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DEF CON 13 is proud to present Marshall Betto with the Protocol Informatics Project.", "tokens": [ 50414, 1057, 558, 13, 10113, 37, 16596, 3705, 307, 4570, 281, 1974, 17279, 6279, 1353, 365, 264, 48753, 34301, 30292, 9849, 13, 50814 ], "temperature": 0.0, "avg_logprob": -0.28835226429833305, "compression_ratio": 1.7702702702702702, "no_speech_prob": 0.2442626953125, "words": [ { "start": 2.4, "end": 2.88, "word": " All", "probability": 0.1490478515625 }, { "start": 2.88, "end": 3.2, "word": " right", "probability": 0.88427734375 }, { "start": 3.2, "end": 3.68, "word": ".", "probability": 0.6884765625 }, { "start": 4.48, "end": 4.96, "word": " DEF", "probability": 0.659912109375 }, { "start": 4.96, "end": 5.12, "word": " CON", "probability": 0.8603515625 }, { "start": 5.12, "end": 5.5, "word": " 13", "probability": 0.78759765625 }, { "start": 5.5, "end": 5.72, "word": " is", "probability": 0.82373046875 }, { "start": 5.72, "end": 5.96, "word": " proud", "probability": 0.84033203125 }, { "start": 5.96, "end": 6.08, "word": " to", "probability": 0.91064453125 }, { "start": 6.08, "end": 6.44, "word": " present", "probability": 0.7451171875 }, { "start": 6.44, "end": 6.9, "word": " Marshall", "probability": 0.57666015625 }, { "start": 6.9, "end": 7.24, "word": " Betto", "probability": 0.60546875 }, { "start": 7.24, "end": 7.5, "word": " with", "probability": 0.79052734375 }, { "start": 7.5, "end": 7.88, "word": " the", "probability": 0.78857421875 }, { "start": 7.88, "end": 8.18, "word": " Protocol", "probability": 0.31494140625 }, { "start": 8.18, "end": 9.7, "word": " Informatics", "probability": 0.619873046875 }, { "start": 9.7, "end": 10.72, "word": " Project", "probability": 0.63134765625 }, { "start": 10.72, "end": 11.2, "word": ".", "probability": 0.97119140625 } ] }, { "id": 2, "seek": 3000, "start": 13.4, "end": 18.1, "text": " Saturday morning at DEF CON. God damn, why am I working Saturday morning at DEF CON?", "tokens": [ 50914, 8803, 2446, 412, 10113, 37, 16596, 13, 1265, 8151, 11, 983, 669, 286, 1364, 8803, 2446, 412, 10113, 37, 16596, 30, 51164 ], "temperature": 0.0, "avg_logprob": -0.28835226429833305, "compression_ratio": 1.7702702702702702, "no_speech_prob": 0.2442626953125, "words": [ { "start": 13.4, "end": 13.88, "word": " Saturday", "probability": 0.327880859375 }, { "start": 13.88, "end": 14.3, "word": " morning", "probability": 0.75341796875 }, { "start": 14.3, "end": 14.5, "word": " at", "probability": 0.892578125 }, { "start": 14.5, "end": 14.68, "word": " DEF", "probability": 0.940673828125 }, { "start": 14.68, "end": 14.88, "word": " CON", "probability": 0.916015625 }, { "start": 14.88, "end": 15.12, "word": ".", "probability": 0.8330078125 }, { "start": 15.12, "end": 15.38, "word": " God", "probability": 0.87890625 }, { "start": 15.38, "end": 15.64, "word": " damn", "probability": 0.442138671875 }, { "start": 15.64, "end": 15.7, "word": ",", "probability": 0.68701171875 }, { "start": 15.7, "end": 15.82, "word": " why", "probability": 0.8046875 }, { "start": 15.82, "end": 15.94, "word": " am", "probability": 0.88720703125 }, { "start": 15.94, "end": 16.04, "word": " I", "probability": 0.9951171875 }, { "start": 16.04, "end": 16.28, "word": " working", "probability": 0.734375 }, { "start": 16.28, "end": 16.66, "word": " Saturday", "probability": 0.86767578125 }, { "start": 16.66, "end": 17.04, "word": " morning", "probability": 0.775390625 }, { "start": 17.04, "end": 17.22, "word": " at", "probability": 0.9013671875 }, { "start": 17.22, "end": 17.42, "word": " DEF", "probability": 0.92822265625 }, { "start": 17.42, "end": 17.62, "word": " CON", "probability": 0.904296875 }, { "start": 17.62, "end": 18.1, "word": "?", "probability": 0.81298828125 } ] }, { "id": 3, "seek": 3000, "start": 18.16, "end": 20.48, "text": " All right. Marshall, take it away.", "tokens": [ 51164, 1057, 558, 13, 17279, 11, 747, 309, 1314, 13, 51264 ], "temperature": 0.0, "avg_logprob": -0.28835226429833305, "compression_ratio": 1.7702702702702702, "no_speech_prob": 0.2442626953125, "words": [ { "start": 18.16, "end": 18.42, "word": " All", "probability": 0.880859375 }, { "start": 18.42, "end": 18.6, "word": " right", "probability": 0.88330078125 }, { "start": 18.6, "end": 18.98, "word": ".", "probability": 0.59033203125 }, { "start": 18.98, "end": 19.46, "word": " Marshall", "probability": 0.75927734375 }, { "start": 19.46, "end": 19.56, "word": ",", "probability": 0.921875 }, { "start": 19.56, "end": 19.72, "word": " take", "probability": 0.82080078125 }, { "start": 19.72, "end": 19.8, "word": " it", "probability": 0.90673828125 }, { "start": 19.8, "end": 20.0, "word": " away", "probability": 0.80615234375 }, { "start": 20.0, "end": 20.48, "word": ".", "probability": 0.99462890625 } ] }, { "id": 4, "seek": 3000, "start": 26.52, "end": 29.76, "text": " All right, so this project is called the Protocol Informatics Project.", "tokens": [ 51364, 1057, 558, 11, 370, 341, 1716, 307, 1219, 264, 48753, 34301, 30292, 9849, 13, 51514 ], "temperature": 0.0, "avg_logprob": -0.28835226429833305, "compression_ratio": 1.7702702702702702, "no_speech_prob": 0.2442626953125, "words": [ { "start": 26.52, "end": 27.0, "word": " All", "probability": 0.1300048828125 }, { "start": 27.0, "end": 27.16, "word": " right", "probability": 0.87744140625 }, { "start": 27.16, "end": 27.2, "word": ",", "probability": 0.439697265625 }, { "start": 27.2, "end": 27.36, "word": " so", "probability": 0.8955078125 }, { "start": 27.36, "end": 27.64, "word": " this", "probability": 0.830078125 }, { "start": 27.64, "end": 27.92, "word": " project", "probability": 0.84375 }, { "start": 27.92, "end": 28.08, "word": " is", "probability": 0.90869140625 }, { "start": 28.08, "end": 28.26, "word": " called", "probability": 0.8017578125 }, { "start": 28.26, "end": 28.38, "word": " the", "probability": 0.75390625 }, { "start": 28.38, "end": 28.64, "word": " Protocol", "probability": 0.80126953125 }, { "start": 28.64, "end": 29.12, "word": " Informatics", "probability": 0.934326171875 }, { "start": 29.12, "end": 29.46, "word": " Project", "probability": 0.87890625 }, { "start": 29.46, "end": 29.76, "word": ".", "probability": 0.9423828125 } ] }, { "id": 5, "seek": 3000, "start": 29.76, "end": 34.86, "text": " It's basically a play on words from bioinformatics, which is kind of my main inspiration for this project.", "tokens": [ 51514, 467, 311, 1936, 257, 862, 322, 2283, 490, 12198, 37811, 30292, 11, 597, 307, 733, 295, 452, 2135, 10249, 337, 341, 1716, 13, 51764 ], "temperature": 0.0, "avg_logprob": -0.28835226429833305, "compression_ratio": 1.7702702702702702, "no_speech_prob": 0.2442626953125, "words": [ { "start": 29.76, "end": 29.92, "word": " It's", "probability": 0.942626953125 }, { "start": 29.92, "end": 30.16, "word": " basically", "probability": 0.88525390625 }, { "start": 30.34, "end": 30.52, "word": " a play", "probability": 0.86962890625 }, { "start": 30.52, "end": 30.64, "word": " on", "probability": 0.8955078125 }, { "start": 30.64, "end": 30.92, "word": " words", "probability": 0.724609375 }, { "start": 30.92, "end": 31.26, "word": " from", "probability": 