6-Prototype Speaker Recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import application\n",
    "import audiostream\n",
    "import audiolib\n",
    "import config\n",
    "import models\n",
    "import charts\n",
    "\n",
    "import time\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resuming at batch 42548\n",
      "Loading model from checkpoints\\voice-embeddings.h5\n",
      "Preloaded model from checkpoints\\voice-embeddings.h5\n"
     ]
    }
   ],
   "source": [
    "application.init()\n",
    "model = application.load_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'RefVariable' object has no attribute 'get_value'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-f3864feeefaa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'RefVariable' object has no attribute 'get_value'"
     ]
    }
   ],
   "source": [
    "model.optimizer.lr.get_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "clip1 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sound_chunks(sound, chunk_seconds=1.6, step_seconds=0.5, sample_rate=44100):\n",
    "    \"\"\"Return a sequence of sound chunks from a sound clip.\n",
    "    Each chunk will be 1.6 seconds of the sound, and each\n",
    "    successive chunk will be advanced by the specified number of seconds.\n",
    "    sound: a numpy array of 16-bit signed integers representing a sound sample.\n",
    "    \"\"\"\n",
    "    chunk_len = int(chunk_seconds * sample_rate)\n",
    "    chunk_step = int(step_seconds * sample_rate)\n",
    "    chunk_count = int(len(sound) / chunk_step)\n",
    "    for i in range(chunk_count):\n",
    "        start = i * chunk_step\n",
    "        end = start + chunk_len\n",
    "        yield sound[start:end]\n",
    "\n",
    "def embeddings_from_sound(model, sound, sample_rate=16000):\n",
    "    \"\"\"Return a sequence of embeddings from the different time slices\n",
    "    in the sound clip.\n",
    "    sound: a numpy array of 16-bit signed integers representing a sound sample.\n",
    "    \"\"\"\n",
    "    # The 1.601 is a hack to make sure we end up with a shape of 160 instead of 159.\n",
    "    # What we actually want is 1.6.\n",
    "    #*TODO: Figure out a better way to fix the 159->160 off by one error than adding .001.\n",
    "    chunk_seconds=1.61\n",
    "    for chunk in sound_chunks(sound, chunk_seconds=chunk_seconds, sample_rate=sample_rate):\n",
    "        # The last portion of the sound may be less than our desired length.\n",
    "        # We can safely skip it because we'll process it later as it shifts down the time window.\n",
    "        lc = len(chunk)\n",
    "        #print('lc=%d sec=%f delta=%f' % (lc, lc/sample_rate, lc/sample_rate - chunk_seconds))\n",
    "        if len(chunk)/sample_rate - chunk_seconds < -0.009:\n",
    "            continue\n",
    "        yield calc_embedding(model, chunk)\n",
    "\n",
    "def calc_embedding(model, sound, sample_rate=16000):\n",
    "    \"\"\"\n",
    "    sound: a numpy array of 16-bit signed sound samples.\n",
    "    \"\"\"\n",
    "    features = audiolib.extract_features(sound, sample_rate=sample_rate, num_filters=config.NUM_FILTERS)\n",
    "    if len(features) < 160:\n",
    "        raise Exception('need exactly 160 features to calculate an embedding, but got %d' % len(features))\n",
    "    emb = models.get_embedding(model, features)\n",
    "    return emb\n",
    "\n",
    "def compare_embeddings(emb1, emb2):\n",
    "    \"\"\"Returns a scalar indicating the difference between 2 embeddings.\n",
    "    Smaller numbers indicate closer.\n",
    "    \"\"\"\n",
    "    dist = np.linalg.norm(emb1 - emb2)\n",
    "    return dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64000, 4.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clip1), len(clip1)/16000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def compare_embedding_permutations(embeddings):\n",
