{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "709f3f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71cec813",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import funcy\n",
    "import json\n",
    "import numpy as np\n",
    "from Bio import pairwise2\n",
    "\n",
    "from suno_utils.audio import Audio\n",
    "from suno_utils.web.mfa import align_text\n",
    "\n",
    "\n",
    "AUDIO_FILEPATH = \"/home/georg/notebooks/tasks/s2t/sample_data/audio_16k/russia.wav\"\n",
    "\n",
    "# with open(\"/home/georg/notebooks/tasks/s2t/sample_data/transcripts.json\") as f:\n",
    "#     transcript_data = json.load(f)[\"a85b9df3-46a4-4461-88ab-c4a52b9fdd96\"]\n",
    "# TRANSCRIPT = transcript_data[\"true\"]\n",
    "# TRANSCRIPT_NORM = transcript_data[\"true_norm\"]\n",
    "# TRANSCRIPT_TSS = align_text(\n",
    "#     [AUDIO_FILEPATH], \n",
    "#     [TRANSCRIPT_NORM], \n",
    "#     \"/home/georg/anaconda3/etc/profile.d/conda.sh\", \n",
    "#     \"mfa\",\n",
    "# )[0]\n",
    "TRANSCRIPT_TSS = [\n",
    "    ('president', (1.57, 2.02)),\n",
    "    ('joe', (2.02, 2.21)),\n",
    "    ('biden', (2.21, 2.79)),\n",
    "    ('backed', (2.82, 3.14)),\n",
    "    ('by', (3.14, 3.25)),\n",
    "    ('the', (3.25, 3.33)),\n",
    "    ('full', (3.33, 3.61)),\n",
    "    ('symbolic', (3.61, 4.13)),\n",
    "    ('power', (4.13, 4.49)),\n",
    "    ('of', (4.49, 4.61)),\n",
    "    ('the', (4.61, 4.66)),\n",
    "    ('western', (4.66, 5.07)),\n",
    "    ('alliance', (5.07, 5.74)),\n",
    "    ('is', (5.81, 5.95)),\n",
    "    ('locked', (5.95, 6.25)),\n",
    "    ('in', (6.25, 6.31)),\n",
    "    ('a', (6.31, 6.36)),\n",
    "    ('showdown', (6.36, 6.93)),\n",
    "    ('with', (6.93, 7.11)),\n",
    "    ('russian', (7.11, 7.46)),\n",
    "    ('president', (7.46, 7.94)),\n",
    "    ('vladimir', (7.94, 8.36)),\n",
    "    ('putin', (8.36, 8.83)),\n",
    "    ('who', (9.14, 9.21)),\n",
    "    ('is', (9.21, 9.3)),\n",
    "    ('using', (9.3, 9.66)),\n",
    "    ('ukraine', (9.66, 10.29)),\n",
    "    ('as', (10.38, 10.49)),\n",
    "    ('a', (10.49, 10.65)),\n",
    "    ('hostage', (10.65, 11.32)),\n",
    "    ('to', (11.32, 11.43)),\n",
    "    ('try', (11.43, 11.67)),\n",
    "    ('to', (11.67, 11.75)),\n",
    "    ('force', (11.75, 12.11)),\n",
    "    ('the', (12.11, 12.18)),\n",
    "    ('u', (12.18, 12.39)),\n",
    "    ('s', (12.39, 12.54)),\n",
    "    ('to', (12.54, 12.66)),\n",
    "    ('renegotiate', (12.66, 13.64)),\n",
    "    ('the', (13.71, 13.8)),\n",
    "    ('settled', (13.8, 14.2)),\n",
    "    ('outcome', (14.2, 14.77)),\n",
    "    ('of', (14.8, 14.91)),\n",
    "    ('the', (14.91, 14.98)),\n",
    "    ('cold', (14.98, 15.27)),\n",
    "    ('war', (15.31, 15.62)),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a302274c",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio = Audio.from_file(AUDIO_FILEPATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8628b797",
   "metadata": {},
   "outputs": [],
   "source": [
    "offs_0 = 0\n",
    "xl = []\n",
    "yl = []\n",
    "for _, (ts, te) in TRANSCRIPT_TSS:\n",
    "    ts = round(ts, 2)\n",
    "    te = round(te, 2)\n",
    "    if ts > offs_0:\n",
    "        xl.extend([offs_0, ts, ts, te])\n",
    "        yl.extend([0, 0, 1, 1])\n",
    "    else:\n",
    "        xl.extend([ts, te])\n",
    "        yl.extend([1, 1])\n",
    "    offs_0 = te\n",
    "\n",
    "ts = TRANSCRIPT_TSS[-1][-1][-1]\n",
    "te = round(audio.duration_s, 2)\n",
    "if ts < te:\n",
    "    xl.extend([ts, te])\n",
    "    yl.extend([0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9dd82c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from suno_utils.utils.vad import predict_vad_speech, VAD_FRAME_DURATION_MS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a84981ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_vad_0 = predict_vad_speech(audio, vad_level=0)\n",
    "y_vad_1 = predict_vad_speech(audio, vad_level=1)\n",
    "y_vad_2 = predict_vad_speech(audio, vad_level=2)\n",
    "y_vad_3 = predict_vad_speech(audio, vad_level=3)\n",
    "assert(len(y_vad_0) + len(y_vad_1) + len(y_vad_2) + len(y_vad_3))\n",
    "y_vad = (y_vad_0 + y_vad_1 + y_vad_2 + y_vad_3) / 4\n",
    "x_vad = [n * VAD_FRAME_DURATION_MS / 1000 for n in range(len(y_vad))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "02937ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 3))\n",
    "# plt.plot([0, audio.duration_s], [0, 0], \"-k\", alpha=1)\n",
    "# plt.plot([0, audio.duration_s], [1, 1], \"-k\", alpha=1)\n",
    "for _, (ts, te) in TRANSCRIPT_TSS:\n",
    "    plt.plot([ts, ts], [0, 1], \"-k\", alpha=0.2)\n",
    "plt.plot(xl, yl, \"-r\", alpha=0.7, label=\"true\")\n",
    "plt.plot(x_vad, y_vad, \"-b\", alpha=0.7, label=\"vad\")\n",
    "plt.axis([0, audio.duration_s, -0.05, 1.05])\n",
    "plt.legend()\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79d73172",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4e15e314",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "daabf24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yrq79dFYK5b0TZtP/zrPrk7ugpVPy9Mpd0rfS6QGs0E8YG8slOQq1iCrSpp/gdSydm531x73PTunIfCv0LV2Dgmn6BbyOpXMjP+pSMvVYoZ8wVmgkhyqckwzDIAdc63wPbNeh47kPaidJ1Lsz3rt8kItvt0u0tJmu/Rp0LhiAc8YOcD8XaUVuZzDPbao7lvsgS6J0hudeLKzQ7wCWbj6A6jnzsP1gF4xXUmQKrYF1RvNOMcpQaB/1BxdtxMvr9xb0Gu1FFvR7DpfOYkcr9BMmzpD3D2+5u0a+vnF/oYvTtWEMjiM+FshlM+C9U5DL5EW2CIUotGnmm/PfxS2/XlLQa7SbTvCsOwIr9BMmn3bUXl1LfnG3HWhQvBG6Awx+nPOCLc7qhHTu0vl0lXKG0dXL31as0O/CvPX+Ae/zhd95Gefd17WDgEVRKJdN3ZZbDC07F529U7J0bazQT5g472tSk0aNrd181SgrlKYvf1F/yyX0DzcWZpOT96VdqKzMLw6l2rlaoZ80ebSjpjyFdjEb6frdRzDrJ6/haFPHbYPHwLxFM2EdZSbLcNNDi/F67b688g0jk+ORFMqEJk/qd4bRRimw/WD3MofGxQr9DuQrf1ud1/Gt2laMhfS/+M6z72LF9kN4o4MnmymHpl/f0IxXN+zDHb9/u035B7ONFriF8iaKCg3R1egqGvQzq3d3dBE6BCv0EyaO6aYrbC3XGVYoMuZ3bGF3tZmr5