SpeechSummarization1.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ZahraArshia/Speech-Summarization/blob/master/SpeechSummarization1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#install nessecery packages"
      ],
      "metadata": {
        "id": "p5QyeDIhdRTZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install subprocess.run\n",
        "!pip install gTTS\n",
        "!pip install ipython\n",
        "!pip install vosk\n",
        "!pip install pydub\n",
        "!pip install torchmetrics"
      ],
      "metadata": {
        "id": "8bvDBCC_dYbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install regex\n",
        "!pip install torch"
      ],
      "metadata": {
        "id": "-X-gy-L2oT5b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Libreries"
      ],
      "metadata": {
        "id": "7B47gHd_deBx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import json\n",
        "from time import sleep\n",
        "from gtts import gTTS\n",
        "import IPython\n",
        "import sys\n",
        "\n",
        "from torchmetrics.functional.text.rouge import rouge_score\n",
        "\n",
        "from vosk import Model, KaldiRecognizer\n",
        "import subprocess"
      ],
      "metadata": {
        "id": "p5BtbsTQdgpC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**MODEL**"
      ],
      "metadata": {
        "id": "T4Sb-gEPaBft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "FRAME_RATE = 16000\n",
        "CHANNELS = 1\n",
        "\n",
        "#load model\n",
        "model = Model(model_name= \"vosk-model-en-us-0.22\")\n",
        "\n",
        "#initialize recognizer\n",
        "rec = KaldiRecognizer(model ,FRAME_RATE)\n",
        "\n",
        "#show text transcript and individual words and the models confidence on the other hand give us information about that\n",
        "rec.SetWords(True)"
      ],
      "metadata": {
        "id": "wc13tquOFILi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#load audio file to pass the recognizer model and for this work we use pydub library\n",
        "from pydub import AudioSegment #this is a class that enable us to load audio and Manipulate it"
      ],
      "metadata": {
        "id": "p2E6A_wsJslI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#load the file\n",
        "path = \"/content/drive/MyDrive/data/Bdb001.interaction.wav\"\n",
        "speech = AudioSegment.from_file(path)\n",
        "#set the number of channel of audio\n",
        "speech = speech.set_channels(CHANNELS)\n",
        "#set frame number of audio\n",
        "speech = speech.set_frame_rate(FRAME_RATE)"
      ],
      "metadata": {
        "id": "Z7_yCz70MvKc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pass audio file into speech recognition model\n",
        "rec.AcceptWaveform(speech.raw_data)  #raw_data is a bi nary representation of the actual data\n",
        "\n",
        "#result of speech recognition\n",
        "result = rec.Result()  #it returns json file format"
      ],
      "metadata": {
        "id": "sLTTGOfnS7JY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = json.loads(result)[\"text\"]  #this can convert it to a python dictionary"
      ],
      "metadata": {
        "id": "_aXq83CvRELR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text"
      ],
      "metadata": {
        "id": "tHKCz-lzSxC1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "json.loads(result)"
      ],
      "metadata": {
        "id": "wKj-Lt7ycySg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cased = subprocess.check_output(\"\", shell=True, text=True, input=text)"
      ],
      "metadata": {
        "id": "kSiPA8fikAwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#A function to trancribe longer audio"
      ],
      "metadata": {
        "id": "1Hz92v8apuVR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def voice_recognition():\n",
        "  #load model\n",
        "  model = Model(model_name= \"vosk-model-en-us-0.22\")\n",
        "\n",
        "  #initialize recognizer\n",
        "  rec = KaldiRecognizer(model ,FRAME_RATE)\n",
        "\n",
        "  #show text transcript and individual words and the models confidence on the other hand give us information about that\n",
        "  rec.SetWords(True)\n",
        "\n",
        "  #load the file\n",
        "  speech = AudioSegment.from_file(path)\n",
        "  #set the number of channel of audio\n",
        "  speech = speech.set_channels(CHANNELS)\n",
        "  #set frame number of audio\n",
        "  speech = speech.set_frame_rate(FRAME_RATE)\n",
        "\n",
        "  step = 45000\n",
        "  transcript = \"\"\n",
        "  for i for range(0, len(speech), step):\n",
        "    print(f\"progress: {i/len(speech)}\")\n",
        "    segment = speech[i:(i+step)]\n",
        "\n",
        "    #pass audio file into speech recognition model\n",
        "    rec.AcceptWaveform(speech.raw_data)  #raw_data is a bi nary representation of the actual data\n",
        "\n",
        "    #result of speech recognition\n",
        "    result = rec.Result()  #it returns json file format\n",
        "\n",
        "    text = json.loads(result)[\"text\"]\n",
        "    transcript += text\n",
        "\n",
        "cased =\n",
        "return cased"
      ],
      "metadata": {
        "id": "WAD5tNDnradu"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "#summerization"
      ],
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      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "summ = pipeline(\"summarization\")"
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stderr",
          "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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        "split_tokens = transcript.split(\"\")\n",
        "docs = []\n",
        "for i range(0 , len(split_tokens), 850):\n",
        "  selection =\" \".join(split_tokens[i:(i+850)])\n",
        "  docs.append(selection)"
      ],
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