VideoLingo_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkeSbYF2HoM_"
      },
      "source": [
        "# Welcome to VideoLingo! 🎉🚀\n",
        "#### This colab file allows you to quickly experience the full functionality in just 5 minutes! ⏱️✨ Before you begin, you may need to prepare some keys. 🔑🗝️ Please read https://videolingo.io/docs/start to get ready. 📚👍\n",
        "#### *Please use a T4 GPU to execute this colab for optimal performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h0jE67Gc-1nO"
      },
      "source": [
        "## 1. Clone VideoLingo repo 📥"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NC3i2T7D51oS",
        "outputId": "19821917-8ee4-4123-a099-fd35405fcdfe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'VideoLingo'...\n",
            "remote: Enumerating objects: 2578, done.\u001b[K\n",
            "remote: Counting objects: 100% (595/595), done.\u001b[K\n",
            "remote: Compressing objects: 100% (221/221), done.\u001b[K\n",
            "remote: Total 2578 (delta 408), reused 378 (delta 374), pack-reused 1983 (from 1)\u001b[K\n",
            "Receiving objects: 100% (2578/2578), 10.44 MiB | 12.60 MiB/s, done.\n",
            "Resolving deltas: 100% (1644/1644), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Huanshere/VideoLingo.git\n",
        "%cd VideoLingo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5sIHzRs8JI1"
      },
      "source": [
        "## 2. Installation 🚀\n",
        "this takes around 4 mins"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSymHAEg6Vzr",
        "outputId": "8c5059e4-37d5-4540-cba5-9c7aa46fe226"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[1;35m╭──────────────────────────╮\u001b[0m\n",
            "\u001b[1;35m│\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35mStarting installation...\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m│\u001b[0m\n",
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            "config.py file has been created. Please fill in the API key and base URL in the config.py file.\n",
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            "\u001b[3m                        Whisper Model Selection                         \u001b[0m\n",
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            "┃\u001b[1m \u001b[0m\u001b[1mOption\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mModel        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mDescription                                \u001b[0m\u001b[1m \u001b[0m┃\n",
            "┡━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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            "└────────┴───────────────┴─────────────────────────────────────────────┘\n",
            "If you're unsure about the differences between models, please see \n",
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            "\u001b[36m│\u001b[0m\u001b[36m \u001b[0m\u001b[36mInstalling PyTorch with CUDA support...\u001b[0m\u001b[36m                                                         \u001b[0m\u001b[36m \u001b[0m\u001b[36m│\u001b[0m\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[?25hBuilding wheels for collected packages: antlr4-python3-runtime, docopt, julius\n",
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            "\u001b[?25hDownloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: pydub, brotli, websockets, watchdog, tenacity, smmap, pycryptodomex, mutagen, json-repair, jiter, h11, azure-cognitiveservices-speech, yt-dlp, resampy, pydeck, pandas, httpcore, gitdb, httpx, gitpython, replicate, openai, streamlit\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "  Attempting uninstall: pandas\n",
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            "    Uninstalling pandas-2.2.2:\n",
            "      Successfully uninstalled pandas-2.2.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.6.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed azure-cognitiveservices-speech-1.40.0 brotli-1.1.0 gitdb-4.0.11 gitpython-3.1.43 h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 jiter-0.5.0 json-repair-0.29.7 mutagen-1.47.0 openai-1.47.0 pandas-2.2.3 pycryptodomex-3.21.0 pydeck-0.9.1 pydub-0.25.1 replicate-0.33.0 resampy-0.4.3 smmap-5.0.1 streamlit-1.38.0 tenacity-8.5.0 watchdog-4.0.2 websockets-13.1 yt-dlp-2024.8.6\n",
            "Downloading UVR model: HP2_all_vocals.pth...\n",
            "Downloaded: 0.01%\n",
            "HP2_all_vocals.pth downloaded successfully.\n",
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            "VR-DeEchoAggressive.pth downloaded successfully.\n",
            "Downloading FFmpeg...\n",
            "FFmpeg has been downloaded to ffmpeg.tar.xz\n",
            "Extracting FFmpeg...\n",
            "Cleaning up...\n",
            "FFmpeg extraction completed.\n",
            "\u001b[1;32m╭───────────────────────────────────────╮\u001b[0m\n",
            "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mAll installation steps are completed!\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
