Speech-to-text-conversion.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRhTDO1k_XYG"
      },
      "source": [
        "#Installing dependancies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fSOEhMM_Wz9",
        "outputId": "bfbcfd52-1fb6-45be-90a0-162bb80e7ade"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Successfully installed google-auth-oauthlib-0.4.6 keras-2.8.0 keras-preprocessing-1.1.2 tensorboard-2.8.0 tensorboard-data-server-0.6.1 tensorflow-2.8.0 tf-estimator-nightly-2.8.0.dev2021122109\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow==2.8.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lE6Dkg28_f-H"
      },
      "outputs": [],
      "source": [
        "from numpy.random import seed\n",
        "from numpy.random import rand\n",
        "import numpy as np                        # for handling large arrays\n",
        "import random\n",
        "\n",
        "rng = np.random.RandomState (seed = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cV19V-B92mi",
        "outputId": "b8f6dcc4-bf4c-4b30-a580-0a4b3692d8a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip -qqq install git+https://github.com/openai/whisper.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ0Yt7rOCl3i",
        "outputId": "3f2302c7-c9cb-475f-8ae1-31252c86c60f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|█████████████████████████████████████| 2.87G/2.87G [02:09<00:00, 23.9MiB/s]\n"
          ]
        }
      ],
      "source": [
        "# Import the libraries \n",
        "import whisper\n",
        "import torch \n",
        "import os\n",
        "import pandas as pd\n",
        "import os\n",
        "import librosa\n",
        "import librosa.display\n",
        "import numpy as np\n",
        "import IPython.display as ipd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "# Set the device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# Load the model \n",
        "whisper_model = whisper.load_model(\"large\", device=device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4HjOru0ZtYY",
        "outputId": "4a02a180-7529-411d-d168-06f87b9b7546"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJR5yDnN__Y-"
      },
      "source": [
        "#Speech to text conversion"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting one audio file to text using whisper ai"
      ],
      "metadata": {
        "id": "JyID_4wFal1o"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kmW8TooqNBZ8"
      },
      "outputs": [],
      "source": [
        "audio_file = \"/content/English-Train/Hate/1.wav\"\n",
        "result = whisper_model.transcribe(audio_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bJjFLj1NcEh7"
      },
      "outputs": [],
      "source": [
        "tx = result[\"text\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converted text"
      ],
      "metadata": {
        "id": "eEEcgeZTawrI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnrQYJI3cPmH",
        "outputId": "619e624e-18d1-4845-f8de-fdc2a444c545"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " picture for your side panel. You know people only follow you to incessantly mock you, right? I'm sure that's what it is because I don't have a lot of followers. I got about 400 and a little over like 350, 400 range. And that's, they're not following me to make fun of me because I just started doing these posts like the other day. Yeah, okay. What does your best friend say when you talk shit about his race or do you keep your mouth shut around him? My best friend, his name is Marcel. Marcel Bichelle. No. He is practically white. Like he is, his skin color is almost, if it was dark in the room you wouldn't be able to find him. He's that black. His parents, they're both surgeons. He has a wealthy family. He hangs out with all white people. He makes fun of his race all the time because he knows that there are niggers who are just ignorant fucks who are doing nothing but make fun of him.\n"
          ]
        }
      ],
      "source": [
        "print(tx)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-pvomdzAH29"
      },
      "source": [
        "#English language"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting hate speech (audio) dataset to text"
      ],
      "metadata": {
        "id": "_8k0RgZVZ47A"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2wD6vDZ5v0w"
      },
      "source": [
        "##English transcribed text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BDWVssdQCBrj"
      },
      "outputs": [],
      "source": [
        "def speech_to_text(audio_path):\n",
        "   \n",
        "    try:\n",
        "        result = whisper_model.transcribe(audio_path)\n",
        "        result[\"text\"]\n",
        "\n",
        "    except Exception as e:\n",
        "        print(\"Error encountered while parsing file: \", file)\n",
        "        return None\n",
        "     \n",
        "    return result[\"text\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dyvBAbsuUswv"
      },
      "outputs": [],
      "source": [
        "from ssl import OPENSSL_VERSION_INFO\n",
        "import csv\n",
        "import scipy.signal\n",
        "import scipy.fftpack\n",
        "\n",
        "\n",
        "# Load various imports \n",
        "\n",
        "dt = []\n",
        "\n",
        "labels = 'Hate Normal'.split()\n",
        "\n",
        "# Iterate through each sound file and extract the features \n",
        "for k in labels:\n",
        "    for filename in os.listdir(f'/content/English-Train/{k}'):\n",
        "        audio_path = f'/content/English-Train/{k}/{filename}'\n",
        "      \n",
        "        result = whisper_model.transcribe(audio_path)\n",
        "        dt.append([filename, result[\"text\"], k])\n",
        "\n",
        "# Convert into a Panda dataframe \n",
        "df = pd.DataFrame(dt, columns=['filename', 'text','label'])\n",
        "\n",
        "\n",
        "print('Finished feature extraction from ', len(df), ' files')\n",
        "df = df.to_csv('/content/E-text.csv', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H1ppqPyfPXCc"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('E-text.csv', on_bad_lines='skip')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6450
        },
        "id": "FJhZgV9kAfpj",
        "outputId": "5ba099ab-5056-4b65-ed45-c5d7595fdf85"
      },
      "outputs": [
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              "4   27.wav   My wife had to spend time at the UP hospital ...  Hate"
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              "2   it's a jungle out there in our city after mid...  Hate\n",
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              "3   And he constantly pushes this out to a large ...  Hate\n",
              "4   My wife had to spend time at the UP hospital ...  Hate"
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