Speech Emotion Recognition using ML - SVM, DT, Random Forest.ipynb

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  "metadata": {
    "colab": {
      "name": "speechemotion_ml_algorithms.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMOovI2U0ot8M87YGFf6HB8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/blhprasanna99/speech_emotion_detection/blob/master/speechemotion_ml_algorithms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWhIg4du-1wV",
        "colab_type": "code",
        "outputId": "339308c7-d31f-4713-fa26-6ae1beddc3ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrWB7JZg_VmF",
        "colab_type": "code",
        "outputId": "7d8250e9-e5ce-4002-b5c4-91886802b6ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "!pip install soundfile"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting soundfile\n",
            "  Downloading https://files.pythonhosted.org/packages/eb/f2/3cbbbf3b96fb9fa91582c438b574cff3f45b29c772f94c400e2c99ef5db9/SoundFile-0.10.3.post1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile) (1.13.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile) (2.19)\n",
            "Installing collected packages: soundfile\n",
            "Successfully installed soundfile-0.10.3.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LCe-eWs_P-c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import soundfile\n",
        "import numpy as np\n",
        "import librosa\n",
        "import glob\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# all emotions on RAVDESS dataset\n",
        "int2emotion = {\n",
        "    \"01\": \"neutral\",\n",
        "    \"02\": \"calm\",\n",
        "    \"03\": \"happy\",\n",
        "    \"04\": \"sad\",\n",
        "    \"05\": \"angry\",\n",
        "    \"06\": \"fearful\",\n",
        "    \"07\": \"disgust\",\n",
        "    \"08\": \"surprised\"\n",
        "}\n",
        "\n",
        "# we allow only these emotions\n",
        "AVAILABLE_EMOTIONS = {\n",
        "    \"angry\",\n",
        "    \"sad\",\n",
        "    \"neutral\",\n",
        "    \"happy\"\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQlrNgst_oa0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def extract_feature(file_name, **kwargs):\n",
        "    \"\"\"\n",
        "    Extract feature from audio file `file_name`\n",
        "        Features supported:\n",
        "            - MFCC (mfcc)\n",
        "            - Chroma (chroma)\n",
        "            - MEL Spectrogram Frequency (mel)\n",
        "            - Contrast (contrast)\n",
        "            - Tonnetz (tonnetz)\n",
        "        e.g:\n",
        "        `features = extract_feature(path, mel=True, mfcc=True)`\n",
        "    \"\"\"\n",
        "    mfcc = kwargs.get(\"mfcc\")\n",
        "    chroma = kwargs.get(\"chroma\")\n",
        "    mel = kwargs.get(\"mel\")\n",
        "    contrast = kwargs.get(\"contrast\")\n",
        "    tonnetz = kwargs.get(\"tonnetz\")\n",
        "    with soundfile.SoundFile(file_name) as sound_file:\n",
        "        X = sound_file.read(dtype=\"float32\")\n",
        "        sample_rate = sound_file.samplerate\n",
        "        if chroma or contrast:\n",
        "            stft = np.abs(librosa.stft(X))\n",
        "        result = np.array([])\n",
        "        if mfcc:\n",
        "            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
        "            result = np.hstack((result, mfccs))\n",
        "        if chroma:\n",
        "            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, chroma))\n",
        "        if mel:\n",
        "            mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, mel))\n",
        "        if contrast:\n",
        "            contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, contrast))\n",
        "        if tonnetz:\n",
        "            tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, tonnetz))\n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWOhBXUk_cNU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_data(test_size=0.2):\n",
        "    X, y = [], []\n",
        "    try :\n",
        "      for file in glob.glob(\"/content/drive/My Drive/wav/Actor_*/*.wav\"):\n",
        "          # get the base name of the audio file\n",
        "          basename = os.path.basename(file)\n",
        "          print(basename)\n",
        "          # get the emotion label\n",
        "          emotion = int2emotion[basename.split(\"-\")[2]]\n",
        "          # we allow only AVAILABLE_EMOTIONS we set\n",
        "          if emotion not in AVAILABLE_EMOTIONS:\n",
        "              continue\n",
        "          # extract speech features\n",
        "          features = extract_feature(file, mfcc=True, chroma=True, mel=True)\n",
        "          # add to data\n",
        "          X.append(features)\n",
        "          y.append(emotion)\n",
        "    except :\n",
        "         pass\n",
        "    # split the data to training and testing and return it\n",
        "    return train_test_split(np.array(X), y, test_size=test_size, random_state=7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okb7d-c3_z4l",
        "colab_type": "code",
        "outputId": "5438af64-6c3e-4dae-dbfd-e884b29570a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X_train, X_test, y_train, y_test = load_data(test_size=0.25)\n",
        "# print some details\n",
        "# number of samples in training data\n",
