{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change audio by detecting onset \n",
    "This notebook contains a method that could change the target video sound with a given audio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython\n",
    "import os\n",
    "import numpy as np\n",
    "from moviepy.editor import *\n",
    "import librosa\n",
    "from IPython.display import Audio\n",
    "from IPython.display import Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read videos\n",
    "origin_video_path = 'data/target.mp4'\n",
    "conditional_video_path = 'data/conditional.mp4'\n",
    "# conditional_video_path = 'data/dog_bark.mp4'\n",
    "\n",
    "ori_videoclip = VideoFileClip(origin_video_path)\n",
    "con_videoclip = VideoFileClip(conditional_video_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"data/target.mp4\" controls  width=\"640\" >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(origin_video_path, width=640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"data/conditional.mp4\" controls  width=\"640\" >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(conditional_video_path, width=640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the audio track from video\n",
    "ori_audioclip = ori_videoclip.audio\n",
    "ori_audio, ori_sr = ori_audioclip.to_soundarray(), ori_audioclip.fps\n",
    "con_audioclip = con_videoclip.audio\n",
    "con_audio, con_sr = con_audioclip.to_soundarray(), con_audioclip.fps\n",
    "\n",
    "ori_audio = ori_audio.mean(-1)\n",
    "con_audio = con_audio.mean(-1)\n",
    "\n",
    "target_sr = 22050\n",
    "ori_audio = librosa.resample(ori_audio, orig_sr=ori_sr, target_sr=target_sr)\n",
    "con_audio = librosa.resample(con_audio, orig_sr=con_sr, target_sr=target_sr)\n",
    "\n",
    "ori_sr, con_sr = target_sr, target_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_onset_of_audio(audio, sample_rate):\n",
    "    onsets = librosa.onset.onset_detect(\n",
    "        y=audio, sr=sample_rate, units='samples', delta=0.3)\n",