vV57C0bJWALvMVxKPJli+K9k0QeIZnsPtyYQO6WQpFT6BNRJRG9RUQriGgNEX2dp48hosVEVEtEfySicp5ewb/X8t+rpbzu4unriWhGwWrVgcR5X7fsb9vimy6iQCUGY1LnE1L5+at2ez+v3nEoXr4Rv1Wmo1+JQnWGsnZcjI6nkG1JLn/URu9j7pqH23+3rHAFsRiJo+k3AbiUMXY6gCkAZhLROQC+DeCHjLHxAA4CuJUffyuAgzz9h/w4ENEpAG4EMBnATAA/I6JUgnXpMmTa+Fbro4jOoI0XHgJRuKCWheU1//tarByjwjsIc9KHzxiRVymTpRi9u3qNxg7YCYyx0jWxdCQ5hT5zOcq/lvE/BuBSAH/m6Y8AuJZ/nsW/g/9+Gblv0iwAjzPGmhhj7wOoBTAtiUp0JuK8rm0W+tppLblmHROgI+2z4sqEpDVTOfZOyBEhP8j97DtbDxagRB1jYtLni+IQdo/k9M6umHREZ9fRxLLpE1GKiJYD2AtgAYCNAOoZY8K1YzsAoRqNALANAPjvhwAMlNMN53Qb4gjJtr7UetY/fmFD2zKKRce/rYwxELnad9hcSXMbOr7I2Dv8xzjPqFArqovRzyZxjUyMTJxOLvVP+uqzHV2EohNL6DPGMoyxKQBGwtXOTypUgYjoNiJaSkRL6+rqCnWZDqWtbpC64CvGhFlHTyMQojX97z//XrvyD8TeCUn3yiPJsI11R0OOakM5FJt+8Sdy2zKiq+pRnvOYzi3yS5O8vHcYY/UAXgZwLoAqIkrzn0YC2ME/7wAwCgD47/0A7JfTDefI13iQMVbDGKsZPHhwPsXrFBTydS2mpaUzKWhRNv22mMpU7x3zitw4QvCvbweab5uRd2csTuydBPIIeSoxtiC2dCBxvHcGE1EV/9wDwBUA1sEV/tfzw2YDeIp/nsu/g//+EnPfoLkAbuTePWMATADwVkL16DQU8n2Nk3XSi4c60mNIeO8QKNkVuRF3Ulwn/IjC9IbZImv6SfC7N7fkPObV95Idrf/2zS2onjMPzd18NXohiaPpDwfwMhGtBLAEwALG2NMAvgjgTiKqhWuzf4gf/xCAgTz9TgBzAIAxtgbAEwDWAngWwB2MsdKbRWkHuvZpWii0fveRRK7VWRR9AgGUbLzzyD1yvfSQidwC3Rh5wFKMPdnDNoQH3Lqv2FafM493Y7S1TfuS3Rvghwtcc16S4TC6ymKypEjnOoAxthLAGYb0TTB43zDGGgF8NCSvewHcm38xuw6F3IxBt2QIAVRIe3BHap3r9xzB1gMNbgeUqKYf8RtT/+tsO1CYhXVFt+nrnZ3U0fzuzS346lNr8JtbzsIlk4bEzqMYeGE5kmwPpSXz7YrcxCmoUV/9alq3lFQD3sqF2/efX59Mhm3keEsm0qY/sn+PRK/HPJu++fc/Ltlm/qGdyJ4wHSGDhLJyrKkVX31qDQBgY44dvGLZ9BOWqGIdRWuCw6ESk/lW6BeSpBt82EIiOTWOG10cxNA914tfDFybvrlen7pkfN75xTLvhIiCQpl3BvT0PWEqcqwKToKw8BMHjjV7aZk2CtZCRSIFfEXn3G8lF0a8q8yhJIUV+gmj2kYTzjskP7nRdhf7pL7AJ6xabRGQysboum07h59+oW5v3x5l3uchfSoLcxGJsHo7Dklpbcv7f1/y148kfb8K0el2k1cmNlboJ4zcgJLWIMJyU6+Z6CU7DLlOhIi6a98PSppqeOa5f4rTwRaKtbsOF/waejVW8bhFskzNVdPw1czx84ji/1bsxA8WqOswCuG109DcceHDOwIr9AtI0gI4TOB0RXe/XMgmAqJwl019ZBMnNEXUaCzrPbTc9uokkfO9d97awlwkguXcW0fWpHO1Jf1nEVwtnfIz0YV2PvzHH97B/S9uUCbPDzaEm44amlsx7d4X8NqG+HsrAPG8kLoTVugnjDxsjiOA84mfE0ezSqKj6QwmInnC1NX0Q4RwG/KW66efL76H3cdCjaTy8fpasa0e1XPmJbomQ9wSOWxCvs2gUBvMLFi7x5iut9PavUex90gTvv3su3nlX4wYVp0JK/QTJl9PmgNxzBEiv1DBJ3U0CUilTiDz1ZpG2PT1W9Luonsum2GafmFuTj4d92OL3UVR7YlQGTaBLZt3WjM5NP2QPJPeaCbMjq/XQVw339Hu7Ie73RrRSKzQLyBxtLeUk8cLEuKnryzsSUAodTYTUdQdasu6CNW8w4y/heVaqHsjBHkcdh9uAgDc83RyZiBTZ5avW6R4FoHN5gs0PNKfBbXRh7+7zIPFxQr9hJHbT9KNSc9uwpDebrrUyvtUlqG9dIZ3QH5xHYdChW1bZHDURKMfe8d8bg7lt828s7U+9rHHE5h4DHrviHSfXOGWh/dTvYzCFraZ3IjX7ow/WR3W6e+sVwMOCtNUZ1NaOhtW6CfM8WY/skScxscY8PhbW3Ha157LqRHp2V160hB+HT9tUO/ckQ/jlKmjkYVSVJTNMBNDdN4Rv/Efw55dW33Xc5HPLV+y+WD7r6dPYPMEOYBdrvY7a8oJsa5lCorX1BodgUX2wvrN65uNx8xbtUv57nBp1hnab2fGCv2E+ds7fuRFFkM+rN55CHP+ugqHG1tzxobXX0LfvVB+UWMXNZRChpKIXQapCFHx9Num6UsTuQHbdnSGbd0AJxdJzBW8vnFfTmHqXS/k+0OvvS+VqW3lCNxTQz4/yrEXxH/+cbn3eXPIntKBxYp8TJDUAsXuihX6BSSO8Fy1Pd6+rm5+2nfDcDoJ4dHZ3pkoTV/vCBeu35szP3PXyb+FmCgEBRP67Tx/zc5D+PgvF+Ob89a17fq8AEs3H/DS8h15hh5nqN0rUvTNI4YVvHEcHMI67M7gfdaZsUK/gMSRD7Ld9JnVuyKONEw6eqtHJc01j/KFXyeBTNpdBsm8E7VHrvZ9/Z72+Vz7tm3zFduyrWC8C/sfP3/FxLxPF0KyNubGLmFtqSzli4R0Klo8hM8LmNPDuO3RZDZH/8lLtQCiQ4fYDsEK/YISx6Yva1PrdkULrDh2bV07++WiTZh6z4Kc5VDzi/9ibDvQUJBVkmpdI+Lpaz/89o0teCTEBuyfE3q6V/cw030RZD4G9q7I/3yeQVx3Sb0aowb0BKD66ecKZhc2YRvmDvqTlzYYJ3CXbQnOUbQl3ELt3uR2MuvOWKFfQOIIfVlzzFcLEUerK3LVY+6dvy6vtQBuOeIdd7ixBRd+52V85W+r8so/VhmkzxQRW1lPbc0y3D13TY68w0dGuTT9D8ecvGwP7XFDbWtsmuqBvXgGflq+K3K9CKWB4xgYY/je8+/hH37yWjCfNo5P/7xsu/KdYlTeKvpW6BcUUwPTBbvsDZKrQQYmck02/QQMPHFzaGhyJw1fSXh3JJ1I7502TeRG/JbjmLIYAd7W7z6CJ/IMwRw1uRwH0TZejRmCIGyEI4vNXPMXYZPBQc8gPy3JOZH3tQ1a4vR3VuZboZ84uaIUNraodgPZYSdXDBA9PxHjJM4q4HwWyOQ94ijwmxQVZbMtNtqo+5UN0Vb933PnP+NHi/CFv6zMr0whn+Py2JtbI39vbMlo90q9iqjXOL72A4gh9EPmBQI1YNGjhhbD4gcRxydp4rSX7m73t0I/Yc4dO9D7HMumLx3zWm20liaO/Nzl7kSfmHSLE3AtHze23Ycacx9UYBSXTUS4bPL/PcpS8fNWzDtho6eQ6+VxH9ssPNpw3ub9vtb7subBdLixBSd99VnFTTLMNJOSTCQ5zTva9+XbXE+0y04eqh3H8p4L2RLiphlFnOikcYqhV7t6zjx8LYfJsCthhX7CODlsorqQmXxC39h5ixezsszh30We8jHmc/MZVv9VWmsQhdhda3+ecwb5EqbpP79mN77+f24ogrzCWcQgiRHT4ePxV856NvU2Ipfrll8vUX6rP+ZqzX99R7WBy4izfyttdp4zDhk/SXS49zy9FoyxQAfcmmXtWiV72UnhWzYCbt1//srGNuevYypp2AKxrogV+gkjywSzTV/9XpmXhurieDtmCU+T3Jr+9oPBCIivbdiH6jnz2ryZ+sN8IU+hfNcFYfH0n5Q6p7xCGIU8o6jomwLZEjGkj+tlc/59L+GqH78aOPbtrQdjl+lDp/sTxG25m7OmjAj9zYvRJAlx02SrTi5BLX7/4sxJXlrdkaaAYrNwfV27hH55jnmUhe/txX3PqJE1w8xDsVZsW/OOJR8U04Gh7eiN/+85TDpK3vxcMW9gXpxlPvfd3cGhr1gX8Ja0ICcqD51CbR0YvI7ZZVNOy0fTDxsZZUM6APWabnrP8pSnne+oP240LeSzGUqc0VoUw/rl5+YprnH6qKrQa+ZcnMX/y/e+OZMNzpNkVfNOvpvLv5dj7YU+TwYANz202HhsZ1ht3tHkFPpENIqIXiaitUS0hog+y9MHENECItrA//fn6URE9xNRLRGtJKKpUl6z+fEbiGh24arVgShChCGTZag70uSnae3z8Ty8PMTLJN4xf9os3EYtiJro29XGOOhOAaX+mp3qSmVTveQONOXE11/iTAqHT+S6v0wY2ienAPnuc/E3lVe9dxh21h9H9Zx5eDLCJCPzxb+0zW32qg8MA2CeoM41D+S1R0not2RYTmVnr/Q+xKEt+zSvDFnpHkvTlz7L5q7uQpw3pRXA5xljpwA4B8AdRHQKgDkAXmSMTQDwIv8OAFcCmMD/bgPwAOB2EgDuBnA2gGkA7hYdRXdCD3N83zPrcNa9L3i+8u0Z5urmHdGClWuG2GHf2LQ/kCYmbH+2ULWH6sJMDiJXLJ7nG2ecPrKfO6IwafrS53QOTX/l9nopLo25k4yjbYt7nSI38Jm8S9OrG9ruusq0z2KB2ef+uCL3uTnalD8yVDsWQDIVmsw7OTV9NQ8AaM1kA4+qqmeZkteh4/nPAd300OI2h2j++v+tQfWcebGPl2/FV/+2OtY5tXuPoHrOvIAbaWckp9BnjO1ijL3NPx8BsA7ACACzADzCD3sEwLX88ywAjzKXNwFUEdFwADMALGCMHWCMHQSwAMDMJCvTGVB3zgKeWr4TAHDgWBNPa4fQ1zR9YadXA67Fzz+uR0/cIF6F4PozRxrDMDDGlB2VdPPO6h2+prftQAM+9JO/4+6n1vBz5XxCPvP/4740H1960teixfyFuN4/S2aEmx5qx2YcejnyGETlcvUVWWUN9TPtySCIq+nLHj+ueUc9b0jfSiX/f/3N0sh8Tby6YR++v2A9BhlWK+dqxr/+++bQ38oNoSbaYgIS80tPr9iZ97nFJi+bPhFVAzgDwGIAQxljIljMbgDCT2sEANlmsZ2nhaXr17iNiJYS0dK6usIu+ikET74tRdlkzBvK/t8K91a1Z85TNEYheH6xaFMgz3yyb+98VSHto8KrafyQPnCIAoLk77XqyEUX+tf8r7/yU+y3K/aBDSNr6AEyWYbfL94qJatCPyn0EUc+u0815QiD4Q0MDfNNvpbuJpwgxcjP5b2jdxyAu9uW3iqyrH3eO4JeFWlMGdUvkJ5P+IWwtS4yuXYMMyHMi10hwmdsoU9EvQH8BcB/MsaUGSrmvgmJ1JYx9iBjrIYxVjN48OAksiwqsrujfEMaubYshMYnLhiTd97CdGPSek2fC00hL/XRM0cCACYNc4W+3lnqo48zRlcF8hB7tuoCNE6xw2367v9iuYjGIfciKve/qSMR9RBZXHHKUPTrUYbKMie3oDZ0gK3ZbODmMZbMxiYTh/QxKk0/fCH+5ut6l2R6ji+sM+/JG4UY7RTaky0JYgl9IiqDK/AfY4z9lSfv4WYb8P9iRcgOAKOk00fytLD0bkvWYC8QbWJ4lTmY1bGmcN9uP3Ssni5fM375Qg81vLTFxp+/cDVJXWjok8i3XzIukMesn/7dPVbbXCMs5EEcLyhRjqQnsdX5hPxueKtBJX9mVTBiq1xk3x4vrun+zzI3LUUUOwyDfC+aW5mX9zWnDed5s9C5pnyYu2Jnu5WagKZvEPoihk8+XkbCStQthD65d+AhAOsYYz+QfpoLYDb/PBvAU1L6zdyL5xwAh7gZ6DkA04moP5/Anc7Tui1yAxCfhNAoMwwrAX/Bkwn/xVQbVjZEiOXCFMfceN34WSaGuHXE9dNAvbTbZ3p5hdeU0HBF2OWw+uheUI0twbmMzqTpi+0yTeGeb3/sbT9v4wXdf0LA+eEnGIgIjhND6AsTkabpi/SLJw7meSej6c9dsbPdbVE/3+T1JdpS3dH4XkbiHnQX8875AG4CcCkRLed/VwG4D8AVRLQBwOX8OwDMB7AJQC2AXwL4FAAwxg4AuAfAEv73DZ7WbVF8wLOqPb4y7S7KGtBL3d7whwtyD1UH80VBvcpTwevk0ejC9mXV7Zxh2lUhPRXENclxNUkhkH+/eKuylZ4gSvPWf1IHYAzv7zuGWT95TYlGypga2kCQzTJ39JFPZWKgd9z1Dbk9XMQ5YTuueVEv+f9dhvAa/kI//9oOuZ1a7iib6mjBLZOfl286irbpjx0UfzVy0jLVqOnz//l4C4l8XlkfnIfcvO8YWnIuby4ecbx3XmOMEWPsNMbYFP43nzG2nzF2GWNsAmPsciHAudfOHYyxcYyxUxljS6W8HmaMjed/vy5kxToDsgZ2Im/YnseDQzhxYE9cOGGQcs7rG4OulQJxbq/yNCrLHPzTOScCiBd7Jx9GVPVUvodFbszlNdIe5IlGhwjZrLuZ9peeXIX//vOKgMoWpXnrv6imFOCBhbVYsf0Qnlm1W0kP8zdPORQrjG8+KGUCi7V+Q5wjJh5/c8tZGCGZDf/KnQqMC9v4f9+846a4TZbyMu/o8Xr0SWI5zcSmPJSHdmv6WkFM7UY823wesair/k7sPdKIS763EPe2cUezQmBX5BYQ2dY6rK/rFeHZhB2z3fRohE1fnEsELgjNNv72MmqAOt8g71daLLKSFils+sIcdeh4S2BCLh9NX6eCj7pkjTnslmaZKxQSX5bWBhPdJr5oacFat7Pq37McO6SFdqaRyqsb6lxfes0041+Tue0rlqav5uGm+TZ9L50lZ+tmjGG8FAkUAE4a1if++dp3k/dOW/rzsHUiIv7Sonas4UgaK/QLiKzp+77S/kSg40TsCGVAHEqkxqNpq3mnM5OVtEXhvePVP8ILxYQeh0VfeStefNn1kYVop4wxryNKEhbyOQ5PLHVX7e7QVlbL5hXBTQ+9hZ8t3BhYWCU7CbRnIlceIaU8TT+5dslY0Ozy4TPC4w6ZzpcxtZuqHmV5l8sJaX9i7m5TG1YVFwor9BPmuql+A8xkGaZyV0IxKeRNUBLBofw0IM/ODdW8oJh3ssHjAeDyk6MjFarXiX1owZDr5DhuXXyP8qBA7l2ZDs3rnqfDh9aMMS9EddS2j5u5CSKTZXCoAOYdZv4ch4+fPRoAUHNif2++B/Dbmp6fPBcjZJVoN1nGQCCkHIr006/dewR/XOKuX5DXN6k2fT/PpJxaGFggAFtVz3hC2jQ3JWvop47oBwCo8IIgtv8ZFzJUSVuxQj9hJp/Qz/vcmmWe9nXXX91VnfLkl0OU12y/d6TmZhemJaohIWJfplOFpHLI9d/JMuYJWtfvWz3OtLJS0NCsmszkW/6Xt7d7UUblyTbGgN+/5cddueR7CwG4100VwLwjC6RvP/tuxJFBxERoZXkKt1/su66KCW/9ecqjmLCJXMeJ1s4v/8EivLfnqJIHwM/R3Fpdm35ymn7vCrWDj9u2swyGuSC/3XgdYBvKms4j9lNHE64eWdqE3Lgz2WxAe5TNFimH8tzRyv1PEOYdMflmnshVPUKSE+UrttVjUcwtEvcebsT6PUdw4YT8FtrtOuR2lm7n6L6r6spP9b5GaVS6S6M8H/A7KRDdQzxUNODOrfzOEKTO7XwKa97J+1ypTcnPXGymoj97xVSmCToxZxHHvCNIBWz6fnkA91r5KDcmV1kAmFY9gHdK6s2PK6QzWQZ9LkixxUtKRb6IPS5OGa7uj9EZ/fat0E8YuQGalnOLRiDc4vLz65U0KHmhjWIaMAv9lw2uZKFXMZQpm2We3VIseorDtG++CADYfN/Vsc8BfGEsTCl6qAldkEe5zesda9gtP21kPy86Y9jLyph7H5LW9dvTJ8uT3nI+vvOAdi3II05/stX9x9PzaJuqpq96qIFfK2xxVq/yFM6RdpsDEOremGUMxBCwusS9dyYvIrnD8j9yM2oej1jc47W7DmPRe3W4yFuj0PmEftcZk3QRdOF09hi3QV/HJ5uUiVyuTY0dHM9P2Z8P4Pl7NltZo4fxs05U5EzTaa9EeB/ow+0kIaHpSzZ9gC/3l4jS9E17sJo4oZ/vtRQWV2jTvmOob2hJXNNvj3CQF7LJgvqGs8QCeL3T878HJnuZ71lmGoVmswxvaG7FunlH3/chmw2v39jBvQO/hd0JYZ0JuODGvHdZFnyqaUXo+xPP+SLfq+fW+K6/VuiXAPoiGzHsO3Gg6qfvmXcY8xZq5YJJL7fc8MNs91ENbn3UxhSG06JerCg3U8FTy9sWcYM87x3VVKWPoqKEcI9y9f6GVSUT0nnKiDULnWV+TnaR1MNVhLn0Msm8o+/NoE7kBm/Cr1/fjI/98k0lLXRxVgw/fXMEVf+zrBBlecEDi+3MWQcw1cek6fvKVMyMod532YuqE1p3rNBPGrWh+JpFhmumJj99vV2YYqm4efsvN0mRJ8NMOlENbu9hdXXmvJV+rBbd9Q+It99rVAjmzz6+POf5YYjFWbIlQjfvRHnTfPLCMcr3MC1e3XYy+Ls8P1NI7518z1Ns+lLBW4TQ187Zd7TJSwyGYfCdDExKw6a6YETLgE1flMfz3gm36RMMdZe+j+zvLxR0ZT4LmNbkOveN8OLKZoPKi+ynH7gXeTwUub3ICklntOlboZ8wTBO64pk3ZVSh78Y3MTfENzeZo1OIowiqthMWWtn00n71b6tRPWce+mvhH+74vR+r5SuGjSNyLdB6e+tBTPrKs/jm/HVt3uxCRllw42mwfqVN8WZMdv3DjS0BF784mr6pA9t/zI/FEkfki1FeHOSO6AMj+kYcqZJlzLvfDgGPSWGgW3gnpdd38ft++/I3URH58YncGLF3vDykGy+bUOSwzWGjTiIKdErzV/sKiLx2SnQosqPMr//+vrJp+cP/clZoOU3mHdl7xyutdC/iItcvoygPVuh3e/SN0cUL+YtX1Nj3wryTMQx9wzRRz7yju9nJgcJkE4U0YBjZvwcYY972b3HaYj7CZwkXJA8u2oSfvFwb+7wwRg/o6Ql+3XuHMSBjGA2Z7PovamFymeHFF8gv65HG4MhGDjega/p+2fw8THu3hiFO69+zDJOG5iP01TYlx9dp1UaXyvX0KJteOdy5E4fUTeD984IoNn1JiZHDNpu05js+OM4172i/bT/oBx2URxErth/yzE+Cr//fWmze7x4/ZVQVpo7ubyihi+ldk236YhHfGxv38XLHF9hyHcS+wwASiS6aNFboJ4xiezZoOLqffj4bTCjmHZhtj9kQLWP7weNG7TiKczWvirj8IEbQuFyIGDcAApuomMw7gNnO3qrt2Wry1fbyzXF7GICzxwzAtDEDApp+BR9NtNlMw//LweUEW/c3oHrOPLxp2PJSbj96/cUEtqlMvgnG97AR5RBhGOKaN/TYOwLZT9/U9O744HizeUdC78jlzl/nt7dOg+MQrj9zpBKDyCubwWVT7lRE3JxfcdddUZfeFWlvU58wZIXh56/424/qq8E7A1boJ4yu6etDZN9lkzwbbMCkGSaU+H/XvGN+0ZjheIHcAHUPDMDvMEb2d1+Yai364XciFg0lPbEpVr4ConOUtCnGQoR+sBCZrCr09e0OZXJ1vmL+RTevAb5Wn2/HKpCFsF6MZ7i54z8fXx4Ihy1Pyur1F7Zl09hGngeQvzPG8vbTly8rzzHImr6e1w9uOB09y9PGTk5Gf05Rj8gLlAbzszRVxxQzR1emXIcL+XcWMGGG3aqbHl5s/qEDsUI/YXSNVG8MstulMO/E1/TFucK8E9Tkorx35IZq2m1I2LRvPvdEAMD4wWpgq58t3BhY3QoAQ/oE9y1tL1nma6HCK0Wujmmy22TTz2jmnCz3dmmL0P/r29u98oS5iLbVhiubW3QN+1vPuJ3t7sON+Mwf3kF5yvF94OG6SIrqXCfFoRHmnWhNWi03Y67QDIunb6p1KmDT5x2r16EE27i4fURBE4h8qDxf0KcyzTV98713pDxNdc7IPaSh7GJhlb9Qzf2f5qMesXL75offwtgvzVfyCXvuudpsR2CFfsLoroVhG56kPD99xPY5k13z5GGxfI3HFm/Bmp2HAulA7lWRR7kd27MRGwSjKYZ+IRwUspIgE5q+F0sGIeYdg0hqblU36haav0nDy6XZfu/593I+q/Zq+inDxKbMe3uOojmT9cqayTJ+r9z6nDfeD9UdFmcfUM1JgHRvGZ9v4qbHTJbhkdc3e+s6TGVTAq5BVk78PPWm53haeQ5NX55H4ZmFLcSTR4amPE2jatEOylOOt6BKHCO/B+/uPoIZP1qEBWv3GEONx+nro55HMbFCP2GCmr1qMpHNCynHbYi6cA7bo9Mfkqv+zfLZm+qO4er7X1OOF+Ta8PkvXJP1bMSRR8vlCrrRtZdMlnkvvJjs811UzXURwuCGmpFe2veeW48/L9vufRceHCZNvznGIi7hMqhr4xOH9vbK3RbkBU1RWejutEs2H3BHRfxeTZDCDot7ZJzI9eaWVJu+CDMhvHeeXrkTd89dg99xB4Amw+S0GnDNF6xRm6iQ8mzD66u4g4J7F4Uf7uVpuofmFblu4U+oqjTsLeBr+gKTWVQ+NoqogH7FxAr9hNEXZwkh4O2cJb3cnveOlscTS90NNH62sBbVc+Z5e3X6eRPkph824RbQ9HMIJN2+G9dOXwBF39VelYlc9Tpml033eNkd9VhzRnFRFN4lZk0/90vJmFmoXHPaCTyP9mn6+qR1Lo43Z93nzKtz+qgqvHHXpejXo8wzJ0RlJ/zURbmF+cQNwwDUN7hzCGIbTzGKlFHnl+LZ9KNMMSK7NH9HPBjvdEPNO6IjMYcszxgUrLTUxk4c6K4JGONteOQeK3tENYasRYkTsqLJCv1uivTs9x9r9hpfa5ahobkVmYyvPaQcx9gQxdfvPLsegGtDlLPWwzCEySpd/uRqmCJf3/YfT+oXwhc5m4Vk3lG1RQZmtI96duKIcmcYw9tb63GwIehVkWsk5F7bvFG78Mlvs9Dn/4V7ar4ny7JxeL8eKEs53sjFlJ+ujYu2IVw2U6SOQkX++poH9zdVAdFj9Zs0bPGMTOYd+TzTrlxhT1eUcenmA9h3tCnQeRr99FNeo8HlJw8FAPzT2Se6xxveq7A1KGGvwD+fM9r7bDX9boosDO55eq0nBPYeacIp/+85z9TgEKHMIbRms8hmgWtOG+6d11MLG+DZ0YUGDiHgwofvQLCB5hJq/hwBv05MTT+bZcZj92kbS9/5xPJ4GcIVQkIgeQHXpOK3ZoO23Tjb3EUJ5Tj7mApTlp6LEFRJaPoHjubeH1cwakAPxaYvKEuRpOmrZZp+ylBlDgGAErJBNu/ozgOmW6uEYZDO8TR9g2LjdehOUGCKMMXlaUeZVzrWnPHK95nLJgTKIcooQj4f0+JLZVnwnZA1fbmzkf/HwdQZbDvQgL2H/XfAavrdlCzzhc4F4wcFGs6zPBhTSph3uCCuLEt5nhcHG1qwfFt9IG9/IpdQd6TJ28g7TM7obfaV9/ZGll0cLrQ+0wtueg