001_YouTube_Video_Summarization_Huggingface.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsECLh62CzhM"
      },
      "source": [
        "# Chapter 1: Download Video"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TX838YHo_9ZW",
        "outputId": "e9f56d7f-bbac-405a-f333-fddce9a88d14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |█████▉                          | 10 kB 28.2 MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 20 kB 32.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 30 kB 38.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 40 kB 33.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 51 kB 36.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 56 kB 3.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install pytube -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SrlmWy4v_pB7"
      },
      "outputs": [],
      "source": [
        "from pytube import YouTube"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ikkNFnIe_3w1"
      },
      "outputs": [],
      "source": [
        "VIDEO_URL = \"https://youtu.be/FT3ODSg1GFE\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cM00J8E7AK__"
      },
      "outputs": [],
      "source": [
        "yt = YouTube(VIDEO_URL)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "eFd0Lpb3AUpV",
        "outputId": "fc70d0cc-e72b-4afd-aafb-df9590d3b305"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/ytaudio.mp4'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "yt.streams \\\n",
        "  .filter(only_audio = True, file_extension = 'mp4') \\\n",
        "  .first() \\\n",
        "  .download(filename = \"ytaudio.mp4\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNbvCgynBaEe",
        "outputId": "9c9bca96-8ea3-4959-9d03-a7cfd04612e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ffmpeg version 3.4.11-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\n",
            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
            "  libavutil      55. 78.100 / 55. 78.100\n",
            "  libavcodec     57.107.100 / 57.107.100\n",
            "  libavformat    57. 83.100 / 57. 83.100\n",
            "  libavdevice    57. 10.100 / 57. 10.100\n",
            "  libavfilter     6.107.100 /  6.107.100\n",
            "  libavresample   3.  7.  0 /  3.  7.  0\n",
            "  libswscale      4.  8.100 /  4.  8.100\n",
            "  libswresample   2.  9.100 /  2.  9.100\n",
            "  libpostproc    54.  7.100 / 54.  7.100\n",
            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'ytaudio.mp4':\n",
            "  Metadata:\n",
            "    major_brand     : dash\n",
            "    minor_version   : 0\n",
            "    compatible_brands: iso6mp41\n",
            "    creation_time   : 2022-09-08T08:17:43.000000Z\n",
            "  Duration: 00:04:19.92, start: 0.000000, bitrate: 48 kb/s\n",
            "    Stream #0:0(eng): Audio: aac (HE-AAC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 1 kb/s (default)\n",
            "    Metadata:\n",
            "      creation_time   : 2022-09-08T08:17:43.000000Z\n",
            "      handler_name    : ISO Media file produced by Google Inc.\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (aac (native) -> pcm_s16le (native))\n",
            "Press [q] to stop, [?] for help\n",
            "Output #0, wav, to 'ytaudio.wav':\n",
            "  Metadata:\n",
            "    major_brand     : dash\n",
            "    minor_version   : 0\n",
            "    compatible_brands: iso6mp41\n",
            "    ISFT            : Lavf57.83.100\n",
            "    Stream #0:0(eng): Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, stereo, s16, 512 kb/s (default)\n",
            "    Metadata:\n",
            "      creation_time   : 2022-09-08T08:17:43.000000Z\n",
            "      handler_name    : ISO Media file produced by Google Inc.\n",
            "      encoder         : Lavc57.107.100 pcm_s16le\n",
            "size=   16245kB time=00:04:19.92 bitrate= 512.0kbits/s speed= 528x    \n",
            "video:0kB audio:16245kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.000469%\n"
          ]
        }
      ],
      "source": [
        "! ffmpeg -i ytaudio.mp4 -acodec pcm_s16le -ar 16000 ytaudio.wav"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UqNEMjeVCap9"
      },
      "source": [
        "# Chapter 2: English Automatic Speech Recognition(ASR) with Huggingsound"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zA2TF6-YCYp1",
        "outputId": "342a1056-4f00-41e0-d53f-d4aaafb3ef12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 25.3 MB 70.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 56.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 57.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 312 kB 72.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 120 kB 73.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 75.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 77.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 57.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 61.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 104 kB 66.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 76.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 50.5 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install huggingsound -q "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "36975e899a21495ea3128fecd290da06",
            "dcdd3de9499a4a849971ac9a78ed8fe2",
            "259758fd994545f382a5aaa859be231c",
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            "b943f191a5d345879d4153474717d8ed",
            "35da013e629a424799c7fa800510c7d6",
            "7f3a745969724c569a44765b3804a033"
          ]
        },
        "id": "g8bl4FiNC9r9",
        "outputId": "9adcc562-ea8f-4918-99bb-599197151911"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36975e899a21495ea3128fecd290da06",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingsound import SpeechRecognitionModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0tt3I1MADNxU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2qkUebn5Dj3c",