    "    return onsets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "onsets = detect_onset_of_audio(ori_audio, ori_sr)\n",
    "plt.figure(dpi=100)\n",
    "\n",
    "time = np.arange(ori_audio.shape[0])\n",
    "plt.plot(time, ori_audio)\n",
    "plt.vlines(onsets, 0, ymax=0.5, colors='r')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method\n",
    "The baseline is quite simple, and it has several steps:\n",
    "- Take the original waveform (encoded and decoded by our codebook) and detect the onsets to determine the timestamp of sound events\n",
    "- (Optional) Assume we don't have original waveform, we can use Andrew's great hit model to predict sound from frames and detect onsets from it.\n",
    "- Detect onsets of conditional waveform (encoded and decoded by our codebook) and clip single onset event from them as sound candicates\n",
    "- For each onset of original waveform, replace with conditional onset event randomly and then generate sound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_onset_audio_range(audio, onsets, i):\n",
    "    if i == 0:\n",
    "        prev_offset = int(onsets[i] // 3)\n",
    "    else:\n",
    "        prev_offset = int((onsets[i] - onsets[i - 1]) // 3)\n",
    "\n",
    "    if i == onsets.shape[0] - 1:\n",
    "        post_offset = int((audio.shape[0] - onsets[i]) // 4 * 2)\n",
    "    else:\n",
    "        post_offset = int((onsets[i + 1] - onsets[i]) // 4 * 2)\n",
    "    return prev_offset, post_offset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "ori_onsets = detect_onset_of_audio(ori_audio, ori_sr)\n",
    "con_onsets = detect_onset_of_audio(con_audio, con_sr)\n",
    "\n",
    "np.random.seed(2022)\n",
    "gen_audio = np.zeros_like(ori_audio)\n",
    "for i in range(ori_onsets.shape[0]):\n",
    "    prev_offset, post_offset = get_onset_audio_range(ori_audio, ori_onsets, i)\n",
    "    j = np.random.choice(con_onsets.shape[0])\n",
    "    prev_offset_con, post_offset_con = get_onset_audio_range(con_audio, con_onsets, j)\n",
    "    prev_offset = min(prev_offset, prev_offset_con)\n",
