ytvideo_whisper_KMN_BOT_Version.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/blusewill/ytvideo-whisper/blob/faster-whisper-refactor/ytvideo_whisper_KMN_BOT_Version.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QQL1GjFTNNp"
      },
      "source": [
        "# ytvideo-whisper 機器狼特別版\n",
        "\n",
        "此專案使用了 [Faster-Whisper](https://github.com/SYSTRAN/faster-whisper) 專案，而非原版的 Whisper 專案  \n",
        "\n",
        "想使用原版 Whisper 專案的朋友，請查看原版 Whisper 分支  \n",
        "\n",
        "[點此進入原版 Whisper 的 Colab](https://colab.research.google.com/github/blusewill/ytvideo-whisper/blob/original-whisper/ytvideo_whisper_KMN_BOT_Version.ipynb)  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrZvIN-oUKdx"
      },
      "source": [
        "# 使用方法\n",
        "\n",
        "1. 請確認你的執行階段有啟用GPU，在 ``執行階段 -> 變更執行階段類型 ``\n",
        "1. 更改設定\n",
        "1. 按下 ``執行階段 -> 執行全部`` 或是 (CTRL+F9)\n",
        "1. 然後連接到Google雲端硬碟\n",
        "1. 然後等一下你的檔案應該就會在 ``雲端硬碟 -> Whisper -> result``\n",
        "\n",
        "> 如果你的 Google Colab 當機的話，**請執行設定過後在按下生產字幕檔**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTpjyRvaUVZh"
      },
      "source": [
        "## 獲得Google雲端硬碟權限\n",
        "(在 雲端硬碟 -> Whisper)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-M2C9UlxTCjE",
        "outputId": "9d661afb-0246-4c49-ed26-5884b70d78a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "#@markdown # **下載選項**\n",
        "#@markdown ---\n",
        "#@markdown ## **停用Google雲端硬碟**\n",
        "disable_google_drive = \"no\" #@param ['yes', 'no']\n",
        "# @markdown **當啟用停用 Google 雲端硬碟的時候 ``結束自動刪除執行階段`` 會直接停用。請記得使用後關機執行階段**\n",
        "if disable_google_drive.lower() == 'no':\n",
        "\n",
        "  from google.colab import drive\n",
        "  import os\n",
        "\n",
        "  path = '/content/gdrive/MyDrive/Whisper/'\n",
        "  path2 = '/content/gdrive/MyDrive/Whisper/result'\n",
        "  drive.mount('/content/gdrive')\n",
        "\n",
        "  if not os.path.exists(path):\n",
        "    os.mkdir(path)\n",
        "    os.mkdir(path2)\n",
        "\n",
        "# Calcuate the Execution Time.\n",
        "from datetime import datetime\n",
        "start_time = datetime.now()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiME-JOSUfhd"
      },
      "source": [
        "# 安裝程式\n",
        "faster-whisper ctranslate2==4.4.0 yt-dlp ipython pysubs2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "aXCqLjY0Uz0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39c56603-32d2-4075-c64a-3b5241de3b2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
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            "\u001b[?25hInstalling collected packages: yt-dlp, pysubs2, jedi, humanfriendly, ctranslate2, av, coloredlogs, onnxruntime, faster-whisper\n",
            "Successfully installed av-13.1.0 coloredlogs-15.0.1 ctranslate2-4.4.0 faster-whisper-1.1.0 humanfriendly-10.0 jedi-0.19.2 onnxruntime-1.20.1 pysubs2-1.7.3 yt-dlp-2024.11.18\n"
          ]
        }
      ],
      "source": [
        "! apt-get install ffmpeg\n",
        "! pip install faster-whisper ctranslate2==4.4.0 yt-dlp ipython pysubs2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWfd0B3nU_5o"
      },
      "source": [
        "# 測試GPU是否有開啟\n",
        "\n",
        "如果沒有東西跑出來的話，請去 ``執行階段 -> 變更執行階段類型`` 更改"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ukF_tYc9VfGS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42b8d5e9-d91e-4658-eb23-32a5580c81d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Dec  1 16:42:10 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   61C    P8              13W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRpdHsXmVqsv"
