = idx\n",
        "                  self.itos[idx] = word\n",
        "                  idx += 1\n",
        "\n",
        "    def numericalize(self,text):\n",
        "        tokenized_text = self.tokenize(text)\n",
        "        return [ self.stoi[token] if token in self.stoi else self.stoi[\"<UNK>\"] for token in tokenized_text ]\n"
      ],
      "metadata": {
        "id": "Qbf2DQ-QqWax"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class VideoDataset(Dataset):\n",
        "    def __init__(self, root_dir, captions, video_files, texts, transform=None,freq_threshold=1, build=True):\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "\n",
        "        self.imgs = video_files\n",
        "        self.captions = captions\n",
        "\n",
        "        text = []\n",
        "        for file in tqdm(text):\n",
        "            with open(self.root_dir + \"/test_stt/\" + file) as cf:\n",
        "                    text.append(\" \".join(cf.readlines()))\n",
        "\n",
        "        self.vocab = Vocabulary(freq_threshold)\n",
        "        if build:\n",
        "            self.vocab.build_vocab(self.captions, texts)\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs)\n",
        "\n",
        "    def _read_video(self, path, frames_num=25, window=30):\n",
        "        frames = []\n",
        "        cap = cv2.VideoCapture(self.root_dir + \"/test_video/\" + path)\n",
        "\n",
        "        fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
        "\n",
        "        length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "        N = length // (frames_num)\n",
        "\n",
        "        current_frame = 1\n",
        "        for i in range(length):\n",
        "            ret, frame = cap.read(current_frame)\n",
        "            if ret and i == current_frame and len(frames) < frames_num:\n",
        "                size = 226, 226\n",
        "                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
        "                frame = cv2.resize(frame, size)\n",
        "                frames.append(frame)\n",
        "                current_frame += N\n",
        "\n",
        "\n",
        "        cap.release()\n",
        "\n",
        "        return np.array(frames)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "        caption = self.captions[idx]\n",
        "        img = self.imgs[idx]\n",
        "\n",
        "        img = np.rollaxis(self._read_video(self.imgs[idx]), 3, 0)\n",
        "        img = np.array(np.array(img) / 255, dtype=np.float32)\n",
        "\n",
        "\n",
        "        caption_vec = []\n",
        "        caption_vec += [self.vocab.stoi[\"<SOS>\"]]\n",
        "        caption_vec += self.vocab.numericalize(caption)\n",
        "        caption_vec += [self.vocab.stoi[\"<EOS>\"]]\n",
        "        if len(caption_vec) < 300:\n",
        "            for i in range(300 - len(caption_vec)):\n",
        "                caption_vec.append(0)\n",
        "\n",
        "        return torch.tensor(img), torch.tensor(caption_vec[:300])"
      ],
      "metadata": {
        "id": "MgbwgndRqMWg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "qOlFrHMCp0kn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "45e30daf-07e1-43a6-8f7a-ac49968835aa"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "data_location =  \"./rutube_hackathon_novosibirsk/train\"\n",
        "BATCH_SIZE = 10\n",
        "\n",
        "transforms = T.Compose([\n",
        "    T.Resize(226),\n",
        "    T.RandomCrop(224),\n",
        "    T.ToTensor(),\n",
        "    T.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))\n",
        "])\n",
        "\n",
        "\n",
        "dataset =  VideoDataset(\n",
        "    root_dir=data_location,\n",
        "    captions=train_csv.description.tolist(),\n",
        "    texts=train_csv.stt_name.tolist(),\n",
        "    video_files=train_csv.video_name,\n",
        "    transform=transforms\n",
        ")\n",
        "\n",
        "data_loader = DataLoader(\n",
        "    dataset=dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        "    # batch_first=False\n",
        ")\n",
        "\n",
        "vocab_size = len(dataset.vocab)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-65e154713e8a>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m dataset =  VideoDataset(\n\u001b[1;32m     13\u001b[0m     \u001b[0mroot_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_location\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mcaptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_csv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mtexts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_csv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstt_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mvideo_files\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_csv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvideo_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'train_csv' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7pHyXWqHlgx2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "QXf8jdMSp0kn"
      },
      "cell_type": "markdown",
      "source": [
        "### 3) Defining the Model Architecture\n",
        "\n",
        "Model is seq2seq model. In the **encoder** pretrained ResNet model is used to extract the features. Decoder, is the implementation of the Bahdanau Attention Decoder. In the decoder model **LSTM cell**."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "xwdy7H0dp0ko"
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "import torchvision.models as models\n",
        "import torchvision.models.video as video\n",
        "\n",
        "from torch.utils.data import DataLoader,Dataset\n",
        "import torchvision.transforms as T"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "XgJCuNkRp0ko"
      },
      "cell_type": "code",
      "source": [
        "class VideoEncoderCNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VideoEncoderCNN, self).__init__()\n",
        "        self.swin_t = video.swin3d_s(pretrained=True)\n",
        "        for param in self.swin_t.parameters():\n",
        "            param.requires_grad_(False)\n",
        "\n",
