SR_SVM_MFCC_GFCC_MGFCC_1_5sec.ipynb

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  "metadata": {
    "colab": {
      "name": "SR_SVM_MFCC_GFCC_MGFCC_1-5sec.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ritikraj660/Speaker-recognition/blob/master/SR_SVM_MFCC_GFCC_MGFCC_1_5sec.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xk59svCYCLhj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import os, signal\n",
        "#os.kill(os.getpid(), signal.SIGKILL)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcyQfgosljmV",
        "colab_type": "code",
        "outputId": "d0cb2c59-84e2-4aa3-bd7d-3beb4d989b1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "!pip install soundfile\n",
        "!pip install spafe"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting soundfile\n",
            "  Downloading https://files.pythonhosted.org/packages/eb/f2/3cbbbf3b96fb9fa91582c438b574cff3f45b29c772f94c400e2c99ef5db9/SoundFile-0.10.3.post1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.6/dist-packages (from soundfile) (1.14.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi>=1.0->soundfile) (2.20)\n",
            "Installing collected packages: soundfile\n",
            "Successfully installed soundfile-0.10.3.post1\n",
            "Collecting spafe\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/d9/d92807657a5125318639aa1636c0a76fb87222d48b5c290996ed9d5be1b6/spafe-0.1.2-py3-none-any.whl (77kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 2.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.18.5)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.6/dist-packages (from spafe) (1.4.1)\n",
            "Installing collected packages: spafe\n",
            "Successfully installed spafe-0.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6t4Z8TJHmwGD",
        "colab_type": "code",
        "outputId": "108d306d-6f12-4d22-aefc-8ce9703f3049",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "import tensorflow\n",
        "tensorflow.test.gpu_device_name() "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXBlWpXQmxLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8Pu4VcMmzl9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYIQUREonGkL",
        "colab_type": "code",
        "outputId": "235e46c7-8e42-43c4-b13a-c30ba8c9df07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "link = 'https://drive.google.com/file/d/1qxZWgx3vttou1HSvyxRDiOYWnaLAK9fj/view?usp=sharing'\n",
        "#fluff, id = link.split('=')\n",
        "id = '1qxZWgx3vttou1HSvyxRDiOYWnaLAK9fj'\n",
        "print (id) "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1qxZWgx3vttou1HSvyxRDiOYWnaLAK9fj\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7X6vFDeUm2qI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "downloaded = drive.CreateFile({'id':id}) \n",
        "downloaded.GetContentFile('LibriSpeech.zip')  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DORbV6fsnAe7",
        "colab_type": "code",
        "outputId": "eed7a32c-efbe-46e5-e6d0-4fcf8e1ad7ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "!pip install patool\n",
        "import os\n",
        "os.listdir()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting patool\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/94/52243ddff508780dd2d8110964320ab4851134a55ab102285b46e740f76a/patool-1.12-py2.py3-none-any.whl (77kB)\n",
            "\r\u001b[K     |████▎                           | 10kB 18.7MB/s eta 0:00:01\r\u001b[K     |████████▌                       | 20kB 2.2MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 30kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 40kB 3.1MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 51kB 2.5MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 61kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 71kB 3.0MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 81kB 2.6MB/s \n",
            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'adc.json', 'LibriSpeech.zip', 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLXzUt1VnOkB",
        "colab_type": "code",
        "outputId": "8d827eac-c3a3-4abe-d172-ad55c24c874f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "%%time\n",
        "import patoolib\n",
        "patoolib.extract_archive(\"LibriSpeech.zip\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "patool: Extracting LibriSpeech.zip ...\n",
            "patool: running /usr/bin/7z x -o./Unpack_rmqbfmyq -- LibriSpeech.zip\n",
            "patool: ... LibriSpeech.zip extracted to `LibriSpeech'.\n",
            "CPU times: user 12.8 ms, sys: 11.6 ms, total: 24.5 ms\n",
            "Wall time: 685 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rx-naY1jnRk1",
        "colab_type": "code",
        "outputId": "8ab7b41d-4bda-41e4-fdd0-2e6bc0591cd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "import librosa\n",
        "import soundfile\n",