        "outputId": "505a292f-e65c-4894-efe2-1d97ded7b149"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda'"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
            "6e45b2a674ef4e1a8a2eebd8da2ae00f",
            "9bae7b73db424ddba759fc53408d2959",
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            "c437b0b01b864b72b92f218c1970a977",
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            "e33afdd8474245deb3ab2619d6a6c8b3",
            "4aff1e18bbd449ab9ed4249f06b498a3",
            "c33fd2dc695c4ef4891c9a12898e938b",
            "8c5bd4da13844e328f3054db276e4d28",
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            "c8f8828bbe4b4b3c96f06ae0d7bdff59",
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            "021efb6b76b246579079230b0b32bd4f",
            "23c818ffb364477aa265c7c8ec7712a4",
            "cbb1d6c73861455684c212a8277f7419",
            "a96a0d314806446e89da221491c740fc",
            "b9c6acde44e749f78e6050c1ac28de55",
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            "4a941c874c634d888fa446a096bc2a68",
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            "b4076701ba1148918dffe4841a6dd55d",
            "9b7c2695cbe349b1a91e12b119c342f3",
            "420a2ffc72f04f9abdcbbb866db400c3",
            "08f38735e35440e18ca338a6f3651303",
            "b2a7b82d6dd240a2b152663448cfcf0a",
            "74628f0378004e5babec5e0e64ab257a",
            "8764a39007a14866a149f27684f45b10",
            "9cae9e20ee914ce295523c03a3f63489",
            "fce8afd864f640b3b3037ef443b0927c",
            "11871a8d6aa34f3bbb826211153df0f4",
            "b4a0710d95304f2aa94afa60e2f4079d",
            "003207e2fed04922aa85333edff83be0",
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            "85e0452ffa2844eaa0efaea473f29a72",
            "fda90ffc39fc4d919531fa5ea1bebe9c",
            "4e470e4d972043f1bef33b693d441a4a",
            "7ee944cbe1c3436f9d8614386ca5acb2",
            "da1217104e9541ebb327bed3dc193abd",
            "343a099d2c7d425f8b4ff76366eda60f",
            "e8ab1ab63c9449e784f86a17da4af760",
            "c8df61c94d474a66beb5266e3cf00c91",
            "7327ab23575b4ee9912fbc76af329ad8"
          ]
        },
        "id": "5Xg2QTbmDlzG",
        "outputId": "c9ce38cb-7eb5-44d4-b42b-7a9a4d989642"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:huggingsound.speech_recognition.model:Loading model...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e45b2a674ef4e1a8a2eebd8da2ae00f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.26G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8f8828bbe4b4b3c96f06ae0d7bdff59",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/262 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b7c2695cbe349b1a91e12b119c342f3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/300 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48e93b3625c74d75a28f1476d2271bbb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/85.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = SpeechRecognitionModel(\"jonatasgrosman/wav2vec2-large-xlsr-53-english\", device = device)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYK7SS_RENjN"
      },
      "source": [
        "# Chapter 2b: Audio Chunking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gwOwdR74ES88"
      },
      "outputs": [],
      "source": [
        "import librosa "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rLDYIn9aEbTH"
      },
      "outputs": [],
      "source": [
        "input_file = '/content/ytaudio.wav'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2VV33xEEha0",
        "outputId": "f9e19d1e-29c8-4f64-fff1-5af64ff45993"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "16000\n"
          ]
        }
      ],
      "source": [
        "print(librosa.get_samplerate(input_file))\n",
        "\n",
        "# Stream over 30 seconds chunks rather than load the full file\n",
        "stream = librosa.stream(\n",
        "    input_file,\n",
        "    block_length=30,\n",
        "    frame_length=16000,\n",
        "    hop_length=16000\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpfLttTWEtF8"
      },
      "outputs": [],
      "source": [
        "import soundfile as sf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LYITYjrLFAKE"
      },
      "outputs": [],
      "source": [
        "for i, speech in enumerate(stream):\n",
        "  sf.write(f'{i}.wav', speech, 16000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtG65YV_FNol"
      },
      "source": [
        "# Chunk Transcription"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fWIbae8WFTzt"
      },
      "outputs": [],
      "source": [
        "audio_path = []\n",
        "for a in range(i+1):\n",
        "  audio_path.append(f'/content/{a}.wav')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnyARl4pFlMd",
        "outputId": "3f7016e9-49c3-4ff3-fd30-eb399f1f892b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['/content/0.wav', '/content/1.wav', '/content/2.wav', '/content/3.wav', '/content/4.wav', '/content/5.wav', '/content/6.wav', '/content/7.wav', '/content/8.wav']\n"
          ]
        }
      ],
      "source": [
        "print(audio_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcZSkGPhFncU",
        "outputId": "c9151553-987e-456d-f7dc-9394f954e6f6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
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            " 67%|██████▋   | 6/9 [00:09<00:02,  1.14it/s]\u001b[A\n",
            " 78%|███████▊  | 7/9 [00:09<00:01,  1.32it/s]\u001b[A\n",
            " 89%|████████▉ | 8/9 [00:10<00:00,  1.46it/s]\u001b[A\n",
            "100%|██████████| 9/9 [00:10<00:00,  1.19s/it]\n"
          ]
        }
      ],
      "source": [
        "transcriptions = model.transcribe(audio_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K15DbVCLF003"
      },
      "outputs": [],
      "source": [
        "full_transcript = ' '"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmX9eJOkF5ZF",
        "outputId": "7d9db906-2f56-4268-fd5a-07c0ad7ea937"
      },
      "outputs": [],
      "source": [
