audio_collectiong_with_whisper_from_transformers.ipynb

{
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vx_BfarlEdL",
        "outputId": "9d70fbda-fe98-45ae-fdeb-138ee11f7bfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers huggingface accelerate datasets pydub tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jc6hk7rmGMiV"
      },
      "source": [
        "# The Goal of this notebook is use whisper to help my collect audio data for arabic sentence detection\n",
        "1. Web scraping to get the urls for the words i need\n",
        "2. Remove the duplicates from the Youtube URLs.\n",
        "3. Use Whisper to collect this data and then clip it with knowing the timestamp of the word\n",
        "4. clean everything and make the url check done also remove the downloaded youtube and audio add 0.5 second above and after the word to get it right\n",
        "5. manually clean this data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tDuouhhEGOla"
      },
      "source": [
        "# Web Scraping Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u4LhY5HE18wX"
      },
      "outputs": [],
      "source": [
        "# from pytube import Search\n",
        "# s = Search('YouTube Rewind')\n",
        "# len(s.results)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "WZGw5cQQGQiR",
        "outputId": "bb43f528-04f9-4a54-c391-95c24e2eb209"
      },
      "outputs": [
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              "      <th>0</th>\n",
              "      <td>qmCHCMlEvQ8</td>\n",
              "      <td>video</td>\n",
              "      <td>$kinny - Never Snitch (Official Video) سكيني -...</td>\n",
              "      <td>3 years ago</td>\n",
              "      <td>2:13</td>\n",
              "      <td>24M views</td>\n",
              "      <td>https://www.youtube.com/watch?v=qmCHCMlEvQ8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>qmCHCMlEvQ8</td>\n",
              "      <td>video</td>\n",
              "      <td>$kinny - Never Snitch (Official Video) سكيني -...</td>\n",
              "      <td>3 years ago</td>\n",
              "      <td>2:13</td>\n",
              "      <td>24,879,443 views</td>\n",
              "      <td>https://www.youtube.com/watch?v=qmCHCMlEvQ8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TLpiGpcJZ8E</td>\n",
              "      <td>video</td>\n",
              "      <td>الحمد لله ♥️🙏  | فارس قطرية - Fares katrya</td>\n",
              "      <td>4 weeks ago</td>\n",
              "      <td>3:12</td>\n",
              "      <td>55K views</td>\n",
              "      <td>https://www.youtube.com/watch?v=TLpiGpcJZ8E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TLpiGpcJZ8E</td>\n",
              "      <td>video</td>\n",
              "      <td>الحمد لله ♥️🙏  | فارس قطرية - Fares katrya</td>\n",
              "      <td>4 weeks ago</td>\n",
              "      <td>3:12</td>\n",
              "      <td>55,991 views</td>\n",
              "      <td>https://www.youtube.com/watch?v=TLpiGpcJZ8E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P2BmrLE2QSI</td>\n",
              "      <td>video</td>\n",
              "      <td>فيلم الكوميديا والمغامرة - 1000 حمدالله ع السل...</td>\n",
              "      <td>1 month ago</td>\n",
              "      <td>1:52:54</td>\n",
              "      <td>2.1M views</td>\n",
              "      <td>https://www.youtube.com/watch?v=P2BmrLE2QSI</td>\n",
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            "text/plain": [
              "            id   type                                              title   \n",
              "0  qmCHCMlEvQ8  video  $kinny - Never Snitch (Official Video) سكيني -...  \\\n",
              "1  qmCHCMlEvQ8  video  $kinny - Never Snitch (Official Video) سكيني -...   \n",
              "2  TLpiGpcJZ8E  video         الحمد لله ♥️🙏  | فارس قطرية - Fares katrya   \n",
              "3  TLpiGpcJZ8E  video         الحمد لله ♥️🙏  | فارس قطرية - Fares katrya   \n",
              "4  P2BmrLE2QSI  video  فيلم الكوميديا والمغامرة - 1000 حمدالله ع السل...   \n",
              "\n",
              "  publishedTime duration         viewCount   \n",
              "0   3 years ago     2:13         24M views  \\\n",
              "1   3 years ago     2:13  24,879,443 views   \n",
              "2   4 weeks ago     3:12         55K views   \n",
              "3   4 weeks ago     3:12      55,991 views   \n",
              "4   1 month ago  1:52:54        2.1M views   \n",
              "\n",
              "                                           url  \n",
              "0  https://www.youtube.com/watch?v=qmCHCMlEvQ8  \n",
              "1  https://www.youtube.com/watch?v=qmCHCMlEvQ8  \n",
              "2  https://www.youtube.com/watch?v=TLpiGpcJZ8E  \n",
              "3  https://www.youtube.com/watch?v=TLpiGpcJZ8E  \n",
              "4  https://www.youtube.com/watch?v=P2BmrLE2QSI  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df= pd.read_csv(\"youtube_urls.csv\")\n",
        "# df[\"checked\"] = 0 #! To Mark if the video has been downloaded before or not \n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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          "height": 337
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        "id": "nau3gDR_gzFG",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>type</th>\n",
              "      <th>title</th>\n",
              "      <th>publishedTime</th>\n",
              "      <th>duration</th>\n",
              "      <th>viewCount</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>qmCHCMlEvQ8</td>\n",
              "      <td>video</td>\n",
              "      <td>$kinny - Never Snitch (Official Video) سكيني -...</td>\n",
              "      <td>3 years ago</td>\n",
              "      <td>0 days 00:02:13</td>\n",
              "      <td>24M views</td>\n",
              "      <td>https://www.youtube.com/watch?v=qmCHCMlEvQ8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>qmCHCMlEvQ8</td>\n",
              "      <td>video</td>\n",
              "      <td>$kinny - Never Snitch (Official Video) سكيني -...</td>\n",
              "      <td>3 years ago</td>\n",
              "      <td>0 days 00:02:13</td>\n",
              "      <td>24,879,443 views</td>\n",
              "      <td>https://www.youtube.com/watch?v=qmCHCMlEvQ8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TLpiGpcJZ8E</td>\n",
              "      <td>video</td>\n",
              "      <td>الحمد لله ♥️🙏  | فارس قطرية - Fares katrya</td>\n",
              "      <td>4 weeks ago</td>\n",
              "      <td>0 days 00:03:12</td>\n",
              "      <td>55K views</td>\n",
              "      <td>https://www.youtube.com/watch?v=TLpiGpcJZ8E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TLpiGpcJZ8E</td>\n",
              "      <td>video</td>\n",
              "      <td>الحمد لله ♥️🙏  | فارس قطرية - Fares katrya</td>\n",
              "      <td>4 weeks ago</td>\n",
