Audio_Visual_Speech_Recongnition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOj5aEgYYtOgbG/9mFhUN0J",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/whitehatjr1001/Lip-Reading-using-Deep-learning/blob/main/Audio_Visual_Speech_Recongnition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyXU6xFRCXMr",
        "outputId": "bb00e8bb-7c31-4f0f-a75d-86f66f81ed62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install opencv-python\n"
      ],
      "metadata": {
        "id": "ZWNFP2vEDGjc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edbcb630-6841-46ff-f4ae-c57b9901a463"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.8.0.76)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from opencv-python) (1.25.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install opencv-python matplotlib imageio gdown tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2S0aZ97wV_W",
        "outputId": "ce585b00-450f-49c2-b05f-96b3024ed530"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.8.0.76)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n",
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            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
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            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.5.2)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (0.7.2)\n",
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            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from typing import List\n",
        "from matplotlib import pyplot as plt\n",
        "import imageio#ImageIO provides ImageReader and ImageWriter plug-ins for the\n",
        "#Graphics Interchange Format (GIF) image format"
      ],
      "metadata": {
        "id": "jm2_uC46DJoe"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.list_physical_devices('GPU')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqxpCRPCEjkn",
        "outputId": "1ab4e07b-d1dc-48b4-9d8b-70e4e6780fe2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "physical_devices = tf.config.list_physical_devices('GPU')\n",
        "try:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "except:\n",
        "    pass"
      ],
      "metadata": {
        "id": "cpq9X1DJjibT"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gdown"
      ],
      "metadata": {
        "id": "ENuK7lY2nZvm"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\n",
        "output = 'data.zip'\n",
        "gdown.download(url, output, quiet=False)\n",
        "gdown.extractall('data.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWn-vE5Pnxwi",
        "outputId": "929197b4-d362-4df7-aad5-d2e64374dd72"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\n",
            "From (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=f0542b5c-6590-4032-b3a3-07df18fab5c2\n",
            "To: /content/data.zip\n",
            "100%|██████████| 423M/423M [00:09<00:00, 44.9MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['data/',\n",
              " 'data/alignments/',\n",
              " 'data/alignments/s1/',\n",
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              " 'data/alignments/s1/sgwp8n.align',\n",
              " 'data/alignments/s1/sgwp9s.align',\n",
              " 'data/alignments/s1/sgwq1a.align',\n",
              " 'data/alignments/s1/sgwqzp.align',\n",
              " 'data/alignments/s1/sgwx2n.align',\n",
              " 'data/alignments/s1/sgwx3s.align',\n",
              " 'data/alignments/s1/sgwx4p.align',\n",
              " 'data/alignments/s1/sgwx5a.align',\n",
              " 'data/alignments/s1/srab1s.align',\n",
              " 'data/alignments/s1/srab2p.align',\n",
              " 'data/alignments/s1/srab3a.align',\n",
              " 'data/alignments/s1/srabzn.align',\n",
              " 'data/alignments/s1/srah4n.align',\n",
              " 'data/alignments/s1/srah5s.align',\n",
              " 'data/alignments/s1/srah6p.align',\n",
              " 'data/alignments/s1/srah7a.align',\n",
              " 'data/alignments/s1/sran8n.align',\n",
              " 'data/alignments/s1/sran9s.align',\n",
              " 'data/alignments/s1/srao1a.align',\n",
              " 'data/alignments/s1/sraozp.align',\n",
              " 'data/alignments/s1/srau2n.align',\n",
              " 'data/alignments/s1/srau3s.align',\n",
              " 'data/alignments/s1/srau4p.align',\n",
              " 'data/alignments/s1/srau5a.align',\n",
              " 'data/alignments/s1/srbb4n.align',\n",
              " 'data/alignments/s1/srbb5s.align',\n",
              " 'data/alignments/s1/srbb6p.align',\n",
              " 'data/alignments/s1/srbb7a.align',\n",
              " 'data/alignments/s1/srbh8n.align',\n",
              " 'data/alignments/s1/srbh9s.align',\n",
              " 'data/alignments/s1/srbi1a.align',\n",
              " 'data/alignments/s1/srbizp.align',\n",
              " 'data/alignments/s1/srbo2n.align',\n",
              " 'data/alignments/s1/srbo3s.align',\n",
              " 'data/alignments/s1/srbo4p.align',\n",
              " 'data/alignments/s1/srbo5a.align',\n",
              " 'data/alignments/s1/srbu6n.align',\n",
              " 'data/alignments/s1/srbu7s.align',\n",
