{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pK6C4xACMmeV",
    "outputId": "09480664-31ef-42eb-f42a-c730916b24dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ffmpeg-python\n",
      "  Downloading https://files.pythonhosted.org/packages/d7/0c/56be52741f75bad4dc6555991fabd2e07b432d333da82c11ad701123888a/ffmpeg_python-0.2.0-py3-none-any.whl\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from ffmpeg-python) (0.16.0)\n",
      "Installing collected packages: ffmpeg-python\n",
      "Successfully installed ffmpeg-python-0.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install ffmpeg-python\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import tensorflow as tf\n",
    "import os,time,librosa,warnings,glob\n",
    "import regex as re\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "import librosa.display\n",
    "from sklearn.preprocessing import MinMaxScaler,OneHotEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Dense,Input,Add,Flatten,Dropout,Activation,AveragePooling1D,Conv1D\n",
    "from keras.models import Model,Sequential,load_model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import LearningRateScheduler,EarlyStopping,ReduceLROnPlateau,ModelCheckpoint\n",
    "from google.colab.output import eval_js\n",
    "from base64 import b64decode\n",
    "from IPython.display import Audio,HTML\n",
    "from scipy.io.wavfile import read as wav_read\n",
    "import io\n",
    "import ffmpeg\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wMK-E6Z4Avys"
   },
   "outputs": [],
   "source": [
    "#decorator function for calculating the total time reqired to execute various function\n",
    "def calc_time(func):\n",
    "  def inner(*args, **kwargs):\n",
    "    st = time.time()\n",
    "    result = func(*args,**kwargs)\n",
    "    end = time.time()-st\n",
    "    print(\"Total time required: {:.3f} ms\".format(end * 1000))\n",
    "    return result\n",
    "  return inner\n",
    "\n",
    "#function for getting ravdess dataset details and labeling\n",
    "def ravdess_data():\n",
    "  #directory of the audio dataset\n",
    "  ravdess = \"/content/drive/MyDrive/Audiofiles/audio_speech_actors_01-24/\"\n",
    "  #label ravdess data\n",
    "  emotion_ravdess = {'01':'neutral','02':'calm','03':'happy','04':'sad','05':'angry','06':'fearful','07':'disgust','08':'surprised'}\n",
    "  #list to store ravdess emotion\n",
    "  ravdess_emotion = []\n",
    "  #list to store ravdess audio path\n",
    "  ravdess_path = []\n",
    "  #get subfolders from the path\n",
    "  ravdess_folder = os.listdir(ravdess)\n",
    "  for i in ravdess_folder:\n",
    "    inner_files = os.listdir(ravdess+i+'/')\n",
    "    for j in inner_files:\n",
    "      #get the split part which contains the emotion information then append it into lists\n",
    "      emotion = j.split('-')[2]\n",
    "      ravdess_path.append(ravdess+i+'/'+j)\n",
    "      ravdess_emotion.append(emotion_ravdess[emotion])\n",
    "\n",
    "  #convert to dataframe\n",
    "  df_ravdess = pd.DataFrame([ravdess_path,ravdess_emotion]).T\n",
    "  df_ravdess.columns = [\"AudioPath\",\"Label\"]\n",
    "  print(\"length of ravdess dataset\",len(df_ravdess))\n",
    "\n",
    "  return df_ravdess\n",
    "\n",
    "#function for getting crema dataset details and labeling\n",
    "def crema_data():\n",
    "  #directory of the audio dataset\n",
    "  crema = \"/content/drive/MyDrive/Audiofiles/AudioWAV/\"\n",
    "  #label ravdess data\n",
    "  emotion_crema = {'SAD':'sad','ANG':'angry','DIS':'disgust','FEA':'fear','HAP':'happy','NEU':'neutral'}\n",
    "  #list to store crema emotion\n",
    "  crema_emotion = []\n",
    "  #list to store crema audio path\n",
    "  crema_path = []\n",
    "  #get crema files in directory\n",
    "  crema_files = os.listdir(crema)\n",
    "  for i in crema_files:\n",
    "    emotion = i.split('_')[2]\n",
    "    crema_emotion.append(emotion_crema[emotion])\n",
    "    crema_path.append(crema+i)\n",
    "\n",
    "  #convert to dataframe\n",
    "  df_crema = pd.DataFrame([crema_path,crema_emotion]).T\n",
    "  df_crema.columns = [\"AudioPath\",\"Label\"]\n",
    "  print(\"length of crema dataset\",len(df_crema))\n",
    "\n",
    "  return df_crema\n",
    "\n",
    "#function for getting tess dataset and labeling\n",
    "def tess_data():\n",
    "  #directory of the audio dataset\n",
    "  tess = \"/content/drive/MyDrive/Audiofiles/TESS Toronto emotional speech set data/\"\n",
    "  tess_emotion = []\n",
    "  tess_path = []\n",
    "  tess_folder = os.listdir(tess)\n",
    "  for i in tess_folder:\n",
    "    emotion = i.split('_',1)[1]\n",
    "    inner_files = os.listdir(tess+i+'/')\n",
    "    for j in inner_files:\n",
    "      tess_path.append(tess+i+'/'+j)\n",
    "      tess_emotion.append(emotion)\n",
    "\n",
    "  #convert to dataframe\n",
    "  df_tess = pd.DataFrame([tess_path,tess_emotion]).T\n",
    "  df_tess.columns = [\"AudioPath\",\"Label\"]\n",
    "  print(\"length of tess dataset\",len(df_tess))\n",
    "\n",
    "  return df_tess\n",
    "\n",
    "#function to get savee dataset and labeling\n",
    "def saveee_data():\n",
    "  #directory of the audio dataset\n",
    "  savee = \"/content/drive/MyDrive/Audiofiles/ALL/\"\n",
    "  emotion_savee = {'a':'anger','d':'disgust','f':'fear','h':'happiness','n':'neutral','sa':'sadness','su':'surprise'}\n",
    "  savee_emotion = []\n",
    "  savee_path = []\n",
    "  savee_files = os.listdir(savee)\n",
    "  for i in savee_files:\n",
    "    emotion = i.split('_')[1]\n",
    "    emotion = re.match(r\"([a-z]+)([0-9]+)\",emotion)[1]\n",
    "    savee_emotion.append(emotion_savee[emotion])\n",
    "    savee_path.append(savee+i)\n",
    "\n",
    "  #convert to dataframe\n",
    "  df_savee = pd.DataFrame([savee_path,savee_emotion]).T\n",
    "  df_savee.columns = [\"AudioPath\",\"Label\"]\n",
    "  print(\"length of savee dataset\",len(df_savee))\n",
    "\n",
    "  return df_savee\n",
    "\n",
    "@calc_time\n",
    "def fetch_data():\n",
    "  #get ravdess data\n",
    "  df_ravdess = ravdess_data()\n",
    "  #get crema data\n",
    "  df_crema = crema_data()\n",
    "  #get tess data\n",
    "  df_tess = tess_data()\n",
    "  #get savee data\n",
    "  df_savee = saveee_data()\n",
    "  #combine all four dataset into one single dataset and create a dataframe \n",
    "  frames = [df_ravdess,df_crema,df_tess,df_savee]\n",
    "  final_combined = pd.concat(frames)\n",
    "  final_combined.reset_index(drop=True,inplace=True)\n",
    "  #save the information of datasets with their path and labels into a csv file\n",
    "  final_combined.to_csv(\"/content/drive/MyDrive/preprocesseddata.csv\",index=False,header=True)\n",
    "  print(\"Total length of the dataset is {}\".format(len(final_combined)))\n",
    "  return final_combined\n",
    "\n",
    "#below are four data agumentation functions for noise, stretch, shift, pitch\n",
    "#function to add noise to audio\n",
    "def noise(data):\n",
    "  noise_amp = 0.035*np.random.uniform()*np.amax(data)\n",
    "  data = data + noise_amp*np.random.normal(size=data.shape[0])\n",
    "  return data\n",
    "\n",
    "#fuction to strech audio\n",
    "def stretch(data, rate=0.8):\n",
    "  return librosa.effects.time_stretch(data, rate)\n",
    "\n",
    "#fucntion to shift audio range\n",
    "def shift(data):\n",
    "  shift_range = int(np.random.uniform(low=-5, high = 5)*1000)\n",
    "  return np.roll(data, shift_range)\n",
    "\n",
    "#function to change pitch\n",
    "def pitch(data, sampling_rate, pitch_factor=0.7):\n",
    "  return librosa.effects.pitch_shift(data, sampling_rate, pitch_factor)\n",
    "\n",
    "#fuction to extract audio features from the audio files given the information of their path\n",
    "#path and label information comes from fetch_data fucntion \n",
    "#also file preprocesseddata.csv stores the information of paths of audio files their label information\n",
    "#the print statements are commented these statements were used to see the number of features returned as output\n",
    "def extract_features(data,sample_rate):  \n",
    "  \n",
    "  #zero crossing rate\n",
    "  result = np.array([])\n",
    "  zcr = np.mean(librosa.feature.zero_crossing_rate(y=data).T, axis=0)\n",
    "  result = np.hstack((result, zcr)) \n",
    "  #print('zcr',result.shape)\n",
    "\n",
    "  #chroma shift\n",
    "  stft = np.abs(librosa.stft(data))\n",
    "  chroma_stft = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, chroma_stft))\n",
    "  #print('chroma',result.shape)\n",
    "  \n",
    "  #mfcc\n",
    "  mfcc = np.mean(librosa.feature.mfcc(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, mfcc))\n",
    "  #print('mfcc',result.shape)\n",
    "  \n",
    "  #rmse\n",
    "  rms = np.mean(librosa.feature.rms(y=data).T, axis=0)\n",
    "  result = np.hstack((result, rms)) \n",
    "  #print('rmse',result.shape)\n",
    "  \n",
    "  #melspectogram\n",
    "  mel = np.mean(librosa.feature.melspectrogram(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, mel)) \n",
    "  #print('mel',result.shape)    \n",
    "\n",
    "  #rollof \n",
    "  rollof = np.mean(librosa.feature.spectral_rolloff(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, rollof))\n",
    "  #print('rollof',result.shape) \n",
    "\n",
    "  #centroids \n",
    "  centroid = np.mean(librosa.feature.spectral_centroid(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, centroid))\n",
    "  #print('centroids',result.shape)\n",
    "\n",
    "  #contrast\n",
    "  contrast = np.mean(librosa.feature.spectral_contrast(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, contrast))\n",
    "  #print('contrast',result.shape)\n",
    "\n",
    "  #bandwidth\n",
    "  bandwidth = np.mean(librosa.feature.spectral_bandwidth(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, bandwidth))\n",
    "  #print('bandwidth',result.shape)\n",
    "\n",
    "  #tonnetz\n",
    "  tonnetz = np.mean(librosa.feature.tonnetz(y=data, sr=sample_rate).T, axis=0)\n",
    "  result = np.hstack((result, tonnetz))\n",
    "  #print('tonnetz',result.shape) \n",
    "\n",
    "  return result\n",
    "\n",
    "#function is used to get all augmented plus original features for given audio file\n",
    "def get_features(path):\n",
    "  #set the duration and offset\n",
    "  #librosa.load takes audio file converts to array and returns array of audio file with its sampling rate\n",
    "  data, sample_rate = librosa.load(path, duration=2.5, offset=0.6)\n",
    "    \n",
    "  #get audio features without augmentation\n",
    "  res1 = extract_features(data,sample_rate)\n",
    "  result = np.array(res1)\n",
    "    \n",
    "  #get audio features with noise\n",
    "  noise_data = noise(data)\n",
    "  res2 = extract_features(noise_data,sample_rate)\n",
    "  result = np.vstack((result, res2))\n",
    "    \n",
    "  #get audio features with stretching and pitching\n",
    "  new_data = stretch(data)\n",
    "  data_stretch_pitch = pitch(new_data, sample_rate)\n",
    "  res3 = extract_features(data_stretch_pitch,sample_rate)\n",
    "  result = np.vstack((result, res3))\n",
    "    \n",
    "  return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aVKIeMnqb-uY",
    "outputId": "fa5a28aa-9343-42b2-e0f1-d465fc3be79c"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "#fucntion one by one takes aduio files from the path extracts features \n",
    "#extracted audio features along with their label information are stored in a csv file \n",
    "@calc_time\n",
    "def Audio_features_extract():\n",
    "  #this function is used to fetch the data from all the four datasets\n",
    "  df = fetch_data()\n",
    "  #count is used to keep a check of number of files processed\n",
    "  count = 0\n",
    "  #list to store audio features and their label information\n",
    "  X_data, Y_label = [], []\n",
    "  #zip audio path and label information and then iterate over them\n",
    "  for path, emotion in zip(df[\"AudioPath\"], df[\"Label\"]):\n",
    "    print(\"Number of files processed \",count)\n",
    "    #get the features \n",
    "    #for one audio file it get three sets of features \n",
    "    #original features, features with noise(agumentation) and feature with change in stretch and pitch\n",
    "    #so one audio file generates three output and the label is same for all the outputs\n",
    "    feature = get_features(path)\n",