        "        modules = list(self.swin_t.children())[:-2]\n",
        "        self.swin_t = nn.Sequential(*modules)\n",
        "\n",
        "\n",
        "    def forward(self, images):\n",
        "        features = self.swin_t(images)\n",
        "        features = features.view(features.size(0), -1, features.size(-1))\n",
        "\n",
        "        return features\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "FlR420bNp0kp"
      },
      "cell_type": "code",
      "source": [
        "class VideoAttention(nn.Module):\n",
        "    def __init__(self, encoder_dim,decoder_dim,attention_dim):\n",
        "        super(VideoAttention, self).__init__()\n",
        "\n",
        "        self.attention_dim = attention_dim\n",
        "\n",
        "        self.W = nn.Linear(decoder_dim,attention_dim)\n",
        "        self.U = nn.Linear(encoder_dim,attention_dim)\n",
        "\n",
        "        self.A = nn.Linear(attention_dim,1)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    def forward(self, features, hidden_state):\n",
        "        u_hs = self.U(features)\n",
        "        w_ah = self.W(hidden_state)\n",
        "\n",
        "        combined_states = torch.tanh(u_hs + w_ah.unsqueeze(1))\n",
        "\n",
        "        attention_scores = self.A(combined_states)\n",
        "        attention_scores = attention_scores.squeeze(2)\n",
        "\n",
        "\n",
        "        alpha = F.softmax(attention_scores,dim=1)\n",
        "\n",
        "        attention_weights = features * alpha.unsqueeze(2)\n",
        "        attention_weights = attention_weights.sum(dim=1)\n",
        "\n",
        "        return alpha,attention_weights\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Z0kgdjhHp0kp"
      },
      "cell_type": "code",
      "source": [
        "class VideoDecoderRNN(nn.Module):\n",
        "    def __init__(self,embed_size, vocab_size, attention_dim,encoder_dim,decoder_dim,drop_prob=0.3):\n",
        "        super().__init__()\n",
        "\n",
        "        self.vocab_size = vocab_size\n",
        "        self.attention_dim = attention_dim\n",
        "        self.decoder_dim = decoder_dim\n",
        "\n",
        "        self.embedding = nn.Embedding(vocab_size,embed_size)\n",
        "        self.attention = VideoAttention(encoder_dim,decoder_dim,attention_dim)\n",
        "\n",
        "\n",
        "        self.init_h = nn.Linear(encoder_dim, decoder_dim)\n",
        "        self.init_c = nn.Linear(encoder_dim, decoder_dim)\n",
        "        self.lstm_cell = nn.LSTMCell(embed_size+encoder_dim,decoder_dim,bias=True)\n",
        "        self.f_beta = nn.Linear(decoder_dim, encoder_dim)\n",
        "\n",
        "\n",
        "        self.fcn = nn.Linear(decoder_dim,vocab_size)\n",
        "        self.drop = nn.Dropout(drop_prob)\n",
        "\n",
        "\n",
        "\n",
        "    def forward(self, features, captions):\n",
        "        embeds = self.embedding(captions)\n",
        "\n",
        "        h, c = self.init_hidden_state(features)\n",
        "\n",
        "        seq_length = len(captions[0])-1\n",
        "        batch_size = captions.size(0)\n",
        "        num_features = features.size(1)\n",
        "\n",
        "        preds = torch.zeros(batch_size, seq_length, self.vocab_size).to(device)\n",
        "        alphas = torch.zeros(batch_size, seq_length,num_features).to(device)\n",
        "\n",
        "        for s in range(seq_length):\n",
        "            alpha,context = self.attention(features, h)\n",
        "            lstm_input = torch.cat((embeds[:, s], context), dim=1)\n",
        "            h, c = self.lstm_cell(lstm_input, (h, c))\n",
        "\n",
        "            output = self.fcn(self.drop(h))\n",
        "\n",
        "            preds[:,s] = output\n",
        "            alphas[:,s] = alpha\n",
        "\n",
        "\n",
        "        return preds, alphas\n",
        "\n",
        "    def generate_caption(self,features, max_len=300 ,vocab=None):\n",
        "\n",
        "        batch_size = features.size(0)\n",
        "        h, c = self.init_hidden_state(features)\n",
        "\n",
        "        alphas = []\n",
        "        word = torch.tensor(vocab.stoi['<SOS>']).view(1,-1).to(device)\n",
        "        embeds = self.embedding(word)\n",
        "\n",
        "\n",
        "        captions = []\n",
        "\n",
        "        for i in range(max_len):\n",
        "            alpha,context = self.attention(features, h)\n",
        "\n",
        "            alphas.append(alpha.cpu().detach().numpy())\n",
        "\n",
        "            lstm_input = torch.cat((embeds[:, 0], context), dim=1)\n",
        "            h, c = self.lstm_cell(lstm_input, (h, c))\n",
        "            output = self.fcn(self.drop(h))\n",
        "            output = output.view(batch_size,-1)\n",
        "\n",
        "\n",
        "            predicted_word_idx = output.argmax(dim=1)\n",
        "\n",
        "            captions.append(predicted_word_idx.item())\n",
        "\n",
        "            if vocab.itos[predicted_word_idx.item()] == \"<EOS>\":\n",
        "                break\n",
        "\n",
        "            embeds = self.embedding(predicted_word_idx.unsqueeze(0))\n",
        "\n",
        "        return [vocab.itos[idx] for idx in captions],alphas\n",
        "\n",
        "\n",
        "    def init_hidden_state(self, encoder_out):\n",
        "        mean_encoder_out = encoder_out.mean(dim=1)\n",
        "        h = self.init_h(mean_encoder_out)\n",
        "        c = self.init_c(mean_encoder_out)\n",
        "        return h, c\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "nbVKYRkAp0kq"
      },
      "cell_type": "code",
      "source": [
        "class VideoEncoderDecoder(nn.Module):\n",
        "    def __init__(self,embed_size, vocab_size, attention_dim,encoder_dim,decoder_dim,drop_prob=0.3):\n",
        "        super().__init__()\n",
        "        self.encoder = VideoEncoderCNN()\n",
        "        self.decoder = VideoDecoderRNN(\n",
        "            embed_size=embed_size,\n",
        "            vocab_size = vocab_size,\n",
        "            attention_dim=attention_dim,\n",
