Demos/13a. 轻松开始你的第一次 AI 视频总结（API 版） - 完整版.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ef54b620-4aa5-4797-bdf4-75dc815d4295",
   "metadata": {},
   "source": [
    "# a. 使用大模型 API 对视频进行快速摘要（音频处理）- 完整版\n",
    "> [HW9: Quick Summary of Lecture Video (演講影片快速摘要)](https://colab.research.google.com/drive/1Ysr25kz6lP7gR8DNTkJMAqOuMp2bhXes?usp=sharing#scrollTo=UULEr1GpDAl6)中文镜像版 | [PDF](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/GenAI_PDF/HW9.pdf)\n",
    "> \n",
    "> 这里的变量名与 HW9 基本保持一致。\n",
    ">\n",
    "> 指导文章：[15. 用 API 实现 AI 视频摘要：动手制作属于你的 AI 视频助手](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/Guide/15.%20用%20API%20实现%20AI%20视频摘要：动手制作属于你的%20AI%20视频助手.md)\n",
    "> \n",
    "\n",
    "**目标：** 学习如何使用现成的 API 快速构建语音识别相关的应用。\n",
    "\n",
    "![Pipeline](../Guide/assets/image-20240926154540268.png)\n",
    "\n",
    "注意，这里没有视觉上的识别。\n",
    "\n",
    "如果你遇到了 443 或者代理端口错误，请查看[《a. 使用 HFD 加快 Hugging Face 模型和数据集的下载》](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/Guide/a.%20使用%20HFD%20加快%20Hugging%20Face%20模型和数据集的下载.md#可能存在的问题443-和-git-clone-failed)。\n",
    "\n",
    "你也可以尝试运行 [b. 精简版](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/Demos/13b.%20轻松开始你的第一次%20AI%20视频总结（API%20版）-%20精简版.ipynb)，适合一键执行查看效果，其中只保留了核心代码。\n",
    "\n",
    "在线链接（精简版）：[Kaggle](https://www.kaggle.com/code/aidemos/13b-ai-api) | [Colab](https://colab.research.google.com/drive/1yh2J4_Cy45fqvydH34LLtDpw10kuWutO?usp=sharing)\n",
    "\n",
    "这里还有一个简单的 [🎡 AI Summarizer 脚本](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/CodePlayground/summarizer.py)供你尝试，命令行执行：\n",
    "```bash\n",
    "python summarizer.py ./example_video.mp4\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20d3388-3394-4988-b8f2-279221ffec2d",
   "metadata": {},
   "source": [
    "## 第1部分 - 准备\n",
    "\n",
    "### 提供的作业演讲视频\n",
    "\n",
    "(1) 为了方便处理，已将其转换为 MP3 文件。\n",
    "\n",
    "(2) 如果你想查看原始视频，请点击以下链接：\n",
    "\n",
    "- 李琳山教授 信号与人生 (2023)\n",
    "\n",
    "  - 视频：[YouTube](https://www.youtube.com/watch?v=MxoQV4M0jY8) | [Bilibili](https://www.bilibili.com/video/BV14P411B7Le)\n",
    "\n",
    "\n",
    "(3) 由于原始演讲视频较长，选取了从 1:43:24 到 2:00:49 的片段用于此次演示。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba593bc-4200-4d9e-82b2-4fc74afae106",
   "metadata": {},
   "source": [
    "### 安装必要的库\n",
    "\n",
    "这里需要一点时间。\n",
    "\n",
    "注意，Colab 的版本在本地运行可能存在一些问题，故有以下修改：\n",
    "1. 官方版本下载的OpenCC是1.1.7，这可能会遇到报错：\n",
    ">ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found\n",
    "\n",
    "这里将版本降到 1.1.6 避免这个问题，解决方法来源: [OpenCC Issues#832](https://github.com/BYVoid/OpenCC/issues/832)\n",
    "\n",
    "2. 本地运行还可能遇到：\n",
    "> ImportError: To support decoding audio files, please install 'librosa' and 'soundfile'.\n",
    "\n",
    "对应增加安装指令即可。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56807783-8e56-4ff1-9d8f-b38295b4434b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install srt\n",
    "!pip install datasets\n",
    "!pip install DateTime\n",
    "!pip install OpenCC==1.1.6\n",
    "!pip install opencv-contrib-python\n",
    "!pip install opencv-python\n",
    "!pip install opencv-python-headless\n",
    "!pip install openpyxl\n",
    "!pip install openai\n",
    "!pip install git+https://github.com/openai/whisper.git@ba3f3cd54b0e5b8ce1ab3de13e32122d0d5f98ab\n",
    "!pip install numpy\n",
    "!pip install soundfile\n",
    "!pip install ipywidgets\n",
    "!pip install librosa\n",
    "#!pip install 'httpx<0.28.0' # 降级 httpx 以解决关键字 'proxies' 被移除的问题，最新的 openai 库不会引发该问题，故默认注释"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9299e157-4bb0-4972-a8a7-a1e2488a1b49",
   "metadata": {},
   "source": [
    "### 导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32ec887d-3ae6-4878-9905-b7ba75ea7753",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准库\n",
    "import os\n",
    "import time\n",
    "import re\n",
    "import pathlib\n",
    "import textwrap\n",
    "import datetime\n",
    "\n",
    "# 第三方库\n",
    "import numpy as np\n",
    "import srt\n",
    "import soundfile as sf\n",
    "from opencc import OpenCC\n",
    "from tqdm import tqdm\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display, Markdown\n",
    "\n",
    "# 项目相关库（如语音识别和API调用等）\n",
    "import whisper\n",
    "from datasets import load_dataset\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b317e682-dd8b-47bc-9b5c-7ad18ba740b9",
   "metadata": {},
   "source": [
    "### 下载数据\n",
    "\n",
    "已经上传至 data 文件夹中，不需要再下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2f6481d-a710-4bc0-9abe-f614a8178d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据集\n",
    "# dataset_name = \"kuanhuggingface/NTU-GenAI-2024-HW9\"\n",
    "# dataset = load_dataset(dataset_name)\n",
    "\n",
    "\n",
    "# 加载本地 Parquet 格式的数据集\n",
    "dataset = load_dataset('parquet', data_files={'test': './data/13/test-00000-of-00001.parquet'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cfef0992-1118-4690-b6c3-957b9652c799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "现在我们将转录音频: 李琳山教授 信号与人生 (2023) (ntu-gen-ai-2024-hw9-16k.mp3)。\n"
     ]
    }
   ],
   "source": [
    "# 准备音频\n",
    "input_audio = dataset[\"test\"][\"audio\"][0]\n",
    "input_audio_name = input_audio[\"path\"]\n",
    "input_audio_array = input_audio[\"array\"].astype(np.float32)\n",
    "sampling_rate = input_audio[\"sampling_rate\"]\n",
    "\n",
    "print(f\"现在我们将转录音频: 李琳山教授 信号与人生 (2023) ({input_audio_name})。\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81fd3ada-646a-4a85-a7fe-3d498bb03560",
   "metadata": {},
   "source": [
    "#### 直接加载 .mp3 文件（二选一）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d71e9869-e2a9-4ebe-96c6-c96364afdcfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "\n",
    "# 指定音频文件路径\n",
    "mp3_file_path = './data/13/audio.mp3'\n",
    "\n",
    "input_audio_name = os.path.basename(mp3_file_path)\n",
    "\n",
    "# 加载音频文件，指定采样率为 16000\n",
    "input_audio_array, sampling_rate = librosa.load(mp3_file_path, sr=16000)\n",
    "\n",
    "# 打印音频数据的采样率和形状，确保加载成功\n",
    "print(f\"采样率: {sampling_rate}\")\n",
    "print(f\"音频数据形状: {input_audio_array.shape}\")\n",
    "\n",
    "print(f\"现在我们将转录音频: ({input_audio_name})。\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7bb38b-bd7a-4ddf-9210-3b55b1b9525e",
   "metadata": {},
   "source": [
    "## 第2部分 - 自动语音识别 (ASR)\n",
    "\n",
    "下图是处理的样例过程，对应于之后所使用的音频：\n",
    "\n",
    "![image-20240926155512340](../Guide/assets/image-20240926155512340.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78c53a6-4df7-4d41-b913-f382ec2d751d",
   "metadata": {},
   "source": [
    "### 定义语音识别函数\n",
    "\n",
    "函数 `speech_recognition()` 旨在将音频转换为字幕。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b9bd0bc-efe9-4bf3-8c53-6de03f3cafdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def speech_recognition(model_name, input_audio, output_subtitle_path, decode_options, cache_dir=\"./\"):\n",
    "    '''\n",
    "        (1) 目标:\n",
    "            - 本函数旨在将音频转换为字幕。\n",
    "\n",
    "        (2) 参数:\n",
    "\n",
    "            - model_name (str):\n",
    "                模型名称。模型有五种尺寸，包括 tiny, base, small, medium, large-v3。\n",
    "                例如，你可以使用 'tiny', 'base', 'small', 'medium', 'large-v3' 来指定模型名称。\n",
    "                详细信息请见 'https://github.com/openai/whisper'。\n",
    "\n",
    "            - input_audio (Union[str, np.ndarray, torch.Tensor]):\n",
    "                音频文件路径或音频波形。\n",
    "                - 例如，如果输入音频路径为 'input.wav'，你可以使用 'input.wav' 来指定音频路径。\n",
    "                - 如果输入音频数组为 'audio_array'，你可以使用 'audio_array' 来指定音频数组。\n",
    "\n",
    "            - output_subtitle_path (str):\n",
    "                输出字幕文件路径。\n",
    "                例如，如果你想将字幕文件保存为 'output.srt'，可以使用 'output.srt' 来指定路径。\n",
    "\n",
    "            - decode_options (dict):\n",
    "                音频文件解码选项，包括 'initial_prompt', 'prompt', 'prefix', 'temperature'。\n",
    "                - initial_prompt (str):\n",
    "                    提供给模型的初始文本prompt。这可以用于提供上下文，以提高识别准确性。\n",
    "                    默认值: None。\n",
    "\n",
    "                详细信息请见 \"https://github.com/openai/whisper/blob/main/whisper/decoding.py\" 和 \"https://github.com/openai/whisper/blob/main/whisper/transcribe.py\"。\n",
    "\n",
    "                - temperature (float):\n",
    "                    采样的温度。值越高，随机性越强。\n",
    "                    默认值: 0.0\n",
    "\n",
    "            - cache_dir (str):\n",
    "                用于保存模型的缓存目录路径。\n",
    "                例如，你可以使用 'cache' 来指定缓存目录。\n",
    "                默认值: './'\n",
    "\n",
    "        (3) 示例:\n",
    "\n",
    "            - 如果你想使用 'base' 模型将 'input.wav' 转换为 'output.srt'，并将缓存文件保存在 'cache' 目录中，\n",
    "              你可以按如下方式调用此函数：\n",
    "\n",
    "                speech_recognition(model_name='base', input_audio_path='input.wav', output_subtitle_path='output.srt', cache_dir='cache')\n",
    "    '''\n",
    "\n",
    "    # 记录开始时间\n",
    "    start_time = time.time()\n",
    "\n",
    "    print(f\"=============== 正在加载 Whisper-{model_name} ===============\")\n",
    "\n",
    "    # 加载模型\n",
    "    model = whisper.load_model(name=model_name, download_root=cache_dir)\n",
    "\n",
    "    print(f\"开始使用 Whisper-{model_name} 转录音频。\")\n",
    "\n",
    "    # 转录音频\n",
    "    transcription = model.transcribe(audio=input_audio, language=decode_options[\"language\"], verbose=False,\n",
    "                                     initial_prompt=decode_options[\"initial_prompt\"], temperature=decode_options[\"temperature\"])\n",
    "\n",
    "    # 记录结束时间\n",
    "    end_time = time.time()\n",
    "\n",
    "    print(f\"语音识别过程耗时 {end_time - start_time} 秒。\")\n",
    "\n",
    "    subtitles = []\n",
    "    # 将转录内容转换为字幕并遍历所有片段\n",
    "    for i, segment in tqdm(enumerate(transcription[\"segments\"])):\n",
    "\n",
    "        # 将开始时间转换为字幕格式\n",
    "        start_time = datetime.timedelta(seconds=segment[\"start\"])\n",
    "\n",
    "        # 将结束时间转换为字幕格式\n",
    "        end_time = datetime.timedelta(seconds=segment[\"end\"])\n",
    "\n",
    "        # 获取字幕文本\n",
    "        text = segment[\"text\"]\n",
    "\n",
    "        # 将字幕添加到字幕列表中\n",
    "        subtitles.append(srt.Subtitle(index=i, start=start_time, end=end_time, content=text))\n",
    "\n",
    "    # 将字幕列表转换为字幕内容\n",
    "    srt_content = srt.compose(subtitles)\n",
    "\n",
    "    print(f\"\\n=============== 正在将字幕保存到 {output_subtitle_path} ===============\")\n",
    "\n",
    "    # 将字幕内容保存到文件中\n",
    "    with open(output_subtitle_path, \"w\", encoding=\"utf-8\") as file:\n",
    "        file.write(srt_content)\n",
    "\n",
    "    print(f\"\\n=============== 字幕保存完成 ===============\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266026e8-8762-4282-8eb6-52ddb0eb630a",
   "metadata": {},
   "source": [
    "### 设置参数\n",
    "\n",
    "注意，这里设置的参数都是 Whisper 相关的，与后续的 AI 摘要不同。\n",
    "\n",
    "你可以通过 model_name 设置不同的模型，使用**标识**指定。通过[官方仓库](https://github.com/openai/whisper)所提供的数据，我们可以看到不同模型需要的显存大小：\n",
    "\n",
    "| Size   | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |\n",
    "| ------ | ---------- | ------------------ | ------------------ | ------------- | -------------- |\n",
    "| tiny   | 39 M       | `tiny.en`          | `tiny`             | ~1 GB         | ~32x           |\n",
    "| base   | 74 M       | `base.en`          | `base`             | ~1 GB         | ~16x           |\n",
    "| small  | 244 M      | `small.en`         | `small`            | ~2 GB         | ~6x            |\n",
    "| medium | 769 M      | `medium.en`        | `medium`           | ~5 GB         | ~2x            |\n",
    "| large  | 1550 M     | N/A                | `large`            | ~10 GB        | 1x             |\n",
    "\n",
    "**解释：**\n",
    "\n",
    "- **Size (大小)**：表示模型的尺寸，不同大小的模型训练时使用的数据量不同，因此性能和精度也不同。较大的模型通常会有更高的精度。`Medium` 是个不错的选择，`tiny` 和 `base` 效果一般，用于学习的话也可以。\n",
    "- **Parameters (参数量)**：模型的参数数量，表示模型的复杂度。参数越多，模型的性能通常越好，但也会占用更多的计算资源。\n",
    "- **English-only model (仅限英文模型)**：模型的**标识**名称，只用于处理英文音频转录，适用于仅需要处理英文语音的场景。\n",
    "- **Multilingual model (多语言模型)**：模型的**标识**名称，用于在代码中加载相应的模型，对应于接下来的 `model_name` 参数。\n",
    "- **Required VRAM (所需显存)**：指运行该模型时所需的显存大小。如果你对参数和显存的对应关系感兴趣，可以阅读之前的文章：[《07. 探究模型参数与显存的关系以及不同精度造成的影响.md》](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/Guide/07.%20探究模型参数与显存的关系以及不同精度造成的影响.md)。\n",
    "- **Relative speed (相对速度)**：相对速度表示模型处理语音转录任务的效率。数字越大，模型处理速度越快，与模型的参数量成反比。\n",
    "\n",
    "直接运行代码，交互式修改所需的参数及输入文件路径，不用关心这里的代码细节，除非你对交互感兴趣。\n",
    "\n",
    "注意：配置完记得点击**提交配置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aed82848-5a9a-423b-b3cd-435077522672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**model_name**<br>请选择要使用的模型名称："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f6936244aff45b6beb313cfae9fb5b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='model_name:', index=3, options=('tiny', 'base', 'small', 'medium', 'large-v3'), value='m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**suffix**<br>设置输出文件名的后缀："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fafcc639a97d46b982a3f2fba1f7cd84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='信号与人生', description='suffix:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "540a503da35c4402be72bc129b76bde2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='./output-信号与人生.srt', description='字幕路径:', disabled=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc746e438949489b8153f9f3c1527930",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='./output-信号与人生.txt', description='原始文本路径:', disabled=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**cache_dir**<br>设置模型和数据集缓存的目录路径："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcfa04669f244de88ae041fc202eb168",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='./', description='cache_dir:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**language**<br>设置演讲视频的语言："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7f1c9915c934d9d8add223b0ad89f81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='zh', description='language:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**initial_prompt**<br>用于提供初始文本prompt（可选）："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "529f0b251eee48919416fcb6965b7c1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='请用中文', description='initial_prompt:')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**temperature**<br>值越高，随机性越强："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2150bac5f8a74a08900dba36285d9d9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0, continuous_update=False, description='temperature:', max=1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c057cf517974d65b69d44c0cf68bfdc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='提交配置', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2633b294611f4bae88f0adf0e91478fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===== 在此代码块中，你可以修改所需的参数及输入文件路径 =====\n",
