ser_mlp.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "kZTfgUu51RCX"
      },
      "outputs": [],
      "source": [
        "#Import All Important Libraries\n",
        "import librosa\n",
        "# from pydub import AudioSegment\n",
        "import subprocess\n",
        "import soundfile\n",
        "import os, glob#, pickle\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "dvGpG07E_3Uo"
      },
      "outputs": [],
      "source": [
        "#function for extracting mfcc, chroma, and mel features from sound file\n",
        "def extract_feature(file_name, mfcc, chroma, mel):\n",
        "    result=np.array([])\n",
        "    with soundfile.SoundFile(file_name) as sound_file:\n",
        "        X = sound_file.read(dtype=\"float32\")\n",
        "        sample_rate=sound_file.samplerate\n",
        "        # if chroma:\n",
        "        if mfcc:\n",
        "            mfccs=np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
        "            result=np.hstack((result, mfccs))\n",
        "        if chroma:\n",
        "            stft=np.abs(librosa.stft(X))\n",
        "            chroma=np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
        "            result=np.hstack((result, chroma))\n",
        "        if mel:\n",
        "            mel=np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n",
        "            result=np.hstack((result, mel))\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "p-UIAg03Xis1"
      },
      "outputs": [],
      "source": [
        "#Define the motions dictionary\n",
        "emotions = {\n",
        "    '01':'neutral',\n",
        "    '02':'calm',\n",
        "    '03':'happy',\n",
        "    '04':'sad',\n",
        "    '05':'angry',\n",
        "    '06':'fearful',\n",
        "    '07':'disgust',\n",
        "    '08':'surprised'\n",
        "}\n",
        "\n",
        "#Emotions we want to observe\n",
        "observed_emotions = ['calm', 'happy', 'fearful', 'disgust']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "154OQUcVZ4tt"
      },
      "outputs": [],
      "source": [
        "#Load the data and extract features for each sound file\n",
        "def load_data(test_size = 0.2):\n",
        "    x, y = [], []\n",
        "    print(0)\n",
        "    # for folder in glob.glob('./Datasets/Actor_*'):\n",
        "    # for folder in glob.glob('./Datasets/Actor_*'):\n",
        "    #     # print(0)\n",
        "    #     print(folder)\n",
        "    for file in glob.glob('./Datasets/*.wav'):\n",
        "        # print(file)\n",
        "        file_name = os.path.basename(file)\n",
        "        emotion = emotions[file_name.split('-')[2]]\n",
        "        if emotion not in observed_emotions:\n",
        "            continue\n",
        "        feature = extract_feature(file, mfcc = True, chroma = True, mel = True)\n",
        "        x.append(feature)\n",
        "        y.append(emotion)\n",
        "    return train_test_split(np.array(x), y, test_size = test_size, random_state = 9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "colab_type": "code",
        "id": "J2f0qX1PjBU0",
        "outputId": "ea97dc04-3154-479b-918f-51a83cbbf2f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n"
          ]
        },
        {
          "ename": "ParameterError",
          "evalue": "Invalid shape for monophonic audio: ndim=2, shape=(172972, 2)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mParameterError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-2b4a89d108d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-4-a7af89c9da86>\u001b[0m in \u001b[0;36mload_data\u001b[0;34m(test_size)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0memotion\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobserved_emotions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mfeature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_feature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmfcc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchroma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memotion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-2-a554c23093c1>\u001b[0m in \u001b[0;36mextract_feature\u001b[0;34m(file_name, mfcc, chroma, mel)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;31m# if chroma:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmfcc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0mmfccs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmfcc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_mfcc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmfccs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mchroma\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/feature/spectral.py\u001b[0m in \u001b[0;36mmfcc\u001b[0;34m(y, sr, S, n_mfcc, dct_type, norm, lifter, **kwargs)\u001b[0m\n\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mS\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1851\u001b[0;31m         \u001b[0mS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpower_to_db\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmelspectrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m     \u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfftpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdct_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn_mfcc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/feature/spectral.py\u001b[0m in \u001b[0;36mmelspectrogram\u001b[0;34m(y, sr, S, n_fft, hop_length, win_length, window, center, pad_mode, power, **kwargs)\u001b[0m\n\u001b[1;32m   1993\u001b[0m     \"\"\"\n\u001b[1;32m   1994\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1995\u001b[0;31m     S, n_fft = _spectrogram(\n\u001b[0m\u001b[1;32m   1996\u001b[0m         \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m         \u001b[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/core/spectrum.py\u001b[0m in \u001b[0;36m_spectrogram\u001b[0;34m(y, S, n_fft, hop_length, power, win_length, window, center, pad_mode)\u001b[0m\n\u001b[1;32m   2510\u001b[0m         S = (\n\u001b[1;32m   2511\u001b[0m             np.abs(\n\u001b[0;32m-> 2512\u001b[0;31m                 stft(\n\u001b[0m\u001b[1;32m   2513\u001b[0m                     \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2514\u001b[0m                     \u001b[0mn_fft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_fft\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/core/spectrum.py\u001b[0m in \u001b[0;36mstft\u001b[0;34m(y, n_fft, hop_length, win_length, window, center, dtype, pad_mode)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;31m# Check audio is valid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m     \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;31m# Pad the time series so that frames are centered\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/.local/lib/python3.8/site-packages/librosa/util/utils.py\u001b[0m in \u001b[0;36mvalid_audio\u001b[0;34m(y, mono)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmono\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         raise ParameterError(\n\u001b[0m\u001b[1;32m    294\u001b[0m             \u001b[0;34m\"Invalid shape for monophonic audio: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;34m\"ndim={:d}, shape={}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mParameterError\u001b[0m: Invalid shape for monophonic audio: ndim=2, shape=(172972, 2)"
          ]
        }
      ],
      "source": [
        "x_train,x_test,y_train,y_test=load_data(test_size=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "colab_type": "code",
        "id": "HpN02uwkpekV",
        "outputId": "78905251-85ce-4d73-92aa-ce7e334840e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(386, 166)\n",
            "Features extracted: 180\n"
          ]
        }
      ],
      "source": [
        "#Shape of train and test set and Number of features extracted\n",
        "print((x_train.shape[0], x_test.shape[0]))\n",
        "print(f'Features extracted: {x_train.shape[1]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ZXwY7HVDuuTv"
      },
      "outputs": [],
      "source": [
        "#Initialise Multi Layer Perceptron Classifier\n",
        "model = MLPClassifier(alpha = 0.01, batch_size = 256, epsilon = 1e-08, hidden_layer_sizes = (1000,), learning_rate = 'adaptive', max_iter = 500)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "colab_type": "code",
        "id": "0_LKbQ3KvJy-",
        "outputId": "429e189c-625a-4187-d6e8-d729e85783ff"
      },
      "outputs": [],
      "source": [
        "history=model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(1000,),\n",
              "              learning_rate='adaptive', max_iter=500)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "glTuS50TwN0I"
      },
      "outputs": [],
      "source": [
        "#Predict for the test set\n",
        "y_pred = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "qqVnMdQmwUXG",
        "outputId": "20d90e48-b15b-47f3-ff7c-62151a3dff82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 93.98%\n"
          ]
        }
      ],
      "source": [
        "#Calculate Accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy: {:.2f}%\".format(accuracy*100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "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": [
        "loss_plot=model.loss_curve_\n",
        "plt.plot(loss_plot)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ffmpeg version 4.4.1 Copyright (c) 2000-2021 the FFmpeg developers\n",
            "  built with gcc 9.4.0 (GCC)\n",
            "  configuration: --prefix=/home/conda/feedstock_root/build_artifacts/ffmpeg_1636205340875/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac --cc=/home/conda/feedstock_root/build_artifacts/ffmpeg_1636205340875/_build_env/bin/x86_64-conda-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-demuxer=dash --enable-gnutls --enable-gpl --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-vaapi --enable-libx264 --enable-libx265 --enable-libaom --enable-libsvtav1 --enable-libxml2 --enable-libvpx --enable-pic --enable-pthreads --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libmp3lame --pkg-config=/home/conda/feedstock_root/build_artifacts/ffmpeg_1636205340875/_build_env/bin/pkg-config\n",
            "  libavutil      56. 70.100 / 56. 70.100\n",
            "  libavcodec     58.134.100 / 58.134.100\n",
            "  libavformat    58. 76.100 / 58. 76.100\n",
            "  libavdevice    58. 13.100 / 58. 13.100\n",
            "  libavfilter     7.110.100 /  7.110.100\n",
            "  libavresample   4.  0.  0 /  4.  0.  0\n",
            "  libswscale      5.  9.100 /  5.  9.100\n",
            "  libswresample   3.  9.100 /  3.  9.100\n",
            "  libpostproc    55.  9.100 / 55.  9.100\n",
            "Input #0, mp3, from './test/agk.mp3':\n",
            "  Duration: 00:00:12.80, start: 0.025057, bitrate: 128 kb/s\n",
            "  Stream #0:0: Audio: mp3, 44100 Hz, stereo, fltp, 128 kb/s\n",
            "    Metadata:\n",
            "      encoder         : LAME3.99r\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))\n",
            "Press [q] to stop, [?] for help\n",
            "Output #0, wav, to './test/test_anger_4.wav':\n",
            "  Metadata:\n",
            "    ISFT            : Lavf58.76.100\n",
            "  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, mono, s16, 768 kb/s\n",
            "    Metadata:\n",
            "      encoder         : Lavc58.134.100 pcm_s16le\n",
            "size=    1198kB time=00:00:12.77 bitrate= 768.1kbits/s speed= 663x    \n",
            "video:0kB audio:1197kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.006361%\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['fearful'] anger\n"
          ]
        }
      ],
      "source": [
        "# file = './test/agk.mp3'\n",
        "# emotion = \"anger\"\n",
        "# opfile = \"./test/test_anger_4.wav\"\n",
        "# if file[-3:] != \"wav\":\n",
        "#     cmd = f\"ffmpeg -i {file} -acodec pcm_s16le -ac 1 -ar 48000 {opfile} -y\"\n",
        "#     process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n",
        "#     output, error = process.communicate()\n",
        "# else:\n",
        "#     opfile = file\n",
        "# file_name = os.path.basename(opfile)\n",
        "# # emotion = emotions[file_name.split('-')[2]]\n",
        "# x = [extract_feature(opfile, True, True, True)]\n",
        "# print(model.predict(x), emotion)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Speech Emotion Recognition Notebook.ipynb",
      "provenance": []
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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