Visual explanation of speech turn segmentation pipeline (and the role of gap_max_duration hyper-parameter)

gap_max_duration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook explains the role of \"gap_min_duration\"  hyper-parameters in the `SpeechTurnSegmentation` pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyannote.core import Segment, Timeline, Annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a sample output of voice activity detection sub-pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Timeline(uri=None, segments=[<Segment(0, 5)>, <Segment(6, 11)>, <Segment(13, 16)>, <Segment(18, 20)>])>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vad = Timeline([Segment(0, 5), Segment(6, 11), Segment(13, 16), Segment(18, 20)])\n",
    "vad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a sample output of speaker change detection sub-pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABH0AAABjCAYAAAAGhXxMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKnUlEQVR4nO3dXaxlZ1kH8P9jp3KBRApD6gjoGGJ6ASrSCbV+dSJSSyUtkEZLjBY/olUxcmH8TLSpMQEEEyVRo9KkCmIVrTaGFppI1JsSZprBUqC2mmnsdGhTa1oaiFD7eHFWy8lh7zN7ej72Oe/+/ZLJ2Wevd+08O/PkXe/6n73Wru4OAAAAAGP5qmUXAAAAAMD2E/oAAAAADEjoAwAAADAgoQ8AAADAgIQ+AAAAAAM6sJWdDx482IcPH96mUgAAAAA4fvz4I939oq2+zpZCn8OHD+fYsWNbrQEAAACASVXdvx2v4/IuAAAAgAEJfQAAAAAGJPQBAAAAGJDQBwAAAGBAQh8AAACAAQl9AAAAAAYk9AEAAAAYkNAHAAAAYEBCHwAAAIABCX0AAAAABiT0AQAAABiQ0AcAAABgQEIfAAAAgAEJfQAAAAAGJPQBAAAAGJDQBwAAAGBAQh8AAACAAQl9AAAAAAYk9AEAAAAYkNAHAAAAYEBCHwAAAIABCX0AAAAABiT0AQAAABjQlkKfBx98cLvqYAmOHj267BKAAZlbAID9wrqF0VV3P/udq3or+7NcVRX/f8B2M7cAAPuFdQt7VVUd7+4jW30dl3cBAAAADOjAVl+gqrajDpbE/x+wE8wtAMB+Yd3CyLYc+vgo3P7lo4zATjC3AAD7hXULe9V2hZFburzr0KFD21IEy3HJJZcsuwRgQOYWAGC/sG5hdFu6kfORI0f62LFj21gOAAAAwGpzI2cAAAAA5hL6AAAAAAxI6AMAAAAwIKEPAAAAwICEPgAAAAADEvoAAAAADEjoAwAAADAgoQ8AAADAgIQ+AAAAAAMS+gAAAAAMSOgDAAAAMCChDwAAAMCAhD4AAAAAAxL6AAAAAAxI6AMAAAAwIKEPAAAAwICEPgAAAAADEvoAAAAADEjoAwAAADAgoQ8AAADAgIQ+AAAAAAMS+gAAAAAMSOgDAAAAMKDq7me/c9XnktyzfeUwuINJHll2EewLeoWzoV9YlF7hbOgXFqVXOBv6hUVd0N3P2+qLHNji/vd095GtFsFqqKpj+oVF6BXOhn5hUXqFs6FfWJRe4WzoFxZVVce243Vc3gUAAAAwIKEPAAAAwIC2Gvr8ybZUwarQLyxKr3A29AuL0iucDf3CovQKZ0O/sKht6ZUt3cgZAAAAgL3J5V0AAAAAAxL6AAAAAAxoodCnqi6rqnuq6r6q+tUZ259TVTdN2z9WVYe3vVL2vKp6aVV9tKo+VVV3V9UvzhhztKoeq6oT07/fXEat7A1VdbKq7pp64Su+krDW/ME0t/xbVb1qGXWyXFV1wbo540RVPV5Vb9swxtyywqrqhqp6uKo+ue65F1TV7VV17/TzvDn7XjONubeqrtm9qlmWOf3yu1X1melYc3NVPX/OvpsetxjLnF65rqpOrTveXD5n303PnxjPnH65aV2vnKyqE3P2NbeskHnnzTu1djnjPX2q6pwk/57ktUkeSPLxJG/u7k+tG/NzSb61u6+tqquTvLG7f3jRN80YqupQkkPdfWdVPS/J8SRv2NArR5P8Une/fjlVspdU1ckkR7r7kTnbL0/yC0kuT3JRkt/v7ot2r0L2mumYdCrJRd19/7rnj8bcsrKq6nuTPJHkz7v7FdNz70zyaHe/fTrhOq+7f2XDfi9IcizJkSSdtePWhd39P7v6BthVc/rl0iT/1N1PVtU7kmRjv0zjTmaT4xZjmdMr1yV5orvftcl+Zzx/Yjyz+mXD9ncneay7r5+x7WTMLStj3nlzkrdkB9Yui3zS59VJ7uvu/+zuLyb5qyRXbhhzZZIbp8cfTPKaqqoFXpuBdPfp7r5zevy5JJ9O8uLlVsU+d2XWDpzd3Xckef40SbK6XpPkP9YHPtDd/5Lk0Q1Pr1+b3Ji1xdRGP5Dk9u5+dFos3Z7ksp2qk71hVr9090e6+8np1zuSvGTXC2PPmTO3LGKR8ycGs1m/TOfGP5TkA7taFHvSJufNO7J2WST0eXGS/1r3+wP5yhP5Z8ZMB8zHkrxwgddmULV2id+3J/nYjM0XV9UnqurWqnr57lbGHtNJPlJVx6vqp2dsX2T+YbVcnfkLJnML653f3aenx59Ncv6MMeYYZvmJJLfO2Xam4xar4a3TpYA3zLn8wtzCRt+T5KHuvnfOdnPLitpw3rwjaxc3cmbbVdXXJPnbJG/r7sc3bL4zyTd297cleU+Sv9/l8thbvru7X5XkdUl+fvpYLMxUVV+d5IokfzNjs7mFuXrtWvbNr2eHJFX1G0meTPL+OUMct/ijJC9L8sokp5O8e6nVsF+8OZt/ysfcsoI2O2/ezrXLIqHPqSQvXff7S6bnZo6pqgNJvjbJf29HgewvVXVu1hr3/d39dxu3d/fj3f3E9PhDSc6tqoO7XCZ7RHefmn4+nOTmrH0cer1F5h9Wx+uS3NndD23cYG5hhoeevhx0+vnwjDHmGJ5RVW9J8vokP9Jzbnq5wHGLwXX3Q939f939VJI/zeweMLfwjOn8+E1Jbpo3xtyyeuacN+/I