Untitled7.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMxLe/l/ZVdPld83m2RBlSp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ritojnan/BlenderWithPython/blob/main/Untitled7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSnvgOWwW0EF",
        "outputId": "990b3635-2eb2-4b03-e59b-80fddf5cdd9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install librosa\n",
        "!pip install tensorflow\n",
        "!pip install numpy\n",
        "!pip install matplotlib\n",
        "!pip install scikit-learn\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O ravdess.zip https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip\n",
        "!unzip ravdess.zip\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNvRP0BXXEI3",
        "outputId": "46543ea7-52ca-4222-fa3c-c24c6a6ffff7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-10-28 10:46:47--  https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip\n",
            "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 MOVED PERMANENTLY\n",
            "Location: /records/1188976/files/Audio_Speech_Actors_01-24.zip [following]\n",
            "--2024-10-28 10:46:47--  https://zenodo.org/records/1188976/files/Audio_Speech_Actors_01-24.zip\n",
            "Reusing existing connection to zenodo.org:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 208468073 (199M) [application/octet-stream]\n",
            "Saving to: ‘ravdess.zip’\n",
            "\n",
            "ravdess.zip         100%[===================>] 198.81M  31.4MB/s    in 7.4s    \n",
            "\n",
            "2024-10-28 10:46:55 (26.7 MB/s) - ‘ravdess.zip’ saved [208468073/208468073]\n",
            "\n",
            "Archive:  ravdess.zip\n",
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            "   creating: Actor_14/\n",
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            "   creating: Actor_15/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import librosa\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "def extract_features(file_path):\n",
        "    audio, sample_rate = librosa.load(file_path, sr=None)\n",
        "    mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n",
        "    mfccs_mean = np.mean(mfccs.T, axis=0)\n",
        "    return mfccs_mean\n",
        "\n",
        "# Define the root directory where all actor folders are located\n",
        "root_dir = '/content'\n",
        "\n",
        "# Create lists to store features and labels\n",
        "features_list = []\n",
        "labels = []\n",
        "\n",
        "# Map emotion labels to integers\n",
        "emotion_map = {\n",
        "    '01': 0,  # Neutral\n",
        "    '02': 1,  # Calm\n",
        "    '03': 2,  # Happy\n",
        "    '04': 3,  # Sad\n",
        "    '05': 4,  # Angry\n",
        "    '06': 5,  # Fearful\n",
        "    '07': 6,  # Disgust\n",
        "    '08': 7   # Surprise\n",
        "}\n",
        "\n",
        "# Walk through each directory and file in root_dir\n",
        "for subdir, _, files in os.walk(root_dir):\n",
        "    for filename in files:\n",
        "        if filename.endswith('.wav'):\n",
        "            # Get the emotion label from the filename\n",
        "            emotion_label = filename.split('-')[2]\n",
        "            if emotion_label in emotion_map:\n",
        "                file_path = os.path.join(subdir, filename)\n",
        "                features = extract_features(file_path)\n",
        "                features_list.append(features)\n",
        "                labels.append(emotion_map[emotion_label])\n",
        "\n",
        "# Convert to numpy arrays\n",
        "X = np.array(features_list)\n",
        "y = np.array(labels)\n"
      ],
      "metadata": {
        "id": "Z7OdzhYoXqmH"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "aEkZyjw1Y51i"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(len(set(y)), activation='softmax'))\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gB9tbZIuY9kM",
        "outputId": "98d62d82-f55d-4e9d-e1b0-bce6c86446ad"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.1412 - loss: 19.6897 - val_accuracy: 0.2155 - val_loss: 3.7656\n",
            "Epoch 2/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1398 - loss: 3.2792 - val_accuracy: 0.2414 - val_loss: 2.4693\n",
            "Epoch 3/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2087 - loss: 2.5214 - val_accuracy: 0.2759 - val_loss: 2.6292\n",
            "Epoch 4/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2608 - loss: 2.3335 - val_accuracy: 0.2155 - val_loss: 2.6691\n",
            "Epoch 5/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.2832 - loss: 2.6092 - val_accuracy: 0.2500 - val_loss: 2.6558\n",
            "Epoch 6/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2741 - loss: 2.6468 - val_accuracy: 0.2845 - val_loss: 2.8433\n",
            "Epoch 7/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3125 - loss: 2.4696 - val_accuracy: 0.3190 - val_loss: 2.0249\n",