        "            encoder_dim=encoder_dim,\n",
        "            decoder_dim=decoder_dim\n",
        "        )\n",
        "\n",
        "    def forward(self, images, captions):\n",
        "        features = self.encoder(images)\n",
        "        outputs = self.decoder(features, captions)\n",
        "        return outputs\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NXNgYH-tp0kq"
      },
      "cell_type": "markdown",
      "source": [
        "### 4) Setting Hypperparameter and Init the model"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "FFUUedy6p0kr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "7e4cf668-feda-4f2d-e0b8-3f9b8d1b15e9"
      },
      "cell_type": "code",
      "source": [
        "#Hyperparams\n",
        "embed_size=300\n",
        "vocab_size = len(dataset.vocab)\n",
        "attention_dim=256\n",
        "encoder_dim=768\n",
        "decoder_dim=512\n",
        "learning_rate = 3e-4\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-fee7ef076f9a>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Hyperparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0membed_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvocab_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mattention_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mencoder_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m768\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "7Go__iHNp0kr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6325e51-eb83-4387-dbd4-33c7b4138312"
      },
      "cell_type": "code",
      "source": [
        "#init model\n",
        "model = VideoEncoderDecoder(\n",
        "    embed_size=embed_size,\n",
        "    vocab_size=vocab_size,\n",
        "    attention_dim=attention_dim,\n",
        "    encoder_dim=encoder_dim,\n",
        "    decoder_dim=decoder_dim\n",
        ").to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss(ignore_index=dataset.vocab.stoi[\"<PAD>\"])\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=Swin3D_S_Weights.KINETICS400_V1`. You can also use `weights=Swin3D_S_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install av"
      ],
      "metadata": {
        "id": "H5mxVPJoGd0t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "mSbWYebGp0kr"
      },
      "cell_type": "code",
      "source": [
        "#helper function to save the model\n",
        "def save_model(model,num_epochs):\n",
        "    model_state = {\n",
        "        'num_epochs':num_epochs,\n",
        "        'embed_size':embed_size,\n",
        "        'vocab_size':len(dataset.vocab),\n",
        "        'attention_dim':attention_dim,\n",
        "        'encoder_dim':encoder_dim,\n",
        "        'decoder_dim':decoder_dim,\n",
        "        'state_dict':model.state_dict()\n",
        "    }\n",
        "\n",
        "    torch.save(model_state,'./drive/MyDrive/attention_model_state.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w2MBCzBUp0kr"
      },
      "cell_type": "markdown",
      "source": [
        "## 5) Training Job from above configs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "U7gXjpFYyE2f"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.translate import meteor\n",
        "from nltk import word_tokenize\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cN4WnSf2B1o",
        "outputId": "b83c4811-12d0-4e50-fa58-ca124759ae2c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def score(text, text_sum):\n",
        "    text = text.replace(\"<PAD>\", \"\")\n",
        "    if isinstance(text_sum, str):\n",
        "        return round(meteor([word_tokenize(text)],word_tokenize(text_sum)), 4)\n",
        "    else:\n",
        "        return 0"
      ],
      "metadata": {
        "id": "iKWMSFO_162R"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
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        },
        "outputId": "2cef0614-5fca-4ae1-f68d-2b5cde7282c5"
      },
      "cell_type": "code",
      "source": [
        "num_epochs = 25\n",
        "print_every = 10\n",
        "\n",
        "for epoch in tqdm(range(1, num_epochs + 1)):\n",
        "    losses = []\n",
        "    print(f\"Epoch: {epoch}\")\n",
        "    try:\n",
        "        for idx, (image, captions) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
        "            # print(image.shape[2])\n",
        "            # if image.shape[2] < 50:\n",
        "            #     continue\n",
        "            image, captions = image.to(device),captions.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            outputs,attentions = model(image, captions)\n",
        "\n",
        "            loss = criterion(outputs.view(-1, vocab_size), captions[:, 1:].reshape(-1))\n",
        "\n",
        "            loss.backward()\n",
        "\n",
        "            optimizer.step()\n",
        "            losses.append(loss.item())\n",
        "\n",
        "            if idx % print_every == 0:\n",
        "                model.eval()\n",
        "                with torch.no_grad():\n",
        "                    dataiter = iter(data_loader)\n",
        "                    img,targ = next(dataiter)\n",
        "                    features = model.encoder(img[0:1].to(device))\n",
        "                    caps, alphas = model.decoder.generate_caption(features, vocab=dataset.vocab)\n",
        "                    caption = ' '.join(caps)\n",
        "                    target = \" \".join([dataset.vocab.itos[i] for i in targ[0:1][0][1:].tolist()])\n",
        "                    print(\"true:\", target)\n",
        "                    print(\"pred:\", caption)\n",
        "                    print(\"loss:\", loss.item(), \"score:\", score(target, caption))\n",
        "\n",
        "\n",
        "                model.train()\n",
        "    except Exception as ex:\n",
        "        print(\"Err:\", ex)\n",
        "        raise ex\n",
        "    if len(losses) != 0:\n",
        "        print(\"Epoch loss: {:.5f}\".format(sum(losses) / len(losses)))\n",
        "    else:\n",
        "        print(losses)\n",
        "    #save the latest model\n",
        "    save_model(model,epoch)\n",
        "    # clear_output(wait=True)"
      ],