    "    for ele in feature:\n",
    "      X_data.append(ele)\n",
    "      Y_label.append(emotion)\n",
    "    count+=1\n",
    "  #create a dataframe of aduio features\n",
    "  Features = pd.DataFrame(X_data)\n",
    "  #add label information \n",
    "  Features['Label'] = Y_label\n",
    "  #store the extracted features in a csv file\n",
    "  Features.to_csv('/content/drive/MyDrive/Audiofiles/Audio_features_All_pr.csv',index=False)\n",
    "\n",
    "#this is just one time process so call this function once only to get the features\n",
    "#once the features are extracted then these features are used for making model\n",
    "Audio_features_extract()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WGQs_brp9NT8"
   },
   "outputs": [],
   "source": [
    "#function to plot loss and accuracy curves on training set\n",
    "def plotgraph(history):\n",
    "  plt.figure(figsize=[8,6])\n",
    "  plt.plot(history.history['loss'],'firebrick',linewidth=3.0)\n",
    "  plt.plot(history.history['accuracy'],'turquoise',linewidth=3.0)\n",
    "  plt.legend(['Training loss','Training Accuracy'],fontsize=18)\n",
    "  plt.xlabel('Epochs ',fontsize=16)\n",
    "  plt.ylabel('Loss and Accuracy',fontsize=16)\n",
    "  plt.title('Loss Curves and Accuracy Curves',fontsize=16)\n",
    "\n",
    "#function carries out additional preprocessing on data\n",
    "#this function includes the EDA carried out on dataset \n",
    "#the selected emotions are kept and others are dropped \n",
    "#renaming of emotions is done to maintian uniformity\n",
    "def additional_preprocess(filepath):\n",
    "  #read the csv file of extrated features\n",
    "  df = pd.read_csv(filepath)\n",
    "  print(\"\\nlabels or emotions present in dataset\\n\",df[\"Label\"].unique())\n",
    "  #replace label names with name common for each emotion \n",
    "  #this is done to maintain uniformity of label names\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"sadness\", \"sad\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"happiness\", \"happy\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"Fear\", \"fear\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"Sad\", \"sad\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"Pleasant_surprise\", \"surprise\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"pleasant_surprised\", \"surprise\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"surprised\", \"surprise\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"fearful\", \"fear\", case = True)\n",
    "  df[\"Label\"] = df[\"Label\"].str.replace(\"anger\", \"angry\", case = True)\n",
    "  #drop labels surprized and clam\n",
    "  #these label dosent contain sufficent amount of data and can lead to missclassification\n",
    "  print(\"\\nUnique count of labels or emotions\\n\",df[\"Label\"].value_counts())\n",
    "  #drop labels or emotions which can lead to misclassifications\n",
    "  df.drop((np.where(df['Label'].isin([\"surprise\",\"calm\"]))[0]), inplace = True)\n",
    "  print(\"\\nUnique count of labels or emotions after dropping selected labels\\n\",df[\"Label\"].value_counts())\n",
    "  print(\"\\nlength of the total data is {}\".format(len(df)))\n",
    "  return df\n",
    "\n",
    "#this fucntion is used to get audio features perform one hot encoding and split datasets into train, test and validation\n",
    "@calc_time\n",
    "def audio_features_final():\n",
    "  df = additional_preprocess(\"/content/drive/MyDrive/Audiofiles/Audio_features_All_pr.csv\")\n",
    "  #get all the aduio features as numpy array from the dataframe \n",
    "  #last column is label so last column is not fetched only 0to:-1\n",
    "  data=df[df.columns[0:-1]].values\n",
    "  #perform one hot encoding on labels\n",
    "  encoder = OneHotEncoder()\n",
    "  #fetch the last column of labels and perform one hot encoding on them\n",
    "  label=df[\"Label\"].values\n",
    "  label = encoder.fit_transform(np.array(label).reshape(-1,1)).toarray()\n",
    "  #min max scaler is used to normalize the data\n",
    "  scaler = MinMaxScaler()\n",
    "  data=scaler.fit_transform(data)\n",
    "  #split the dataframe into train and test 80% train, 10% validation and 10% test datasets\n",
    "  x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.20, random_state=42,shuffle=True)\n",
    "  x_test, x_val, y_test, y_val = train_test_split(x_test, y_test, test_size=0.50, random_state=42, shuffle=True)\n",
    "  print(\"\\nlength of train data is {}, test data is {} and validation set is {}\".format(len(x_train),len(x_test),len(x_val))) \n",
    "  print(\"\\n shape of train features and label is {}\".format(x_train.shape, y_train.shape))\n",
    "  print(\"\\n shape of test features and label is {}\".format(x_test.shape, y_test.shape))\n",
    "  print(\"\\n shape of validation features and label is {}\".format(x_val.shape,y_val.shape))\n",
    "  return x_train, x_test, y_train, y_test, x_val, y_val, encoder\n",
    "\n",
    "#fucntion trains the model and saves the best model at the checkpoint\n",
    "@calc_time\n",
    "def emotion_recognition_model(x_train,y_train,x_val,y_val):\n",
    "  #reduce the laerning rate if plateau is encountered\n",
    "  reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=5, min_lr=0.001)\n",
    "  #early stopping method is used to montior the loss if there are no significant reductions in loss then halt the training\n",
    "  es = EarlyStopping(monitor='loss', patience=20)\n",
    "  #checkpoint to save the best model with highest validation accuracy\n",
    "  filepath = \"/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\"\n",
    "  checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\n",
    "  #create a combined list of reduce learning rate, early stopping and checkpoint\n",
    "  callbacks_list = [reduce_lr,es,checkpoint]\n",
    "  def residual_block(x, filters, conv_num=3, activation=\"relu\"):\n",
    "    #fucntion is used to create residual blocks and add residual blocks\n",
    "    s = Conv1D(filters, 1, padding=\"same\")(x)\n",
    "    for i in range(conv_num - 1):\n",
    "      x = Conv1D(filters, 3, padding=\"same\")(x)\n",
    "      x = Activation(activation)(x)\n",
    "    x = Conv1D(filters, 3, padding=\"same\")(x)\n",
    "    x = Add()([x, s])\n",
    "    x = Activation(activation)(x)\n",
    "    return x\n",
    "  #fucntion to build the model \n",
    "  def build_model():\n",
    "    inputs =  Input(shape=(x_train.shape[1],1))\n",
    "    x = Dense(256, activation=\"relu\")(inputs)\n",
    "    x = residual_block(x, 16, 2)\n",
    "    x = residual_block(x, 32, 2)\n",
    "    x = residual_block(x, 32, 2)\n",
    "    x = residual_block(x, 64, 3)\n",
    "    x = residual_block(x, 64, 3)\n",
    "    x = residual_block(x, 128, 3)\n",
    "    x = residual_block(x, 128, 3)\n",
    "    #perform the average pooling after last residual block \n",
    "    x = AveragePooling1D(pool_size=3, strides=3)(x)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(256, activation=\"relu\")(x)\n",
    "    x = Dense(128, activation=\"relu\")(x)\n",
    "    outputs = Dense(6, activation=\"softmax\", name=\"output\")(x)\n",
    "    return Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "  res_model = build_model() \n",
    "  #display the summary of the model\n",
    "  res_model.summary() \n",
    "  #complie the model\n",
    "  res_model.compile(loss='categorical_crossentropy',optimizer = Adam(lr=1e-4, decay=1e-4 / 50) , metrics=['accuracy'])\n",
    "  history = res_model.fit(np.expand_dims(x_train,-1),y_train,\n",
    "                validation_data=(np.expand_dims(x_val, -1), y_val), \n",
    "                epochs=500,\n",
    "                batch_size=32,\n",
    "                shuffle=True,\n",
    "                workers=50,\n",
    "                verbose=1,\n",
    "                use_multiprocessing=True,\n",
    "                callbacks = callbacks_list)\n",
    "  \n",
    "  #plot loss and accuracy curves\n",
    "  plotgraph(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Zwe90AjYKbcD"
   },
   "outputs": [],
   "source": [
    "#this javascript is used to tell colab cell to open microphone and record audio\n",
    "AUDIO_HTML = \"\"\"\n",
    "<script>\n",
    "var my_div = document.createElement(\"DIV\");\n",
    "var my_p = document.createElement(\"P\");\n",
    "var my_btn = document.createElement(\"BUTTON\");\n",
    "var t = document.createTextNode(\"Press to start recording\");\n",
    "\n",
    "my_btn.appendChild(t);\n",
    "//my_p.appendChild(my_btn);\n",
    "my_div.appendChild(my_btn);\n",
    "document.body.appendChild(my_div);\n",
    "\n",
    "var base64data = 0;\n",
    "var reader;\n",
    "var recorder, gumStream;\n",
    "var recordButton = my_btn;\n",
    "\n",
    "var handleSuccess = function(stream) {\n",
    "  gumStream = stream;\n",
    "  var options = {\n",
    "    //bitsPerSecond: 8000, //chrome seems to ignore, always 48k\n",
    "    mimeType : 'audio/webm;codecs=opus'\n",
    "    //mimeType : 'audio/webm;codecs=pcm'\n",
    "  };            \n",
    "  //recorder = new MediaRecorder(stream, options);\n",
    "  recorder = new MediaRecorder(stream);\n",
    "  recorder.ondataavailable = function(e) {            \n",
    "    var url = URL.createObjectURL(e.data);\n",
    "    var preview = document.createElement('audio');\n",
    "    preview.controls = true;\n",
    "    preview.src = url;\n",
    "    document.body.appendChild(preview);\n",
    "\n",
    "    reader = new FileReader();\n",
    "    reader.readAsDataURL(e.data); \n",
    "    reader.onloadend = function() {\n",
    "      base64data = reader.result;\n",
    "      //console.log(\"Inside FileReader:\" + base64data);\n",
    "    }\n",
    "  };\n",
    "  recorder.start();\n",
    "  };\n",
    "\n",
    "recordButton.innerText = \"Recording... press to stop\";\n",
    "\n",
    "navigator.mediaDevices.getUserMedia({audio: true}).then(handleSuccess);\n",
    "\n",
    "\n",
    "function toggleRecording() {\n",
    "  if (recorder && recorder.state == \"recording\") {\n",
    "      recorder.stop();\n",
    "      gumStream.getAudioTracks()[0].stop();\n",
    "      recordButton.innerText = \"Saving the recording... pls wait!\"\n",
    "  }\n",
    "}\n",
    "\n",
    "// https://stackoverflow.com/a/951057\n",
    "function sleep(ms) {\n",
    "  return new Promise(resolve => setTimeout(resolve, ms));\n",
    "}\n",
    "\n",
    "var data = new Promise(resolve=>{\n",
    "//recordButton.addEventListener(\"click\", toggleRecording);\n",
    "recordButton.onclick = ()=>{\n",
    "toggleRecording()\n",
    "\n",
    "sleep(2000).then(() => {\n",
    "  // wait 2000ms for the data to be available...\n",
    "  // ideally this should use something like await...\n",
    "  //console.log(\"Inside data:\" + base64data)\n",
    "  resolve(base64data.toString())\n",
    "\n",
    "});\n",
    "\n",
    "}\n",
    "});\n",
    "      \n",
    "</script>\n",
    "\"\"\"\n",
    "#fucntion is used to invoke microphone on laptop and record the voice \n",
    "#it used javascript to work as colab dosent have inbuilt method to record audio\n",
    "def get_audio():\n",
    "  display(HTML(AUDIO_HTML))\n",
    "  data = eval_js(\"data\")\n",
    "  binary = b64decode(data.split(',')[1])\n",
    "  \n",
    "  process = (ffmpeg\n",
    "    .input('pipe:0')\n",
    "    .output('pipe:1', format='wav')\n",
    "    .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True, quiet=True, overwrite_output=True)\n",
    "  )\n",
    "  output, err = process.communicate(input=binary)\n",
    "  \n",
    "  riff_chunk_size = len(output) - 8\n",
    "  # Break up the chunk size into four bytes, held in b.\n",
    "  q = riff_chunk_size\n",
    "  b = []\n",
    "  for i in range(4):\n",
    "      q, r = divmod(q, 256)\n",
    "      b.append(r)\n",
    "\n",
    "  # Replace bytes 4:8 in proc.stdout with the actual size of the RIFF chunk.\n",
    "  riff = output[:4] + bytes(b) + output[8:]\n",
    "\n",
    "  sr, audio = wav_read(io.BytesIO(riff))\n",
    "\n",
    "  return audio, sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OW5lMB2fKM1b"
   },
   "outputs": [],
   "source": [
    "#function is used to get the audio features recorded from the microphone \n",
    "def get_features_recorded(data,sr): \n",
    "\n",
    "  #get features for recorded audio using mircophone\n",
    "  res1 = extract_features(data,sr)\n",