0.80859375 }, { "start": 31.26, "end": 32.12, "word": " bioinformatics", "probability": 0.88720703125 }, { "start": 32.12, "end": 32.2, "word": ",", "probability": 0.7734375 }, { "start": 32.2, "end": 32.32, "word": " which", "probability": 0.9072265625 }, { "start": 32.32, "end": 32.46, "word": " is", "probability": 0.88671875 }, { "start": 32.46, "end": 32.64, "word": " kind", "probability": 0.68701171875 }, { "start": 32.64, "end": 32.72, "word": " of", "probability": 0.9052734375 }, { "start": 32.72, "end": 32.9, "word": " my", "probability": 0.8857421875 }, { "start": 32.9, "end": 33.24, "word": " main", "probability": 0.80908203125 }, { "start": 33.24, "end": 33.64, "word": " inspiration", "probability": 0.81982421875 }, { "start": 33.64, "end": 33.86, "word": " for", "probability": 0.89697265625 }, { "start": 33.86, "end": 34.02, "word": " this", "probability": 0.88720703125 }, { "start": 34.02, "end": 34.38, "word": " project", "probability": 0.8408203125 }, { "start": 34.38, "end": 34.86, "word": ".", "probability": 0.99560546875 } ] }, { "id": 6, "seek": 5828, "start": 37.2, "end": 41.7, "text": " Yeah, it's not that loud. Should I talk closer? Okay, there we go. Thanks.", "tokens": [ 50414, 865, 11, 309, 311, 406, 300, 6588, 13, 6454, 286, 751, 4966, 30, 1033, 11, 456, 321, 352, 13, 2561, 13, 50614 ], "temperature": 0.0, "avg_logprob": -0.19817708445092042, "compression_ratio": 1.5958188153310104, "no_speech_prob": 0.1329345703125, "words": [ { "start": 37.2, "end": 37.64, "word": " Yeah", "probability": 0.03363037109375 }, { "start": 37.64, "end": 37.78, "word": ",", "probability": 0.853515625 }, { "start": 37.78, "end": 37.84, "word": " it's", "probability": 0.769775390625 }, { "start": 37.84, "end": 37.96, "word": " not", "probability": 0.88818359375 }, { "start": 37.96, "end": 38.08, "word": " that", "probability": 0.88232421875 }, { "start": 38.08, "end": 38.3, "word": " loud", "probability": 0.90869140625 }, { "start": 38.3, "end": 38.54, "word": ".", "probability": 0.92626953125 }, { "start": 38.54, "end": 38.64, "word": " Should", "probability": 0.66455078125 }, { "start": 38.64, "end": 38.74, "word": " I", "probability": 0.99560546875 }, { "start": 38.74, "end": 38.88, "word": " talk", "probability": 0.80224609375 }, { "start": 38.88, "end": 39.22, "word": " closer", "probability": 0.80322265625 }, { "start": 39.22, "end": 39.66, "word": "?", "probability": 0.96240234375 }, { "start": 39.74, "end": 39.96, "word": " Okay", "probability": 0.751953125 }, { "start": 39.96, "end": 40.28, "word": ",", "probability": 0.6826171875 }, { "start": 40.28, "end": 40.58, "word": " there", "probability": 0.84033203125 }, { "start": 40.58, "end": 40.66, "word": " we", "probability": 0.87646484375 }, { "start": 40.66, "end": 40.82, "word": " go", "probability": 0.904296875 }, { "start": 40.82, "end": 41.04, "word": ".", "probability": 0.409912109375 }, { "start": 41.04, "end": 41.26, "word": " Thanks", "probability": 0.68603515625 }, { "start": 41.26, "end": 41.7, "word": ".", "probability": 0.984375 } ] }, { "id": 7, "seek": 5828, "start": 42.42, "end": 46.7, "text": " So, my name is Marshall Betto. I'm a research scientist at McAfee.", "tokens": [ 50664, 407, 11, 452, 1315, 307, 17279, 6279, 1353, 13, 286, 478, 257, 2132, 12662, 412, 4050, 32104, 1653, 13, 50864 ], "temperature": 0.0, "avg_logprob": -0.19817708445092042, "compression_ratio": 1.5958188153310104, "no_speech_prob": 0.1329345703125, "words": [ { "start": 42.42, "end": 42.86, "word": " So", "probability": 0.7119140625 }, { "start": 42.86, "end": 43.08, "word": ",", "probability": 0.55810546875 }, { "start": 43.08, "end": 43.26, "word": " my", "probability": 0.8974609375 }, { "start": 43.26, "end": 43.36, "word": " name", "probability": 0.81201171875 }, { "start": 43.36, "end": 43.44, "word": " is", "probability": 0.91015625 }, { "start": 43.44, "end": 43.68, "word": " Marshall", "probability": 0.63916015625 }, { "start": 43.68, "end": 44.02, "word": " Betto", "probability": 0.63232421875 }, { "start": 44.02, "end": 44.4, "word": ".", "probability": 0.7587890625 }, { "start": 44.46, "end": 44.9, "word": " I'm", "probability": 0.821044921875 }, { "start": 45.08, "end": 45.3, "word": " a research", "probability": 0.681640625 }, { "start": 45.3, "end": 45.6, "word": " scientist", "probability": 0.765625 }, { "start": 45.6, "end": 45.8, "word": " at", "probability": 0.88525390625 }, { "start": 45.8, "end": 46.3, "word": " McAfee", "probability": 0.9446614583333334 }, { "start": 46.3, "end": 46.7, "word": ".", "probability": 0.7763671875 } ] }, { "id": 8, "seek": 5828, "start": 46.7, "end": 51.22, "text": " However, this is pretty much all independent research. My company's never even seen this stuff.", "tokens": [ 50864, 2908, 11, 341, 307, 1238, 709, 439, 6695, 2132, 13, 1222, 2237, 311, 1128, 754, 1612, 341, 1507, 13, 51114 ], "temperature": 0.0, "avg_logprob": -0.19817708445092042, "compression_ratio": 1.5958188153310104, "no_speech_prob": 0.1329345703125, "words": [ { "start": 46.7, "end": 47.08, "word": " However", "probability": 0.8076171875 }, { "start": 47.08, "end": 47.26, "word": ",", "probability": 0.96728515625 }, { "start": 47.26, "end": 47.46, "word": " this", "probability": 0.880859375 }, { "start": 47.46, "end": 47.58, "word": " is", "probability": 0.90380859375 }, { "start": 47.58, "end": 47.76, "word": " pretty", "probability": 0.85888671875 }, { "start": 47.76, "end": 47.86, "word": " much", "probability": 0.8125 }, { "start": 47.86, "end": 48.04, "word": " all", "probability": 0.8994140625 }, { "start": 48.04, "end": 48.38, "word": " independent", "probability": 0.77880859375 }, { "start": 48.38, "end": 48.98, "word": " 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What is bioinformatics?", "tokens": [ 51414, 407, 11, 12198, 37811, 30292, 13, 708, 307, 12198, 37811, 30292, 30, 51564 ], "temperature": 0.0, "avg_logprob": -0.19739584003885588, "compression_ratio": 1.6176470588235294, "no_speech_prob": 0.0012645721435546875, "words": [ { "start": 383.02, "end": 383.18, "word": " So", "probability": 0.7255859375 }, { "start": 383.18, "end": 383.34, "word": ",", "probability": 0.5263671875 }, { "start": 383.34, "end": 383.86, "word": " bioinformatics", "probability": 0.91015625 }, { "start": 383.86, "end": 384.0, "word": ".", "probability": 0.65673828125 }, { "start": 384.0, "end": 384.14, "word": " What", "probability": 0.8154296875 }, { "start": 384.14, "end": 384.48, "word": " is", "probability": 0.89990234375 }, { "start": 384.48, "end": 385.0, "word": " bioinformatics", "probability": 0.9578450520833334 }, { "start": 385.0, "end": 385.46, "word": "?", "probability": 0.99560546875 } ] }, { "id": 87, "seek": 38228, "start": 385.86, "end": 388.6, 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" and insert gaps into either sequence to make them the same length.", "tokens": [ 50364, 293, 8969, 15031, 666, 2139, 8310, 281, 652, 552, 264, 912, 4641, 13, 50564 ], "temperature": 0.0, "avg_logprob": -0.2155975862814669, "compression_ratio": 1.7914893617021277, "no_speech_prob": 0.0164031982421875, "words": [ { "start": 699.31, "end": 699.41, "word": " and", "probability": 0.038299560546875 }, { "start": 699.41, "end": 699.57, "word": " insert", "probability": 0.78173828125 }, { "start": 699.57, "end": 699.93, "word": " gaps", "probability": 0.84716796875 }, { "start": 699.93, "end": 700.07, "word": " into", "probability": 0.4228515625 }, { "start": 700.07, "end": 700.31, "word": " either", "probability": 0.8330078125 }, { "start": 700.31, "end": 700.77, "word": " sequence", "probability": 0.8779296875 }, { "start": 700.77, "end": 700.99, "word": " to", "probability": 0.88671875 }, { "start": 700.99, "end": 701.17, "word": " make", "probability": 0.873046875 }, { "start": 701.17, 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programming algorithm, meaning that it operates on matrices.", "tokens": [ 50814, 307, 257, 8546, 9410, 9284, 11, 3620, 300, 309, 22577, 322, 32284, 13, 51014 ], "temperature": 0.0, "avg_logprob": -0.24862636249144, "compression_ratio": 1.5964912280701755, "no_speech_prob": 0.012237548828125, "words": [ { "start": 734.97, "end": 735.17, "word": " is", "probability": 0.1640625 }, { "start": 735.71, "end": 735.99, "word": " a dynamic", "probability": 0.72607421875 }, { "start": 735.99, "end": 736.43, "word": " programming", "probability": 0.63818359375 }, { "start": 736.43, "end": 736.83, "word": " algorithm", "probability": 0.8193359375 }, { "start": 736.83, "end": 736.93, "word": ",", "probability": 0.68603515625 }, { "start": 736.93, "end": 737.09, "word": " meaning", "probability": 0.81298828125 }, { "start": 737.09, "end": 737.33, "word": " that", "probability": 0.81982421875 }, { "start": 737.33, "end": 737.49, "word": " it", "probability": 0.89794921875 }, { "start": 737.49, 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0.6806640625 }, { "start": 884.15, "end": 884.31, "word": " what", "probability": 0.75537109375 }, { "start": 884.31, "end": 884.43, "word": " you", "probability": 0.892578125 }, { "start": 884.43, "end": 884.63, "word": " do", "probability": 0.90087890625 }, { "start": 884.63, "end": 884.93, "word": " is", "probability": 0.904296875 }, { "start": 884.93, "end": 885.37, "word": ",", "probability": 0.391357421875 } ] }, { "id": 201, "seek": 87442, "start": 885.37, "end": 888.43, "text": " you go through and you score every single similarity", "tokens": [ 51114, 291, 352, 807, 293, 291, 6175, 633, 2167, 32194, 51264 ], "temperature": 0.0, "avg_logprob": -0.20154671325828089, "compression_ratio": 1.808695652173913, "no_speech_prob": 0.0384521484375, "words": [ { "start": 885.37, "end": 885.51, "word": " you", "probability": 0.89306640625 }, { "start": 885.51, "end": 885.71, "word": " go", "probability": 0.9091796875 }, { "start": 885.71, "end": 886.03, "word": " through", "probability": 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It's really simple.", "tokens": [ 50864, 467, 311, 257, 2487, 12, 6032, 10336, 13, 467, 311, 534, 2199, 13, 50964 ], "temperature": 0.0, "avg_logprob": -0.17144494975378755, "compression_ratio": 1.6798418972332017, "no_speech_prob": 0.1710205078125, "words": [ { "start": 992.17, "end": 992.29, "word": " It's", "probability": 0.94091796875 }, { "start": 992.41, "end": 992.53, "word": " a text", "probability": 0.92138671875 }, { "start": 992.53, "end": 992.69, "word": "-based", "probability": 0.78759765625 }, { "start": 992.69, "end": 993.09, "word": " protocol", "probability": 0.8408203125 }, { "start": 993.09, "end": 993.55, "word": ".", "probability": 0.69384765625 }, { "start": 993.61, "end": 993.75, "word": " It's", "probability": 0.9482421875 }, { "start": 993.75, "end": 993.97, "word": " really", "probability": 0.7890625 }, { "start": 993.97, "end": 994.35, "word": " simple", "probability": 0.91162109375 }, { "start": 994.35, "end": 994.67, "word": ".", "probability": 0.9638671875 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use", "probability": 0.8291015625 }, { "start": 996.41, "end": 996.57, "word": " this", "probability": 0.88232421875 }, { "start": 996.57, "end": 997.01, "word": " program", "probability": 0.74169921875 } ] }, { "id": 236, "seek": 98552, "start": 997.01, "end": 1000.71, "text": " to do text-based protocols just because they're so easy.", "tokens": [ 51114, 281, 360, 2487, 12, 6032, 20618, 445, 570, 436, 434, 370, 1858, 13, 51314 ], "temperature": 0.0, "avg_logprob": -0.17144494975378755, "compression_ratio": 1.6798418972332017, "no_speech_prob": 0.1710205078125, "words": [ { "start": 997.01, "end": 997.23, "word": " to", "probability": 0.9052734375 }, { "start": 997.23, "end": 997.49, "word": " do", "probability": 0.90380859375 }, { "start": 997.49, "end": 997.75, "word": " text", "probability": 0.90673828125 }, { "start": 997.75, "end": 997.91, "word": "-based", "probability": 0.894775390625 }, { "start": 997.91, "end": 998.37, "word": " protocols", "probability": 0.75 }, { "start": 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example's", "probability": 0.732666015625 }, { "start": 1003.97, "end": 1004.13, "word": " sake", "probability": 0.86474609375 }, { "start": 1004.13, "end": 1004.19, "word": ",", "probability": 0.8984375 } ] }, { "id": 238, "seek": 98552, "start": 1004.19, "end": 1006.95, "text": " I use it just because it's small enough", "tokens": [ 51464, 286, 764, 309, 445, 570, 309, 311, 1359, 1547, 51614 ], "temperature": 0.0, "avg_logprob": -0.17144494975378755, "compression_ratio": 1.6798418972332017, "no_speech_prob": 0.1710205078125, "words": [ { "start": 1004.19, "end": 1004.37, "word": " I", "probability": 0.99951171875 }, { "start": 1004.37, "end": 1004.59, "word": " use", "probability": 0.80908203125 }, { "start": 1004.59, "end": 1004.71, "word": " it", "probability": 0.90283203125 }, { "start": 1004.71, "end": 1004.89, "word": " just", "probability": 0.8759765625 }, { "start": 1004.89, "end": 1005.37, "word": " because", "probability": 0.841796875 }, { "start": 1005.37, "end": 1005.97, "word": " it's", "probability": 0.951904296875 }, { "start": 1005.97, "end": 1006.77, "word": " small", "probability": 0.84375 }, { "start": 1006.77, "end": 1006.95, "word": " enough", "probability": 0.783203125 } ] }, { "id": 239, "seek": 98552, "start": 1006.95, "end": 1008.69, "text": " to fit into a PowerPoint presentation.", "tokens": [ 51614, 281, 3318, 666, 257, 25584, 5860, 13, 51714 ], "temperature": 0.0, "avg_logprob": -0.17144494975378755, "compression_ratio": 1.6798418972332017, "no_speech_prob": 0.1710205078125, "words": [ { "start": 1006.95, "end": 1007.07, "word": " to", "probability": 0.88671875 }, { "start": 1007.07, "end": 1007.17, "word": " fit", "probability": 0.88916015625 }, { "start": 1007.17, "end": 1007.27, "word": " into", "probability": 0.736328125 }, { "start": 1007.39, "end": 1007.63, "word": " a PowerPoint", "probability": 0.86279296875 }, { "start": 1007.63, "end": 1008.23, "word": " presentation", "probability": 0.7587890625 }, { "start": 1008.23, "end": 1008.69, "word": ".", "probability": 0.998046875 } ] }, { "id": 240, "seek": 101462, "start": 1008.69, "end": 1014.57, "text": " So the first sequence is placed in the topmost row,", "tokens": [ 50364, 407, 264, 700, 8310, 307, 7074, 294, 264, 1192, 1761, 5386, 11, 50614 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1008.69, "end": 