    "# 模型名称部分\n",
    "model_name_markdown = Markdown(\"**model_name**<br>请选择要使用的模型名称：\")\n",
    "model_name_widget = widgets.Dropdown(\n",
    "    options=['tiny', 'base', 'small', 'medium', 'large-v3'],\n",
    "    value='medium',\n",
    "    description='model_name:',\n",
    ")\n",
    "\n",
    "# 输出文件的后缀部分\n",
    "suffix_markdown = Markdown(\"**suffix**<br>设置输出文件名的后缀：\")\n",
    "suffix_widget = widgets.Text(\n",
    "    value='信号与人生',\n",
    "    description='suffix:',\n",
    ")\n",
    "\n",
    "# 输出字幕文件和原始文本文件路径部分\n",
    "output_subtitle_path_widget = widgets.Text(\n",
    "    value=f\"./output-信号与人生.srt\",\n",
    "    description='字幕路径:',\n",
    "    disabled=True  # 禁用手动修改\n",
    ")\n",
    "\n",
    "output_raw_text_path_widget = widgets.Text(\n",
    "    value=f\"./output-信号与人生.txt\",\n",
    "    description='原始文本路径:',\n",
    "    disabled=True  # 禁用手动修改\n",
    ")\n",
    "\n",
    "# 模型和数据集缓存目录部分\n",
    "cache_dir_markdown = Markdown(\"**cache_dir**<br>设置模型和数据集缓存的目录路径：\")\n",
    "cache_dir_widget = widgets.Text(\n",
    "    value='./',\n",
    "    description='cache_dir:',\n",
    ")\n",
    "\n",
    "# 演讲视频的语言部分\n",
    "language_markdown = Markdown(\"**language**<br>设置演讲视频的语言：\")\n",
    "language_widget = widgets.Text(\n",
    "    value='zh',\n",
    "    description='language:',\n",
    ")\n",
    "\n",
    "# 用于提供初始文本prompt（可选）\n",
    "initial_prompt_markdown = Markdown(\"**initial_prompt**<br>用于提供初始文本prompt（可选）：\")\n",
    "initial_prompt_widget = widgets.Text(\n",
    "    value='请用中文',\n",
    "    description='initial_prompt:',\n",
    ")\n",
    "\n",
    "# 采样温度部分\n",
    "temperature_markdown = Markdown(\"**temperature**<br>值越高，随机性越强：\")\n",
    "temperature_widget = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=0,\n",
    "    max=1,\n",
    "    step=0.1,\n",
    "    description='temperature:',\n",
    "    continuous_update=False\n",
    ")\n",
    "\n",
    "# 创建输出区域来显示打印内容\n",
    "output_area = widgets.Output()\n",
    "\n",
    "# 获取用户输入值并更新路径\n",
    "def on_button_click(b):\n",
    "    global model_name, suffix, output_subtitle_path, output_raw_text_path, cache_dir, language, initial_prompt, temperature\n",
    "    with output_area:  # 使用 Output 小部件捕获输出\n",
    "        output_area.clear_output()  # 清除之前的输出\n",
    "        model_name = model_name_widget.value\n",
    "        suffix = suffix_widget.value\n",
    "        output_subtitle_path = f\"./output-{suffix}.srt\"  # 更新全局变量\n",
    "        output_raw_text_path = f\"./output-{suffix}.txt\"  # 更新全局变量\n",
    "        cache_dir = cache_dir_widget.value\n",
    "        language = language_widget.value\n",
    "        initial_prompt = initial_prompt_widget.value\n",
    "        temperature = temperature_widget.value\n",
    "        \n",
    "        # 更新字幕文件路径和原始文本路径\n",
    "        output_subtitle_path_widget.value = output_subtitle_path\n",
    "        output_raw_text_path_widget.value = output_raw_text_path\n",
    "        \n",
    "        # 打印配置\n",
    "        print(f\"模型名称: {model_name}\")\n",
    "        print(f\"输出文件后缀: {suffix}\")\n",
    "        print(f\"字幕文件路径: {output_subtitle_path}\")\n",
    "        print(f\"原始文本路径: {output_raw_text_path}\")\n",
    "        print(f\"缓存目录: {cache_dir}\")\n",
    "        print(f\"语言: {language}\")\n",
    "        print(f\"初始prompt: {initial_prompt}\")\n",
    "        print(f\"采样温度: {temperature}\")\n",
    "\n",
    "# 创建提交按钮\n",
    "submit_button = widgets.Button(description=\"提交配置\")\n",
    "submit_button.on_click(on_button_click)\n",
    "\n",
    "# 显示带有说明的所有小部件\n",
    "display(model_name_markdown, model_name_widget)\n",
    "display(suffix_markdown, suffix_widget)\n",
    "display(output_subtitle_path_widget, output_raw_text_path_widget)\n",
    "display(cache_dir_markdown, cache_dir_widget)\n",
    "display(language_markdown, language_widget)\n",
    "display(initial_prompt_markdown, initial_prompt_widget)\n",
    "display(temperature_markdown, temperature_widget)\n",
    "\n",
    "# 显示提交按钮\n",
    "display(submit_button)\n",
    "\n",
    "# 显示输出区域\n",
    "display(output_area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb1358e-49a7-4601-8c8f-32d1c6a67ec4",
   "metadata": {},
   "source": [
    "构建 DecodingOptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8058b4a-ea9c-4771-8361-c2bba6f1667f",
   "metadata": {},
   "outputs": [],
   "source": [
    "decode_options = {\n",
    "    \"language\": language,\n",
    "    \"initial_prompt\": initial_prompt,\n",
    "    \"temperature\": temperature\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcda6b1e-e501-4844-998a-dea936322217",
   "metadata": {},
   "source": [
    "**打印完整配置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0503049b-a8f4-4ddd-852c-014b065018d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "设置: (1) 模型: whisper-medium (2) 语言: zh (3) 初始prompt: 请用中文 (4) 温度: 0.0\n",
      "转录 李琳山教授 信号与人生 (2023)\n"
     ]
    }
   ],
   "source": [
    "message = \"转录 李琳山教授 信号与人生 (2023)\"\n",
    "print(f\"设置: (1) 模型: whisper-{model_name} (2) 语言: {language} (3) 初始prompt: {initial_prompt} (4) 温度: {temperature}\")\n",
    "print(message)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7cb886f-c86f-4145-bec2-b029b204ca8d",
   "metadata": {},
   "source": [
    "预计需要花费 4 分钟时间（包括下载和识别，识别大概1分钟）（由模型大小，网速和你的显卡决定）\n",
    "\n",
    "下载+识别：\n",
    "\n",
    "![结果](../Guide/assets/image-20240924005355363.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb77f557-5acf-42c1-b887-9fad7b6b135b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============== 正在加载 Whisper-medium ===============\n",
      "开始使用 Whisper-medium 转录音频。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████| 104500/104500 [01:10<00:00, 1480.45frames/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "语音识别过程耗时 75.73528480529785 秒。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "385it [00:00, 821784.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============== 正在将字幕保存到 ./output-信号与人生.srt ===============\n",
      "\n",
      "=============== 字幕保存完成 ===============\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 运行 ASR\n",
    "speech_recognition(model_name=model_name, input_audio=input_audio_array, output_subtitle_path=output_subtitle_path, decode_options=decode_options, cache_dir=cache_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1f09e8-baa0-4951-b91f-4c8c46161659",
   "metadata": {},
   "source": [
    "**检查结果**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a69d62f-cf79-454f-a941-f491e13f9405",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "00:00:00,000 --> 00:00:04,000\n",
      "每次说 学问是做出来的\n",
      "\n",
      "2\n",
      "00:00:06,000 --> 00:00:08,000\n",
      "什么意思\n",
      "\n",
      "3\n",
      "00:00:08,000 --> 00:00:12,000\n",
      "要做 才会获得学问\n",
      "\n",
      "4\n",
      "00:00:13,000 --> 00:00:16,000\n",
      "你如果每天光是坐在那里听\n",
      "\n",
      "5\n",
      "00:00:17,000 --> 00:00:20,000\n",
      "学问很可能是左耳近右耳出的\n",
      "\n",
      "6\n",
      "00:00:21,000 --> 00:00:23,000\n",
      "你光是坐在那儿读\n",
      "\n",
      "7\n",
      "00:00:23,000 --> 00:00:26,000\n",
      "学问可能从眼睛进入脑海之后就忘掉了\n",
      "\n",
      "8\n",
      "00:00:26,000 --> 00:00:29,000\n",
      "如何能够学问在脑海里面\n",
      "\n",
      "9\n",
      "00:00:31,000 --> 00:00:33,000\n",
      "真的变成你自己学问\n",
      "\n",
      "10\n",
      "00:00:33,000 --> 00:00:35,000\n",
      "就是要做\n",
      "\n",
      "11\n",
      "00:00:36,000 --> 00:00:39,000\n",
      "可能有很多同学有这个经验\n",
      "\n",
      "12\n",
      "00:00:39,000 --> 00:00:41,000\n",
      "你如果去修某一门课\n",
      "\n",
      "13\n",
      "00:00:41,000 --> 00:00:44,000\n",
      "或者做某一个实验\n",
      "\n",
      "14\n",
      "00:00:44,000 --> 00:00:47,000\n",
      "在期末就是要教一个final project\n",
      "\n",
      "15\n",
      "00:00:48,000 --> 00:00:50,000\n",
      "那个final project就是要你把\n",
      "\n",
      "16\n",
      "00:00:51,000 --> 00:00:53,000\n",
      "学到的很多东西\n",
      "\n",
      "17\n",
      "00:00:53,000 --> 00:00:56,000\n",
      "最后整合在你的final project里面\n",
      "\n",
      "18\n",
      "00:00:56,000 --> 00:00:58,000\n",
      "最后做出来的时候\n",
      "\n",
      "19\n",
      "00:00:58,000 --> 00:01:00,000\n",
      "就是把它们都整合了\n",
      "\n",
      "20\n",
      "00:01:00,000 --> 00:01:02,000\n",
      "当你学期结束\n",
      "\n",
      "21\n",
      "00:01:02,000 --> 00:01:04,000\n",
      "真的把final project做完的时候\n",
      "\n",
      "22\n",
      "00:01:04,000 --> 00:01:05,000\n",
      "你会忽然发现\n",
      "\n",
      "23\n",
      "00:01:05,000 --> 00:01:07,000\n",
      "我真的学到很多东西\n",
      "\n",
      "24\n",
      "00:01:07,000 --> 00:01:10,000\n",
      "那就是做出来的学问\n",
      "\n",
      "25\n",
      "00:01:11,000 --> 00:01:13,000\n",
      "也许可以举另外一个例子\n",
      "\n",
      "26\n",
      "00:01:13,000 --> 00:01:15,000\n",
      "就是你如果学了某一些\n",
      "\n",
      "27\n",
      "00:01:15,000 --> 00:01:17,000\n",
      "很复杂的演算法或者什么\n",
      "\n",
      "28\n",
      "00:01:17,000 --> 00:01:21,000\n",
      "好像觉得那些不见得在你的脑海里\n",
      "\n",
      "29\n",
      "00:01:21,000 --> 00:01:24,000\n",
      "可是后来老师出了个习题\n",
      "\n",
      "30\n",
      "00:01:24,000 --> 00:01:26,000\n",
      "那个习题叫你写一个很大的程式\n",
      "\n",
      "31\n",
      "00:01:26,000 --> 00:01:28,000\n",
      "要把所有东西都包进去\n",
      "\n",
      "32\n",
      "00:01:29,000 --> 00:01:31,000\n",
      "当你把这个程式写完的时候\n",
      "\n",
      "33\n",
      "00:01:31,000 --> 00:01:32,000\n",
      "你会发现\n",
      "\n",
      "34\n",
      "00:01:32,000 --> 00:01:34,000\n",
      "你忽然把演算法\n",
      "\n",
      "35\n",
      "00:01:34,000 --> 00:01:35,000\n",
      "所有东西都弄通了\n",
      "\n",
      "36\n",
      "00:01:35,000 --> 00:01:38,000\n",
      "那就是学问是做出来的\n",
      "\n",
      "37\n",
      "00:01:38,000 --> 00:01:40,000\n",
      "所以我们永远要记得\n",
      "\n",
      "38\n",
      "00:01:40,000 --> 00:01:44,000\n",
      "尽量多动手多做\n",
      "\n",
      "39\n",
      "00:01:44,000 --> 00:01:46,000\n",
      "在动手跟做的过程之中\n",
      "\n",
      "40\n",
      "00:01:46,000 --> 00:01:49,000\n",
      "学问才可以变成是自己的\n",
      "\n",
      "41\n",
      "00:01:49,000 --> 00:01:51,000\n",
      "同样的情形就是说\n",
      "\n",
      "42\n",
      "00:01:51,000 --> 00:01:54,000\n",
      "很多时候这样动手或者做的\n",
      "\n",
      "43\n",
      "00:01:56,000 --> 00:01:57,000\n",
      "表现或者成绩\n",
      "\n",
      "44\n",
      "00:01:57,000 --> 00:02:00,000\n",
      "没有一个成绩单上的数字\n",
      "\n",
      "45\n",
      "00:02:00,000 --> 00:02:03,000\n",
      "使得很多人觉得那不重要\n",
      "\n",
      "46\n",
      "00:02:03,000 --> 00:02:05,000\n",
      "很多人甚至觉得\n",
      "\n",
      "47\n",
      "00:02:05,000 --> 00:02:07,000\n",
      "这门课要做final project\n",
      "\n",
      "48\n",
      "00:02:07,000 --> 00:02:09,000\n",
      "我就不修了太累了\n",
      "\n",
      "49\n",
      "00:02:09,000 --> 00:02:11,000\n",
      "或者说那门课需要\n",
      "\n",
      "50\n",
      "00:02:11,000 --> 00:02:13,000\n",
      "怎么样太累我就不要做了\n",
      "\n",
      "51\n",
      "00:02:13,000 --> 00:02:14,000\n",
      "而不知道\n",
      "\n",
      "52\n",
      "00:02:14,000 --> 00:02:17,000\n",
      "其实那个才是让你做的机会\n",
      "\n",
      "53\n",
      "00:02:17,000 --> 00:02:19,000\n",
      "然后可以学到最多\n",
      "\n",
      "54\n",
      "00:02:19,000 --> 00:02:22,000\n",
      "也就是说虽然很可能\n",
      "\n",
      "55\n",
      "00:02:22,000 --> 00:02:24,000\n",
      "那么辛苦的做很多事\n",
      "\n",
      "56\n",
      "00:02:24,000 --> 00:02:28,000\n",
      "没有让你获得什么具体成绩\n",
      "\n",
      "57\n",
      "00:02:28,000 --> 00:02:30,000\n",
      "对你的overfitting可能没有帮助\n",
      "\n",
      "58\n",
      "00:02:30,000 --> 00:02:32,000\n",
      "可是对你的全面学习\n",
      "\n",
      "59\n",
      "00:02:32,000 --> 00:02:35,000\n",
      "是很有帮助是该学的\n",
      "\n",
      "60\n",
      "00:02:35,000 --> 00:02:38,000\n",
      "不要漏掉这些事\n",
      "\n",
      "61\n",
      "00:02:38,000 --> 00:02:41,000\n",
      "这是我所说的\n",
      "\n",
      "62\n",
      "00:02:41,000 --> 00:02:46,000\n",
      "这个课业内可以做的这些事\n",
      "\n",
      "63\n",
      "00:02:46,000 --> 00:02:50,000\n",
      "刚才我们讲到思考的时候\n",
      "\n",
      "64\n",
      "00:02:50,000 --> 00:02:52,000\n",
      "我觉得我漏掉一点\n",
      "\n",
      "65\n",
      "00:02:52,000 --> 00:02:55,000\n",
      "你如果修我的信号课\n",
      "\n",
      "66\n",
      "00:02:55,000 --> 00:02:56,000\n",
      "你可能会发现\n",
      "\n",
      "67\n",
      "00:02:56,000 --> 00:03:00,000\n",
      "我上课没讲到一个数学试字的时候\n",
      "\n",
      "68\n",
      "00:03:00,000 --> 00:03:02,000\n",
      "我通常都不推它的\n",
      "\n",
      "69\n",
      "00:03:02,000 --> 00:03:06,000\n",
      "我是在解释那个数学试字在说什么话\n",
      "\n",
      "70\n",
      "00:03:06,000 --> 00:03:10,000\n",
      "同样的没讲到一个什么事情的时候\n",
      "\n",
      "71\n",
      "00:03:10,000 --> 00:03:13,000\n",
      "我通常就在解释他在说什么话\n",
      "\n",
      "72\n",
      "00:03:13,000 --> 00:03:17,000\n",
      "也就是说我在讲的就是\n",
      "\n",
      "73\n",
      "00:03:17,000 --> 00:03:20,000\n",
      "我读到特本那里的时候\n",
      "\n",
      "74\n",
      "00:03:20,000 --> 00:03:21,000\n",
      "我心里怎么想的\n",
      "\n",
      "75\n",
      "00:03:21,000 --> 00:03:24,000\n",
      "也就是我在告诉同学\n",
      "\n",
      "76\n",
      "00:03:24,000 --> 00:03:28,000\n",
      "如何读书的时候\n",
      "\n",
      "77\n",