    "  result = np.array(res1)\n",
    "  \n",
    "  #get audio features with noise\n",
    "  noise_data = noise(data)\n",
    "  res2 = extract_features(noise_data,sr)\n",
    "  result = np.vstack((result, res2))\n",
    "    \n",
    "  #get audio features with stretching and pitching\n",
    "  new_data = stretch(data)\n",
    "  data_stretch_pitch = pitch(new_data, sr)\n",
    "  res3 = extract_features(data_stretch_pitch,sr)\n",
    "  result = np.vstack((result, res3))\n",
    "  \n",
    "  return result\n",
    "\n",
    "#fucntion is used to evaualte performance of model on recorded audio using microphone\n",
    "@calc_time\n",
    "def test_realtime(encoder):\n",
    "  \n",
    "  #load the best model\n",
    "  res_model = load_model(\"/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\")\n",
    "\n",
    "  #record the aduio\n",
    "  audio, sr = get_audio()\n",
    "  #save audio in a file \n",
    "  files = []\n",
    "  os.chdir('/content/drive/MyDrive/Audiofiles/realtimetested')\n",
    "  for file in glob.glob(\"*.npy\"):\n",
    "\t  files.append(file)\n",
    "\n",
    "  #save the audio file for reference\n",
    "  np.save('/content/drive/MyDrive/Audiofiles/realtimetested/audiorec{}.npy'.format(len(files)),audio)\n",
    "  #plot the recorded audio\n",
    "  plt.figure(figsize=(5,5))\n",
    "  plt.plot(audio)\n",
    "  plt.show()\n",
    "  #save the plot of audio file\n",
    "  plt.savefig(\"audiorec{}.png\".format(len(files)))\n",
    "  #convert int to float\n",
    "  audio = audio.astype('float')\n",
    "  #get audio features from the recorded voice\n",
    "  feature = get_features_recorded(audio,sr)\n",
    "  #apply min max scaling\n",
    "  scaler = MinMaxScaler()\n",
    "  feature = scaler.fit_transform(feature)\n",
    "  #get the predicted label\n",
    "  label=res_model.predict(feature)\n",
    "  #get the label information by reversing one hot encoded output\n",
    "  label_predicted=encoder.inverse_transform(label)\n",
    "  print(\"\\nThe Emotion Predicted For Recorded Audio Using Microphone is {}\".format(label_predicted[0]))\n",
    "  #create a dataframe for recorded aduio features\n",
    "  df = pd.DataFrame(index=range(0,3),columns=['path','label','audio'])\n",
    "  for i in range(0,3):\n",
    "    df[\"path\"][i] = '/content/drive/MyDrive/Audiofiles/realtimetested/audiorec{}.npy'.format(len(files))\n",
    "    df[\"label\"][i] = label_predicted[i]\n",
    "    df[\"audio\"][i] = feature[i]\n",
    "\n",
    "  #store the real time predicted features in a csv file\n",
    "  df.to_csv('/content/drive/MyDrive/Audiofiles/realtimetested/real_time_predicted_audio_features.csv', mode='a', index=False)\n",
    " \n",
    "#function to evaluate the model performance once the best model is saved\n",
    "#it loads the best model and then evaluates the performance\n",
    "@calc_time\n",
    "def evaluate_model(x_train, x_test, y_train, y_test, x_val, y_val):\n",
    "  #load the best model\n",
    "  model = load_model(\"/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\")\n",
    "  #evaluate training accuracy \n",
    "  _,train_acc = model.evaluate(np.expand_dims(x_train,-1),y_train, batch_size=1)\n",
    "  #evaluate testing acuracy\n",
    "  _,test_acc = model.evaluate(np.expand_dims(x_test,-1),y_test, batch_size=1)\n",
    "  #evaluate validation accuracy\n",
    "  _,val_acc = model.evaluate(np.expand_dims(x_val,-1),y_val, batch_size=1)\n",
    "  print(\"\\n**********************************************\")\n",
    "  print(\"\\n Training accuracy of the model is {}\".format(np.round(float(train_acc*100),2)))\n",
    "  print(\"\\n Testing accuracy of the model is {}\".format(np.round(float(test_acc*100),2)))\n",
    "  print(\"\\n Validation accuracy of the model is {}\".format(np.round(float(val_acc*100),2)))\n",
    "  print(\"**********************************************\")\n",
    "  #predict the outcome of the model\n",
    "  y_pred = model.predict(x_test)\n",
    "  y_pred=np.argmax(y_pred, axis=1)\n",
    "  y_test=np.argmax(y_test, axis=1)\n",
    "  #View the classification report for test data and predictions\n",
    "  print(\"\\nClassification report for Emotion Recognition\")\n",
    "  print(classification_report(y_test, y_pred))  \n",
    "  #View confusion matrix for test data and predictions\n",
    "  print(\"\\nConfusion matrix for Emotion Recognition\")\n",
    "  print(confusion_matrix(y_test, y_pred))\n",
    "  print(\"*****************************\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "93xZIcuLXHo5",
    "outputId": "1707a63f-f1ba-42d0-af25-e87d8cb83e8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "labels or emotions present in dataset\n",
      " ['calm' 'sad' 'angry' 'neutral' 'happy' 'fearful' 'surprised' 'disgust'\n",
      " 'fear' 'Fear' 'pleasant_surprised' 'Sad' 'Pleasant_surprise' 'anger'\n",
      " 'happiness' 'sadness' 'surprise']\n",
      "\n",
      "Unique count of labels or emotions\n",
      " fear        5769\n",
      "happy       5769\n",
      "angry       5769\n",
      "disgust     5769\n",
      "sad         5769\n",
      "neutral     5109\n",
      "surprise    1956\n",
      "calm         576\n",
      "Name: Label, dtype: int64\n",
      "\n",
      "Unique count of labels or emotions after dropping selected labels\n",
      " fear       5769\n",
      "happy      5769\n",
      "angry      5769\n",
      "disgust    5769\n",
      "sad        5769\n",
      "neutral    5109\n",
      "Name: Label, dtype: int64\n",
      "\n",
      "length of the total data is 33954\n",
      "\n",
      "length of train data is 27163, test data is 3395 and validation set is 3396\n",
      "\n",
      " shape of train features and label is (27163, 178)\n",
      "\n",
      " shape of test features and label is (3395, 178)\n",
      "\n",
      " shape of validation features and label is (3396, 178)\n",
      "Total time required: 4707.916 ms\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 178, 1)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 178, 256)     512         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 178, 16)      12304       dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 178, 16)      0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 178, 16)      784         activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 178, 16)      4112        dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 178, 16)      0           conv1d_2[0][0]                   \n",
      "                                                                 conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 178, 16)      0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 178, 32)      1568        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 178, 32)      0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 178, 32)      3104        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 178, 32)      544         activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 178, 32)      0           conv1d_5[0][0]                   \n",
      "                                                                 conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 178, 32)      0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 178, 32)      3104        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 178, 32)      0           conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 178, 32)      3104        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 178, 32)      1056        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 178, 32)      0           conv1d_8[0][0]                   \n",
      "                                                                 conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 178, 32)      0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 178, 64)      6208        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 178, 64)      0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 178, 64)      12352       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 178, 64)      0           conv1d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_12 (Conv1D)              (None, 178, 64)      12352       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 178, 64)      2112        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 178, 64)      0           conv1d_12[0][0]                  \n",
      "                                                                 conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 178, 64)      0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, 178, 64)      12352       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 178, 64)      0           conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, 178, 64)      12352       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 178, 64)      0           conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, 178, 64)      12352       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_13 (Conv1D)              (None, 178, 64)      4160        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 178, 64)      0           conv1d_16[0][0]                  \n",
      "                                                                 conv1d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 178, 64)      0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_18 (Conv1D)              (None, 178, 128)     24704       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 178, 128)     0           conv1d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_19 (Conv1D)              (None, 178, 128)     49280       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 178, 128)     0           conv1d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_20 (Conv1D)              (None, 178, 128)     49280       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_17 (Conv1D)              (None, 178, 128)     8320        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 178, 128)     0           conv1d_20[0][0]                  \n",
      "                                                                 conv1d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 178, 128)     0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_22 (Conv1D)              (None, 178, 128)     49280       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 178, 128)     0           conv1d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_23 (Conv1D)              (None, 178, 128)     49280       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 178, 128)     0           conv1d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_24 (Conv1D)              (None, 178, 128)     49280       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_21 (Conv1D)              (None, 178, 128)     16512       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 178, 128)     0           conv1d_24[0][0]                  \n",
      "                                                                 conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 178, 128)     0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling1d (AveragePooli (None, 59, 128)      0           activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 7552)         0           average_pooling1d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          1933568     flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          32896       dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 6)            774         dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 2,367,606\n",
      "Trainable params: 2,367,606\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/500\n",