/C3o2a/3lB+S72bI2DLMiER4s82ZjJBjfTkEcGYUSZTsICs/3041P988Gfb8AkoWrMFWnHOG8QWi6hHTukrPwN46Nn+vMWsk1fUJZyPBOYXitZYZBNMCLV363MMB1q6FH1KJuCtJS33kb9TiQY7uG0kf0AAD/6xynQt0nYWHc0dLtK/XbHMW96igV8xwXPJGtoDmGLv+Rjrz7VVeAu/M7LeH6tPz/XZTR9InqYiPYS0WopbQARLSCiDfx/f55ORHQ/EdUS0UoimiqdM5sfv4GIZhemOh1PljH0rkgj5RCmjKoKFYgph5BOEVq4FkQAelb4Gv61hvDF8kQuADy3Zo93zbCyyBw1rDJV8/eH+PmYGfTrTBlV5a38bSvZgJ++bzZYs/MwfvvmFm/HK4HsERLGa7XmTd4B/2UXk+4CMUk7ZlAv34dbujuXTBrsPRPRiZelnDx3RRN18DXVKESH1yq1H5l0ijxvkaiOTh+h6BO5+oRvrm5MjrIpm44ibfpaHuLeDuvXQzHvAO5ojBDs5Ny81DSmyVh9zQbgd0yizoC6ZkHHZN4CtHcg5CZ1Je+d3wCYqaXNAfAiY2wCgBf5dwC4EsAE/ncbgAcAt5MAcDeAswFMA3C36Ci6G4z5IRZasyzUju4QIc1t+t45OewpntlFa1VhwiUwpM6hecrDWjF5Ggf9sH1Hm/BVQ/yefMgySQsjdYJQUKG9gLLv95OfOs9LL5MCuNz5xIrQawrzjq6hOw5hWvUADOpdjuXb6gMue7dfPM4XnprAizsp69v0440OhPAR5hT9tPKUEzmR63UyjjpH4eZFXjx9fbWv0MJl1I3R/bw9TV/qPASygNbLN3+VOxrOZFmgzaYdJ+cmNl+YOck93zBXFjQz+YpFSnuGptcqTHAz3llOHNrb6/gD53YVTZ8xtgiAHgFsFoBH+OdHAFwrpT/KXN4EUEVEwwHMALCAMXaAMXYQwAIEO5JugfAvTzuuTTVMCxeeCS38GLchm1vyeePccAhyGIZhfSu9peHhmr77/4OTXP/j0QN6Go8TCO3cNxcE8zVdqkGzncaZEM2FvCKXyByuQhfOJB1/0jB/2XzcePrCHKLnu/9oM97afABLNh80npdOOb6mz2f/0pqpICfMPC8SRgUPx93KBaouHI80tmIBNy2YSiDSCOrqWzFqEPH09dHlmEHBvR/KQgKupaR7oN8GccqRxlZvlzSBmPc63NgSUITcNSrRDsJ9+P4OpnUqQXOVVCYuDeV7oRPWtsU7k3KcUEWvywj9EIYyxkQovN0AhvLPIwBsk47bztPC0rsdQksWmn7YbH/KIT7Zxjw3OZMbISBpdd4L6LqX9Qpp3ALRSQzjm4PkmqjcdsB9+cTEbO+K4IbTYYJJjqm/Wwvb3Bb0xVmuu140JP3PR4AKxP0p0/Y7bc1mjcIOAE4c2BNTR1f5Nn1+i3WvGABYt+sw6o6Y7fXC/1zEf8nFHn6PhUDVRwg76o8jy9ytB/XmsWW/H9CMCIFdsjzzDvPDXURtMOJvJK6OyMSErMlDTQjA5dvqsedwyBwGM3XsfpnD0EMkC0zl8LyI5IncKKEf4ionK3thHb3u2NBRtHsil7mSpf2qHYeIbiOipUS0tK4u/hZ/nQUxkSsefpii5zj+EFoMDW+5YIzxWLFnqL+AB8r+umGyPOu9fG5jNmm8Yks99Ty345o0rA9+HRGqtpCoAddUW7Eg4AYomXfimklkhBYXEDQgnDN2IAb19sNNiGt/6pJxisAQL7xv2vDzufLHr+LS7y00XjvK/9xEvx5uhyxs+mEysElbkQy4HYucJO+SJUyNYg8D3ePEZK6SzWzy3Iu3BsAwSntW2l0qDAaGFdvrlbTGlmzOZ6uvN5HLrqfJk/8prWMz6VJho8asZBYLW5WdKzx5sWir0N/DzTbg/4VbyA4Ao6TjRvK0sPQAjLEHGWM1jLGawYPz20y7M+AFrHJc74mwXj/FbYhiCO0QMFCLcS8QL56s6cv764b6DvOXT0x4mnzbJwztHUiT7ekfPGmI8ptenwrJtpwksj3XD7gWLKcJV2jlf02hxckba7j5iU7c/f1S6Z7IHkai3IDv/60LuyMhu4wxFpz/GxsyugCAK04Zyq+X5ffBXOGmlozxPnmeYBCTtvDK66a5dRH7CjRr+0HIyEJfFqyyeUc/La6pw7i3BAEHj4W7terPQpDR/X6hKQr6mgWDLhu1wZFoJ0m/C0nTVqE/F8Bs/nk2gKek9Ju5F885AA5xM9BzAKYTUX8+gTudp3U7hAAvSxF/Ic0NQGjrQguKslKKCI7yQhl5k4twP333v5jINGkpYYHHZGXqodk13md9eCtMT3E30Y6/n6m8wtJsFw6rN6Ftmn5LiKY/sHcF0twU16cyjdEDegYEmz6R65k28pjI1Yt8ckQ4X897J+MKMv0xfu+jpwNw205UlE13VKRO5MpKhRDOQvEwyTwiwiv/fQkAbt7h6VF++p+4cCwAbbMcqGG5GQN6lAW3EiX44Y9NeHvzGmz6AddRqM9PHkGbLDlhWrxq1lVPFGGeRSDDjiaOy+YfALwBYBIRbSeiWwHcB+AKItoA4HL+HQDmA9gEoBbALwF8CgAYYwcA3ANgCf/7Bk/rdgRs+iEvvQi45noUuC+e7o0iENqW70mhTr6FCRYvdogQ+oZWXHNi/0AaY6rQvOzkofjFTWcCcIWM7A0jNhuJ643291pz7BJT2cVlyhwHrdmgmULX5Hz7bNts+gLdpj9+SG93Yp5LNPneyB5GbpnUiVwhQEzbDMq4mr5a6LA5HsAfvWWyjK9eJu136ZlH9jsUVCC4eSyblYR+S7imDwCj+LaGWcl9R/aG0WXl0L6uqeyM0f09s9neI424/8UN3jFZBnzlmpODJW6zeSeY5rmOSt99751gXcPmxbyJXAra9Blj6MPduDsD4RtKchhjHwv56TLDsQzAHSH5PAzg4bxK1wURDz+XTT8thcfNZF2TUFhjFi+c70lBSujbXEvDhYBoaVWP+8yl4zGgl2+nns01kYxhtWuVYkMOXivu6sXHFm/BBRMG5TxONu+kpQnvONeMupdxcAx9bzrldjwOOdy3XIy6/IlAt9zu8fqip5310ZPbDEH7TpQLb7kw2YXY9GUt23SX5DSHSDJpSCYfxjwNXygeYSM1UVTG/FGLE6Hp+5q1/xx3afdIDsPQqzzlrbAlAN/88Kn40pOrjGUR74OulZsmchX7Dvw1IaIuOibvnY/+/HUs2XwQfSrTSKcITdp7xuCOzLq6944lBHlxS2sm3HtHXrHZkskaNdPPXT4RHz5jREDT170EcvnpezZ9TdMnzfYtvHzkSVRBWhMygvE8qmNcN81nVu/G6h2HUD1nHrbsD4Zp9souuWyWpRy0ZIKavn5vHfX9bTNpg9Qv45o+k64DQJlsBqSJXC2QWc4yGWz6QwyT7ALPrMY7Yb2TE8Ky1WBPP21kP8X9V9kli/kTuZls0LwTpsS4Ha1v0xel8bcEDRH6kmast7lp1QO8+zZJMgM55E9km/jVa+7WpH9etk1JN22XSNp/xUEipqYvXHk9s1hA07dCv1sjTCPl6RSaWrOhNkB5I4zWrNmmP2ZwL/TvWS4Nrd10T9P3hqHmsvhC32zTl+ONyMebhIgwNbRmssqLc0JVD+XcOAg/7BfWhYeFyDJfcAnTirjE/1z7AQDBl1Je2h+HL848yctLxjQMTzkOGOPPikgadYn/qh1ZhOzNMjc43CPS5t0mGIImGtkGXJ52lIVRFdKKXNfzx1wHk3YrCyXif394axvqjjQpSkvWIPSj1h14E+6SJ5Ir1INt1LtvDkn3TK1E/17lXj7yvZGVlZOHB+c9RHgSfatC0/4W8ryRXN6WTNbLR4ThIIpe8yE8gMTaCe+6cEOGdKUVuZY8EEPtirSDptaM8SVJc3dNWTsUn//pbD8qX3mKUFHmSFqWr53Jbna5FmcJzVXXUhwCFkrxeLzwugZzgTcvoOVRLtmW47J212EA0TsJZZhs3nHcURO/hJjc06co9E7uu9efFlmO2y8ZhynSJtYCec5i3mcu4GVw05pbzaMykSY6eXlx1qNvbFFisJgwBa0b3s/X9D8ydSTuutK3b/tCPRuYg5HLK7Z4lNGf1V6+duCb89d5phnfvOOOMptaos07gOxaK9nIufkmbHW4I7Vj0xwGaceLNNEZmMIiXH+m6yh46oh+Srp5ElqrA++EPv/ECnz3OTfK7bQxA7D5vqvdcNVSozONXoSmL99jxtz3xGr63RShJVfw4Vwmy/DhM0bgnLED0JsvphIN9Zevvu+dJxqfHE+mLOWgMp3ydkqS46CkJU0/TOAyTQjqQtZxCC+uCwp9U9RG0XHoG42Up82dgeATF4zBTz8+VXm53uLx7SsNnhnzV+3Cfh4W1/HuCaEl63uh9OBRSHVNX5Rf3N9/OP0EY5nU8gdfAVnjnDTUNSukFaETFE76wh55A5H6htxRM4Ut/UtXneRfR7ppf3hrKzbt8yeDvZFXqE1fGgnoZjHJzCFfw1vdy0012azvqtnIBdaBiLqQp+nrmnNwHkiUX3Y9jkKZ3yA///JU8FlMn+y6sw7r10Opu8l1VL9vouOau2Jn4Ji04yiafsATiNx4Wq1ZdQ9na9Pv5ojhcUVZCovfP4DdhxtRWZbC47edi7GDXb9rIWjO5+EVAP8llQVOWcpBBY/T3tya9RqZZ97JhNse3bL4+QBBga0L9qzXiYRrjnrDLffyNjdoxyFcfdpwLP9/0720wXxPXeHBITjW1IpPPfY2LvnuQnfnLKHpc9OKEOpC09dfd1FfUSa5UxHxWAQ3njVKOVZG7nhT0mhDkHbk0Mqq+UG87KKjzWYRa5KBcWXhY9NGhx5TL+0BoIc40J+X5zlj0PTf23NU8dMXZCUvG7FdojAtNrVkwBjD797cGlo+R7Hpy6M0fz7mex89HVd+YBiqepbzc8gbsZnasehsxOpzt8y+WdLUacumSFkwy+adL8ychF/fclZwLsRgkxf3Wg5XLfLT65923Pq2aMdVWPNON0bY9BXB4f4XwkT8NmvKiMAxaV3o80bd1JpR/fRlj4scNv10yOIsh4B/Pse3GyvmHa1liHLpQt/P21wIseJXnngToQj01Z4ijyNNrUrsHb3DER2hHmVTn7hWftNe5G9dd6p7rEFoyL7hQijIJh9TCIDA4izJT1+sqI7C3YYR6FMZPkEpm3vEyMs1ewVngdWFUcFnIwRtcAMUzU+ftxkxao1CiYbKsxXCThTh0pOG4IF/PlMqJ6JHrPy3/j39+yI6FwDe3r2muuuLI+XYOycP64sPThoSOFf23hGINrDrUCP+pGy9GbxuOkVoyajXzTI3VlKcdlAMrNBPGDHUljdCES+otwkzFzRCeAG+ZiYLrPI0edpqY4uvtRARUql8vHdULVTgECkNX/ZPDmr6brlWaHH+l2x2TTVh5p0oE4vegaj2Ul8gifILASTuUQ9tsxmvvgZBPl7aO/bhf6nxbcKGDkLfxAZQR2BphyTziPq7sIGLIGRZxvDgok2B/HSYsO9oyJ2NWIULaMHBctn0pcd+2sh+7mI3oUBI1RcdBBG8NSSeph/hlOCViYKb3aQcwuNLtkkxfEzn8HZnaEI3nDUK/3bxWHzuioleGhG8YHJvb60PnFOW0jpEqX7edzGRHEPTl8vcq1yOMxR8n8SaEtkMlHLUubmOxgr9hBECc4AUUsHRBLoQ+rJGKTRD3aYva/q+TV9MFKseFR/SBKxok2UhJhgiNQiUPzFsECK85eseEeKosAYdtR5FL0+rppX5ox+xzsA9vn/PckwY0hv3XXeqkr9n05cE5aZvXoVnPnshZn7A35lMtslXlgVfgUqD0JcXbKWkSXjxTPzO2RX