      },
      "source": [
        "# 設定\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IccTHdDOVxgL"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "#@markdown # **設定 (星號代表基礎設定)**\n",
        "#@markdown ---\n",
        "#@markdown ## **上傳自己的音檔 (目前只限 mp3 檔)**\n",
        "upload_own_audio_file = \"no\" #@param ['yes', 'no']\n",
        "#@markdown ## **YouTube 模式**\n",
        "#@markdown ---\n",
        "#@markdown ## ***影片連接**\n",
        "# YouTube Link\n",
        "YouTube_Video_Link = \"https://www.youtube.com/watch?v=Y-hOsUinkm8\" #@param {type:\"string\"}\n",
        "# File Name Change\n",
        "#@markdown ## ***檔案名稱**\n",
        "new_filename = \"【ChatGPT】自製GPT真的超簡單！機器狼的AI對話魔法！【GPTs】【入門筆記／精華版】#1-1\" #@param {type:\"string\"}\n",
        "#@markdown ## ***語言 (自動，中文，英文，日文)**\n",
        "language = \"Auto\" #@param [\"Auto\", \"zh\", \"en\", \"ja\"]\n",
        "#@markdown ## ***模式 ``生成 / 翻譯``**\n",
        "task = \"transcribe\" #@param ['transcribe', 'translate']\n",
        "# Change it to srt file type\n",
        "new_filename = os.path.splitext(new_filename)[0] + \".srt\"\n",
        "new_filename2 = os.path.splitext(new_filename)[0] + \" Transcript.txt\"\n",
        "# Change the Model of the Whisper\n",
        "#@markdown # **其他選項**\n",
        "#@markdown ---\n",
        "#@markdown ## **生產逐字稿**\n",
        "Generate_Plain_Document = \"yes\" #@param ['yes','no']\n",
        "#@markdown ## **結束自動刪除執行階段**\n",
        "shutdown_after_complete = \"yes\" #@param ['yes','no']\n",
        "#@markdown ## **模型**\n",
        "model_user = \"medium\" #@param ['tiny.en','tiny','base.en','base','small.en','small','medium.en','medium','large','large-v1','large-v2','large-v3','Custom']\n",
        "#@markdown ## **自訂模型（請先在上面選擇 Custom）**\n",
        "model_custom = \"XA9/faster-whisper-large-v2-cantonese-1\" #@param {type:\"string\"}\n",
        "if model_user == \"Custom\":\n",
        "  model_user = model_custom#@markdown ## **Cookies驗證**\n",
        "enable_cookies = \"no\" #@param ['yes', 'no']\n",
        "\n",
        "try:\n",
        "  disable_google_drive\n",
        "except NameError:\n",
        "    print(\"Checking if the Google Drive is mounted\")\n",
        "    gdrive_crash_check = os.path.exists(\"/content/gdrive\")\n",
        "    if gdrive_crash_check == True:\n",
        "      print(\"Crash Fallback : Google Drive is mounted!\")\n",
        "      disable_google_drive = \"no\"\n",
        "    else:\n",
        "      print(\"Crash Fallback : Google Drive is not mounted!\")\n",
        "      disable_google_drive = \"yes\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 如果你有啟動Cookies驗證\n",
        "請在這裡上傳Cookies檔案"
      ],
      "metadata": {
        "id": "MQa-cRGkAopn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "if enable_cookies.lower() == \"yes\":\n",
        "  print(\"It is not recommended to upload your main account's cookies files here, as this is a shared environment.\")\n",
        "  print(\"Your cookies might get leaked, and I will not take any responsibility if your account gets stolen.\")\n",
        "  print(\"The recommended approach is to use another Google Account's cookies to run this tool.\")\n",
        "  print(\"If it is a private video, you can share the video with that Google Account to grant access to it.\")\n",
        "  print(\"\")\n",
        "\n",
        "  from google.colab import files\n",
        "  cookies = files.upload()\n",
        "\n",
        "  # Specify the path where the files are located\n",
        "  path_cookies = \"/content\"\n",
        "\n",
        "  def rename_files(path_cookies):\n",
        "      for filename in os.listdir(path_cookies):\n",