              "      <td>0 days 00:03:12</td>\n",
              "      <td>55,991 views</td>\n",
              "      <td>https://www.youtube.com/watch?v=TLpiGpcJZ8E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4CSUrZFwVVI</td>\n",
              "      <td>video</td>\n",
              "      <td>Sharmoofers - Al Hamdullah | Official Lyrics V...</td>\n",
              "      <td>3 months ago</td>\n",
              "      <td>0 days 00:03:30</td>\n",
              "      <td>357K views</td>\n",
              "      <td>https://www.youtube.com/watch?v=4CSUrZFwVVI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id   type                                              title   \n",
              "0  qmCHCMlEvQ8  video  $kinny - Never Snitch (Official Video) سكيني -...  \\\n",
              "1  qmCHCMlEvQ8  video  $kinny - Never Snitch (Official Video) سكيني -...   \n",
              "2  TLpiGpcJZ8E  video         الحمد لله ♥️🙏  | فارس قطرية - Fares katrya   \n",
              "3  TLpiGpcJZ8E  video         الحمد لله ♥️🙏  | فارس قطرية - Fares katrya   \n",
              "6  4CSUrZFwVVI  video  Sharmoofers - Al Hamdullah | Official Lyrics V...   \n",
              "\n",
              "  publishedTime        duration         viewCount   \n",
              "0   3 years ago 0 days 00:02:13         24M views  \\\n",
              "1   3 years ago 0 days 00:02:13  24,879,443 views   \n",
              "2   4 weeks ago 0 days 00:03:12         55K views   \n",
              "3   4 weeks ago 0 days 00:03:12      55,991 views   \n",
              "6  3 months ago 0 days 00:03:30        357K views   \n",
              "\n",
              "                                           url  \n",
              "0  https://www.youtube.com/watch?v=qmCHCMlEvQ8  \n",
              "1  https://www.youtube.com/watch?v=qmCHCMlEvQ8  \n",
              "2  https://www.youtube.com/watch?v=TLpiGpcJZ8E  \n",
              "3  https://www.youtube.com/watch?v=TLpiGpcJZ8E  \n",
              "6  https://www.youtube.com/watch?v=4CSUrZFwVVI  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def parse_duration(duration_str):\n",
        "    try:\n",
        "        if isinstance(duration_str, str):\n",
        "            parts = duration_str.split(':')\n",
        "            if len(parts) == 2:\n",
        "                hours, minutes, seconds = 0, int(parts[0]), int(parts[1])\n",
        "            elif len(parts) == 3:\n",
        "                hours, minutes, seconds = int(parts[0]), int(parts[1]), int(parts[2])\n",
        "            else:\n",
        "                raise ValueError(f\"Invalid duration format: {duration_str}\")\n",
        "\n",
        "            return pd.Timedelta(hours=hours, minutes=minutes, seconds=seconds)\n",
        "        else:\n",
        "            return pd.NaT\n",
        "    except ValueError:\n",
        "        return pd.NaT\n",
        "\n",
        "df['duration'] = df['duration'].apply(parse_duration)\n",
        "filtered_df = df[df['duration'] < pd.Timedelta(minutes=15)]\n",
        "\n",
        "filtered_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "HH-UXGHDxNwi"
      },
      "outputs": [],
      "source": [
        "filtered_df = filtered_df.drop_duplicates()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nogYVJUQx0tO"
      },
      "source": [
        "## Download the Youtube URLS and MAKE them as downloaded completed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwSdV-bZsFI8",
        "outputId": "38e5a969-c4bf-4577-b777-12472fc3666b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pytube\n",
            "  Using cached pytube-15.0.0-py3-none-any.whl (57 kB)\n",
            "Installing collected packages: pytube\n",
            "Successfully installed pytube-15.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pytube"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "S93PRn0jtc4N",
        "outputId": "612e8fb0-f232-400d-9b8e-a7a867ab31bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<Stream: itag=\"17\" mime_type=\"video/3gpp\" res=\"144p\" fps=\"12fps\" vcodec=\"mp4v.20.3\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">, <Stream: itag=\"18\" mime_type=\"video/mp4\" res=\"360p\" fps=\"24fps\" vcodec=\"avc1.42001E\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">, <Stream: itag=\"22\" mime_type=\"video/mp4\" res=\"720p\" fps=\"24fps\" vcodec=\"avc1.64001F\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">, <Stream: itag=\"137\" mime_type=\"video/mp4\" res=\"1080p\" fps=\"24fps\" vcodec=\"avc1.640028\" progressive=\"False\" type=\"video\">, <Stream: itag=\"216\" mime_type=\"video/mp4\" res=\"None\" fps=\"1fps\" vcodec=\"avc1.640028\" progressive=\"False\" type=\"video\">, <Stream: itag=\"248\" mime_type=\"video/webm\" res=\"1080p\" fps=\"24fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"136\" mime_type=\"video/mp4\" res=\"720p\" fps=\"24fps\" vcodec=\"avc1.4d401f\" progressive=\"False\" type=\"video\">, <Stream: itag=\"214\" mime_type=\"video/mp4\" res=\"None\" fps=\"1fps\" vcodec=\"avc1.4d401f\" progressive=\"False\" type=\"video\">, <Stream: itag=\"247\" mime_type=\"video/webm\" res=\"720p\" fps=\"24fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"135\" mime_type=\"video/mp4\" res=\"480p\" fps=\"24fps\" vcodec=\"avc1.4d401e\" progressive=\"False\" type=\"video\">, <Stream: itag=\"244\" mime_type=\"video/webm\" res=\"480p\" fps=\"24fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"134\" mime_type=\"video/mp4\" res=\"360p\" fps=\"24fps\" vcodec=\"avc1.4d401e\" progressive=\"False\" type=\"video\">, <Stream: itag=\"243\" mime_type=\"video/webm\" res=\"360p\" fps=\"24fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"133\" mime_type=\"video/mp4\" res=\"240p\" fps=\"24fps\" vcodec=\"avc1.4d4015\" progressive=\"False\" type=\"video\">, <Stream: itag=\"242\" mime_type=\"video/webm\" res=\"240p\" fps=\"24fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"160\" mime_type=\"video/mp4\" res=\"144p\" fps=\"24fps\" vcodec=\"avc1.4d400c\" progressive=\"False\" type=\"video\">, <Stream: itag=\"278\" mime_type=\"video/webm\" res=\"144p\" fps=\"24fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"139\" mime_type=\"audio/mp4\" abr=\"48kbps\" acodec=\"mp4a.40.5\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"140\" mime_type=\"audio/mp4\" abr=\"128kbps\" acodec=\"mp4a.40.2\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"249\" mime_type=\"audio/webm\" abr=\"50kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"250\" mime_type=\"audio/webm\" abr=\"70kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"251\" mime_type=\"audio/webm\" abr=\"160kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pytube import YouTube\n",
        "YouTube('https://youtu.be/9bZkp7q19f0').streams.first().download()\n",
        "yt = YouTube('http://youtube.com/watch?v=9bZkp7q19f0')\n",
        "yt.streams\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "gHbrcBzU5LcV"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "15.0.0\n",
