SPEECH_EMOTION.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7th28u4xpMUP"
      },
      "source": [
        "# **LIBRIRIES**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "XNMaB4xFpJFr"
      },
      "outputs": [],
      "source": [
        "        ## DADOS ##\n",
        "\n",
        "import os\n",
        "import cv2\n",
        "import keras\n",
        "import pickle\n",
        "import random\n",
        "import shutil\n",
        "import librosa\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from librosa import display\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "        ## NEURAL NETWORK ##\n",
        "\n",
        "from keras.models import Model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "from tensorflow.keras.applications import Xception\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "from tensorflow.keras.applications import ResNet50"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXoOBWNLlveE"
      },
      "source": [
        "# **AUDIOS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o3JdRLv0npAs"
      },
      "source": [
        "#### **SONG**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1VUAhHF0nycm"
      },
      "outputs": [],
      "source": [
        "         ### CRIAR AS PASTAS NECESSÁRIAS!!! ###\n",
        "\n",
        "classes_dir = ['neutral','calm','happy','sad','angry','fear','disgust','surprised','song', 'speech']\n",
        "\n",
        "\n",
        "for category in classes_dir:\n",
        "    if not os.path.isdir(category):\n",
        "        os.makedirs(category)\n",
        "        print(\"Pasta \" + category + \" foi criada!!\")\n",
        "    else:\n",
        "        print(\"Pasta \" + category + \" já criada!!\")\n",
        "\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/RAV_song.zip\" -d \"/content/song\"  ## UNZIP AUDIOS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cygr0QKwgJvP"
      },
      "outputs": [],
      "source": [
        "path = \"/content/song\"\n",
        "pathfiles = os.listdir(path)\n",
        "for files in pathfiles:\n",
        "    subfiles = os.path.join(path, files)\n",
        "    audios = os.listdir(subfiles)\n",
        "    for so in audios:\n",
        "        files_song = os.path.join(subfiles, so)\n",
        "        emo_label = files_song[30]\n",
        "        if emo_label == '1':\n",
        "            shutil.copy(files_song, \"/content/neutral\")\n",
        "        if emo_label == '2':\n",
        "            shutil.copy(files_song, \"/content/calm\")\n",
        "        if emo_label == '3':\n",
        "            shutil.copy(files_song, \"/content/happy\")\n",
        "        if emo_label == '4':\n",
        "            shutil.copy(files_song, \"/content/sad\")\n",
        "        if emo_label == '5':\n",
        "            shutil.copy(files_song, \"/content/angry\")\n",
        "        if emo_label == '6':\n",
        "            shutil.copy(files_song, \"/content/fear\")\n",
        "        #if emo_label == '7':\n",
        "            #shutil.copy(files_song, \"/content/disgust\")       ## NÃO TEM!!\n",
        "        #if emo_label == '8':\n",
        "            #shutil.copy(files_song, \"/content/surprised\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKnYrd14nz3T"
      },
      "source": [
        "#### **SPEECH**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aLwB6OuOdKwF"
      },
      "outputs": [],
      "source": [
        "         ### CRIAR AS PASTAS NECESSÁRIAS!!! ###\n",
        "\n",
        "classes_dir = ['neutral','calm','happy','sad','angry','fear','disgust','surprised','song', 'speech']\n",
        "\n",
        "\n",
        "for category in classes_dir:\n",
        "    if not os.path.isdir(category):\n",
        "        os.makedirs(category)\n",
        "        print(\"Pasta \" + category + \" foi criada!!\")\n",
        "    else:\n",
        "        print(\"Pasta \" + category + \" já criada!!\")\n",
        "\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/RAV_speech.zip\" -d \"/content/speech\"  ## UNZIP AUDIOS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lTmmfQchdRhU"
      },
      "outputs": [],
      "source": [
        "path_speech = \"/content/speech\"\n",
        "pathfiles_speech = os.listdir(path_speech)\n",
        "for files_speech in pathfiles_speech:\n",
        "    subfiles_speech = os.path.join(path_speech, files_speech)\n",
        "    audios_speech = os.listdir(subfiles_speech)\n",
        "    for so_speech in audios_speech:\n",
        "        files_speech = os.path.join(subfiles_speech, so_speech)\n",
        "        emo_label = files_speech[32]\n",
        "        if emo_label == '1':\n",
        "            shutil.copy(files_speech, \"/content/neutral\")\n",