6ac+8E3+C23SrRDRNQDVlpCWbvcmm75ubfC177qfPx2UnubtmiVGVvgGKGDSITuw4r09TayaneYJIclHlabsONYIxYN7KnfwYtaDe/sfMHIa8Ip3CXVeejL7SSJFAkQLU1/RVT7PmVv+77rUjl8ekwQPuQsbTpYn/gPsn+WtKZAWmzCFUplPGTeU7Aiv0E0ZM5Pbv6Qt9IQCE1lZu0FQ9E1BaNe8IYdLUmvUEiEOECu4SCvixvPUVvbor3FYpuqJIl00VQuhmWLgLYP9eqvlB2Ofloes9BjdIILj6V38JFKEvLc4KavqEBXdejOmTh2Hl12Zg9ddnAAh2coBritHd+uTfZaEq6FkWXLMoP5eUQ54AFs9APKdjTRnlGqa9eE2ImE06v711GgDg6x+aHOh4ABF7xzTx7o80xF0lkFcPMSKRRzBif1th3gEkod+SzbkWo4wvYJMncgUikmbYIjLd40VHnsglAm7i7qym6Ke+TV/V9FsyvqlL5Kbfcccxrf/wzYzyPdBnS4j8NSXycWVph2v61rzTLRFeJ/16+IJDnwysMGj6noBzzJr+8WY/cJYjeQcJDcldG6A+TtHeRR6rdhxSfk87qh9KY4vfiQSW9TuqkBPcyiODykL/JmmeQM7nax+arJyra47y5hOyeUfcNzFklkvduyLteUX53krRzVo2mZQZvD96lAfPL0/J5jryQgeITkPc44bmVqUMC9bmjiYJCD999/Pzn7sIf7ztHABumIJVX5uOm889UemgVe+dYH4pSfDJC7H05yg/HxHSQZh3AN9m3tiayRmaW95tS/dwEp2HyZwCqJE47/3wB/DmXZcZjxP1EIu1/mu6OkEP+O1l75EmZfTQksn6HWCIeafMcdCSDQpzgAt0SYNvaM7gT0v9BWCM+WtKZAWmR1kKFWnHe786Giv0E0aYRgZKYXiFliI0fM8mLZt3DBOG5SnHGw0cb/Encsnx5wOaWrPIZN3z5dC/blnc48XQeNxgVStKOY6iCQlf7Gw2GAIg5fnpu8cLrV2sGg1r0P0lM9fEoWpwrSibfks2690nfRI5lzNMTqEvdY4m7bpHeVDTlztUhwj/PWMSvnL1yZjO7exC0xcbyojnqD+TsPgrWUnTnzi0D84e63t29aksC5pFHPLMKaZO2rSBiTA/AH4nLZfHDc7G54x4dYWwbsmwnOaJMoe4Nh18RmKrzrBytkqTn2MG9cKwfupqZMVllgh9K8uw+b6rcfVpw6Ej2u4f3tqqbJloMu/I3nGAcK1UNXJ5vk3W4B967X38959Xet+zWeatKZGdJirLUqgsS1lNv7si7KtXneo3RtGwhQAXAqRSMe/whiUJl7I0eR3DcWkzDGHeAbjZh3vb/Ov5Y7xz5ZWzDhH6VqYDrntphzBj8jDcUDMSvSvSnoajR9kUxwLwXog7Pjgem++72tOyxYIrwcL/ugR/uf08JU3XqoNCX9X0RbwfIcQ38qBluYR+eTr6ANPkrUxPw/oBxZ6eIvSqSOMTF471tG8xN+ALffM1wswYBgccI4u/dBmWfPlytxzSSuUwb6tWaWaVQF653tjoBr6T+6BDx1u4lu4LRbmsR0PCQnvXFO6ZCNZFjOr0Zye8un62sNZf42B4wKKTgyFvHWFydDeS8cvfLO1kJ/Looc3FiLky+b54I06HlNGO7g2XYcxbUyJP5H5h5iRlDq6jsUI/YbLcoJlyCP9yXjUAX6Dri7PUiVxfmxCUpRz05Fqna95RJ3IB7srJ3Rv79SzD9WeOBOBqZsrxZSk0coF0AteiUo7rHfSd60/HCVWV3kRp1ESuZ2LhP4dFBq0e1AtnajZ8XVsNTORqNmPxTWinT6/cZcxHPz63eSf69wG9g/sayB2WSVv3bfqqecc0AjKZSUx2cBND+1Z6Qk2YU158d2/g3smLw2RPI1GPxXyBnCxMjza1elq6XEex/eCx5lxCn9CSFZq+e/5jnzhbOUZvV6Kz/OnLGwP7C+uIzjrXfQoLQdEiKUKifEIRE8+pgk+4ym1MNu/oiolMJuuvKRFt+5c31+C8cYM8l824ocULiRX6CSOvJBXCXTRC3RXQtAioXBP6PSSzgW7TB4CDx1pcTxfeMkV88pasupirPOVg5yE3imGZVi5xLWFeESMHmRQXDr6JhZQ6tgVd09c14CeWuPZSPQBaLtkYJtTlRTZR9DKYd4R5IgzPpt+imnfEkP7WC8bgK1e7YRRMXjCuLIij6/ukHQevrHd3l9O3WVRs+jxNTDQqxxHh3y8eB8DtsMSksGxOEebBXJp+mSM0febV5Pzxg3DRRH8zJF1gyxPp4vmHbYUoBHTc2EqnDO+r2PTFHJhcjp6S+RRw23NTa0bR9EVbd33w/Wend4KMsYD5THyvLHOQZeHx+IuJFfoJY/IvF8NWsfXbwvXBbSBNmn5F2vE0oUbZpk/+vqQzfrTIHY56q1e5liNp+kTqBKzoSGTvg3TK8VbsNjS3BjavICL0Lk/jcKPrjeJ1bDm0Zh15d7DfvL4Z1XPmeUve9Xj/owe6MdqPacJG39lKJ0zopw33GABeuPMifPJC3zRm0iSnjK7yPushgAHJpq9p+uK+Xzd1hBTi2jTMz29jdMBtM0cazd5Bpl2rHKKA4pFyCF+YMQmThvbB/mPN2He0ifuV+8f1qXQ7Qb3j+/JVJ+P1OZcqebVym74slwca3JcF8rMQI7mwkNLC7BbnPp03biB6Vahukj9buBEf/9VipRw9uKeWbN5pzpi3ZHRX26reQDLCvAP4nYiYOJfNsR2NFfoJI/tMC48PoR1E7eZkmsgtSzme3V8eGsqaPgAcamjxXvKUFAJZPl72xxcak9yAy1OEltYs6hua8eamA164WJm+Pcq81bSiEeuavmlhk8yN00YF0j7zh3cABIXhl65yNeNeFWqeFanoa4R1RCJdL/P4IX28aKGAq+n/+MYp3u5TADCkjz+xaNqMRjyPNzbt52V2hcnC9Xu9a4vrmiI1xrXpy6QdCoTW8H/zVwTLdmy97m6cHcLMDwwD4LqYphxSOn1hTtI1/YnD1PtWlvL3h5XrIrsv60p8g6Qti+irYeYd8S7o79E/1owKhNnoWZ7GsaaMErpCRtf05dXeTS3ZwMI/gMeAkjR9U/RScd+FKVXW9AF0ilW5VugnjOyFIXyixYspJneF5gRIrmO8ocvL8FN8UQcA/M+8dYq5Ro4rc7ixRQoKJSbwVPOOjHih5aGmMO+ccc+C0Lr1qUx7ERmFkNP93Fd/bUbo+QDQNyLMgG72EJN8p46sUtLLQiZqKcfv4gU0db6yoKnqWYZZU0Z48yM6pgVXxF1mhW1YlH1j3TF+bcdfJGfQ9uSwE3FJOYRDx80B0IRFTOwB4JbR3+kMcNdTiE7g2jOkkCBa+xrZ3xXs+ohL18jLUr4bsWwTHyCt7dDnY+TO1Ct7yH0Q7VbvuL59/Wn41CXjlbReFSk0NLeGmqTEFYTQF++fWP9imsxPp9RNz3WtPZsNavppKV+gcwj9nDtnWfLDnQR1P/vbtrkN5cazRuGs6v6KdkRwJ/GEENU3h5A1f3liVtaojzS2etcSL+bTK3ZheFWld7yapz8aEKRTDo7xiTwA+Nsd5wfq1rdHmWd3FUJBt4+H2WMFct11dGEo6lgVuCfRNnvTJigAcO64gZi/ardxJCAL/QEhG9QLwuyylVIkxb6G5+hpewbXvdYsy3t+JM1dJM2/cU1f2XyGvPv/patOUtZTjKjqwSNMuvNHsqZ/At9cRwhQETFSX5y072gTNu9v8EY7gv4R9/PsMQNw6wVj8JC0522Ypi+uH6dz7FmexrHmTMCrTCCek9DoZbfqptaMMQaSG2VT8tPXOpQMY94aAdH5i+9i5CcW73UkVtNPGNln2t81ibz/44f08TxyAD9okxASuruhrBmJmOtE/ggAAA4f9807Yr7g3vnrPPONnEePshSG8OF6RmrAfSrSONLYihO5HV2PRQ6oWrropORNseNwzWnD8etbzvI0ILFa9lhTa0BzEtfoWZ5SJhbD9o6tMMxVyPzghimY95kL0K9n8IWWF8WZQj7LhIVWkM8boXVu8qS8KUjY8eZMzglmnZR0/JwrT1J+E55ij765xUsjAs6qHoC5nz4fn7hgrHJ8edrx2uI72w4qK6/F3IowlcgbuMh88KRgADMAGNAzXOg7DuFOKa5OFCLMtWyqDKNXeQoNTa1YFyL0a6oHAPCfmXh/Ksrc0Vovg3mnPK3a9A9rcxwtGeZ17GLuK+2N4Pm8SFO8FdqFxAr9hJHdHU/hAm3MoJ45z+vLG8WJA4PLyj/PX4qDDS1e3pNP8EMLbNp3zEt/+F/O8tLvf8ndZNoh4K4rT8K1U07AuntmetqN/M4O6l2OTfuOYcv+Blw4YZBR25KH93KwOFOZwyByg7wJgSE2Mdl7pEnR9B+aXaN0luKlGdS7PGAiEPzu1rNxy/nVGKwtiBJUlqUw+YRgZwb42v1Yw7J+wWcvmwAgPJS13jHJC4fSDgXi8whaMlk8u2a3ZwqKy7YDxwG4HbnwwBGIUcPW/Q2BBUmnjayKjBT6zQ+fqiykEwJyPfcQEqaKjDbKuPsfJhuFZZSmD6hhk4Hw4H0f5xsM7Tp0PDI/AOhZkUZDSwbVvMNa/fUZ3oLCr39osnd/ROc4a4obt6pvZRmONLYY27+7LsIv27It6rxXJss8xei5Ne5KbGFS7M3b75EcXmDFoOhCn4hmEtF6IqolojnFvn6hkRc2XTJpMP5y+7m4oSY4eakjhpNlKUcJIwv42vDGuqO+6cghzJw8zDtGKKrlaQd/5x4Vm7gQSTmEf7t4HH504xkAgOumurbqy072NTN560Ldc0dQJZUrKgRwPpzCO6+9hxs9of/Ev52Ly04eqhw3lK/8/dcLxiCMScP64O5/mBzaKUQxkPvmmybwBJ+8aCxmTB6Kz3DhryOLwCxjnn874LrJVkoL7cRGMQCw+1D0pum5CDOHzJw8DNWDegUWJIUhRqAj+/dUTGgjqnpgUO8KrNl5CIDfuZm6vjXfmBlIGxqx168JMdrUEXMLqRDznUyv8hQYA/YdbUZ5ykHvijQev+0cPP+5izCbr58BXEVg2Vcux9d5iJBBvcuRZcB+vlXiPbP80CEV6VTkdomA/168w6N/CjObaAu53F6LQVGFPhGlAPwUwJUATgHwMSI6pZhlKDSuCcbXUM88cUAsISRP7i6482Is+u8Pet+rufa5qe6YktfPbzrT0/jlSbURVT3w5av8rfWqeqia1pRRVdh839WKhv4NqXGHeeDIAlfX6Ab1Lvdi5+eD0L521B9HE9fwqg0v/Rmj++eddz4I81pUHXpXpPGLm2qMk4+AKlR7VaQVDbbM8d1vv/zkapz5Py/g839aAQDYWe9qrgNzaMQ6t1/iavdhgmRYv0rsOdToLzwyRBSV+fSl7mToCVX+Hghijql/zzLPnPEhrhVPHNrbkEsQ8TzlNh7GG3ddGqpQjBvcG1++6mTcGxLQT6Ynv/d7Djd6WnY65QRCgQDAwN4Vnu19EDd9btnfgIsnDsZN51Z7x4lOJ4q+PdQ6lmma/qNvbAmcU2yKPZE7DUAtY2wTABDR4wBmAVhb5HIUjKNNrRgRo3EIvnXdqZi7fKdikhjUuwKQ3qfRA9yXZvfhxsAKWKGVb9YiaH7yorGYPKIv5i7fGWiIJi47eShuOudE/PbNLaEhdM/iw/whfSoCHdlrX7w0r83Rl33lcvdeVfVAz/IU/rZ8J87gph7ThKaIh7/jYO6hfVsYN7g3vv/R05XRT75UD+qFnYca8R+XjsfEoX2UOZB0irxFXzu4kP/r2zvwT2efiC382f1ZC1uRixvPGoUHFm4MDV89tG8ljjS1Yke9m78peqjMv188DrecX+2ZbxZ87iLPC2XG5GHYsLcWAHBDzSjcecXE0LmPR/91mtIREREW/tclkXMlv//E2Xjkjc2hHarI55MXjQ39XaY3d/PdVHcs4PIbxdhB/ounz72MGxLeyZWlCB+ZOhLD+6nvvqizUCbeev8AFr1XhwsnDDIqg4wxPLZ4Ky6eOBijBuQ2C7eFYgv9EQC2Sd+3Azg75Ng28+7uw/j0713fb3VXegkW/Bh2LFOODS7Dln/fUX8cF0+MLzg+Nm105BZ5gCsEz6rujyWbDwa8Qm67aCyW/naZsrhIcN64QThv3KDYZZkxeRh+++aWyMa24u7pim+1INfkp87A3hVeULrPXT4R33xmHRa9V4e0Q0YTy8wPDMODizbiuqkjAr8lxUdCXDTjcs1pJ+D1jfu9SfAPjPDnXdIOKff1X86rxm9e34yPPPA6AHd0NTrPl3z0gJ74/BUTQydQTxruarU/fXkjylKEnjGEn+yCO0Qa9Xx++kS8sG4P3t93DEP6VkQ+b3kFrqA6Yq4EAM4bPwjnjY/fVnMhNPr1e47gtJHmeRwTJw/3RwL6e3Dy8D6o5BO9MycPw7Nr/Aiqf/r387z5KSJfJghljojcUcr8dbj54bdAFDSjlqUcHG5s8c792LRR+NZ1p8Uue1yomLEgiOh6ADMZY5/g328CcDZj7NPSMbcBuA0ARo8efeaWLfkPhzbvO+btZO9mavyoxtfw0uIfK38RS8MryhzcfvG4xHvpTJZh7oodGDOot9e4koYxhnW7juCkYX1yul4mzdLN7oKwk4b1CRVinZ1MlmHl9nqczidKGWP407LtmDS0j7f5Rmsmi7qjTRjerwdeWLsHa3cdxrHmVlw8YXCiQg9wn+dza/Zg3a7DOKt6AC6Y0L78Dze24Ehja8AzqbPy5qb9eHvrQVwwfhBO09Z6RNHYksHfa/dh2pgBAVPTlv3HsHTzQVxz+nBs3HsMowf2xNLNB3DxxMGejNh7pBEvrtuLU4b3VTZdac1kMW/VLrxeuz8w+mY8RAMRULv3KEb274Epo6rwj2dFK4RhENEyxliN8bciC/1zAXyNMTaDf78LABhj3zIdX1NTw5YuXVq08lksFkt3IEroF9t7ZwmACUQ0hojKAdwIYG6Ry2CxWCwlS1Ft+oyxViL6NIDnAKQAPMwYW1PMMlgsFkspU/QwDIyx+QDmF/u6FovFYrErci0Wi6WksELfYrFYSggr9C0Wi6WEsELfYrFYSggr9C0Wi6WEKOrirHwhojoA7YlQNAjAvoSK05kplXoCtq7dFVvXZDmRMRaMh4FOLvTbCxEtDVuV1p0olXoCtq7dFVvX4mHNOxaLxVJCWKFvsVgsJUR3F/oPdnQBikSp1BOwde2u2LoWiW5t07dYLBaLSnfX9C0Wi8Ui0S2FflfefJ2INhPRKiJaTkRLedoAIlpARBv4//48nYjofl7PlUQ0VcpnNj9+AxHNltLP5PnX8nOLtlsKET1MRHuJaLWUVvC6hV2jyPX8GhHt4M91ORFdJf12Fy/zeiKaIaUb2zEPTb6Yp/+RhykHEVXw77X89+pC1pNfcxQRvUxEa4loDRF9lqd3x+caVteu9WwZY93qD27I5o0AxgIoB7ACwCkdXa48yr8ZwCAt7TsA5vDPcwB8m3++CsAzcPfvOgfAYp4+AMAm/r8//9yf//YWP5b4uVcWsW4XAZgKYHUx6xZ2jSLX82sA/stw7Cm8jVYAGMPbbiqqHQN4AsCN/PPPAdzOP38KwM/55xsB/LEIz3Q4gKn8cx8A7/E6dcfnGlbXLvVsi/KyF/MPwLkAnpO+3wXgro4uVx7l34yg0F8PYLjU8Nbzz78A8DH9OAAfA/ALKf0XPG04gHeldOW4ItWvGqowLHjdwq5R5HqGCQalfcLda+LcsHbMBd8+AGme7h0nzuWf0/w4KvLzfQrAFd31uYbUtUs92+5o3jFtvl643bSThwF4noiWkbtfMAAMZYzt4p93AxjKP4fVNSp9uyG9IylG3cKuUWw+zU0aD0umiHzrORBAPWOsVUtX8uK/H+LHFwVucjgDwGJ08+eq1RXoQs+2Owr9rs4FjLGpAK4EcAcRXST/yNyuvlu6XBWjbh14/x4AMA7AFAC7AHy/A8pQMIioN4C/APhPxthh+bfu9lwNde1Sz7Y7Cv0dAEZJ30fytC4BY2wH/78XwJMApgHYQ0TDAYD/38sPD6trVPpIQ3pHUoy6hV2jaDDG9jDGMoyxLIBfwn2uQP713A+giojSWrqSF/+9Hz++oBBRGVwh+Bhj7K88uVs+V1Ndu9qz7Y5Cv8tuvk5EvYioj/gMYDqA1XDLL7wZZsO1JYKn38w9Is4BcIgPd58DMJ2I+vOh5nS4tsFdAA4T0TncA+JmKa+Oohh1C7tG0RDCifNhuM8VcMt2I/fOGANgAtyJS2M75hrtywCu5+fr90zU83oAL/HjCwa/1w8BWMcY+4H0U7d7rmF17XLPtpgTH0WcYLkK7sz6RgBf7ujy5FHusXBn8lcAWCPKDtd29yKADQBeADCApxOAn/J6rgJQI+X1rwBq+d8tUnoNb5QbAfwERZzoA/AHuMPfFrj2yluLUbewaxS5nr/l9VgJ9wUeLh3/ZV7m9ZC8qcLaMW8nb/H6/wlABU+v5N9r+e9ji/BML4BrVlkJYDn/u6qbPtewunapZ2tX5FosFksJ0R3NOxaLxWIJwQp9i8ViKSGs0LdYLJYSwgp9i8ViKSGs0LdYLJYSwgp9i8ViKSGs0LdYLJYSwgp9i8ViKSH+P8CZSSEiNLNnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = pd.Series(\n",
    "    np.abs(np.array(audio.array))\n",
    ").rolling(250, center=True, min_periods=1).mean().iloc[::50]\n",
    "yc = (s.values - s.min()) / (s.max() - s.min())\n",
    "xc = np.arange(0, audio.duration_s, audio.duration_s / len(yc))\n",
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "efc30c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 3))\n",
    "# plt.plot([0, audio.duration_s], [0, 0], \"-k\", alpha=1)\n",
    "# plt.plot([0, audio.duration_s], [1, 1], \"-k\", alpha=1)\n",
    "for _, (ts, te) in TRANSCRIPT_TSS:\n",
    "    plt.fill_between([ts, te], -0.1, 1.1, color=\"k\", alpha=0.1)\n",
    "#     plt.plot([ts, ts], [0, 1], \"-k\", alpha=0.2)\n",
    "plt.plot(x_vad, y_vad_smooth, \"-r\", alpha=0.7, label=\"vad\")\n",
    "plt.plot(xc, yc, \"-b\", alpha=0.7, label=\"audio\")\n",
    "# plt.axis([0, audio.duration_s, -0.05, 1.05])\n",
    "plt.axis([0, 4, -0.1, 1.1])\n",
    "# plt.legend()\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "06a8401c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<audio controls=\"controls\" autobuffer=\"autobuffer\" style=\"\">\n",
       "  <source src=\"data:audio/mpeg;base64,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\"/>\n",
       "  Your browser does not support the audio element.\n",
       "</audio>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "audio.play(1.5,2.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "746daac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import webrtcvad\n",
    "from suno_utils.utils.vad import N_FRAME_BYTES, VAD_SAMPLE_RATE\n",
    "\n",
    "n_subdivide = 16\n",
    "\n",
    "x_vad = []\n",
    "y_vad = []\n",
    "vad = webrtcvad.Vad(3)\n",
    "for n in range(len(audio.bytes) // N_FRAME_BYTES):\n",
    "    for nn in range(n_subdivide):\n",
    "        from_idx = n * N_FRAME_BYTES + nn * (N_FRAME_BYTES // n_subdivide)\n",
    "        to_idx = (n + 1) * N_FRAME_BYTES + nn * (N_FRAME_BYTES // n_subdivide)\n",
    "        frame_bytes = audio.bytes[from_idx:to_idx]\n",
    "        if len(frame_bytes) != N_FRAME_BYTES:\n",
    "            continue\n",
    "        y_vad.append(vad.is_speech(frame_bytes, VAD_SAMPLE_RATE))\n",
    "        x_vad.append(((from_idx + to_idx) / 2) / len(audio.bytes) * audio.duration_s)\n",
    "y_vad_smooth = pd.Series(y_vad).rolling(16, center=True, min_periods=1).mean().values\n",
    "plt.figure(figsize=(16, 3))\n",
    "plt.plot(x_vad, y_vad_smooth);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6c547b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8500674",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ad931d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73406898",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "143e513d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c1c8f85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba157eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import webrtcvad\n",
    "\n",
    "from .numbers import interpolate_1d_time_array\n",
    "\n",