            "[<Stream: itag=\"17\" mime_type=\"video/3gpp\" res=\"144p\" fps=\"6fps\" vcodec=\"mp4v.20.3\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">, <Stream: itag=\"18\" mime_type=\"video/mp4\" res=\"360p\" fps=\"25fps\" vcodec=\"avc1.42001E\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">, <Stream: itag=\"22\" mime_type=\"video/mp4\" res=\"720p\" fps=\"25fps\" vcodec=\"avc1.64001F\" acodec=\"mp4a.40.2\" progressive=\"True\" type=\"video\">, <Stream: itag=\"136\" mime_type=\"video/mp4\" res=\"720p\" fps=\"25fps\" vcodec=\"avc1.4d401f\" progressive=\"False\" type=\"video\">, <Stream: itag=\"247\" mime_type=\"video/webm\" res=\"720p\" fps=\"25fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"135\" mime_type=\"video/mp4\" res=\"480p\" fps=\"25fps\" vcodec=\"avc1.4d401e\" progressive=\"False\" type=\"video\">, <Stream: itag=\"244\" mime_type=\"video/webm\" res=\"480p\" fps=\"25fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"134\" mime_type=\"video/mp4\" res=\"360p\" fps=\"25fps\" vcodec=\"avc1.4d401e\" progressive=\"False\" type=\"video\">, <Stream: itag=\"243\" mime_type=\"video/webm\" res=\"360p\" fps=\"25fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"133\" mime_type=\"video/mp4\" res=\"240p\" fps=\"25fps\" vcodec=\"avc1.4d4015\" progressive=\"False\" type=\"video\">, <Stream: itag=\"242\" mime_type=\"video/webm\" res=\"240p\" fps=\"25fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"160\" mime_type=\"video/mp4\" res=\"144p\" fps=\"25fps\" vcodec=\"avc1.4d400c\" progressive=\"False\" type=\"video\">, <Stream: itag=\"278\" mime_type=\"video/webm\" res=\"144p\" fps=\"25fps\" vcodec=\"vp9\" progressive=\"False\" type=\"video\">, <Stream: itag=\"139\" mime_type=\"audio/mp4\" abr=\"48kbps\" acodec=\"mp4a.40.5\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"140\" mime_type=\"audio/mp4\" abr=\"128kbps\" acodec=\"mp4a.40.2\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"249\" mime_type=\"audio/webm\" abr=\"50kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"250\" mime_type=\"audio/webm\" abr=\"70kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">, <Stream: itag=\"251\" mime_type=\"audio/webm\" abr=\"160kbps\" acodec=\"opus\" progressive=\"False\" type=\"audio\">]\n"
          ]
        }
      ],
      "source": [
        "import pytube as pt\n",
        "import os\n",
        "print(pt.__version__)\n",
        "yt = pt.YouTube('https://www.youtube.com/watch?v=_brWPpALvBo')\n",
        "print(yt.streams)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5B3_eiqMy2Zy",
        "outputId": "ee7ba66a-5505-4914-b528-b0d8b5848551"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pytube\n",
            "  Downloading pytube-15.0.0-py3-none-any.whl (57 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.6/57.6 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pytube\n",
            "Successfully installed pytube-15.0.0\n"
          ]
        }
      ],
      "source": [
        "# !pip install pytube"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "oajrh8sw4IlU",
        "outputId": "cd0ead4c-f00a-429e-f1ca-5ced9d52ee0a"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "'NoneType' object has no attribute 'download'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[8], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m filename \u001b[39m=\u001b[39m row[\u001b[39m\"\u001b[39m\u001b[39mid\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m      5\u001b[0m yt \u001b[39m=\u001b[39m pytube\u001b[39m.\u001b[39mYouTube(url)\n\u001b[0;32m----> 6\u001b[0m yt\u001b[39m.\u001b[39;49mstreams\u001b[39m.\u001b[39;49mfilter(abr\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m160kbps\u001b[39;49m\u001b[39m\"\u001b[39;49m,progressive\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\u001b[39m.\u001b[39;49mfirst()\u001b[39m.\u001b[39;49mdownload(filename\u001b[39m=\u001b[39mfilename)\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'download'"
          ]
        }
      ],
      "source": [
        "import pytube\n",
        "for index, row in filtered_df.iterrows():\n",
        "    url = row[\"url\"]\n",
        "    filename = row[\"id\"]\n",
        "    yt = pytube.YouTube(url)\n",
        "    yt.streams.filter(abr=\"160kbps\",progressive=True).first().download(filename=filename)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "falwZQwIGQ_k"
      },
      "source": [
        "### Imports and Mount Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aNkL8g8HGDu",
        "outputId": "e97e13aa-d975-4b04-b658-447951fa284e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNIEODmWGUQq"
      },
      "source": [
        "# USE Whisper fine-tuned on arabic to Get the timestamps back and clip them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YsOxs-KW94lN"
      },
      "outputs": [],
      "source": [
        "# from datasets import Dataset,Audio\n",
        "# from tqdm import tqdm\n",
        "# from transformers.pipelines.pt_utils import KeyDataset\n",
        "\n",
        "# files = [...] # alist of audio paths\n",
        "# dataset = Dataset.from_dict({\"audio\":files}).cast_column(\"audio\",Audio(sampling_rate=16_000))\n",
        "\n",
        "# transcriptions = []\n",
        "# with torch.no_grad():\n",
        "#     for tt in tqdm(model(KeyDataset(dataset,\"audio\"),batch_size=10,truncation=True)):\n",
        "#         transcriptions.append(tt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3h5aDH1veSu6"
      },
      "source": [
        "# Cleand Verison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "77ybkPrKeSd1"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "import torch\n",
        "import re\n",
        "import pandas as pd\n",
        "from pydub import AudioSegment\n",
        "import datetime\n",
        "import random\n",
        "import string\n",
        "import tqdm\n",
        "import glob\n",
        "import os\n",
        "\n",
        "completed_files = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uWYxz2AlPmjz"
      },
      "outputs": [],
      "source": [
        "def convert_to_hms(seconds: float) -> str:\n",
        "    hours, remainder = divmod(seconds, 3600)\n",
        "    minutes, seconds = divmod(remainder, 60)\n",
        "    milliseconds = round((seconds % 1) * 1000)\n",
        "    output = f\"{int(hours):02}:{int(minutes):02}:{int(seconds):02},{milliseconds:03}\"\n",
        "    return output\n",
        "\n",
        "def convert_chunk(chunk: dict) -> str:\n",
        "    start = convert_to_hms(chunk[\"timestamp\"][0])\n",
        "    end = convert_to_hms(chunk[\"timestamp\"][1])\n",
        "    text = chunk[\"text\"].strip()\n",
        "    return f\"{start} --> {end}\\n{text}\\n\\n\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2fiHBj0AetLP"
      },
      "outputs": [],
      "source": [
        "def find_subtitles_with_word(file_path, my_list):\n",
        "    with open(file_path, \"r\") as file:\n",