1009.53, "word": " So", "probability": 0.63037109375 }, { "start": 1009.53, "end": 1010.25, "word": " the", "probability": 0.416015625 }, { "start": 1010.25, "end": 1010.45, "word": " first", "probability": 0.798828125 }, { "start": 1010.45, "end": 1010.75, "word": " sequence", "probability": 0.8720703125 }, { "start": 1010.75, "end": 1011.13, "word": " is", "probability": 0.89990234375 }, { "start": 1011.13, "end": 1011.67, "word": " placed", "probability": 0.8564453125 }, { "start": 1011.67, "end": 1011.95, "word": " in", "probability": 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1015.25, "word": " slash", "probability": 0.435546875 }, { "start": 1015.25, "end": 1015.51, "word": " index", "probability": 0.74951171875 }, { "start": 1015.51, "end": 1016.03, "word": ".html", "probability": 0.90380859375 }, { "start": 1016.03, "end": 1016.39, "word": " HTTP", "probability": 0.2327880859375 }, { "start": 1016.39, "end": 1016.73, "word": " 1", "probability": 0.33447265625 }, { "start": 1016.73, "end": 1017.33, "word": ".0", "probability": 0.830322265625 }, { "start": 1017.33, "end": 1017.51, "word": ".", "probability": 0.353271484375 } ] }, { "id": 242, "seek": 101462, "start": 1017.51, "end": 1020.97, "text": " And the second sequence is git slash HTTP 1.0.", "tokens": [ 50764, 400, 264, 1150, 8310, 307, 18331, 17330, 33283, 502, 13, 15, 13, 50964 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1017.51, "end": 1017.81, "word": " And", "probability": 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11, 51064 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1021.35, "end": 1021.95, "word": " So", "probability": 0.8759765625 }, { "start": 1021.95, "end": 1022.25, "word": " as", "probability": 0.69091796875 }, { "start": 1022.25, "end": 1022.51, "word": " mentioned", "probability": 0.72607421875 }, { "start": 1022.51, "end": 1022.91, "word": " earlier", "probability": 0.904296875 }, { "start": 1022.91, "end": 1023.21, "word": ",", "probability": 0.94921875 } ] }, { "id": 244, "seek": 101462, "start": 1023.21, "end": 1024.89, "text": " you just kind of iterate through the matrix", "tokens": [ 51064, 291, 445, 733, 295, 44497, 807, 264, 8141, 51164 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1023.21, "end": 1023.43, "word": " you", "probability": 0.89306640625 }, { "start": 1023.43, "end": 1023.61, "word": " just", "probability": 0.88623046875 }, { "start": 1023.61, "end": 1023.79, "word": " kind", "probability": 0.51416015625 }, { "start": 1023.79, "end": 1023.85, "word": " of", "probability": 0.90771484375 }, { "start": 1023.85, "end": 1024.09, "word": " iterate", "probability": 0.859375 }, { "start": 1024.09, "end": 1024.35, "word": " through", "probability": 0.7939453125 }, { "start": 1024.35, "end": 1024.51, "word": " the", "probability": 0.80859375 }, { "start": 1024.51, "end": 1024.89, "word": " matrix", "probability": 0.833984375 } ] }, { "id": 245, "seek": 101462, "start": 1024.89, "end": 1031.35, "text": " and you perform the scoring based on the similarity between each character.", "tokens": [ 51164, 293, 291, 2042, 264, 22358, 2361, 322, 264, 32194, 1296, 1184, 2517, 13, 51464 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1024.89, "end": 1025.15, "word": " and", "probability": 0.5625 }, { "start": 1025.15, "end": 1025.33, "word": " you", "probability": 0.89501953125 }, { "start": 1025.33, "end": 1025.83, "word": " perform", "probability": 0.7626953125 }, { "start": 1025.83, "end": 1026.13, "word": " the", "probability": 0.79150390625 }, { "start": 1026.13, "end": 1026.43, "word": " scoring", "probability": 0.87841796875 }, { "start": 1026.43, "end": 1026.73, "word": " based", "probability": 0.8125 }, { "start": 1026.73, "end": 1026.91, "word": " on", "probability": 0.908203125 }, { "start": 1026.91, "end": 1027.19, "word": " the", "probability": 0.814453125 }, { "start": 1027.19, "end": 1028.41, "word": " similarity", "probability": 0.814453125 }, { "start": 1028.41, "end": 1028.91, "word": " between", "probability": 0.783203125 }, { "start": 1028.91, "end": 1029.51, "word": " each", "probability": 0.8681640625 }, { "start": 1029.51, "end": 1030.83, "word": " character", "probability": 0.65185546875 }, { "start": 1030.83, "end": 1031.35, "word": ".", "probability": 0.93310546875 } ] }, { "id": 246, "seek": 101462, "start": 1031.35, "end": 1034.33, "text": " So right here, the Gs match,", "tokens": [ 51464, 407, 558, 510, 11, 264, 460, 82, 2995, 11, 51614 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1031.35, "end": 1031.55, "word": " So", "probability": 0.8720703125 }, { "start": 1031.55, "end": 1032.29, "word": " right", "probability": 0.8115234375 }, { "start": 1032.29, "end": 1032.61, "word": " here", "probability": 0.81640625 }, { "start": 1032.61, "end": 1033.31, "word": ",", "probability": 0.69287109375 }, { "start": 1033.31, "end": 1033.45, "word": " the", "probability": 0.8095703125 }, { "start": 1033.45, "end": 1033.73, "word": " Gs", "probability": 0.686767578125 }, { "start": 1033.73, "end": 1034.05, "word": " match", "probability": 0.7548828125 }, { "start": 1034.05, "end": 1034.33, "word": ",", "probability": 0.56640625 } ] }, { "id": 247, "seek": 101462, "start": 1034.33, "end": 1037.79, "text": " so you score with one and so on.", "tokens": [ 51614, 370, 291, 6175, 365, 472, 293, 370, 322, 13, 51814 ], "temperature": 0.0, "avg_logprob": -0.23216540765280674, "compression_ratio": 1.5825688073394495, "no_speech_prob": 0.0104827880859375, "words": [ { "start": 1034.33, "end": 1034.33, "word": " so", "probability": 0.888671875 }, { "start": 1034.33, "end": 1035.33, "word": " you", "probability": 0.88720703125 }, { "start": 1035.33, "end": 1035.63, "word": " score", "probability": 0.68505859375 }, { "start": 1035.63, "end": 1035.81, "word": " with", "probability": 0.810546875 }, { "start": 1035.81, "end": 1036.11, "word": " one", "probability": 0.7841796875 }, { "start": 1036.11, "end": 1037.23, "word": " and", "probability": 0.5869140625 }, { "start": 1037.23, "end": 1037.45, "word": " so", "probability": 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1180.13, "word": " this", "probability": 0.8818359375 }, { "start": 1180.13, "end": 1180.47, "word": " case", "probability": 0.81494140625 }, { "start": 1180.47, "end": 1181.01, "word": ",", "probability": 0.8564453125 }, { "start": 1181.01, "end": 1181.95, "word": " typically", "probability": 0.84228515625 }, { "start": 1181.95, "end": 1182.63, "word": ",", "probability": 0.52197265625 } ] }, { "id": 307, "seek": 118156, "start": 1182.63, "end": 1186.03, "text": " the maximum cell is always going to be your bottom right.", "tokens": [ 50714, 264, 6674, 2815, 307, 1009, 516, 281, 312, 428, 2767, 558, 13, 50964 ], "temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1182.63, "end": 1182.81, "word": " the", "probability": 0.80712890625 }, { "start": 1182.81, "end": 1183.15, "word": " maximum", "probability": 0.78515625 }, { "start": 1183.15, "end": 1183.65, "word": " cell", "probability": 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1190.57, "word": " highest", "probability": 0.82275390625 }, { "start": 1190.57, "end": 1190.93, "word": " value", "probability": 