SR_SVM_MGFCC_1sec_overlapping_window.ipynb

{
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  "metadata": {
    "colab": {
      "name": "SR_SVM_MGFCC_1sec_overlapping_window.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "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_MGFCC_1sec_overlapping_window.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": "80c211a2-85b7-4cf3-e17e-6ea9ebf92abb",
        "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 2.2MB/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": "1f94da05-ac00-4496-e032-579a0ac58240",
        "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": [
              "'/device:GPU:0'"
            ]
          },
          "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": "7df81626-a5de-4663-bb76-62e2c58d9ada",
        "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": "0d510ff2-cfa3-4841-91b7-20ebe2fa00b6",
        "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 17.7MB/s eta 0:00:01\r\u001b[K     |████████▌                       | 20kB 1.6MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 30kB 2.1MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 40kB 2.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 51kB 1.9MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 61kB 2.1MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 71kB 2.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 81kB 2.1MB/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": "d7320f82-157a-41ad-8092-6810a62b43f3",
        "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_u0uk2qf0 -- LibriSpeech.zip\n",
            "patool: ... LibriSpeech.zip extracted to `LibriSpeech'.\n",
            "CPU times: user 16.1 ms, sys: 13.5 ms, total: 29.6 ms\n",
            "Wall time: 630 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rx-naY1jnRk1",
        "colab_type": "code",
        "outputId": "971674e7-feb3-417c-fd51-64e9243437a7",
        "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": 9,
      "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,number_of_speaker=10):\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",
        "    user_name = ['1272','1462','1673','174','1919','251','422','652','777','84']\n",
        "    choose_ = []\n",
        "    for i in range(number_of_speaker):\n",
        "      choose_.append(user_name[i])\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",
        "        if user in choose_:\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,number_of_speaker=10):\n",
        "  \n",
        "  x,y = s2c(mfcc=mfcc,gmfcc=gmfcc,gfcc=gfcc,cep=cep,t_s=t_s,frame_overlapp = frame_overlapp,number_of_speaker=number_of_speaker)\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": "488744b4-d37e-463c-e6eb-4e739641b317",
        "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,number_of_speaker=10):\n",
        "  x,y = pre_pro(mfcc=False,gmfcc=True,gfcc=False,cep=20,dim=1,t_s=2,frame_overlapp = frame_overlapp,number_of_speaker=number_of_speaker)\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": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
            "Wall time: 5.48 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": "932aed5a-48ca-4b93-a13f-5cd548ce6ef5",
        "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,number_of_speaker=10):\n",
        "  x,y = x_y(mfcc=mfcc,gmfcc=gmfcc,gfcc=gfcc,cep=cep,dim=1,t_s=t_s,frame_overlapp = frame_overlapp,number_of_speaker=number_of_speaker)\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=42)\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",
        "  total_user = list(set(y_test))\n",
        "  fig, ax = plt.subplots(figsize=(10,10))\n",
        "  sns.heatmap(conf_mat, annot=True, fmt='d',\n",
        "              xticklabels=total_user, yticklabels=total_user)\n",
        "  plt.ylabel('Actual')\n",
        "  plt.xlabel('Predicted')\n",
        "  plt.show()\n",
        "\n"
      ],
      "execution_count": 16,
      "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": "aad2ca08-4c21-412e-855d-918431f6a6de",
        "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",
        "speaker_ = [2,3,4,5,6,7,8,9]\n",
        "print(cep_fea,cep_num,time_,win_over)"
      ],
      "execution_count": 18,
      "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": "a80528e8-a1b4-41c5-e1d8-46c916bdd1cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in speaker_:\n",
        "    print(\"*****************************************************************************\")\n",
        "    print(\"***************time = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker {}**********************\".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 = 0.005,number_of_speaker=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": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*****************************************************************************\n",
            "***************time = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 2**********************\n",
            "*****************************************************************************\n",
            "(3067, 2480) (3067,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1}\n",
            "Training shape: (2453, 2480), Training label shape : (2453,)\n",
            "Training shape: (614, 2480), Training label shape : (614,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.9869706840390879\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 3**********************\n",
            "*****************************************************************************\n",
            "(4616, 2480) (4616,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2}\n",
            "Training shape: (3692, 2480), Training label shape : (3692,)\n",
            "Training shape: (924, 2480), Training label shape : (924,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.9913419913419913\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 4**********************\n",
            "*****************************************************************************\n",
            "(6158, 2480) (6158,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3}\n",
            "Training shape: (4926, 2480), Training label shape : (4926,)\n",
            "Training shape: (1232, 2480), Training label shape : (1232,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.9602272727272727\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 5**********************\n",
            "*****************************************************************************\n",
            "(7725, 2480) (7725,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4}\n",
            "Training shape: (6180, 2480), Training label shape : (6180,)\n",
            "Training shape: (1545, 2480), Training label shape : (1545,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.945631067961165\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 6**********************\n",
            "*****************************************************************************\n",
            "(9263, 2480) (9263,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5}\n",
            "Training shape: (7410, 2480), Training label shape : (7410,)\n",
            "Training shape: (1853, 2480), Training label shape : (1853,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.9244468429573665\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 7**********************\n",
            "*****************************************************************************\n",
            "(10875, 2480) (10875,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6}\n",
            "Training shape: (8700, 2480), Training label shape : (8700,)\n",
            "Training shape: (2175, 2480), Training label shape : (2175,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.9016091954022989\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 8**********************\n",
            "*****************************************************************************\n",
            "(12464, 2480) (12464,) <class 'numpy.ndarray'> <class 'numpy.ndarray'>\n",
            "{'1272': 0, '1462': 1, '1673': 2, '174': 3, '1919': 4, '251': 5, '422': 6, '652': 7}\n",
            "Training shape: (9971, 2480), Training label shape : (9971,)\n",
            "Training shape: (2493, 2480), Training label shape : (2493,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.8900922583233053\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 = 1 second, Cepstral feature= MFCC+GFCC, Frame Overlapping =80% number of speaker 9**********************\n",
            "*****************************************************************************\n",
            "(14004, 2480) (14004,) <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}\n",
            "Training shape: (11203, 2480), Training label shape : (11203,)\n",
            "Training shape: (2801, 2480), Training label shape : (2801,)\n",
            "Enteres into model moking\n",
            "Entering into training Model\n",
            "entering into predicting value\n",
            "0.8811138878971796\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": []
    }
  ]
}