              " 'data/alignments/s1/srbu8p.align',\n",
              " 'data/alignments/s1/srbu9a.align',\n",
              " 'data/alignments/s1/sria6n.align',\n",
              " 'data/alignments/s1/sria7s.align',\n",
              " 'data/alignments/s1/sria8p.align',\n",
              " 'data/alignments/s1/sria9a.align',\n",
              " 'data/alignments/s1/srih1s.align',\n",
              " 'data/alignments/s1/srih2p.align',\n",
              " 'data/alignments/s1/srih3a.align',\n",
              " 'data/alignments/s1/srihzn.align',\n",
              " 'data/alignments/s1/srin4n.align',\n",
              " 'data/alignments/s1/srin5s.align',\n",
              " 'data/alignments/s1/srin6p.align',\n",
              " 'data/alignments/s1/srin7a.align',\n",
              " 'data/alignments/s1/srit8n.align',\n",
              " 'data/alignments/s1/srit9s.align',\n",
              " 'data/alignments/s1/sriu1a.align',\n",
              " 'data/alignments/s1/sriuzp.align',\n",
              " 'data/alignments/s1/srwb8n.align',\n",
              " 'data/alignments/s1/srwb9s.align',\n",
              " 'data/alignments/s1/srwc1a.align',\n",
              " 'data/alignments/s1/srwczp.align',\n",
              " 'data/alignments/s1/srwi2n.align',\n",
              " 'data/alignments/s1/srwi3s.align',\n",
              " 'data/alignments/s1/srwi4p.align',\n",
              " 'data/alignments/s1/srwi5a.align',\n",
              " 'data/alignments/s1/srwo6n.align',\n",
              " 'data/alignments/s1/srwo7s.align',\n",
              " 'data/alignments/s1/srwo8p.align',\n",
              " 'data/alignments/s1/srwo9a.align',\n",
              " 'data/alignments/s1/srwv1s.align',\n",
              " 'data/alignments/s1/srwv2p.align',\n",
              " 'data/alignments/s1/srwv3a.align',\n",
              " 'data/alignments/s1/srwvzn.align',\n",
              " 'data/alignments/s1/swab6n.align',\n",
              " 'data/alignments/s1/swab7s.align',\n",
              " 'data/alignments/s1/swab8p.align',\n",
              " 'data/alignments/s1/swab9a.align',\n",
              " 'data/alignments/s1/swai1s.align',\n",
              " 'data/alignments/s1/swai2p.align',\n",
              " 'data/alignments/s1/swai3a.align',\n",
              " 'data/alignments/s1/swaizn.align',\n",
              " 'data/alignments/s1/swao4n.align',\n",
              " 'data/alignments/s1/swao5s.align',\n",
              " 'data/alignments/s1/swao6p.align',\n",
              " 'data/alignments/s1/swao7a.align',\n",
              " 'data/alignments/s1/swau8n.align',\n",
              " 'data/alignments/s1/swau9s.align',\n",
              " 'data/alignments/s1/swav1a.align',\n",
              " 'data/alignments/s1/swavzp.align',\n",
              " 'data/alignments/s1/swbc1s.align',\n",
              " 'data/alignments/s1/swbc2p.align',\n",
              " 'data/alignments/s1/swbc3a.align',\n",
              " 'data/alignments/s1/swbczn.align',\n",
              " 'data/alignments/s1/swbi4n.align',\n",
              " 'data/alignments/s1/swbi5s.align',\n",
              " 'data/alignments/s1/swbi6p.align',\n",
              " 'data/alignments/s1/swbi7a.align',\n",
              " 'data/alignments/s1/swbo8n.align',\n",
              " 'data/alignments/s1/swbo9s.align',\n",
              " 'data/alignments/s1/swbp1a.align',\n",
              " 'data/alignments/s1/swbpzp.align',\n",
              " 'data/alignments/s1/swbv2n.align',\n",
              " 'data/alignments/s1/swbv3s.align',\n",
              " 'data/alignments/s1/swbv4p.align',\n",
              " 'data/alignments/s1/swbv5a.align',\n",
              " 'data/alignments/s1/swib2n.align',\n",
              " 'data/alignments/s1/swib3s.align',\n",
              " 'data/alignments/s1/swib4p.align',\n",
              " 'data/alignments/s1/swib5a.align',\n",
              " 'data/alignments/s1/swih6n.align',\n",
              " 'data/alignments/s1/swih7s.align',\n",
              " 'data/alignments/s1/swih8p.align',\n",
              " 'data/alignments/s1/swih9a.align',\n",
              " 'data/alignments/s1/swio1s.align',\n",
              " 'data/alignments/s1/swio2p.align',\n",
              " 'data/alignments/s1/swio3a.align',\n",
              " 'data/alignments/s1/swiozn.align',\n",
              " 'data/alignments/s1/swiu4n.align',\n",
              " 'data/alignments/s1/swiu5s.align',\n",
              " 'data/alignments/s1/swiu6p.align',\n",
              " 'data/alignments/s1/swiu7a.align',\n",
              " 'data/alignments/s1/swwc4n.align',\n",
              " 'data/alignments/s1/swwc5s.align',\n",
              " 'data/alignments/s1/swwc6p.align',\n",
              " 'data/alignments/s1/swwc7a.align',\n",
              " 'data/alignments/s1/swwi8n.align',\n",
              " 'data/alignments/s1/swwi9s.align',\n",
              " 'data/alignments/s1/swwj1a.align',\n",
              " 'data/alignments/s1/swwjzp.align',\n",
              " 'data/alignments/s1/swwp2n.align',\n",
              " 'data/alignments/s1/swwp3s.align',\n",
              " 'data/alignments/s1/swwp4p.align',\n",
              " 'data/alignments/s1/swwp5a.align',\n",