0.9248046875 }, { "start": 1190.93, "end": 1191.37, "word": ".", "probability": 0.382568359375 } ] }, { "id": 310, "seek": 118156, "start": 1192.37, "end": 1193.67, "text": " We look at the neighboring cells", "tokens": [ 51214, 492, 574, 412, 264, 31521, 5438, 51264 ], "temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1192.37, "end": 1192.75, "word": " We", "probability": 0.58935546875 }, { "start": 1192.75, "end": 1192.91, "word": " look", "probability": 0.90625 }, { "start": 1192.91, "end": 1192.97, "word": " at", "probability": 0.89208984375 }, { "start": 1192.97, "end": 1193.09, "word": " the", "probability": 0.791015625 }, { "start": 1193.09, "end": 1193.31, "word": " neighboring", "probability": 0.81201171875 }, { "start": 1193.31, "end": 1193.67, "word": " cells", "probability": 0.66943359375 } ] }, { "id": 311, "seek": 118156, "start": 1193.67, "end": 1195.27, "text": " and we choose the cell with the maximum value", "tokens": [ 51264, 293, 321, 2826, 264, 2815, 365, 264, 6674, 2158, 51364 ], "temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1193.67, "end": 1193.77, "word": " and", "probability": 0.58056640625 }, { "start": 1193.77, "end": 1193.89, "word": " we", "probability": 0.88427734375 }, { "start": 1193.89, "end": 1194.15, "word": " choose", "probability": 0.80029296875 }, { "start": 1194.15, "end": 1194.27, "word": " the", "probability": 0.52001953125 }, { "start": 1194.27, "end": 1194.43, "word": " cell", "probability": 0.8212890625 }, { "start": 1194.43, "end": 1194.53, "word": " with", "probability": 0.7021484375 }, { "start": 1194.53, "end": 1194.63, "word": " the", "probability": 0.796875 }, { "start": 1194.63, "end": 1194.91, "word": " maximum", "probability": 0.70849609375 }, { "start": 1194.91, "end": 1195.27, "word": " value", "probability": 0.9287109375 } ] }, { "id": 312, "seek": 118156, "start": 1195.27, "end": 1196.61, "text": " and traverse there.", "tokens": [ 51364, 293, 45674, 456, 13, 51464 ], "temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1195.27, "end": 1195.49, "word": " and", "probability": 0.8681640625 }, { "start": 1195.49, "end": 1195.81, "word": " traverse", "probability": 0.67236328125 }, { "start": 1195.81, "end": 1196.17, "word": " there", "probability": 0.85107421875 }, { "start": 1196.17, "end": 1196.61, "word": ".", "probability": 0.99365234375 } ] }, { "id": 313, "seek": 118156, "start": 1197.63, "end": 1198.99, "text": " We start here at E", "tokens": [ 51464, 492, 722, 510, 412, 462, 51564 ], "temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1197.63, "end": 1197.83, "word": " We", "probability": 0.77587890625 }, { "start": 1197.83, "end": 1198.11, "word": " start", "probability": 0.8798828125 }, { "start": 1198.11, "end": 1198.45, "word": " here", "probability": 0.80224609375 }, { "start": 1198.45, "end": 1198.81, "word": " at", "probability": 0.8115234375 }, { "start": 1198.81, "end": 1198.99, "word": " E", "probability": 0.9287109375 } ] }, { "id": 314, "seek": 118156, "start": 1199.49, "end": 1201.97, "text": " and we notice that our neighbors are all Ds.", "tokens": [ 51564, 293, 321, 3449, 300, 527, 12512, 366, 439, 413, 82, 13, 51664 ], "temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1199.49, "end": 1199.93, "word": " and", "probability": 0.476806640625 }, { "start": 1199.93, "end": 1200.05, "word": " we", 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"temperature": 0.0, "avg_logprob": -0.19149999475479126, "compression_ratio": 1.848605577689243, "no_speech_prob": 0.0316162109375, "words": [ { "start": 1203.61, "end": 1203.91, "word": " because", "probability": 0.7890625 }, { "start": 1203.91, "end": 1204.07, "word": " they're", "probability": 0.76806640625 }, { "start": 1204.07, "end": 1204.21, "word": " all", "probability": 0.90234375 }, { "start": 1204.21, "end": 1204.49, "word": " equal", "probability": 0.80615234375 }, { "start": 1204.49, "end": 1204.73, "word": ".", "probability": 0.95458984375 } ] }, { "id": 317, "seek": 121100, "start": 1204.73, "end": 1206.71, "text": " We can go left, we can go up, we can go diagonal.", "tokens": [ 50364, 492, 393, 352, 1411, 11, 321, 393, 352, 493, 11, 321, 393, 352, 21539, 13, 50514 ], "temperature": 0.0, "avg_logprob": -0.19733796213512067, "compression_ratio": 1.7555555555555555, "no_speech_prob": 0.189453125, "words": [ { "start": 1204.73, "end": 1204.81, "word": " We", "probability": 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}, { "start": 1268.65, "end": 1268.75, "word": " the", "probability": 0.7919921875 }, { "start": 1268.75, "end": 1269.13, "word": " completed", "probability": 0.6220703125 }, { "start": 1269.13, "end": 1269.55, "word": " traverse", "probability": 0.61962890625 } ] }, { "id": 343, "seek": 126786, "start": 1269.55, "end": 1271.53, "text": " all the way back to the beginning of the matrix.", "tokens": [ 50764, 439, 264, 636, 646, 281, 264, 2863, 295, 264, 8141, 13, 50864 ], "temperature": 0.0, "avg_logprob": -0.2131164976123239, "compression_ratio": 1.8531746031746033, "no_speech_prob": 0.7822265625, "words": [ { "start": 1269.55, "end": 1269.97, "word": " all", "probability": 0.5791015625 }, { "start": 1269.97, "end": 1270.05, "word": " the", "probability": 0.81640625 }, { "start": 1270.05, "end": 1270.13, "word": " way", "probability": 0.89599609375 }, { "start": 1270.13, "end": 1270.31, "word": " back", "probability": 0.79638671875 }, { "start": 1270.31, "end": 1270.41, "word": " to", 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So", "probability": 0.884765625 }, { "start": 1275.11, "end": 1275.21, "word": " at", "probability": 0.8798828125 }, { "start": 1275.21, "end": 1275.37, "word": " this", "probability": 0.8798828125 }, { "start": 1275.37, "end": 1275.59, "word": " point", "probability": 0.8525390625 }, { "start": 1275.59, "end": 1275.87, "word": ",", "probability": 0.86279296875 }, { "start": 1275.87, "end": 1276.43, "word": " we're", "probability": 0.86865234375 }, { "start": 1276.43, "end": 1276.65, "word": " looking", "probability": 0.794921875 }, { "start": 1276.65, "end": 1276.99, "word": " around", "probability": 0.876953125 }, { "start": 1276.99, "end": 1277.09, "word": ".", "probability": 0.52685546875 } ] }, { "id": 347, "seek": 126786, "start": 1277.09, "end": 1279.15, "text": " We notice that 5 to our left is the maximum,", "tokens": [ 51114, 492, 3449, 300, 1025, 281, 527, 1411, 307, 264, 6674, 11, 51214 ], "temperature": 0.0, "avg_logprob": -0.2131164976123239, "compression_ratio": 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126786, "start": 1279.15, "end": 1279.85, "text": " so we're going to go there.", "tokens": [ 51214, 370, 321, 434, 516, 281, 352, 456, 13, 51264 ], "temperature": 0.0, "avg_logprob": -0.2131164976123239, "compression_ratio": 1.8531746031746033, "no_speech_prob": 0.7822265625, "words": [ { "start": 1279.15, "end": 1279.21, "word": " so", "probability": 0.89697265625 }, { "start": 1279.21, "end": 1279.33, "word": " we're", "probability": 0.9267578125 }, { "start": 1279.33, "end": 1279.39, "word": " going", "probability": 0.78466796875 }, { "start": 1279.39, "end": 1279.43, "word": " to", "probability": 0.90283203125 }, { "start": 1279.43, "end": 1279.53, "word": " go", "probability": 0.89501953125 }, { "start": 1279.53, "end": 1279.73, "word": " there", "probability": 0.84765625 }, { "start": 1279.73, "end": 1279.85, "word": ".", "probability": 0.90576171875 } ] }, { "id": 349, "seek": 126786, "start": 1279.85, "end": 1280.53, "text": " Same thing.", "tokens": [ 51264, 10635, 551, 13, 51314 ], "temperature": 0.0, "avg_logprob": -0.2131164976123239, "compression_ratio": 1.8531746031746033, "no_speech_prob": 0.7822265625, "words": [ { "start": 1279.85, "end": 1280.05, "word": " Same", "probability": 0.76171875 }, { "start": 1280.05, "end": 1280.33, "word": " thing", "probability": 0.81005859375 }, { "start": 1280.33, "end": 1280.53, "word": ".", "probability": 0.654296875 } ] }, { "id": 350, "seek": 126786, "start": 1280.71, "end": 1283.19, "text": " We're surrounded by two 4s and a 5 on the left,", "tokens": [ 51314, 492, 434, 13221, 538, 732, 1017, 82, 293, 257, 1025, 322, 264, 1411, 11, 51414 ], "temperature": 0.0, "avg_logprob": -0.2131164976123239, "compression_ratio": 1.8531746031746033, "no_speech_prob": 0.7822265625, "words": [ { "start": 1280.71, "end": 1281.07, "word": " We're", "probability": 0.912353515625 }, { "start": 1281.07, "end": 1281.35, "word": " surrounded", "probability": 0.65234375 }, { "start": 1281.35, "end": 1281.55, "word": " by", 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[ { "start": 1289.47, "end": 1289.83, "word": " And", "probability": 0.85791015625 }, { "start": 1289.83, "end": 1290.19, "word": " all", "probability": 0.85888671875 }, { "start": 1290.19, "end": 1290.27, "word": " of", "probability": 0.90234375 }, { "start": 1290.27, "end": 1290.35, "word": " them", "probability": 0.72607421875 }, { "start": 1290.35, "end": 1290.45, "word": " are", "probability": 0.8896484375 }, { "start": 1290.45, "end": 1290.63, "word": " equal", "probability": 0.81396484375 }, { "start": 1290.63, "end": 1290.93, "word": " again", "probability": 0.91796875 }, { "start": 1290.93, "end": 1291.03, "word": ",", "probability": 0.9208984375 } ] }, { "id": 354, "seek": 129684, "start": 1291.03, "end": 1292.71, "text": " so we decide that we're going to go diagonal.", "tokens": [ 50364, 370, 321, 4536, 300, 321, 434, 516, 281, 352, 21539, 13, 50464 ], "temperature": 0.0, "avg_logprob": -0.194840607227095, "compression_ratio": 1.8362989323843417, "no_speech_prob": 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1303.15, "end": 1304.67, "text": " So what did this do?", "tokens": [ 50964, 407, 437, 630, 341, 360, 30, 51064 ], "temperature": 0.0, "avg_logprob": -0.194840607227095, "compression_ratio": 1.8362989323843417, "no_speech_prob": 0.0094146728515625, "words": [ { "start": 1303.15, "end": 1303.51, "word": " So", "probability": 0.88232421875 }, { "start": 1303.51, "end": 1303.87, "word": " what", "probability": 0.78857421875 }, { "start": 1303.87, "end": 1303.95, "word": " did", "probability": 0.771484375 }, { "start": 1303.95, "end": 1304.11, "word": " this", "probability": 0.8818359375 }, { "start": 1304.11, "end": 1304.31, "word": " do", "probability": 0.9013671875 }, { "start": 1304.31, "end": 1304.67, "word": "?", "probability": 0.99072265625 } ] }, { "id": 360, "seek": 129684, "start": 1304.75, "end": 1306.61, "text": " We computed a path through the matrix,", "tokens": [ 51064, 492, 40610, 257, 3100, 807, 264, 8141, 11, 51164 ], "temperature": 0.0, "avg_logprob": -0.194840607227095, 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wanted to analyze this really simple example,", "tokens": [ 50664, 407, 11, 498, 291, 1415, 281, 12477, 341, 534, 2199, 1365, 11, 50864 ], "temperature": 0.0, "avg_logprob": -0.21061466252508243, "compression_ratio": 1.6384615384615384, "no_speech_prob": 0.00307464599609375, "words": [ { "start": 1356.03, "end": 1356.51, "word": " So", "probability": 0.56298828125 }, { "start": 1356.51, "end": 1356.99, "word": ",", "probability": 0.60107421875 }, { "start": 1356.99, "end": 1357.23, "word": " if", "probability": 0.8955078125 }, { "start": 1357.23, "end": 1357.29, "word": " you", "probability": 0.892578125 }, { "start": 1357.29, "end": 1357.41, "word": " wanted", "probability": 0.72705078125 }, { "start": 1357.41, "end": 1357.55, "word": " to", "probability": 0.9052734375 }, { "start": 1357.55, "end": 1357.83, "word": " analyze", "probability": 0.662109375 }, { "start": 1357.83, "end": 1357.99, "word": " this", "probability": 0.87890625 }, { "start": 1357.99, "end": 1358.17, "word": " 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54, 771, 1601, 307, 767, 1143, 257, 688, 51564 ], "temperature": 0.0, "avg_logprob": -0.1887500047683716, "compression_ratio": 1.8549019607843138, "no_speech_prob": 0.00359344482421875, "words": [ { "start": 1586.36, "end": 1586.84, "word": " Smith", "probability": 0.1773681640625 }, { "start": 1586.84, "end": 1587.18, "word": "-Waterman", "probability": 0.9356689453125 }, { "start": 1587.18, "end": 1587.34, "word": " is", "probability": 0.8447265625 }, { "start": 1587.34, "end": 1587.58, "word": " actually", "probability": 0.78076171875 }, { "start": 1587.58, "end": 1587.86, "word": " used", "probability": 0.8037109375 }, { "start": 1587.96, "end": 1588.08, "word": " a lot", "probability": 0.88818359375 } ] }, { "id": 467, "seek": 155838, "start": 1588.08, "end": 1590.26, "text": " to determine the distance between two sequences.", "tokens": [ 51564, 281, 6997, 264, 4560, 1296, 732, 22978, 13, 51664 ], "temperature": 0.0, "avg_logprob": -0.1887500047683716, "compression_ratio": 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51814 ], "temperature": 0.0, "avg_logprob": -0.1887500047683716, "compression_ratio": 1.8549019607843138, "no_speech_prob": 0.00359344482421875, "words": [ { "start": 1590.26, "end": 1590.4, "word": " So", "probability": 0.69775390625 }, { "start": 1590.4, "end": 1591.06, "word": " if", "probability": 0.81494140625 }, { "start": 1591.06, "end": 1591.14, "word": " you", "probability": 0.89599609375 }, { "start": 1591.14, "end": 1591.26, "word": " want", "probability": 0.8173828125 }, { "start": 1591.26, "end": 1591.34, "word": " to", "probability": 0.908203125 }, { "start": 1591.34, "end": 1591.48, "word": " find", "probability": 0.7822265625 }, { "start": 1591.48, "end": 1591.78, "word": " the", "probability": 0.7919921875 }, { "start": 1591.78, "end": 1592.04, "word": " distance", "probability": 0.806640625 }, { "start": 1592.04, "end": 1592.34, "word": " between", "probability": 0.80322265625 }, { "start": 1592.34, "end": 1592.6, "word": " sequence", "probability": 0.85595703125 }, { 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1.6981132075471699, "no_speech_prob": 0.00047278404235839844, "words": [ { "start": 1607.1, "end": 1607.38, "word": " Just", "probability": 0.71826171875 }, { "start": 1607.38, "end": 1608.56, "word": " pretty", "probability": 0.80908203125 }, { "start": 1608.56, "end": 1608.82, "word": " simple", "probability": 0.90576171875 }, { "start": 1608.82, "end": 1609.34, "word": ".", "probability": 0.99755859375 } ] }, { "id": 476, "seek": 158762, "start": 1610.78, "end": 1612.58, "text": " Basically, here's how it's used.", "tokens": [ 