    "    \"\"\"Compare multiple embeddings against each other.\n",
    "    Given a list of multiple embeddings, compare all the combinations of them taken 2 at a time.\n",
    "    Returns a list of scalars representing the various comparisons.\n",
    "    \"\"\"\n",
    "    for emb1, emb2 in itertools.permutations(embeddings, 2):\n",
    "        yield compare_embeddings(emb1, emb2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "rico1_embs = list(embeddings_from_sound(model, clip1, sample_rate=16000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "rico1_comparisons = list(compare_embedding_permutations(rico1_embs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PA1uAc5NsGao9SdKehjyCfy7w11V1a1U9BHwAeMmA7UmSZkhVDbPj5Gxga1W9slv/aeAfVtXPzdpuG7CtWz0B+OogBcFRwLcH2vdqa6Uv9mNtaaUf0E5f+vTj2KpaP9cD61a+nr+TOe7b669JVW0Htg9Yx6iYZLKqJoZuZzW00hf7sba00g9opy/L7ceQQzR3AMfMWN8I3Dlge5KkGYYM+C8Bxyd5WpInAucAHxmwPUnSDIMN0VTVw0l+DvgEcCBwSVXdOFR7PQw+DLSKWumL/VhbWukHtNOXZfVjsDdZJUnj5SdZJalRBrwkNaq5gF9oeoQkv5TkpiRfSfK/khw7jjoX0qMfP5vk+iTXJfncWv6UcN8pK5KcnaSSrMnT23q8Juclmepek+uSvHIcdS6kz+uR5Ke635Mbk7x/tWvso8fr8dYZr8VfJbl3HHX20aMvm5J8Osm1XXad0WvHVdXMjdGbuX8DHAc8EfhLYMusbU4DDumWXw18cNx1L7Efh89YPgv4+LjrXmpfuu0OAz4LXANMjLvuJb4m5wEXj7vWFejH8cC1wN/r1p8y7rqX+nM1Y/vXMTrRY+y1L/E12Q68ulveAuzqs+/WjuAXnB6hqj5dVd/pVq9hdH7+WtOnH7tnrD6ZOT5Etkb0nbLit4DfBb67msUtQitTb/Tpx6uAt1fV/wWoqrtXucY+Fvt6nAtctiqVLV6fvhRweLd8BD0/U9RawB8N3D5j/Y7uvsfzCuBjg1a0NL36keS1Sf6GUTD+/CrVtlgL9iXJicAxVXXlaha2SH1/tv5F9y/0jiTHzPH4uPXpxzOAZyT5fJJrkmxdter66/273g3DPg34i1Woayn69OXNwMuT3AH8GaP/SBbUWsD3mh4BIMnLgQngPw9a0dL0nebh7VX1dOB84E2DV7U08/YlyQHAW4FfXrWKlqbPa/JRYHNV/TDwKeDdg1e1eH36sY7RMM0LGB35vivJkQPXtVi9f9cZfchyR1U9MmA9y9GnL+cCf1RVG4EzgEu73515tRbwvaZHSPIi4NeAs6rqwVWqbTEWO83DB4CfGLSipVuoL4cBzwauSrILeB7wkTX4RuuCr0lV3TPj5+mdwMmrVNti9PnZugP406r6XlV9ndEEgMevUn19LeZ35BzW7vAM9OvLK4DLAarqauAgRhORzW/cbzCs8JsV64BbGf07Nv1mxbNmbXMiozc0jh93vcvsx/Ezll8MTI677qX2Zdb2V7E232Tt85psmLH8UuCacde9xH5sBd7dLR/FaPjg+8dd+1J+rhjNULuL7kOda/HW8zX5GHBet/xDjP4ALNinsXdugG/WGcBfdSH+a919/5HR0TqM/nX+FnBdd/vIuGteYj8uAm7s+vDp+UJz3LeF+jJr2zUZ8D1fk//UvSZ/2b0mzxx3zUvsR4ALgZuA64Fzxl3zUn+uGI1d/864a12B12QL8PnuZ+s64Mf67NepCiSpUa2NwUuSOga8JDXKgJekRhnwktQoA16SGmXAa7+S5Kok/2zWfa9P8ofzPOf+4SuTVp4Br/3NZYw+2TjTWv+ko7QkBrz2NzuAM5M8CSDJZuAHgOu66wN8uZtnf6+ZCZO8IMmVM9YvTnJet3xyks8k2ZnkE0k2rEZnpPkY8NqvVNU9wBcZfRwfRkfvHwQeAF5aVScxumbA7yeZaxKovSR5AvBfgLOr6mTgEuAtK127tFjrxl2ANAbTwzR/2n39t4w+nv/bSZ4PPMpoutanAnf12N8JjCZM+2T3N+FA4JsrX7a0OAa89kdXABcmOQk4uKq+3A21rAdOrqrvdTNbHjTreQ+z53+9048HuLGqThm2bGlxHKLRfqeq7mc0qdklPPbm6hHA3V24nwbMda3e24AtSZ6U5Ajghd39XwXWJzkFRkM2SZ41ZB+kPjyC1/7qMuBPeOyMmvcBH00yyWi2vltmP6Gqbk9yOfAV4GuMrltKVT2U5GzgD7rgXwe8jdHMktLYOJukJDXKIRpJapQBL0mNMuAlqVEGvCQ1yoCXpEYZ8JLUKANekhr1/wHSrjwSS5Gj2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.histogram(rico1_comparisons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40245754"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_embeddings(rico1_embs[0], emb1[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "rico_clip2 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "rico2_embs = list(embeddings_from_sound(model, rico_clip2, sample_rate=16000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "rico2_comparisons = list(compare_embedding_permutations(rico2_embs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.histogram(rico2_comparisons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60666585"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_embeddings(rico1_embs[3], rico2_embs[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "cor_clip1 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "cor_clip2 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "cor_clip3 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor1_embs = list(embeddings_from_sound(model, cor_clip1, sample_rate=16000))\n",