2mWR0OfjSb65qr5p+ivr1Ulu2TDmliRP3zX6qqzdCM9f1FbMdK3qe5N8urt/b86Yr3v6fk9V9eqs9aCAcAVV1XOnG5elqp6b5NIkn9ww7JYkP1ZrviNrN787HVbV3L+SmVuYYf3a5Jok/zBjzIeTXFpV502XaFw6PceKqarLkvxykiu6+/Nzxixy3GJwG+4t+MbM7oFFzp9YHd+f5DPd/cCsjeaW1bPJefOOrF0OnKmg6VsM3jq90DlJbujuu6vq+iTHuvuWqeC/qKr7snbzqqvP9LoM6buS/GiSu+rLX0f460m+IUm6+4+zFgr+bFU9meQLSa4WEK6s85PcPJ2nH0jyl919W1VdmzzTLx/K2jd33Zfk80l+fEm1smTTIui1SX5m3XPre8XcssKq6gNJjiY5WFUPJPmtJG9P8tdV9ZNJ7s/aDTRTVUeSXNvdP9Xdj1bVb2ftBC1Jru/uZ3PTVvaROf3ya0mek+T26bh0x/SttF+f5M+6+/LMOW4t4S2wS+b0ytGqemXWLrs4mem4tL5X5p0/7f47YDfN6pfufm9m3I/Q3LLy5p0378ja5Yxf2Q4AAADA/uNGzgAAAAADEvoAAAAADEjoAwAAADAgoQ8AAADAgIQ+AAAAAAMS+gAA+1JVvbCqTkz/PltVp6bHT1TVHy67PgCAZfOV7QDAvldV1yV5orvftexaAAD2Cp/0AQCGUlVHq+ofp8fXVdWNVfWvVXV/Vb2pqt5ZVXdV1W1Vde407sKq+ueqOl5VH66qQ8t9FwAAWyf0AQBG97Ik35fkiiTvS/LR7v6WJF9I8oNT8POeJFd194VJbkjyO8sqFgBguxxYdgEAADvs1u7+UlXdleScJLdNz9+V5HCSC5K8IsntVZVpzOkl1AkAsK2EPgDA6P43Sbr7qar6Un/5hoZPZW0tVEnu7u6Ll1UgAMBOcHkXALDq7knyoqq6OEmq6tyqevmSawIA2DKhDwCw0rr7i0muSvKOqvpEkhNJvnOpRQEAbANf2Q4AAAAwIJ/0AQAAABiQ0AcAAABgQEIfAAAAgAEJfQAAAAAGJPQBAAAAGJDQBwAAAGBAQh8AAACAAf0/Js2UR0mk1w8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Timeline(uri=None, segments=[<Segment(0, 2)>, <Segment(2, 7)>, <Segment(7, 17)>, <Segment(17, 20)>])>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scd = Timeline([Segment(0, 2), Segment(2, 7), Segment(7, 17), Segment(17, 20)])\n",
    "scd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the model is *not*  trained to detect non-speech/speaker changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "gap_max_duration = 1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the gap between first and second `vad` segments is shorter than `gap_max_duration` and since `scd` did not detect any speaker change between those two segments, we expect the following speech turn segmentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<pyannote.core.annotation.Annotation at 0x1167b7a60>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speech_turns = Annotation()\n",
    "speech_turns[Segment(0, 2)] = 'A'\n",
    "speech_turns[Segment(2, 5)] = 'B'\n",
    "speech_turns[Segment(6, 7)] = 'B'  # same label as previous segment\n",
    "speech_turns[Segment(7, 11)] = 'C'\n",
    "speech_turns[Segment(13, 16)] = 'D'\n",
    "speech_turns[Segment(18, 20)] = 'E'\n",
    "speech_turns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go step by step.\n",
    "\n",
    "We start by computing `vad` support with filled short gaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Timeline(uri=None, segments=[<Segment(0, 11)>, <Segment(13, 16)>, <Segment(18, 20)>])>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vad_support = vad.support(collar=gap_max_duration)\n",
    "vad_support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then split resulting speech regions at speaker change points detected by `scd`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<pyannote.core.annotation.Annotation at 0x1166b4fa0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scd_support = scd.crop(vad_support).to_annotation(generator=\"string\")\n",
    "scd_support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we go back to the original `vad` because we incorrectly filled short gaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<pyannote.core.annotation.Annotation at 0x116869a60>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speech_turns = scd_support.crop(vad)\n",
    "speech_turns"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}