{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bccAucKjnPHm"
   },
   "source": [
    "### Dependencies and inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cSih95WFmwgi"
   },
   "outputs": [],
   "source": [
    "!pip -q install pydub\n",
    "from google.colab import output\n",
    "from base64 import b64decode, b64encode\n",
    "from io import BytesIO\n",
    "import numpy as np\n",
    "from pydub import AudioSegment\n",
    "from IPython.display import HTML, display\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import moviepy.editor as mpe\n",
    "from matplotlib.animation import FuncAnimation, FFMpegWriter\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "\n",
    "torch.set_num_threads(1)\n",
    "\n",
    "model, _ = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
    "                              model='silero_vad',\n",
    "                              force_reload=True)\n",
    "\n",
    "def int2float(sound):\n",
    "    abs_max = np.abs(sound).max()\n",
    "    sound = sound.astype('float32')\n",
    "    if abs_max > 0:\n",
    "        sound *= 1/32768\n",
    "    sound = sound.squeeze()\n",
    "    return sound\n",
    "\n",
    "AUDIO_HTML = \"\"\"\n",
    "<script>\n",
    "var my_div = document.createElement(\"DIV\");\n",
    "var my_p = document.createElement(\"P\");\n",
    "var my_btn = document.createElement(\"BUTTON\");\n",
    "var t = document.createTextNode(\"Press to start recording\");\n",
    "\n",
    "my_btn.appendChild(t);\n",
    "//my_p.appendChild(my_btn);\n",
    "my_div.appendChild(my_btn);\n",
    "document.body.appendChild(my_div);\n",
    "\n",
    "var base64data = 0;\n",
    "var reader;\n",
    "var recorder, gumStream;\n",
    "var recordButton = my_btn;\n",
    "\n",
    "var handleSuccess = function(stream) {\n",
    "  gumStream = stream;\n",
    "  var options = {\n",
    "    //bitsPerSecond: 8000, //chrome seems to ignore, always 48k\n",
    "    mimeType : 'audio/webm;codecs=opus'\n",
    "    //mimeType : 'audio/webm;codecs=pcm'\n",
    "  };            \n",
    "  //recorder = new MediaRecorder(stream, options);\n",
    "  recorder = new MediaRecorder(stream);\n",
    "  recorder.ondataavailable = function(e) {            \n",
    "    var url = URL.createObjectURL(e.data);\n",
    "    // var preview = document.createElement('audio');\n",
    "    // preview.controls = true;\n",
    "    // preview.src = url;\n",
    "    // document.body.appendChild(preview);\n",
    "\n",
    "    reader = new FileReader();\n",
    "    reader.readAsDataURL(e.data); \n",
    "    reader.onloadend = function() {\n",
    "      base64data = reader.result;\n",
    "      //console.log(\"Inside FileReader:\" + base64data);\n",
    "    }\n",
    "  };\n",
    "  recorder.start();\n",
    "  };\n",
    "\n",
    "recordButton.innerText = \"Recording... press to stop\";\n",
    "\n",
    "navigator.mediaDevices.getUserMedia({audio: true}).then(handleSuccess);\n",
    "\n",
    "\n",
    "function toggleRecording() {\n",
    "  if (recorder && recorder.state == \"recording\") {\n",
    "      recorder.stop();\n",
    "      gumStream.getAudioTracks()[0].stop();\n",
    "      recordButton.innerText = \"Saving recording...\"\n",
    "  }\n",
    "}\n",
    "\n",
    "// https://stackoverflow.com/a/951057\n",
    "function sleep(ms) {\n",
    "  return new Promise(resolve => setTimeout(resolve, ms));\n",
    "}\n",
    "\n",
    "var data = new Promise(resolve=>{\n",
    "//recordButton.addEventListener(\"click\", toggleRecording);\n",
    "recordButton.onclick = ()=>{\n",
    "toggleRecording()\n",
    "\n",
    "sleep(2000).then(() => {\n",
    "  // wait 2000ms for the data to be available...\n",