      "849/849 [==============================] - 69s 22ms/step - loss: 1.6402 - accuracy: 0.2741 - val_loss: 1.2397 - val_accuracy: 0.5082\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.50824, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 2/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 1.2004 - accuracy: 0.5124 - val_loss: 1.1701 - val_accuracy: 0.5218\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.50824 to 0.52179, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 3/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 1.1178 - accuracy: 0.5541 - val_loss: 1.0932 - val_accuracy: 0.5580\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.52179 to 0.55801, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 4/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 1.0456 - accuracy: 0.5767 - val_loss: 1.0678 - val_accuracy: 0.5598\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.55801 to 0.55978, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 5/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 1.0083 - accuracy: 0.5999 - val_loss: 1.0611 - val_accuracy: 0.5721\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.55978 to 0.57214, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 6/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.9601 - accuracy: 0.6161 - val_loss: 1.0362 - val_accuracy: 0.5745\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.57214 to 0.57450, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 7/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.9279 - accuracy: 0.6313 - val_loss: 1.0129 - val_accuracy: 0.5886\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.57450 to 0.58863, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 8/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.8885 - accuracy: 0.6492 - val_loss: 1.0158 - val_accuracy: 0.5960\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.58863 to 0.59600, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 9/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.8369 - accuracy: 0.6681 - val_loss: 0.9962 - val_accuracy: 0.6042\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.59600 to 0.60424, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 10/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.7977 - accuracy: 0.6860 - val_loss: 0.9810 - val_accuracy: 0.6098\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.60424 to 0.60984, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 11/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.7550 - accuracy: 0.7047 - val_loss: 1.0102 - val_accuracy: 0.6246\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.60984 to 0.62456, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 12/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.7076 - accuracy: 0.7273 - val_loss: 0.9346 - val_accuracy: 0.6325\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.62456 to 0.63251, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 13/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.6548 - accuracy: 0.7494 - val_loss: 0.9741 - val_accuracy: 0.6302\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.63251\n",
      "Epoch 14/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.6001 - accuracy: 0.7711 - val_loss: 0.9939 - val_accuracy: 0.6446\n",
      "\n",
      "Epoch 00014: val_accuracy improved from 0.63251 to 0.64458, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 15/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.5607 - accuracy: 0.7839 - val_loss: 1.0616 - val_accuracy: 0.6425\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.64458\n",
      "Epoch 16/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.5011 - accuracy: 0.8108 - val_loss: 1.0867 - val_accuracy: 0.6569\n",
      "\n",
      "Epoch 00016: val_accuracy improved from 0.64458 to 0.65695, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 17/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.4611 - accuracy: 0.8279 - val_loss: 1.0927 - val_accuracy: 0.6684\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.65695 to 0.66843, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 18/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.4157 - accuracy: 0.8475 - val_loss: 1.0871 - val_accuracy: 0.6749\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.66843 to 0.67491, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 19/500\n",
      "849/849 [==============================] - 15s 18ms/step - loss: 0.3620 - accuracy: 0.8659 - val_loss: 1.1498 - val_accuracy: 0.6773\n",
      "\n",
      "Epoch 00019: val_accuracy improved from 0.67491 to 0.67727, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 20/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.3391 - accuracy: 0.8728 - val_loss: 1.2412 - val_accuracy: 0.6711\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.67727\n",
      "Epoch 21/500\n",
      "849/849 [==============================] - 15s 18ms/step - loss: 0.3044 - accuracy: 0.8900 - val_loss: 1.2211 - val_accuracy: 0.6835\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.67727 to 0.68345, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 22/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.2704 - accuracy: 0.8991 - val_loss: 1.2830 - val_accuracy: 0.6837\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.68345 to 0.68375, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 23/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.2383 - accuracy: 0.9134 - val_loss: 1.3101 - val_accuracy: 0.6858\n",
      "\n",
      "Epoch 00023: val_accuracy improved from 0.68375 to 0.68581, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 24/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.2177 - accuracy: 0.9230 - val_loss: 1.5786 - val_accuracy: 0.6770\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.68581\n",
      "Epoch 25/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.2146 - accuracy: 0.9222 - val_loss: 1.5391 - val_accuracy: 0.6790\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.68581\n",
      "Epoch 26/500\n",
      "849/849 [==============================] - 15s 18ms/step - loss: 0.1723 - accuracy: 0.9379 - val_loss: 1.5472 - val_accuracy: 0.6879\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.68581 to 0.68787, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 27/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.1587 - accuracy: 0.9456 - val_loss: 1.7719 - val_accuracy: 0.6808\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.68787\n",
      "Epoch 28/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.1403 - accuracy: 0.9505 - val_loss: 1.6434 - val_accuracy: 0.6929\n",
      "\n",
      "Epoch 00028: val_accuracy improved from 0.68787 to 0.69287, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 29/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.1320 - accuracy: 0.9572 - val_loss: 1.5942 - val_accuracy: 0.6926\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.69287\n",
      "Epoch 30/500\n",
      "849/849 [==============================] - 15s 18ms/step - loss: 0.1267 - accuracy: 0.9558 - val_loss: 1.8359 - val_accuracy: 0.6899\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.69287\n",
      "Epoch 31/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.1155 - accuracy: 0.9610 - val_loss: 1.9143 - val_accuracy: 0.6855\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.69287\n",
      "Epoch 32/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.1097 - accuracy: 0.9632 - val_loss: 1.8671 - val_accuracy: 0.6938\n",
      "\n",
      "Epoch 00032: val_accuracy improved from 0.69287 to 0.69376, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 33/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0887 - accuracy: 0.9693 - val_loss: 2.0030 - val_accuracy: 0.6979\n",
      "\n",
      "Epoch 00033: val_accuracy improved from 0.69376 to 0.69788, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 34/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0943 - accuracy: 0.9666 - val_loss: 1.8802 - val_accuracy: 0.7008\n",
      "\n",
      "Epoch 00034: val_accuracy improved from 0.69788 to 0.70082, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 35/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0729 - accuracy: 0.9744 - val_loss: 2.0523 - val_accuracy: 0.7058\n",
      "\n",
      "Epoch 00035: val_accuracy improved from 0.70082 to 0.70583, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 36/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0908 - accuracy: 0.9701 - val_loss: 2.2384 - val_accuracy: 0.6932\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.70583\n",
      "Epoch 37/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.1005 - accuracy: 0.9697 - val_loss: 2.1976 - val_accuracy: 0.7061\n",
      "\n",
      "Epoch 00037: val_accuracy improved from 0.70583 to 0.70612, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 38/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0729 - accuracy: 0.9765 - val_loss: 2.1763 - val_accuracy: 0.7041\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.70612\n",
      "Epoch 39/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0752 - accuracy: 0.9753 - val_loss: 2.0716 - val_accuracy: 0.6949\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.70612\n",
      "Epoch 40/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0702 - accuracy: 0.9787 - val_loss: 2.1251 - val_accuracy: 0.6952\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.70612\n",
      "Epoch 41/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0711 - accuracy: 0.9766 - val_loss: 1.9895 - val_accuracy: 0.7035\n",
      "\n",
      "Epoch 00041: val_accuracy did not improve from 0.70612\n",
      "Epoch 42/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0471 - accuracy: 0.9857 - val_loss: 2.2080 - val_accuracy: 0.7020\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.70612\n",
      "Epoch 43/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 2.3020 - val_accuracy: 0.6964\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.70612\n",
      "Epoch 44/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0596 - accuracy: 0.9806 - val_loss: 2.2774 - val_accuracy: 0.7008\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.70612\n",
      "Epoch 45/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0503 - accuracy: 0.9831 - val_loss: 2.2544 - val_accuracy: 0.7188\n",
      "\n",
      "Epoch 00045: val_accuracy improved from 0.70612 to 0.71879, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 46/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0426 - accuracy: 0.9864 - val_loss: 2.2005 - val_accuracy: 0.6976\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.71879\n",
      "Epoch 47/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0444 - accuracy: 0.9859 - val_loss: 2.4465 - val_accuracy: 0.7079\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.71879\n",
      "Epoch 48/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0429 - accuracy: 0.9864 - val_loss: 2.8449 - val_accuracy: 0.7005\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.71879\n",
      "Epoch 49/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0547 - accuracy: 0.9813 - val_loss: 2.2216 - val_accuracy: 0.7150\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.71879\n",
      "Epoch 50/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0484 - accuracy: 0.9851 - val_loss: 2.5731 - val_accuracy: 0.7123\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.71879\n",
      "Epoch 51/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0642 - accuracy: 0.9811 - val_loss: 2.1752 - val_accuracy: 0.7182\n",
      "\n",
      "Epoch 00051: val_accuracy did not improve from 0.71879\n",
      "Epoch 52/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0329 - accuracy: 0.9902 - val_loss: 2.2871 - val_accuracy: 0.7176\n",
      "\n",
      "Epoch 00052: val_accuracy did not improve from 0.71879\n",
      "Epoch 53/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0301 - accuracy: 0.9913 - val_loss: 2.2334 - val_accuracy: 0.7185\n",
      "\n",
      "Epoch 00053: val_accuracy did not improve from 0.71879\n",
      "Epoch 54/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0424 - accuracy: 0.9883 - val_loss: 2.4543 - val_accuracy: 0.7191\n",
      "\n",
      "Epoch 00054: val_accuracy improved from 0.71879 to 0.71908, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 55/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0333 - accuracy: 0.9905 - val_loss: 2.4032 - val_accuracy: 0.7117\n",
      "\n",
      "Epoch 00055: val_accuracy did not improve from 0.71908\n",
      "Epoch 56/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0411 - accuracy: 0.9883 - val_loss: 2.3787 - val_accuracy: 0.7052\n",
      "\n",
      "Epoch 00056: val_accuracy did not improve from 0.71908\n",
      "Epoch 57/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0323 - accuracy: 0.9909 - val_loss: 2.4338 - val_accuracy: 0.7306\n",
      "\n",
      "Epoch 00057: val_accuracy improved from 0.71908 to 0.73057, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 58/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0227 - accuracy: 0.9928 - val_loss: 2.2404 - val_accuracy: 0.7097\n",
      "\n",