        "        content = file.read()\n",
        "    subtitles = content.split(\"\\n\\n\")\n",
        "    found_subtitles = []\n",
        "\n",
        "    for subtitle in subtitles:\n",
        "        lines = subtitle.split(\"\\n\")\n",
        "        if len(lines) >= 3:\n",
        "            time_line = lines[1]\n",
        "            subtitle_text = \" \".join(lines[2:])\n",
        "\n",
        "            for word in my_list:\n",
        "                if word.lower() in time_line.lower() or word.lower() in subtitle_text.lower():\n",
        "                    start_time, end_time = re.findall(r'\\d+:\\d+:\\d+,\\d+', time_line)\n",
        "                    found_subtitles.append({\n",
        "                        \"start_time\": start_time,\n",
        "                        \"end_time\": end_time,\n",
        "                        \"subtitle\": subtitle_text\n",
        "                    })\n",
        "    return found_subtitles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "139qUCLKevJ2"
      },
      "outputs": [],
      "source": [
        "def clip_audio(file_path, start_time, end_time):\n",
        "    audio = AudioSegment.from_file(file_path)\n",
        "    start_ms = timestamp_to_milliseconds(start_time)\n",
        "    end_ms = timestamp_to_milliseconds(end_time)\n",
        "    clipped_audio = audio[start_ms:end_ms]\n",
        "    return clipped_audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3afracLexVg"
      },
      "outputs": [],
      "source": [
        "def timestamp_to_milliseconds(timestamp):\n",
        "    time_obj = datetime.datetime.strptime(timestamp, \"%H:%M:%S,%f\")\n",
        "    milliseconds = (time_obj.minute * 60 + time_obj.second) * 1000 + int(time_obj.microsecond / 1000)\n",
        "    return milliseconds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "rvlg8iH-eywa",
        "outputId": "0dea84f9-32c2-4ffc-db7e-e653096bda20"
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              "Downloading (…)olve/main/vocab.json: 0.00B [00:00, ?B/s]"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 19&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">22</span>                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/pipelines/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">automatic_speech_recognition.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">72</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__call__</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"there\", \"timestamps\": (1.0, 1.5)}]`. The original full text can</span>   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">270 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">`\"\".join(chunk[\"text\"] for chunk in output[\"chunks\"])`.</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">271 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>272 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">super</span>().<span style=\"color: #00ff00; text-decoration-color: #00ff00\">__call__</span>(inputs, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">273 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">274 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_sanitize_parameters</span>(                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">275 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>,                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/pipelines/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">base.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1112</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__call__</span>          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1109 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">elif</span> is_iterable:                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1110 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.iterate(inputs, preprocess_params, forward_params, postprocess_p  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1111 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">elif</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.framework == <span style=\"color: #808000; text-decoration-color: #808000\">\"pt\"</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">and</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, ChunkPipeline):                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1112 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">next</span>(                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1113 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">iter</span>(                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1114 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.get_iterator(                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1115 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   </span>[inputs], num_workers, batch_size, preprocess_params, forward_pa  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/pipelines/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">pt_utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">124</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__next__</span>       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.loader_batch_item()                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">122 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">123 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># We're out of items within a batch</span>                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>124 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>item = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">next</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.iterator)                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">125 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>processed = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.infer(item, **<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.params)                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">126 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># We now have a batch of \"inferred things\".