    "\n",
    "# vad defaults\n",
    "VAD_LEVEL = 3  # 3 is less speech, 0 is more\n",
    "VAD_FRAME_DURATION_MS = 10\n",
    "VAD_SAMPLE_RATE = 16_000\n",
    "VAD_BYTE_WIDTH = 2\n",
    "VAD_N_CHANNELS = 1\n",
    "N_FRAME_BYTES = int(VAD_SAMPLE_RATE * VAD_FRAME_DURATION_MS / 1000) * VAD_BYTE_WIDTH\n",
    "\n",
    "\n",
    "def get_vad_speech_frames(audio, vad_level=VAD_LEVEL):\n",
    "    vad = webrtcvad.Vad(vad_level)\n",
    "    vad_audio = audio.convert(VAD_SAMPLE_RATE, VAD_BYTE_WIDTH, VAD_N_CHANNELS)\n",
    "    is_speech_frames = []\n",
    "    for n in range(len(vad_audio.bytes) // N_FRAME_BYTES):\n",
    "        frame_bytes = vad_audio.bytes[n * N_FRAME_BYTES : (n + 1) * N_FRAME_BYTES]\n",
    "        is_speech_frames.append(vad.is_speech(frame_bytes, VAD_SAMPLE_RATE))\n",
    "    return is_speech_frames\n",
    "\n",
    "\n",
    "def predict_vad_speech(audio, vad_level=VAD_LEVEL, frame_duration_ms=VAD_FRAME_DURATION_MS):\n",
    "    is_speech_frames = get_vad_speech_frames(audio, vad_level=vad_level)\n",
    "    is_speech_arr = np.array(is_speech_frames).astype(float)\n",
    "    model_frame_duration_ms = audio.duration_s / len(is_speech_arr) * 1_000\n",
    "    is_speech_arr = interpolate_1d_time_array(\n",
    "        is_speech_arr, model_frame_duration_ms, frame_duration_ms\n",
    "    )\n",
    "    return is_speech_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2026fd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd1c6767",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1069afc1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63e8ce9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b5e0e16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "588381ef",
   "metadata": {},
   "source": [
    "### Try nemo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c36e1c22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2022-10-17 20:34:57 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_marblenet/versions/1.10.0/files/vad_multilingual_marblenet.nemo to /home/georg/.cache/torch/NeMo/NeMo_1.12.0rc0/vad_multilingual_marblenet/670f425c7f186060b7a7268ba6dfacb2/vad_multilingual_marblenet.nemo\n",
      "100% [............................................................................] 501760 / 501760[NeMo I 2022-10-17 20:34:59 common:910] Instantiating model from pre-trained checkpoint\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2022-10-17 20:34:59 modelPT:142] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
      "    Train config : \n",
      "    manifest_filepath: /manifests/ami_train_0.63.json,/manifests/freesound_background_train.json,/manifests/freesound_laughter_train.json,/manifests/fisher_2004_background.json,/manifests/fisher_2004_speech_sampled.json,/manifests/google_train_manifest.json,/manifests/icsi_all_0.63.json,/manifests/musan_freesound_train.json,/manifests/musan_music_train.json,/manifests/musan_soundbible_train.json,/manifests/mandarin_train_sample.json,/manifests/german_train_sample.json,/manifests/spanish_train_sample.json,/manifests/french_train_sample.json,/manifests/russian_train_sample.json\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 256\n",
      "    shuffle: true\n",
      "    is_tarred: false\n",
      "    tarred_audio_filepaths: null\n",
      "    tarred_shard_strategy: scatter\n",
      "    augmentor:\n",
      "      shift:\n",
      "        prob: 0.5\n",
      "        min_shift_ms: -10.0\n",
      "        max_shift_ms: 10.0\n",
      "      white_noise:\n",
      "        prob: 0.5\n",
      "        min_level: -90\n",
      "        max_level: -46\n",
      "        norm: true\n",
      "      noise:\n",
      "        prob: 0.5\n",
      "        manifest_path: /manifests/noise_0_1_musan_fs.json\n",
      "        min_snr_db: 0\n",
      "        max_snr_db: 30\n",
      "        max_gain_db: 300.0\n",
      "        norm: true\n",
      "      gain:\n",
      "        prob: 0.5\n",
      "        min_gain_dbfs: -10.0\n",
      "        max_gain_dbfs: 10.0\n",
      "        norm: true\n",
      "    num_workers: 16\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2022-10-17 20:34:59 modelPT:149] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
      "    Validation config : \n",
      "    manifest_filepath: /manifests/ami_dev_0.63.json,/manifests/freesound_background_dev.json,/manifests/freesound_laughter_dev.json,/manifests/ch120_moved_0.63.json,/manifests/fisher_2005_500_speech_sampled.json,/manifests/google_dev_manifest.json,/manifests/musan_music_dev.json,/manifests/mandarin_dev.json,/manifests/german_dev.json,/manifests/spanish_dev.json,/manifests/french_dev.json,/manifests/russian_dev.json\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 256\n",
      "    shuffle: false\n",
      "    val_loss_idx: 0\n",
      "    num_workers: 16\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2022-10-17 20:34:59 modelPT:155] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
      "    Test config : \n",
      "    manifest_filepath: null\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    shuffle: false\n",
      "    test_loss_idx: 0\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2022-10-17 20:34:59 features:225] PADDING: 16\n",
      "[NeMo I 2022-10-17 20:35:01 save_restore_connector:243] Model EncDecClassificationModel was successfully restored from /home/georg/.cache/torch/NeMo/NeMo_1.12.0rc0/vad_multilingual_marblenet/670f425c7f186060b7a7268ba6dfacb2/vad_multilingual_marblenet.nemo.\n",
      "[NeMo I 2022-10-17 20:35:01 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_telephony_marblenet/versions/1.0.0rc1/files/vad_telephony_marblenet.nemo to /home/georg/.cache/torch/NeMo/NeMo_1.12.0rc0/vad_telephony_marblenet/44c033a250118838ab1637e5a7ba06c2/vad_telephony_marblenet.nemo\n",