        "          file_path = os.path.join(path_cookies, filename)\n",
        "          if os.path.isfile(file_path) and filename.endswith(\".txt\"):\n",
        "              new_path = os.path.join(path_cookies, \"cookies.txt\")\n",
        "              os.rename(file_path, new_path)\n",
        "\n",
        "  rename_files(path_cookies)"
      ],
      "metadata": {
        "id": "hPMZ1BQLAoY9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1vEu7rbV6xJ"
      },
      "source": [
        "# 下載影片/上傳音檔\n",
        "使用yt-dlp跟ffmpeg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "o6o0YbciV_aS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e850d71-4c35-4dba-97d2-bb5b5fcd4d4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[youtube] Extracting URL: https://www.youtube.com/watch?v=Y-hOsUinkm8\n",
            "[youtube] Y-hOsUinkm8: Downloading webpage\n",
            "[youtube] Y-hOsUinkm8: Downloading ios player API JSON\n",
            "[youtube] Y-hOsUinkm8: Downloading mweb player API JSON\n",
            "[youtube] Y-hOsUinkm8: Downloading player b46bb280\n",
            "[youtube] Y-hOsUinkm8: Downloading m3u8 information\n",
            "[info] Y-hOsUinkm8: Downloading 1 format(s): 251\n",
            "[download] Destination: content/audio\n",
            "[download] 100% of    3.24MiB in 00:00:00 at 7.50MiB/s   \n",
            "[ExtractAudio] Destination: content/audio.mp3\n",
            "Deleting original file content/audio (pass -k to keep)\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import shutil\n",
        "import yt_dlp\n",
        "\n",
        "if upload_own_audio_file == \"yes\":\n",
        "  if not os.path.isfile('audio.mp3'):\n",
        "      own_audio = files.upload()\n",
        "      if not os.path.exists('content'):\n",
        "        os.mkdir('content')\n",
        "        for filename in own_audio.keys():\n",
        "            os.rename(filename, 'audio.mp3')\n",
        "            os.replace('audio.mp3', 'content/audio.mp3')\n",
        "\n",
        "else:\n",
        "    if enable_cookies.lower() == \"yes\":\n",
        "        ydl_opts = {\n",
        "            'format': 'bestaudio/best',\n",
        "            'postprocessors': [{\n",
        "                'key': 'FFmpegExtractAudio',\n",
        "                'preferredcodec': 'mp3',\n",
        "                'preferredquality': '192',\n",
        "            }],\n",
        "            'outtmpl': 'content/audio',\n",
        "            'cookiefile': 'cookies.txt'\n",
        "        }\n",
        "    else:\n",
        "        ydl_opts = {\n",
        "            'format': 'bestaudio/best',\n",
        "            'postprocessors': [{\n",
        "                'key': 'FFmpegExtractAudio',\n",
        "                'preferredcodec': 'mp3',\n",
        "                'preferredquality': '192',\n",
        "            }],\n",
        "            'outtmpl': 'content/audio',\n",
        "        }\n",
        "\n",
        "    with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n",
        "        ydl.download([YouTube_Video_Link])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzO86nhoWPu2"
      },
      "source": [
        "# 生成字幕檔"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ZnFM1sy3WTBh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "71218221be7b4f3a90c08cd7669b9b48",
            "36763a94985a4ba2b288d6d727be5ac4",
            "fb835c20db1241ebaaed35479a86e1fe",
            "7aa0d015be7c43ae8c6e83fe00b7cce5",
            "c57cee0fb74f482a8c82b0f2fd014309",
            "868b1222333746e19ba2e3e615585e05",
            "94384a5fd8d44419a9acd3979aa1ac24",