</span>                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">127 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.loader_batch_size <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/pipelines/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">pt_utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">266</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__next__</span>       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">263 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> accumulator                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">264 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">265 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">while</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> is_last:                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>266 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>processed = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.infer(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">next</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.iterator), **<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.params)                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">267 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.loader_batch_size <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">268 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(processed, torch.Tensor):                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">269 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>first_tensor = processed                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/pipelines/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">base.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1018</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1015 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> inference_context                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1016 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1017 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, model_inputs, **forward_params):                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1018 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.device_placement():                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1019 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.framework == <span style=\"color: #808000; text-decoration-color: #808000\">\"tf\"</span>:                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1020 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>model_inputs[<span style=\"color: #808000; text-decoration-color: #808000\">\"training\"</span>] = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">False</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1021 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>model_outputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward(model_inputs, **forward_params)             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/lib/python3.10/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">contextlib.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">135</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__enter__</span>                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">132 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># they are only needed for recreation, which is not possible anymore</span>               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">133 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">del</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.args, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.kwds, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.func                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">134 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>135 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">next</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.gen)                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">136 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">StopIteration</span>:                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">137 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">RuntimeError</span>(<span style=\"color: #808000; text-decoration-color: #808000\">\"generator didn't yield\"</span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">from</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">None</span>                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">138 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/pipelines/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">base.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">905</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">device_placement</span>   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 902 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">yield</span>                                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 903 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 904 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.device.type == <span style=\"color: #808000; text-decoration-color: #808000\">\"cuda\"</span>:                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 905 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>torch.cuda.set_device(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.device)                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 906 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 907 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">yield</span>                                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 908 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/cuda/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">__init__.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">348</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">set_device</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 345 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">device (torch.device or int): selected device. This function is a no-op</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 346 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">if this argument is negative.</span>                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 347 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 348 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>device = _get_device_index(device)                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 349 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> device &gt;= <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>:                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 350 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>torch._C._cuda_setDevice(device)                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 351 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/cuda/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">36</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_get_device_index</span>             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">33 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> torch.jit.is_scripting():                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">34 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(device, torch.cuda.device):                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">35 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> device.idx                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>36 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> _torch_get_device_index(device, optional, allow_cpu)                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">37 </span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">38 </span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">39 </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_dummy_type</span>(name: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">str</span>) -&gt; <span style=\"color: #00ffff; text-decoration-color: #00ffff\">type</span>:                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">_utils.