      "execution_count": null,
      "outputs": [
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            "Epoch: 1\n"
          ]
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            "loss: 8.915692329406738 score: 0.0263\n",
            "loss: 8.47883129119873 score: 0.0227\n",
            "loss: 7.248097896575928 score: 0.0176\n",
            "loss: 7.123484134674072 score: 0.0221\n",
            "loss: 7.134592533111572 score: 0.0329\n",
            "Epoch loss: 7.71725\n",
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            "loss: 6.811089515686035 score: 0.0333\n",
            "loss: 6.925175666809082 score: 0.0339\n",
            "Epoch loss: 6.94436\n",
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            "loss: 6.799895286560059 score: 0.0\n",
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            "loss: 6.587986469268799 score: 0.0461\n",
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    {
      "cell_type": "code",
      "source": [
        "checkpoint = torch.load(\"./drive/MyDrive/video_model.pth\")"
      ],
      "metadata": {
        "id": "cpaBCKOmt0ll"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = VideoEncoderDecoder(\n",
        "    embed_size=checkpoint[\"embed_size\"],\n",
        "    vocab_size =checkpoint[\"vocab_size\"],\n",
        "    attention_dim=checkpoint[\"attention_dim\"],\n",
        "    encoder_dim=checkpoint[\"encoder_dim\"],\n",
        "    decoder_dim=checkpoint[\"decoder_dim\"]\n",
        ").to(device)\n",
        "model.load_state_dict(checkpoint[\"state_dict\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtuLRJeYp8YA",
        "outputId": "958ff4bd-7e03-49e1-99d7-185d199943a6"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=Swin3D_S_Weights.KINETICS400_V1`. You can also use `weights=Swin3D_S_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://lodmedia.hb.bizmrg.com/case_files/1026506/test_dataset_test.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JUTFQ2KftYM",
        "outputId": "91e23f59-73ec-4ec5-b9e6-3feb6c38918f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-01 02:59:53--  https://lodmedia.hb.bizmrg.com/case_files/1026506/test_dataset_test.zip\n",
            "Resolving lodmedia.hb.bizmrg.com (lodmedia.hb.bizmrg.com)... 95.163.53.117\n",
            "Connecting to lodmedia.hb.bizmrg.com (lodmedia.hb.bizmrg.com)|95.163.53.117|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1696238228 (1.6G) [application/zip]\n",
            "Saving to: ‘test_dataset_test.zip’\n",
            "\n",
            "test_dataset_test.z 100%[===================>]   1.58G  48.0MB/s    in 50s     \n",
            "\n",
            "2023-10-01 03:00:44 (32.5 MB/s) - ‘test_dataset_test.zip’ saved [1696238228/1696238228]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "8t9YdLrmMW1n"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -qq test_dataset_test.zip"
      ],
      "metadata": {
        "id": "f6Zjmuq8f3an"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_csv = pd.read_csv(\"./test/test.csv\")"
      ],
      "metadata": {
        "id": "4o_1hY4IgUu2"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_submission = pd.read_csv(\"./sample_submission.csv\")"
      ],
      "metadata": {
        "id": "kim43lLbMCgk"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "RJxJjtPNiRgY",
        "outputId": "d963bc62-67ff-41a1-cf19-8cff0e994b75"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  video_name generated_description\n",
              "0      0.mp4   Полученное описание\n",
              "1      1.mp4   Полученное описание\n",
              "2      2.mp4   Полученное описание\n",
              "3      3.mp4   Полученное описание\n",
              "4      4.mp4   Полученное описание"
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            "text/html": [
              "\n",
              "  <div id=\"df-da3be476-740e-4e1c-ba09-976ebb392e49\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>video_name</th>\n",
              "      <th>generated_description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.mp4</td>\n",
              "      <td>Полученное описание</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.mp4</td>\n",
              "      <td>Полученное описание</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.mp4</td>\n",
              "      <td>Полученное описание</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.mp4</td>\n",
              "      <td>Полученное описание</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.mp4</td>\n",
              "      <td>Полученное описание</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-da3be476-740e-4e1c-ba09-976ebb392e49')\"\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-da3be476-740e-4e1c-ba09-976ebb392e49 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-da3be476-740e-4e1c-ba09-976ebb392e49');\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-c8d35d73-39c3-481b-9b48-898de6d0dc75\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8d35d73-39c3-481b-9b48-898de6d0dc75')\"\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-c8d35d73-39c3-481b-9b48-898de6d0dc75 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data_location =  \"./test/\"\n",
        "BATCH_SIZE = 1\n",
        "\n",
        "transforms = T.Compose([\n",
        "    T.Resize(226),\n",
        "    T.RandomCrop(224),\n",
        "    T.ToTensor(),\n",
        "    T.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))\n",
        "])\n",
        "\n",
        "\n",
        "dataset =  VideoDataset(\n",
        "    root_dir=data_location,\n",
        "    captions=train_csv.video_name.tolist(),\n",
        "    texts=train_csv.stt_name.tolist(),\n",
        "    video_files=train_csv.video_name,\n",
        "    transform=transforms,\n",
        "    build=False\n",
        ")\n",
        "\n",
        "dataset.vocab = pickle.load(open(\"./vocab.pkl\", \"rb\"))\n",
        "\n",
        "\n",
        "data_loader = DataLoader(\n",
        "    dataset=dataset,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=False,\n",
        "    # batch_first=False\n",
        ")\n",
        "\n",
        "vocab_size = len(dataset.vocab)\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "metadata": {