      "00:03:28,000 --> 00:03:31,000\n",
      "如何一面读一面练习思考\n",
      "\n",
      "78\n",
      "00:03:31,000 --> 00:03:35,000\n",
      "这个才是最重要的一件事\n",
      "\n",
      "79\n",
      "00:03:35,000 --> 00:03:40,000\n",
      "如何培养自己思考的能力\n",
      "\n",
      "80\n",
      "00:03:40,000 --> 00:03:42,000\n",
      "跟培养思考的习惯\n",
      "\n",
      "81\n",
      "00:03:42,000 --> 00:03:45,000\n",
      "我觉得最好的办法就是\n",
      "\n",
      "82\n",
      "00:03:45,000 --> 00:03:46,000\n",
      "读书的时候\n",
      "\n",
      "83\n",
      "00:03:46,000 --> 00:03:49,000\n",
      "凡是读到一个数学试字\n",
      "\n",
      "84\n",
      "00:03:49,000 --> 00:03:50,000\n",
      "都去想一想\n",
      "\n",
      "85\n",
      "00:03:50,000 --> 00:03:53,000\n",
      "那个数学试字到底在说什么\n",
      "\n",
      "86\n",
      "00:03:53,000 --> 00:03:56,000\n",
      "凡是读到特本上讲什么\n",
      "\n",
      "87\n",
      "00:03:56,000 --> 00:03:57,000\n",
      "就去想一想\n",
      "\n",
      "88\n",
      "00:03:57,000 --> 00:04:00,000\n",
      "那个到底在说什么\n",
      "\n",
      "89\n",
      "00:04:00,000 --> 00:04:03,000\n",
      "你要真的了解他在说什么的时候\n",
      "\n",
      "90\n",
      "00:04:03,000 --> 00:04:06,000\n",
      "你就用了很多思考的功夫\n",
      "\n",
      "91\n",
      "00:04:06,000 --> 00:04:10,000\n",
      "你就在练习自己思考的能力了\n",
      "\n",
      "92\n",
      "00:04:10,000 --> 00:04:15,000\n",
      "好 以上说的是课业内的部分\n",
      "\n",
      "93\n",
      "00:04:15,000 --> 00:04:18,000\n",
      "那当然除了课业内之外\n",
      "\n",
      "94\n",
      "00:04:18,000 --> 00:04:21,000\n",
      "还有一大堆是不在课业内的\n",
      "\n",
      "95\n",
      "00:04:21,000 --> 00:04:25,000\n",
      "那就是课业外的\n",
      "\n",
      "96\n",
      "00:04:25,000 --> 00:04:27,000\n",
      "课业外也有很多式的\n",
      "\n",
      "97\n",
      "00:04:27,000 --> 00:04:33,000\n",
      "那我们可以举例来说\n",
      "\n",
      "98\n",
      "00:04:39,000 --> 00:04:41,000\n",
      "课业外有什么可以学习的\n",
      "\n",
      "99\n",
      "00:04:41,000 --> 00:04:45,000\n",
      "那我通常把学习定义成为\n",
      "\n",
      "100\n",
      "00:04:45,000 --> 00:04:47,000\n",
      "什么是学习\n",
      "\n",
      "101\n",
      "00:04:47,000 --> 00:04:50,000\n",
      "学习就是一种增长\n",
      "\n",
      "102\n",
      "00:04:53,000 --> 00:04:56,000\n",
      "一种进步\n",
      "\n",
      "103\n",
      "00:04:57,000 --> 00:05:01,000\n",
      "然后获得快乐\n",
      "\n",
      "104\n",
      "00:05:01,000 --> 00:05:03,000\n",
      "这就是学习\n",
      "\n",
      "105\n",
      "00:05:03,000 --> 00:05:07,000\n",
      "所以即使是课业外的任何事情\n",
      "\n",
      "106\n",
      "00:05:07,000 --> 00:05:09,000\n",
      "只要你觉得是有增长的\n",
      "\n",
      "107\n",
      "00:05:09,000 --> 00:05:11,000\n",
      "是有进步的\n",
      "\n",
      "108\n",
      "00:05:11,000 --> 00:05:12,000\n",
      "让你觉得快乐的\n",
      "\n",
      "109\n",
      "00:05:12,000 --> 00:05:16,000\n",
      "那应该就是值得学习的地方\n",
      "\n",
      "110\n",
      "00:05:16,000 --> 00:05:19,000\n",
      "那我们可以举很多例子\n",
      "\n",
      "111\n",
      "00:05:19,000 --> 00:05:21,000\n",
      "那譬如说\n",
      "\n",
      "112\n",
      "00:05:21,000 --> 00:05:23,000\n",
      "很多同学喜欢打球\n",
      "\n",
      "113\n",
      "00:05:23,000 --> 00:05:26,000\n",
      "打球是不是学习\n",
      "\n",
      "114\n",
      "00:05:26,000 --> 00:05:28,000\n",
      "当然是\n",
      "\n",
      "115\n",
      "00:05:28,000 --> 00:05:30,000\n",
      "打球中间有没有增长\n",
      "\n",
      "116\n",
      "00:05:30,000 --> 00:05:32,000\n",
      "当然有增长\n",
      "\n",
      "117\n",
      "00:05:32,000 --> 00:05:35,000\n",
      "打球不只是对健康有增长\n",
      "\n",
      "118\n",
      "00:05:35,000 --> 00:05:37,000\n",
      "而且可能对于\n",
      "\n",
      "119\n",
      "00:05:37,000 --> 00:05:39,000\n",
      "比如说手脑协调\n",
      "\n",
      "120\n",
      "00:05:39,000 --> 00:05:41,000\n",
      "譬如说团队精神\n",
      "\n",
      "121\n",
      "00:05:41,000 --> 00:05:44,000\n",
      "譬如说个人之间的互动\n",
      "\n",
      "122\n",
      "00:05:44,000 --> 00:05:46,000\n",
      "可能都有帮助\n",
      "\n",
      "123\n",
      "00:05:46,000 --> 00:05:48,000\n",
      "所以打球当然是有增长的\n",
      "\n",
      "124\n",
      "00:05:48,000 --> 00:05:50,000\n",
      "那当然是很好的学习的机会\n",
      "\n",
      "125\n",
      "00:05:50,000 --> 00:05:52,000\n",
      "有人喜欢爬山\n",
      "\n",
      "126\n",
      "00:05:52,000 --> 00:05:54,000\n",
      "爬山是不是好的学习机会\n",
      "\n",
      "127\n",
      "00:05:54,000 --> 00:05:55,000\n",
      "当然是\n",
      "\n",
      "128\n",
      "00:05:55,000 --> 00:05:57,000\n",
      "这个我以前两年前就讲过很多\n",
      "\n",
      "129\n",
      "00:05:57,000 --> 00:05:59,000\n",
      "爬山可以学到很多的\n",
      "\n",
      "130\n",
      "00:05:59,000 --> 00:06:02,000\n",
      "那爬山当然是一种学习\n",
      "\n",
      "131\n",
      "00:06:02,000 --> 00:06:04,000\n",
      "有人说我不喜欢爬山\n",
      "\n",
      "132\n",
      "00:06:04,000 --> 00:06:05,000\n",
      "我去旅行好不好\n",
      "\n",
      "133\n",
      "00:06:05,000 --> 00:06:07,000\n",
      "旅行当然好\n",
      "\n",
      "134\n",
      "00:06:07,000 --> 00:06:09,000\n",
      "旅行可以增长建设\n",
      "\n",
      "135\n",
      "00:06:09,000 --> 00:06:11,000\n",
      "可以扩增事业\n",
      "\n",
      "136\n",
      "00:06:11,000 --> 00:06:13,000\n",
      "可以增加很多很多\n",
      "\n",
      "137\n",
      "00:06:13,000 --> 00:06:14,000\n",
      "当然是有进步的\n",
      "\n",
      "138\n",
      "00:06:14,000 --> 00:06:16,000\n",
      "所以当然是很好的学习\n",
      "\n",
      "139\n",
      "00:06:16,000 --> 00:06:18,000\n",
      "你凡是获得快乐\n",
      "\n",
      "140\n",
      "00:06:18,000 --> 00:06:20,000\n",
      "都是很好的事\n",
      "\n",
      "141\n",
      "00:06:20,000 --> 00:06:23,000\n",
      "那这些都值得下功夫去\n",
      "\n",
      "142\n",
      "00:06:23,000 --> 00:06:25,000\n",
      "把它看成是学习\n",
      "\n",
      "143\n",
      "00:06:25,000 --> 00:06:27,000\n",
      "都值得下功夫去做的\n",
      "\n",
      "144\n",
      "00:06:27,000 --> 00:06:29,000\n",
      "我们再讲另外一系列\n",
      "\n",
      "145\n",
      "00:06:29,000 --> 00:06:30,000\n",
      "譬如说\n",
      "\n",
      "146\n",
      "00:06:30,000 --> 00:06:34,000\n",
      "有人说谈恋爱是不是学习\n",
      "\n",
      "147\n",
      "00:06:34,000 --> 00:06:37,000\n",
      "谈恋爱除了你在谈恋爱上\n",
      "\n",
      "148\n",
      "00:06:37,000 --> 00:06:39,000\n",
      "会有收获以外\n",
      "\n",
      "149\n",
      "00:06:39,000 --> 00:06:41,000\n",
      "本身也是有收获的\n",
      "\n",
      "150\n",
      "00:06:41,000 --> 00:06:44,000\n",
      "因为让你体验到人跟人之间\n",
      "\n",
      "151\n",
      "00:06:44,000 --> 00:06:45,000\n",
      "的各种感觉\n",
      "\n",
      "152\n",
      "00:06:45,000 --> 00:06:48,000\n",
      "人跟人之间的各种期待等等\n",
      "\n",
      "153\n",
      "00:06:48,000 --> 00:06:50,000\n",
      "有没有帮助\n",
      "\n",
      "154\n",
      "00:06:50,000 --> 00:06:52,000\n",
      "当然有帮助\n",
      "\n",
      "155\n",
      "00:06:52,000 --> 00:06:54,000\n",
      "对每一个人都是很好的学习\n",
      "\n",
      "156\n",
      "00:06:54,000 --> 00:06:57,000\n",
      "所以谈恋爱当然是一件很好的事\n",
      "\n",
      "157\n",
      "00:06:57,000 --> 00:07:00,000\n",
      "有人会说那要靠缘分\n",
      "\n",
      "158\n",
      "00:07:00,000 --> 00:07:02,000\n",
      "没有缘分没有办法\n",
      "\n",
      "159\n",
      "00:07:02,000 --> 00:07:03,000\n",
      "对不对\n",
      "\n",
      "160\n",
      "00:07:03,000 --> 00:07:06,000\n",
      "但是你不是一定要谈恋爱吗\n",
      "\n",
      "161\n",
      "00:07:06,000 --> 00:07:07,000\n",
      "你可以交朋友\n",
      "\n",
      "162\n",
      "00:07:07,000 --> 00:07:10,000\n",
      "交朋友是不是学习\n",
      "\n",
      "163\n",
      "00:07:10,000 --> 00:07:11,000\n",
      "当然是\n",
      "\n",
      "164\n",
      "00:07:11,000 --> 00:07:13,000\n",
      "交朋友也一样\n",
      "\n",
      "165\n",
      "00:07:13,000 --> 00:07:16,000\n",
      "让我们学到很多人际的互动\n",
      "\n",
      "166\n",
      "00:07:16,000 --> 00:07:20,000\n",
      "学到很多人跟人之间的沟通\n",
      "\n",
      "167\n",
      "00:07:20,000 --> 00:07:22,000\n",
      "人跟人之间的期待\n",
      "\n",
      "168\n",
      "00:07:22,000 --> 00:07:24,000\n",
      "人跟人之间的感觉\n",
      "\n",
      "169\n",
      "00:07:24,000 --> 00:07:26,000\n",
      "这都是交朋友之后学到的\n",
      "\n",
      "170\n",
      "00:07:26,000 --> 00:07:28,000\n",
      "对我们电机系的同学而言\n",
      "\n",
      "171\n",
      "00:07:28,000 --> 00:07:31,000\n",
      "你四周有一大群好同学\n",
      "\n",
      "172\n",
      "00:07:31,000 --> 00:07:34,000\n",
      "都是很好的交朋友的对象\n",
      "\n",
      "173\n",
      "00:07:34,000 --> 00:07:36,000\n",
      "你下一番功夫交朋友好不好\n",
      "\n",
      "174\n",
      "00:07:36,000 --> 00:07:37,000\n",
      "好\n",
      "\n",
      "175\n",
      "00:07:37,000 --> 00:07:40,000\n",
      "当然是有帮助的\n",
      "\n",
      "176\n",
      "00:07:40,000 --> 00:07:42,000\n",
      "另外当然我们可以举很多\n",
      "\n",
      "177\n",
      "00:07:42,000 --> 00:07:44,000\n",
      "我们最现成的例子\n",
      "\n",
      "178\n",
      "00:07:44,000 --> 00:07:47,000\n",
      "譬如说我们的系学会办各种活动\n",
      "\n",
      "179\n",
      "00:07:47,000 --> 00:07:49,000\n",
      "那些活动有没有帮助\n",
      "\n",
      "180\n",
      "00:07:49,000 --> 00:07:50,000\n",
      "当然有\n",
      "\n",
      "181\n",
      "00:07:50,000 --> 00:07:54,000\n",
      "我们举例来讲电业\n",
      "\n",
      "182\n",
      "00:07:54,000 --> 00:07:57,000\n",
      "你如果去参加某一个舞跳个舞\n",
      "\n",
      "183\n",
      "00:07:57,000 --> 00:08:00,000\n",
      "或者参加某个剧演个剧\n",
      "\n",
      "184\n",
      "00:08:00,000 --> 00:08:01,000\n",
      "有没有帮助\n",
      "\n",
      "185\n",
      "00:08:01,000 --> 00:08:02,000\n",
      "当然有帮助\n",
      "\n",
      "186\n",
      "00:08:02,000 --> 00:08:05,000\n",
      "你在这中间一定发现有所增长\n",
      "\n",
      "187\n",
      "00:08:05,000 --> 00:08:06,000\n",
      "有所进步\n",
      "\n",
      "188\n",
      "00:08:06,000 --> 00:08:08,000\n",
      "那是为什么有那么多同学\n",
      "\n",
      "189\n",
      "00:08:08,000 --> 00:08:09,000\n",
      "要去参加\n",
      "\n",
      "190\n",
      "00:08:09,000 --> 00:08:11,000\n",
      "就是因为发现那个确实是有增长\n",
      "\n",
      "191\n",
      "00:08:11,000 --> 00:08:13,000\n",
      "有进步\n",
      "\n",
      "192\n",
      "00:08:13,000 --> 00:08:15,000\n",
      "有的人说\n",
      "\n",
      "193\n",
      "00:08:15,000 --> 00:08:17,000\n",
      "我不去跳那个舞\n",
      "\n",
      "194\n",
      "00:08:17,000 --> 00:08:20,000\n",
      "或者演那个剧\n",
      "\n",
      "195\n",
      "00:08:20,000 --> 00:08:22,000\n",
      "我做幕后的\n",
      "\n",
      "196\n",
      "00:08:22,000 --> 00:08:25,000\n",
      "譬如说是幕后的什么什么规划\n",
      "\n",
      "197\n",
      "00:08:25,000 --> 00:08:29,000\n",
      "或者说是什么这个光舞的什么软体组\n",
      "\n",
      "198\n",
      "00:08:29,000 --> 00:08:32,000\n",
      "还是什么服装道具组\n",
      "\n",
      "199\n",
      "00:08:32,000 --> 00:08:33,000\n",
      "一样\n",
      "\n",
      "200\n",
      "00:08:33,000 --> 00:08:36,000\n",
      "那个都是可以获得很多的增长\n",
      "\n",
      "201\n",
      "00:08:36,000 --> 00:08:37,000\n",
      "很多进步的\n",
      "\n",
      "202\n",
      "00:08:37,000 --> 00:08:39,000\n",
      "当然都是很有用的\n",
      "\n",
      "203\n",
      "00:08:39,000 --> 00:08:42,000\n",
      "都是很好的学习\n",
      "\n",
      "204\n",
      "00:08:42,000 --> 00:08:47,000\n",
      "那当然也包括电业以外的戏学会\n",
      "\n",
      "205\n",
      "00:08:47,000 --> 00:08:50,000\n",
      "其他的各种活动都一样\n",
      "\n",
      "206\n",
      "00:08:50,000 --> 00:08:55,000\n",
      "也包括电机系以外的其他的校内\n",
      "\n",
      "207\n",
      "00:08:55,000 --> 00:08:59,000\n",
      "或者校外的各种活动几乎都一样\n",
      "\n",
      "208\n",
      "00:08:59,000 --> 00:09:01,000\n",
      "都可以让人有所增长\n",
      "\n",
      "209\n",
      "00:09:01,000 --> 00:09:02,000\n",
      "有所进步\n",
      "\n",
      "210\n",
      "00:09:02,000 --> 00:09:04,000\n",
      "都是很好的学习的机会\n",
      "\n",
      "211\n",
      "00:09:04,000 --> 00:09:06,000\n",
      "都是很好的学习\n",
      "\n",
      "212\n",
      "00:09:06,000 --> 00:09:08,000\n",
      "同样的问题是\n",
      "\n",
      "213\n",
      "00:09:08,000 --> 00:09:11,000\n",
      "这些东西都没有考试\n",
      "\n",
      "214\n",
      "00:09:11,000 --> 00:09:12,000\n",
      "没有成绩\n",
      "\n",
      "215\n",
      "00:09:12,000 --> 00:09:14,000\n",
      "不能显示在成绩单上\n",
      "\n",
      "216\n",
      "00:09:14,000 --> 00:09:18,000\n",
      "因此对有一些同学会认为那个浪费时间\n",
      "\n",
      "217\n",
      "00:09:18,000 --> 00:09:20,000\n",
      "我不需要花时间去做那个\n",
      "\n",
      "218\n",
      "00:09:20,000 --> 00:09:22,000\n",
      "因为不影响我的\n",
      "\n",
      "219\n",
      "00:09:22,000 --> 00:09:24,000\n",
      "overfitting的目标\n",
      "\n",
      "220\n",
      "00:09:24,000 --> 00:09:26,000\n",
      "里面没有这个嘛\n",
      "\n",
      "221\n",
      "00:09:26,000 --> 00:09:29,000\n",
      "具体成绩没有这些嘛\n",
      "\n",
      "222\n",
      "00:09:29,000 --> 00:09:30,000\n",
      "那不要这样想\n",
      "\n",
      "223\n",
      "00:09:30,000 --> 00:09:33,000\n",
      "因为那些都非常的重要\n",
      "\n",
      "224\n",
      "00:09:33,000 --> 00:09:36,000\n",
      "都对你发展非常的重要\n",
      "\n",
      "225\n",
      "00:09:36,000 --> 00:09:39,000\n",
      "那我们说电机工程\n",
      "\n",
      "226\n",
      "00:09:39,000 --> 00:09:41,000\n",
      "今天的电机工程\n",
      "\n",
      "227\n",
      "00:09:41,000 --> 00:09:44,000\n",
      "很少什么事情自己一个人可以做成功的\n",
      "\n",
      "228\n",
      "00:09:44,000 --> 00:09:47,000\n",
      "你必须跟很多人一起\n",
      "\n",
      "229\n",
      "00:09:47,000 --> 00:09:50,000\n",
      "才可能做成功一个非常重要的\n",
      "\n",
      "230\n",
      "00:09:50,000 --> 00:09:52,000\n",
      "有意义的工作\n",
      "\n",
      "231\n",
      "00:09:52,000 --> 00:09:55,000\n",
      "那当你跟一群人在一起做的时候\n",
      "\n",
      "232\n",
      "00:09:55,000 --> 00:09:59,000\n",
      "你必须学会如何进入一个团队\n",
      "\n",
      "233\n",
      "00:09:59,000 --> 00:10:03,000\n",
      "从边缘开始慢慢进入核心\n",
      "\n",
      "234\n",
      "00:10:03,000 --> 00:10:06,000\n",
      "从底层开始慢慢变成leader\n",
      "\n",
      "235\n",
      "00:10:06,000 --> 00:10:10,000\n",
      "然后如何可以推动你想做的事\n",
      "\n",
      "236\n",
      "00:10:10,000 --> 00:10:14,000\n",
      "如何变成可以做到你想做的事等等\n",
      "\n",
      "237\n",
      "00:10:14,000 --> 00:10:16,000\n",
      "这些都是很重要的\n",