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">731</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_get_device_index</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">728 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">729 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>device_idx = _get_current_device_index()                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">730 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>731 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">ValueError</span>(                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">732 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"Expected a torch.device with a specified index \"</span>                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">733 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"or an integer, but got:{}\"</span>.format(device)                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">734 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>)                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">ValueError: </span>Expected a torch.device with a specified index or an integer, but got:cuda\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 19>\u001b[0m:\u001b[94m22\u001b[0m                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/\u001b[0m\u001b[1;33mautomatic_speech_recognition.py\u001b[0m:\u001b[94m2\u001b[0m \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[94m72\u001b[0m in \u001b[92m__call__\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m269 \u001b[0m\u001b[2;33m│   │   │   │   │   │   \u001b[0m\u001b[33m\"there\", \"timestamps\": (1.0, 1.5)}]`. The original full text can\u001b[0m   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m270 \u001b[0m\u001b[2;33m│   │   │   │   │   │   \u001b[0m\u001b[33m`\"\".join(chunk[\"text\"] for chunk in output[\"chunks\"])`.\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m271 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m272 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96msuper\u001b[0m().\u001b[92m__call__\u001b[0m(inputs, **kwargs)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m273 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m274 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_sanitize_parameters\u001b[0m(                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m275 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[96mself\u001b[0m,                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/\u001b[0m\u001b[1;33mbase.py\u001b[0m:\u001b[94m1112\u001b[0m in \u001b[92m__call__\u001b[0m          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1109 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melif\u001b[0m is_iterable:                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1110 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.iterate(inputs, preprocess_params, forward_params, postprocess_p  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1111 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melif\u001b[0m \u001b[96mself\u001b[0m.framework == \u001b[33m\"\u001b[0m\u001b[33mpt\u001b[0m\u001b[33m\"\u001b[0m \u001b[95mand\u001b[0m \u001b[96misinstance\u001b[0m(\u001b[96mself\u001b[0m, ChunkPipeline):                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1112 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mnext\u001b[0m(                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1113 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[96miter\u001b[0m(                                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1114 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.get_iterator(                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1115 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m[inputs], num_workers, batch_size, preprocess_params, forward_pa  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/\u001b[0m\u001b[1;33mpt_utils.py\u001b[0m:\u001b[94m124\u001b[0m in \u001b[92m__next__\u001b[0m       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m121 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.loader_batch_item()                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m122 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m123 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# We're out of items within a batch\u001b[0m                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m124 \u001b[2m│   │   \u001b[0mitem = \u001b[96mnext\u001b[0m(\u001b[96mself\u001b[0m.iterator)                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m125 \u001b[0m\u001b[2m│   │   \u001b[0mprocessed = \u001b[96mself\u001b[0m.infer(item, **\u001b[96mself\u001b[0m.params)                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m126 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# We now have a batch of \"inferred things\".\u001b[0m                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m127 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.loader_batch_size \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m:                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/\u001b[0m\u001b[1;33mpt_utils.py\u001b[0m:\u001b[94m266\u001b[0m in \u001b[92m__next__\u001b[0m       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m263 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mreturn\u001b[0m accumulator                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m264 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m265 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mwhile\u001b[0m \u001b[95mnot\u001b[0m is_last:                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m266 \u001b[2m│   │   │   \u001b[0mprocessed = \u001b[96mself\u001b[0m.infer(\u001b[96mnext\u001b[0m(\u001b[96mself\u001b[0m.iterator), **\u001b[96mself\u001b[0m.params)                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m267 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.loader_batch_size \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m:                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m268 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96misinstance\u001b[0m(processed, torch.Tensor):                