    "    post_offset = min(post_offset, post_offset_con)\n",
    "    gen_audio[ori_onsets[i] - prev_offset: ori_onsets[i] + post_offset] = con_audio[con_onsets[j] - prev_offset: con_onsets[j] + post_offset]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zlmv2R81P+rVgXaxZd84qmuYTzVu9LewQEIDmGiKmL67ShQ83nzje8cz7AUWETJBEIGN+mxuRHM30QGYWpZggj4+lcJFEQDM+qNKSDen7SvBmzd5LS5pPFCa9vkJzVzEm3o+XFm/Uso3bww4DeTTl7dV8kSlAJBHQNY+9rXPvfz3sMErS799KPWTt0p/P1PYshzBCuvbxORr309fCDgN59O2nF+jFRbTeFRqSCDSYnuYNyneAYPxt/vqwQyhac+kzEim3TJmvugN8EhUSkgg0eOWDqpTHaUoMxnf/ulBrP9kVdhhFafaKzFekRfHbtbcu7cgm5BdJBBocSJMkkEIE50fPLg47hKJEXhs9v3zto7BDQBySCDT4/VtrtC9uPHZiy8OarXxbDsrU9zeEHUJRuveFDxrNIZB4zbKEeLTQWpp/JBFo5Ojbn9f+ugP645w16j/hH42Obd6R3bobyE5VTfLZQdN9MP5l3lqd+ZMZWr5pRxBhFbxP3f68nHOaPGtVk2s223U3UBwWNzPkM13/okXra3TGT2bo+QUfBxFWJJFEoImXllTpv//0XpP9GxL+yN37whL9+B/RbYZPNYueH5+9P/log7ufj90Dds5p/pptTUZzfGPKu/po805d8Wh0Z7d8dekmfS/J6o9bExacu+u5RfrJCx/kKywE5LwHko8mu2XK/IbHyzft0LptuxsdH//g61qxeadu/N07QYYXKSQRaOKGyXOT7r/zmUUNjxd/XKOfvbJcj772kbbsiGaT8Uebczvb57Zm5v6vvwf8mzdW6qKfvaET70g+vW9VhJvur3ns7aT775u2tOHxm8s3a9LrK/TwKx+qlqXZ8+pAHkdU1B1w2rJjj87631f1mbtfztvrRhVJBDK2OS5ZuOChfzY8XrIhmpP+PDV7dc7P2dyti6Ubt+uHzy5KegzNmxk3tfgXJx1cwXZ9wjdUBGt3AEnbrGamjZ+zcqseeGlZw8979pMwBokkAr7sj/tmEdV78c++l/v7qhc8/E99srNp35PPMrFSTq3fxuq0+bS/LvctEZc/OitpZ+/LHp2lJ2auaviZ1T2DRRKBrEx5e7WenLWq0b57pkbzHvO2XbnvaLpwXY2G/vDFtOV27d2f89dO5nezV+nBuG91xegv89Zq8syVjfbdP31p8sIIxL4DwSxpP+aeV9KWqV/MDsHISxJhZjeZ2QozqzWzuWY2Jk3507xytWb2kZl9JR9xIr1vP71A303owLZjz34dedtzKZsNN23fU3I95feHOHPecd9/QR9WBdsCVLuvTrf/ZaHue3Gpfjd7VfonFKhvTHlX3/tb45Ue56z6REfe9lyjIc2JqmpqtWNPfpK1Urd3fzBJRCaufXyOJka4A3jQWgf9AmZ2maT7Jd0k6Q1JN0h63syOc841ualsZv0l/UPSJElfkvQZSY+Y2Sbn3NNBx1tMCm3NhYHfnZq2zA2nDdBNp31KlYe0abS/7oBTK5PMLKjwSs7Z970a6Pmrdx+8vm7/y0Ld/peFeu+Oz6qiXZsUz0ot2a2aMB19+/Npy3zrnIH60qh+qmzf9Jota8X1monrHp8T6uv/8rWPmKQqIBb05BxmNlvSO865G+P2LZb0V+fchCTl/0fShc65QXH7fiFpsHPulCTlyyWVx+3qJGltdXW1KioqcvI7vPJBlZ6eu7bxjI2u/p/Yg/hqdKmOJZSJnwfy4PNiXl6SehrqUnTaMd3UqV1rJamehvqUUtd3k+NqWjZZvTct65p9/qtLN6X4LQrbBYN7ZVTupcUbtWsvndLSOXtQd7VrUyapmVldk+x0CTuTfQwn3ZfJ85KF0GRn01KJZZKfJ8nz0pynmN