{
 "cells": [
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    "tags": []
   },
   "source": [
    "# Example of usage "
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Install library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# !pip install praat-textgrids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "start_time": "2022-05-13T08:04:46.576938",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Load data from kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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    "tags": []
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   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import math\n",
    "from glob import glob\n",
    "import pickle\n",
    "import os\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory"
   ]
  },
  {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "### Define the function extracting the ground truth labels"
   ]
  },
  {
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   "source": [
    "It's important for this kind of tasks to perform short time analysis on the signal, so it needs to assign the lables (SPEECH/NONSPEECH) to very little portions of the signal. I decide to split the data into portion of 30 milliseconds."
   ]
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   "source": [
    "import textgrids\n",
    "\n",
    "FRAME_DURATION = 20 # 20 msec\n",
    "\n",
    "def readFile(path : str, frame_len_ms : int = 20 ):\n",
    "    '''\n",
    "    Read the file and return the list of SPEECH/NONSPEECH labels for each frame\n",
    "    '''\n",
    "    frame_len = frame_len_ms * 16\n",
    "    labeled_list  = []\n",
    "    grid = textgrids.TextGrid(path)\n",
    "    len_x = math.ceil(grid.xmax * 16_000)\n",
    "    for interval in grid['silences']:\n",
    "        label = int(interval.text)\n",
    "        \n",
    "        dur = interval.dur\n",
    "        len_dur = int(round(dur * 16_000))\n",
    "        if label == 0:\n",
    "            labeled_list = labeled_list + [0] * len_dur\n",
    "        else: \n",
    "            labeled_list = labeled_list + [1] * len_dur\n",
    "            \n",
    "    len_l = len(labeled_list)\n",
    "    extra = len_x - len_l\n",
    "    if extra > 0:\n",
    "        if label == 0:\n",
    "            labeled_list = labeled_list + [0] * extra\n",
    "        else: \n",
    "            labeled_list = labeled_list + [1] * extra\n",
    "    else:\n",
    "        labeled_list = labeled_list[:len_x]\n",
    "\n",
    "    labeled_np = np.array(labeled_list)\n",
    "\n",
    "    framed_label = []\n",
    "    for i in range(0, len_x,frame_len):\n",
    "        if np.mean(labeled_np[i:i+frame_len]) > 0.5:\n",
    "            framed_label = framed_label + [1]\n",
    "        else:\n",
    "            framed_label = framed_label + [0]\n",
    "\n",
    "    return framed_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "539"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "labels_filenames = glob(\"Annotation/Female/*/*.TextGrid\") + \\\n",
    "    glob(\"Annotation/Male/*/*.TextGrid\") + glob(\"Annotation/NoNoise/*.TextGrid\")\n",
    "len(labels_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for annotation_path  in labels_filenames:\n",
    "    framed_label= readFile(annotation_path)\n",
    "    file_name = annotation_path.split(\"Annotation/\")[-1].split(\".TextGrid\")[0].replace(\"/\", \"_\")\n",
    "    with open(os.path.join(\"/home/dllabsharif/Documents/DATASETS/clean_dataset/vad_labels/TIMIT\",\n",
    "                        file_name+'.txt'), 'wb') as handle:\n",
    "        pickle.dump(framed_label, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/home/dllabsharif/Documents/DATASETS/clean_dataset/vad_labels/TIMIT/Female_PTDB-TUG_mic_F02_si822.txt', 'rb') as handle:\n",
    "    b = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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    {
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       "  0])"
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     },
     "execution_count": 12,
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   ],
   "source": [
    "len(b), b[200:]"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Load a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   "source": [
    "import librosa\n",
    "\n",
    "root ='/Female/TIMIT/SA2'\n",
    "annotation_path = \"Annotation/Female/TMIT/SI2220.TextGrid\"\n",
    "audio_path = \"Audio/Female/TMIT/SI2220.wav\"\n",
    "\n",
    "# read wav file\n",
    "data, fs = librosa.load(audio_path, sr = 16_000)\n",
    "\n",
    "# read annotaion\n",
    "framed_label= readFile(annotation_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'label_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fs, \u001b[39mlen\u001b[39m(data), \u001b[39mlen\u001b[39m(label_list), \u001b[39mlen\u001b[39m(framed_label)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'label_list' is not defined"
     ]
    }
   ],
   "source": [
    "fs, len(data), len(label_list), len(framed_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46720"
      ]
     },
     "execution_count": 122,
     "metadata": {},
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    }
   ],
   "source": [
    "147 * 320 - 320"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "## Plot signal"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "figure = plt.Figure(figsize=(10, 7), dpi=85)\n",
    "\n",
    "plt.plot(data)\n",
    "plt.plot(0.25*np.array(label_list))\n",
    "plt.plot(0.25*np.array(label_list))\n",
    "plt.title(\"Ground truth labels\")\n",
    "plt.legend(['Signal', 'Speech'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-13T08:04:58.923831Z",
     "iopub.status.busy": "2022-05-13T08:04:58.922763Z",
     "iopub.status.idle": "2022-05-13T08:04:58.930309Z",
     "shell.execute_reply": "2022-05-13T08:04:58.929625Z",
     "shell.execute_reply.started": "2022-05-13T07:52:16.641220Z"
    },
    "papermill": {
     "duration": 0.056337,
     "end_time": "2022-05-13T08:04:58.930437",
     "exception": false,
     "start_time": "2022-05-13T08:04:58.874100",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46797\n",
      "46797\n",
      "46797\n"
     ]
    }
   ],
   "source": [
    "print(len(label_list))\n",
    "print(len(t))\n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "papermill": {
   "duration": 535.307432,
   "end_time": "2022-05-13T08:13:25.559897",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-05-13T08:04:30.252465",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}