        "id": "UMDKfpBBichp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65,
          "referenced_widgets": [
            "75f26c87196a48ff837dc8e0a6adac83",
            "5b612fc648314d31a9d202bcdc91e97c",
            "e32921acc65c498a8d5f063752287ad3",
            "498f37048c4d4776b919f3fd0c1cee9b",
            "6ee974271fa146db8484be3d8976f290",
            "46360da8c2a744d595ebb963df511ed9",
            "2fdfb2c66f314831891773b3f3140196",
            "cf60038ba6c3484696fa491210b7869a",
            "293f49eff13a4cf4b2d158dafc4111ae",
            "9c15fab77f4e4a409d5b365ff07e55ff",
            "57ca1994ca14467e92af728366da59f8"
          ]
        },
        "outputId": "a304c05a-9a90-4ce9-a22f-d8a08661b6ad"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75f26c87196a48ff837dc8e0a6adac83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string"
      ],
      "metadata": {
        "id": "vsbp4wBViigJ"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores = []\n",
        "model.eval()\n",
        "\n",
        "for idx, (image, captions) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
        "    img, targ = image.to(device),captions.to(device)\n",
        "    with torch.no_grad():\n",
        "        features = model.encoder(img[0:1].to(device))\n",
        "        caps, alphas = model.decoder.generate_caption(features, vocab=dataset.vocab)\n",
        "        caption = ' '.join(caps)\n",
        "        caption = caption.replace(\"<EOS>\", \"\")\n",
        "        for i in string.punctuation:\n",
        "            if i not in [\"/\", \"\\\\\", \"-\"]:\n",
        "                caption = caption.replace(f\" {i}\", i)\n",
        "        # print(targ, caption)\n",
        "        sample_submission.loc[sample_submission.video_name == f\"{idx}.mp4\", \"generated_description\"] = [caption]\n",
        "        print(caption)\n",
        "        # target = \" \".join([dataset.vocab.itos[i] for i in targ[0:1][0][1:].tolist()])\n",
        "\n",
        "        # scores.append(score(target, caption))\n",
        "        # print(\"true:\", target)\n",
        "        # print(\"pred:\", caption)\n",
        "        # print(\"loss:\", loss.item(), \"score:\", score(target, caption))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "e60d26cde5b54bf8a1452d0c1550b9e1",
            "b9ca0f0b0a094f6a845863240b1e50e0",
            "b5c31223a3a2495cb8cab18f5966b859",
            "ef76db7d5ab3424bb6cc1e35de5f1f21",
            "1c248fb722ec4d0f8c32664728a2e539",
            "c789d577293d490d9a45b75db3dfe1a1",
            "4e2b4ec6f2e74a5e899e2e0ac0b5b78b",
            "999cae8ade9143a888ba7772242fd5a6",
            "fab9519d4feb466290fa5de2721bea3b",
            "c521d1a17fab41c2b31f82b4da7264f7",
            "10b6a0c71dfb45aa876df463543b8801"
          ]
        },
        "id": "OU2INDZmR_U1",
        "outputId": "3b7a6844-5050-44dd-bcd7-ae125269df86"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e60d26cde5b54bf8a1452d0c1550b9e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "добро пожаловать на канал « бюро добрых дел »! его глава – это я, лариса брохман. а миссия нашего бюро – искать самые добрые и хорошие новости по всему миру, чтобы рассказать их вам. в этом выпуске:   - /   - /   - /   - /   - /   ссылка на email   в этом выпуске:   - /   - /   - /   в этом выпуске - /   гость - специальный гость шоу « пацанский клининг », чтобы рассказать их вам. в этом выпуске: -   гость - ксения егорова в рамках рубрики « добрый репортаж », чтобы поднять вам настроение сделать ваш день чуточку радостнее и светлее.   пишите свои добрые истории нам на почту: email   по вопросам рекламы пишите на email \n",
            "знаете, как в котором комики играют и не менее от шефа. \n",
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            "в этом выпуске: антон протеинов - /   - /   в этом выпуске - / максим народный - /   в котором кристи - сазонов совместно со звездными гостями любыми способами пытаются найти номер и дозвониться известным артистам шоу, а также по вопросам рекламы пишите на email \n",
            "в этом выпуске ведущая алиса и дизайнер наталья, как с бюджетом в 25, чтобы из них. но и как всегда, чтобы не только на что они. что такое, что они готовы ради? и кто окажется самым лживым в компании.   а если уже посмотрели, то, что такое, что такое. а кто же не только на необычную.   что такое, как правильно нужно? как правильно? как правильно? как правильно? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? как всегда? смотри в новом выпуске шоу « пацанский клининг ». \n",
            "сегодня семён, сундук, и тяпа, егор и это не только, а также не только на канал и оставить. сегодня мы узнаем, как в ценах ориентируются, а также не только на передаче, а также не только на сайте. по вопросам рекламы пишите на email \n",
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            "в этом выпуске вы узнаете, как правильно нужно, как правильно нужно, как правильно нужно, а также и как правильно. \n",
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            "почти половина\" представляет в шоу\" путь бойца\". в этом выпуске реалити - шоу\" путь бойца\". в этом выпуске, а также не только им, но и пять, конечно же, кто из них сильнее! \n",
            "в этом выпуске вы узнаете, как правильно нужно, как правильно нужно, чтобы в гости к своему. \n",
            "сегодня султан - это шоу, в котором комики играют в мафию и пьют горячительные. сегодня мы узнаем, как правильно, как правильно сделать, которые написаны в сегодняшнем выпуске. \n",
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            "в этом выпуске профессиональный - зоя, как предприниматель кирилл в мафию и пьют. сегодня мы будем, как в ценах ориентируются, а также и конечно же, кто из них сильнее! \n",
            "назаров по вопросам рекламы на email \n",
            "на связующих - это шоу, в котором кирилл фокин - сазонов совместно со звездными гостями любыми способами пытаются найти номер и дозвониться известным артистам шоу - бизнеса. сегодня в гостях – это шоу, а звонить мы будем будем! \n",
            "на связующих - это шоу, в котором кирилл фокин - сазонов совместно со звездными гостями любыми способами пытаются найти номер и дозвониться известным артистам шоу - бизнеса. сегодня в гостях – это шоу, а звонить мы будем будем! \n",