            "a9b8e439ea4943e9bc0d8118cad16edd",
            "293be31501f440e186d33ca1d2afd965",
            "89b83a7f7b4d4984a93ffeaf79dba382",
            "88f07c68a66a4e89a1fb74e2da382519",
            "e568dcec0da1414486f3f7fdd0cba635",
            "27c1464462b24828b7c5ef3ebbcde3ce",
            "6760731455c64f7a8d125440a17aacd4",
            "9ad7aa08579e4acda9a6839a51ee0b61",
            "48aaf8b719d44f99bf271b5d54a78155",
            "9a9d441e53644452b036924a35323984",
            "84ede3caf4674dc5bfdb6e986ed3f57f",
            "94346e5e718543ddba841dcbf529ccba",
            "4c98e25263ec421281db023c8eafee2b",
            "79f6bae8fe1e4b689ee23cea2f020165",
            "0bb971a830a945f7871134d1480105ff",
            "8bb83be8e98c433fa65d195845b19ae3",
            "0b468c40258e45c28d9ecf98d714076f",
            "86114e02f2224d4cb44e0243c9d9255a",
            "bf6b25fb15064520b8ac3d41251b7125",
            "561354dbd2a94e8a8ea0e5b14d78c7fd",
            "526c186b5da3454b9c7f4786d11ac177",
            "a28e99be30b54ae7b865e00818344651",
            "e785d7253f8d4e8e8d230b7918f0f42f",
            "598273137e8142bb81509914b7adb6b6",
            "83e911b4f2c5419ba924883bc2391a7d",
            "ae7c5c58ac24468b93fcdf4d33e318c7",
            "3e8e5b8fbc0b4a51a13cebb39e83cd6e",
            "9bd6a3022cd2480ba7204f636a5204e2",
            "c3fa53a2555d4e3294248cdbee3d910a",
            "12fbe0eb4f754411af3585e6ef136889",
            "4ca18386bb03471b873a1816dc81ca53",
            "ddd292f82a144745b6fac8f9afa5d861",
            "9f828badca4449669a058f928e85d159",
            "56308ce2dfed45f392b1f2a97e9213ff",
            "a8e39360e6504b88a2882d46f1f883cc",
            "eb8c3ed09de54107929174c33fa9c0f5",
            "8393483530b043ff889c9998e31be765"
          ]
        },
        "outputId": "6589b377-c2db-4752-ceea-d4ae53a9d316"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.bin:   0%|          | 0.00/1.53G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71218221be7b4f3a90c08cd7669b9b48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/2.26k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e568dcec0da1414486f3f7fdd0cba635"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.20M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bb83be8e98c433fa65d195845b19ae3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocabulary.txt:   0%|          | 0.00/460k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e8e5b8fbc0b4a51a13cebb39e83cd6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Whisper model loaded.\n",
            "Detected language 'zh' with probability 0.996094\n",
            "Starting transcription with verbose output...\n",
            "[0.00s -> 14.00s] 接下來就來帶大家看一下GPT商店\n",
            "[14.00s -> 18.00s] 然後教大家怎麼找到機器人的GPT\n",
            "[18.00s -> 22.00s] 基本上在這邊左上角會有個Explore GPT\n",
            "[22.00s -> 24.00s] 或者是如果你有中文的話\n",
            "[24.00s -> 30.00s] 會看到這邊是一個探索GPTS\n",
            "[30.00s -> 32.00s] 然後你就會看到\n",
            "[32.00s -> 36.00s] 它上面就有很多很多人家寫好的GPT\n",
            "[36.00s -> 38.00s] 寫作相關的GPT\n",
            "[38.00s -> 40.00s] 會有這個生產力\n",
            "[40.00s -> 43.00s] 就是幫你生產什麼Powerpoint的生產海報\n",
            "[43.00s -> 45.00s] 生產PDF的這種GPT\n",
            "[45.00s -> 49.00s] 或者是研究跟分析資料的GPT\n",
            "[49.00s -> 50.00s] 它有很多很多\n",
            "[50.00s -> 53.00s] 大家有興趣可以自己研究看看\n",
            "[53.00s -> 55.00s] 你就在這邊打上機器人\n",
            "[55.00s -> 57.00s] 你就把它點進來\n",
            "[57.00s -> 60.00s] 然後你就會看到它的一些資訊\n",
            "[60.00s -> 61.00s] 這我們等一下會講\n",
            "[61.00s -> 65.00s] 阿Chet開始跟機器人聊天\n",
            "[65.00s -> 69.00s] 這樣你就可以找到機器人GPT了\n",
            "[69.00s -> 71.00s] 基本上先講大條件\n",