              " 'data/alignments/s1/swwv6n.align',\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_video(path:str) -> List[float]:\n",
        "\n",
        "    cap = cv2.VideoCapture(path)\n",
        "    frames = []\n",
        "    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):\n",
        "        ret, frame = cap.read()\n",
        "        frame = tf.image.rgb_to_grayscale(frame)\n",
        "        frames.append(frame[190:236,80:220,:])\n",
        "    cap.release()\n",
        "\n",
        "    mean = tf.math.reduce_mean(frames)\n",
        "    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n",
        "    return tf.cast((frames - mean), tf.float32) / std"
      ],
      "metadata": {
        "id": "ghhad-cy5kET"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]"
      ],
      "metadata": {
        "id": "D3MdSGZY-2x8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import StringLookup"
      ],
      "metadata": {
        "id": "C4kG4Yh3xe6u"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#One hot encoding the text\n",
        "char_to_num = StringLookup(vocabulary=vocab,oov_token=\"\")\n",
        "num_to_char = StringLookup(\n",
        "    vocabulary = char_to_num.get_vocabulary(),oov_token=\"\",invert=True\n",
        ")\n",
        "print(\n",
        "    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n",
        "    f\"(size ={char_to_num.vocabulary_size()})\"\n",
        ")"
      ],
      "metadata": {
        "id": "s9LEWFU0-5Hb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b37239aa-67a0-42a8-df59-48d74e81acd1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loading alignments\n",
        "def load_alignments(path:str) -> List[str]:\n",
        "    with open(path, 'r') as f:\n",
        "        lines = f.readlines()\n",
        "    tokens = []\n",
        "    for line in lines:\n",
        "        line = line.split()\n",
        "        if line[2] != 'sil':\n",
        "            tokens = [*tokens,' ',line[2]]\n",
        "    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]\n"
      ],
      "metadata": {
        "id": "GqkOyU_Hvyuu"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loding video and alignments simintaniously\n",
        "def load_data(path):\n",
        "  path = path.numpy().decode()\n",
        "  filename = path.split('/')[-1].split('.')[0]\n",
        "  video_path = os.path.join('/content','data','s1',f'{filename}.mpg')\n",
        "  alignment_path = os.path.join('/content','data','alignments','s1',f'{filename}.align')\n",
        "  frames = load_video(video_path)\n",
        "  alignments = load_alignments(alignment_path)\n",
        "  return frames,alignments"
      ],
      "metadata": {
        "id": "qdiNkKpUzW1r"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_path = '/content/data/s1/bbaf2n.mpg'"
      ],
      "metadata": {
        "id": "SbL9r_er_UTu"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames, alignments = load_data(tf.convert_to_tensor(test_path))"
      ],
      "metadata": {
        "id": "Ij-IkJC_7qRj"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(frames[40])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "qfTYHEk5-WS9",
        "outputId": "4827f7f4-d7dd-4478-dd86-dfdc1978d7a3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x794e20f53340>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alignments"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GKOXZ74_ECu",
        "outputId": "801e16e2-8a77-4ef4-c100-611550111bc8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(21,), dtype=int64, numpy=\n",
              "array([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39,  6, 39, 20, 23, 15,\n",
              "       39, 14, 15, 23])>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szEB0yVk_JdY",
        "outputId": "0af7139e-2061-4941-a4cf-5dc5d213d782"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=string, numpy=b'bin blue at f two now'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mappable_function(path:str) ->List[str]:\n",
        "    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n",
        "    return result"
      ],
      "metadata": {
        "id": "Owtlkq_yFYUC"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data pipe line"
      ],
      "metadata": {
        "id": "9EdllEBUFkVw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "yzOAe3-hFii0"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = tf.data.Dataset.list_files('/content/data/s1/*.mpg')\n",
        "data = data.shuffle(500, reshuffle_each_iteration=False)\n",
        "data = data.map(mappable_function)\n",
        "data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\n",
        "data = data.prefetch(tf.data.AUTOTUNE)\n",
        "# Added for split\n",
        "train = data.take(450)\n",
        "test = data.skip(450)"
      ],
      "metadata": {
        "id": "reev0Yz2Fuih"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wfldsbY7GB-f"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames, alignments = data.as_numpy_iterator().next()"
      ],
      "metadata": {
        "id": "jj0LZTIIGHj7"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "len()"
      ],
      "metadata": {