            "Epoch 8/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3243 - loss: 2.0151 - val_accuracy: 0.1897 - val_loss: 2.4382\n",
            "Epoch 9/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3029 - loss: 2.0241 - val_accuracy: 0.2328 - val_loss: 2.5152\n",
            "Epoch 10/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3397 - loss: 2.0345 - val_accuracy: 0.3534 - val_loss: 2.0722\n",
            "Epoch 11/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3158 - loss: 2.0861 - val_accuracy: 0.3621 - val_loss: 1.8451\n",
            "Epoch 12/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3590 - loss: 2.0588 - val_accuracy: 0.3879 - val_loss: 2.0095\n",
            "Epoch 13/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3372 - loss: 2.0128 - val_accuracy: 0.2672 - val_loss: 2.2893\n",
            "Epoch 14/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3483 - loss: 1.8914 - val_accuracy: 0.2759 - val_loss: 2.1860\n",
            "Epoch 15/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3659 - loss: 1.8481 - val_accuracy: 0.3362 - val_loss: 1.9065\n",
            "Epoch 16/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3960 - loss: 1.7824 - val_accuracy: 0.3448 - val_loss: 1.8427\n",
            "Epoch 17/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4041 - loss: 1.7110 - val_accuracy: 0.4052 - val_loss: 1.6563\n",
            "Epoch 18/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4174 - loss: 1.6758 - val_accuracy: 0.3879 - val_loss: 1.9179\n",
            "Epoch 19/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4290 - loss: 1.5833 - val_accuracy: 0.2931 - val_loss: 1.9426\n",
            "Epoch 20/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4127 - loss: 1.6436 - val_accuracy: 0.4483 - val_loss: 1.6320\n",
            "Epoch 21/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4406 - loss: 1.5868 - val_accuracy: 0.2931 - val_loss: 1.9961\n",
            "Epoch 22/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3728 - loss: 1.9559 - val_accuracy: 0.4138 - val_loss: 1.6918\n",
            "Epoch 23/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4592 - loss: 1.5748 - val_accuracy: 0.3707 - val_loss: 1.7539\n",
            "Epoch 24/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4389 - loss: 1.6018 - val_accuracy: 0.3534 - val_loss: 1.7372\n",
            "Epoch 25/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4099 - loss: 1.6734 - val_accuracy: 0.3276 - val_loss: 2.5355\n",
            "Epoch 26/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4257 - loss: 1.9155 - val_accuracy: 0.3707 - val_loss: 1.8132\n",
            "Epoch 27/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4600 - loss: 1.5539 - val_accuracy: 0.4052 - val_loss: 2.0509\n",
            "Epoch 28/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4870 - loss: 1.4879 - val_accuracy: 0.3879 - val_loss: 1.6196\n",
            "Epoch 29/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4643 - loss: 1.4240 - val_accuracy: 0.4483 - val_loss: 1.5958\n",
            "Epoch 30/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4824 - loss: 1.4771 - val_accuracy: 0.3707 - val_loss: 2.5722\n",
            "Epoch 31/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5019 - loss: 1.4502 - val_accuracy: 0.3190 - val_loss: 2.7019\n",
            "Epoch 32/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4180 - loss: 2.0626 - val_accuracy: 0.3879 - val_loss: 1.6589\n",
            "Epoch 33/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4218 - loss: 1.9284 - val_accuracy: 0.4310 - val_loss: 1.7308\n",
            "Epoch 34/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4827 - loss: 1.5786 - val_accuracy: 0.4138 - val_loss: 1.6975\n",
            "Epoch 35/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4827 - loss: 1.4584 - val_accuracy: 0.4224 - val_loss: 1.5797\n",
            "Epoch 36/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4998 - loss: 1.5147 - val_accuracy: 0.4569 - val_loss: 1.5384\n",
            "Epoch 37/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4940 - loss: 1.4944 - val_accuracy: 0.3966 - val_loss: 1.5713\n",
            "Epoch 38/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5015 - loss: 1.3787 - val_accuracy: 0.3621 - val_loss: 2.0111\n",
            "Epoch 39/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4627 - loss: 1.6753 - val_accuracy: 0.4483 - val_loss: 1.6364\n",
            "Epoch 40/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4801 - loss: 1.5201 - val_accuracy: 0.4052 - val_loss: 1.6886\n",
            "Epoch 41/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4032 - loss: 1.8843 - val_accuracy: 0.4741 - val_loss: 1.8139\n",
            "Epoch 42/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4782 - loss: 1.5217 - val_accuracy: 0.5000 - val_loss: 1.4570\n",
            "Epoch 43/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5611 - loss: 1.3055 - val_accuracy: 0.3621 - val_loss: 1.8319\n",