      "\n",
      "238\n",
      "00:10:16,000 --> 00:10:18,000\n",
      "那我们通常称这些东西\n",
      "\n",
      "239\n",
      "00:10:18,000 --> 00:10:21,000\n",
      "是所谓的soft skills\n",
      "\n",
      "240\n",
      "00:10:21,000 --> 00:10:25,000\n",
      "也就是软实力\n",
      "\n",
      "241\n",
      "00:10:25,000 --> 00:10:31,000\n",
      "所谓软实力就是硬实力以外的软实力\n",
      "\n",
      "242\n",
      "00:10:31,000 --> 00:10:34,000\n",
      "那硬实力是说你电子学的功力\n",
      "\n",
      "243\n",
      "00:10:34,000 --> 00:10:36,000\n",
      "数学的功力\n",
      "\n",
      "244\n",
      "00:10:36,000 --> 00:10:40,000\n",
      "这个城市能力这种是硬实力\n",
      "\n",
      "245\n",
      "00:10:40,000 --> 00:10:45,000\n",
      "软实力我们主要就是讲各种人际之间的\n",
      "\n",
      "246\n",
      "00:10:45,000 --> 00:10:49,000\n",
      "在人跟人之间的各种能力\n",
      "\n",
      "247\n",
      "00:10:49,000 --> 00:10:52,000\n",
      "包括沟通能力协调能力\n",
      "\n",
      "248\n",
      "00:10:52,000 --> 00:10:53,000\n",
      "交朋友的能力\n",
      "\n",
      "249\n",
      "00:10:53,000 --> 00:10:55,000\n",
      "这个说服人的能力\n",
      "\n",
      "250\n",
      "00:10:55,000 --> 00:10:59,000\n",
      "这个团队精神领导能力等等\n",
      "\n",
      "251\n",
      "00:10:59,000 --> 00:11:02,000\n",
      "那那些就是所谓的soft skills\n",
      "\n",
      "252\n",
      "00:11:02,000 --> 00:11:03,000\n",
      "重要不重要\n",
      "\n",
      "253\n",
      "00:11:03,000 --> 00:11:04,000\n",
      "重要\n",
      "\n",
      "254\n",
      "00:11:04,000 --> 00:11:07,000\n",
      "你看到任何一个成功的电机工程师\n",
      "\n",
      "255\n",
      "00:11:07,000 --> 00:11:09,000\n",
      "他都有一堆这种\n",
      "\n",
      "256\n",
      "00:11:09,000 --> 00:11:12,000\n",
      "这个才是他成功的一个非常重要的关键\n",
      "\n",
      "257\n",
      "00:11:12,000 --> 00:11:15,000\n",
      "这种东西怎么来\n",
      "\n",
      "258\n",
      "00:11:15,000 --> 00:11:20,000\n",
      "我们刚才讲的各种课业外的各种学习增长机会\n",
      "\n",
      "259\n",
      "00:11:20,000 --> 00:11:26,000\n",
      "都可以帮助一个人塑造他的soft skills\n",
      "\n",
      "260\n",
      "00:11:26,000 --> 00:11:30,000\n",
      "是有少数人的这些soft skills是天生的\n",
      "\n",
      "261\n",
      "00:11:30,000 --> 00:11:31,000\n",
      "他天生就厉害\n",
      "\n",
      "262\n",
      "00:11:31,000 --> 00:11:32,000\n",
      "有没有\n",
      "\n",
      "263\n",
      "00:11:32,000 --> 00:11:33,000\n",
      "有\n",
      "\n",
      "264\n",
      "00:11:33,000 --> 00:11:35,000\n",
      "但这种人毕竟没那么多\n",
      "\n",
      "265\n",
      "00:11:35,000 --> 00:11:37,000\n",
      "对很多人而言\n",
      "\n",
      "266\n",
      "00:11:37,000 --> 00:11:42,000\n",
      "他的soft skills是自己努力慢慢培养起来的\n",
      "\n",
      "267\n",
      "00:11:42,000 --> 00:11:45,000\n",
      "我刚才一开始前面讲的那一段\n",
      "\n",
      "268\n",
      "00:11:45,000 --> 00:11:49,000\n",
      "我说我在这个进台大电机系以前\n",
      "\n",
      "269\n",
      "00:11:49,000 --> 00:11:51,000\n",
      "我几乎不会交朋友\n",
      "\n",
      "270\n",
      "00:11:51,000 --> 00:11:53,000\n",
      "我不太会说话\n",
      "\n",
      "271\n",
      "00:11:53,000 --> 00:11:57,000\n",
      "我在读大学的四年里面改变我自己\n",
      "\n",
      "272\n",
      "00:11:57,000 --> 00:12:02,000\n",
      "让我变成有很多这方面的能力的人\n",
      "\n",
      "273\n",
      "00:12:02,000 --> 00:12:06,000\n",
      "其实最重要就是我的很多soft skills\n",
      "\n",
      "274\n",
      "00:12:06,000 --> 00:12:07,000\n",
      "都是我自己培养\n",
      "\n",
      "275\n",
      "00:12:07,000 --> 00:12:10,000\n",
      "在读台大电机系的四年里面\n",
      "\n",
      "276\n",
      "00:12:10,000 --> 00:12:15,000\n",
      "获得的非常多这方面的收获的\n",
      "\n",
      "277\n",
      "00:12:15,000 --> 00:12:21,000\n",
      "那是为什么我每次都要强调这个东西有多么重要\n",
      "\n",
      "278\n",
      "00:12:21,000 --> 00:12:29,000\n",
      "那我之前曾经在几年前的这个信号与人生里面\n",
      "\n",
      "279\n",
      "00:12:29,000 --> 00:12:31,000\n",
      "有说到这一件事\n",
      "\n",
      "280\n",
      "00:12:31,000 --> 00:12:33,000\n",
      "我现在不要重复\n",
      "\n",
      "281\n",
      "00:12:33,000 --> 00:12:36,000\n",
      "但是我简单的summarize\n",
      "\n",
      "282\n",
      "00:12:36,000 --> 00:12:39,000\n",
      "那我说我们电机系的\n",
      "\n",
      "283\n",
      "00:12:39,000 --> 00:12:45,000\n",
      "电机工程师的一生career的发展\n",
      "\n",
      "284\n",
      "00:12:45,000 --> 00:12:48,000\n",
      "那黄金实在是在什么时候\n",
      "\n",
      "285\n",
      "00:12:48,000 --> 00:12:52,000\n",
      "我认为是在35岁到55岁\n",
      "\n",
      "286\n",
      "00:12:52,000 --> 00:12:56,000\n",
      "这20年是我们的黄金时代\n",
      "\n",
      "287\n",
      "00:12:56,000 --> 00:12:58,000\n",
      "在这以前当然更好\n",
      "\n",
      "288\n",
      "00:12:58,000 --> 00:13:01,000\n",
      "只是说可能各方面尚未具备\n",
      "\n",
      "289\n",
      "00:13:01,000 --> 00:13:03,000\n",
      "还没有完全训练的好\n",
      "\n",
      "290\n",
      "00:13:03,000 --> 00:13:05,000\n",
      "在这以后是最好的\n",
      "\n",
      "291\n",
      "00:13:05,000 --> 00:13:10,000\n",
      "这以后年纪大了难免有一些要打个折扣等等\n",
      "\n",
      "292\n",
      "00:13:10,000 --> 00:13:15,000\n",
      "就这里面我们看到我们的电机系的毕业的同学\n",
      "\n",
      "293\n",
      "00:13:15,000 --> 00:13:18,000\n",
      "过去有几千人毕业我都看到\n",
      "\n",
      "294\n",
      "00:13:18,000 --> 00:13:22,000\n",
      "那我觉得有的人的发展是像这样\n",
      "\n",
      "295\n",
      "00:13:22,000 --> 00:13:24,000\n",
      "有一定的斜率\n",
      "\n",
      "296\n",
      "00:13:24,000 --> 00:13:28,000\n",
      "但到某一个阶段它会慢慢saturate\n",
      "\n",
      "297\n",
      "00:13:28,000 --> 00:13:31,000\n",
      "有的人也许开始向上比较晚\n",
      "\n",
      "298\n",
      "00:13:31,000 --> 00:13:33,000\n",
      "但它斜率比较高\n",
      "\n",
      "299\n",
      "00:13:33,000 --> 00:13:38,000\n",
      "它最后会saturate在比较高的地方\n",
      "\n",
      "300\n",
      "00:13:38,000 --> 00:13:41,000\n",
      "也有的人也许开始的比较快\n",
      "\n",
      "301\n",
      "00:13:41,000 --> 00:13:46,000\n",
      "但是后来会overshoot之后收敛在比较低的地方等等\n",
      "\n",
      "302\n",
      "00:13:46,000 --> 00:13:47,000\n",
      "这个每一个人都不一样\n",
      "\n",
      "303\n",
      "00:13:47,000 --> 00:13:49,000\n",
      "但是当然也有一种人\n",
      "\n",
      "304\n",
      "00:13:49,000 --> 00:13:53,000\n",
      "你会看到它一直向上走\n",
      "\n",
      "305\n",
      "00:13:53,000 --> 00:13:57,000\n",
      "完全没有saturate\n",
      "\n",
      "306\n",
      "00:13:57,000 --> 00:14:00,000\n",
      "这些人这些差别在哪里\n",
      "\n",
      "307\n",
      "00:14:00,000 --> 00:14:03,000\n",
      "这些东西差别在哪里\n",
      "\n",
      "308\n",
      "00:14:03,000 --> 00:14:05,000\n",
      "我以前已经说过这件事\n",
      "\n",
      "309\n",
      "00:14:05,000 --> 00:14:07,000\n",
      "我不要多重复\n",
      "\n",
      "310\n",
      "00:14:07,000 --> 00:14:10,000\n",
      "我说最主要因素有四个\n",
      "\n",
      "311\n",
      "00:14:10,000 --> 00:14:12,000\n",
      "就是实力\n",
      "\n",
      "312\n",
      "00:14:12,000 --> 00:14:16,000\n",
      "努力\n",
      "\n",
      "313\n",
      "00:14:16,000 --> 00:14:20,000\n",
      "大致\n",
      "\n",
      "314\n",
      "00:14:20,000 --> 00:14:23,000\n",
      "跟self skill这四件事情\n",
      "\n",
      "315\n",
      "00:14:23,000 --> 00:14:26,000\n",
      "那我认为\n",
      "\n",
      "316\n",
      "00:14:26,000 --> 00:14:29,000\n",
      "真正影响这个的\n",
      "\n",
      "317\n",
      "00:14:29,000 --> 00:14:32,000\n",
      "不是因为电子学考得好不好\n",
      "\n",
      "318\n",
      "00:14:32,000 --> 00:14:35,000\n",
      "不是因为信号与系统念得好不好\n",
      "\n",
      "319\n",
      "00:14:35,000 --> 00:14:37,000\n",
      "也就我刚才讲\n",
      "\n",
      "320\n",
      "00:14:37,000 --> 00:14:41,000\n",
      "你把每一门必修课当成是单一跑道\n",
      "\n",
      "321\n",
      "00:14:41,000 --> 00:14:43,000\n",
      "跑到第一名并不表示怎样\n",
      "\n",
      "322\n",
      "00:14:43,000 --> 00:14:45,000\n",
      "我们最后不看那个的\n",
      "\n",
      "323\n",
      "00:14:45,000 --> 00:14:47,000\n",
      "最后看的是这个\n",
      "\n",
      "324\n",
      "00:14:47,000 --> 00:14:49,000\n",
      "那这个是怎么样影响\n",
      "\n",
      "325\n",
      "00:14:49,000 --> 00:14:51,000\n",
      "我认为是这四件事\n",
      "\n",
      "326\n",
      "00:14:51,000 --> 00:14:53,000\n",
      "就是实力努力\n",
      "\n",
      "327\n",
      "00:14:53,000 --> 00:14:56,000\n",
      "大致跟self skills\n",
      "\n",
      "328\n",
      "00:14:56,000 --> 00:14:59,000\n",
      "这四件事里面我们现在可以summarize\n",
      "\n",
      "329\n",
      "00:14:59,000 --> 00:15:01,000\n",
      "我刚才讲的\n",
      "\n",
      "330\n",
      "00:15:01,000 --> 00:15:03,000\n",
      "什么是实力\n",
      "\n",
      "331\n",
      "00:15:03,000 --> 00:15:06,000\n",
      "实力就是所有的这些我们电机工程的\n",
      "\n",
      "332\n",
      "00:15:06,000 --> 00:15:09,000\n",
      "专业领域里面的各种东西的实力\n",
      "\n",
      "333\n",
      "00:15:09,000 --> 00:15:11,000\n",
      "实力怎么厉害法\n",
      "\n",
      "334\n",
      "00:15:11,000 --> 00:15:13,000\n",
      "就是我刚才讲的\n",
      "\n",
      "335\n",
      "00:15:13,000 --> 00:15:16,000\n",
      "你如果都是在做全面的学习的话\n",
      "\n",
      "336\n",
      "00:15:16,000 --> 00:15:18,000\n",
      "你就会学到各种该学到的\n",
      "\n",
      "337\n",
      "00:15:18,000 --> 00:15:20,000\n",
      "最后你的实力就是很强的\n",
      "\n",
      "338\n",
      "00:15:20,000 --> 00:15:25,000\n",
      "所以实力最主要就是不要overfitting\n",
      "\n",
      "339\n",
      "00:15:25,000 --> 00:15:31,000\n",
      "要尽量都做学到该学的全面的学习\n",
      "\n",
      "340\n",
      "00:15:31,000 --> 00:15:33,000\n",
      "努力是没有疑问\n",
      "\n",
      "341\n",
      "00:15:33,000 --> 00:15:34,000\n",
      "每个人都了解\n",
      "\n",
      "342\n",
      "00:15:34,000 --> 00:15:38,000\n",
      "确实我们可以看到一个人在未来的几十年里面\n",
      "\n",
      "343\n",
      "00:15:38,000 --> 00:15:40,000\n",
      "有的人他一直努力\n",
      "\n",
      "344\n",
      "00:15:40,000 --> 00:15:42,000\n",
      "有的人慢慢不太努力等等\n",
      "\n",
      "345\n",
      "00:15:42,000 --> 00:15:44,000\n",
      "这个是有明显差别的\n",
      "\n",
      "346\n",
      "00:15:44,000 --> 00:15:46,000\n",
      "那self skills我刚才已经讲了\n",
      "\n",
      "347\n",
      "00:15:46,000 --> 00:15:50,000\n",
      "就是很多我们平常没有算成绩\n",
      "\n",
      "348\n",
      "00:15:50,000 --> 00:15:52,000\n",
      "觉得大家不重视的事情\n",
      "\n",
      "349\n",
      "00:15:52,000 --> 00:15:54,000\n",
      "那其他常常是很重要的\n",
      "\n",
      "350\n",
      "00:15:54,000 --> 00:15:56,000\n",
      "你如果好好的\n",
      "\n",
      "351\n",
      "00:15:56,000 --> 00:15:59,000\n",
      "多在各种课业外的事情上\n",
      "\n",
      "352\n",
      "00:15:59,000 --> 00:16:01,000\n",
      "增长进步的话\n",
      "\n",
      "353\n",
      "00:16:01,000 --> 00:16:03,000\n",
      "你这些东西会很强\n",
      "\n",
      "354\n",
      "00:16:03,000 --> 00:16:05,000\n",
      "这个东西会很厉害的\n",
      "\n",
      "355\n",
      "00:16:05,000 --> 00:16:07,000\n",
      "当然对少数人而言\n",
      "\n",
      "356\n",
      "00:16:07,000 --> 00:16:08,000\n",
      "他天生就有\n",
      "\n",
      "357\n",
      "00:16:08,000 --> 00:16:09,000\n",
      "他可能不需要\n",
      "\n",
      "358\n",
      "00:16:09,000 --> 00:16:11,000\n",
      "就是每一个人不一样的\n",
      "\n",
      "359\n",
      "00:16:11,000 --> 00:16:13,000\n",
      "这三个我都提过了\n",
      "\n",
      "360\n",
      "00:16:13,000 --> 00:16:15,000\n",
      "那么大致我还没有提\n",
      "\n",
      "361\n",
      "00:16:15,000 --> 00:16:18,000\n",
      "其实大致没有什么要特别说的\n",
      "\n",
      "362\n",
      "00:16:18,000 --> 00:16:21,000\n",
      "那应该就是我刚才前面有讲过\n",
      "\n",
      "363\n",
      "00:16:21,000 --> 00:16:26,000\n",
      "就是每一个人可以有你自己的长程目标\n",
      "\n",
      "364\n",
      "00:16:26,000 --> 00:16:29,000\n",
      "那有的人本来就有了\n",
      "\n",
      "365\n",
      "00:16:29,000 --> 00:16:32,000\n",
      "有的人也许我平常没有想过\n",
      "\n",
      "366\n",
      "00:16:32,000 --> 00:16:37,000\n",
      "那你可以在适当时机开始想\n",
      "\n",
      "367\n",
      "00:16:37,000 --> 00:16:40,000\n",
      "我有没有想要做什么事情\n",
      "\n",
      "368\n",
      "00:16:40,000 --> 00:16:43,000\n",
      "哪些事情可能是我的长程目标\n",
      "\n",
      "369\n",
      "00:16:43,000 --> 00:16:48,000\n",
      "我希望最后让我花个5年10年\n",
      "\n",
      "370\n",
      "00:16:48,000 --> 00:16:50,000\n",
      "15年或者更长\n",
      "\n",
      "371\n",
      "00:16:50,000 --> 00:16:53,000\n",
      "我把我的很多的努力都来\n",
      "\n",
      "372\n",
      "00:16:53,000 --> 00:16:55,000\n",
      "把某一些事情做得非常漂亮\n",
      "\n",
      "373\n",
      "00:16:55,000 --> 00:16:57,000\n",
      "那是我很想做的事\n",
      "\n",
      "374\n",
      "00:16:57,000 --> 00:17:00,000\n",
      "那就是长程目标\n",
      "\n",
      "375\n",
      "00:17:00,000 --> 00:17:02,000\n",
      "如果我觉得做那些事情\n",
      "\n",
      "376\n",
      "00:17:02,000 --> 00:17:04,000\n",
      "会让我非常的\n",
      "\n",
      "377\n",
      "00:17:04,000 --> 00:17:06,000\n",
      "觉得有意义\n",
      "\n",
      "378\n",
      "00:17:06,000 --> 00:17:08,000\n",
      "愿意花功夫下去做的\n",
      "\n",
      "379\n",
      "00:17:08,000 --> 00:17:10,000\n",
      "那就是我的长程目标\n",
      "\n",
      "380\n",
      "00:17:10,000 --> 00:17:13,000\n",
      "有的人如果可以想出这个来的话\n",
      "\n",
      "381\n",
      "00:17:13,000 --> 00:17:16,000\n",
      "那就是他的大致\n",
      "\n",
      "382\n",
      "00:17:16,000 --> 00:17:18,000\n",
      "越是有这种大致的人\n",
      "\n",
      "383\n",
      "00:17:18,000 --> 00:17:21,000\n",
      "也比较容易向上冲\n",
      "\n",
      "384\n",
      "00:17:21,000 --> 00:17:22,000\n",
      "那我感觉起来\n",
      "\n",
      "385\n",
      "00:17:22,000 --> 00:17:25,000\n",
      "真正影响的就是这四件事\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "''' 打开 SRT 文件并读取其内容。\n",
    "SRT 文件格式为：\n",
    "\n",
    "[索引]\n",
    "[开始时间] (小时:分钟:秒) --> [结束时间] (小时:分钟:秒)\n",
    "[转录内容]\n",
    "'''\n",
    "\n",
    "with open(output_subtitle_path, 'r', encoding='utf-8') as file:\n",
    "    content = file.read()\n",
    "\n",
    "print(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc74a4c9-73d0-4b70-b089-9ff861c5dbd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_and_save_text(srt_filename, output_filename, convert_to_tradition_chinese=False):\n",
    "    '''\n",
    "    (1) 目标:\n",
    "        - 该函数提取SRT文件中的文本并将其保存到新的文本文件中。\n",
    "        - 如果 convert_to_tradition_chinese 为 True，则将简体中文转换为繁体中文。\n",
    "\n",
    "    (2) 参数:\n",
    "\n",