        "print(\"[+] Number of training samples:\", X_train.shape[0])\n",
        "# number of samples in testing data\n",
        "print(\"[+] Number of testing samples:\", X_test.shape[0])\n",
        "# number of features used\n",
        "# this is a vector of features extracted \n",
        "# using utils.extract_features() method\n",
        "print(\"[+] Number of features:\", X_train.shape[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
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            "03-02-06-02-02-02-03.wav\n",
            "[+] Number of training samples: 985\n",
            "[+] Number of testing samples: 329\n",
            "[+] Number of features: 180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-2WVGMZDPGu",
        "colab_type": "text"
      },
      "source": [
        "**DECISION TREE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJSh1g49ALTV",
        "colab_type": "code",
        "outputId": "b564fb35-a537-403a-915c-ebf7c0ccdbb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "dtree_model = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train) \n",
        "dtree_predictions = dtree_model.predict(X_test) \n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=dtree_predictions))\n",
        "print(classification_report(y_test,dtree_predictions)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test, dtree_predictions) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5501519756838906\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.88      0.47      0.61        90\n",
            "       happy       0.49      0.66      0.56        94\n",
            "     neutral       0.28      0.27      0.28        44\n",
            "         sad       0.59      0.64      0.61       101\n",
            "\n",
            "    accuracy                           0.55       329\n",
            "   macro avg       0.56      0.51      0.51       329\n",
            "weighted avg       0.60      0.55      0.55       329\n",
            "\n",
            "[[42 34 11  3]\n",
            " [ 6 62  9 17]\n",
            " [ 0  6 12 26]\n",
            " [ 0 25 11 65]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jo_-cYFVDMqB",
        "colab_type": "code",
        "outputId": "80db6b34-3a64-410f-cb19-facf4e030351",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "dtree_model = DecisionTreeClassifier(max_depth = 6).fit(X_train, y_train) \n",
        "dtree_predictions = dtree_model.predict(X_test) \n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=dtree_predictions))\n",
        "print(classification_report(y_test,dtree_predictions)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test, dtree_predictions) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6595744680851063\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.74      0.74      0.74        90\n",
            "       happy       0.57      0.53      0.55        94\n",
            "     neutral       0.58      0.68      0.62        44\n",
            "         sad       0.71      0.69      0.70       101\n",
            "\n",
            "    accuracy                           0.66       329\n",
            "   macro avg       0.65      0.66      0.65       329\n",
            "weighted avg       0.66      0.66      0.66       329\n",
            "\n",
            "[[67 21  0  2]\n",
            " [17 50  8 19]\n",
            " [ 4  3 30  7]\n",
            " [ 3 14 14 70]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2domdsBGPWd",
        "colab_type": "text"
      },
      "source": [
        "DT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rr-ksNHtEHfe",
        "colab_type": "code",
        "outputId": "e54ef6c4-f642-413f-8d08-9c5b709fae3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "dtree_model = DecisionTreeClassifier(max_depth = 9,random_state=0).fit(X_train, y_train) \n",
        "dtree_predictions = dtree_model.predict(X_test) \n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=dtree_predictions))\n",
        "print(classification_report(y_test,dtree_predictions)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test, dtree_predictions) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6656534954407295\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.66      0.76      0.70        90\n",
            "       happy       0.63      0.55      0.59        94\n",
            "     neutral       0.67      0.64      0.65        44\n",
            "         sad       0.70      0.70      0.70       101\n",
            "\n",
            "    accuracy                           0.67       329\n",
            "   macro avg       0.66      0.66      0.66       329\n",
            "weighted avg       0.66      0.67      0.66       329\n",
            "\n",
            "[[68 16  0  6]\n",
            " [22 52  3 17]\n",
            " [ 4  4 28  8]\n",
            " [ 9 10 11 71]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64KjFNzqDqd-",
        "colab_type": "text"
      },
      "source": [
        "**SUPPORT VECTOR MACHINE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81nRiRWwDxo2",
        "colab_type": "code",
        "outputId": "cd39a985-8b31-47cb-db9d-09575ebcbb91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from sklearn.svm import SVC \n",
        "svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) \n",
        "svm_predictions = svm_model_linear.predict(X_test) \n",