8r9TJ9z4TliyP66pSjDsvJuWpqalRZWSlJlc65tMs7B9oSYWZtJQ2TdHfCoWmSRjfztFO84/FekHStmbVxziV+/Z4g6QctjTWVFZt2BtKJDk2VwgdOoXvmXRb8yqXpi6OX7EdNob9nxhzdVacoN0lEtoK+ndFVUpmkxOUhN0rq0cxzejRTvrV3vsS/5hMl3Rf3cydJa/0E25yRAw7VHRccJ6lxc3v9Q0vcEbfvYJnmnxffgt9QzqQfPbtINbX7dcWIvvr9W7kfTliI6utZOljXjesnTor6ju1rWueZlE320BJe65v/926zv0Mh69qxXDefcVRGZd9fX6M/zfX3Vrr05D56+p2cvg0LVvw1KyW/JZfsLl2TXcmel+T1EotZklKZvF7yMk1OnmFM1myZ15ZtKuqVai85ubdO7F0ZdhgpDT2ic2ivHXifCE9ii5cl2ZeufLL9cs7tkdQwgUEQ99SP71Wp43vl/yL6wvAjGh5PvOTEJsedc02m+Q3bib0r9eS1I/XbN1fqp9OX6vbxg9StU3nDTHKL7zxX7duWhRtkDlw6rI+OvO25sMNoYuXd5+fsXFt27GmURDz275/WGQO7Z/z8//3C4Cb7DhxwGvCdwrpmR/Q/VJOuHq5fvLpcP5+xXD/83PFqU9ZKt/15gSRp6Y/OU9vWDGTz69JhfTS8X5cmnVvDlsv3SpQFnURsllSnpq0O3dW0taHehmbK75fEur9xCqkT4jWjj9QdFx7f8PPXzz5aXz/76Iafzzuxh0xWUh/G3TqVh7bA0+eH9dG9n4/9kQ4qmamI60j49//8jE7q07nF52xVQB0Rbz7jKH3rnGMbfv72ucfq2+ce/Pmiob1V1srUpqx0rtmwnHN8j1CTiBUTx8vMCjLxL3aBvjucc3slzZU0LuHQOElvNvO0mUnKf1bSnCT9IZBnD1w+pEmP9K+e+alGCUQy5a3LSiqBkKTJ144I7bXrE4ggtSlrpVkTztLr/31GThKIsNx/2ZAm++77wuBGCUQy7dqUkUDkSPeKdqG99o8vPrGgvnCVmny8Q+6TdJ2ZfdnMBpnZTyX1lfQLSTKziWb2RFz5X0jqZ2b3eeW/LOlaST/JQ6xI43NDemv5j8frW+cMbNh3/kk9Q4woPOWtg7kt89GPxwdyXj96VLbTEYceEnYYLXLR0N5aeff5umpUv4Z9o4/qGmJEyJVMbkkM7NEpD5FEV+BJhHNuiqRbJH1f0nxJYyWNd87Vz17TU7Gkor78CknjJZ3ulf+epK8xR0T4Tuh9cMjszWd8KsRICkMQLfO3jx+UtMn/zzc1N5jp4P/Ls189NfcBFbnLP32wX9G3zh2YoiRKxWP//mmdPejwhp87lh+8a//d82MzB3ztTD6/ciUvHSudc49IeqSZY9ck2feqpJMDDgtZuu7UAUn39wixqTJMyXrFt9SXT+3fZN8VI/rq5L5dNP7EHvrHgg1Njj/71ZQTwEbavw7r0/C4fZuDLUeJE0eh+Lz1nbOS7j9jYHcN69dFJ3mr3XZsd/DP3HVjBui6Mck/x+BPvkZnoIh89/xBmrl8i15KmOwqviVCkl75r9NVu69OnQ9pm8/wCkaub7NOv3Vs0hkQf3zxCZKkh644WT0qFmvkgENz+8Il4IcXnaBn5q/XWyu3Ntrf77AODY/blLXS9FtP0wHnSmKEUJTddfEJSftZ3HZerJ9LRbs2+uFFJ2h77T717tw+3+FFCkkEGnn5m6epf9cOum7MAFXv2qfBdx6c9ytxJrr+XTsoynKdRHyqe/J7t/Wdwspamb6fMCcBYtfsgG4dddWofvpk595GC5glzvD4qe4d8x0eAnDlyH5J97eOS8Lj+8AgOCQRaHDzGUdpQLeDH7KJ61uEt9xUYcplj+/TB3bL2bmi5Pbxgxpds106JLSKcdFGCmuZ5B/jl9CgjGFQWeneqTx9oQw9eMXQnJ0rSrhko+fKkX2bPTY4xJkbo4okAg3SfbPuWRnNDpTNyeUcAvGd/pC5Vmmu2Qo6UJacwSnmLDm5b5f8BQJJJBGIc9HQ3imPd2rBEtBo3l0Xn8CkRj6dc0JzS/DEtCM5Kyk3jB2gS+NG3CB8fHKhwZGHFfekQoVoxJGpR1K0LWvVbCcxpEfP+2iZMH4Q/R4KDEkEEKAnrxsZdgglqy2tNyUl2fTkKHyMzoi4Yf266DfXfFqtWxnzywcg3XohP7uSOdWyddnwI/Sd8YNKbi2WqEvXKfLG0zNbwh75RRIRcZee3IfZ+0LCEtP+XDikV5PhxyhtD14xVBcO7hV2GEiCTzCkdHyvivSFkLUB3TqQQPiUrr2sa8fcDb1F/qTq6nDa0cyjUqj4FENKg3qSROTaHRccp2m3jA07jJJ1ct/OYYcAH5pbi2bRnefQ8lTASCIirncXerfnW4fy1mpNp0Dfkq2ZgOLXXJesQ9py172Q8UkWcdyuyD86sLZMuqHIzHRdnMykY3skXz8GhYskIuLS/TlLXHQLCBtJWOk6jtunRYckImKOOZxVDMM2iqW8s8J6CNFgZnQ2LkL8j0XIPf96kv5286mN9vGtLnhPXtt4wilGD2Tu0auGacr1oxrt44otTSbp1nHHaEC3Dg37zh7UPbyAkBGSiAg5tkcntW/LWgL5durRXXXVKKa29mNQz4qs17/gFlzx6l7RTi9/8/SsnsP8EeEiiYiQZEOo0vaJoJtaKIYyTBER47dRdPyJPXMbCLJCEhEhyd6knRl/nRfZfkDec+lJ6t25vf7n0hODCahIJKu3VizAhDhnHNtNx/eq0BUj+oYdSiQxADfCvvyZ/vSJyJP4Ws6kyo8+vJPeuO3MwOIpVrecfXTYISAgzU02lU556zI997UxOY4GmaIlIkKO6tZ4ZAa3KlDoDk+YWIr+DtHC/3fhI4mIkMROlZkspXzKgMOCCgdIq03CNdqmLP231bHHdJXEUuHFpksHbq0WI25nRNB3xh+rP7+zTl85Lf3Supee3EeHtG2twUdU5iEy4KCO5Qc/nr457hg9v3CDrh59ZNrnXTmyn7oc0lbDj+wSYHTItfLWjBwrRiQREXT92KN0/dj0CYQU68R2/kn0fm6p+L4nfu/9Rk18LX31rKP11bMy6w9R1sp0AcP+gLygvQ8AUJAO0Cmi4JFERMShHdqGHQKQlf5xMxcCKEwkERHxw8+dEHYIQFYmnDco7BAApEESERG0RBQOpubITEV7umwBhY4kooScdky3hsfXnto/xEiQiMQhuc8P69Pw+IaxA0KMBIWIHhGFL9Akwsy6mNlkM6v2tslm1jnNcy4xsxfMbLOZOTMbEmSMpWRE/0PVu3N7SdLNZ3wq5GiA9Ib27aKuHWOtZDefyTUbFWcPOjzsEJAjQbcXPiWpj6RzvZ8flTRZ0gUpntNB0huS/k/SpECjKzGtzDTjW6dr9746VbRj4pZ8u2hIL/11/vqkx+hknlwrk9687Szt2V+nTgnXLENhS9d/nztQ0xdvDDsM5EBgSYSZDVIseRjlnJvt7fsPSTPNbKBz7oNkz3POTfbKHpnh65RLKo/b1aklcRezL47oqzZlrZrM8of8yDRP4E/jQRcO6aW2rVupbWuuWaAYBfnOPUVSdX0CIUnOuVmSqiWNzuHrTPDOWb+tzeG5i0qndnREK1T0iUjukLbNX7Os7QJa8ApfkElED0lVSfZXecdyZaKkyritT+riQDBSfeDRNA8cRHJQOrJOIszsDq/DY6ptuFc82aVizez3xTm3xzlXU79J2p6rcxebVJXKN2EUGxIvoPD5af9+WNIf0pRZKekkScm64HaTRI8alJyM+0SQ0SHiuFVVOrJOIpxzmyVtTlfOzGZKqjSzEc65t7x9IxW75fBmtq8LAAAKS2B9IpxziyVNlTTJzEaZ2SjFhmw+Gz8yw8yWmNnFcT8f6s0NcZy3a6CZDTGzXPajiJxWfPsNXLeO5c0ei69+xw3hjLRiwEbJapfhst+8Uwpf0G/TKyUtkDTN296TdFVCmYGKtU7Uu1DSPEnPeT//wfv5K4FGWuKG9esSdggl7+tnN79UNSlc9gYeHtnR2iXvyK4srlYqAh0T6JzbKulLacpYws+/lfTb4KKKprJW/BkLWmX7zCb4ok9EZqgnoPDRYAgAKEjc+it8JBEAAMAXkgggDw7teHApdhrpgcyc0LsyfSGEiiQCyIMuhxxMIlrRPwXIyNiju4UdAtIgiSgh3D8EUEroW1v4SCIAAAWJ70WFjyQCyAO+UAEoRSQRAADAF5IIAADgC0kEAKAgsdpn4SOJKCG83QoXvcwBlCKSCAAA4AtJBAAA8IUkooQwphpASeEzreCRRJQQOiEVLmOmCKCR9m3Kwg4BOUASUUJoiQBQLIYf2SXsEJADJBFAHpS34a0GZKt1Ge+bQtc67ACAKBh/Yk/9ae5aDe93aNihAAWhW8fyZo/dMHaAlm/aqeH9aK0odCQRQB60KWulydeODDsMoGCcf1JP/XneuqTHJowflOdo4BdtRQCAvGvVis7GpYAkAgjIF4b3CTsEAAgUSUQJYXRGYRnal/u5AEobSQQAAPCFJAIICC1DAEodSUQJacdcBACAPOKvTgkx1psOXXlr3lJAJo7rWRF2CMgBPvGAHLrkZEZkAJk4vKJd2CEgB0gigByiMQhAlJBEADlEDgEgSkgigByiJQJAlASaRJhZFzObbGbV3jbZzDqnKN/GzP7HzBaY2U4zW29mT5hZryDjBAAA2Qu6JeIpSUMknettQyRNTlH+EEknS/qh9+8lko6R9PcggwRy5dAOB1cmdGKiCAClLbBVPM1skGKJwyjn3Gxv339ImmlmA51zHyQ+xzlXLWlcwnm+KuktM+vrnFud5HXKJcWvKdsph78GkJVxgw7Xgy8tCzsMAMiLIFsiTpFUXZ9ASJJzbpakakmjszhPpSQnaVszxyd456zf1voJFsgF+kQAiJIgk4gekqqS7K/yjqVlZu0k3S3pKedcTTPFJiqWaNRvDNQHACAPsk4izOwOM3NptuFe8WQ3ha2Z/Ymv00bSH7wYb2qunHNuj3Oupn6TtD3b3wkAAGTPT5+IhxX7457KSkknSTo8ybFukjamerKXQPxRUn9JZ6ZohQAKCrczAERJ1kmEc26zpM3pypnZTEmVZjbCOfeWt2+kYrcc3kzxvPoE4mhJZzjntmQbY5QM7lOpd9dWhx0GkLH+XTtoxeadYYcBIAcC6xPhnFssaaqkSWY2ysxGSZok6dn4kRlmtsTMLvYet5b0J0nDJV0pqczMenhb26BiLWp89S0oZa34/0iHGgJKR9DzRFwpaYGkad72nqSrEsoMVKx1Qop1irzQ+3e+pI/jtmxGdAChGHj4wRHGjmkigIwc2oHviMUqsHkiJMk5t1XSl9KUsbjHK8UXFRSh+gYhlmMHkjvz2O56eUmyAXtS+zZleY4GucLaGQAAwBeSCCAg3M0AUOpIIopc/8MOCTsEICsDunUIOwQAORJonwgE7wcXHK+2rVvp88OPCDsUICMTLzlJXaYu0RdH9g07FAAtRBJR5Lp0aKt7/nVw2GEAGevWqVz3fp5rNmocw5VKErczgBxgTAaAKCKJAILCNy8gI7RSFC+SCAAA4AtJBAAA8IUkAgAA+EISAQAAfCGJAAAAvpBEAAAAX0giAACALyQRAADAF5IIICBMnwNkhvdK8SKJAAAEjkShNJFEADlgxuoZAKKHJAIAAPhCEgEAAHwhiQAAAL6QRAAAAF9IIgAUnDZldFQFigFJRATwgRwOx5g23yrbtw07BOQR75XiRRIBAAB8IYkAAAC+kEQAAABfSCIiwESfiKD16twu7BCAgnZYh/KwQ0AAWocdAFDMnr5xtB54aZm+/y+Dwg4FKGgTxh+rrTv36PIRfcMOBTlEEgG0wLB+XfTEl0eEHUbJYSmS0tO1Y7ke+3feK6Um0NsZZtbFzCabWbW3TTazzmmec4eZLTGznWb2iZlNN7ORQcYJAACyF3SfiKckDZF0rrcNkTQ5zXOWSvpPSSdKOlXSSknTzKxbUEECQRjYo1PYIQAFrd9hh0iSzj6ue8iRwK/AbmeY2SDFEodRzrnZ3r7/kDTTzAY65z5I9jzn3FMJ57lV0rWSTpL0UpLXKZcU32OHT+5ENA3n1dRbxujDqh0aNeCwsEMBCtqfvjJaryyp0gWDe4UdCnwKsiXiFEnV9QmEJDnnZkmqljQ6kxOYWVtJ13vPebeZYhO84/Xb2hbEXJLIIfLr2B4V+peT+FBsCa7ZaOjWqVxf+