    "        - srt_filename: SRT文件的路径。\n",
    "\n",
    "        - output_filename: 输出文本文件的名称。\n",
    "\n",
    "        - convert_to_tradition_chinese: 布尔值，指示是否将简体中文转换为繁体中文。默认值为 False。\n",
    "\n",
    "    (3) 示例:\n",
    "        - 如果你的SRT文件名为 'subtitle.srt'，并且希望将提取的文本保存为名为 'output.txt' 的文件，不进行简繁转换，可以按如下方式使用该函数:\n",
    "            extract_and_save_text('subtitle.srt', 'output.txt')\n",
    "\n",
    "        - 如果需要将简体中文转换为繁体中文，可以设置 convert_to_tradition_chinese=True:\n",
    "            extract_and_save_text('subtitle.srt', 'output.txt', convert_to_tradition_chinese=True)\n",
    "    '''\n",
    "\n",
    "    # 打开SRT文件并读取其内容\n",
    "    with open(srt_filename, 'r', encoding='utf-8') as file:\n",
    "        content = file.read()\n",
    "\n",
    "    # 使用正则表达式去除时间码\n",
    "    pure_text = re.sub(r'\\d+\\n\\d{2}:\\d{2}:\\d{2},\\d{3} --> \\d{2}:\\d{2}:\\d{2},\\d{3}\\n', '', content)\n",
    "\n",
    "    # 去除空行\n",
    "    pure_text = re.sub(r'\\n\\n+', '\\n', pure_text)\n",
    "\n",
    "    # 如果 convert_to_tradition_chinese 为 True，则将简体中文转换为繁体中文\n",
    "    if convert_to_tradition_chinese:\n",
    "        cc = OpenCC('s2t')\n",
    "        pure_text = cc.convert(pure_text)\n",
    "\n",
    "    # 将提取的文本写入新文件\n",
    "    with open(output_filename, 'w', encoding='utf-8') as output_file:\n",
    "        output_file.write(pure_text)\n",
    "\n",
    "    print(f'提取的文本已保存到 {output_filename}。\\n\\n')\n",
    "\n",
    "    return pure_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc8dca8e-46ed-430f-bdd6-b514d16cfb81",
   "metadata": {},
   "source": [
    "## 第3部分 - 处理自动语音识别的结果\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65936d9d-7e43-4978-9295-eb4bfbca47dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunk_text(text, max_length):\n",
    "    \"\"\"\n",
    "    (1) 目标:\n",
    "        - 此函数用于将长字符串拆分为指定长度的较小字符串。\n",
    "\n",
    "    (2) 参数:\n",
    "        - text: str, 要拆分的长字符串。\n",
    "        - max_length: int, 每个较小字符串的最大长度。\n",
    "\n",
    "    (3) 返回:\n",
    "        - split_text: list, 拆分后的字符串列表。\n",
    "\n",
    "    (3) 示例:\n",
    "        - 如果你想将一个名为 \"long_string\" 的字符串拆分为长度为 100 的较小字符串，可以按如下方式使用该函数:\n",
    "            chunk_text(long_string, 100)\n",
    "    \"\"\"\n",
    "\n",
    "    return textwrap.wrap(text, max_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d74e1f3-1dc6-4801-bc98-0640b3d3fb90",
   "metadata": {},
   "source": [
    "这里可以自定义修改文本块参数，修改后执行下一段代码查看结果。\n",
    "\n",
    "如果你更偏好繁体，修改 convert_to_tradition_chinese = True。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c99846f4-87c7-4702-90a1-f67a327a9c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文本块的长度\n",
    "chunk_length = 512\n",
    "\n",
    "# 决定是否转为繁体\n",
    "convert_to_tradition_chinese = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2bc48dbb-664c-49c8-b6bd-a1b3bb09e135",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "提取的文本已保存到 ./output-信号与人生.txt。\n",
      "\n",
      "\n",
      "查看将长文本拆分为多个短文本的结果。\n",
      "\n",
      "\n",
      "========== 拆分后的第 1 段（506 字） ==========\n",
      "\n",
      "\n",
      "每次说 学问是做出来的 什么意思 要做 才会获得学问 你如果每天光是坐在那里听 学问很可能是左耳近右耳出的 你光是坐在那儿读 学问可能从眼睛进入脑海之后就忘掉了\n",
      "\n",
      "如何能够学问在脑海里面 真的变成你自己学问 就是要做 可能有很多同学有这个经验 你如果去修某一门课 或者做某一个实验 在期末就是要教一个final\n",
      "\n",
      "project 那个final project就是要你把 学到的很多东西 最后整合在你的final project里面 最后做出来的时候 就是把它们都整合了\n",
      "\n",
      "当你学期结束 真的把final project做完的时候 你会忽然发现 我真的学到很多东西 那就是做出来的学问 也许可以举另外一个例子 就是你如果学了某一些\n",
      "\n",
      "很复杂的演算法或者什么 好像觉得那些不见得在你的脑海里 可是后来老师出了个习题 那个习题叫你写一个很大的程式 要把所有东西都包进去 当你把这个程式写完的时候\n",
      "\n",
      "你会发现 你忽然把演算法 所有东西都弄通了 那就是学问是做出来的 所以我们永远要记得 尽量多动手多做 在动手跟做的过程之中 学问才可以变成是自己的\n",
      "\n",
      "同样的情形就是说 很多时候这样动手或者做的 表现或者成绩 没有一个成绩单上的数字\n",
      "\n",
      "\n",
      "========== 拆分后的第 2 段（500 字） ==========\n",
      "\n",
      "\n",
      "使得很多人觉得那不重要 很多人甚至觉得 这门课要做final project 我就不修了太累了 或者说那门课需要 怎么样太累我就不要做了 而不知道\n",
      "\n",
      "其实那个才是让你做的机会 然后可以学到最多 也就是说虽然很可能 那么辛苦的做很多事 没有让你获得什么具体成绩 对你的overfitting可能没有帮助\n",
      "\n",
      "可是对你的全面学习 是很有帮助是该学的 不要漏掉这些事 这是我所说的 这个课业内可以做的这些事 刚才我们讲到思考的时候 我觉得我漏掉一点 你如果修我的信号课\n",
      "\n",
      "你可能会发现 我上课没讲到一个数学试字的时候 我通常都不推它的 我是在解释那个数学试字在说什么话 同样的没讲到一个什么事情的时候 我通常就在解释他在说什么话\n",
      "\n",
      "也就是说我在讲的就是 我读到特本那里的时候 我心里怎么想的 也就是我在告诉同学 如何读书的时候 如何一面读一面练习思考 这个才是最重要的一件事\n",
      "\n",
      "如何培养自己思考的能力 跟培养思考的习惯 我觉得最好的办法就是 读书的时候 凡是读到一个数学试字 都去想一想 那个数学试字到底在说什么 凡是读到特本上讲什么\n",
      "\n",
      "就去想一想 那个到底在说什么 你要真的了解他在说什么的时候 你就用了很多思考的功夫\n",
      "\n",
      "\n",
      "========== 拆分后的第 3 段（503 字） ==========\n",
      "\n",
      "\n",
      "你就在练习自己思考的能力了 好 以上说的是课业内的部分 那当然除了课业内之外 还有一大堆是不在课业内的 那就是课业外的 课业外也有很多式的 那我们可以举例来说\n",
      "\n",
      "课业外有什么可以学习的 那我通常把学习定义成为 什么是学习 学习就是一种增长 一种进步 然后获得快乐 这就是学习 所以即使是课业外的任何事情\n",
      "\n",
      "只要你觉得是有增长的 是有进步的 让你觉得快乐的 那应该就是值得学习的地方 那我们可以举很多例子 那譬如说 很多同学喜欢打球 打球是不是学习 当然是\n",
      "\n",
      "打球中间有没有增长 当然有增长 打球不只是对健康有增长 而且可能对于 比如说手脑协调 譬如说团队精神 譬如说个人之间的互动 可能都有帮助 所以打球当然是有增长的\n",
      "\n",
      "那当然是很好的学习的机会 有人喜欢爬山 爬山是不是好的学习机会 当然是 这个我以前两年前就讲过很多 爬山可以学到很多的 那爬山当然是一种学习 有人说我不喜欢爬山\n",
      "\n",
      "我去旅行好不好 旅行当然好 旅行可以增长建设 可以扩增事业 可以增加很多很多 当然是有进步的 所以当然是很好的学习 你凡是获得快乐 都是很好的事\n",
      "\n",
      "那这些都值得下功夫去 把它看成是学习 都值得下功夫去做的 我们再讲另外一系列 譬如说\n",
      "\n",
      "\n",
      "========== 拆分后的第 4 段（509 字） ==========\n",
      "\n",
      "\n",
      "有人说谈恋爱是不是学习 谈恋爱除了你在谈恋爱上 会有收获以外 本身也是有收获的 因为让你体验到人跟人之间 的各种感觉 人跟人之间的各种期待等等 有没有帮助\n",
      "\n",
      "当然有帮助 对每一个人都是很好的学习 所以谈恋爱当然是一件很好的事 有人会说那要靠缘分 没有缘分没有办法 对不对 但是你不是一定要谈恋爱吗 你可以交朋友\n",
      "\n",
      "交朋友是不是学习 当然是 交朋友也一样 让我们学到很多人际的互动 学到很多人跟人之间的沟通 人跟人之间的期待 人跟人之间的感觉 这都是交朋友之后学到的\n",
      "\n",
      "对我们电机系的同学而言 你四周有一大群好同学 都是很好的交朋友的对象 你下一番功夫交朋友好不好 好 当然是有帮助的 另外当然我们可以举很多 我们最现成的例子\n",
      "\n",
      "譬如说我们的系学会办各种活动 那些活动有没有帮助 当然有 我们举例来讲电业 你如果去参加某一个舞跳个舞 或者参加某个剧演个剧 有没有帮助 当然有帮助\n",
      "\n",
      "你在这中间一定发现有所增长 有所进步 那是为什么有那么多同学 要去参加 就是因为发现那个确实是有增长 有进步 有的人说 我不去跳那个舞 或者演那个剧 我做幕后的\n",
      "\n",
      "譬如说是幕后的什么什么规划 或者说是什么这个光舞的什么软体组 还是什么服装道具组 一样\n",
      "\n",
      "\n",
      "========== 拆分后的第 5 段（504 字） ==========\n",
      "\n",
      "\n",
      "那个都是可以获得很多的增长 很多进步的 当然都是很有用的 都是很好的学习 那当然也包括电业以外的戏学会 其他的各种活动都一样 也包括电机系以外的其他的校内\n",
      "\n",
      "或者校外的各种活动几乎都一样 都可以让人有所增长 有所进步 都是很好的学习的机会 都是很好的学习 同样的问题是 这些东西都没有考试 没有成绩 不能显示在成绩单上\n",
      "\n",
      "因此对有一些同学会认为那个浪费时间 我不需要花时间去做那个 因为不影响我的 overfitting的目标 里面没有这个嘛 具体成绩没有这些嘛 那不要这样想\n",
      "\n",
      "因为那些都非常的重要 都对你发展非常的重要 那我们说电机工程 今天的电机工程 很少什么事情自己一个人可以做成功的 你必须跟很多人一起 才可能做成功一个非常重要的\n",
      "\n",
      "有意义的工作 那当你跟一群人在一起做的时候 你必须学会如何进入一个团队 从边缘开始慢慢进入核心 从底层开始慢慢变成leader 然后如何可以推动你想做的事\n",
      "\n",
      "如何变成可以做到你想做的事等等 这些都是很重要的 那我们通常称这些东西 是所谓的soft skills 也就是软实力 所谓软实力就是硬实力以外的软实力\n",
      "\n",
      "那硬实力是说你电子学的功力 数学的功力 这个城市能力这种是硬实力\n",
      "\n",
      "\n",
      "========== 拆分后的第 6 段（512 字） ==========\n",
      "\n",
      "\n",
      "软实力我们主要就是讲各种人际之间的 在人跟人之间的各种能力 包括沟通能力协调能力 交朋友的能力 这个说服人的能力 这个团队精神领导能力等等\n",
      "\n",
      "那那些就是所谓的soft skills 重要不重要 重要 你看到任何一个成功的电机工程师 他都有一堆这种 这个才是他成功的一个非常重要的关键 这种东西怎么来\n",
      "\n",
      "我们刚才讲的各种课业外的各种学习增长机会 都可以帮助一个人塑造他的soft skills 是有少数人的这些soft skills是天生的 他天生就厉害 有没有\n",
      "\n",
      "有 但这种人毕竟没那么多 对很多人而言 他的soft skills是自己努力慢慢培养起来的 我刚才一开始前面讲的那一段 我说我在这个进台大电机系以前\n",
      "\n",
      "我几乎不会交朋友 我不太会说话 我在读大学的四年里面改变我自己 让我变成有很多这方面的能力的人 其实最重要就是我的很多soft skills 都是我自己培养\n",
      "\n",
      "在读台大电机系的四年里面 获得的非常多这方面的收获的 那是为什么我每次都要强调这个东西有多么重要 那我之前曾经在几年前的这个信号与人生里面 有说到这一件事\n",
      "\n",
      "我现在不要重复 但是我简单的summarize 那我说我们电机系的 电机工程师的一生career的发展\n",
      "\n",
      "\n",
      "========== 拆分后的第 7 段（507 字） ==========\n",
      "\n",
      "\n",
      "那黄金实在是在什么时候 我认为是在35岁到55岁 这20年是我们的黄金时代 在这以前当然更好 只是说可能各方面尚未具备 还没有完全训练的好 在这以后是最好的\n",
      "\n",
      "这以后年纪大了难免有一些要打个折扣等等 就这里面我们看到我们的电机系的毕业的同学 过去有几千人毕业我都看到 那我觉得有的人的发展是像这样 有一定的斜率\n",
      "\n",
      "但到某一个阶段它会慢慢saturate 有的人也许开始向上比较晚 但它斜率比较高 它最后会saturate在比较高的地方 也有的人也许开始的比较快\n",
      "\n",
      "但是后来会overshoot之后收敛在比较低的地方等等 这个每一个人都不一样 但是当然也有一种人 你会看到它一直向上走 完全没有saturate\n",
      "\n",
      "这些人这些差别在哪里 这些东西差别在哪里 我以前已经说过这件事 我不要多重复 我说最主要因素有四个 就是实力 努力 大致 跟self skill这四件事情\n",
      "\n",
      "那我认为 真正影响这个的 不是因为电子学考得好不好 不是因为信号与系统念得好不好 也就我刚才讲 你把每一门必修课当成是单一跑道 跑到第一名并不表示怎样\n",
      "\n",
      "我们最后不看那个的 最后看的是这个 那这个是怎么样影响 我认为是这四件事 就是实力努力 大致跟self\n",
      "\n",
      "\n",
      "========== 拆分后的第 8 段（509 字） ==========\n",
      "\n",
      "\n",
      "skills 这四件事里面我们现在可以summarize 我刚才讲的 什么是实力 实力就是所有的这些我们电机工程的 专业领域里面的各种东西的实力 实力怎么厉害法\n",
      "\n",
      "就是我刚才讲的 你如果都是在做全面的学习的话 你就会学到各种该学到的 最后你的实力就是很强的 所以实力最主要就是不要overfitting\n",
      "\n",
      "要尽量都做学到该学的全面的学习 努力是没有疑问 每个人都了解 确实我们可以看到一个人在未来的几十年里面 有的人他一直努力 有的人慢慢不太努力等等\n",
      "\n",
      "这个是有明显差别的 那self skills我刚才已经讲了 就是很多我们平常没有算成绩 觉得大家不重视的事情 那其他常常是很重要的 你如果好好的\n",
      "\n",
      "多在各种课业外的事情上 增长进步的话 你这些东西会很强 这个东西会很厉害的 当然对少数人而言 他天生就有 他可能不需要 就是每一个人不一样的 这三个我都提过了\n",
      "\n",
      "那么大致我还没有提 其实大致没有什么要特别说的 那应该就是我刚才前面有讲过 就是每一个人可以有你自己的长程目标 那有的人本来就有了 有的人也许我平常没有想过\n",
      "\n",
      "那你可以在适当时机开始想 我有没有想要做什么事情 哪些事情可能是我的长程目标 我希望最后让我花个5年10年\n",
      "\n",
      "\n",
      "========== 拆分后的第 9 段（154 字） ==========\n",
      "\n",
      "\n",
      "15年或者更长 我把我的很多的努力都来 把某一些事情做得非常漂亮 那是我很想做的事 那就是长程目标 如果我觉得做那些事情 会让我非常的 觉得有意义\n",
      "\n",
      "愿意花功夫下去做的 那就是我的长程目标 有的人如果可以想出这个来的话 那就是他的大致 越是有这种大致的人 也比较容易向上冲 那我感觉起来 真正影响的就是这四件事\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 从SRT文件中提取文本并保存为新文本文件\n",
    "pure_text = extract_and_save_text(srt_filename=output_subtitle_path, output_filename=output_raw_text_path, convert_to_tradition_chinese=convert_to_tradition_chinese)\n",
    "\n",
    "# 将长文本拆分为指定长度的较小块\n",
    "chunks = chunk_text(text=pure_text, max_length=512)\n",
    "\n",
    "# 你可以查看每段的字数和内容\n",
    "print(\"查看将长文本拆分为多个短文本的结果。\\n\")\n",
    "for index, chunk in enumerate(chunks):\n",
    "    if index == 0:\n",
    "        print(f\"\\n========== 拆分后的第 {index + 1} 段（{len(chunk)} 字） ==========\\n\\n\")\n",
    "        for text in textwrap.wrap(chunk, 80):\n",
    "            print(f\"{text}\\n\")\n",
    "    elif index == 1:\n",
    "        print(f\"\\n========== 拆分后的第 {index + 1} 段（{len(chunk)} 字） ==========\\n\\n\")\n",
    "        for text in textwrap.wrap(chunk, 80):\n",
    "            print(f\"{text}\\n\")\n",
    "    elif index == 2:\n",
    "        print(f\"\\n========== 拆分后的第 {index + 1} 段（{len(chunk)} 字） ==========\\n\\n\")\n",
    "        for text in textwrap.wrap(chunk, 80):\n",
    "            print(f\"{text}\\n\")\n",
    "    else:\n",
    "        print(f\"\\n========== 拆分后的第 {index + 1} 段（{len(chunk)} 字） ==========\\n\\n\")\n",
    "        for text in textwrap.wrap(chunk, 80):\n",
    "            print(f\"{text}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43b6c25-df8b-4339-b2f0-217e81543ac2",
   "metadata": {},
   "source": [
    "## 第4部分 - 摘要\n",
    "\n",
    "这里提供的方法同 ChatGPT，因为一样使用 OpenAI 库进行。\n",
    "\n",
    "你可以通过[《00. 大模型 API 获取步骤》](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/Guide/00.%20大模型%20API%20获取步骤.md)获取 API 密钥。\n",
    "\n",
    "其他模型可以参阅[模型广场 -- 阿里云百炼](https://bailian.console.aliyun.com/?spm=5176.29619931.J__Z58Z6CX7MY__Ll8p1ZOR.1.4d1d59fcWwSqvr#/model-market)，点击对应模型的`查看详情`。\n",
    "\n",
    "![查看详情](../Guide/assets/image-20240924091151684.png)\n",
    "\n",
    "在界面左上角可以看到对应的英文名称。\n",
    "\n",
    "![对应模型名](../Guide/assets/image-20240924091414350.png)\n",
    "\n",
    "复制它。\n",
    "\n",
    "现在你可以随意更换为你想要的模型，不过你可能要先申请使用（通过大概要几个小时，会有短信提示）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "374d7006-e3f5-420f-9a25-58ab554105ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarization(client, summarization_prompt, model_name=\"qwen-turbo\", temperature=0.0, top_p=1.0, max_tokens=512):\n",
    "    \"\"\"\n",
    "    (1) 目标:\n",
    "        - 使用 OpenAI Chat API 对给定文本进行摘要。\n",
    "\n",
    "    (2) 参数:\n",
    "        - client: OpenAI Chat API客户端。\n",
    "        - summarization_prompt: 包含需要摘要的文本的摘要prompt。\n",
    "        - model_name: 模型名称，默认是 \"qwen-turbo\"。\n",
    "        - temperature: 控制响应的随机性。较低的值使响应更具确定性，默认是 0.0。\n",
    "        - top_p: 通过核采样控制多样性。较高的值会导致更多样化的响应，默认是 1.0。\n",
    "        - max_tokens: 完成任务时生成的最大tokens，默认是 512。\n",
    "\n",
    "    (3) 返回:\n",
    "        - 摘要文本。\n",
    "\n",
    "    (4) 示例:\n",
    "        - 如果文本是 \"ABC\"，摘要prompt是 \"DEF\"，model_name 是 \"qwen-turbo\"，temperature 是 0.0，top_p 是 1.0，max_tokens 是 512，则可以按如下方式调用此函数:\n",
    "\n",
    "              summarization(client=client, text=\"ABC\", summarization_prompt=\"DEF\", model_name=\"qwen-turbo\", temperature=0.0, top_p=1.0, max_tokens=512)\n",
    "    \"\"\"\n",
    "\n",
    "    # 用户prompt是 summarization_prompt 和文本的结合\n",
    "    user_prompt = summarization_prompt\n",
    "\n",
    "    while True:\n",
    "\n",
    "        try:\n",
    "            # 使用 OpenAI Chat API 对文本进行摘要\n",
    "            chat_completion = client.chat.completions.create(\n",
    "                messages=[\n",
    "                    {\n",
    "                        \"role\": \"user\",\n",
    "                        \"content\": user_prompt,\n",
    "                    }\n",
    "                ],\n",
    "                    model=model_name,\n",
    "                    temperature=temperature,\n",
    "                    top_p=top_p,\n",
    "                    max_tokens=max_tokens\n",
    "            )\n",
    "\n",
    "            break\n",
    "\n",
    "        except:\n",
    "            print(\"API调用失败，等待1秒后重试。\")\n",
    "            time.sleep(1)\n",
    "\n",
    "    return chat_completion.choices[0].message.content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e5feb3-d1ef-4b08-8a65-9a836cb7ca94",
   "metadata": {},
   "source": [
    "直接运行代码，交互式修改参数和 API，不用关心这里的代码细节。\n",
    "\n",