        "\n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=svm_predictions))\n",
        "print(classification_report(y_test,svm_predictions)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test, svm_predictions) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7507598784194529\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.78      0.79      0.78        90\n",
            "       happy       0.73      0.70      0.71        94\n",
            "     neutral       0.65      0.73      0.69        44\n",
            "         sad       0.80      0.77      0.78       101\n",
            "\n",
            "    accuracy                           0.75       329\n",
            "   macro avg       0.74      0.75      0.74       329\n",
            "weighted avg       0.75      0.75      0.75       329\n",
            "\n",
            "[[71 13  4  2]\n",
            " [12 66  4 12]\n",
            " [ 3  3 32  6]\n",
            " [ 5  9  9 78]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_oElZsGEwkG",
        "colab_type": "code",
        "outputId": "ded15a68-2a37-4f6b-e016-435259c57972",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "from sklearn.svm import SVC \n",
        "svm_model_linear = SVC().fit(X_train, y_train) \n",
        "svm_predictions = svm_model_linear.predict(X_test) \n",
        "\n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=svm_predictions))\n",
        "print(classification_report(y_test,svm_predictions)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test, svm_predictions) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.48632218844984804\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.69      0.66      0.67        90\n",
            "       happy       0.36      0.40      0.38        94\n",
            "     neutral       0.00      0.00      0.00        44\n",
            "         sad       0.46      0.62      0.53       101\n",
            "\n",
            "    accuracy                           0.49       329\n",
            "   macro avg       0.38      0.42      0.39       329\n",
            "weighted avg       0.43      0.49      0.45       329\n",
            "\n",
            "[[59 27  0  4]\n",
            " [24 38  0 32]\n",
            " [ 1  5  0 38]\n",
            " [ 2 36  0 63]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kfaciZaHcp2",
        "colab_type": "text"
      },
      "source": [
        "**RANDOM FOREST**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjB-aXmmHAN3",
        "colab_type": "code",
        "outputId": "c32564b3-b384-4110-bf6b-7a94e86b070e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "  \n",
        " # create regressor object \n",
        "classifier = RandomForestClassifier(n_estimators = 100, random_state = 0) \n",
        "  \n",
        "# fit the regressor with x and y data \n",
        "classifier.fit(X_train, y_train)   \n",
        "\n",
        "c_p = classifier.predict(X_test) \n",
        "\n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=c_p))\n",
        "print(classification_report(y_test,c_p)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test,c_p) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7659574468085106\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.87      0.87      0.87        90\n",
            "       happy       0.70      0.72      0.71        94\n",
            "     neutral       0.88      0.64      0.74        44\n",
            "         sad       0.71      0.77      0.74       101\n",
            "\n",
            "    accuracy                           0.77       329\n",
            "   macro avg       0.79      0.75      0.76       329\n",
            "weighted avg       0.77      0.77      0.77       329\n",
            "\n",
            "[[78 10  0  2]\n",
            " [ 9 68  1 16]\n",
            " [ 0  2 28 14]\n",
            " [ 3 17  3 78]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiozFUzAIdCW",
        "colab_type": "code",
        "outputId": "47518f76-73a9-43d7-b6a8-2af330dd6fab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "  \n",
        " # create regressor object \n",
        "classifier = RandomForestClassifier(n_estimators = 20,random_state = 0) \n",
        "  \n",
        "# fit the regressor with x and y data \n",
        "classifier.fit(X_train, y_train)   \n",
        "\n",
        "c_p = classifier.predict(X_test) \n",
        "\n",
        "\n",
        "print(accuracy_score(y_true=y_test,y_pred=c_p))\n",
        "print(classification_report(y_test,c_p)) \n",
        "# creating a confusion matrix \n",
        "print(confusion_matrix(y_test,c_p) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7142857142857143\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       angry       0.79      0.86      0.82        90\n",
            "       happy       0.68      0.61      0.64        94\n",
            "     neutral       0.72      0.59      0.65        44\n",
            "         sad       0.68      0.74      0.71       101\n",
            "\n",
            "    accuracy                           0.71       329\n",
            "   macro avg       0.72      0.70      0.70       329\n",
            "weighted avg       0.71      0.71      0.71       329\n",
            "\n",
            "[[77 10  0  3]\n",
            " [14 57  4 19]\n",
            " [ 0  4 26 14]\n",
            " [ 7 13  6 75]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXzMm5uoKZJN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}