      "Epoch 00058: val_accuracy did not improve from 0.73057\n",
      "Epoch 59/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0372 - accuracy: 0.9880 - val_loss: 2.4441 - val_accuracy: 0.7197\n",
      "\n",
      "Epoch 00059: val_accuracy did not improve from 0.73057\n",
      "Epoch 60/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0305 - accuracy: 0.9901 - val_loss: 2.4972 - val_accuracy: 0.7238\n",
      "\n",
      "Epoch 00060: val_accuracy did not improve from 0.73057\n",
      "Epoch 61/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0222 - accuracy: 0.9937 - val_loss: 2.6436 - val_accuracy: 0.7076\n",
      "\n",
      "Epoch 00061: val_accuracy did not improve from 0.73057\n",
      "Epoch 62/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0452 - accuracy: 0.9865 - val_loss: 2.4889 - val_accuracy: 0.7253\n",
      "\n",
      "Epoch 00062: val_accuracy did not improve from 0.73057\n",
      "Epoch 63/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0184 - accuracy: 0.9950 - val_loss: 2.5470 - val_accuracy: 0.7097\n",
      "\n",
      "Epoch 00063: val_accuracy did not improve from 0.73057\n",
      "Epoch 64/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0471 - accuracy: 0.9841 - val_loss: 2.0595 - val_accuracy: 0.7167\n",
      "\n",
      "Epoch 00064: val_accuracy did not improve from 0.73057\n",
      "Epoch 65/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0290 - accuracy: 0.9922 - val_loss: 2.3475 - val_accuracy: 0.7309\n",
      "\n",
      "Epoch 00065: val_accuracy improved from 0.73057 to 0.73086, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 66/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0139 - accuracy: 0.9967 - val_loss: 2.2733 - val_accuracy: 0.7267\n",
      "\n",
      "Epoch 00066: val_accuracy did not improve from 0.73086\n",
      "Epoch 67/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0405 - accuracy: 0.9885 - val_loss: 2.5342 - val_accuracy: 0.7232\n",
      "\n",
      "Epoch 00067: val_accuracy did not improve from 0.73086\n",
      "Epoch 68/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0250 - accuracy: 0.9929 - val_loss: 2.1508 - val_accuracy: 0.7126\n",
      "\n",
      "Epoch 00068: val_accuracy did not improve from 0.73086\n",
      "Epoch 69/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0323 - accuracy: 0.9909 - val_loss: 2.3647 - val_accuracy: 0.7185\n",
      "\n",
      "Epoch 00069: val_accuracy did not improve from 0.73086\n",
      "Epoch 70/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0229 - accuracy: 0.9937 - val_loss: 2.2424 - val_accuracy: 0.7338\n",
      "\n",
      "Epoch 00070: val_accuracy improved from 0.73086 to 0.73380, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 71/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0191 - accuracy: 0.9944 - val_loss: 2.4073 - val_accuracy: 0.7200\n",
      "\n",
      "Epoch 00071: val_accuracy did not improve from 0.73380\n",
      "Epoch 72/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0308 - accuracy: 0.9913 - val_loss: 2.5449 - val_accuracy: 0.7338\n",
      "\n",
      "Epoch 00072: val_accuracy did not improve from 0.73380\n",
      "Epoch 73/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0297 - accuracy: 0.9913 - val_loss: 2.5445 - val_accuracy: 0.7256\n",
      "\n",
      "Epoch 00073: val_accuracy did not improve from 0.73380\n",
      "Epoch 74/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0174 - accuracy: 0.9945 - val_loss: 2.6775 - val_accuracy: 0.7314\n",
      "\n",
      "Epoch 00074: val_accuracy did not improve from 0.73380\n",
      "Epoch 75/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0283 - accuracy: 0.9923 - val_loss: 2.5167 - val_accuracy: 0.7294\n",
      "\n",
      "Epoch 00075: val_accuracy did not improve from 0.73380\n",
      "Epoch 76/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0404 - accuracy: 0.9880 - val_loss: 2.5142 - val_accuracy: 0.7282\n",
      "\n",
      "Epoch 00076: val_accuracy did not improve from 0.73380\n",
      "Epoch 77/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0151 - accuracy: 0.9956 - val_loss: 2.6490 - val_accuracy: 0.7270\n",
      "\n",
      "Epoch 00077: val_accuracy did not improve from 0.73380\n",
      "Epoch 78/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0251 - accuracy: 0.9924 - val_loss: 2.4232 - val_accuracy: 0.7188\n",
      "\n",
      "Epoch 00078: val_accuracy did not improve from 0.73380\n",
      "Epoch 79/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0124 - accuracy: 0.9963 - val_loss: 2.5776 - val_accuracy: 0.7176\n",
      "\n",
      "Epoch 00079: val_accuracy did not improve from 0.73380\n",
      "Epoch 80/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0335 - accuracy: 0.9910 - val_loss: 2.4985 - val_accuracy: 0.7088\n",
      "\n",
      "Epoch 00080: val_accuracy did not improve from 0.73380\n",
      "Epoch 81/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0229 - accuracy: 0.9938 - val_loss: 2.8743 - val_accuracy: 0.7261\n",
      "\n",
      "Epoch 00081: val_accuracy did not improve from 0.73380\n",
      "Epoch 82/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0273 - accuracy: 0.9915 - val_loss: 2.3166 - val_accuracy: 0.7365\n",
      "\n",
      "Epoch 00082: val_accuracy improved from 0.73380 to 0.73645, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 83/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0112 - accuracy: 0.9974 - val_loss: 2.3049 - val_accuracy: 0.7261\n",
      "\n",
      "Epoch 00083: val_accuracy did not improve from 0.73645\n",
      "Epoch 84/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0215 - accuracy: 0.9927 - val_loss: 2.5888 - val_accuracy: 0.7356\n",
      "\n",
      "Epoch 00084: val_accuracy did not improve from 0.73645\n",
      "Epoch 85/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0237 - accuracy: 0.9934 - val_loss: 2.6167 - val_accuracy: 0.7347\n",
      "\n",
      "Epoch 00085: val_accuracy did not improve from 0.73645\n",
      "Epoch 86/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0181 - accuracy: 0.9950 - val_loss: 2.4843 - val_accuracy: 0.7356\n",
      "\n",
      "Epoch 00086: val_accuracy did not improve from 0.73645\n",
      "Epoch 87/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0178 - accuracy: 0.9949 - val_loss: 2.2852 - val_accuracy: 0.7365\n",
      "\n",
      "Epoch 00087: val_accuracy did not improve from 0.73645\n",
      "Epoch 88/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0136 - accuracy: 0.9959 - val_loss: 2.5536 - val_accuracy: 0.7326\n",
      "\n",
      "Epoch 00088: val_accuracy did not improve from 0.73645\n",
      "Epoch 89/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0199 - accuracy: 0.9943 - val_loss: 2.6703 - val_accuracy: 0.7344\n",
      "\n",
      "Epoch 00089: val_accuracy did not improve from 0.73645\n",
      "Epoch 90/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0236 - accuracy: 0.9939 - val_loss: 2.4169 - val_accuracy: 0.7370\n",
      "\n",
      "Epoch 00090: val_accuracy improved from 0.73645 to 0.73704, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 91/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0239 - accuracy: 0.9930 - val_loss: 2.4991 - val_accuracy: 0.7365\n",
      "\n",
      "Epoch 00091: val_accuracy did not improve from 0.73704\n",
      "Epoch 92/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0176 - accuracy: 0.9953 - val_loss: 2.4130 - val_accuracy: 0.7138\n",
      "\n",
      "Epoch 00092: val_accuracy did not improve from 0.73704\n",
      "Epoch 93/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0204 - accuracy: 0.9939 - val_loss: 2.4510 - val_accuracy: 0.7300\n",
      "\n",
      "Epoch 00093: val_accuracy did not improve from 0.73704\n",
      "Epoch 94/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0224 - accuracy: 0.9945 - val_loss: 2.7440 - val_accuracy: 0.7312\n",
      "\n",
      "Epoch 00094: val_accuracy did not improve from 0.73704\n",
      "Epoch 95/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0071 - accuracy: 0.9982 - val_loss: 2.2506 - val_accuracy: 0.7147\n",
      "\n",
      "Epoch 00095: val_accuracy did not improve from 0.73704\n",
      "Epoch 96/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0209 - accuracy: 0.9941 - val_loss: 2.6790 - val_accuracy: 0.7359\n",
      "\n",
      "Epoch 00096: val_accuracy did not improve from 0.73704\n",
      "Epoch 97/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0105 - accuracy: 0.9972 - val_loss: 2.2672 - val_accuracy: 0.7312\n",
      "\n",
      "Epoch 00097: val_accuracy did not improve from 0.73704\n",
      "Epoch 98/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0186 - accuracy: 0.9947 - val_loss: 2.5985 - val_accuracy: 0.7441\n",
      "\n",
      "Epoch 00098: val_accuracy improved from 0.73704 to 0.74411, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 99/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0098 - accuracy: 0.9974 - val_loss: 2.4103 - val_accuracy: 0.7253\n",
      "\n",
      "Epoch 00099: val_accuracy did not improve from 0.74411\n",
      "Epoch 100/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0194 - accuracy: 0.9940 - val_loss: 2.5387 - val_accuracy: 0.7317\n",
      "\n",
      "Epoch 00100: val_accuracy did not improve from 0.74411\n",
      "Epoch 101/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0193 - accuracy: 0.9956 - val_loss: 2.5939 - val_accuracy: 0.7341\n",
      "\n",
      "Epoch 00101: val_accuracy did not improve from 0.74411\n",
      "Epoch 102/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0127 - accuracy: 0.9965 - val_loss: 2.6493 - val_accuracy: 0.7338\n",
      "\n",
      "Epoch 00102: val_accuracy did not improve from 0.74411\n",
      "Epoch 103/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0349 - accuracy: 0.9905 - val_loss: 2.5545 - val_accuracy: 0.7312\n",
      "\n",
      "Epoch 00103: val_accuracy did not improve from 0.74411\n",
      "Epoch 104/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 2.6546 - val_accuracy: 0.7312\n",
      "\n",
      "Epoch 00104: val_accuracy did not improve from 0.74411\n",
      "Epoch 105/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0161 - accuracy: 0.9955 - val_loss: 2.5936 - val_accuracy: 0.7338\n",
      "\n",
      "Epoch 00105: val_accuracy did not improve from 0.74411\n",
      "Epoch 106/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 2.6816 - val_accuracy: 0.7282\n",
      "\n",
      "Epoch 00106: val_accuracy did not improve from 0.74411\n",
      "Epoch 107/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0270 - accuracy: 0.9923 - val_loss: 2.5505 - val_accuracy: 0.7285\n",
      "\n",
      "Epoch 00107: val_accuracy did not improve from 0.74411\n",
      "Epoch 108/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0132 - accuracy: 0.9955 - val_loss: 2.8920 - val_accuracy: 0.7350\n",
      "\n",
      "Epoch 00108: val_accuracy did not improve from 0.74411\n",
      "Epoch 109/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0089 - accuracy: 0.9975 - val_loss: 3.0162 - val_accuracy: 0.7365\n",
      "\n",
      "Epoch 00109: val_accuracy did not improve from 0.74411\n",
      "Epoch 110/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0113 - accuracy: 0.9965 - val_loss: 2.9934 - val_accuracy: 0.7253\n",
      "\n",
      "Epoch 00110: val_accuracy did not improve from 0.74411\n",
      "Epoch 111/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0315 - accuracy: 0.9916 - val_loss: 2.5055 - val_accuracy: 0.7288\n",
      "\n",
      "Epoch 00111: val_accuracy did not improve from 0.74411\n",
      "Epoch 112/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0154 - accuracy: 0.9959 - val_loss: 2.6502 - val_accuracy: 0.7323\n",
      "\n",
      "Epoch 00112: val_accuracy did not improve from 0.74411\n",
      "Epoch 113/500\n",
      "849/849 [==============================] - 13s 16ms/step - loss: 0.0073 - accuracy: 0.9981 - val_loss: 2.7770 - val_accuracy: 0.7317\n",
      "\n",
      "Epoch 00113: val_accuracy did not improve from 0.74411\n",
      "Epoch 114/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 2.8094 - val_accuracy: 0.7432\n",
      "\n",
      "Epoch 00114: val_accuracy did not improve from 0.74411\n",
      "Epoch 115/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0192 - accuracy: 0.9934 - val_loss: 2.6607 - val_accuracy: 0.7376\n",
      "\n",
      "Epoch 00115: val_accuracy did not improve from 0.74411\n",