            "Epoch 44/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5481 - loss: 1.3812 - val_accuracy: 0.5086 - val_loss: 1.5221\n",
            "Epoch 45/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5516 - loss: 1.3920 - val_accuracy: 0.4138 - val_loss: 1.6148\n",
            "Epoch 46/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5047 - loss: 1.5411 - val_accuracy: 0.3793 - val_loss: 1.8764\n",
            "Epoch 47/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4808 - loss: 1.5002 - val_accuracy: 0.4138 - val_loss: 1.7659\n",
            "Epoch 48/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5241 - loss: 1.4507 - val_accuracy: 0.4741 - val_loss: 1.4371\n",
            "Epoch 49/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4943 - loss: 1.4084 - val_accuracy: 0.4224 - val_loss: 1.7715\n",
            "Epoch 50/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4791 - loss: 1.5763 - val_accuracy: 0.4569 - val_loss: 1.8107\n",
            "Epoch 51/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4396 - loss: 1.7513 - val_accuracy: 0.4397 - val_loss: 1.9048\n",
            "Epoch 52/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4762 - loss: 1.6319 - val_accuracy: 0.4483 - val_loss: 1.6426\n",
            "Epoch 53/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5149 - loss: 1.4416 - val_accuracy: 0.3793 - val_loss: 2.0636\n",
            "Epoch 54/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5281 - loss: 1.4122 - val_accuracy: 0.4655 - val_loss: 1.4288\n",
            "Epoch 55/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5765 - loss: 1.3143 - val_accuracy: 0.5000 - val_loss: 1.3774\n",
            "Epoch 56/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5627 - loss: 1.3458 - val_accuracy: 0.3793 - val_loss: 1.8344\n",
            "Epoch 57/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5200 - loss: 1.3562 - val_accuracy: 0.4052 - val_loss: 2.0248\n",
            "Epoch 58/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5335 - loss: 1.2960 - val_accuracy: 0.3879 - val_loss: 2.2970\n",
            "Epoch 59/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4877 - loss: 1.5925 - val_accuracy: 0.3534 - val_loss: 2.1387\n",
            "Epoch 60/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5161 - loss: 1.5385 - val_accuracy: 0.4224 - val_loss: 1.5955\n",
            "Epoch 61/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5829 - loss: 1.3106 - val_accuracy: 0.4138 - val_loss: 1.9169\n",
            "Epoch 62/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5549 - loss: 1.3231 - val_accuracy: 0.5259 - val_loss: 1.4121\n",
            "Epoch 63/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5697 - loss: 1.1898 - val_accuracy: 0.4483 - val_loss: 1.8935\n",
            "Epoch 64/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5285 - loss: 1.3240 - val_accuracy: 0.4569 - val_loss: 1.4668\n",
            "Epoch 65/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5507 - loss: 1.2310 - val_accuracy: 0.4138 - val_loss: 2.2325\n",
            "Epoch 66/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5408 - loss: 1.4236 - val_accuracy: 0.5000 - val_loss: 1.5928\n",
            "Epoch 67/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4990 - loss: 1.3714 - val_accuracy: 0.3362 - val_loss: 1.7299\n",
            "Epoch 68/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5264 - loss: 1.2673 - val_accuracy: 0.4655 - val_loss: 1.6563\n",
            "Epoch 69/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5244 - loss: 1.4508 - val_accuracy: 0.4483 - val_loss: 1.6651\n",
            "Epoch 70/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5650 - loss: 1.2561 - val_accuracy: 0.4138 - val_loss: 1.7876\n",
            "Epoch 71/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5764 - loss: 1.2120 - val_accuracy: 0.4052 - val_loss: 1.8173\n",
            "Epoch 72/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4954 - loss: 1.5551 - val_accuracy: 0.4224 - val_loss: 1.8544\n",
            "Epoch 73/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5294 - loss: 1.4067 - val_accuracy: 0.4828 - val_loss: 1.5891\n",
            "Epoch 74/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5591 - loss: 1.2422 - val_accuracy: 0.4569 - val_loss: 1.8119\n",
            "Epoch 75/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5299 - loss: 1.3522 - val_accuracy: 0.3362 - val_loss: 2.0181\n",
            "Epoch 76/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5206 - loss: 1.4170 - val_accuracy: 0.4138 - val_loss: 1.7853\n",
            "Epoch 77/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5468 - loss: 1.3383 - val_accuracy: 0.3966 - val_loss: 1.9648\n",
            "Epoch 78/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5415 - loss: 1.5139 - val_accuracy: 0.4397 - val_loss: 1.6821\n",
            "Epoch 79/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5495 - loss: 1.2827 - val_accuracy: 0.3793 - val_loss: 2.0662\n",
            "Epoch 80/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5689 - loss: 1.3145 - val_accuracy: 0.4483 - val_loss: 1.5792\n",