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m269 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mfirst_tensor = processed                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/\u001b[0m\u001b[1;33mbase.py\u001b[0m:\u001b[94m1018\u001b[0m in \u001b[92mforward\u001b[0m           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1015 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m inference_context                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1016 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1017 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mforward\u001b[0m(\u001b[96mself\u001b[0m, model_inputs, **forward_params):                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1018 \u001b[2m│   │   \u001b[0m\u001b[94mwith\u001b[0m \u001b[96mself\u001b[0m.device_placement():                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1019 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.framework == \u001b[33m\"\u001b[0m\u001b[33mtf\u001b[0m\u001b[33m\"\u001b[0m:                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1020 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mmodel_inputs[\u001b[33m\"\u001b[0m\u001b[33mtraining\u001b[0m\u001b[33m\"\u001b[0m] = \u001b[94mFalse\u001b[0m                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1021 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mmodel_outputs = \u001b[96mself\u001b[0m._forward(model_inputs, **forward_params)             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/lib/python3.10/\u001b[0m\u001b[1;33mcontextlib.py\u001b[0m:\u001b[94m135\u001b[0m in \u001b[92m__enter__\u001b[0m                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m132 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# they are only needed for recreation, which is not possible anymore\u001b[0m               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m133 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mdel\u001b[0m \u001b[96mself\u001b[0m.args, \u001b[96mself\u001b[0m.kwds, \u001b[96mself\u001b[0m.func                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m134 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m135 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mnext\u001b[0m(\u001b[96mself\u001b[0m.gen)                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m136 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mStopIteration\u001b[0m:                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m137 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mRuntimeError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mgenerator didn\u001b[0m\u001b[33m'\u001b[0m\u001b[33mt yield\u001b[0m\u001b[33m\"\u001b[0m) \u001b[94mfrom\u001b[0m \u001b[96mNone\u001b[0m                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m138 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/\u001b[0m\u001b[1;33mbase.py\u001b[0m:\u001b[94m905\u001b[0m in \u001b[92mdevice_placement\u001b[0m   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 902 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[94myield\u001b[0m                                                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 903 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 904 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.device.type == \u001b[33m\"\u001b[0m\u001b[33mcuda\u001b[0m\u001b[33m\"\u001b[0m:                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 905 \u001b[2m│   │   │   │   \u001b[0mtorch.cuda.set_device(\u001b[96mself\u001b[0m.device)                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 906 \u001b[0m\u001b[2m│   │   │   \u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 907 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94myield\u001b[0m                                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 908 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/cuda/\u001b[0m\u001b[1;33m__init__.py\u001b[0m:\u001b[94m348\u001b[0m in \u001b[92mset_device\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 345 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33mdevice (torch.device or int): selected device. This function is a no-op\u001b[0m           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 346 \u001b[0m\u001b[2;33m│   │   │   \u001b[0m\u001b[33mif this argument is negative.\u001b[0m                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 347 \u001b[0m\u001b[2;33m│   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 348 \u001b[2m│   \u001b[0mdevice = _get_device_index(device)                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 349 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m device >= \u001b[94m0\u001b[0m:                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 350 \u001b[0m\u001b[2m│   │   \u001b[0mtorch._C._cuda_setDevice(device)                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 351 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/cuda/\u001b[0m\u001b[1;33m_utils.py\u001b[0m:\u001b[94m36\u001b[0m in \u001b[92m_get_device_index\u001b[0m             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m33 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m torch.jit.is_scripting():                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m34 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96misinstance\u001b[0m(device, torch.cuda.device):                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m35 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m device.idx                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m36 \u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m _torch_get_device_index(device, optional, allow_cpu)                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m37 \u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m38 \u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m39 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_dummy_type\u001b[0m(name: \u001b[96mstr\u001b[0m) -> \u001b[96mtype\u001b[0m:                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/\u001b[0m\u001b[1;33m_utils.py\u001b[0m:\u001b[94m731\u001b[0m in \u001b[92m_get_device_index\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m728 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m729 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mdevice_idx = _get_current_device_index()                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m730 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m731 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m732 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mExpected a torch.device with a specified index \u001b[0m\u001b[33m\"\u001b[0m                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m733 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mor an integer, but got:\u001b[0m\u001b[33m{}\u001b[0m\u001b[33m\"\u001b[0m.format(device)                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m734 \u001b[0m\u001b[2m│   │   │   \u001b[0m)                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mValueError: \u001b[0mExpected a torch.device with a specified index or an integer, but got:cuda\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Initialize pipeline\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model = pipeline(\n",