      "Epoch 116/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0088 - accuracy: 0.9977 - val_loss: 2.4299 - val_accuracy: 0.7229\n",
      "\n",
      "Epoch 00116: val_accuracy did not improve from 0.74411\n",
      "Epoch 117/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0172 - accuracy: 0.9942 - val_loss: 2.4194 - val_accuracy: 0.7226\n",
      "\n",
      "Epoch 00117: val_accuracy did not improve from 0.74411\n",
      "Epoch 118/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0129 - accuracy: 0.9967 - val_loss: 2.6365 - val_accuracy: 0.7456\n",
      "\n",
      "Epoch 00118: val_accuracy improved from 0.74411 to 0.74558, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 119/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0051 - accuracy: 0.9988 - val_loss: 2.7210 - val_accuracy: 0.7373\n",
      "\n",
      "Epoch 00119: val_accuracy did not improve from 0.74558\n",
      "Epoch 120/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0242 - accuracy: 0.9925 - val_loss: 2.9423 - val_accuracy: 0.7291\n",
      "\n",
      "Epoch 00120: val_accuracy did not improve from 0.74558\n",
      "Epoch 121/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0208 - accuracy: 0.9939 - val_loss: 2.7740 - val_accuracy: 0.7320\n",
      "\n",
      "Epoch 00121: val_accuracy did not improve from 0.74558\n",
      "Epoch 122/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0138 - accuracy: 0.9957 - val_loss: 2.7350 - val_accuracy: 0.7420\n",
      "\n",
      "Epoch 00122: val_accuracy did not improve from 0.74558\n",
      "Epoch 123/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0070 - accuracy: 0.9979 - val_loss: 2.9365 - val_accuracy: 0.7300\n",
      "\n",
      "Epoch 00123: val_accuracy did not improve from 0.74558\n",
      "Epoch 124/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0203 - accuracy: 0.9935 - val_loss: 2.7160 - val_accuracy: 0.7276\n",
      "\n",
      "Epoch 00124: val_accuracy did not improve from 0.74558\n",
      "Epoch 125/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0197 - accuracy: 0.9942 - val_loss: 2.4821 - val_accuracy: 0.7250\n",
      "\n",
      "Epoch 00125: val_accuracy did not improve from 0.74558\n",
      "Epoch 126/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0102 - accuracy: 0.9969 - val_loss: 2.7057 - val_accuracy: 0.7412\n",
      "\n",
      "Epoch 00126: val_accuracy did not improve from 0.74558\n",
      "Epoch 127/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0060 - accuracy: 0.9986 - val_loss: 2.6830 - val_accuracy: 0.7314\n",
      "\n",
      "Epoch 00127: val_accuracy did not improve from 0.74558\n",
      "Epoch 128/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0060 - accuracy: 0.9980 - val_loss: 2.8443 - val_accuracy: 0.7497\n",
      "\n",
      "Epoch 00128: val_accuracy improved from 0.74558 to 0.74971, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 129/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0072 - accuracy: 0.9977 - val_loss: 2.6526 - val_accuracy: 0.7158\n",
      "\n",
      "Epoch 00129: val_accuracy did not improve from 0.74971\n",
      "Epoch 130/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0212 - accuracy: 0.9927 - val_loss: 2.4477 - val_accuracy: 0.7367\n",
      "\n",
      "Epoch 00130: val_accuracy did not improve from 0.74971\n",
      "Epoch 131/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 2.8274 - val_accuracy: 0.7409\n",
      "\n",
      "Epoch 00131: val_accuracy did not improve from 0.74971\n",
      "Epoch 132/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0247 - accuracy: 0.9941 - val_loss: 2.2859 - val_accuracy: 0.7367\n",
      "\n",
      "Epoch 00132: val_accuracy did not improve from 0.74971\n",
      "Epoch 133/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0044 - accuracy: 0.9990 - val_loss: 2.6345 - val_accuracy: 0.7412\n",
      "\n",
      "Epoch 00133: val_accuracy did not improve from 0.74971\n",
      "Epoch 134/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0135 - accuracy: 0.9958 - val_loss: 2.8281 - val_accuracy: 0.7188\n",
      "\n",
      "Epoch 00134: val_accuracy did not improve from 0.74971\n",
      "Epoch 135/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0193 - accuracy: 0.9943 - val_loss: 2.9265 - val_accuracy: 0.7432\n",
      "\n",
      "Epoch 00135: val_accuracy did not improve from 0.74971\n",
      "Epoch 136/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0167 - accuracy: 0.9953 - val_loss: 2.6185 - val_accuracy: 0.7438\n",
      "\n",
      "Epoch 00136: val_accuracy did not improve from 0.74971\n",
      "Epoch 137/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0045 - accuracy: 0.9989 - val_loss: 2.7476 - val_accuracy: 0.7388\n",
      "\n",
      "Epoch 00137: val_accuracy did not improve from 0.74971\n",
      "Epoch 138/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0070 - accuracy: 0.9980 - val_loss: 2.4789 - val_accuracy: 0.7323\n",
      "\n",
      "Epoch 00138: val_accuracy did not improve from 0.74971\n",
      "Epoch 139/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0152 - accuracy: 0.9958 - val_loss: 2.6375 - val_accuracy: 0.7503\n",
      "\n",
      "Epoch 00139: val_accuracy improved from 0.74971 to 0.75029, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 140/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 2.4794 - val_accuracy: 0.7379\n",
      "\n",
      "Epoch 00140: val_accuracy did not improve from 0.75029\n",
      "Epoch 141/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0067 - accuracy: 0.9987 - val_loss: 2.8469 - val_accuracy: 0.7294\n",
      "\n",
      "Epoch 00141: val_accuracy did not improve from 0.75029\n",
      "Epoch 142/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0247 - accuracy: 0.9925 - val_loss: 2.4374 - val_accuracy: 0.7468\n",
      "\n",
      "Epoch 00142: val_accuracy did not improve from 0.75029\n",
      "Epoch 143/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0060 - accuracy: 0.9984 - val_loss: 2.6606 - val_accuracy: 0.7432\n",
      "\n",
      "Epoch 00143: val_accuracy did not improve from 0.75029\n",
      "Epoch 144/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0042 - accuracy: 0.9991 - val_loss: 2.6615 - val_accuracy: 0.7420\n",
      "\n",
      "Epoch 00144: val_accuracy did not improve from 0.75029\n",
      "Epoch 145/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0206 - accuracy: 0.9936 - val_loss: 2.7128 - val_accuracy: 0.7468\n",
      "\n",
      "Epoch 00145: val_accuracy did not improve from 0.75029\n",
      "Epoch 146/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0022 - accuracy: 0.9996 - val_loss: 2.7716 - val_accuracy: 0.7547\n",
      "\n",
      "Epoch 00146: val_accuracy improved from 0.75029 to 0.75471, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 147/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 2.8686 - val_accuracy: 0.7529\n",
      "\n",
      "Epoch 00147: val_accuracy did not improve from 0.75471\n",
      "Epoch 148/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 2.1786 - val_accuracy: 0.7473\n",
      "\n",
      "Epoch 00148: val_accuracy did not improve from 0.75471\n",
      "Epoch 149/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 2.5741 - val_accuracy: 0.7521\n",
      "\n",
      "Epoch 00149: val_accuracy did not improve from 0.75471\n",
      "Epoch 150/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 2.6562 - val_accuracy: 0.7409\n",
      "\n",
      "Epoch 00150: val_accuracy did not improve from 0.75471\n",
      "Epoch 151/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0187 - accuracy: 0.9946 - val_loss: 2.4825 - val_accuracy: 0.7438\n",
      "\n",
      "Epoch 00151: val_accuracy did not improve from 0.75471\n",
      "Epoch 152/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0037 - accuracy: 0.9993 - val_loss: 2.3794 - val_accuracy: 0.7415\n",
      "\n",
      "Epoch 00152: val_accuracy did not improve from 0.75471\n",
      "Epoch 153/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 2.5872 - val_accuracy: 0.7488\n",
      "\n",
      "Epoch 00153: val_accuracy did not improve from 0.75471\n",
      "Epoch 154/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 2.7284 - val_accuracy: 0.7482\n",
      "\n",
      "Epoch 00154: val_accuracy did not improve from 0.75471\n",
      "Epoch 155/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0073 - accuracy: 0.9974 - val_loss: 2.6109 - val_accuracy: 0.7359\n",
      "\n",
      "Epoch 00155: val_accuracy did not improve from 0.75471\n",
      "Epoch 156/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0121 - accuracy: 0.9966 - val_loss: 2.7225 - val_accuracy: 0.7397\n",
      "\n",
      "Epoch 00156: val_accuracy did not improve from 0.75471\n",
      "Epoch 157/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0169 - accuracy: 0.9956 - val_loss: 2.6024 - val_accuracy: 0.7444\n",
      "\n",
      "Epoch 00157: val_accuracy did not improve from 0.75471\n",
      "Epoch 158/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0105 - accuracy: 0.9972 - val_loss: 2.6336 - val_accuracy: 0.7485\n",
      "\n",
      "Epoch 00158: val_accuracy did not improve from 0.75471\n",
      "Epoch 159/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0192 - accuracy: 0.9950 - val_loss: 2.3242 - val_accuracy: 0.7420\n",
      "\n",
      "Epoch 00159: val_accuracy did not improve from 0.75471\n",
      "Epoch 160/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.6017 - val_accuracy: 0.7565\n",
      "\n",
      "Epoch 00160: val_accuracy improved from 0.75471 to 0.75648, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 161/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 6.2872e-04 - accuracy: 0.9999 - val_loss: 2.7451 - val_accuracy: 0.7591\n",
      "\n",
      "Epoch 00161: val_accuracy improved from 0.75648 to 0.75913, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 162/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 7.0849e-04 - accuracy: 0.9999 - val_loss: 2.6902 - val_accuracy: 0.7488\n",
      "\n",
      "Epoch 00162: val_accuracy did not improve from 0.75913\n",
      "Epoch 163/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0356 - accuracy: 0.9905 - val_loss: 2.2528 - val_accuracy: 0.7585\n",
      "\n",
      "Epoch 00163: val_accuracy did not improve from 0.75913\n",
      "Epoch 164/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 2.4723 - val_accuracy: 0.7591\n",
      "\n",
      "Epoch 00164: val_accuracy did not improve from 0.75913\n",
      "Epoch 165/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 8.4305e-04 - accuracy: 0.9999 - val_loss: 2.4655 - val_accuracy: 0.7612\n",
      "\n",
      "Epoch 00165: val_accuracy improved from 0.75913 to 0.76119, saving model to /content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\n",
      "Epoch 166/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0026 - accuracy: 0.9995 - val_loss: 2.2733 - val_accuracy: 0.7235\n",
      "\n",
      "Epoch 00166: val_accuracy did not improve from 0.76119\n",
      "Epoch 167/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0213 - accuracy: 0.9942 - val_loss: 2.2600 - val_accuracy: 0.7397\n",
      "\n",
      "Epoch 00167: val_accuracy did not improve from 0.76119\n",
      "Epoch 168/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 2.3888 - val_accuracy: 0.7447\n",
      "\n",
      "Epoch 00168: val_accuracy did not improve from 0.76119\n",
      "Epoch 169/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0023 - accuracy: 0.9992 - val_loss: 2.6382 - val_accuracy: 0.7532\n",
      "\n",
      "Epoch 00169: val_accuracy did not improve from 0.76119\n",
      "Epoch 170/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 2.7475 - val_accuracy: 0.7373\n",
      "\n",
      "Epoch 00170: val_accuracy did not improve from 0.76119\n",
      "Epoch 171/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0092 - accuracy: 0.9971 - val_loss: 2.7844 - val_accuracy: 0.7456\n",
      "\n",
      "Epoch 00171: val_accuracy did not improve from 0.76119\n",
      "Epoch 172/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0151 - accuracy: 0.9958 - val_loss: 2.5690 - val_accuracy: 0.7400\n",
      "\n",
      "Epoch 00172: val_accuracy did not improve from 0.76119\n",
      "Epoch 173/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 2.7857 - val_accuracy: 0.7524\n",
      "\n",
      "Epoch 00173: val_accuracy did not improve from 0.76119\n",