        "        if emo_label == '2':\n",
        "            shutil.copy(files_speech, \"/content/calm\")\n",
        "        if emo_label == '3':\n",
        "            shutil.copy(files_speech, \"/content/happy\")\n",
        "        if emo_label == '4':\n",
        "            shutil.copy(files_speech, \"/content/sad\")\n",
        "        if emo_label == '5':\n",
        "            shutil.copy(files_speech, \"/content/angry\")\n",
        "        if emo_label == '6':\n",
        "            shutil.copy(files_speech, \"/content/fear\")\n",
        "        if emo_label == '7':\n",
        "            shutil.copy(files_speech, \"/content/disgust\")       \n",
        "        if emo_label == '8':\n",
        "            shutil.copy(files_speech, \"/content/surprised\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h9T-qaWKg1Jl"
      },
      "outputs": [],
      "source": [
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")\n",
        "shutil.copy(files_speech, \"/content/neutral\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ppfeefb0bmQK"
      },
      "outputs": [],
      "source": [
        "!zip -r neutral.zip neutral/\n",
        "!zip -r calm.zip calm/\n",
        "!zip -r happy.zip happy/\n",
        "!zip -r fear.zip fear/\n",
        "!zip -r angry.zip angry/\n",
        "!zip -r sad.zip sad/\n",
        "!zip -r disgust.zip disgust/\n",
        "!zip -r surprised.zip surprised/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QtJhiayDcVdw"
      },
      "outputs": [],
      "source": [
        "!cp neutral.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "!cp calm.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "!cp happy.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "!cp fear.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "!cp angry.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "!cp sad.zip \"/content/drive/MyDrive/PROJETOS\"  \n",
        "!cp disgust.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "!cp surprised.zip \"/content/drive/MyDrive/PROJETOS\"\n",
        "\n",
        "## SALVO!!! NÃO PRECISA CORRER MAIS O CÓDIGO ACIMA..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "duBwe7SlgtAg"
      },
      "source": [
        "# **MEL_SPECTOGRAMA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e-do0tNbgx1G"
      },
      "outputs": [],
      "source": [
        "!mkdir \"/content/emo_audios\"\n",
        "\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/neutral.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/calm.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/happy.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/fear.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/angry.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/sad.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/disgust.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n",
        "!unzip \"/content/drive/MyDrive/PROJETOS/surprised.zip\" -d \"/content/emo_audios\"  ## UNZIP AUDIOS\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7h1Fl2vYeYRn"
      },
      "outputs": [],
      "source": [
        "classes = ['neutral','calm','happy','sad','angry','fear','disgust','surprised']\n",
        "DATADIR = \"/content/emo_audios\"\n",
        "data = []\n",
        "\n",
        "for category in classes:\n",
        "    path = os.path.join(DATADIR, category)\n",
        "    label = classes.index(category)\n",
        "    files = os.listdir(path)\n",
        "    #print(path)\n",
        "    for audios in files:\n",
        "        #name = audios[:-4]\n",
        "        aud = os.path.join(path, audios)\n",
        "        X, sample_rate = librosa.load(aud, res_type='kaiser_fast')\n",
        "        mel_spec = librosa.feature.melspectrogram(y=X, sr=sample_rate, n_mels= 320)\n",
        "        mel_db = (librosa.power_to_db(mel_spec, ref=np.max) + 40)/40\n",
        "        img = cv2.resize(mel_db,(100,100))\n",
        "        img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)\n",
        "        image = img.reshape(100,100,3)\n",
        "        data.append([image, label])\n",
        "        #break  ## Para ler um audio de cada emoção e testar abaixo\n",
        "    #break  ## Para ler um audio apenas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTDugLewOQ0N",
        "outputId": "5008873e-71b6-46bc-d42d-3bd58cb509da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(100, 100, 3)"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "usiHmYjjhH-f"
      },
      "outputs": [],
      "source": [
        "random.shuffle(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3YdeuXBehNDo"
      },
      "outputs": [],
      "source": [