    "注意：配置完记得点击**提交配置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "545d70a0-c55e-4c5f-b7df-d349f4e49c57",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Markdown' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# ===== 在此代码块中，你可以修改所需的参数并设置OpenAI API密钥 =====\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# 你的OpenAI API密钥部分\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m api_key_markdown \u001b[38;5;241m=\u001b[39m \u001b[43mMarkdown\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m**openai_api_key**<br>请输入你的 OpenAI API 密钥：\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m openai_api_key_widget \u001b[38;5;241m=\u001b[39m widgets\u001b[38;5;241m.\u001b[39mPassword(\n\u001b[1;32m      6\u001b[0m     value\u001b[38;5;241m=\u001b[39mos\u001b[38;5;241m.\u001b[39mgetenv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOPENAI_API_KEY\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      7\u001b[0m     description\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopenai_api_key:\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      8\u001b[0m     placeholder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m请输入你的OpenAI API密钥\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      9\u001b[0m     style\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdescription_width\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minitial\u001b[39m\u001b[38;5;124m'\u001b[39m}\n\u001b[1;32m     10\u001b[0m )\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# 模型名称部分\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Markdown' is not defined"
     ]
    }
   ],
   "source": [
    "# ===== 在此代码块中，你可以修改所需的参数并设置OpenAI API密钥 =====\n",
    "\n",
    "# 你的OpenAI API密钥部分\n",
    "api_key_markdown = Markdown(\"**openai_api_key**<br>请输入你的 OpenAI API 密钥：\")\n",
    "openai_api_key_widget = widgets.Password(\n",
    "    value=os.getenv('OPENAI_API_KEY'),  # 默认使用环境变量\n",
    "    description='openai_api_key:',\n",
    "    placeholder='请输入你的OpenAI API密钥（可以不填写，默认使用环境变量）',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 模型名称部分\n",
    "model_name_markdown = Markdown(\"**model_name**<br>请选择模型名称，默认是 'qwen-turbo'：\")\n",
    "model_name_widget = widgets.Text(\n",
    "    value='qwen-turbo',\n",
    "    description='model_name:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 控制响应的随机性部分\n",
    "temperature_markdown = Markdown(\"**temperature**<br>通过温度值来控制响应的随机性，较低的值使响应更具确定性：\")\n",
    "temperature_widget = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=0,\n",
    "    max=1,\n",
    "    step=0.1,\n",
    "    description='temperature (随机性):',\n",
    "    continuous_update=False,\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 控制多样性 (Top-P) 部分\n",
    "top_p_markdown = Markdown(\"**控制多样性 (Top-P)**<br>通过 Top-P 核采样控制多样性，较高的值会导致更多样化的响应：\")\n",
    "top_p_widget = widgets.FloatSlider(\n",
    "    value=0,\n",
    "    min=0,\n",
    "    max=1,\n",
    "    step=0.1,\n",
    "    description='Top-P (多样性):',\n",
    "    continuous_update=False,\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 创建输出区域来显示打印内容\n",
    "output_area = widgets.Output()\n",
    "\n",
    "# 获取用户输入值并显示\n",
    "def on_button_click(b):\n",
    "    with output_area:\n",
    "        global openai_api_key, model_name, temperature, top_p\n",
    "        output_area.clear_output()  # 清除之前的输出\n",
    "        openai_api_key = openai_api_key_widget.value\n",
    "        model_name = model_name_widget.value\n",
    "        temperature = temperature_widget.value\n",
    "        top_p = top_p_widget.value\n",
    "        \n",
    "        # 打印配置\n",
    "        print(f\"OpenAI API密钥: {'*' * len(openai_api_key)}\")  # 为了安全性，只显示密钥长度\n",
    "        print(f\"模型名称: {model_name}\")\n",
    "        print(f\"温度 (随机性): {temperature}\")\n",
    "        print(f\"Top-P (多样性): {top_p}\")\n",
    "\n",
    "# 创建提交按钮\n",
    "submit_button = widgets.Button(description=\"提交配置\")\n",
    "submit_button.on_click(on_button_click)\n",
    "\n",
    "# 显示带有说明的所有小部件\n",
    "display(api_key_markdown, openai_api_key_widget)\n",
    "display(model_name_markdown, model_name_widget)\n",
    "display(temperature_markdown, temperature_widget)\n",
    "display(top_p_markdown, top_p_widget)\n",
    "\n",
    "# 显示提交按钮\n",
    "display(submit_button)\n",
    "\n",
    "# 显示输出区域\n",
    "display(output_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12a54687-e82c-4f1e-9084-24ac4a8ee317",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建 OpenAI 客户端\n",
    "client = OpenAI(\n",
    "    api_key=openai_api_key,\n",
    "    base_url=\"https://dashscope.aliyuncs.com/compatible-mode/v1\", # 这里使用的是阿里云的大模型，如果需要使用其他平台，请参考对应的开发文档后对应修改。如果使用 GPT 的 API，删除这行就可以直接运行。\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c2be0de4-f959-460e-9cdf-befc446d90f7",
   "metadata": {},
   "source": [
    "#### 这里演示两种摘要方式\n",
    "参考：[Text Summarization of Large Documents using LangChain 🦜🔗](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-summarization/summarization_large_documents_langchain.ipynb)\n",
    "\n",
    "分别对应于 `MapReduce` 和 `Refine`，你可以通过接下来的代码来感受二者的区别。\n",
    "\n",
    "不要修改 `<text>`，这是一个占位符，如果你不记得占位符的概念，回看[《03. 进阶指南：自定义 Prompt 提升大模型解题能力》](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN/blob/master/Guide/03.%20进阶指南：自定义%20Prompt%20提升大模型解题能力.md#设计prompt解决数学问题)。\n",
    "\n",
    "##### 方法一：多段摘要方法（Multi-Stage Summarization）- MapReduce\n",
    "\n",
    "![image.png](../Guide/assets/image-20240924092040340.png)\n",
    "\n",
    "1. 将长文本分成多个较小的部分，并分别获取每个小段落的摘要\n",
    "\n",
    "注意：配置完记得点击**提交配置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "461d5e62-0dbe-4f5c-9340-eceb99ec4826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**多段摘要：段落设置**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**max_tokens**<br>设置生成的最大tokens："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db338226853e4c5383425fdea9820ac6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntText(value=350, description='max_tokens:', style=DescriptionStyle(description_width='initial'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**summarization_prompt_template**<br>你可以修改摘要prompt，但不要修改 `<text>` 部分："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92438a88deef41418cda70cf53987f92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Textarea(value='用 300 个字内写出这段文字的摘要，其中包括要点和所有重要细节：<text>', description='summarization_prompt_template:', layout…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "734936ee2af9428398b05130cdc2660d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='提交配置', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b62c270647fe486ca0226f26955ca8af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===== 多段摘要：段落设置 =====\n",
    "\n",
    "# 你可以修改摘要prompt和生成的最大tokens，但不要修改 <text> 部分\n",
    "instructions = Markdown(\"**多段摘要：段落设置**\")\n",
    "\n",
    "# 设置最大生成tokens\n",
    "max_tokens_markdown = Markdown(\"**max_tokens**<br>设置生成的最大tokens：\")\n",
    "max_tokens_widget = widgets.IntText(\n",
    "    value=350,\n",
    "    description='max_tokens:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 摘要prompt模板\n",
    "summarization_prompt_markdown = Markdown(\"**summarization_prompt_template**<br>你可以修改摘要prompt，但不要修改 `<text>` 部分：\")\n",
    "summarization_prompt_template_widget = widgets.Textarea(\n",
    "    value=\"用 300 个字内写出这段文字的摘要，其中包括要点和所有重要细节：<text>\",\n",
    "    description='summarization_prompt_template:',\n",
    "    layout=widgets.Layout(width='500px', height='100px'),\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 创建输出区域来显示打印内容\n",
    "output_area = widgets.Output()\n",
    "\n",
    "# 获取用户输入值并显示\n",
    "def on_button_click(b):\n",
    "    global max_tokens, summarization_prompt_template\n",
    "    with output_area:\n",
    "        output_area.clear_output()  # 清除之前的输出\n",
    "        max_tokens = max_tokens_widget.value\n",
    "        summarization_prompt_template = summarization_prompt_template_widget.value\n",
    "        \n",
    "        # 打印配置\n",
    "        print(f\"最大tokens: {max_tokens}\")\n",
    "        print(f\"摘要prompt模板: {summarization_prompt_template}\")\n",
    "\n",
    "# 创建提交按钮\n",
    "submit_button = widgets.Button(description=\"提交配置\")\n",
    "submit_button.on_click(on_button_click)\n",
    "\n",
    "# 显示带有说明的所有小部件\n",
    "display(instructions)\n",
    "display(max_tokens_markdown, max_tokens_widget)\n",
    "display(summarization_prompt_markdown, summarization_prompt_template_widget)\n",
    "\n",
    "# 显示提交按钮\n",
    "display(submit_button)\n",
    "\n",
    "# 显示输出区域\n",
    "display(output_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "884abd01-4297-42ee-86e0-c042c0632170",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------第 1 段的摘要----------------------------\n",
      "\n",
      "这段文字强调了“学问是做出来的”这一观点。它指出，理论知识若仅通过听讲或阅读获取，容易遗忘，只有通过实践操作，将所学内容应用到实际中，才能真正掌握并内化为个人的\n",
      "\n",
      "知识。具体而言，这涉及到两个核心概念：一是参与课程学习和实验活动，特别是完成期末项目（final project），这要求学生综合运用所学知识，从而加深理解和记\n",
      "\n",
      "忆；二是通过解决复杂问题，如编写大型程序，来巩固对算法等复杂概念的理解。这种实践性学习方式有助于学生将理论知识转化为实际技能，并最终成为其自身的知识体系。因此，\n",
      "\n",
      "文中反复强调动手实践的重要性，认为这是获取真知灼见的关键途径。此外，文中还提醒读者，实践成果的价值往往难以仅通过分数体现，真正的学习成果应体现在能力的提升和个人\n",
      "\n",
      "知识结构的构建上。\n",
      "\n",
      "第 1 段摘要的长度: 329\n",
      "生成第 1 段摘要的时间: 4.59 秒。\n",
      "\n",
      "----------------------------第 2 段的摘要----------------------------\n",
      "\n",
      "这段文字强调了在课程学习中，尤其是面对需要付出大量努力的项目或内容时，人们往往容易忽视其中蕴含的学习机会。作者指出，看似辛苦的项目实际上提供了最深入学习的机会，\n",
      "\n",
      "对个人的全面发展具有重要意义，而不仅仅是短期成绩的提升。  作者以自己的教学经验为例，说明在教授信号课时，他更注重引导学生理解数学公式背后的含义，而非机械地推导\n",
      "\n",
      "公式。他认为，通过解读数学概念、思考其实际应用，学生能够培养深度思考和独立学习的能力。这种方法不仅涉及理解文本内容，更在于如何边阅读边实践思考，这有助于养成良好\n",
      "\n",
      "的思考习惯。  总之，这段文字的核心要点在于鼓励学生抓住学习过程中看似艰难的机遇，通过深入理解概念、公式等，培养批判性思维和自主学习能力，从而实现知识的真正掌握\n",
      "\n",
      "和能力的全面提升。\n",
      "\n",
      "第 2 段摘要的长度: 329\n",
      "生成第 2 段摘要的时间: 4.61 秒。\n",
      "\n",
      "----------------------------第 3 段的摘要----------------------------\n",
      "\n",
      "本文强调了学习不仅限于课业之内，课业之外同样充满学习机会。学习的本质在于增长、进步与获得快乐。无论是打球、爬山还是旅行，只要能促进个人成长、提升技能或增进身心健\n",
      "\n",
      "康，都是值得学习的活动。这些课外活动能够增强手脑协调能力、培养团队精神、改善人际交往等，同时带来乐趣与满足感。因此，作者鼓励大家将日常活动视为学习机会，积极投入\n",
      "\n",
      "并从中收获成长与快乐。除此之外，文章还提及了其他类型的课外学习方式，比如阅读、艺术创作、志愿服务等，均能促进全面发展。总之，学习无处不在，关键在于我们如何发现并\n",
      "\n",
      "利用生活中的各种机会进行自我提升。\n",
      "\n",
      "第 3 段摘要的长度: 257\n",
      "生成第 3 段摘要的时间: 3.57 秒。\n",
      "\n",
      "----------------------------第 4 段的摘要----------------------------\n",
      "\n",
      "这段文字强调了恋爱、交友和参与社团活动对于个人成长的重要性。核心观点包括：  1.\n",
      "\n",
      "**恋爱与学习**：恋爱不仅提供了情感体验，还让人学会了与人相处的各种技巧，如理解他人的期待、感受以及处理人际关系的能力。这是一段宝贵的个人学习经历。  2. \n",
      "\n",
      "**友情的价值**：交朋友同样能带来人际互动和沟通技巧的学习，帮助个体更好地理解和适应社会环境。电机系的同学可以通过广泛结交朋友来丰富自己的社交圈，增进人际交往\n",
      "\n",
      "能力。  3. **参与社团活动的好处**：加入学生会或参与各类活动，如跳舞、戏剧表演等，能够促进个人在技能、团队合作、领导力等方面的成长。这些活动不仅提供实践\n",
      "\n",
      "机会，还能增加个人的成就感和自信心。  4. **多元化角色的贡献**：无论是作为活动的参与者还是幕后工作者，如策划、技术、服装道具等，每种角色都能为团队贡献价\n",
      "\n",
      "值，同时促进个人在不同领域的技能提升和经验积累。\n",
      "\n",
      "总之，这段文字鼓励通过恋爱、交友和参与各种活动来获取多方面的学习和发展机会，强调了在大学生活中积极主动参与的重要性。\n",
      "\n",
      "第 4 段摘要的长度: 446\n",
      "生成第 4 段摘要的时间: 5.59 秒。\n",
      "\n",
      "----------------------------第 5 段的摘要----------------------------\n",
      "\n",
      "这段文字强调了除了专业学习外，参与各类活动对个人成长与学习的重要性。它涵盖了电业之外的活动，无论是在校内还是校外，都能提供增长和进步的机会。尽管这些活动往往不包\n",
      "\n",
      "含考试和成绩，不会体现在成绩单上，但它们对于个人发展同样至关重要。  作者指出，虽然电机工程领域的项目很少能由个人独立完成，需要团队合作才能成功，这要求参与者学\n",
      "\n",
      "会融入团队，从边缘逐渐成为核心，甚至成为领导者。在这个过程中，如何推动团队达成目标、实现个人理想等软实力技能变得尤为重要。这些软实力，如沟通、协作、领导力和问题\n",
      "\n",
      "解决能力，虽不属于传统的专业知识范畴，但对个人的职业发展和团队协作能力有着不可忽视的影响。\n",
      "\n",
      "综上所述，这段文字强调了广泛参与活动对于个人成长的重要性，特别是那些提升软实力的机会，这些软实力与专业技能同等重要，共同构成了个人全面发展的关键要素。\n",
      "\n",
      "第 5 段摘要的长度: 362\n",
      "生成第 5 段摘要的时间: 4.6 秒。\n",
      "\n",
      "----------------------------第 6 段的摘要----------------------------\n",
      "\n",
      "本文讨论了软实力在个人发展中的重要性。软实力包括沟通、协调、交友、说服、团队精神及领导力等能力，是个人成功的关键因素。软实力不仅影响职业发展，还关乎人际关系的建\n",
      "\n",
      "立。尽管有些人天生具备这些技能，但对于大多数人来说，软实力需要通过后天的学习和实践来培养。  作者以自己的经历为例，讲述了在进入台湾大学电机工程系前，自己在社交\n",
      "\n",
      "和沟通方面的能力较弱，但在大学期间通过各种课外活动和学习机会，逐渐培养出了这些软实力。这些软实力的提升对于他在大学生涯乃至未来的职业发展起到了至关重要的作用。\n",
      "\n",
      "因此，作者强调软实力的重要性，并鼓励大家通过不断学习和实践来提高自己的软实力。尽管在几年前的《信号与人生》一书中已经提到过这一点，但其重要性不容忽视，值得再次强\n",
      "\n",
      "调。总之，软实力的培养对电机工程师乃至任何职业人士的生涯发展至关重要。\n",
      "\n",
      "第 6 段摘要的长度: 356\n",
      "生成第 6 段摘要的时间: 4.66 秒。\n",
      "\n",
      "----------------------------第 7 段的摘要----------------------------\n",
      "\n",
      "这段文字探讨了个人发展黄金时代的概念，指出35岁至55岁为多数人的最佳发展阶段。在这段时间之前和之后，发展可能会受限于成熟度、能力或年龄带来的挑战。文章以电机系\n",
      "\n",
      "毕业生为例，描述了个人发展曲线的多样性，包括斜率的变化、饱和点以及最终达到的高度，强调每个人的发展路径都是独一无二的。  作者提出了四个主要因素影响个人发展的观\n",
      "\n",
      "点：实力、努力、大致（可能指目标或方向）以及自我技能。他认为，真正的关键不在于单科成绩的优异与否，而在于综合考虑实力、努力、方向和自我提升的能力，这些因素共同作\n",
      "\n",
      "用于个人的成长过程。文章最后提到，有些人可能从一开始就展现出持续向上的发展趋势，而不会遇到明显的饱和点。\n",
      "\n",
      "总之，这段文字强调了个人发展过程中的多样性和复杂性，并提出实力、努力、目标和自我技能是决定个人成长的关键因素。\n",
      "\n",
      "第 7 段摘要的长度: 349\n",
      "生成第 7 段摘要的时间: 4.41 秒。\n",
      "\n",
      "----------------------------第 8 段的摘要----------------------------\n",
      "\n",
      "实力主要指在电机工程专业领域的广泛且深入学习与掌握。实力的获得关键在于全面学习，避免过度专注于某一领域而忽视其他相关知识，从而达到精通的程度。努力是实现这一目标\n",
      "\n",
      "的基础，但不同人对于努力的理解和实践会有差异，有的人在未来几十年内始终保持持续的努力，而有的人则逐渐放松。此外，自我技能的重要性不容忽视，包括但不限于课程之外的\n",
      "\n",
      "实践活动、团队合作、领导力培养等，这些软技能能够显著增强个人竞争力。  每个人都有独特的长程目标，有些人自小就对此有清晰的认识，而有些人可能需要在适当的时候思考\n",
      "\n",
      "自己想要追求的方向。设定并追求这些目标，比如花费5年到10年的时间，可以帮助个人实现职业或个人发展上的突破。总的来说，实力的构建是一个综合性的过程，不仅涉及专业\n",
      "\n",
      "知识的学习，还包括个人技能的培养以及明确目标的设定。\n",
      "\n",
      "第 8 段摘要的长度: 346\n",
      "生成第 8 段摘要的时间: 4.42 秒。\n",
      "\n",
      "----------------------------第 9 段的摘要----------------------------\n",
      "\n",
      "这段文字的核心内容围绕着设定和追求长期目标的重要性展开。关键点如下：  1.\n",
      "\n",
      "**长期努力的价值**：作者强调了将大量精力投入到某些特别事项中，以实现卓越成果，这代表了他的长期目标。这些目标是他热衷追求的事物，赋予他意义感。  2. **\n",
      "\n",
      "目标与意义的联系**：对于作者而言，那些能让他感到有意义、愿意投入时间和精力去完成的事情构成了他的长期目标。这意味着目标不仅仅是外在的成就，更是内在的满足感和动\n",
      "\n",
      "力源泉。  3. **目标设定的好处**：能够明确自己的长期目标的人，往往更容易朝着更高的成就迈进。这表明目标设定在个人成长和进步过程中扮演着关键角色。  4.\n",
      "\n",
      "**影响因素**：作者认为真正能推动人们向上发展的因素有四个，尽管具体是哪四个并未详细列出，但可以推测可能包括目标设定、持续努力、内在动机以及自我反思等。  综\n",
      "\n",
      "上所述，这段文字通过阐述长期目标在个人发展中的重要性，鼓励读者设定并追求能赋予他们意义的目标，同时暗示了成功的关键因素可能包括目标设定、持续努力、内在动机和自我\n",
      "\n",
      "反思。\n",
      "\n",
      "第 9 段摘要的长度: 443\n",
      "生成第 9 段摘要的时间: 5.66 秒。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "paragraph_summarizations = []\n",
    "\n",
    "# 首先，我们分别对每个拆分的部分进行摘要。\n",
    "for index, chunk in enumerate(chunks):\n",