      "100% [............................................................................] 355642 / 355642[NeMo I 2022-10-17 20:35:03 common:910] Instantiating model from pre-trained checkpoint\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2022-10-17 20:35:03 modelPT:142] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
      "    Train config : \n",
      "    manifest_filepath: /manifests/manifest_check_15/background_training_manifest.json,/manifests/manifest_check_15/musan_music_train_sample.json,/manifests/manifest_check_15/musan_sndbible_train.json,/manifests/manifest_check_15/new_ami_manifest_train.json,/gpfs/fs1/jbalam/vad/manifests/fisher_swbd_nithin/150ms_fisher_swbd.json\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    num_workers: 20\n",
      "    shuffle: true\n",
      "    augmentor:\n",
      "      shift:\n",
      "        prob: 0.5\n",
      "        min_shift_ms: -50.0\n",
      "        max_shift_ms: 50.0\n",
      "      white_noise:\n",
      "        prob: 0.5\n",
      "        min_level: -90\n",
      "        max_level: -46\n",
      "      gain:\n",
      "        prob: 0.5\n",
      "        min_gain_dbfs: -10.0\n",
      "        max_gain_dbfs: 0\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2022-10-17 20:35:03 modelPT:149] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
      "    Validation config : \n",
      "    manifest_filepath: /manifests/manifest_check_15/background_validation_manifest.json,/manifests/manifest_check_15/musan_music_validation_sample.json,/manifests/manifest_check_15/new_ami_manifest_development.json,/gpfs/fs1/jbalam/vad/manifests/fisher_swbd_nithin/swbd_validation_150ms.json\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    shuffle: false\n",
      "    val_loss_idx: 0\n",
      "    num_workers: 20\n",
      "    pin_memory: true\n",
      "    \n",
      "[NeMo W 2022-10-17 20:35:03 modelPT:155] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
      "    Test config : \n",
      "    manifest_filepath: null\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    shuffle: false\n",
      "    test_loss_idx: 0\n",
      "    num_workers: 20\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2022-10-17 20:35:03 features:225] PADDING: 16\n",
      "[NeMo I 2022-10-17 20:35:03 save_restore_connector:243] Model EncDecClassificationModel was successfully restored from /home/georg/.cache/torch/NeMo/NeMo_1.12.0rc0/vad_telephony_marblenet/44c033a250118838ab1637e5a7ba06c2/vad_telephony_marblenet.nemo.\n",
      "[NeMo I 2022-10-17 20:35:03 cloud:66] Downloading from: https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_marblenet/versions/1.0.0rc1/files/vad_marblenet.nemo to /home/georg/.cache/torch/NeMo/NeMo_1.12.0rc0/vad_marblenet/10477085f32c378938ef41e65dc2e1b3/vad_marblenet.nemo\n",
      "100% [............................................................................] 369247 / 369247[NeMo I 2022-10-17 20:35:05 common:910] Instantiating model from pre-trained checkpoint\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NeMo W 2022-10-17 20:35:06 modelPT:142] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
      "    Train config : \n",
      "    vad_stream: false\n",
      "    manifest_filepath: /home/fjia/code/manifest64/train.json\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    num_workers: 20\n",
      "    shuffle: true\n",
      "    augmentor:\n",
      "      shift:\n",
      "        prob: 0.8\n",
      "        min_shift_ms: -5.0\n",
      "        max_shift_ms: 5.0\n",
      "      white_noise:\n",
      "        prob: 0.8\n",
      "        min_level: -90\n",
      "        max_level: -46\n",
      "    \n",
      "[NeMo W 2022-10-17 20:35:06 modelPT:149] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
      "    Validation config : \n",
      "    vad_stream: false\n",
      "    manifest_filepath: /home/fjia/code/manifest64/validation.json\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    shuffle: false\n",
      "    val_loss_idx: 0\n",
      "    num_workers: 20\n",
      "    \n",
      "[NeMo W 2022-10-17 20:35:06 modelPT:155] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
      "    Test config : \n",
      "    vad_stream: false\n",
      "    manifest_filepath: null\n",
      "    sample_rate: 16000\n",
      "    labels:\n",
      "    - background\n",
      "    - speech\n",
      "    batch_size: 128\n",
      "    shuffle: false\n",
      "    test_loss_idx: 0\n",
      "    num_workers: 20\n",
      "    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NeMo I 2022-10-17 20:35:06 audio_preprocessing:491] Numba CUDA SpecAugment kernel is being used\n",
      "[NeMo I 2022-10-17 20:35:06 save_restore_connector:243] Model EncDecClassificationModel was successfully restored from /home/georg/.cache/torch/NeMo/NeMo_1.12.0rc0/vad_marblenet/10477085f32c378938ef41e65dc2e1b3/vad_marblenet.nemo.\n"
     ]
    }
   ],
   "source": [
    "import nemo\n",
    "import nemo.collections.asr as nemo_asr\n",
    "vad_model_1 = nemo_asr.models.EncDecClassificationModel.from_pretrained(model_name=\"vad_multilingual_marblenet\")\n",
    "vad_model_2 = nemo_asr.models.EncDecClassificationModel.from_pretrained(model_name=\"vad_telephony_marblenet\")\n",
    "vad_model_3 = nemo_asr.models.EncDecClassificationModel.from_pretrained(model_name=\"vad_marblenet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df2d621a",
   "metadata": {},
   "outputs": [],
   "source": [
    "vad_model_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76d61360",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f0b38a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd34b0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b9ae82d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1636607",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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