        "import tqdm\n",
        "from scipy.io import wavfile\n",
        "import glob\n",
        "import os,pickle\n",
        "import numpy as np\n",
        "from spafe.features.mfcc import mfcc, imfcc\n",
        "import csv\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import tensorflow.python.keras\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from tensorflow.python.keras.layers import Dense, Dropout, Flatten, BatchNormalization\n",
        "from tensorflow.python.keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxXrNH__DKN9",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBzHCtXXnVA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from spafe.features.lpc import lpc\n",
        "from spafe.features.gfcc import gfcc\n",
        "\n",
        "def mfcc_(sig,fs,cep,frame_overlapp):\n",
        "    mfccs  = mfcc(sig, fs=fs, num_ceps=cep, pre_emph=1, \n",
        "                  pre_emph_coeff=0.95,win_len=0.025, win_hop=frame_overlapp, win_type='hamming',\n",
        "                  nfilts=26, nfft=512, low_freq=None, high_freq=None, scale='constant', \n",
        "                  dct_type=2, use_energy=False, lifter=22)\n",
        "    #print(fs)\n",
        "    return mfccs\n",
        "\n",
        "def gfcc_(sig,fs,cep,frame_overlapp):\n",
        "  gfccs = gfcc(sig, fs=fs, num_ceps=cep, pre_emph=0, \n",
        "             pre_emph_coeff=0.97, win_len=0.025, win_hop=frame_overlapp, win_type='hamming', \n",
        "             nfilts=26, nfft=512, low_freq=None, high_freq=None, scale='constant', \n",
        "             dct_type=2, use_energy=False, lifter=22, normalize=1)\n",
        "  return gfccs\n",
        "\n",
        "# combine mfcc and gfcc ceptral features\n",
        "def gfcc_mfcc(y,sr,cep,frame_overlapp):\n",
        "    g = gfcc_(y,sr,cep,frame_overlapp)\n",
        "    m = mfcc_(y,sr,cep,frame_overlapp)\n",
        "    if g.shape[0] != m.shape[0]:\n",
        "        pass\n",
        "    result = np.zeros([g.shape[1]+m.shape[1]])\n",
        "    for i in range(g.shape[0]):\n",
        "        merge = np.concatenate((m[i],g[i]))\n",
        "        result = np.vstack((result,merge))\n",
        "    return result[1:,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA85kmM7nxBe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def label(user,total): # its creating lable for splited audio data\n",
        "    k_train = []\n",
        "    for i in range(total):\n",
        "        k_train.append(user)\n",
        "    y_train=np.array(k_train).T\n",
        "    return y_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRa8gDbjrNFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def audioLengthInSecond(sr,m,t_s):#t_s will gives time in second[1 for 1sec, 2 for 2sec]\n",
        "    t = round(sr/512) # Number of frames in one second\n",
        "    totalFrame = t*t_s # Number of frames in one second * time(second)\n",
        "    t1 = round(m.shape[0]/totalFrame) # total_audio_time/ t_s\n",
        "    #print(time_in_sec)\n",
        "    #print(\" Actual audio length in second :\",t1,\"\\n\",\"Number of frames in one second :\",t)\n",
        "    j=0\n",
        "    total = totalFrame*m.shape[1]\n",
        "    result = np.zeros([1,total])\n",
        "    #print(result.shape)\n",
        "    for i in range(t1):\n",
        "            temp = m[j:j+totalFrame,:].reshape(1,-1)\n",
        "            diff = total-temp.shape[1]\n",
        "            if round(total*0.3)>diff: # if more 30% padding need done then we will not add that audio file\n",
        "                #print(diff)\n",
        "                if diff!=0:\n",
        "                    temp = np.concatenate((temp[0,:],temp[0,diff:diff+diff])).reshape(1,-1)\n",
        "                #print(\"temp\",temp.shape)\n",
        "                result = np.concatenate((result,temp))\n",
        "                j += totalFrame\n",
        "            else:\n",
        "                pass\n",
        "    return result[1:,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC-U22v0U7ih",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pathlib import Path\n",
        "def s2c(mfcc=False,gmfcc=False,gfcc=False,cep=20,t_s=1, frame_overlapp = 0.01):\n",
        "    x,y=[],[]\n",
        "    if gmfcc==True:\n",
        "      ceps=cep+cep\n",
        "    else:\n",
        "      ceps=cep\n",
        "    t = round(16000/512)\n",
        "    totalFrame = t*t_s\n",
        "    total = totalFrame*ceps    \n",
        "    data = np.empty([1,total])\n",
        "    data_label = np.asarray([])\n",
        "    count =0  # E:\\dataset\\LibriSpeech\\dev-clean\n",
        "    #for file in tqdm(glob.iglob('/LibriSpeech/test-clean//**/*.flac', recursive=True)):\n",
        "    for file in Path('LibriSpeech/dev-clean').glob('**/*.flac'):\n",