            "\u001b[1;32m╰───────────────────────────────────────╯\u001b[0m\n",
            "Please use the following command to start Streamlit:\n",
            "\u001b[1;36mstreamlit run st.py\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python install.py 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5J2jjEY2BrM3"
      },
      "source": [
        "## 3. Register and Obtain Ngrok Token 🔑\n",
        "\n",
        "1. Visit the [Ngrok official website](https://ngrok.com/) and register for an account.\n",
        "2. After logging in, find the \"Your Authtoken\" section on the dashboard page, or directly visit [Ngrok Token](https://dashboard.ngrok.com/get-started/your-authtoken).\n",
        "3. Copy your Ngrok Authtoken.\n",
        "\n",
        "After completing these steps, please fill in your ngrok token in the next section of code and proceed.\n",
        "\n",
        "---\n",
        "\n",
        "## 3. 注册并获取 Ngrok 令牌 🔑\n",
        "\n",
        "1. 访问 [Ngrok 官方网站](https://ngrok.com/) 并注册账户。\n",
        "2. 登录后，在仪表板页面找到\"Your Authtoken\"部分，或直接访问 [Ngrok 令牌](https://dashboard.ngrok.com/get-started/your-authtoken)。\n",
        "3. 复制您的 Ngrok Authtoken。\n",
        "\n",
        "完成这些步骤后，请在下一节代码中填入您的 ngrok 令牌并继续。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tm25rblnBqhl",
        "outputId": "93691228-1e7b-48e7-b7f3-c27007a4a13f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyngrok in /usr/local/lib/python3.10/dist-packages (7.2.0)\n",
            "Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install pyngrok\n",
        "from pyngrok import ngrok\n",
        "\n",
        "#! SET Ngrok Authtoken Here\n",
        "ngrok.set_auth_token(\"YOUR_TOKEN_HERE\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDJXtgMuGXMH"
      },
      "source": [
        "## 🎈 4. Streamlit GO !!!\n",
        "Click the NgrokChannel URL to start your VideoLingo Journey.\n",
        "\n",
        "Make sure to change `whisper method` in sidebar to `whisperx 💻` to run whisperx locally.\n",
        "\n",
        "> tips: You can set your Language down the sidebar on the left."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "qr_a4mS29k5_",
        "outputId": "e6915708-d030-45d0-d6a8-cb177960c137"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭───────────────────────────────────╮\n",
              "│ Streamlit is available at Ngrok ⬇️ │\n",
              "╰───────────────────────────────────╯\n",
              "</pre>\n"
            ],
            "text/plain": [
              "╭───────────────────────────────────╮\n",
              "│ Streamlit is available at Ngrok ⬇️ │\n",
              "╰───────────────────────────────────╯\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Click 👉 NgrokTunnel: \"https://0308-34-125-196-161.ngrok-free.app\" -> \"http://localhost:8501\"\n",
            "\n",
            "  You can now view your Streamlit app in your browser.\n",
            "\n",
            "  Local URL: http://localhost:8501\n",
            "  Network URL: http://172.28.0.12:8501\n",
            "  External URL: http://34.125.196.161:8501\n",
            "\n",
            "  Stopping...\n",
            "Interrupted by user, shutting down...\n"
          ]
        }
      ],
      "source": [
        "import subprocess\n",
        "import threading\n",
        "import sys\n",
        "from pyngrok import ngrok\n",
        "from rich import print as rprint\n",
        "from rich.panel import Panel\n",
        "\n",
        "def print_output(process):\n",
        "    for line in iter(process.stdout.readline, ''):\n",
        "        sys.stdout.write(line)\n",
        "    for line in iter(process.stderr.readline, ''):\n",
        "        sys.stderr.write(line)\n",
        "\n",
        "# Start Streamlit\n",
        "streamlit_process = subprocess.Popen(\n",
        "    [\"streamlit\", \"run\", \"st.py\"],\n",
        "    stdout=subprocess.PIPE,\n",
        "    stderr=subprocess.PIPE,\n",
        "    universal_newlines=True,\n",
        "    bufsize=1\n",
        ")\n",
        "\n",
        "# Create and start the output printing thread\n",
        "output_thread = threading.Thread(target=print_output, args=(streamlit_process,))\n",
        "output_thread.start()\n",
        "\n",
        "# Create a tunnel using ngrok\n",
        "public_url = ngrok.connect(8501)\n",
        "rprint(Panel(f\"Streamlit is available at Ngrok ⬇️\", expand=False))\n",
        "print(f\"Click 👉 {public_url}\")\n",
        "\n",
        "# Keep the program running\n",
        "ngrok_process = ngrok.get_ngrok_process()\n",
        "try:\n",
        "    streamlit_process.wait()\n",
        "except KeyboardInterrupt:\n",
        "    print(\"Interrupted by user, shutting down...\")\n",
        "finally:\n",
        "    ngrok.kill()\n",
        "    streamlit_process.terminate()\n",
        "    output_thread.join()\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}