    "\n",
    "    # 记录开始时间\n",
    "    start = time.time()\n",
    "\n",
    "    # 构建摘要prompt\n",
    "    summarization_prompt = summarization_prompt_template.replace(\"<text>\", chunk)\n",
    "\n",
    "    # 分别对每个拆分的部分进行摘要\n",
    "    response = summarization(client=client, summarization_prompt=summarization_prompt, model_name=model_name, temperature=temperature, top_p=top_p, max_tokens=max_tokens)\n",
    "\n",
    "    # 计算执行时间，并保留两位小数\n",
    "    cost_time = round(time.time() - start, 2)\n",
    "\n",
    "    # 打印摘要及其长度\n",
    "    print(f\"----------------------------第 {index + 1} 段的摘要----------------------------\\n\")\n",
    "    for text in textwrap.wrap(response, 80):\n",
    "        print(f\"{text}\\n\")\n",
    "    print(f\"第 {index + 1} 段摘要的长度: {len(response)}\")\n",
    "    print(f\"生成第 {index + 1} 段摘要的时间: {cost_time} 秒。\\n\")\n",
    "\n",
    "    # 记录结果\n",
    "    paragraph_summarizations.append(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf766a69-e672-4b8e-b7fb-08224bbb765a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 1 段的摘要: 这段文字强调了“学问是做出来的”这一观点。它指出，理论知识若仅通过听讲或阅读获取，容易遗忘，只有通过实践操作，将所学内容应用到实际中，才能真正掌握并内化为个人的知识。具体而言，这涉及到两个核心概念：一是参与课程学习和实验活动，特别是完成期末项目（final project），这要求学生综合运用所学知识，从而加深理解和记忆；二是通过解决复杂问题，如编写大型程序，来巩固对算法等复杂概念的理解。这种实践性学习方式有助于学生将理论知识转化为实际技能，并最终成为其自身的知识体系。因此，文中反复强调动手实践的重要性，认为这是获取真知灼见的关键途径。此外，文中还提醒读者，实践成果的价值往往难以仅通过分数体现，真正的学习成果应体现在能力的提升和个人知识结构的构建上。\n",
      "第 2 段的摘要: 这段文字强调了在课程学习中，尤其是面对需要付出大量努力的项目或内容时，人们往往容易忽视其中蕴含的学习机会。作者指出，看似辛苦的项目实际上提供了最深入学习的机会，对个人的全面发展具有重要意义，而不仅仅是短期成绩的提升。\n",
      "\n",
      "作者以自己的教学经验为例，说明在教授信号课时，他更注重引导学生理解数学公式背后的含义，而非机械地推导公式。他认为，通过解读数学概念、思考其实际应用，学生能够培养深度思考和独立学习的能力。这种方法不仅涉及理解文本内容，更在于如何边阅读边实践思考，这有助于养成良好的思考习惯。\n",
      "\n",
      "总之，这段文字的核心要点在于鼓励学生抓住学习过程中看似艰难的机遇，通过深入理解概念、公式等，培养批判性思维和自主学习能力，从而实现知识的真正掌握和能力的全面提升。\n",
      "第 3 段的摘要: 本文强调了学习不仅限于课业之内，课业之外同样充满学习机会。学习的本质在于增长、进步与获得快乐。无论是打球、爬山还是旅行，只要能促进个人成长、提升技能或增进身心健康，都是值得学习的活动。这些课外活动能够增强手脑协调能力、培养团队精神、改善人际交往等，同时带来乐趣与满足感。因此，作者鼓励大家将日常活动视为学习机会，积极投入并从中收获成长与快乐。除此之外，文章还提及了其他类型的课外学习方式，比如阅读、艺术创作、志愿服务等，均能促进全面发展。总之，学习无处不在，关键在于我们如何发现并利用生活中的各种机会进行自我提升。\n",
      "第 4 段的摘要: 这段文字强调了恋爱、交友和参与社团活动对于个人成长的重要性。核心观点包括：\n",
      "\n",
      "1. **恋爱与学习**：恋爱不仅提供了情感体验，还让人学会了与人相处的各种技巧，如理解他人的期待、感受以及处理人际关系的能力。这是一段宝贵的个人学习经历。\n",
      "\n",
      "2. **友情的价值**：交朋友同样能带来人际互动和沟通技巧的学习，帮助个体更好地理解和适应社会环境。电机系的同学可以通过广泛结交朋友来丰富自己的社交圈，增进人际交往能力。\n",
      "\n",
      "3. **参与社团活动的好处**：加入学生会或参与各类活动，如跳舞、戏剧表演等，能够促进个人在技能、团队合作、领导力等方面的成长。这些活动不仅提供实践机会，还能增加个人的成就感和自信心。\n",
      "\n",
      "4. **多元化角色的贡献**：无论是作为活动的参与者还是幕后工作者，如策划、技术、服装道具等，每种角色都能为团队贡献价值，同时促进个人在不同领域的技能提升和经验积累。\n",
      "\n",
      "总之，这段文字鼓励通过恋爱、交友和参与各种活动来获取多方面的学习和发展机会，强调了在大学生活中积极主动参与的重要性。\n",
      "第 5 段的摘要: 这段文字强调了除了专业学习外，参与各类活动对个人成长与学习的重要性。它涵盖了电业之外的活动，无论是在校内还是校外，都能提供增长和进步的机会。尽管这些活动往往不包含考试和成绩，不会体现在成绩单上，但它们对于个人发展同样至关重要。\n",
      "\n",
      "作者指出，虽然电机工程领域的项目很少能由个人独立完成，需要团队合作才能成功，这要求参与者学会融入团队，从边缘逐渐成为核心，甚至成为领导者。在这个过程中，如何推动团队达成目标、实现个人理想等软实力技能变得尤为重要。这些软实力，如沟通、协作、领导力和问题解决能力，虽不属于传统的专业知识范畴，但对个人的职业发展和团队协作能力有着不可忽视的影响。\n",
      "\n",
      "综上所述，这段文字强调了广泛参与活动对于个人成长的重要性，特别是那些提升软实力的机会，这些软实力与专业技能同等重要，共同构成了个人全面发展的关键要素。\n",
      "第 6 段的摘要: 本文讨论了软实力在个人发展中的重要性。软实力包括沟通、协调、交友、说服、团队精神及领导力等能力，是个人成功的关键因素。软实力不仅影响职业发展，还关乎人际关系的建立。尽管有些人天生具备这些技能，但对于大多数人来说，软实力需要通过后天的学习和实践来培养。\n",
      "\n",
      "作者以自己的经历为例，讲述了在进入台湾大学电机工程系前，自己在社交和沟通方面的能力较弱，但在大学期间通过各种课外活动和学习机会，逐渐培养出了这些软实力。这些软实力的提升对于他在大学生涯乃至未来的职业发展起到了至关重要的作用。\n",
      "\n",
      "因此，作者强调软实力的重要性，并鼓励大家通过不断学习和实践来提高自己的软实力。尽管在几年前的《信号与人生》一书中已经提到过这一点，但其重要性不容忽视，值得再次强调。总之，软实力的培养对电机工程师乃至任何职业人士的生涯发展至关重要。\n",
      "第 7 段的摘要: 这段文字探讨了个人发展黄金时代的概念，指出35岁至55岁为多数人的最佳发展阶段。在这段时间之前和之后，发展可能会受限于成熟度、能力或年龄带来的挑战。文章以电机系毕业生为例，描述了个人发展曲线的多样性，包括斜率的变化、饱和点以及最终达到的高度，强调每个人的发展路径都是独一无二的。\n",
      "\n",
      "作者提出了四个主要因素影响个人发展的观点：实力、努力、大致（可能指目标或方向）以及自我技能。他认为，真正的关键不在于单科成绩的优异与否，而在于综合考虑实力、努力、方向和自我提升的能力，这些因素共同作用于个人的成长过程。文章最后提到，有些人可能从一开始就展现出持续向上的发展趋势，而不会遇到明显的饱和点。\n",
      "\n",
      "总之，这段文字强调了个人发展过程中的多样性和复杂性，并提出实力、努力、目标和自我技能是决定个人成长的关键因素。\n",
      "第 8 段的摘要: 实力主要指在电机工程专业领域的广泛且深入学习与掌握。实力的获得关键在于全面学习，避免过度专注于某一领域而忽视其他相关知识，从而达到精通的程度。努力是实现这一目标的基础，但不同人对于努力的理解和实践会有差异，有的人在未来几十年内始终保持持续的努力，而有的人则逐渐放松。此外，自我技能的重要性不容忽视，包括但不限于课程之外的实践活动、团队合作、领导力培养等，这些软技能能够显著增强个人竞争力。\n",
      "\n",
      "每个人都有独特的长程目标，有些人自小就对此有清晰的认识，而有些人可能需要在适当的时候思考自己想要追求的方向。设定并追求这些目标，比如花费5年到10年的时间，可以帮助个人实现职业或个人发展上的突破。总的来说，实力的构建是一个综合性的过程，不仅涉及专业知识的学习，还包括个人技能的培养以及明确目标的设定。\n",
      "第 9 段的摘要: 这段文字的核心内容围绕着设定和追求长期目标的重要性展开。关键点如下：\n",
      "\n",
      "1. **长期努力的价值**：作者强调了将大量精力投入到某些特别事项中，以实现卓越成果，这代表了他的长期目标。这些目标是他热衷追求的事物，赋予他意义感。\n",
      "\n",
      "2. **目标与意义的联系**：对于作者而言，那些能让他感到有意义、愿意投入时间和精力去完成的事情构成了他的长期目标。这意味着目标不仅仅是外在的成就，更是内在的满足感和动力源泉。\n",
      "\n",
      "3. **目标设定的好处**：能够明确自己的长期目标的人，往往更容易朝着更高的成就迈进。这表明目标设定在个人成长和进步过程中扮演着关键角色。\n",
      "\n",
      "4. **影响因素**：作者认为真正能推动人们向上发展的因素有四个，尽管具体是哪四个并未详细列出，但可以推测可能包括目标设定、持续努力、内在动机以及自我反思等。\n",
      "\n",
      "综上所述，这段文字通过阐述长期目标在个人发展中的重要性，鼓励读者设定并追求能赋予他们意义的目标，同时暗示了成功的关键因素可能包括目标设定、持续努力、内在动机和自我反思。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 收集之前获得的所有摘要并打印它们。\n",
    "\n",
    "collected_summarization = \"\"\n",
    "for index, paragraph_summarization in enumerate(paragraph_summarizations):\n",
    "    collected_summarization += f\"第 {index + 1} 段的摘要: {paragraph_summarization}\\n\"\n",
    "\n",
    "print(collected_summarization)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f858025-b73b-4906-9ac6-c05899622f61",
   "metadata": {},
   "source": [
    "2. 在分别获取每个小段落的摘要后，处理这些摘要以生成最终的摘要。\n",
    "\n",
    "直接运行代码，交互式修改，不用关心这里的代码细节。\n",
    "\n",
    "注意：配置完记得点击**提交配置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4aa1fb50-dab9-4d3f-9c0e-a5777ca13386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**多段摘要：总摘要**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**max_tokens**<br>我们设置生成的最大tokens，确保最终摘要不超过 550 个标记。"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc69f092d0024c2fb9f4480f856f3d6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntText(value=550, description='max_tokens:', style=DescriptionStyle(description_width='initial'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**summarization_prompt**<br>你可以修改摘要prompt，但不要修改 `<text>` 部分："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b03d7a27147946bcbbb75e5a498d1aa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Textarea(value='在500字以内写出以下文字的简洁摘要：<text>', description='summarization_prompt:', layout=Layout(height='100px',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8fd09d5b70c4045a6dcbf1ba4d5f9b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='提交配置', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac38dd834248435bb14ffe93604842e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===== 多段摘要：总摘要设置 =====\n",
    "\n",
    "# 你可以修改摘要prompt和生成的最大tokens，但不要修改 <text> 部分\n",
    "instructions = Markdown(\"**多段摘要：总摘要**\")\n",
    "\n",
    "# 设置生成的最大tokens\n",
    "max_tokens_markdown = Markdown(\"**max_tokens**<br>我们设置生成的最大tokens，确保最终摘要不超过 550 个标记。\")\n",
    "max_tokens_widget = widgets.IntText(\n",
    "    value=550,\n",
    "    description='max_tokens:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 摘要prompt模板\n",
    "summarization_prompt_markdown = Markdown(\"**summarization_prompt**<br>你可以修改摘要prompt，但不要修改 `<text>` 部分：\")\n",
    "summarization_prompt_widget = widgets.Textarea(\n",
    "    value=\"在500字以内写出以下文字的简洁摘要：<text>\",\n",
    "    description='summarization_prompt:',\n",
    "    layout=widgets.Layout(width='500px', height='100px'),\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 创建输出区域来显示打印内容\n",
    "output_area = widgets.Output()\n",
    "\n",
    "# 获取用户输入值并显示\n",
    "def on_button_click(b):\n",
    "    global max_tokens, summarization_prompt_template\n",
    "    with output_area:\n",
    "        output_area.clear_output()  # 清除之前的输出\n",
    "        max_tokens = max_tokens_widget.value\n",
    "        summarization_prompt_template = summarization_prompt_widget.value\n",
    "        \n",
    "        # 打印配置\n",
    "        print(f\"最大tokens: {max_tokens}\")\n",
    "        print(f\"摘要prompt模板: {summarization_prompt_template}\")\n",
    "\n",
    "# 创建提交按钮\n",
    "submit_button = widgets.Button(description=\"提交配置\")\n",
    "submit_button.on_click(on_button_click)\n",
    "\n",
    "# 显示带有说明的所有小部件\n",
    "display(instructions)\n",
    "display(max_tokens_markdown, max_tokens_widget)\n",
    "display(summarization_prompt_markdown, summarization_prompt_widget)\n",
    "\n",
    "# 显示提交按钮\n",
    "display(submit_button)\n",
    "\n",
    "# 显示输出区域\n",
    "display(output_area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77af801d-2e6b-401a-8bc2-107c11b56bd0",
   "metadata": {},
   "source": [
    "实际运行时间与 API 有关。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b5f75dc8-fbcd-4246-8b55-cb8d98703a74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------最终摘要----------------------------\n",
      "\n",
      "第一段摘要：本文强调了实践在学习中的关键作用，认为通过动手操作和应用所学知识，能更有效地掌握和内化知识，形成个人知识体系。实践性学习方式，如参与课程学习、实验活\n",
      "动、完成期末项目和解决复杂问题，有助于学生将理论知识转化为实际技能。文章指出，实践成果的价值难以仅通过分数衡量，真正的学习成果体现在能力提升和个人知识结构的构建\n",
      "上。  第二段摘要：文章指出，在课程学习中，人们往往忽视看似艰难的项目或内容背后的学习机会。通过深入理解数学公式、解读概念和思考实际应用，学生能培养深度思考和独\n",
      "立学习能力。这种方法强调理解文本内容与实践思考相结合，有助于养成良好的思考习惯，实现知识的真正掌握和能力的全面提升。  第三段摘要：本文提倡将日常活动视为学习机\n",
      "会，强调学习无处不在，关键在于发现并利用生活中的各种机会进行自我提升。无论是体育运动、旅行、艺术创作还是志愿服务，只要能促进个人成长、提升技能或增进身心健康，都\n",
      "应被视为有价值的活动。这些活动不仅能增强手脑协调能力、培养团队精神和人际交往能力，还能带来乐趣与满足感。  第四段摘要：文章强调了恋爱、交友和参与社团活动对于个\n",
      "人成长与学习的重要性。恋爱和交友提供了情感体验和人际互动的机会，培养了沟通、协作、领导力等软实力。参与社团活动则能促进技能提升、团队合作和领导力的发展，这些软实\n",
      "力与专业技能同样关键，共同构成个人全面发展的要素。  第五段摘要：本文强调了软实力在个人发展中的重要性，指出沟通、协调、交友、说服、团队精神及领导力等能力是个人\n",
      "成功的关键。软实力的培养对于职业发展和人际关系建立至关重要，虽然有些人在这些方面天生具备优势，但大多数人都需要通过后天学习和实践来提高软实力。  第六段摘要：本\n",
      "文讨论了个人发展黄金时代的概念，指出35岁至55岁为多数人的最佳发展阶段。这段时间内，个人在成熟度、能力和年龄等方面的优势有利于职业发展和自我提升。作者以电机系\n",
      "毕业生为例，强调了个人发展曲线的多样性，包括斜率变化、饱和点和最终高度，并强调了实力、努力、目标和自我技能在个人成长过程中的关键作用。  第七段摘要：本文探讨了\n",
      "个人发展黄金时代及其影响因素，指出实力、努力、目标和自我技能是决定个人成长的关键因素。实力主要指电机工程专业领域的深入学习与掌握，努力是实现这一目标的基础，而目\n",
      "标和自我技能则进一步推动个人发展\n",
      "\n",
      "最终摘要的长度: 976\n",
      "生成最终摘要所需的时间: 12.76 秒。\n"
     ]
    }
   ],
   "source": [
    "# 记录开始时间\n",
    "start = time.time()\n",
    "\n",
    "# 运行最终摘要生成\n",
    "summarization_prompt = summarization_prompt_template.replace(\"<text>\", collected_summarization)\n",
    "final_summarization = summarization(client=client, summarization_prompt=summarization_prompt, model_name=model_name, temperature=temperature, top_p=top_p, max_tokens=max_tokens)\n",
    "\n",
    "# 计算执行时间并四舍五入到两位小数\n",
    "cost_time = round(time.time() - start, 2)\n",
    "\n",
    "# 打印摘要及其长度\n",
    "print(f\"----------------------------最终摘要----------------------------\\n\")\n",
    "for text in textwrap.wrap(final_summarization, 80):\n",
    "        print(f\"{text}\")\n",
    "print(f\"\\n最终摘要的长度: {len(final_summarization)}\")\n",
    "print(f\"生成最终摘要所需的时间: {cost_time} 秒。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "193dfad3-a71c-42dd-9873-4f762894bb29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终摘要已保存到 ./final-summary-信号与人生-llm-multi-stage.txt\n",
      "\n",
      "===== 以下是最终摘要 (976 字) =====\n",
      "\n",
      "第一段摘要：本文强调了实践在学习中的关键作用，认为通过动手操作和应用所学知识，能更有效地掌握和内化知识，形成个人知识体系。实践性\n",
      "学习方式，如参与课程学习、实验活动、完成期末项目和解决复杂问题，有助于学生将理论知识转化为实际技能。文章指出，实践成果的价值难以\n",
      "仅通过分数衡量，真正的学习成果体现在能力提升和个人知识结构的构建上。  第二段摘要：文章指出，在课程学习中，人们往往忽视看似艰难\n",
      "的项目或内容背后的学习机会。通过深入理解数学公式、解读概念和思考实际应用，学生能培养深度思考和独立学习能力。这种方法强调理解文本\n",
      "内容与实践思考相结合，有助于养成良好的思考习惯，实现知识的真正掌握和能力的全面提升。  第三段摘要：本文提倡将日常活动视为学习机\n",
      "会，强调学习无处不在，关键在于发现并利用生活中的各种机会进行自我提升。无论是体育运动、旅行、艺术创作还是志愿服务，只要能促进个人\n",
      "成长、提升技能或增进身心健康，都应被视为有价值的活动。这些活动不仅能增强手脑协调能力、培养团队精神和人际交往能力，还能带来乐趣与\n",
      "满足感。  第四段摘要：文章强调了恋爱、交友和参与社团活动对于个人成长与学习的重要性。恋爱和交友提供了情感体验和人际互动的机会，\n",
      "培养了沟通、协作、领导力等软实力。参与社团活动则能促进技能提升、团队合作和领导力的发展，这些软实力与专业技能同样关键，共同构成个\n",
      "人全面发展的要素。  第五段摘要：本文强调了软实力在个人发展中的重要性，指出沟通、协调、交友、说服、团队精神及领导力等能力是个人\n",
      "成功的关键。软实力的培养对于职业发展和人际关系建立至关重要，虽然有些人在这些方面天生具备优势，但大多数人都需要通过后天学习和实践\n",
      "来提高软实力。  第六段摘要：本文讨论了个人发展黄金时代的概念，指出35岁至55岁为多数人的最佳发展阶段。这段时间内，个人在成熟\n",
      "度、能力和年龄等方面的优势有利于职业发展和自我提升。作者以电机系毕业生为例，强调了个人发展曲线的多样性，包括斜率变化、饱和点和最\n",
      "终高度，并强调了实力、努力、目标和自我技能在个人成长过程中的关键作用。  第七段摘要：本文探讨了个人发展黄金时代及其影响因素，指\n",
      "出实力、努力、目标和自我技能是决定个人成长的关键因素。实力主要指电机工程专业领域的深入学习与掌握，努力是实现这一目标的基础，而目\n",
      "标和自我技能则进一步推动个人发展\n"
     ]
    }
   ],
   "source": [
    "# 你可以修改最终摘要的输出路径。\n",
    "output_path = f\"./final-summary-{suffix}-llm-multi-stage.txt\"\n",
    "\n",
    "# 如果需要将简体中文转换为繁体中文，请将此选项设置为 True；否则，设置为 False。\n",
    "convert_to_tradition_chinese = False\n",
    "\n",
    "if convert_to_tradition_chinese == True:\n",
    "    # 创建一个 OpenCC 实例用于简体到繁体中文转换。\n",
    "    cc = OpenCC('s2t')\n",
    "    final_summarization = cc.convert(final_summarization)\n",
    "\n",
    "# 输出最终摘要\n",
    "with open(output_path, \"w\") as fp:\n",
    "    fp.write(final_summarization)\n",
    "\n",
    "# 显示结果\n",
    "print(f\"最终摘要已保存到 {output_path}\")\n",
    "print(f\"\\n===== 以下是最终摘要 ({len(final_summarization)} 字) =====\\n\")\n",
    "for text in textwrap.wrap(final_summarization, 64):\n",
    "    print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b6f8e0-b0cc-4e9f-ae74-81fd75225d22",
   "metadata": {},
   "source": [
    "##### 方法二：精炼方法（the method of Refinement) - Refine\n",
    "\n",
    "Refinement 就是把每次的文本和之前的摘要结合起来丢给大模型，类似于迭代：\n",
    "\n",
    "![Refinement](../Guide/assets/image-20240924092753352.png)\n",
    "\n",
    "步骤（Pipeline）如下：\n",
    "- 第1步：从一小部分数据开始，运行prompt生成初始输出。\n",
    "- 第2步：对后续每个文档，将前一个输出与新文档结合输入。\n",
    "- 第3步：LLM 根据新文档中的信息精炼输出。\n",
    "- 第4步：此过程持续迭代，直到处理完所有文档。\n",
    "\n",
    "对应的核心代码：\n",
    "```python\n",
    "        # 第1步\n",
    "        first_paragraph_summarization = summarization(client=client, summarization_prompt=summarization_prompt, ...)\n",
    "        # 第2步\n",
    "        chunk_text = f\"\"\"前 {index} 段的摘要: {paragraph_summarizations[-1]}\\n第 {index + 1} 段的内容: {chunk}\"\"\"\n",
    "        # 第3步\n",
    "        paragraph_summarization = summarization(client=client, summarization_prompt=summarization_prompt, ...)\n",
    "```\n",
    "\n",
    "直接运行代码，交互式修改，不用关心这里的代码细节。\n",
    "\n",