            "[71.00s -> 73.00s] 你要製作GPT你要付錢\n",
            "[73.00s -> 78.00s] 你要付GPT它的Plus訂閱費\n",
            "[78.00s -> 80.00s] 免費仔是不能自己做的\n",
            "[80.00s -> 82.00s] 但是免費仔可以用\n",
            "[82.00s -> 84.00s] 付費仔做出來的GPT\n",
            "[84.00s -> 86.00s] 如果你要建立自己的GPT的話\n",
            "[86.00s -> 90.00s] 你就在這邊按右上角有個建立\n",
            "[90.00s -> 93.00s] 它就會跑出一個這樣子的視窗\n",
            "[93.00s -> 97.00s] 左邊的話就是幫你的GPT命名\n",
            "[97.00s -> 99.00s] 然後這邊給它一些指令\n",
            "[99.00s -> 104.00s] 就是告訴GPT說你希望它做哪些事情\n",
            "[104.00s -> 108.00s] 然後或者是如果你覺得填表格不太熟悉\n",
            "[108.00s -> 111.00s] 它也有一個建立的對話方塊\n",
            "[111.00s -> 115.00s] 你就可以想像成是GPT的AI客服人員\n",
            "[115.00s -> 120.00s] 然後帶你一步步建立你的客製化GPT\n",
            "[121.00s -> 124.00s] 基本上就是大概像這種案子\n",
            "[124.00s -> 125.00s] 還可以上傳頭像\n",
            "[125.00s -> 127.00s] 你就是用指令跟它講說\n",
            "[127.00s -> 128.00s] 它現在要怎樣怎樣\n",
            "[128.00s -> 130.00s] 那因為機器人GPT\n",
            "[130.00s -> 131.00s] 所以就會跟它說\n",
            "[131.00s -> 133.00s] 你現在是叫做機器人的獸人男孩\n",
            "[133.00s -> 137.00s] 今年13歲是來自未來高科技獸人\n",
            "[137.00s -> 140.00s] 然後你給大家看那個這個這個\n",
            "[140.00s -> 143.00s] 有夠長有夠長\n",
            "[143.00s -> 145.00s] 好這個就是它的指令\n",
            "[145.00s -> 147.00s] 你指令可以盡量塞\n",
            "[147.00s -> 149.00s] 現在的GPT似乎很聰明\n",
            "[149.00s -> 151.00s] 你塞越多它表現就會越好\n",
            "[151.00s -> 153.00s] 然後它會很聽話\n",
            "[153.00s -> 156.00s] 大家有興趣客製化自己的GPT的話\n",
            "[156.00s -> 160.00s] 也可以也可以來玩玩看這樣\n",
            "[160.00s -> 166.00s] 好總之我們現在就調校好了一隻機器人GPT\n",
            "[166.00s -> 168.00s] 剛剛還有講到說\n",
            "[168.00s -> 170.00s] 就是機器人剛剛那一長串指令\n",
            "[170.00s -> 173.00s] 有修正一些GPT常見的問題\n",
            "[173.00s -> 174.00s] 你就跟它說\n",
            "[174.00s -> 177.00s] 你要怎樣怎樣你不要怎樣怎樣\n",
            "[177.00s -> 180.00s] 來避免GPT常見的問題\n",
            "[180.00s -> 181.00s] 它唬爛嘴之類的\n",
            "[181.00s -> 185.00s] 這邊就可以看到這個機器人GPT\n",
            "[185.00s -> 187.00s] 然後一些簡單的按鈕\n",
            "[187.00s -> 188.00s] 然後還有一個對話框\n",
            "[188.00s -> 191.00s] 可以傳訊息給機器人玩\n",
            "[196.00s -> 197.50s] by bwd6\n",
            "SRT file saved at: /content/gdrive/MyDrive/Whisper/result/【ChatGPT】自製GPT真的超簡單！機器狼的AI對話魔法！【GPTs】【入門筆記／精華版】#1-1.srt\n",
            "Generated both SRT and TXT files.\n",
            "Transcription complete!\n"
          ]
        }
      ],
      "source": [
        "from faster_whisper import WhisperModel\n",
        "import pysubs2\n",
        "import os\n",
        "\n",
        "try:\n",
        "    start_time\n",
        "except NameError:\n",
        "    print(\"Crash Fallback : Starting to Calculate the Time from now\")\n",
        "    from datetime import datetime\n",
        "    start_time = datetime.now()\n",
        "\n",
        "# Load model\n",
        "model = WhisperModel(model_user)\n",
        "print(\"Whisper model loaded.\")\n",
        "\n",
        "filename = \"audio.mp3\"\n",
        "input_directory = \"content\"\n",
        "input_file = f\"{input_directory}/{filename}\"\n",
        "\n",
        "# Transcribe with or without specific language setting\n",
        "if task == \"translate\":\n",
        "    segments, info = model.transcribe(input_file, task=\"translate\", language=language)\n",
        "else:\n",
        "  if language.strip() == 'Auto':\n",
        "      segments, info = model.transcribe(input_file)\n",
        "      print(\"Detected language '%s' with probability %f\" % (info.language, info.language_probability))\n",
        "  else:\n",