    "cor2_embs = list(embeddings_from_sound(model, cor_clip1, sample_rate=16000))\n",
    "cor3_embs = list(embeddings_from_sound(model, cor_clip1, sample_rate=16000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5730244"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_embeddings(rico1_embs[2], cor1_embs[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "jecka_clip1 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "jecka_clip2 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording\n",
      "4 seconds remaining\n",
      "3 seconds remaining\n",
      "2 seconds remaining\n",
      "1 seconds remaining\n",
      "Recording finished\n"
     ]
    }
   ],
   "source": [
    "jecka_clip3 = audiostream.record(4, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "audiolib.save_wav(r'd:\\tmp\\rico1.wav', clip1, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\rico2.wav', rico_clip2, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\cor_clip1.wav', cor_clip1, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\cor_clip2.wav', cor_clip2, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\cor_clip3.wav', cor_clip3, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\jecka_clip1.wav', jecka_clip1, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\jecka_clip2.wav', jecka_clip2, rate=16000)\n",
    "audiolib.save_wav(r'd:\\tmp\\jecka_clip3.wav', jecka_clip3, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "jecka1_embs = list(embeddings_from_sound(model, jecka_clip1, sample_rate=16000))\n",
    "jecka2_embs = list(embeddings_from_sound(model, jecka_clip2, sample_rate=16000))\n",
    "jecka3_embs = list(embeddings_from_sound(model, jecka_clip3, sample_rate=16000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2861824"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_embeddings(jecka1_embs[0],rico1_embs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8714248"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_embeddings(jecka1_embs[1],jecka2_embs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1345081"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_embeddings(cor1_embs[1],jecka2_embs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KnownSpeaker:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.embeddings = [] # A list of embeddings of known utterances by this speaker.\n",
    "    \n",
    "    def add_embeddings(self, embeddings):\n",
    "        self.embeddings.extend(embeddings)\n",
    "    \n",
    "    def distance(self, anchor_embedding):\n",
    "        \"\"\"Returns the average distance of the embedding to known\n",
    "        utterances by this speaker.\n",
    "        \"\"\"\n",
    "        distances = [compare_embeddings(anchor_embedding, emb) for emb in self.embeddings]\n",
    "        return np.mean(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "rico = KnownSpeaker('Rico')\n",
    "rico.add_embeddings(rico1_embs)\n",
    "rico.add_embeddings(rico2_embs)\n",
    "cor = KnownSpeaker('Cor')\n",
    "cor.add_embeddings(cor1_embs)\n",
    "cor.add_embeddings(cor2_embs)\n",
    "cor.add_embeddings(cor3_embs)\n",
    "jecka = KnownSpeaker('Jecka')\n",
    "jecka.add_embeddings(jecka1_embs)\n",
    "jecka.add_embeddings(jecka2_embs)\n",
    "jecka.add_embeddings(jecka2_embs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.49930662, 1.5563623, 1.2464644)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = rico2_embs[3]\n",
    "#emb = cor1_embs[3]\n",
    "#emb = jecka2_embs[3]\n",
    "rico.distance(emb), cor.distance(emb), jecka.distance(emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RecognizerPrototype:\n",
    "    def __init__(self, known_speakers, model):\n",
    "        self.known_speakers = known_speakers\n",
    "        self.model = model\n",