    "注意：配置完记得点击**提交配置**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6fb36291-a394-4e12-bb5d-3e9250cb464a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**精炼摘要的prompt设置**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**max_tokens**<br>设置最大生成tokens"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "977f24fb072a46b58f7de4b87fbb2c85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntText(value=550, description='max_tokens:', style=DescriptionStyle(description_width='initial'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**summarization_prompt**<br>你可以修改初始摘要prompt，但不要修改 `<text>` 部分："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71e0bc60240f4675ad9f811177d3f941",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Textarea(value='请在 300 字以内，提供以下文字的简洁摘要:<text>', description='summarization_prompt:', layout=Layout(height='100…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**summarization_prompt_refinement**<br>你可以修改精炼摘要prompt，但不要修改 `<text>` 部分："
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "909271c655ab475fac3624f50cb1a72a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Textarea(value='请在 500 字以内，结合原先的摘要和新的内容，提供简洁的摘要:<text>', description='summarization_prompt_refinement:', layou…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ff934a77c6245e48ce9174b74532edd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='提交配置', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5ca47030ae44042a61b328b6c116930",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===== 精炼摘要的prompt设置 =====\n",
    "\n",
    "# 你可以修改摘要prompt和最大生成tokens，但不要修改 <text> 部分\n",
    "instructions = Markdown(\"**精炼摘要的prompt设置**\")\n",
    "\n",
    "# 设置最大生成tokens\n",
    "max_tokens_markdown = Markdown(\"**max_tokens**<br>设置最大生成tokens\")\n",
    "max_tokens_widget = widgets.IntText(\n",
    "    value=550,\n",
    "    description='max_tokens:',\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 初始摘要prompt\n",
    "summarization_prompt_markdown = Markdown(\"**summarization_prompt**<br>你可以修改初始摘要prompt，但不要修改 `<text>` 部分：\")\n",
    "summarization_prompt_widget = widgets.Textarea(\n",
    "    value=\"请在 300 字以内，提供以下文字的简洁摘要:<text>\",\n",
    "    description='summarization_prompt:',\n",
    "    layout=widgets.Layout(width='500px', height='100px'),\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 精炼摘要prompt\n",
    "summarization_prompt_refinement_markdown = Markdown(\"**summarization_prompt_refinement**<br>你可以修改精炼摘要prompt，但不要修改 `<text>` 部分：\")\n",
    "summarization_prompt_refinement_widget = widgets.Textarea(\n",
    "    value=\"请在 500 字以内，结合原先的摘要和新的内容，提供简洁的摘要:<text>\",\n",
    "    description='summarization_prompt_refinement:',\n",
    "    layout=widgets.Layout(width='500px', height='100px'),\n",
    "    style={'description_width': 'initial'}\n",
    ")\n",
    "\n",
    "# 创建输出区域来显示打印内容\n",
    "output_area = widgets.Output()\n",
    "\n",
    "# 获取用户输入值并显示\n",
    "def on_button_click(b):\n",
    "    global max_tokens, summarization_prompt_template, summarization_prompt_refinement_template\n",
    "    with output_area:\n",
    "        output_area.clear_output()  # 清除之前的输出\n",
    "        max_tokens = max_tokens_widget.value\n",
    "        summarization_prompt_template = summarization_prompt_widget.value\n",
    "        summarization_prompt_refinement_template = summarization_prompt_refinement_widget.value\n",
    "        \n",
    "        # 打印配置\n",
    "        print(f\"最大tokens: {max_tokens}\")\n",
    "        print(f\"初始摘要prompt: {summarization_prompt_template}\")\n",
    "        print(f\"精炼摘要prompt: {summarization_prompt_refinement_template}\")\n",
    "\n",
    "# 创建提交按钮\n",
    "submit_button = widgets.Button(description=\"提交配置\")\n",
    "submit_button.on_click(on_button_click)\n",
    "\n",
    "# 显示 Markdown 和所有小部件\n",
    "display(instructions)\n",
    "display(max_tokens_markdown, max_tokens_widget)\n",
    "display(summarization_prompt_markdown, summarization_prompt_widget)\n",
    "display(summarization_prompt_refinement_markdown, summarization_prompt_refinement_widget)\n",
    "\n",
    "# 显示提交按钮\n",
    "display(submit_button)\n",
    "\n",
    "# 显示输出区域\n",
    "display(output_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d8afe5b7-2604-40bf-97c9-a8dd91f0e40e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------第 1 段摘要----------------------------\n",
      "\n",
      "这段文字强调“学问是做出来的”这一观点。意味着通过实践操作，而非仅仅听讲或阅读，人们才能真正掌握和内化知识。作者以课程学习和实验为例，指出在完成期末项目时，将所\n",
      "\n",
      "学知识整合应用，是知识转化为个人学问的关键过程。同样地，学习复杂算法等知识，通过实际编程解决问题，也能促使理解和记忆的深化。因此，作者呼吁大家积极参与实践，通过\n",
      "\n",
      "动手操作，让学问成为自己的一部分，强调实践的重要性，而非仅依赖于考试分数或理论理解。\n",
      "\n",
      "第 1 段摘要的长度: 202\n",
      "生成第 1 段摘要所需的时间: 3.1 秒。\n",
      "\n",
      "----------------------------前 2 段摘要----------------------------\n",
      "\n",
      "这段文字强调实践对学问形成的重要性，并指出理论学习与实践操作相辅相成。通过参与期末项目、实际编程解决问题等实践活动，个体能将知识转化为个人学问，深化理解和记忆。\n",
      "\n",
      "然而，许多人因担心项目繁重而选择逃避，实际上，这些实践机会是深入学习、培养思考能力的关键。即便付出努力却未直接带来可量化的成绩，这样的实践对全面发展和长期学习习\n",
      "\n",
      "惯的建立极为有益。作者还特别强调了在阅读过程中培养思考能力的重要性，建议读者在遇到数学公式或理论时，主动思考其含义，通过这样的实践锻炼，有效提升思考能力和习惯。\n",
      "\n",
      "前 2 段摘要的长度: 240\n",
      "生成前 2 段摘要所需的时间: 3.3 秒。\n",
      "\n",
      "----------------------------前 3 段摘要----------------------------\n",
      "\n",
      "本文探讨实践在学问形成中的关键作用，指出理论学习与实践操作相辅相成的重要性。通过参与项目、编程等实践活动，个体能将知识内化为个人学问，深化理解并建立长期学习习惯\n",
      "\n",
      "。尽管实践中可能面临挑战，但付出的努力对于全面发展至关重要。作者强调，在阅读中培养思考能力同样重要，鼓励读者主动思考数学公式或理论的含义，以此锻炼思考能力。  \n",
      "\n",
      "除了课业内的学习，课业外的学习也同样重要。作者将学习定义为增长、进步以及获得快乐的过程。无论是体育运动、户外探险还是旅行，只要能够促进个人成长、带来快乐，都应被\n",
      "\n",
      "视为学习的机会。例如，打球可以增进手脑协调、培养团队精神；爬山则有助于提高体力、增强意志力；旅行则能拓宽视野、丰富经验。总之，无论何时何地，只要有增长、有进步、\n",
      "\n",
      "让人感到快乐的活动，都值得投入时间和精力，将其视为学习的一部分。\n",
      "\n",
      "前 3 段摘要的长度: 352\n",
      "生成前 3 段摘要所需的时间: 5.04 秒。\n",
      "\n",
      "----------------------------前 4 段摘要----------------------------\n",
      "\n",
      "本文探讨实践在学问形成中的核心作用，强调理论学习与实践经验相辅相成的必要性。通过参与项目、编程等活动，个体能将知识内化为个人学问，并建立持久的学习习惯。实践中虽\n",
      "\n",
      "然充满挑战，但努力对个人发展至关重要。此外，作者认为阅读同样重要，鼓励读者深入思考数学公式或理论的含义，以提升思考能力。学习不应局限于课堂，体育运动、户外探险、\n",
      "\n",
      "旅行等都能促进个人成长和快乐，这些活动同样被视为学习机会。恋爱、交友和参与社团活动亦是学习过程，它们教会人际交往、沟通技巧和团队合作，对个人发展有益。参与各种活\n",
      "\n",
      "动不仅能够增长知识、提高技能，还能带来乐趣，因此鼓励大家积极参与，把握每一次学习机会。\n",
      "\n",
      "前 4 段摘要的长度: 283\n",
      "生成前 4 段摘要所需的时间: 4.13 秒。\n",
      "\n",
      "----------------------------前 5 段摘要----------------------------\n",
      "\n",
      "本文探讨实践在学问形成中的核心作用，强调理论学习与实践经验相辅相成的重要性。通过参与多样化的活动，如项目、编程、体育运动、户外探险、旅行、社团活动等，个体能将知\n",
      "\n",
      "识内化为个人学问，并建立持久的学习习惯。实践中虽有挑战，但努力对个人发展至关重要。阅读也是关键，鼓励深入理解数学公式或理论，以提升思考能力。学习不应局限于课堂，\n",
      "\n",
      "各种活动都能促进个人成长和快乐，被视为宝贵的学习机会。这些活动虽然无法直接体现在成绩单上，但对培养人际交往、沟通技巧、团队合作等软实力至关重要。软实力指的是除专\n",
      "\n",
      "业技能外的人际关系处理、领导力等能力，对于现代电机工程领域的成功不可或缺。因此，参与各种活动不仅增长知识、提高技能，还能带来乐趣，鼓励大家积极参与，把握每一次学\n",
      "\n",
      "习机会。\n",
      "\n",
      "前 5 段摘要的长度: 324\n",
      "生成前 5 段摘要所需的时间: 4.43 秒。\n",
      "\n",
      "----------------------------前 6 段摘要----------------------------\n",
      "\n",
      "本文探讨实践在学问形成中的核心作用，指出理论学习与实践经验相结合的重要性。通过参与多样化的活动，如项目、编程、体育运动、旅行等，个体能够将知识转化为个人学问，建\n",
      "\n",
      "立持久的学习习惯。实践中虽有挑战，但努力对个人发展至关重要。阅读同样关键，鼓励深入理解数学公式或理论，以提升思考能力。学习不应局限于课堂，各种活动都能促进个人成\n",
      "\n",
      "长和快乐，被视为宝贵的学习机会。这些活动虽然无法直接体现在成绩单上，但对培养人际交往、沟通技巧、团队合作等软实力至关重要。  软实力是指人际关系处理、领导力等非\n",
      "\n",
      "专业技能，对于现代电机工程领域的成功不可或缺。它包括沟通能力、协调能力、交朋友的能力、说服人的能力以及团队精神和领导能力等。软实力的培养对于大多数工程师而言，是\n",
      "\n",
      "通过参与各种课外学习和增长机会来实现的。尽管有些人可能天生在这方面表现出色，但大多数人的软实力是通过自我努力逐步培养起来的。  作者通过自己的经历说明，在进入台\n",
      "\n",
      "湾大学电机系之前，他并不擅长社交和沟通。然而，在大学四年的学习过程中，他通过各种机会逐渐培养了自己的软实力，这对他后来的职业生涯发展起到了重要作用。因此，强调软\n",
      "\n",
      "实力的重要性对于电机工程师来说至关重要。\n",
      "\n",
      "前 6 段摘要的长度: 500\n",
      "生成前 6 段摘要所需的时间: 6.74 秒。\n",
      "\n",
      "----------------------------前 7 段摘要----------------------------\n",
      "\n",
      "本文探讨实践与理论学习相结合在学问形成中的核心作用，强调参与多样化的活动能促进知识内化和个人成长。通过项目、编程、体育、旅行等活动，个体能将理论知识转化为实践，\n",
      "\n",
      "建立持久的学习习惯，并通过阅读加深理解，从而提升思考能力。学习不应仅限于课堂，而是要通过各种活动促进个人全面发展。软实力，包括人际交往、沟通、团队合作等非专业技\n",
      "\n",
      "能，对电机工程领域成功至关重要，且通常需要通过自我努力逐步培养。作者分享个人经历，从不擅长社交到大学期间通过各种机会提升软实力，认识到这对其职业生涯的影响。文章\n",
      "\n",
      "指出，35至55岁是个人发展的黄金时期，电机系毕业生的发展路径各不相同，主要受到实力、努力、大致方向及自我技能等因素的影响。文章强调，真正的关键在于综合实力、努\n",
      "\n",
      "力程度、正确的方法论以及个人的自我技能，而非单一学科成绩。\n",
      "\n",
      "前 7 段摘要的长度: 349\n",
      "生成前 7 段摘要所需的时间: 4.74 秒。\n",
      "\n",
      "----------------------------前 8 段摘要----------------------------\n",
      "\n",
      "本文探讨实践与理论学习相结合在学问形成中的关键作用，强调通过参与多样化活动如项目、编程、体育、旅行等，将理论知识转化为实践，建立持久的学习习惯，并通过阅读深化理\n",
      "\n",
      "解，以提升思考能力。学习不应局限于课堂，应通过各种活动促进个人全面发展。软实力，包括人际交往、沟通、团队合作等非专业技能，在电机工程领域尤为重要，通常需通过自我\n",
      "\n",
      "努力逐步培养。作者分享了从不擅长社交到大学期间通过各种机会提升软实力的经历，认识到这对其职业生涯的影响。文章指出，电机工程毕业生的发展路径多样，受实力、努力、大\n",
      "\n",
      "致方向及自我技能等因素影响。实力主要体现在全面学习专业领域的知识，避免过度专注于某一领域而忽视其他重要方面。努力程度显著影响个人未来的发展，持续的努力与不同努力\n",
      "\n",
      "程度之间的差异明显。此外，非学术性技能（self skills）对于个人发展同样重要，鼓励在课外活动中增长和提高这些技能。文章建议每个人设定长期目标，根据自身情\n",
      "\n",
      "况在适当时机开始思考自己希望追求的事物，并为之努力。\n",
      "\n",
      "前 8 段摘要的长度: 426\n",
      "生成前 8 段摘要所需的时间: 5.36 秒。\n",
      "\n",
      "----------------------------前 9 段摘要----------------------------\n",
      "\n",
      "本文探讨实践与理论学习相结合对学问形成的关键作用，强调通过参与多样化活动，如项目、编程、体育、旅行等，将理论知识转化为实践，建立持久的学习习惯。作者分享了自己从\n",
      "\n",
      "不擅长社交到通过各种机会提升软实力的经历，认识到软实力对职业发展的重要性。文章指出，电机工程毕业生的发展路径多样，受实力、努力、大致方向及自我技能等因素影响。实\n",
      "\n",
      "力体现在全面学习专业领域的知识，避免过度专注某一领域。努力程度显著影响未来的发展，而非学术性技能对于个人发展同样重要。作者建议设定长期目标，找到那些能带来意义感\n",
      "\n",
      "、愿意投入精力去做的事情作为长程目标，认为这有助于个人向上发展。\n",
      "\n",
      "前 9 段摘要的长度: 272\n",
      "生成前 9 段摘要所需的时间: 3.54 秒。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "paragraph_summarizations = []\n",
    "\n",
    "# 首先，我们分别对拆分后的每个部分进行摘要。\n",
    "for index, chunk in enumerate(chunks):\n",
    "\n",
    "    if index == 0:\n",
    "        # 记录开始时间\n",
    "        start = time.time()\n",
    "\n",
    "        # 构建初始摘要prompt\n",
    "        summarization_prompt = summarization_prompt_template.replace(\"<text>\", chunk)\n",
    "\n",
    "        # 第1步: 从一小部分数据开始，运行prompt生成初始输出\n",
    "        first_paragraph_summarization = summarization(client=client, summarization_prompt=summarization_prompt, model_name=model_name, temperature=temperature, top_p=top_p, max_tokens=max_tokens)\n",
    "\n",
    "        # 记录结果\n",
    "        paragraph_summarizations.append(first_paragraph_summarization)\n",
    "\n",
    "        # 计算执行时间并四舍五入到两位小数\n",
    "        cost_time = round(time.time() - start, 2)\n",
    "\n",
    "        # 打印摘要及其长度\n",
    "        print(f\"----------------------------第 {index + 1} 段摘要----------------------------\\n\")\n",
    "        for text in textwrap.wrap(first_paragraph_summarization, 80):\n",
    "            print(f\"{text}\\n\")\n",
    "        print(f\"第 {index + 1} 段摘要的长度: {len(first_paragraph_summarization)}\")\n",
    "        print(f\"生成第 {index + 1} 段摘要所需的时间: {cost_time} 秒。\\n\")\n",
    "\n",
    "    else:\n",
    "        # 记录开始时间\n",
    "        start = time.time()\n",
    "\n",
    "        # 第2步：将前一个输出与新文档结合输入\n",
    "        chunk_text = f\"\"\"前 {index} 段的摘要: {paragraph_summarizations[-1]}\\n第 {index + 1} 段的内容: {chunk}\"\"\"\n",
    "\n",
    "        # 构建精炼摘要prompt\n",
    "        summarization_prompt = summarization_prompt_refinement_template.replace(\"<text>\", chunk_text)\n",
    "\n",
    "        # 第3步：LLM 被指示根据新文档中的信息精炼输出\n",
    "        paragraph_summarization = summarization(client=client, summarization_prompt=summarization_prompt, model_name=model_name, temperature=temperature, top_p=top_p, max_tokens=max_tokens)\n",
    "\n",
    "        # 记录结果\n",
    "        paragraph_summarizations.append(paragraph_summarization)\n",
    "\n",
    "        # 计算执行时间并四舍五入到两位小数\n",
    "        cost_time = round(time.time() - start, 2)\n",
    "\n",
    "        # 打印结果\n",
    "        print(f\"----------------------------前 {index + 1} 段摘要----------------------------\\n\")\n",
    "        for text in textwrap.wrap(paragraph_summarization, 80):\n",
    "            print(f\"{text}\\n\")\n",
    "        print(f\"前 {index + 1} 段摘要的长度: {len(paragraph_summarization)}\")\n",
    "        print(f\"生成前 {index + 1} 段摘要所需的时间: {cost_time} 秒。\\n\")\n",
    "\n",
    "    # 第4步：此过程持续迭代，直到处理完所有文档。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5fe9f4e4-e0ff-4891-9443-cdaab9898451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终摘要已保存到 ./final-summary-信号与人生-llm-refinement.txt\n",
      "\n",
      "===== 以下是最终摘要 (272 字) =====\n",
      "\n",
      "本文探讨实践与理论学习相结合对学问形成的关键作用，强调通过参与多样化活动，如项目、编程、体育、旅行等，将理论知识转化为实践，建立持久的学习习惯。作者分享了自己从\n",
      "不擅长社交到通过各种机会提升软实力的经历，认识到软实力对职业发展的重要性。文章指出，电机工程毕业生的发展路径多样，受实力、努力、大致方向及自我技能等因素影响。实\n",
      "力体现在全面学习专业领域的知识，避免过度专注某一领域。努力程度显著影响未来的发展，而非学术性技能对于个人发展同样重要。作者建议设定长期目标，找到那些能带来意义感\n",
      "、愿意投入精力去做的事情作为长程目标，认为这有助于个人向上发展。\n"
     ]
    }
   ],
   "source": [
    "# 你可以修改最终摘要的输出路径。\n",
    "\n",
    "output_path = f\"./final-summary-{suffix}-llm-refinement.txt\"\n",
    "\n",
    "# 如果需要将简体中文转换为繁体中文，请将此选项设置为 True；否则，设置为 False。\n",
    "convert_to_tradition_chinese = False\n",
    "\n",
    "if convert_to_tradition_chinese == True:\n",
    "    # 创建一个 OpenCC 实例用于简体到繁体中文转换。\n",
    "    cc = OpenCC('s2t')\n",
    "    paragraph_summarizations[-1] = cc.convert(paragraph_summarizations[-1])\n",
    "\n",
    "# 输出最终摘要\n",
    "with open(output_path, \"w\") as fp:\n",
    "    fp.write(paragraph_summarizations[-1])\n",
    "\n",
    "# 显示结果\n",
    "print(f\"最终摘要已保存到 {output_path}\")\n",
    "print(f\"\\n===== 以下是最终摘要 ({len(paragraph_summarizations[-1])} 字) =====\\n\")\n",
    "for text in textwrap.wrap(paragraph_summarizations[-1], 80):\n",
    "    print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ec97da-65e1-4c42-ae77-1806d8297cde",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "通过这个代码文件，你将了解如何：\n",
    "\n",
    "1. 使用 Whisper 模型从视频音频生成字幕。\n",
    "2. 将字幕文件中的内容提取并处理，转换为文本格式。\n",
    "3. 使用 OpenAI API 为文本生成摘要。\n",
    "\n",
    "你也将：\n",
    "1. 理解 AI 视频总结助手实际上是如何工作的。\n",
    "2. 发现 AI 应用实际真的并不难，你完全有能力制作一个属于自己的 AI 应用。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a87465b2-c48c-4769-b463-cad424d8b9d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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