      "Epoch 174/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 2.7038 - val_accuracy: 0.7344\n",
      "\n",
      "Epoch 00174: val_accuracy did not improve from 0.76119\n",
      "Epoch 175/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0098 - accuracy: 0.9966 - val_loss: 2.6748 - val_accuracy: 0.7468\n",
      "\n",
      "Epoch 00175: val_accuracy did not improve from 0.76119\n",
      "Epoch 176/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 2.7827 - val_accuracy: 0.7447\n",
      "\n",
      "Epoch 00176: val_accuracy did not improve from 0.76119\n",
      "Epoch 177/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0151 - accuracy: 0.9964 - val_loss: 2.8339 - val_accuracy: 0.7356\n",
      "\n",
      "Epoch 00177: val_accuracy did not improve from 0.76119\n",
      "Epoch 178/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0052 - accuracy: 0.9985 - val_loss: 2.9358 - val_accuracy: 0.7350\n",
      "\n",
      "Epoch 00178: val_accuracy did not improve from 0.76119\n",
      "Epoch 179/500\n",
      "849/849 [==============================] - 14s 17ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 2.7629 - val_accuracy: 0.7550\n",
      "\n",
      "Epoch 00179: val_accuracy did not improve from 0.76119\n",
      "Epoch 180/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 0.0022 - accuracy: 0.9997 - val_loss: 2.7110 - val_accuracy: 0.7600\n",
      "\n",
      "Epoch 00180: val_accuracy did not improve from 0.76119\n",
      "Epoch 181/500\n",
      "849/849 [==============================] - 14s 16ms/step - loss: 9.9683e-04 - accuracy: 0.9998 - val_loss: 2.7727 - val_accuracy: 0.7485\n",
      "\n",
      "Epoch 00181: val_accuracy did not improve from 0.76119\n",
      "Total time required: 2626042.307 ms\n",
      "27163/27163 [==============================] - 117s 4ms/step - loss: 9.1506e-04 - accuracy: 0.9999\n",
      "3395/3395 [==============================] - 15s 4ms/step - loss: 2.2317 - accuracy: 0.7596\n",
      "3396/3396 [==============================] - 15s 4ms/step - loss: 2.4655 - accuracy: 0.7612\n",
      "\n",
      "**********************************************\n",
      "\n",
      " Training accuracy of the model is 99.99\n",
      "\n",
      " Testing accuracy of the model is 75.96\n",
      "\n",
      " Validation accuracy of the model is 76.12\n",
      "**********************************************\n",
      "\n",
      "Classification report for Emotion Recognition\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.85      0.84       601\n",
      "           1       0.75      0.69      0.72       600\n",
      "           2       0.77      0.73      0.75       582\n",
      "           3       0.75      0.73      0.74       549\n",
      "           4       0.74      0.81      0.77       522\n",
      "           5       0.72      0.74      0.73       541\n",
      "\n",
      "    accuracy                           0.76      3395\n",
      "   macro avg       0.76      0.76      0.76      3395\n",
      "weighted avg       0.76      0.76      0.76      3395\n",
      "\n",
      "\n",
      "Confusion matrix for Emotion Recognition\n",
      "[[513  21  19  38   7   3]\n",
      " [ 32 417  27  30  50  44]\n",
      " [ 21  26 426  33  22  54]\n",
      " [ 45  31  29 401  26  17]\n",
      " [  8  29  12  15 422  36]\n",
      " [  3  34  38  21  45 400]]\n",
      "*****************************\n",
      "Total time required: 177169.208 ms\n",
      "Total time required: 2807919.949 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#function calculates the above code in sequence\n",
    "#it runs the model and also evaluates the model performance\n",
    "@calc_time\n",
    "def main():\n",
    "  #get train,test data and labels \n",
    "  x_train, x_test, y_train, y_test, x_val, y_val, encoder = audio_features_final()\n",
    "  #call the emotion recognition model\n",
    "  emotion_recognition_model(x_train,y_train,x_val,y_val)\n",
    "  #evaluate the model performance\n",
    "  evaluate_model(x_train, x_test, y_train, y_test, x_val, y_val)\n",
    "\n",
    "if __name__:main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "syTb5TPSPxzb",
    "outputId": "0330abf3-e94c-44c2-a6d6-1ab0aa5d18e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "labels or emotions present in dataset\n",
      " ['calm' 'sad' 'angry' 'neutral' 'happy' 'fearful' 'surprised' 'disgust'\n",
      " 'fear' 'Fear' 'pleasant_surprised' 'Sad' 'Pleasant_surprise' 'anger'\n",
      " 'happiness' 'sadness' 'surprise']\n",
      "\n",
      "Unique count of labels or emotions\n",
      " fear        5769\n",
      "happy       5769\n",
      "angry       5769\n",
      "disgust     5769\n",
      "sad         5769\n",
      "neutral     5109\n",
      "surprise    1956\n",
      "calm         576\n",
      "Name: Label, dtype: int64\n",
      "\n",
      "Unique count of labels or emotions after dropping selected labels\n",
      " fear       5769\n",
      "happy      5769\n",
      "angry      5769\n",
      "disgust    5769\n",
      "sad        5769\n",
      "neutral    5109\n",
      "Name: Label, dtype: int64\n",
      "\n",
      "length of the total data is 33954\n",
      "\n",
      "length of train data is 27163, test data is 3395 and validation set is 3396\n",
      "\n",
      " shape of train features and label is (27163, 178)\n",
      "\n",
      " shape of test features and label is (3395, 178)\n",
      "\n",
      " shape of validation features and label is (3396, 178)\n",
      "Total time required: 1990.703 ms\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test, x_val, y_val, encoder = audio_features_final()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x-7A4ZF1ZY1F",
    "outputId": "181e39bb-40a0-4a6a-dfae-045c0ff8d7d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "one hot encoding array\n",
      " [[0. 0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0.]]\n",
      "\n",
      "One hot encoding mapping to actual label\n",
      " [['sad']\n",
      " ['neutral']\n",
      " ['happy']\n",
      " ['fear']\n",
      " ['disgust']\n",
      " ['angry']]\n"
     ]
    }
   ],
   "source": [
    "#mapping of the one hot ecoding with respect to their labels\n",
    "print(\"\\none hot encoding array\\n\",np.unique(y_train,axis=0))\n",
    "print(\"\\nOne hot encoding mapping to actual label\\n\",encoder.inverse_transform(np.unique(y_train,axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "id": "F214nBXPvW4T",
    "outputId": "a4fcb4f1-5ad4-4dc0-cc6a-f596cf572c83"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "var my_div = document.createElement(\"DIV\");\n",
       "var my_p = document.createElement(\"P\");\n",
       "var my_btn = document.createElement(\"BUTTON\");\n",
       "var t = document.createTextNode(\"Press to start recording\");\n",
       "\n",
       "my_btn.appendChild(t);\n",
       "//my_p.appendChild(my_btn);\n",
       "my_div.appendChild(my_btn);\n",
       "document.body.appendChild(my_div);\n",
       "\n",
       "var base64data = 0;\n",
       "var reader;\n",
       "var recorder, gumStream;\n",
       "var recordButton = my_btn;\n",
       "\n",
       "var handleSuccess = function(stream) {\n",
       "  gumStream = stream;\n",
       "  var options = {\n",
       "    //bitsPerSecond: 8000, //chrome seems to ignore, always 48k\n",
       "    mimeType : 'audio/webm;codecs=opus'\n",
       "    //mimeType : 'audio/webm;codecs=pcm'\n",
       "  };            \n",
       "  //recorder = new MediaRecorder(stream, options);\n",
       "  recorder = new MediaRecorder(stream);\n",
       "  recorder.ondataavailable = function(e) {            \n",
       "    var url = URL.createObjectURL(e.data);\n",
       "    var preview = document.createElement('audio');\n",
       "    preview.controls = true;\n",
       "    preview.src = url;\n",
       "    document.body.appendChild(preview);\n",
       "\n",
       "    reader = new FileReader();\n",
       "    reader.readAsDataURL(e.data); \n",
       "    reader.onloadend = function() {\n",
       "      base64data = reader.result;\n",
       "      //console.log(\"Inside FileReader:\" + base64data);\n",
       "    }\n",
       "  };\n",
       "  recorder.start();\n",
       "  };\n",
       "\n",
       "recordButton.innerText = \"Recording... press to stop\";\n",
       "\n",
       "navigator.mediaDevices.getUserMedia({audio: true}).then(handleSuccess);\n",
       "\n",
       "\n",
       "function toggleRecording() {\n",
       "  if (recorder && recorder.state == \"recording\") {\n",
       "      recorder.stop();\n",
       "      gumStream.getAudioTracks()[0].stop();\n",
       "      recordButton.innerText = \"Saving the recording... pls wait!\"\n",
       "  }\n",
       "}\n",
       "\n",
       "// https://stackoverflow.com/a/951057\n",
       "function sleep(ms) {\n",
       "  return new Promise(resolve => setTimeout(resolve, ms));\n",
       "}\n",
       "\n",
       "var data = new Promise(resolve=>{\n",
       "//recordButton.addEventListener(\"click\", toggleRecording);\n",
       "recordButton.onclick = ()=>{\n",
       "toggleRecording()\n",
       "\n",
       "sleep(2000).then(() => {\n",
       "  // wait 2000ms for the data to be available...\n",
       "  // ideally this should use something like await...\n",
       "  //console.log(\"Inside data:\" + base64data)\n",
       "  resolve(base64data.toString())\n",
       "\n",
       "});\n",
       "\n",
       "}\n",
       "});\n",
       "      \n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oL6oalCV6UuV/znFfQP2+YA+e+Nw3i75bFs57+D/L8U3+L1XSuz02C4s3crHFeJS/86DTWaiCAdXGyRKqc3mYYVl6uPi4u25rfvvyd3jlf+vx0+Z9aDtmOvIKKq/PGL6fdxwI+Zzi4yV4e8FGXPvqwir73LIonhd9u243cp9lR/9QMKDaKG/ld6AWtbRUeWa5jNkrfUF/wfrdxq55JIzp5Do97K7bvniyYut+p7PgKRwpZcPJEqrdUslslwrde4s24fSWGU5ngxIMS6g2xKE1pfzd7h4rcf62taRU4d8LN1Wofnhhnnunf3vwo2W45DnvLehHoVto8A4pUgyoNspLqDF+3rwC+8aTR6etqPDYiTrVdmOmY8zHy6J+e300wjrPg0dPGMoJecVVExc4nYVyDKh2ykdKxTakbvjFvtUyb+PJRp/TH5mJTg/PwHIXdVKfb9O6Hq4X5uVHdP4Ff//STEaIwsCAakMcmmD6X19vsE0vi+tHT5TggC6B/XvRplhlK2DXqAXrzbXy749waZOdB47i23XmAjwB/14Yu8+a1zGg2khy6p7fj7JeB5v3HC5Pi+WHfOW2wC291rw57cnPVzudhbgyXvc5psAYUG042W3KjYKp+Th3wjwjz2VioMCSQvdUh1Bk3lu0yWi3vGhjQK2GW7orbd5zJKzz7nrvR7y7MLLx2MsKi/DhD4FnHjI1YIrBkMqs2FqEBz9ahuE2jU5rK/WI6fGXWbHKVrXYD9WGU3Woodp14Cga161hu2/4xO+wYP0eTFuyFdf2OSXs57j0+eC7HpWWKiQlOT9sl8zaX+xMz4nqVmW98OmvKjzee/g4duwvRlO9YrFTWEK1UXbLfyiGXXCCmYjicKVRRPP0LOv5Ow9W6UplsqEoWK/OXx/z56T4dPRECT5buq388c791S8rDgB9/vZFNLMUFAZUG2UB9a+frcLc1Tti8pzBzJB+2fPfVHh8/4dL8dEPhRj0j//h/qnm1mHaVnQEx8MYTPCfH7dy7DwZMb5Sw2JvFwTLYDCg2jp523rfB9FZMK6yz8OcKu3eKUsAANOWbC1PWxVEq7w/B4qPo98Tc3HVK9+FfO7Kbfsx7rOVYT83uVc4E+DMXb0j7Dp8u9UV3DZJkB0GVBvW2fv2HAq8xEYsbCsK3DD1Tf4v2LzncJWlgHs9PhujdOANpKxa4YdN+0LPJDgrfry64uXQv2BvejMPYz9ebqxfcKeHZ2DN9tBnI4slBlQbldtVskd/hsPHnB3S2O+JuQGPWb39gG3JcvehY0G11AOosN5r9ujPgs1euUiGjlZuuSXvso6vv+ZfC/GtodF0K7YWhTUjWawwoNqwW0b65x3Rm8zW1DIOxcdLsLUocOV9dSItYUYyN7eX+htSVT/vOFC+/PQjleafuMZmrtpwLFi/G4/9d4Xf/be/s9jRenwGVBt2QeGXA0ej9nwrDc05OWFm4AXrArl5Ul5E57ul7y6ZZ9frZe2OA8ge/RkmzFyNi57+ClfqqoG1UZpNf0peIbbs81/99fny7fhxk3MrNDCg2rArZO2MYkA94FA/v2hwS50zmdflkZnYXukO6Nm5vslsyqZyXLPjAB7/dCVKIhjpEWhpk6/XVl99cNXEBWFVV5nAgGrDbk2pMR8vi9rzXf/6oqhdOxTB9PULZFsEVQ4/htkQRrETzCxnr823n+TnvAnzgqrWmb5sW8Bj3IoB1WFuarXcZGiCkw/yNod13kc/Bu6LS8665a28Ch3uQ1Gw+7DtMNLK/jH757CuX9ltb8d+tVsGVBv+Gla2VlN3E65/fR270UVnPDYLL36Zj4NHT2DfYd+t+Sv/W1fercVU9eefpi7Fla98Z7sKKXnfHf/+oXzb9JzBJhuUyha6/HnHAdw/dUlE1RDB4lh+G0mWiFo7LRmHdDeNs8bPRcH4XKPPFcuJoouOHMdTM9bgqRm+xquC8bl4Qo9I6de2Ib4z2Mq+aMMeXPvqwqD/Xuwy5T1b9h3Bp2GUVt9duNHv/BJvfVcQWaYqsdaljhzQDu2b1Cl/vHxLEdbtOohh3Vsaez6WUG1YC6if3nVO1J5nxvLtWO3gLb/1w2YymIbj9nd/CHwQucakbwvQf3zgvtF2xn68HKu32/dsOXQ0en1Mn5pxcjjrW98V4JLn5uPuyT8ZfQ4GVBsFuw+VbydXuv830XBT5rZ3Yl/HE2unBbkGVaCWXXKXyv1MQ2U3PHrtjgN4eo6Z+lM7s1buwF7dC+XPn0SWf38YUG3MWbWzfDspCRVuWwdEOJFyaanCqm37HevWEWtHjpcEnM2/rDM4JY4/vr8ESwv34YGpS7GssAh3vfdjlSn5oqHH47MjXresOp6pQxWRIQCeAZAM4FWl1PhYPG9ypXGoxcfDqzRftGEP/j5zDRaFMcmE153z1DxMHtkXPVvXR1rKye/wz5ZuwyPTluOXg+y7mojKZk97P8xeIeEyMQDGH08EVBFJBvACgAsBFAL4XkSmKaWiPrVR5Vv+UP20eR9GvpUX1YEBXhBMdxkir/PKLX9vAPlKqfVKqWMAJgMYZuriSimc0PN/Vh5XbzcDfbDdp1ZsLcLlL3yT8MGUyM1Mdv3ySkBtCcB6X1Co04zYVlSM9mM/R/sx09HxoYqNKGUl1OmW1v6zgmjdLClVyH02+OVDiMgZJrvSeiWgBkVERopInojk7dq1K+jzaqel4N4LT8XIAW1RKy25wr6yEmrnFvVCyku7MdNDOp6InFGagCXULQBaWR5n6bQKlFITlVI5Sqmcxo0bB33xjFqpuGtgB9w/pBNW/mVIhX2VG6XKM1TNbf/732+q9vnaN6mDDU9cHHT+iMgbvBJQvwfQQUTaiEgagOEApsXiia2NUo3qnFxhtP/4ubYzK23ddwQPfGg/kUq/tg1RMD4Xc+49FyKCvm0bmM+wi60dN7TKF0mD2mkO5YbIJyXZXBj0REBVSp0AcCeAmQBWAZiilIpOz9xKkix/oYcvOa3Cvp6Pz64wVG7j7kPV1q++N7Jvhcdv3NDbRBY9YfFDg5CanAQRwfwHzscr1/VCwfhc/PDwhXj26h5OZ4/ICE8EVABQSk1XSp2qlGqnlBoXq+e1llCHdW+JO89vX2H/E9N9w9n2HT6Gcyd8WeX8l67tiU//cDbyHhpUZV/NtGT86/ocsxl2mQ5N6uD7sYPQ0FK6z6pfC4O7NCt/fNkZLZzIGrnIB7f1i8nzrHhssO3/oimeCaixZL0NrVyHet/gjhUeHzleguzRn6H7X2ZXuc6CBwdi6OnN0bVlRoXqAqsLOzc1kGP3um9wRzSua//aKbEtGjsQYy7uhPxxQ3FmdgO8ccOZUX/O9NRkNKpTA/MfOB8AcPHpzQKcERoGVBvWEGo32fTX959f7fmz/jgABeNz0Swj3XDOoqtto9rGr3lWu4ZBHVe55E/u99hlXTByQNuwzn3u6h5oUjcdIwe0K6/DbNWglsns2SorIGXVr4WC8bl48dpeRq/PgGrDLohatcis6XffwjEDcWrTuiE939xR54Z0vCllQezi05uhYHwu5t53ntHrf3BbP9RNTw3q2FEXnWr0uSn6runTGk3CuPtomVkTl9pU87RvUgdTfhedW/86NVIweminqFzbyhNDT2Mt0GjT5CTBf+88Gz9u3lth1po1fx2CGinJ1Zxpr23jOoEPMmDsxafhlnPaYMf+o1BQWLfzEJ6fl4+m9cyWpNs2ro25o84L6ZxAX2LkPqnJSUgNo4U8qZpTwgnQ1cmqXxOdmtXDqyNi01bBgGojmH/t07MycHpWBi7p1gI7DxSjU7PQOv47YUjXZhCR8qqIZvXS8ezVPXCR4XrcNIPdUMidFo4ZCAC46sxWIU/ld1bbRn73mfxeXfLni5BRK7g7JFMYUG2E8qY2qJ3mmb6UNVIrBjoRiUoL+1NXdDN+TXKPSTf1Lr+rSU8N/Y7ssWFd/O6ToIoz1fvDBe2RkpQU82AKMKDaMvGmus2VOVloUjf6jWTndGiEblmZUX8eckakSwD1bJ1ZbRAOVJg5r2NjfLmm+mHloy7qWO3+aGJAteFEdV5qsuB4SfQWETu/Y5OgjmuekR7RUtCdm7u/6oPM+upP52Pltv3o164h0pKTkJosaD/28yrHmRhu/fL/64WZK7YbX7rEFFZ22UhyIKLedm47Y9ca1r3qbXywobqsB8NDuacFONJezbTQbwHLvHKd2S4sFButG9bCkK7NkFEzFTXTkpGSnGT7GRSRgI2PTepV3yiVnppsdFE90xhQPeI5P8MzP7z9rAqPG9ROw63nVO0bWKdGcDcjE6/rhX9e1T3sPrQ3ntUmrPMAX3cacq8FDw4M+tjGfgayBFIjJdlvtcJ9Huhax4Bqo7puHU6x67cHAN1bVayv/OHhC9G1ZUaFEV5/GtwR53Tw37Jq1bBODVzeo2XQ/Uetzm7fKKKGAAZUdwvlS7byiEITOlp60pzfMfjZ5GLJhaHDeU40SvVuE3jmqXsGdaiSZg2cqx8/OfXg48O6AgBqpibj5rPbhNzPc0CHRnjqN93w99+eEfQ5KcmR/d3qe6S3BAUWTuu/1SkNT46a+tPgjvhi1LkVhmlf1++UiK4fLQyoNpxolDqnQ+Bv3HsGVbzleeTSzhUeWz/E1/RpjYLxuVj1+JCwPtwigivPbIXf9Ay+vuqa3q1Dfp5omcCuW0bN+uOAiM5vVCe0L8umlh4pfds2RLtKg1/cOhCEAdWGE41S4SibsSmat8qhfHAbhllvZlqD2mm4oleW09mIK/VrRXb30LdtcHM6lLF+7HqdUr/Kfn+DRyZXmiIz1hhQbTgVTgd3CW3EUlmL/Jd/Og8//3VoNLLkSf/9w9muLcF4VaQzhj2U2znwQRadmlU/H4bdShr92zcMOXCbxoBqx6H/xV/3DL5UZb3dT01OqrDevWntGgc3C1Wgf4JYYeOWO5TV+d/Uvw2aBugOVdmYAN327P5FTS62Fy4GVBtuLtuM+5WvsSnYVnsTJo8MPAPQ7wa0Re0gu2aRt5zRKryRb/cMOhUF43Px50s7h3zHEGg+CLvr5dhUDcQa/wNsOHW72LdNQ9sRUw0trd/X9G6N3NObIzPCOq1QBHW7Z+hP1qdNAyzcsMfMxciIbi0zHHvu1n7mSLX+i7bISMc7t/TBKQ3Nz+cbKpZQbfwuzElzI5VRKxVrx1UdnjfT0sIqIjENprH2mxCqPQL5dvQFuPns8AcakHNEBBOv6+V3aRTr93ed9BS0bVzH7wrFscSAasPpoW3LHr0I9S0d5P0tnxJLgQrtw890T5epMi0ya+J0B0tX8UIFPXDZrIu6NPM7V6/183iHi1Z7YEC14XQDcd30VFzU2exaN5Ha8IT/WYbO69gYbUwtn2L4b5/kglKL152Z7cblzn3va/dWmY4XgKwYUG244V/w4Us7Y9BpTcoXE3OD927tiytzqt6SPzDE3NISpv/2Q7u664vJi4a48G/odKHHHwZUG27ow1inRgpeHXEmsupHf+GyYPVr1xBPXXEGTqs0RV/lx5EIpx9hwfhc/PvWPgCAq3u3qrAvnCU6yP3KJl85M9v5ln0rtvLbcD6cutvU2/qhcO8RDP7nV8avnVU/vD6kZ7VrhOev6YGBneJ7We5Y+3XPlq5c0qZVg1r4YtS5OCUGK6WGggHVhgsKqK5Wu0YKOjSpg97ZDXBNH/c0Rl3SzX5GrpaZNbFl35EY58b7Ip2dP9oqj+93A/d99biAG2753S4pSTDltn64vIfZBoFo/O1vP8/c5N1E1WFAJU/LDGL+VX4/UqwwoJKnTbgi8HytbhjjTYmBAZU8zYXtJZ7x7i19nM5C3OHHkTytQe3Ao8hYQLXXv30j1zc8eQ0DKnnWe7f2rbKmFgVn3n3nOZ2FuMSASp7Vr12QgwBYiVpFoKHCXzLghoUBlVwnlIUByZwb+2eXb2ebmpshwTCg+vHopZ0dX58mUZleD4rl0+A4sdpvvOFIKT9u6M95NJ2UJEApI2FMiQDT7uyPTXsOO50Vz2IJlVwp0G1/15bBT8gyrHvLmC4Z41UCoFtWpt8hvBQYAyq5UqDRTe/cHHwfyoyaqXg7hOMTVd30wKPOqHoMqORKF5/eHD1b++8SFc/LwDjltvOcWXDe+NUAAA4VSURBVPonnjCgkivVSEnG01d1r5L+p8Edw24s7NvWjTPPx94Xo86t8Lh5hm+ZkRopyU5kJ66wUYpcq/Iqlu/d2jf4vqc2MmuyVAtUnfbuw9vPwg+b9jqUm/jCgEqulpIkOKGb+9s1Zt/IaGiRWRMtMsOb2JsqiuiWX0R+KyIrRKRURHIq7XtQRPJFZI2IDLakD9Fp+SIy2pLeRkQW6vT3RSRNp9fQj/P1/uxI8kzeMuG33cq33bBMMFF1Iq1DXQ7g1wAqrIUhIp0BDAfQBcAQAC+KSLKIJAN4AcBQAJ0BXK2PBYAnATytlGoPYC+Am3X6zQD26vSn9XGUIM7p0Lh8u6ELltMmqk5EAVUptUoptcZm1zAAk5VSR5VSGwDkA+itf/KVUuuVUscATAYwTHzTtF8AYKo+fxKAyy3XmqS3pwIYKJxSP2E0qlMDn911tpHJPG4dwFZsiq5otfK3BLDZ8rhQp/lLbwhgn1LqRKX0CtfS+4v08VWIyEgRyRORvF27dhl6KeS0Li0yAk7mEYxep8RuhcyBnZrE7LnIPQI2SonIHAB2C3OPVUp9Yj5L4VNKTQQwEQBycnI4cJEcww9fYgoYUJVSg8K47hYA1gXSs3Qa/KTvBpApIim6FGo9vuxahSKSAiBDH0/kWg1qs4tWIorWLf80AMN1C30bAB0ALALwPYAOukU/Db6Gq2lKKQVgHoAr9PkjAHxiudYIvX0FgLn6eCLXeuyyLk5ngRwQabepX4lIIYB+AD4TkZkAoJRaAWAKgJUAZgC4QylVokufdwKYCWAVgCn6WAB4AMC9IpIPXx3pazr9NQANdfq9AMq7WhG5Ve0a7OKdiCJ615VSHwP42M++cQDG2aRPBzDdJn09fL0AKqcXA/htJPkkcoP6tVKx9/Bxp7NBUcSx/EQx4tRY+Y5N65ZvLxoz0JE8JAoGVKIQ1E4LPyjGuvf09f1OQadmdXGDZWmTJvXSY5uJBMOAShSCV67LCXyQS/xlWFfMuGcAFzaJIQZUoihbOGYg3rqpN9o3qRP4YPI0BlSiKGtaLx0DTm2MF67t6cjzs49h7DCgElXjzOyqw1XPaOV/JQGr3m0aoJalzrUelxiJe+wsRxSiuwe2x01v5gU8bsrv+oV87d7ZDVAnPQUFuw9h/a5D4WSPHMQSKlEIRIALOjVFwfjcsM7/269OL99+9NLOFfa9en0OXr0hB6/fcCba6slgbrS00JP7MaASBfD45V1t08MJdtf0aV2+nVW/Fr4dfQHObt8ID+WehkGdm1qqBdg270W85Seqxu/Pb4/zOzbBgeLjeGrGmgpLhTSMcAKUM9s0QEbNVLxzi/klrgedVnX6wCtzsow/D1XEgEoJqXZaMg4dKwl4XLZeKPC2Ae3w6x5ZaJZxsmP88N6t8eWaXcjbGNoCd3NHnYut+4qRUTNwI1W4o6uyG1adPzaJ87JHHW/5KSGd1rxeSMcnJUmFYAr4VhOYevtZVY59ZnjV5a+t2jaug7M7NArqeXu0zsRDuacFn1HtrPYn52BvUte3dEyrBrVCvg6FhgGVyLBh3VsGPihIAuCWc9qiboizV3VtkVG+fUGnJnjjxjNx27ntjOWL7DGgEvnRIiMdreoHXl557qhzo56XdEt/1sxaofVnFRGc37EJV42NAQZUSki1gijxffvgQKQkB/4Xads4+kNK66WfzG/d9CBKq4ydjmBApYQ08pzorID6nzv6R+W6Vhxx5V4MqJSQ+re3XTg3YmdkZQQ+KAKXndECE68PPOOVsIjqCAZUSkhiuAvRGzeciQsMLh19/5CO6JaVgbPa+3oDlE1wctfA9miZGbhel5zBfqhEBpzfqQnONxhQT21aF9PuPNtmT3BfBI3qcNVVJ7CESuQFIc7BZ7oETsFhQCWy0TzDnUuFME66G2/5iSp57LIuGHFWttPZIA9iCZWoEqXcN8d92cxWZcNIyZ1YQiXygOv6ZeO6ftlOZ4MCYAmVyIOGdW/hdBbIBgMqUSW10tx/45Ye5rR+FF0MqJTwep3iW4gvt1tzPJR7Gn7Ty/0TMQ89vZnTWSAb7v8qJoqRGslJuCVKY/xNO6+juUEEZA5LqJTw3NiqH4mRA7zxpRCPGFAp4cVXOAVuYB9axzCgUsI7p30jpCZL3HTmb8HJUxzDgEoJq19b3xR+jerWwNpxF+OMVpkO54i8jgGVElb7Jr6Z9uOsCpUcxIBKCSs91ffx51pLZAq7TVHCunvQqUhJTsKVOa2czkpY0pKTcKyk1OlskAVLqJSw6tRIwQNDOiEtxZv/BtFaxoXC581PElGYbj2nDRrUjo/Z7F+8tpfTWaBKeMtPCWVsbmeMze3sdDaMqJnG8fxuwxIqEZEhDKhERIZEFFBFZIKIrBaRpSLysYhkWvY9KCL5IrJGRAZb0ofotHwRGW1JbyMiC3X6+yKSptNr6Mf5en92JHkmIoqWSEuoswF0VUp1A/AzgAcBQEQ6AxgOoAuAIQBeFJFkEUkG8AKAoQA6A7haHwsATwJ4WinVHsBeADfr9JsB7NXpT+vjiIhcJ6KAqpSapZQ6oR8uAFA2keQwAJOVUkeVUhsA5APorX/ylVLrlVLHAEwGMEx8a95eAGCqPn8SgMst15qkt6cCGChcI5eIXMhkHepNAD7X2y0BbLbsK9Rp/tIbAthnCc5l6RWupfcX6eOJEt45HRo5nQWyCBhQRWSOiCy3+RlmOWYsgBMA3o1mZgMRkZEikiciebt27XIyK0QxcVP/Nk5ngSwC9kNVSg2qbr+I3ADgEgAD1cmZercAsI7ny9Jp8JO+G0CmiKToUqj1+LJrFYpICoAMfbxdXicCmAgAOTk5nPKCiGIq0lb+IQDuB3CZUuqwZdc0AMN1C30bAB0ALALwPYAOukU/Db6Gq2k6EM8DcIU+fwSATyzXGqG3rwAwV8XbFOtE4WJrgqtEOlLqeQA1AMzW7UQLlFK3KaVWiMgUACvhqwq4QylVAgAicieAmQCSAbyulFqhr/UAgMki8lcAPwJ4Tae/BuBtEckHsAe+IExE5DoRBVTdlcnfvnEAxtmkTwcw3SZ9PXy9ACqnFwP4bST5JCKKBY6UIooD557a2OksEBhQiYiMYUAl8rAuzesBAK7t09rhnBDA6fuIPK1JvXQUjM91OhuksYRKFEe6ZWU4nYWExhIqUZxY9uhFnl3OJV4woBLFibrpqU5nIeHx64yIyBAGVCIiQxhQiYgMYUAlIjKEAZWIyBAGVCIiQxhQiYgMYUAlIjKEAZWIyBAGVCIiQyRel2cSkV0ANoZ4WiMAv0QhO07ia/KOeHxd8fiaTlFK2c7oHbcBNRwikqeUynE6HybxNXlHPL6ueHxN1eEtPxGRIQyoRESGMKBWNNHpDEQBX5N3xOPrisfX5BfrUImIDGEJlYjIEAZUACIyRETWiEi+iIx2Oj+ViUgrEZknIitFZIWI3K3TG4jIbBFZq3/X1+kiIs/q17NURHparjVCH79WREZY0nuJyDJ9zrMiIjF6bcki8qOIfKoftxGRhTof74tImk6voR/n6/3Zlms8qNPXiMhgS7oj76uIZIrIVBFZLSKrRKSf198rEfmj/uwtF5H3RCQ9Ht4r45RSCf0DIBnAOgBtAaQBWAKgs9P5qpTH5gB66u26AH4G0BnAUwBG6/TRAJ7U2xcD+ByAAOgLYKFObwBgvf5dX2/X1/sW6WNFnzs0Rq/tXgD/BvCpfjwFwHC9/TKA2/X27wG8rLeHA3hfb3fW71kNAG30e5ns5PsKYBKAW/R2GoBML79XAFoC2ACgpuU9uiEe3ivTPyyhAr0B5Cul1iuljgGYDGCYw3mqQCm1TSn1g94+AGAVfB/yYfD980L/vlxvDwPwlvJZACBTRJoDGAxgtlJqj1JqL4DZAIboffWUUguU75P/luVaUSMiWQByAbyqHwuACwBM9fOayl7rVAAD9fHDAExWSh1VSm0AkA/fe+rI+yoiGQAGAHgNAJRSx5RS++Dx9wq+9edqikgKgFoAtsHj71U0MKD6AtNmy+NCneZK+vapB4CFAJoqpbbpXdsBNNXb/l5TdemFNunR9k8A9wMo1Y8bAtinlDphk4/yvOv9Rfr4UF9rtLUBsAvAG7oq41URqQ0Pv1dKqS0A/g5gE3yBtAjAYnj/vTKOAdVDRKQOgA8B3KOU2m/dp0srnumyISKXANiplFrsdF4MSwHQE8BLSqkeAA7Bd4tfzoPvVX34SoxtALQAUBvAEEcz5VIMqMAWAK0sj7N0mquISCp8wfRdpdRHOnmHvgWE/r1Tp/t7TdWlZ9mkR1N/AJeJSAF8t3gXAHgGvlvesuXNrfkoz7venwFgN0J/rdFWCKBQKbVQP54KX4D18ns1CMAGpdQupdRxAB/B9/55/b0yz+lKXKd/4CtRrIfv27esQryL0/mqlEeBr67sn5XSJ6BiQ8dTejsXFRs6Fun0BvA1LtTXPxsANND7Kjd0XBzD13ceTjZKfYCKDR2/19t3oGJDxxS93QUVGzrWw9fI4dj7CuBrAB319qP6ffLsewWgD4AV8NWdCnz1o3+Ih/fK+N/K6Qy44Qe+ltaf4WtpHOt0fmzydzZ8t4hLAfykfy6Gr17qCwBrAcyx/MMJgBf061kGIMdyrZvgawzIB3CjJT0HwHJ9zvPQgz5i9PqsAbWtDhj5+h+2hk5P14/z9f62lvPH6nyvgaXF26n3FUB3AHn6/fqPDoiefq8APAZgtX7et3VQ9Px7ZfqHI6WIiAxhHSoRkSEMqEREhjCgEhEZwoBKRGQIAyoRkSEMqEREhjCgEhEZwoBKRGTI/wez1zRbU2JhJQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The Emotion Predicted For Recorded Audio Using Microphone is ['fear']\n",
      "Total time required: 12865.033 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this function call is used to record audio using microphone and test the model in real time on speaker voice\n",
    "test_realtime(encoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IeIQnZ6uo33C",
    "outputId": "05ccc85b-4eb9-42ae-b666-7587699f09ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Total time required: 461948.864 ms\n"
     ]
    }
   ],
   "source": [
    "#function to check on audio sample in different language\n",
    "@calc_time\n",
    "def unknown_audio():\n",
    "  #list for storing features, labels and datapath\n",
    "  datapath = []\n",
    "  X = []\n",
    "  label = []\n",
    "  path = '/content/drive/MyDrive/Audiofiles/realtimetested/testing on sample voices/'\n",
    "  #get emotion informations\n",
    "  emotions = os.listdir(path)\n",
    "  #get path of emotions   \n",
    "  data = [os.listdir(path+i) for i in emotions]\n",
    "  for i in range(0,len(data)):\n",
    "    count=0\n",
    "    for j in data[i]:\n",
    "      print(\"Number of files processed \",count)\n",
    "      #librosa.load takes audio file converts to array and returns array of audio file with its sampling rate\n",
    "      audio, sr = librosa.load(path+emotions[i]+'/'+j, duration=2.5, offset=0.6)\n",
    "      features = get_features_recorded(audio,sr)\n",
    "      for ele in features:\n",
    "        X.append(ele)\n",
    "        label.append(emotions[i])\n",
    "        datapath.append(path+emotions[i]+'/'+j)\n",
    "      count+=1\n",
    "  #create a dataframe of aduio features\n",
    "  Features = pd.DataFrame(X)\n",
    "  #add label information \n",
    "  Features['Label'] = label\n",
    "  Features['AudioPath'] = datapath\n",
    "  #store the extracted features in a csv file\n",
    "  Features.to_csv('/content/drive/MyDrive/Audiofiles/realtimetested/unkonwaudio.csv',index=False)  \n",
    "\n",
    "#this is one time process to extract audio features of audio in different languages\n",
    "unknown_audio()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Bq19qmRNvoZ-",
    "outputId": "ebc7439e-bb70-4739-d318-7099db202cff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "original neutral predicted neutral\n",
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      "original neutral predicted neutral\n",
      "Total count of samples to be tested for different language 1317\n",
      "Total count of samples predicted correctly for different language 289\n",
      "Total time required: 3384.318 ms\n"
     ]
    }
   ],
   "source": [
    "#function to find accuracy of model on different language audio\n",
    "@calc_time\n",
    "def diff_lang_test():\n",
    "  #read different language file audio features and labels\n",
    "  df = pd.read_csv('/content/drive/MyDrive/Audiofiles/realtimetested/unkonwaudio.csv')\n",
    "  #load the best model\n",
    "  res_model = load_model(\"/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5\")\n",
    "  #apply min max scaling\n",
    "  scaler = MinMaxScaler()\n",
    "  feature = scaler.fit_transform(df[df.columns[:-2]].values)\n",
    "  #get the predicted label\n",
    "  label=res_model.predict(feature)\n",
    "  #get the label information by reversing one hot encoded output\n",
    "  label_predicted=encoder.inverse_transform(label)\n",
    "  #print the count of samples that match the predicted label by the model\n",
    "  count=0\n",
    "  for i in range(0,len(df[\"Label\"])):\n",
    "    if (df[\"Label\"][i]).lower()==(label_predicted[i][0]).lower():\n",
    "      print(\"original {} predicted {}\".format((df[\"Label\"][i]).lower(),(label_predicted[i][0]).lower()))\n",
    "      count+=1\n",
    "    else:\n",
    "      pass\n",
    "  print(\"Total count of samples to be tested for different language {}\".format(len(df[\"Label\"])))\n",
    "  print(\"Total count of samples predicted correctly for different language {}\".format(count))\n",
    "\n",
    "diff_lang_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mE67sr5I8cBz"
   },
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "GPU",
  "colab": {
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   "name": "1130532_ResearchMethodology_Project_Final.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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