whisper_bilibili_ipynb.ipynb

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        "**<font size='5'>以下顺次点击下方每个单元格左侧的“运行”图标，不可跳过步骤</font>**\n",
        "**</br>【重要】:** 务必在\"修改\"->\"笔记本设置\"->\"硬件加速器\"中选择GPU！否则处理速度会非常慢。\n",
        " **</br>【IMPORTANT】:** Make sure you select GPU as hardware accelerator in notebook settings, otherwise the processing speed will be very slow."
      ]
    },
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      "source": [
        "#@title **通用参数/Required settings:**\n",
        "\n",
        "\n",
        "# @markdown **【IMPORTANT】:**<font size=\"2\">Select uploaded file type.\n",
        "# @markdown **</br>【重要】:** 选择上传的文件类型(视频-video/音频-audio）</font>\n",
        "\n",
        "# encoding:utf-8\n",
        "file_type = \"audio\"  # @param [\"audio\",\"video\"]\n",
        "\n",
        "# @markdown #### **Youtube video or playlist**\n",
        "url = \"https://www.bilibili.com/video/BV1HT411K74R\"  # @param {type:\"string\"}\n",
        "filename=\"1\"  # @param {type:\"string\"}\n",
        "\n",
        "model_size = \"large-v2\"  # @param [\"base\",\"small\",\"medium\", \"large-v1\",\"large-v2\"]\n",
        "language = \"en\"  # @param [\"en\",\"cn\"]\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **运行Whisper/Run Whisper**\n",
        "#@markdown 完成后srt文件将自动下载到本地/srt file will be auto downloaded after finish.\n",
        "! pip install faster-whisper\n",
        "! pip install ffmpeg\n",
        "! pip install torch\n",
        "! pip install pysubs2\n",
        "! pip install you-get\n",
        "from IPython.display import clear_output \n",
        "clear_output()\n",
        "print('语音识别库配置完毕，将开始转换')\n",
        "import os\n",
        "import subprocess\n",
        "import torch\n",
        "from google.colab import files\n",
        "from faster_whisper import WhisperModel\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "import pandas as pd\n",
        "import requests\n",
        "from urllib.parse import quote_plus\n",
        "from pathlib import Path\n",
        "import sys\n",
        "import pysubs2\n",
        "import gc\n",
        "import re\n",
        "\n",
        "# assert file_name != \"\"\n",
        "# assert language != \"\"\n",
        "file_basenames = []\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "print('加载模型 Loading model...')\n",
        "model = WhisperModel(model_size)"
      ],
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            "语音识别库配置完毕，将开始转换\n",
            "加载模型 Loading model...\n"
          ]
        },
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              "Downloading (…)37e8b/vocabulary.txt:   0%|          | 0.00/460k [00:00<?, ?B/s]"
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          "text": [
            "site:                Bilibili\n",
            "title:               【直播回放】卡尔·弗里斯顿：我和我的马尔可夫毯：能动推理和最小自由能原理\n",
            "stream:\n",
            "    - format:        \u001b[7mdash-flv480\u001b[0m\n",
            "      container:     mp4\n",
            "      quality:       清晰 480P\n",
            "      size:          328.1 MiB (344054083 bytes)\n",
            "    # download-with: \u001b[4myou-get --format=dash-flv480 [URL]\u001b[0m\n",
            "\n",
            "Downloading 1.mp4 ...\n",
            " 100% (328.1/328.1MB) ├████████████████████████████████████████┤[2/2]   33 MB/s\n",
            "Merging video parts... Merged into 1.mp4\n",
            "\n",
            "Downloading 【直播回放】卡尔·弗里斯顿：我和我的马尔可夫毯：能动推理和最小自由能原理.cmt.xml ...\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "from you_get import common as you_get       #导入you-get库\n",
        " \n",
        "#设置下载目录\n",
        "directory = %pwd  \n",
        "\n",
        "#设置要下载的视频地址    \n",
        "sys.argv = ['you-get', '-l', '-o', directory,'-O',f'{filename}', url]\n",
        " \n",
        "you_get.main()\n"
      ]
    },
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      "source": [
        "\n",
        "tic = time.time()\n",
        "print('识别中 Transcribe in progress...')\n",
        "segments, info = model.transcribe(audio = f'{filename}.mp4',beam_size=5,language=language)\n",
        "\n",
        "# segments is a generator so the transcription only starts when you iterate over it\n",
        "# to use pysubs2, the argument must be a segment list-of-dicts\n",
        "total_duration = round(info.duration, 2)  # Same precision as the Whisper timestamps.\n",
        "results= []\n",
        "with tqdm(total=total_duration, unit=\" seconds\") as pbar:\n",
        "    for s in segments:\n",
        "        segment_dict = {'start':s.start,'end':s.end,'text':s.text}\n",
        "        results.append(segment_dict)\n",
        "        segment_duration = s.end - s.start\n",
        "        pbar.update(segment_duration)\n",
        "\n",
        "\n",
        "#Time comsumed\n",
        "toc = time.time()\n",
        "print('识别完毕 Done')\n",
        "print(f'Time consumpution {toc-tic}s')\n",
        "\n",
        "srt_file_path = filename+'.srt'\n",
        "subs = pysubs2.load_from_whisper(results)\n",
        "subs.save(srt_file_path)\n",
        "\n",
        "files.download(srt_file_path)\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "print('字幕生成完毕 All done!')"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "识别中 Transcribe in progress...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 96%|█████████▌| 8088.9000000000115/8437.16 [11:08<00:28, 12.09 seconds/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "识别完毕 Done\n",
            "Time consumpution 705.3728559017181s\n"
          ]
        },
        {
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          "text": [
            "\n"
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            "字幕生成完毕 All done!\n"
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