PQRat+2LOxQ4FOQSUQPSVVJ9ld5x5plZv9iZjsk1Ur6hqRxzrnNzRSfKKkybuvjO2IABYGOlUBxyDqJ8Do+ujTbcK94shnRrZn98V5RrP/EaElTJf3RzJLeNHPO7XHO1dRvkrZn+zsBAIDs+ekT8bCkP6Qps1KxPgyHJznWTdLGVE92zu2U9KG3zTKzZYr1i5iYbbAAACAYWScR3m2F5m4tNDCzmZIqzWyEc+4tb99IxW45vJnly5oad54EAAAhC6xPhHNusWK3IiaZ2SgzGyVpkqRn40dmeHNCXOw97mBmP/bK9zOzk83sV4r1c/i/oGItddxfBgAEIeh5Iq6UtEDSNG97T9JVCWUGKtY6IUl1ko6V9LRi80U8q9jtjzHOufcDjhVAgWC9F6A4BDrttXNuq6QvpSljcY9rJV0SZEwAACA3WMUTAAD4QhIBAAB8IYkAAAC+kEREAJ3UUGwYUQQUB5IIAADgC0kEAADwhSQCAAD4QhIBAAB8IYmIADqpAQCCQBIBoOCQ9wLFgSQCAAD4QhIBAAB8IYkAAAC+kESUsO6dyiVJpww4LORIgMwc0rZMkvTp/oeGHAmATJhzLuwYcsrMKiRVV1dXq6KiIuxwQrVm6y79Zd46XTWqn7p0aBt2OEBaq7bs1N/mr9e/jT5Sle3bhB0OEDk1NTWqrKyUpErnXE268iQRAABAUvZJBLczAACALyQRAADAF5IIAADgC0kEAADwhSQCAAD4QhIBAAB8IYkAAAC+kEQAAABfSCIAAIAvJBEAAMCX1mEHEJSamrSzdQIAgDjZ/u0sxbUzektaG3YcAAAUsT7OuXXpCpViEmGSeknanuNTd1IsOekTwLmjiPrMPeo0t6jP3KI+cy+oOu0kab3LIEEoudsZ3i+dNnvKViw3kSRtz2RlM6RGfeYedZpb1GduUZ+5F2CdZnwuOlYCAABfSCIAAIAvJBGZ2yPp/3n/ouWoz9yjTnOL+swt6jP3Qq/TkutYCQAA8oOWCAAA4AtJBAAA8IUkAgAA+EISAQAAfCGJAAAAvpBEZMDMbjKzFWZWa2ZzzWxM2DHlm5mNNbNnzGy9mTkzuyjhuJnZHd7x3WY2w8yOTyhTbmYPmdlmM9tpZn83sz4JZbqY2WQzq/a2yWbWOaFMXy+Wnd65HjSztkH97kEwswlm9raZbTezKjP7q5kNTChDnWbBzG40s/fMrMbbZprZeXHHqc8W8K5ZZ2b3x+2jTrPg1ZVL2DbEHS+++nTOsaXYJF0maa+k6yQNknS/pB2S+oYdW57r4TxJP5J0iSQn6aKE499WbKrUSySdIOkPktZL6hRX5ueKzfN+tqShkl6WNF9SWVyZ5yUtkHSKty2Q9Ezc8TJv38veOc5WbJrzh8Kuoyzrc6qkayQdL2mwpGclrZLUgTr1XacXSBov6Rhvu8t77x5Pfba4bj8taYWkdyXdzzXqux7vkLRQUo+4rVsx12folVrom6TZkn6esG+xpIlhxxZinTRKIiSZpI8lfTtuX7mkbZJu8H6uVOwD/bK4Mr0k1Uk6x/t5kHfukXFlRnn7Bno/n+c9p1dcmcsl1UqqCLtuWlCn3bzfcyx1mtN63SrpWuqzRXXYUdJS7w/NDHlJBHXqqy7vkDS/mWNFWZ/czkjBa9oZJmlawqFpkkbnP6KC1V+xjLqhnpxzeyS9qoP1NExSm4Qy6xXLyuvLnCKp2jk3O67MLEnVCWUWes+t94Jib7ZhufuV8q7S+3er9y912gJmVmZml0vqIGmmqM+W+Jmk55xz0xP2U6f+HO3drlhhZn8wswHe/qKsz5JbxTPHuirW7LMxYf9Gxf6zEVNfF8nqqV9cmb3OuU+SlOkRV6YqyfmrEso0eh3n3CdmtldF+n9iZibpPkn/dM4t9HZTpz6Y2YmKJQ3tFLvteLFzbpGZ1X94Up9Z8BKxkxW7nZGIazR7syVdrVjLzuGSvivpTa/fQ1HWJ0lEZhLnBrck++CvnhLLJCvvp0wxeVjSSZJOTXKMOs3OB5KGSOos6VJJj5vZaXHHqc8MmdkRkh6Q9FnnXG2KotRphpxzz8f9uMDMZkpaLunfJM2qL5bwtIKuT25npLZZsftGiZlZdzXNFqOsvndxqnraIKmtmXVJU+bwJOfvllCm0et452yjIvw/MbOHJF0o6Qzn3Nq4Q9SpD865vc65D51zc5xzExTrCPh1UZ9+DFPsd59rZvvNbL+k0yR9zXtc/7tQpz4553Yq1sHxaBXpNUoSkYJzbq+kuZLGJRwaJ+nN/EdUsFYodlE21JPXn+Q0HaynuZL2JZTpqVgP5PoyMyVVmtmIuDIjFesvEF/mBO+59T6r2Cp2c3P3KwXLG8r1sGK9sM90zq1IKEKd5oYpdp+X+szeS5JOVKxlp36bI+l33uOPRJ22iJmVK9YR8mMV6zUadm/VQt90cIjnl73/7J8qdq+1X9ix5bkeOurgB4mT9A3vcV/v+LcV60V8sXdBP6XkQ5PWSDpLsWFFLyn50KR3FetNPErSe0o+NGm6d46zvHMW21CvR7z6Ok2Nh3u1jytDnWZXpz+WNEbSkYr98btLsZbEcdRnzup4hpoO8aROM6+/n3jv+f6SRkp6RrEhnf2KtT5Dr9Ri2CTdJGmlDmZpY8OOKYQ6OF2x5CFx+6133BQbvvSxYsOEXpV0QsI52kl6SNIWSbu8N9ARCWUOlfSk98aq8R53TijTV7F5FXZ553pIUnnYdZRlfSarSyfpmrgy1Gl2dfrruPdplfcBOY76zGkdz1DjJII6za7+6ud92KvYvAxPSzqumOvTvJMBAABkhT4RAADAF5IIAADgC0kEAADwhSQCAAD4QhIBAAB8IYkAAAC+kEQAAABfSCIAAIAvJBEAAMAXkggAAOALSQQAAPDl/wMsTYwZlKLTRwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(dpi=100)\n",
    "time = np.arange(gen_audio.shape[0])\n",
    "plt.plot(time, gen_audio)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save audio\n",
    "import soundfile as sf\n",
    "sf.write('data/gen_audio.wav', gen_audio, ori_sr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "t:   0%|          | 0/49 [00:00<?, ?it/s, now=None]    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Building video data/generate.mp4.\n",
      "MoviePy - Writing audio in generateTEMP_MPY_wvf_snd.mp3\n",
      "MoviePy - Done.\n",
      "Moviepy - Writing video data/generate.mp4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Done !\n",
      "Moviepy - video ready data/generate.mp4\n"
     ]
    }
   ],
   "source": [
    "gen_audioclip = AudioFileClip(\"data/gen_audio.wav\")\n",
    "gen_videoclip = ori_videoclip.set_audio(gen_audioclip)\n",
    "gen_videoclip.write_videofile('data/generate.mp4')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"data/generate.mp4\" controls  width=\"640\" >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video('data/generate.mp4', width=640)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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