            "Epoch 81/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5521 - loss: 1.2482 - val_accuracy: 0.5000 - val_loss: 1.4339\n",
            "Epoch 82/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5924 - loss: 1.1480 - val_accuracy: 0.4483 - val_loss: 1.6395\n",
            "Epoch 83/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5617 - loss: 1.3901 - val_accuracy: 0.4828 - val_loss: 1.6119\n",
            "Epoch 84/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5982 - loss: 1.1081 - val_accuracy: 0.3793 - val_loss: 1.7731\n",
            "Epoch 85/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5612 - loss: 1.2875 - val_accuracy: 0.4569 - val_loss: 1.6682\n",
            "Epoch 86/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5440 - loss: 1.2472 - val_accuracy: 0.4741 - val_loss: 1.4707\n",
            "Epoch 87/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6028 - loss: 1.1376 - val_accuracy: 0.4052 - val_loss: 1.8472\n",
            "Epoch 88/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5698 - loss: 1.2627 - val_accuracy: 0.4397 - val_loss: 1.8347\n",
            "Epoch 89/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5206 - loss: 1.3990 - val_accuracy: 0.5603 - val_loss: 1.4511\n",
            "Epoch 90/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6127 - loss: 1.0560 - val_accuracy: 0.4828 - val_loss: 1.5810\n",
            "Epoch 91/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6203 - loss: 1.0677 - val_accuracy: 0.4741 - val_loss: 1.6066\n",
            "Epoch 92/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5922 - loss: 1.1520 - val_accuracy: 0.4655 - val_loss: 1.7419\n",
            "Epoch 93/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5933 - loss: 1.1675 - val_accuracy: 0.4138 - val_loss: 1.7787\n",
            "Epoch 94/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5753 - loss: 1.1973 - val_accuracy: 0.4310 - val_loss: 1.9282\n",
            "Epoch 95/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5794 - loss: 1.2082 - val_accuracy: 0.4741 - val_loss: 1.5022\n",
            "Epoch 96/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6040 - loss: 1.1359 - val_accuracy: 0.4741 - val_loss: 1.5465\n",
            "Epoch 97/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6298 - loss: 1.0579 - val_accuracy: 0.4655 - val_loss: 1.6068\n",
            "Epoch 98/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5888 - loss: 1.1031 - val_accuracy: 0.4914 - val_loss: 1.4868\n",
            "Epoch 99/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5814 - loss: 1.1926 - val_accuracy: 0.4224 - val_loss: 1.7573\n",
            "Epoch 100/100\n",
            "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5561 - loss: 1.2548 - val_accuracy: 0.5000 - val_loss: 1.7627\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a35a1893610>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(f'Accuracy: {accuracy * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIwDp7VQZOMM",
        "outputId": "2b9e0a27-506d-4f1c-b725-10f8bd2a83cc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4891 - loss: 1.8029 \n",
            "Accuracy: 52.08%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_emotion(file_path):\n",
        "    features = extract_features(file_path).reshape(1, -1)  # Reshape for prediction\n",
        "    prediction = model.predict(features)\n",
        "    return np.argmax(prediction)\n",
        "\n",
        "# Example prediction (update the path to your audio file)\n",
        "predicted_emotion = predict_emotion('/content/Actor_23/03-01-06-01-01-02-23.wav')\n",
        "print(f'Predicted Emotion: {predicted_emotion}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnXcJO55ZRZd",
        "outputId": "20ac7b2f-d999-4c38-ed36-8f07b14bb21f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "Predicted Emotion: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the entire model\n",
        "model.save('emotion_recognition_model.keras')\n"
      ],
      "metadata": {
        "id": "PUWJdjZQzEtB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('emotion_recognition_model.keras')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Er9f94g0zMCk",
        "outputId": "9c65229a-dc4e-41d7-a80e-5a6cd231a625"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_8e3507b5-4245-4a66-88cd-b96316d79675\", \"emotion_recognition_model.keras\", 191103)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install SpeechRecognition pyttsx3 sounddevice\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "891gG285zjVB",
        "outputId": "07f4de0f-1ced-4f5e-bf6d-b0f06feca54a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting SpeechRecognition\n",
            "  Downloading SpeechRecognition-3.11.0-py2.py3-none-any.whl.metadata (28 kB)\n",
            "Collecting pyttsx3\n",
            "  Downloading pyttsx3-2.98-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting sounddevice\n",