        "        file_name=os.path.basename(file)\n",
        "        user = file_name.split(\"-\")[0]\n",
        "        #print(f)\n",
        "        with soundfile.SoundFile(file) as sound_file:\n",
        "                signal = sound_file.read(dtype=\"float32\")\n",
        "                sr=sound_file.samplerate\n",
        "        #signal = norm_(signal,mean = False)\n",
        "        #signal = zero_handling(signal)\n",
        "        #print(signal.shape,sr)\n",
        "        if mfcc:\n",
        "            feature = mfcc_(signal,sr,cep,frame_overlapp)\n",
        "            #print(feature.shape[0])\n",
        "        if gmfcc:\n",
        "            feature = gfcc_mfcc(signal,sr,cep,frame_overlapp)\n",
        "            ceps = feature.shape[1]   \n",
        "        #if feature.shape[0]>256:\n",
        "        if gfcc:\n",
        "            feature = gfcc_(signal,sr,cep,frame_overlapp)\n",
        "        #t_s = 1\n",
        "        re_data = audioLengthInSecond(sr,feature,t_s) # return (3,400)\n",
        "        #print(\"shape of feature return \",re_data.shape)\n",
        "        data=np.concatenate((data,re_data),axis=0)\n",
        "        #print(\"Updated data length\",data.shape)\n",
        "        re_label = label(user,re_data.shape[0])\n",
        "        data_label = np.concatenate((data_label,re_label),axis=0)\n",
        "    return data[1:,:],data_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK39L9gupDO2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pre_pro(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=1,frame_overlapp = 0.01):\n",
        "  \n",
        "  x,y = s2c(mfcc=mfcc,gmfcc=gmfcc,gfcc=gfcc,cep=cep,t_s=t_s,frame_overlapp = frame_overlapp)\n",
        "  #write_(x,y)\n",
        "  print(x.shape,y.shape,type(x),type(y))\n",
        "  if dim ==2:\n",
        "    if gmfcc == True:\n",
        "      ceps = cep+cep\n",
        "    else:\n",
        "      ceps = cep   \n",
        "    d = np.empty([1,round(x.shape[1]/ceps),ceps])\n",
        "    for i in tqdm(range(x.shape[0])):\n",
        "      t = np.reshape(x[i],(1,-1,40))\n",
        "      d = np.concatenate((d,t),axis=0)\n",
        "    print(d.shape)\n",
        "    x=d\n",
        "  return x,y  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qigx27vfYJpb",
        "colab_type": "code",
        "outputId": "23d1a1a8-6d3b-45ae-af39-cd19f77ae53e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "%%time\n",
        "def x_y(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=2,frame_overlapp = 0.01):\n",
        "  x,y = pre_pro(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=2,frame_overlapp = frame_overlapp)\n",
        "  u = np.unique(y)\n",
        "  num_classes = len(u)\n",
        "  #print(u)\n",
        "  d = dict()\n",
        "  for enu,key in enumerate(u,0):\n",
        "      d[key]=enu\n",
        "  print(d)\n",
        "  for i in range(len(y)):\n",
        "      y[i] = d[y[i]]\n",
        "  return x,y    "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 6 µs, sys: 0 ns, total: 6 µs\n",
            "Wall time: 11.2 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUUYoIwMKx3p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3GFCnSCOhQi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NX8bh2HHnZA_",
        "colab_type": "text"
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_xqmREHY_Ms",
        "colab_type": "code",
        "outputId": "2989a307-13cc-44cc-ca90-e7eda04f13ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas.util.testing as tm\n",
        "import seaborn as sns\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.multiclass import OneVsOneClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "def svm_(mfcc=False,gmfcc=False,gfcc=False,cep=20,dim=1,t_s=1,frame_overlapp = 0.01):\n",
        "  x,y = x_y(mfcc=mfcc,gmfcc=gmfcc,gfcc=gfcc,cep=cep,dim=1,t_s=t_s,frame_overlapp = frame_overlapp)\n",
        "  x_train, x_test, y_train, y_test = train_test_split(x,y ,test_size=0.20, shuffle= True)\n",
        "\n",
        "  print(\"Training shape: {0}, Training label shape : {1}\".format(x_train.shape,y_train.shape))\n",
        "  print(\"Training shape: {0}, Training label shape : {1}\".format(x_test.shape,y_test.shape))\n",
        "\n",
        "  print(\"Enteres into model moking\")\n",
        "  model = SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, \n",
        "                shrinking=True, probability=True, tol=0.001, cache_size=200, \n",
        "                class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr',\n",
        "                break_ties=False, random_state=None)\n",
        "  # fit model\n",
        "  print(\"Entering into training Model\")\n",
        "  his = model.fit(x_train, y_train)\n",
        "  # make predictions\n",
        "  print(\"entering into predicting value\")\n",