        "id": "8Gy_0lViG1-J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample = data.as_numpy_iterator()\n"
      ],
      "metadata": {
        "id": "owsuTljLG6rG"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val = sample.next(); val[0]"
      ],
      "metadata": {
        "id": "Ri5FnvYDHJ-q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ef2e38f-702b-4d03-88e5-0600031f2f56"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[[1.4293033 ],\n",
              "          [1.4293033 ],\n",
              "          [1.39169   ],\n",
              "          ...,\n",
              "          [9.478538  ],\n",
              "          [9.252858  ],\n",
              "          [9.064792  ]],\n",
              "\n",
              "         [[1.4293033 ],\n",
              "          [1.4293033 ],\n",
              "          [1.39169   ],\n",
              "          ...,\n",
              "          [8.914339  ],\n",
              "          [8.876725  ],\n",
              "          [9.215245  ]],\n",
              "\n",
              "         [[1.2788503 ],\n",
              "          [1.2788503 ],\n",
              "          [1.2788503 ],\n",
              "          ...,\n",
              "          [9.328085  ],\n",
              "          [9.591377  ],\n",
              "          [9.591377  ]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[0.9403311 ],\n",
              "          [0.9403311 ],\n",
              "          [0.9027178 ],\n",
              "          ...,\n",
              "          [9.591377  ],\n",
              "          [9.553764  ],\n",
              "          [9.553764  ]],\n",
              "\n",
              "         [[0.9403311 ],\n",
              "          [0.9403311 ],\n",
              "          [0.9027178 ],\n",
              "          ...,\n",
              "          [9.51615   ],\n",
              "          [9.553764  ],\n",
              "          [9.553764  ]],\n",
              "\n",
              "         [[0.9403311 ],\n",
              "          [0.9403311 ],\n",
              "          [0.9027178 ],\n",
              "          ...,\n",
              "          [9.553764  ],\n",
              "          [9.553764  ],\n",
              "          [9.51615   ]]],\n",
              "\n",
              "\n",
              "        [[[1.3164636 ],\n",
              "          [1.3164636 ],\n",
              "          [1.3540767 ],\n",
              "          ...,\n",
              "          [9.553764  ],\n",
              "          [9.140018  ],\n",
              "          [9.140018  ]],\n",
              "\n",
              "         [[1.3164636 ],\n",
              "          [1.3164636 ],\n",
              "          [1.3540767 ],\n",
              "          ...,\n",
              "          [8.57582   ],\n",
              "          [9.102405  ],\n",
              "          [9.252858  ]],\n",
              "\n",
              "         [[1.2036238 ],\n",
              "          [1.2036238 ],\n",
              "          [1.241237  ],\n",
              "          ...,\n",
              "          [9.290471  ],\n",
              "          [9.553764  ],\n",
              "          [9.553764  ]],\n",
              "\n",
              "         ...,\n",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJLI_BAsAExE",
        "outputId": "6172d38b-7a56-40e6-c8da-bd3f902df459"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=string, numpy=b'lay blue by q eight now'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow"
      ],
      "metadata": {
        "id": "LGwxosqzDiAB"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler"
      ],
      "metadata": {
        "id": "kkPt8xubDXNr"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.as_numpy_iterator().next()[0][0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4BmVLLsDlOL",
        "outputId": "46d69de8-fa59-4c79-a1cd-1deed750f9f7"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(75, 46, 140, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPool3D((1,2,2)))\n",
        "\n",
        "model.add(Conv3D(256, 3, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPool3D((1,2,2)))\n",
        "\n",
        "model.add(Conv3D(75, 3, padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPool3D((1,2,2)))\n",
        "\n",
        "model.add(TimeDistributed(Flatten()))\n",
        "\n",
        "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
        "model.add(Dropout(.5))\n",
        "\n",
        "model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\n",
        "model.add(Dropout(.5))\n",
        "\n",
        "model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))"
      ],
      "metadata": {
        "id": "ksdulV7xEu1W"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-kiFmpVFSmF",
        "outputId": "ede0fdbc-5789-4683-c6e3-0b2fa9b7bc91"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv3d_3 (Conv3D)           (None, 75, 46, 140, 128   3584      \n",
            "                             )                                   \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 75, 46, 140, 128   0         \n",