        "    task=\"automatic-speech-recognition\",\n",
        "    model=\"openai/whisper-medium\",\n",
        "    device=device,\n",
        "    chunk_length_s=30,\n",
        "    generate_kwargs={\"num_beams\": 5}  # same as \"open whisper\" default\n",
        ")\n",
        "audio_directory_path = \"/content/\"\n",
        "audio_formats = [\"*.mp3\",\"*.wav\"]\n",
        "file_path_dir = []\n",
        "for audio_format in audio_formats:\n",
        "    file_path_dir.extend(glob.glob(audio_directory_path+audio_format))\n",
        "print(file_path_dir,\"\\n\")\n",
        "\n",
        "\n",
        "# Iterate over the audio files, transcribe, and generate subtitles\n",
        "with torch.no_grad():\n",
        "    for audio_file_path in file_path_dir:\n",
        "        # Transcribe audio and retrieve the result\n",
        "        result = model(audio_file_path, return_timestamps=True, generate_kwargs={\"language\": \"arabic\", \"task\": \"transcribe\"})\n",
        "\n",
        "        new_file_name = os.path.basename(audio_file_path)\n",
        "        file_name = new_file_name[:-4] + \".srt\"\n",
        "        file_path = f\"/content/drive/MyDrive/ZIKIR/{file_name}\"\n",
        "\n",
        "        # Save subtitles to SRT file\n",
        "        def save_subtitles_to_srt(result, file_path):\n",
        "            with open(file_path, \"w\") as f:\n",
        "                for i, chunk in enumerate(result[\"chunks\"], start=1):\n",
        "                    f.write(f\"{i}\\n{convert_chunk(chunk)}\")\n",
        "\n",
        "\n",
        "\n",
        "        # Save subtitles to SRT file\n",
        "        save_subtitles_to_srt(result, file_path)\n",
        "        print(f\"Saved file: {file_path}\")\n",
        "\n",
        "        # Update the progress bar description\n",
        "        print(f\"Processed: {audio_file_path}\")\n",
        "        completed_files.extend(audio_file_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "9CCoqioDe2Zv"
      },
      "outputs": [],
      "source": [
        "# Find matching subtitles with given words\n",
        "\n",
        "directory_path = \"/content/drive/MyDrive/ZIKIR/\"\n",
        "str_formats = [\"*.srt\"]\n",
        "transcriped_files_path = []\n",
        "for str_format in str_formats:\n",
        "    transcriped_files_path.extend(glob.glob(directory_path+str_format))\n",
        "print(transcriped_files_path,\"\\n\")\n",
        "words_to_search = [\"محمد\", \"لله\"]\n",
        "for file_path in transcriped_files_path:\n",
        "    matching_subtitles = find_subtitles_with_word(file_path, words_to_search)\n",
        "    for subtitle in matching_subtitles:\n",
        "        print(\"Start time:\", subtitle[\"start_time\"])\n",
        "        print(\"End time:\", subtitle[\"end_time\"])\n",
        "        print(\"Subtitle Text:\", subtitle[\"subtitle\"])\n",
        "        print(\"---\")\n",
        "\n",
        "    # Save matching subtitles to a CSV file\n",
        "    subtitle_data = []\n",
        "    for subtitle in matching_subtitles:\n",
        "        subtitle_data.append({\n",
        "            \"start_time\": subtitle[\"start_time\"],\n",
        "            \"end_time\": subtitle[\"end_time\"],\n",
        "            \"subtitle\": subtitle[\"subtitle\"],\n",
        "        })\n",
        "\n",
        "    subtitle_df = pd.DataFrame(subtitle_data)\n",
        "    output_path = f\"{file_path[:-3]}csv\"\n",
        "    subtitle_df.to_csv(output_path, index=False)\n",
        "    completed_files.extend(output_path)\n",
        "    print(\"Subtitle DataFrame saved successfully.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "UbxAGksFe4yz"
      },
      "outputs": [],
      "source": [
        "# Clip audio based on subtitles\n",
        "audio_directory_path #path to the audio files\n",
        "output_directory = \"/content/drive/MyDrive/ZIKIR/cliped_audio/\"\n",
        "\n",
        "csv_path = \"/content/drive/MyDrive/ZIKIR/\"\n",
        "file_pattern =\"*.csv\"\n",
        "csv_files = glob.glob(csv_path+file_pattern)\n",
        "print(csv_files)\n",
        "for df_path in csv_files:\n",
        "    subtitle_df = pd.read_csv(df_path)\n",
        "    audio_file_name = os.path.splitext(os.path.basename(df_path))[0]\n",
        "    wav_filename = audio_directory_path+audio_file_name + \".mp3\"\n",
        "    for index, row in subtitle_df.iterrows():\n",
        "        start_time = row[\"start_time\"]\n",
        "        end_time = row[\"end_time\"]\n",
        "        subtitle_text = row[\"subtitle\"]\n",
        "\n",
        "        # Generate a unique name for the clipped audio file based on the subtitle\n",
        "        unique_chars = ''.join(random.choices(string.ascii_letters + string.digits, k=4))  # Generate a random suffix\n",
        "        file_name = audio_file_name + \"_\" + unique_chars + \".wav\"\n",
        "        print(wav_filename)\n",
        "        # Clip the audio\n",
        "        clipped_audio = clip_audio(wav_filename, start_time, end_time)\n",
        "\n",
        "        output_path = output_directory + file_name\n",
        "        clipped_audio.export(output_path, format=\"wav\")\n",
        "\n",
        "        print(f\"Clipped audio saved: {output_path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "8aRfuaUvuEkc",
        "outputId": "e62aa586-3ae7-4702-b0c8-45e9927e2d39"
      },
      "outputs": [
        {
          "data": {
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              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 1&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">NameError: </span>name <span style=\"color: #008000; text-decoration-color: #008000\">'completed_files'</span> is not defined\n",
              "</pre>\n"
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            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 1>\u001b[0m:\u001b[94m1\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mNameError: \u001b[0mname \u001b[32m'completed_files'\u001b[0m is not defined\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(completed_files)\n",
        "os.remove(f for f in completed_files)\n",
        "print(f\"Deleted: \",completed_files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eR4mA3fp5HyR"
      },
      "outputs": [],
      "source": []
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