51214, 8537, 11, 510, 311, 577, 309, 311, 1143, 13, 51314 ], "temperature": 0.0, "avg_logprob": -0.14960355089226765, "compression_ratio": 1.6981132075471699, "no_speech_prob": 0.00047278404235839844, "words": [ { "start": 1610.78, "end": 1611.3, "word": " Basically", "probability": 0.021484375 }, { "start": 1611.3, "end": 1611.64, "word": ",", "probability": 0.767578125 }, { "start": 1611.64, "end": 1611.88, "word": " here's", "probability": 0.90087890625 }, { "start": 1611.88, "end": 1612.0, "word": " how", "probability": 0.8955078125 }, { "start": 1612.0, "end": 1612.14, "word": " it's", "probability": 0.94775390625 }, { "start": 1612.14, "end": 1612.38, "word": " used", "probability": 0.8154296875 }, { "start": 1612.38, "end": 1612.58, "word": ".", "probability": 0.9765625 } ] }, { "id": 477, "seek": 158762, "start": 1612.58, "end": 1615.36, "text": " In my program, it's not necessarily used", "tokens": [ 51314, 682, 452, 1461, 11, 309, 311, 406, 4725, 1143, 51414 ], "temperature": 0.0, "avg_logprob": -0.14960355089226765, "compression_ratio": 1.6981132075471699, "no_speech_prob": 0.00047278404235839844, "words": [ { "start": 1612.58, "end": 1613.02, "word": " In", "probability": 0.91552734375 }, { "start": 1613.02, "end": 1613.48, "word": " my", "probability": 0.90966796875 }, { "start": 1613.48, "end": 1614.06, "word": " program", "probability": 0.74462890625 }, { "start": 1614.06, "end": 1614.18, "word": ",", "probability": 0.87890625 }, { "start": 1614.18, "end": 1614.36, "word": " it's", "probability": 0.9521484375 }, { "start": 1614.36, "end": 1614.58, "word": " not", "probability": 0.89111328125 }, { "start": 1614.58, "end": 1614.96, "word": " necessarily", "probability": 0.75927734375 }, { "start": 1614.96, "end": 1615.36, "word": " used", "probability": 0.81005859375 } ] }, { "id": 478, "seek": 158762, "start": 1615.36, "end": 1616.72, "text": " to determine the protocol fields.", "tokens": [ 51414, 281, 6997, 264, 10336, 7909, 13, 51514 ], "temperature": 0.0, "avg_logprob": -0.14960355089226765, "compression_ratio": 1.6981132075471699, "no_speech_prob": 0.00047278404235839844, "words": [ { "start": 1615.36, "end": 1615.56, "word": " to", "probability": 0.8994140625 }, { "start": 1615.56, "end": 1615.86, "word": " determine", "probability": 0.75390625 }, { "start": 1615.86, "end": 1616.0, "word": " the", "probability": 0.8125 }, { "start": 1616.0, "end": 1616.28, "word": " protocol", "probability": 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1.6981132075471699, "no_speech_prob": 0.00047278404235839844, "words": [ { "start": 1621.08, "end": 1621.76, "word": " kind", "probability": 0.352783203125 }, { "start": 1621.76, "end": 1621.84, "word": " of", "probability": 0.90283203125 }, { "start": 1621.84, "end": 1621.96, "word": " like", "probability": 0.76416015625 }, { "start": 1622.6, "end": 1622.92, "word": " a relationship", "probability": 0.83349609375 }, { "start": 1622.92, "end": 1623.32, "word": " matrix", "probability": 0.82763671875 } ] }, { "id": 482, "seek": 161476, "start": 1623.32, "end": 1625.52, "text": " and understand which packets", "tokens": [ 50364, 293, 1223, 597, 30364, 50464 ], "temperature": 0.0, "avg_logprob": -0.20108173053998213, "compression_ratio": 1.7060810810810811, "no_speech_prob": 0.0079345703125, "words": [ { "start": 1623.32, "end": 1623.54, "word": " and", "probability": 0.2939453125 }, { "start": 1623.54, "end": 1623.96, "word": " understand", "probability": 0.7197265625 }, { "start": 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So, basically, similar", "tokens": [ 50964, 938, 6838, 13, 407, 11, 1936, 11, 2531, 51064 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2122.01, "end": 2122.19, "word": " long", "probability": 0.85107421875 }, { "start": 2122.19, "end": 2122.47, "word": " tail", "probability": 0.7216796875 }, { "start": 2122.47, "end": 2122.65, "word": ".", "probability": 0.88525390625 }, { "start": 2122.65, "end": 2122.77, "word": " So", "probability": 0.8037109375 }, { "start": 2122.77, "end": 2123.13, "word": ",", "probability": 0.51953125 }, { "start": 2123.13, "end": 2123.47, "word": " basically", "probability": 0.88134765625 }, { "start": 2123.47, "end": 2123.79, "word": ",", "probability": 0.78515625 }, { "start": 2123.79, "end": 2123.99, "word": " similar", "probability": 0.87060546875 } ] }, { "id": 716, "seek": 209106, "start": 2123.99, "end": 2126.07, "text": " situation, apply it to packets.", "tokens": [ 51064, 2590, 11, 3079, 309, 281, 30364, 13, 51164 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2123.99, "end": 2124.57, "word": " situation", "probability": 0.78564453125 }, { "start": 2124.57, "end": 2124.73, "word": ",", "probability": 0.56689453125 }, { "start": 2124.73, "end": 2124.99, "word": " apply", "probability": 0.67041015625 }, { "start": 2124.99, "end": 2125.13, "word": " it", "probability": 0.8603515625 }, { "start": 2125.13, "end": 2125.23, "word": " to", "probability": 0.90478515625 }, { "start": 2125.23, "end": 2125.55, "word": " packets", "probability": 0.84130859375 }, { "start": 2125.55, "end": 2126.07, "word": ".", "probability": 0.9951171875 } ] }, { "id": 717, "seek": 209106, "start": 2138.29, "end": 2140.01, "text": " So, to create", "tokens": [ 51264, 407, 11, 281, 1884, 51364 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2138.29, "end": 2138.59, "word": " So", "probability": 0.71826171875 }, { "start": 2138.59, "end": 2139.33, "word": ",", "probability": 0.86279296875 }, { "start": 2139.33, "end": 2139.79, "word": " to", "probability": 0.88916015625 }, { "start": 2139.79, "end": 2140.01, "word": " create", "probability": 0.8154296875 } ] }, { "id": 718, "seek": 209106, "start": 2140.01, "end": 2142.23, "text": " polygenetic trees, there's an algorithm called Upcoma", "tokens": [ 51364, 6754, 1766, 3532, 5852, 11, 456, 311, 364, 9284, 1219, 5858, 1112, 64, 51464 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2140.01, "end": 2140.69, "word": " polygenetic", "probability": 0.5972493489583334 }, { "start": 2140.69, "end": 2140.97, "word": " trees", "probability": 0.8095703125 }, { "start": 2140.97, "end": 2141.05, "word": ",", "probability": 0.81591796875 }, { "start": 2141.05, "end": 2141.21, "word": " there's", "probability": 0.89697265625 }, { "start": 2141.21, "end": 2141.31, "word": " an", "probability": 0.78759765625 }, { "start": 2141.31, "end": 2141.57, "word": " algorithm", "probability": 0.81787109375 }, { "start": 2141.57, "end": 2141.87, "word": " called", "probability": 0.79541015625 }, { "start": 2141.87, "end": 2142.23, "word": " Upcoma", "probability": 0.4936726888020833 } ] }, { "id": 719, "seek": 209106, "start": 2142.23, "end": 2144.33, "text": " which is the unweighted paired group method using", "tokens": [ 51464, 597, 307, 264, 517, 12329, 292, 25699, 1594, 3170, 1228, 51564 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2142.23, "end": 2142.39, "word": " which", "probability": 0.1396484375 }, { "start": 2142.39, "end": 2142.53, "word": " is", "probability": 0.89111328125 }, { "start": 2142.53, "end": 2142.65, "word": " the", "probability": 0.5732421875 }, { "start": 2142.65, "end": 2143.09, "word": " unweighted", "probability": 