Main_speech-emotion-recognition-ravdess-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61084ce6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d995d8fa",
   "metadata": {},
   "source": [
    "# Speech Emotion Recognition \n",
    "\n",
    "In this Python project, we are going to learn how to recognize emotions from speech. \n",
    "\n",
    "For that, We will use an MLPClassifier: This is a Multi-layer Perceptron Classifier; it optimizes the log-loss function using LBFGS or stochastic gradient descent.The MLPClassifier has an internal neural network for the purpose of classification. This is a feedforward ANN model.\n",
    "\n",
    "We also use Librosa with is the Python library for analyzing audio and music\n",
    "\n",
    "And Sklearn libraries \n",
    "\n",
    "And the RAVDESS dataset : Ryerson Audio-Visual Database of Emotional Speech and Song dataset https://drive.google.com/file/d/1wWsrN2Ep7x6lWqOXfr4rpKGYrJhWc8z7/view "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f089134f",
   "metadata": {},
   "source": [
    "## 1- Importing libraries and packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f08fe87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import soundfile\n",
    "import os, glob, pickle\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#import maya.cmds as cmds\n",
    "#import maya.mel as mel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c035c1",
   "metadata": {},
   "source": [
    "## 2- Extraction of sound features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43a60ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract features (mfcc, chroma, mel) from a sound file\n",
    "\n",
    "def extract_feature(file_name, mfcc, chroma, mel):\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:\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",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1815e2f6",
   "metadata": {},
   "source": [
    "## 3- Dictionary to hold numbers and the emotions available in the RAVDESS dataset, \n",
    "\n",
    "## - And a list to hold those we want- calm, happy, fearful, disgust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f52e0b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Emotions in the RAVDESS dataset\n",
    "\n",
    "emotions={\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43967f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Emotions to observe\n",
    "\n",
    "observed_emotions=['calm', 'happy', 'fearful', 'disgust']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d190dc92",
   "metadata": {},
   "source": [
    "## 4- loading the data and extract the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5ab62d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data and extract features for each sound file\n",
    "\n",
    "def load_data(test_size=0.2):\n",
    "    x,y=[],[]\n",
    "    for file in glob.glob(\"/home/ther/LAB/cv/Speech_Emotion_Recognition/speech-emotion-recognition-ravdess-data/Actor_*/*.wav\"):\n",
    "        file_name=os.path.basename(file)\n",
    "        emotion=emotions[file_name.split(\"-\")[2]]\n",
    "        if emotion not in observed_emotions:\n",
    "            continue\n",
    "        feature=extract_feature(file, mfcc=True, chroma=True, mel=True)\n",
    "        x.append(feature)\n",
    "        y.append(emotion)\n",
    "    return train_test_split(np.array(x), y, test_size=test_size, random_state=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f16186",
   "metadata": {},
   "source": [
    "## 5- Split the dataset into training and testing sets! Let’s keep the test set 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50c19725",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the dataset\n",
    "\n",
    "x_train,x_test,y_train,y_test=load_data(test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a043cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(614, 154)\n"
     ]
    }
   ],
   "source": [
    "#Get the shape of the training and testing datasets\n",
    "\n",
    "print((x_train.shape[0], x_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "46713b4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features extracted: 180\n"
     ]
    }
   ],
   "source": [
    "#Get the number of features extracted\n",
    "\n",
    "print(f'Features extracted: {x_train.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebee970",
   "metadata": {},
   "source": [
    "## 6- Initialization of our MLPClassifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd49b2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize the Multi Layer Perceptron Classifier\n",
    "\n",
    "model=MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071b56aa",
   "metadata": {},
   "source": [
    "## 7- Fit and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1ce9f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
       "              learning_rate='adaptive', max_iter=500)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model\n",
    "\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cd07ef",
   "metadata": {},
   "source": [
    "## 8- Prediction of the test set (the predicted emotions for the features in the test set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e077bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Predict for the test set\n",
    "\n",
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d1956a",
   "metadata": {},
   "source": [
    "## 9- Accuracy of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "648cf7d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 75.32%\n"
     ]
    }
   ],
   "source": [
    "#The accuracy of our model\n",
    "\n",
    "accuracy=accuracy_score(y_true=y_test, y_pred=y_pred)\n",
    "\n",
    "\n",
    "# Print the accuracy\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912abf89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "384ba048",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "315da9d1",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "clf = SVC(random_state=0)\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2344427a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27  8  0  1]\n",
      " [ 2 39  1  2]\n",
      " [ 3  6 26  6]\n",
      " [ 0  6  3 24]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfe698ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}