    "  // ideally this should use something like await...\n",
    "  //console.log(\"Inside data:\" + base64data)\n",
    "  resolve(base64data.toString())\n",
    "\n",
    "});\n",
    "\n",
    "}\n",
    "});\n",
    "      \n",
    "</script>\n",
    "\"\"\"\n",
    "\n",
    "def record(sec=10):\n",
    "    display(HTML(AUDIO_HTML))\n",
    "    s = output.eval_js(\"data\")\n",
    "    b = b64decode(s.split(',')[1])\n",
    "    audio = AudioSegment.from_file(BytesIO(b))\n",
    "    audio.export('test.mp3', format='mp3')\n",
    "    audio = audio.set_channels(1)\n",
    "    audio = audio.set_frame_rate(16000)\n",
    "    audio_float = int2float(np.array(audio.get_array_of_samples()))\n",
    "    audio_tens = torch.tensor(audio_float )\n",
    "    return audio_tens\n",
    "\n",
    "def make_animation(probs, audio_duration, interval=40):\n",
    "    fig = plt.figure(figsize=(16, 9))\n",
    "    ax = plt.axes(xlim=(0, audio_duration), ylim=(0, 1.02))\n",
    "    line, = ax.plot([], [], lw=2)\n",
    "    x = [i / 16000 * 512 for i in range(len(probs))]\n",
    "    plt.xlabel('Time, seconds', fontsize=16)\n",
    "    plt.ylabel('Speech Probability', fontsize=16)\n",
    "\n",
    "    def init():\n",
    "        plt.fill_between(x, probs, color='#064273')\n",
    "        line.set_data([], [])\n",
    "        line.set_color('#990000')\n",
    "        return line,\n",
    "\n",
    "    def animate(i):\n",
    "        x = i * interval / 1000 - 0.04\n",
    "        y = np.linspace(0, 1.02, 2)\n",
    "        \n",
    "        line.set_data(x, y)\n",
    "        line.set_color('#990000')\n",
    "        return line,\n",
    "\n",
    "    anim = FuncAnimation(fig, animate, init_func=init, interval=interval, save_count=audio_duration / (interval / 1000))\n",
    "\n",
    "    f = r\"animation.mp4\" \n",
    "    writervideo = FFMpegWriter(fps=1000/interval) \n",
    "    anim.save(f, writer=writervideo)\n",
    "    plt.close('all')\n",
    "\n",
    "def combine_audio(vidname, audname, outname, fps=25): \n",
    "    my_clip = mpe.VideoFileClip(vidname, verbose=False)\n",
    "    audio_background = mpe.AudioFileClip(audname)\n",
    "    final_clip = my_clip.set_audio(audio_background)\n",
    "    final_clip.write_videofile(outname,fps=fps,verbose=False)\n",
    "\n",
    "def record_make_animation():\n",
    "  tensor = record()\n",
    "\n",
    "  print('Calculating probabilities...')\n",
    "  speech_probs = []\n",
    "  window_size_samples = 512\n",
    "  for i in range(0, len(tensor), window_size_samples):\n",
    "      if len(tensor[i: i+ window_size_samples]) < window_size_samples:\n",
    "        break\n",
    "      speech_prob = model(tensor[i: i+ window_size_samples], 16000).item()\n",
    "      speech_probs.append(speech_prob)\n",
    "  model.reset_states()\n",
    "  print('Making animation...')\n",
    "  make_animation(speech_probs, len(tensor) / 16000)\n",
    "\n",
    "  print('Merging your voice with animation...')\n",
    "  combine_audio('animation.mp4', 'test.mp3', 'merged.mp4')\n",
    "  print('Done!')\n",
    "  mp4 = open('merged.mp4','rb').read()\n",
    "  data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
    "  display(HTML(\"\"\"\n",
    "  <video width=800 controls>\n",
    "        <source src=\"%s\" type=\"video/mp4\">\n",
    "  </video>\n",
    "  \"\"\" % data_url))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IFVs3GvTnpB1"
   },
   "source": [
    "## Record example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5EBjrTwiqAaQ"
   },
   "outputs": [],
   "source": [
    "record_make_animation()"
   ]
  }
 ],
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