        "  yhat = model.predict(x_test)\n",
        "  acc = accuracy_score(y_test,yhat)\n",
        "  print(acc)\n",
        "  conf_mat = confusion_matrix(y_test, yhat)\n",
        "  fig, ax = plt.subplots(figsize=(10,10))\n",
        "  sns.heatmap(conf_mat, annot=True, fmt='d',\n",
        "              xticklabels=[0,1,2,3,4,5,6,7,8,9], yticklabels=[0,1,2,3,4,5,6,7,8,9])\n",
        "  plt.ylabel('Actual')\n",
        "  plt.xlabel('Predicted')\n",
        "  plt.show()\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnDyr3qPGtot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#svm_(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=1,frame_overlapp = 0.005)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBOzAJQPwnB_",
        "colab_type": "code",
        "outputId": "275f1371-b649-48fc-a959-5fef97287fe5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "cep_fea = ['mfcc','gfcc','gmfcc']\n",
        "cep_num = [16,18,19,20,22]\n",
        "time_ = [2,3,4,5]\n",
        "win_over = [0.015,0.01,0.0075,0.005]\n",
        "print(cep_fea,cep_num,time_,win_over)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['mfcc', 'gfcc', 'gmfcc'] [16, 18, 19, 20, 22] [2, 3, 4, 5] [0.015, 0.01, 0.0075, 0.005]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQmsuLb3OZ0_",
        "colab_type": "code",
        "outputId": "76467e36-f2d2-40f4-a33b-9d4583284b30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in win_over:\n",
        "    print(\"*****************************************************************************\")\n",
        "    print(\"***************time = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping {}**********************\".format(i))\n",
        "    print(\"*****************************************************************************\")\n",
        "    #if i == 0.01:\n",
        "    svm_(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=1,frame_overlapp = i)\n",
        "      #speaker(batch_sizes=16,epoch=200,mfcc=m,gmfcc=gm,gfcc=g,cep=20,dim=1,t_s=j,frame_overlapp = 0.01)\n",
        "      #%%time\n",
        "    print(\"*****************************************************************************\")\n",
        "    print(\"*****************************************************************************\")\n",
        "\n",
        "      "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "***************time = 2 second, Cepstral feature= mfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7488643737832577\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 2 second, Cepstral feature= gfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7410772225827384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 2 second, Cepstral feature= gmfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7475665152498377\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 3 second, Cepstral feature= mfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.754055807916937\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 3 second, Cepstral feature= gfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7618429591174561\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 3 second, Cepstral feature= gmfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7560025957170668\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 4 second, Cepstral feature= mfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7216093445814407\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 4 second, Cepstral feature= gfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7495133030499675\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 4 second, Cepstral feature= gmfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7508111615833875\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 5 second, Cepstral feature= mfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7326411421155095\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 5 second, Cepstral feature= gfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7482154445165476\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "*****************************************************************************\n",
            "***************time = 5 second, Cepstral feature= gmfcc**********************\n",
            "*****************************************************************************\n",
            "(7704, 2480) (7704,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7, '777': 8, '84': 9}\n",
            "Training shape: (6163, 2480), Training label shape : (6163,)\n",
            "Training shape: (1541, 2480), Training label shape : (1541,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.7423750811161584\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "*****************************************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCa7b2PCdNQu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}