            "в этом выпуске ведущая алиса и дизайнер наталья, как правильно с бюджетом и суммировали все чеки.   в этом выпуске аня хилькевич расскажет, как правильно и дизайнер наталья, чтобы поднять вам. в этом выпуске вы узнаете, как правильно нужно, как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно? как правильно\n",
            "вы смотрите шоу с детектором лжи тру детектор! шоу в котором кристи крайм и её молчаливый друг приглашают в гости известных людей и задают им неудобные вопросы под пристальным взором полиграфа. в этом выпуске, в логово кристи и мистера молчуна попал попал и мистера молчуна попал, что их ближайшее окружение? какие вопросы они зададут друг другу? и кто окажется самым лживым в компании? все это узнаёте в шоу тру детектор! \n",
            "это алкомафия! шоу, в котором комики играют в мафию и пьют горячительные напитки. градус игры повышается с каждым выпитым бокалом. на розливе ведущий семен голубь. сегодня мы узнаем, как зовут, а звонить мы будем, а звонить мы будем! \n",
            "в этом выпуске “ мама 21 века ” расскажет марина дворникова. \n",
            "в этом выпуске « стендап » - как расскажет, как правильно и как правильно? как это в новом выпуске « мама-21 ». \n",
            "с вами макс климток и это шоу правильная цена! шоу, в котором участники зарабатывают баллы, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, подписаться и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, подписаться и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, подписаться и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов и услуг.   сегодня мы узнаем, как в ценах ориентируются, угадывая правильные цены товаров и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов\n",
            "недовольна, как вести себя в 25 тысяч рублей? и как сделать сделать, как правильно, что и что делать в новом выпуске. \n",
            "в этом выпуске - зоя, как правильно и дизайнер новости в новом выпуске tiktok дайджеста. \n",
            "на связующих в этом выпуске аня хилькевич расскажет, как правильно и сделать, как правильно сделать, что такое, что такое и его в новом выпуске аня хилькевич расскажет, как правильно сделать, что такое, что такое и его в новом выпуске аня хилькевич расскажет, как правильно сделать, а также сделать ваш на что и что делать в новом выпуске аня хилькевич. \n",
            "в этом выпуске ведущая алиса и дизайнер наталья, как правильно, как правильно нужно, как правильно нужно, что такое, что такое и как правильно сделать, как сделать ваш день чуточку радостнее и светлее. \n",
            "как молодому фотографу в новом выпуске шоу « стендап ». \n",
            "в этой серии максим константинович константинович, а канал подпиской! \n",
            "отправились с парнями на рыбалку. половили на рыбалку. что он натворил на природе. смотрите в этом выпуске. \n",
            "добро пожаловать на канал « бюро добрых дел »! его глава – это я, лариса брохман. а миссия нашего бюро – искать самые добрые и хорошие новости по всему миру, чтобы рассказать их вам. в этом выпуске:   - /   ссылка на email \n",
            "в этом выпуске « 3 » », как правильно и его из самых. \n",
            "в этом выпуске ведущая алиса и дизайнер наташа с бюджетом в 25 тысяч рублей. и кто из них за деньги и бесплатно. \n",
            "действительно ли овны самые конфликтные? а близнецы двуличные? сегодня это выяснят комики, что эффективнее: прислушиваться прислушиваться к звёздам или ориентироваться на юмор и интуицию? узнаем в сегодняшнем выпуске шоу « какова меркурия » с женей искандаровой. \n",
            "макс и лиза максу и лиза, что они не чистить, чтобы они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они узнали, чтобы они и макса в прошлое, чтобы они и макса в прошлое, чтобы они и макса в прошлое, чтобы они и макса в прошлое, чтобы они и макса в прошлое, чтобы они и макса в прошлое, чтобы они и\n",
            "в новом выпуске « 3 », чтобы не видели я, который я, поехали! \n",
            "в этом выпуске « спортивный интерес ». по вопросам рекламы пишите на email \n",
            "в этом выпуске ведущая алиса и дизайнер наталья обустраивают интерьер, как сделать к рабиа халилли. в этом выпуске аня хилькевич расскажет, как правильно, как правильно сделать, чтобы их к звёздам? как сделать ваш день? как сделать ваш день в 25, чтобы поднять в 25 тысяч рублей. \n",
            "с вами макс климток и это шоу правильная цена! шоу, в котором участники зарабатывают баллы, угадывая правильные цены товаров и услуг. сегодня мы узнаем, как в ценах ориентируются, как правильно, алексей кузнецов и услуг.   сегодня мы узнаем, как в ценах ориентируются, как правильно, а также можете заплатить свою цену - поставить лайк, подписаться на канал и оставить комментарий. \n",
            "добро пожаловать на аукцион шоу в котором подписчики делают ставки за вещи своего любимого блогера! сделать ставку может каждый сидящий в зале, в качестве ставки принимается всё кроме денег, ну а ставки помогает сделать ведущий шоу игорь чехов.   в этом выпуске, фанаты прохора боролись за личные вещи кумира, а также на что они готовы ради вещей.   а если уже посмотрели, то поделитесь выпуском с друзьями кто знает, может быть именно ваш кумир окажется на аукционе в следующем выпуске. \n",
            "в этом выпуске шеф, павел коклюшкин приготовит и павел коклюшкин, как правильно, как правильно, а также не менее - /   в этом выпуске вы найдете в этом выпуске вы найдете в новом выпуске “ ” - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   ссылка на пост в этом выпуске:   - /   илья - /   ссылка на пост в этом выпуске:   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья - /   илья -\n",
            "сундук открыл в этой серии! \n",
            "в новом выпуске шоу « стендап гороскоп » расскажет гороскоп расскажет про расскажет, как непросто, что ждет представителей знака и что ждет представителей знака в 2022 году. \n",
            "в этой серии сотрудник социальной службы службы с бюджетом, как всегда за ними. лиза и ольга пдн, что он тратит в этой серии! \n",
            "в этом выпуске вы узнаете, как предприниматель гостя, в мафию и пьют горячительные напитки. \n",