        "data[60]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SM1lmMwHhUCu"
      },
      "outputs": [],
      "source": [
        "X = []\n",
        "y = []\n",
        "\n",
        "for features, labels in data:\n",
        "    X.append(features)\n",
        "    y.append(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5S1ZY3jh01d",
        "outputId": "a019f82a-bf77-48e5-d4c9-0baa44e6c623"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2452"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train = np.array(X)\n",
        "y_train = np.array(y)\n",
        "len(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8G2hReNI9kKR",
        "outputId": "141261ba-fccf-405d-b92d-f7c0f58d1feb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2452, 100, 100, 3)\n",
            "(2452,)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSb8yN1nmRbK",
        "outputId": "b06abdc5-b05b-4b88-8ba6-c07b63e58ef8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2452, 100, 100, 3)"
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape # (,100,100,3) (quantidade de imagens, tamanho, tamanho, 1 ou 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gLEZI8Dozum"
      },
      "source": [
        "#### **SAVE AND COPY**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ggwQjRJDiCsg"
      },
      "outputs": [],
      "source": [
        "pickle.dump(X_train, open('data_train.pkl', 'wb'))\n",
        "pickle.dump(y_train, open('labels_train.pkl', 'wb'))\n",
        "\n",
        "# Salvos os dados processados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F6LDC52aiJqy"
      },
      "outputs": [],
      "source": [
        "!cp data_train.pkl '/content/drive/MyDrive/PROJETOS'\n",
        "!cp labels_train.pkl '/content/drive/MyDrive/PROJETOS' \n",
        "\n",
        "# Copiados os dados processados no Drive, eles podem ser apenas carregados a partir daqui com Pickle;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTO6HyZXpD-U"
      },
      "source": [
        "#### **LOAD DATA WITH PICKLE**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i_zm9Y89jnCh"
      },
      "outputs": [],
      "source": [
        "!cp /content/drive/MyDrive/PROJETOS/data_train.pkl '/content' \n",
        "!cp /content/drive/MyDrive/PROJETOS/labels_train.pkl '/content'  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XJlBMMcelsKq"
      },
      "outputs": [],
      "source": [
        "X_train = pickle.load(open('data_train.pkl', 'rb'))\n",
        "y_train = pickle.load(open('labels_train.pkl', 'rb'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfIOIwXIqGFg"
      },
      "source": [
        "# **NEURAL NETWORK**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mppck1OmqOYO"
      },
      "outputs": [],
      "source": [
        "models = VGG19(input_shape=X_train.shape[1:], weights='imagenet', include_top=False) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEn_ks-artL4",
        "outputId": "8330413c-ca4a-42a8-d535-bd4efbf167fa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 100, 100, 3) dtype=float32 (created by layer 'input_5')>"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "models.input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fsU2fEbGrw6L"
      },
      "outputs": [],
      "source": [
        "for layer in models.layers: \n",
        "  layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qEcHUSNNrzR-"
      },
      "outputs": [],
      "source": [
        "x = Flatten()(models.output) \n",
        "prediction = Dense(8, activation='softmax')(x)\n",
        "model = Model(inputs=models.input, outputs=prediction)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YaD1_O5zsNJ3"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maS_Qx_isT9U",
        "outputId": "553540e0-2974-444b-ecb2-2f3c8b3469c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "230/230 [==============================] - 7s 28ms/step - loss: 1.6909 - accuracy: 0.3524 - val_loss: 1.4413 - val_accuracy: 0.4437\n",
            "Epoch 2/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 1.3222 - accuracy: 0.5014 - val_loss: 1.3362 - val_accuracy: 0.4976\n",
            "Epoch 3/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 1.1848 - accuracy: 0.5606 - val_loss: 1.2949 - val_accuracy: 0.5204\n",
            "Epoch 4/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 1.0760 - accuracy: 0.6030 - val_loss: 1.2364 - val_accuracy: 0.5400\n",
            "Epoch 5/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 1.0058 - accuracy: 0.6465 - val_loss: 1.3292 - val_accuracy: 0.5139\n",
            "Epoch 6/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.9224 - accuracy: 0.6721 - val_loss: 1.2808 - val_accuracy: 0.5188\n",