        "      segments, info = model.transcribe(input_file, language=language)\n",
        "\n",
        "# Store results and show detailed information\n",
        "results = []\n",
        "print(\"Starting transcription with verbose output...\")\n",
        "for segment in segments:\n",
        "    print(\"[%.2fs -> %.2fs] %s\" % (segment.start, segment.end, segment.text))\n",
        "    segment_dict = {'start': segment.start, 'end': segment.end, 'text': segment.text}\n",
        "    results.append(segment_dict)\n",
        "\n",
        "# Save to Google Drive or local directory as specified\n",
        "if disable_google_drive.lower() == 'no':\n",
        "    transcription_root = \"/content/gdrive/MyDrive/Whisper/result/\"\n",
        "    os.makedirs(transcription_root, exist_ok=True)\n",
        "else:\n",
        "    transcription_root = \"./result/\"\n",
        "    os.makedirs(transcription_root, exist_ok=True)\n",
        "\n",
        "# Save as SRT\n",
        "subs = pysubs2.load_from_whisper(results)\n",
        "srt_path = transcription_root + new_filename\n",
        "subs.save(srt_path)\n",
        "print(f\"SRT file saved at: {srt_path}\")\n",
        "\n",
        "# Optionally generate plain text file\n",
        "if Generate_Plain_Document.lower() == \"yes\":\n",
        "    txt_path = transcription_root + new_filename2\n",
        "    with open(txt_path, \"w\") as txt_file:\n",
        "        for segment in results:\n",
        "            txt_file.write(f\"{segment['text']}\\n\")\n",
        "    print(\"Generated both SRT and TXT files.\")\n",
        "\n",
        "else:\n",
        "    print(\"Generated only SRT file.\")\n",
        "\n",
        "print(\"Transcription complete!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 壓縮檔案並上傳\n",
        "如果雲端硬碟被停用了"
      ],
      "metadata": {
        "id": "7rWCkivc2Jhx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if disable_google_drive.lower() == 'yes':\n",
        "    # Creating the ZIP file\n",
        "    filenametime = datetime.now().strftime(\"%m-%d-%Y-%H-%M-%S\")\n",
        "    archived = shutil.make_archive(filenametime, 'zip', '/content/result')\n",
        "    if os.path.exists(archived):\n",
        "      print(archived)\n",
        "    if shutdown_after_complete.lower() == \"yes\":\n",
        "      print(f\"Turning off Auto Shutdown. Due to file download doesn't support when the runtime is shutdown.\")\n",
        "      shutdown_after_complete = \"no\"\n",
        "    files.download(archived)\n",
        "    print(\"File downloaded successfully.\")"
      ],
      "metadata": {
        "id": "H-PkcMx22JTF"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM61x2JuYBl-"
      },
      "source": [
        "# 自動關閉執行階段"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "dUKHF0-rYGvq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "3c15f3c3-3fb0-4127-e04a-a09ba0e0239e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "生產總時長: 0:01:36.578171\n",
            "Shutting Down the Runtime Because Shutdown After Complete is on\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "end_time = datetime.now()\n",
        "print('\\u751f\\u7522\\u7e3d\\u6642\\u9577: {}'.format(end_time - start_time))\n",
        "time.sleep (2)\n",
        "\n",
        "if shutdown_after_complete.lower() == \"yes\":\n",
        "    print(\"Shutting Down the Runtime Because Shutdown After Complete is on\")\n",
        "    from google.colab import runtime\n",
        "    runtime.unassign()\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "HiME-JOSUfhd"
      ],
      "provenance": [],
      "include_colab_link": true
    },
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