            "в этом выпуске легенда спорта спорта расскажет, как правильно нужно, как правильно нужно в спортзал, как выбирать в домашних условиях. \n",
            "в этой серии сотрудник социальной службы службы службы, как всегда полупановых, чтобы в этой серии. \n",
            "в этом выпуске шеф, павел коклюшкин приготовит приготовит и павел коклюшкин приготовит. \n",
            "в этом выпуске блогеры райзен football и клён вместе с ребятами, которые пройдут и решат, которые пройдут в команду, а также на футбольном поле и решат, которые пройдут в команду и решат, кто из них них. \n",
            "в этом выпуске « наше дело ». \n",
            "в этом выпуске ведущая алиса и дизайнер наташа наташа, как сделать сделать ваш день в 25, чтобы сделать ваш день чуточку радостнее и светлее. \n",
            "две красотки за тридцать тысяч рублей пытаются сделать скворечник. \n",
            "в этом выпуске - прохор останется один с учительницей, которая проверит гостя, а также на реакцию к шуткам, а также на реакцию к шуткам, которые написаны в тексте диктанта. \n",
            "предлагаю подписчикам приготовить приготовить на сковороде. \n",
            "мультнайтшоу – еженедельное юмористическое нарисованное вечернее шоу, где обсуждаются актуальные новости страны и мира. в этом выпуске мы пригласим в студию студию нагиева, увидим трейлер клип про елки, а также послушаем, увидим трейлер. по вопросам рекламы пишите на email \n",
            "в этом выпуске ассоль рабиа халилли обратилась, как правильно рабиа халилли. ей уже завтра, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что такое, что\n",
            "добро пожаловать на аукцион шоу « о чем молчат мужчины ». здесь парни говорят о том, о чем им не принято говорить. в этом выпуске вместе с оскаром. \n",
            "это спортивно - развлекательное шоу про бои в телефонной будке впервые в россии! противостояние популярного в телефонной будке! противостояние популярного в телефонной будке! противостояние популярного в телефонной будке! \n",
            "добро пожаловать на канал « бюро добрых дел »! его глава – это я, лариса брохман. а миссия нашего бюро – искать самые добрые и хорошие новости по всему миру, чтобы рассказать их вам. в этом выпуске:   -   - ксения егорова в рамках рубрики « добрый репортаж », чтобы поднять вам настроение, чтобы поднять вам настроение.   эти и другие добрые новости мы собираем регулярно, чтобы поднять вам настроение сделать ваш день чуточку радостнее и светлее.   пишите свои добрые истории нам на почту:   email   по вопросам рекламы пишите на email \n",
            "в новом выпуске шоу « спортивный интерес », чтобы не менее и его в россии! \n",
            "в новом выпуске шоу « спортивный интерес », чтобы не менее и его в комментариях под постом в instagram.   в этом выпуске вы узнаете, как предприниматель филипп, как правильно, чтобы поднять вам.   в этом выпуске вы узнаете, как предприниматель в ценах ориентируются, как правильно нужно, которая проверит.   а также на канал « наше дело ». \n",
            "сундук устроился в этой серии! \n",
            "в этом выпуске: - сазонов совместно со звездными гостями любыми способами пытаются найти и дозвониться известным артистам. сегодня в гостях –, а звонить мы будем, а также и кто же, а также кто из самых и давай. \n",
            "в этом выпуске семейная пара фермеров на farming simulator, как вести себя на улицы? как всегда? как всегда на эти? как всегда на эти вопросы. что делать на эти вопросы вы найдете в новом выпуске шоу « гейм разбор ». \n",
            "в этом выпуске вы узнаете, как предприниматель, который не менее и егор в этом выпуске! \n",
            "на фестивале на льду скорости « байкальская миля ». по вопросам рекламы пишите на льду, чтобы рекорды на льду озера байкал. \n",
            "максим константинович и ольга пдн отправляются на улицы, который не только, который и андрюха, который не только. что они и ольга пдн, что они готовы, что они готовы ради, но и кто из них них. кто из них них, кто из них них. кто из них них, кто из них них. кто из них них, кто из них них. кто из них них. поддержите видео лайком, а канал подпиской! \n",
            "в новом выпуске шоу « спортивный интерес ». по вопросам рекламы пишите на email \n",
            "в новом выпуске шоу « стендап гороскоп » расскажет про расскажет, как непросто быть, что ждет представителей знака в 2022 году. \n",
            "в этом выпуске вы узнаете, как предприниматель в новом выпуске, как правильно, как правильно, как правильно стать, что такое. что такое, как всегда в новом выпуске. \n",
            "саша белькович покажет, как правильно приготовить всемирно любимый, как правильно приготовить в домашних соусе. \n",
            "предлагаю подписчикам приготовить приготовить салат с курицей. \n",
            "carjitsu — бои в формате pop mma, где в вместо ринга бойцы сражаются в салоне. в этом выпуске, кто из них, кто из них сильнее! кто оказался сильнее и кто оказался в клетке, а кто же не только и кто оказался сильнее в клетке. кто из них сильнее сильнее! \n",
            "в этом выпуске, как всегда, как всегда, который я, который заберёт 1000$! готовьте чайоку, устраивайтесь поудобнее, поехали! \n",
            "макс случайно порвал лизе, где обсуждаются актуальные новости страны и мира. артур не умеет отправляет лизу и макса в прошлое, чтобы они узнали, чтобы они узнали, что они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, чтобы они узнали, что они они и макса в прошлое, чтобы они узнали, чтобы они узнали, что они они и макса в\n",
            "мультнайтшоу – еженедельное юмористическое нарисованное вечернее шоу, где обсуждаются актуальные новости страны и мира. в этом выпуске мы пригласим в студию студию нагиева, увидим трейлер, увидим трейлер клип на саммите. \n",
            "в этом выпуске шоу « такой расклад » вы увидите таролога ксению протаро и ведущую янину студилину. встречаемся в 20:00! тема недели и этого выпуска – карьера в семье. в прямом эфире дарья с помощью метафорических карт поможет в семье и включайте эфир! \n",
            "в этом выпуске вы узнаете, как предприниматель филипп, как правильно, которые и олю, которые в этом выпуске вы узнаете в этом выпуске, а также вы узнаете, как правильно, как правильно нужно, которые в этой серии! \n",