            "Epoch 7/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.8920 - accuracy: 0.6683 - val_loss: 1.1518 - val_accuracy: 0.5742\n",
            "Epoch 8/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.8312 - accuracy: 0.6977 - val_loss: 1.2401 - val_accuracy: 0.5579\n",
            "Epoch 9/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.7763 - accuracy: 0.7276 - val_loss: 1.1124 - val_accuracy: 0.5824\n",
            "Epoch 10/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.7359 - accuracy: 0.7499 - val_loss: 1.1696 - val_accuracy: 0.5644\n",
            "Epoch 11/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.6805 - accuracy: 0.7765 - val_loss: 1.1644 - val_accuracy: 0.5546\n",
            "Epoch 12/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.6541 - accuracy: 0.7858 - val_loss: 1.0931 - val_accuracy: 0.5954\n",
            "Epoch 13/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.6261 - accuracy: 0.7950 - val_loss: 1.1319 - val_accuracy: 0.5889\n",
            "Epoch 14/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.6061 - accuracy: 0.7955 - val_loss: 1.1073 - val_accuracy: 0.6020\n",
            "Epoch 15/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.5801 - accuracy: 0.7993 - val_loss: 1.1742 - val_accuracy: 0.5595\n",
            "Epoch 16/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.5411 - accuracy: 0.8309 - val_loss: 1.0656 - val_accuracy: 0.5971\n",
            "Epoch 17/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.5096 - accuracy: 0.8554 - val_loss: 1.1655 - val_accuracy: 0.5775\n",
            "Epoch 18/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.4946 - accuracy: 0.8494 - val_loss: 1.2235 - val_accuracy: 0.5546\n",
            "Epoch 19/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.4890 - accuracy: 0.8515 - val_loss: 1.0668 - val_accuracy: 0.6052\n",
            "Epoch 20/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.4719 - accuracy: 0.8624 - val_loss: 1.1121 - val_accuracy: 0.6117\n",
            "Epoch 21/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.4565 - accuracy: 0.8586 - val_loss: 1.1568 - val_accuracy: 0.6003\n",
            "Epoch 22/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.4142 - accuracy: 0.8793 - val_loss: 1.1594 - val_accuracy: 0.5938\n",
            "Epoch 23/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.4194 - accuracy: 0.8798 - val_loss: 1.1505 - val_accuracy: 0.5971\n",
            "Epoch 24/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3944 - accuracy: 0.8972 - val_loss: 1.0847 - val_accuracy: 0.6069\n",
            "Epoch 25/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3941 - accuracy: 0.8918 - val_loss: 1.1313 - val_accuracy: 0.6134\n",
            "Epoch 26/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3783 - accuracy: 0.8923 - val_loss: 1.1374 - val_accuracy: 0.5971\n",
            "Epoch 27/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3466 - accuracy: 0.9184 - val_loss: 1.0876 - val_accuracy: 0.6264\n",
            "Epoch 28/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3320 - accuracy: 0.9152 - val_loss: 1.1587 - val_accuracy: 0.6036\n",
            "Epoch 29/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3115 - accuracy: 0.9288 - val_loss: 1.0630 - val_accuracy: 0.6395\n",
            "Epoch 30/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.3010 - accuracy: 0.9299 - val_loss: 1.1680 - val_accuracy: 0.5742\n",
            "Epoch 31/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2817 - accuracy: 0.9413 - val_loss: 1.2369 - val_accuracy: 0.6020\n",
            "Epoch 32/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2816 - accuracy: 0.9413 - val_loss: 1.1077 - val_accuracy: 0.6150\n",
            "Epoch 33/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2535 - accuracy: 0.9538 - val_loss: 1.2286 - val_accuracy: 0.5889\n",
            "Epoch 34/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2673 - accuracy: 0.9440 - val_loss: 1.1862 - val_accuracy: 0.6150\n",
            "Epoch 35/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2728 - accuracy: 0.9342 - val_loss: 1.1169 - val_accuracy: 0.6264\n",
            "Epoch 36/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2407 - accuracy: 0.9532 - val_loss: 1.1589 - val_accuracy: 0.6166\n",
            "Epoch 37/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.2327 - accuracy: 0.9554 - val_loss: 1.1757 - val_accuracy: 0.6101\n",
            "Epoch 38/50\n",
            "230/230 [==============================] - 6s 27ms/step - loss: 0.2235 - accuracy: 0.9554 - val_loss: 1.1130 - val_accuracy: 0.6215\n",
            "Epoch 39/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.2084 - accuracy: 0.9663 - val_loss: 1.1151 - val_accuracy: 0.6232\n",