    "    \n",
    "    def run(self, duration_seconds=20):\n",
    "        rate = 16000\n",
    "        stream = audiostream.AudioStream(seconds=4, rate=rate)\n",
    "        stream.start()\n",
    "        seconds_remaining = duration_seconds\n",
    "        speaker_name = '-'\n",
    "        speaker = None\n",
    "        step_duration = 0.5\n",
    "        dist = 999\n",
    "        while seconds_remaining > 0:\n",
    "            print('%d remaining. dist=%f Speaker=%s' % (seconds_remaining, dist, speaker_name))\n",
    "            time.sleep(step_duration)\n",
    "            \n",
    "            sound = stream.sound_array()\n",
    "            if len(sound)/rate < 1.61:\n",
    "                continue\n",
    "            embs = list(embeddings_from_sound(self.model, sound, sample_rate=rate))\n",
    "            # To avoid the rounding error of 159 frames instead of 160, we need at least 2 full chunks.\n",
    "            if len(embs) <1:\n",
    "                continue\n",
    "            # Later, we could change this to use multiple embeddings form the last few seconds.\n",
    "            # For now we'll just get the last full chunk.\n",
    "            embedding = embs[-1]\n",
    "            dist, speaker = self.determine_speaker(embedding)\n",
    "            if dist < 0.99:\n",
    "                speaker_name = speaker.name\n",
    "            else:\n",
    "                speaker_name = '-'\n",
    "            \n",
    "            seconds_remaining -= step_duration\n",
    "    \n",
    "    def determine_speaker(self, embedding):\n",
    "        best_speaker = None\n",
    "        best_dist = 999\n",
    "        for speaker in self.known_speakers:\n",
    "            dist = speaker.distance(embedding)\n",
    "            if dist < best_dist:\n",
    "                best_dist = dist\n",
    "                best_speaker = speaker\n",
    "        return best_dist, best_speaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 remaining. dist=999.000000 Speaker=-\n",
      "15 remaining. dist=999.000000 Speaker=-\n",
      "15 remaining. dist=999.000000 Speaker=-\n",
      "15 remaining. dist=999.000000 Speaker=-\n",
      "14 remaining. dist=1.161672 Speaker=-\n",
      "14 remaining. dist=0.963942 Speaker=Cor\n",
      "13 remaining. dist=0.787207 Speaker=Cor\n",
      "13 remaining. dist=0.941732 Speaker=Cor\n",
      "12 remaining. dist=0.850635 Speaker=Cor\n",
      "12 remaining. dist=0.895779 Speaker=Cor\n",
      "11 remaining. dist=1.052427 Speaker=-\n",
      "11 remaining. dist=0.857238 Speaker=Cor\n",
      "10 remaining. dist=1.042673 Speaker=-\n",
      "10 remaining. dist=1.156600 Speaker=-\n",
      "9 remaining. dist=1.305294 Speaker=-\n",
      "9 remaining. dist=1.246918 Speaker=-\n",
      "8 remaining. dist=1.161736 Speaker=-\n",
      "8 remaining. dist=0.900886 Speaker=Rico\n",
      "7 remaining. dist=1.151994 Speaker=-\n",
      "7 remaining. dist=0.781099 Speaker=Rico\n",
      "6 remaining. dist=0.743346 Speaker=Rico\n",
      "6 remaining. dist=1.173527 Speaker=-\n",
      "5 remaining. dist=1.402816 Speaker=-\n",
      "5 remaining. dist=1.385853 Speaker=-\n",
      "4 remaining. dist=1.395822 Speaker=-\n",
      "4 remaining. dist=1.351745 Speaker=-\n",
      "3 remaining. dist=1.267563 Speaker=-\n",
      "3 remaining. dist=1.295461 Speaker=-\n",
      "2 remaining. dist=1.383031 Speaker=-\n",
      "2 remaining. dist=1.380281 Speaker=-\n",
      "1 remaining. dist=1.378038 Speaker=-\n",
      "1 remaining. dist=1.373088 Speaker=-\n",
      "0 remaining. dist=1.425356 Speaker=-\n"
     ]
    }
   ],
   "source": [
    "r = RecognizerPrototype([rico, cor, jecka], model)\n",
    "r.run(duration_seconds=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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