        "transcriptions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oqoZx-n5GVhl"
      },
      "outputs": [],
      "source": [
        "for item in transcriptions:\n",
        "  full_transcript += ''.join(item['transcription'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "O1PBqfFLGsK8",
        "outputId": "acd50264-f243-4e2a-80a4-98748c96a0c3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\" it's time to go beyond what a smartphone can dowith a massive camera upgrade the a-six-team bionic ship an entirely new category of safety innovations and never before seen display technologythis is yephone fourteen-prowe always on display artfully dims your wallpaper to create a unique low-powered lock screenmaking go-to information available at just a glands and introducing a remarkable innovation that is both hardware and software and something wethe dynamic islandan interactive place that bubbles up alerts notifications and activities fluidly trans forming itself as neededit's an entirely newway to experience ephoneyou are now looking at the most impressive iphone camera system yet in a major leap the yephone forteen-prose main camera has a forty-eight mega-pixal quad-pixtal sensor it capthers breathtaking detail depth and color nse the equivalent of four prolences at the readywith every photo the quad-pixel sensor captures four times more information than ever beforethen the a-sixteen bionic powering the new clotonic engine analyzes and perfects every fixel with up to four trilian operations achieving an astonishing new level of sharpness in detailowpro-photographers will be able to capture an uncompressed forty-egt mega-pixel pro-raw image giving you even more resolution to rescale retouch and reimagine your photosbuilding off iphone sleap ahead in progress and dubivision video technologyiphone fortin pro now includes new action mode allowing gimblelike stabilization for all your action scenesin cinematic mode will be even more powerful for pro-filmmakers with forq resolution and twenty-four frames per second recordingiphone forting prow is capable of shots that promost film cameras are simply impossibleyou can rely on your iphone every day and now it can even help in emergency situationsfor the first time yphone fourtein-pro will be able to communicate with satellites with emergencysallowing you to deliver critical information to emergency services in places where wi-fy and cell service are unavailablean a feature we hope you never have to usecrash detectionusing a high-g-force accelerometer giroscope sensors and machine learning algorithms yphone forting pro can detect when there has been a severe car crash and automatically call emergency servicessupercharged by nearly sixteen billion transistors the a-sixteen bionic is the fastest ship in a smart phone and powers the most impressive range of pro-innovations yetthe eyephone forcing pro goes above and beyond redefining what i smartpone can beagain\""
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "full_transcript"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sxRVM9fyGwk0"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrXDwZHAG08c"
      },
      "source": [
        "# Chapter 3: Text Summarization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JS7j1uAYG4MF"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
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        "id": "NvvgqkJhG657",
        "outputId": "484b3561-4395-44e8-8586-5710351d35ff"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
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              "Downloading:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
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        {
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        {
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      "source": [
        "summarization = pipeline('summarization')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9Z50-aqG-0b",
        "outputId": "5b19adee-2ab4-41bf-b84c-6f0416c346e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'summary_text': ' The iphone fortin pro now includes new action mode allowing gimblelike stabilization for all your action scenes . The a-sixteen bionic is the fastest ship in a smart phone and powers the most impressive range of pro-innovations yet . For the first time yphone fourtein-pro will be able to communicate with satellites with emergencys .'}]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summarization(full_transcript)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yEXmsa9YHnkc"
      },
      "source": [
        "Text chunking before Summarization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co5E7eSyHsD_",
        "outputId": "16702040-3187-4c6a-9453-e6aac1e3c0da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Summarized text\n",
            " The yephone forteen-prose main camera has a forty-eight mega\n",
            "Summarized text\n",
            " iphone fortin pro now includes new action mode allowing gimblelike stabilization for\n",
            "Summarized text\n",
            " The eyephone forcing pro goes above and beyond redefining what i smartpone\n",
            "[' The yephone forteen-prose main camera has a forty-eight mega', ' iphone fortin pro now includes new action mode allowing gimblelike stabilization for', ' The eyephone forcing pro goes above and beyond redefining what i smartpone']\n"
          ]
        }
      ],
      "source": [
        "num_iters = int(len(full_transcript)/1000)\n",
        "summarized_text = []\n",
        "for i in range(0, num_iters + 1):\n",
        "  start = 0\n",
        "  start = i * 1000\n",
        "  end = (i + 1) * 1000\n",
        "  #print(\"input text \\n\" + full_transcript[start:end])\n",
        "  out = summarization(full_transcript[start:end], min_length = 5, max_length=20)\n",
        "  out = out[0]\n",
        "  out = out['summary_text']\n",
        "  print(\"Summarized text\\n\"+out)\n",
        "  summarized_text.append(out)\n",
        "\n",
        "print(summarized_text)"
      ]
    }
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