            "Epoch 40/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1950 - accuracy: 0.9695 - val_loss: 1.1803 - val_accuracy: 0.6199\n",
            "Epoch 41/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.2024 - accuracy: 0.9685 - val_loss: 1.1937 - val_accuracy: 0.6117\n",
            "Epoch 42/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1850 - accuracy: 0.9728 - val_loss: 1.1363 - val_accuracy: 0.6085\n",
            "Epoch 43/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1842 - accuracy: 0.9728 - val_loss: 1.2905 - val_accuracy: 0.5954\n",
            "Epoch 44/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1825 - accuracy: 0.9734 - val_loss: 1.1136 - val_accuracy: 0.6297\n",
            "Epoch 45/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1654 - accuracy: 0.9810 - val_loss: 1.1833 - val_accuracy: 0.6215\n",
            "Epoch 46/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1690 - accuracy: 0.9793 - val_loss: 1.1457 - val_accuracy: 0.6427\n",
            "Epoch 47/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1599 - accuracy: 0.9826 - val_loss: 1.1664 - val_accuracy: 0.5987\n",
            "Epoch 48/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1583 - accuracy: 0.9799 - val_loss: 1.1581 - val_accuracy: 0.6166\n",
            "Epoch 49/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1416 - accuracy: 0.9842 - val_loss: 1.2051 - val_accuracy: 0.6264\n",
            "Epoch 50/50\n",
            "230/230 [==============================] - 6s 28ms/step - loss: 0.1467 - accuracy: 0.9826 - val_loss: 1.1607 - val_accuracy: 0.6117\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(X_train, y_train, batch_size=8, epochs = 50, validation_split=0.25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "47xurb3Av39p",
        "outputId": "0b9ea8d5-1a40-4d43-aa00-1de8d6a49a3b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "EY-dm8z2wA88",
        "outputId": "05e79c3b-a363-4ce7-de70-9d48797a8303"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pklYT8iJh1Z",
        "outputId": "1fbd06df-297a-43ce-db4e-5e5112e9b247"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/librosa/core/audio.py:165: UserWarning: PySoundFile failed. Trying audioread instead.\n",
            "  warnings.warn(\"PySoundFile failed. Trying audioread instead.\")\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 54ms/step\n",
            "surprised\n"
          ]
        }
      ],
      "source": [
        "IMG_SIZE = 100\n",
        "classes = ['neutral','calm','happy','sad','angry','fear','disgust','surprised']\n",
        "aud = '/content/surprised.ogg'\n",
        "\n",
        "X, sample_rate = librosa.load(aud, res_type='kaiser_fast')\n",
        "mel_spec = librosa.feature.melspectrogram(y=X, sr=sample_rate, n_mels= 320)\n",
        "mel_db = (librosa.power_to_db(mel_spec, ref=np.max) + 40)/40\n",
        "img = cv2.resize(mel_db,(100,100))\n",
        "img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)\n",
        "image = img.reshape(-1,100,100,3)\n",
        "prediction = model.predict(image)\n",
        "label = classes[np.argmax(prediction)]\n",
        "print(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "IMG_SIZE = 100\n",
        "classes = ['neutral','happy','sad','angry','fear']\n",
        "#aud = '/content/TESTE_MODEL/angry/OAF_back_angry.wav'\n",
        "#aud = \"/content/TESTE_MODEL/fear/OAF_back_fear.wav\"\n",
        "aud = \"D:\\speech_emotion\\happy.ogg\"\n",
        "\n",
        "model = tf.keras.models.load_model('D:\\speech_emotion\\Audio_model.h5')\n",
        "#classes = ['neutral','happy','sad','angry','fear']\n",
        "\n",
        "X, sample_rate = librosa.load(aud, res_type='kaiser_fast')\n",
        "mel_spec = librosa.feature.melspectrogram(y=X, sr=sample_rate, n_mels= 320)\n",
        "mel_db = (librosa.power_to_db(mel_spec, ref=np.max) + 40)/40\n",
        "img = cv2.resize(mel_db,(100,100))\n",
        "img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)\n",
        "image = img.reshape(-1,100,100,3)\n",
        "prediction = model.predict(image)\n",
        "label = classes[np.argmax(prediction)]\n",
        "print(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHkCrUZuRrav",
        "outputId": "521adfce-7771-418f-ca98-77eab78c5279"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MODEL SAVED\n"
          ]
        }
      ],
      "source": [
        "model.save('Audio_model.h5')\n",
        "!cp Audio_model.h5 '/content/drive/MyDrive/PROJETOS'\n",
        "print(\"MODEL SAVED\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "authorship_tag": "ABX9TyNNwe2oGEgvVp4lkvyG3DVy",
      "mount_file_id": "1jSh6L58rZ-GEfn4v61tMwt_dNKtzExOB",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}