            "  Downloading sounddevice-0.5.1-py3-none-any.whl.metadata (1.4 kB)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from SpeechRecognition) (2.32.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from SpeechRecognition) (4.12.2)\n",
            "Requirement already satisfied: CFFI>=1.0 in /usr/local/lib/python3.10/dist-packages (from sounddevice) (1.17.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from CFFI>=1.0->sounddevice) (2.22)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26.0->SpeechRecognition) (2024.8.30)\n",
            "Downloading SpeechRecognition-3.11.0-py2.py3-none-any.whl (32.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.8/32.8 MB\u001b[0m \u001b[31m53.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyttsx3-2.98-py3-none-any.whl (34 kB)\n",
            "Downloading sounddevice-0.5.1-py3-none-any.whl (32 kB)\n",
            "Installing collected packages: pyttsx3, SpeechRecognition, sounddevice\n",
            "Successfully installed SpeechRecognition-3.11.0 pyttsx3-2.98 sounddevice-0.5.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import speech_recognition as sr\n",
        "import pyttsx3\n",
        "import queue\n",
        "import sounddevice as sd\n",
        "\n",
        "# Initialize recognizer and TTS engine\n",
        "recognizer = sr.Recognizer()\n",
        "engine = pyttsx3.init()\n",
        "\n",
        "# Define a queue to hold audio chunks for streaming\n",
        "audio_queue = queue.Queue()\n",
        "\n",
        "# Define a function for continuous listening\n",
        "def listen_stream():\n",
        "    with sr.Microphone() as source:\n",
        "        recognizer.adjust_for_ambient_noise(source)\n",
        "        print(\"Listening...\")\n",
        "\n",
        "        # Continuously capture audio from the microphone\n",
        "        while True:\n",
        "            try:\n",
        "                audio = recognizer.listen(source, timeout=1, phrase_time_limit=5)\n",
        "                audio_queue.put(audio)\n",
        "            except Exception as e:\n",
        "                print(f\"Error while listening: {e}\")\n",
        "\n",
        "# Define a function to process and respond\n",
        "def process_and_respond():\n",
        "    while True:\n",
        "        try:\n",
        "            # Fetch audio from the queue\n",
        "            audio = audio_queue.get(block=True)\n",
        "            text = recognizer.recognize_google(audio)\n",
        "            print(f\"User said: {text}\")\n",
        "\n",
        "            # Respond and use TTS to stream back the response\n",
        "            response = generate_response(text)  # Custom function for your response logic\n",
        "            print(f\"Response: {response}\")\n",
        "            engine.say(response)\n",
        "            engine.runAndWait()\n",
        "\n",
        "        except sr.UnknownValueError:\n",
        "            print(\"Could not understand audio\")\n",
        "        except sr.RequestError as e:\n",
        "            print(f\"API error: {e}\")\n",
        "\n",
        "# Placeholder function for generating responses (replace with any NLP model or logic)\n",
        "def generate_response(text):\n",
        "    return f\"I heard you say: {text}\"\n",
        "\n",
        "# Run both functions in parallel\n",
        "import threading\n",
        "\n",
        "listen_thread = threading.Thread(target=listen_stream)\n",
        "response_thread = threading.Thread(target=process_and_respond)\n",
        "\n",
        "listen_thread.start()\n",
        "response_thread.start()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "T62mxuNFzm3D",
        "outputId": "7f183228-45ed-4837-e2b3-9ef4418af6c5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "PortAudio library not found",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-3ed1ef594753>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyttsx3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msounddevice\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Initialize recognizer and TTS engine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sounddevice.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PortAudio library not found'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0m_lib\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_libname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: PortAudio library not found"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "# Load the model\n",
        "model = load_model('emotion_recognition_model.keras')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fh9HPYDO0JyX",
        "outputId": "494dea16-23c4-43ae-c7db-8fd7bb204d23"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:576: UserWarning: Skipping variable loading for optimizer 'rmsprop', because it has 8 variables whereas the saved optimizer has 14 variables. \n",
            "  saveable.load_own_variables(weights_store.get(inner_path))\n"
          ]
        }
      ]
    }
  ]
}