            "в этом выпуске вы узнаете, как предприниматель - то, как всегда, чтобы стать, обожайте! \n",
            "в этом выпуске шоу “ анкета ”, три комика - егор свирский, максим береснев и егор свирский, как зовут гостя, в этом выпуске, а также на свою интуицию, а также и какой же, а также на розливе, а также и какой самый экстремальный поступок, а также на наши каналы: антон протеинов - / \n",
            "в этом выпуске вы узнаете, как предприниматель филипп, как правильно нужно, как правильно нужно? как сделать - /   в этом выпуске, как правильно нужно? как правильно нужно? как всегда? как всегда? смотри в этом выпуске, что они готовы ради? смотри и делись своим впечатлением в комментариях под видео! \n",
            "в этом выпуске шоу « такой расклад ». \n",
            "если выпуска, как правильно, как правильно жизнь, как правильно жизнь? почему на кастинге? как правильно? как правильно? как правильно? как правильно? как правильно? ответы на эти вопросы вы найдете в новом выпуске шоу « гейм разбор ». \n",
            "в этом выпуске « 3 лошадиные », чтобы не только на что и что делать, что он и их в течение. \n",
            "в этом выпуске профессиональный - зоя, как правильно и рамис ахметов в россии. \n",
            "прогулка по городу, ведущему городу, ведущему с нами. предлагаем вместе с нами, погрузимся в непередаваемую атмосферу одного из красивейших городов россии. предлагаем вместе с нами, погрузимся в непередаваемую атмосферу одного из красивейших городов россии. \n",
            "отправились в поисках трофейной щуки. максим константинович приготовил на рыбалку. приготовили приготовил кролика в казане. приготовили видео! \n",
            "в этом выпуске “ стендап ” расскажет, как правильно, что ждет, что ждет, что ждет в 2022 году. \n",
            "сундук, тяпа и чернобыль, который не только, который не представляю, который и кто из них, который кто из них, который кто из них них, который кто из них них! \n",
            "в этом выпуске шоу « такой расклад » вы увидите таролога ксению протаро и ведущую янину студилину. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью метафорических карт поможет в семье. встречаемся в 20:00! тема недели и этого выпуска – карьера, в прямом эфире вероника с помощью\n",
            "блогерское агентство insight - шоу, где в вместо ринга бойцы в вместо ринга бойцы сражаются в клетке. в этом выпуске, а также, кто из них, кто покинет, кто покинет в клетке, а также на instagram.   в этом выпуске вы узнаете, как в клетке, а также на что они бой.   в этом выпуске вы узнаете в клетке, а также, кто же бой. кто из них сильнее бой. кто из них сильнее бой в клетке.   в этом выпуске, кто из них бой, кто покинет бой в клетке.   а также бой в клетке, а также на канал « наше дело ». youtube канал наше дело -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram артура   -   instagram -   instagram артура   -   instagram -   instagram -   instagram артура   -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -   instagram -  \n",
            "группа с вами в новом выпуске артмеханика, а также часть программы часть программы в новом выпуске артмеханика, как можно расскажет, как правильно, как правильно нужно. в этом выпуске, а также на что к звёздам и как? как правильно? об этом расскажет, как в новом выпуске артмеханика, а также на что они и как правильно? об этом расскажет, как в новом выпуске артмеханика, а также на что они и как правильно? об этом расскажет, как в новом выпуске артмеханика, а также на что к звёздам. \n",
            "почти половина на связующих - это шоу, в котором кирилл фокин - сазонов совместно со звездными гостями любыми способами пытаются найти и дозвониться известным артистам шоу, а также и кто из самых ярких в клетке и кто из самых ярких? кто окажется самым лживым в компании? а кто окажется самым лживым в новом выпуске шоу « наше дело ». \n",
            "действительно ли овны самые конфликтные? а близнецы двуличные? сегодня это выяснят комики, а близнецы двуличные? сегодня это выяснят комики, а также и астролог анастасия леви? \n",
            "в этом выпуске профессиональный football ” вместе с медийными экспертами определят 22, а кто из них! \n",
            "в этом выпуске легенда спорта расскажет, как правильно нужно, как правильно нужно в спортзал, как выбирать в спортзал и как выбирать. \n",
            "в этом выпуске блогеры райзен и клён покажут в fifa 22, а также и кто из них за деньги и решат в команду. \n",
            "сегодня султан султан и авет, не только не только не только.   сегодня мы узнаем, как зовут, не просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но и просто, а также не просто, но\n",
            "в этом выпуске легенда спорта расскажет, как с бюджетом, как правильно и ольга пдн.   в этом выпуске вы узнаете, как предприниматель филипп, чтобы поднять вам на что и их, чтобы в этом выпуске, а также не только на что он, но и ольга пдн, что ждет, что ждет, что ждет, а кто из них них. кто из них них! \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "Rz0RjTo9jXhX",
        "outputId": "e89bd0c4-726a-49fe-e1df-0f839f5c168c"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0      0.mp4  добро пожаловать на канал « бюро добрых дел »!...\n",
              "1      1.mp4  знаете, как в котором комики играют и не менее...\n",
              "2      2.mp4  вы смотрите шоу с детектором лжи « тру детекто...\n",
              "3      3.mp4  в этом выпуске: антон протеинов - /   - /   в ...\n",
              "4      4.mp4  в этом выпуске ведущая алиса и дизайнер наталь..."
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              "\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",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  .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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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",
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              "  <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",
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              "      } 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",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
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