            "                             )                                   \n",
            "                                                                 \n",
            " max_pooling3d_3 (MaxPoolin  (None, 75, 23, 70, 128)   0         \n",
            " g3D)                                                            \n",
            "                                                                 \n",
            " conv3d_4 (Conv3D)           (None, 75, 23, 70, 256)   884992    \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 75, 23, 70, 256)   0         \n",
            "                                                                 \n",
            " max_pooling3d_4 (MaxPoolin  (None, 75, 11, 35, 256)   0         \n",
            " g3D)                                                            \n",
            "                                                                 \n",
            " conv3d_5 (Conv3D)           (None, 75, 11, 35, 75)    518475    \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 75, 11, 35, 75)    0         \n",
            "                                                                 \n",
            " max_pooling3d_5 (MaxPoolin  (None, 75, 5, 17, 75)     0         \n",
            " g3D)                                                            \n",
            "                                                                 \n",
            " time_distributed_1 (TimeDi  (None, 75, 6375)          0         \n",
            " stributed)                                                      \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirecti  (None, 75, 256)           6660096   \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 75, 256)           0         \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirecti  (None, 75, 256)           394240    \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 75, 256)           0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 75, 41)            10537     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8471924 (32.32 MB)\n",
            "Trainable params: 8471924 (32.32 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#learning rate shedulin g\n",
        "def schleduler(epoch, lr):\n",
        "  if epoch < 30:\n",
        "    return lr\n",
        "  else :\n",
        "    lr= lr*tf.math.exp(-0.1)"
      ],
      "metadata": {
        "id": "tFFudSvQGMp1"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ctc loss  this part of the code is taken from keras learning tutorial\n",
        "def CTCLoss(y_true, y_pred):\n",
        "    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
        "    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
        "    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
        "\n",
        "    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "\n",
        "    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n",
        "    return loss\n"
      ],
      "metadata": {
        "id": "b4JFjTSYGsqj"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "class ProduceExample(tf.keras.callbacks.Callback):\n",
        "    def __init__(self, dataset) -> None:\n",
        "        self.dataset = dataset.as_numpy_iterator()\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None) -> None:\n",
        "        data = self.dataset.next()\n",
        "        yhat = self.model.predict(data[0])\n",
        "        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n",
        "        for x in range(len(yhat)):\n",
        "            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n",
        "            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n",
        "            print('~'*100)"
      ],
      "metadata": {
        "id": "d6hwMLflG_gG"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss=CTCLoss)"
      ],
      "metadata": {
        "id": "RpndOLa_HLXz"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True)"
      ],
      "metadata": {
        "id": "vh4MCMIaHOYy"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "schedule_callback = LearningRateScheduler(schleduler)"
      ],
      "metadata": {
        "id": "2u84-NfyHRBM"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example_callback = ProduceExample(test)"
      ],
      "metadata": {
        "id": "_glR3JDgIBQC"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train,validation_data=test,epochs = 1,callbacks=[checkpoint_callback,schedule_callback,example_callback])"
      ],
      "metadata": {
        "id": "N8ot_b93Hl9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f565f21d-c8eb-497d-c36d-9a92c998a82d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 3s 3s/step\n",
            "Original: place blue at o nine soon\n",
            "Prediction: le e e e e o\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Original: set red with i three soon\n",
            "Prediction: le e e e e o\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "450/450 [==============================] - 927s 2s/step - loss: 85.5936 - val_loss: 70.6770 - lr: 1.0000e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x794d9e452cb0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    }
  ]
}