0.87744140625 }, { "start": 2143.09, "end": 2143.35, "word": " paired", "probability": 0.548828125 }, { "start": 2143.35, "end": 2143.63, "word": " group", "probability": 0.55712890625 }, { "start": 2143.63, "end": 2144.01, "word": " method", "probability": 0.89990234375 }, { "start": 2144.01, "end": 2144.33, "word": " using", "probability": 0.79931640625 } ] }, { "id": 720, "seek": 209106, "start": 2144.33, "end": 2146.53, "text": " arithmetic averages. It's pretty simple,", "tokens": [ 51564, 42973, 42257, 13, 467, 311, 1238, 2199, 11, 51664 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2144.33, "end": 2144.77, "word": " arithmetic", "probability": 0.77783203125 }, { "start": 2144.77, "end": 2145.27, "word": " averages", "probability": 0.8037109375 }, { "start": 2145.27, "end": 2145.65, "word": ".", "probability": 0.97900390625 }, { "start": 2145.65, "end": 2145.89, "word": " It's", "probability": 0.951171875 }, { "start": 2145.89, "end": 2146.05, "word": " pretty", "probability": 0.8935546875 }, { "start": 2146.05, "end": 2146.39, "word": " simple", "probability": 0.91162109375 }, { "start": 2146.39, "end": 2146.53, "word": ",", "probability": 0.61865234375 } ] }, { "id": 721, "seek": 209106, "start": 2146.53, "end": 2148.29, "text": " actually. This little", "tokens": [ 51664, 767, 13, 639, 707, 51764 ], "temperature": 0.0, "avg_logprob": -0.23840331751853228, "compression_ratio": 1.5833333333333333, "no_speech_prob": 0.182373046875, "words": [ { "start": 2146.53, "end": 2146.75, "word": " actually", "probability": 0.78271484375 }, { "start": 2146.75, "end": 2147.27, "word": ".", "probability": 0.98583984375 }, { "start": 2147.63, "end": 2148.03, "word": " This", "probability": 0.81005859375 }, { "start": 2148.03, "end": 2148.29, "word": " little", "probability": 0.80078125 } ] }, { "id": 722, "seek": 211904, "start": 2148.29, "end": 2150.13, "text": " equation right here just signifies", "tokens": [ 50364, 5367, 558, 510, 445, 1465, 11221, 50464 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2148.29, "end": 2148.67, "word": " equation", "probability": 0.1787109375 }, { "start": 2148.67, "end": 2148.89, "word": " right", 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2151.51, "word": " two", "probability": 0.7861328125 }, { "start": 2151.51, "end": 2151.79, "word": " clusters", "probability": 0.80615234375 }, { "start": 2151.79, "end": 2152.01, "word": " is", "probability": 0.892578125 }, { "start": 2152.01, "end": 2152.41, "word": " taken", "probability": 0.67724609375 }, { "start": 2152.41, "end": 2152.89, "word": ".", "probability": 0.80224609375 } ] }, { "id": 724, "seek": 211904, "start": 2153.25, "end": 2154.05, "text": " Basically,", "tokens": [ 50564, 8537, 11, 50664 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2153.25, "end": 2153.73, "word": " Basically", "probability": 0.8330078125 }, { "start": 2153.73, "end": 2154.05, "word": ",", "probability": 0.459228515625 } ] }, { "id": 725, "seek": 211904, "start": 2154.05, "end": 2155.75, "text": " the dpq", "tokens": [ 50664, 264, 274, 79, 80, 50764 ], "temperature": 0.0, "avg_logprob": 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] }, { "id": 727, "seek": 211904, "start": 2158.17, "end": 2159.99, "text": " the distance between each", "tokens": [ 50864, 264, 4560, 1296, 1184, 50964 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2158.17, "end": 2158.33, "word": " the", "probability": 0.81103515625 }, { "start": 2158.33, "end": 2158.55, "word": " distance", "probability": 0.8134765625 }, { "start": 2158.55, "end": 2158.95, "word": " between", "probability": 0.78759765625 }, { "start": 2158.95, "end": 2159.99, "word": " each", "probability": 0.859375 } ] }, { "id": 728, "seek": 211904, "start": 2160.65, "end": 2162.27, "text": " sequence in cluster i and", "tokens": [ 50964, 8310, 294, 13630, 741, 293, 51064 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2160.65, "end": 2161.13, "word": " sequence", 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And it's basically just", "tokens": [ 51064, 13630, 361, 13, 400, 309, 311, 1936, 445, 51164 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2162.27, "end": 2162.55, "word": " cluster", "probability": 0.8388671875 }, { "start": 2162.55, "end": 2162.83, "word": " j", "probability": 0.9931640625 }, { "start": 2162.83, "end": 2163.27, "word": ".", "probability": 0.51220703125 }, { "start": 2163.27, "end": 2163.41, "word": " And", "probability": 0.67626953125 }, { "start": 2163.41, "end": 2163.65, "word": " it's", "probability": 0.9072265625 }, { "start": 2163.65, "end": 2163.93, "word": " basically", "probability": 0.888671875 }, { "start": 2163.93, "end": 2164.17, "word": " just", "probability": 0.8017578125 } ] }, { "id": 730, "seek": 211904, "start": 2164.17, "end": 2165.97, "text": " taking the mean of that and returning that back as", "tokens": [ 51164, 1940, 264, 914, 295, 300, 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case, the dpq", "tokens": [ 51364, 682, 341, 1389, 11, 264, 274, 79, 80, 51464 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2168.25, "end": 2168.73, "word": " In", "probability": 0.17822265625 }, { "start": 2168.73, "end": 2168.91, "word": " this", "probability": 0.88232421875 }, { "start": 2168.91, "end": 2169.11, "word": " case", "probability": 0.81005859375 }, { "start": 2169.11, "end": 2169.43, "word": ",", "probability": 0.84716796875 }, { "start": 2169.43, "end": 2169.69, "word": " the", "probability": 0.79052734375 }, { "start": 2169.69, "end": 2170.37, "word": " dpq", "probability": 0.9778645833333334 } ] }, { "id": 733, "seek": 211904, "start": 2170.37, "end": 2172.49, "text": " is going to be the Smith-Waterman score and the d", "tokens": [ 51464, 307, 516, 281, 312, 264, 8538, 12, 54, 771, 1601, 6175, 293, 264, 274, 51564 ], "temperature": 0.0, "avg_logprob": 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So, this is just", "tokens": [ 51664, 741, 293, 361, 13, 407, 11, 341, 307, 445, 51764 ], "temperature": 0.0, "avg_logprob": -0.16145833333333334, "compression_ratio": 1.915929203539823, "no_speech_prob": 0.5234375, "words": [ { "start": 2174.55, "end": 2174.75, "word": " j", "probability": 0.9990234375 }, { "start": 2174.75, "end": 2175.23, "word": ".", "probability": 0.99169921875 }, { "start": 2175.27, "end": 2175.45, "word": " So", "probability": 0.58935546875 }, { "start": 2175.45, "end": 2175.81, "word": ",", "probability": 0.395751953125 }, { "start": 2175.81, "end": 2175.99, "word": " this", "probability": 0.8701171875 }, { "start": 2175.99, "end": 2176.11, "word": " is", "probability": 0.900390625 }, { "start": 2176.11, "end": 2176.27, "word": " just", "probability": 0.87353515625 } ] }, { "id": 736, "seek": 214714, "start": 2176.27, "end": 2178.29, "text": " the operation that's used each step of the", "tokens": [ 50364, 264, 6916, 300, 311, 1143, 1184, 1823, 295, 264, 50464 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Use the Upcoma", "tokens": [ 50864, 680, 13630, 11, 1936, 13, 8278, 264, 5858, 1112, 64, 50964 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, "compression_ratio": 1.8345864661654134, "no_speech_prob": 0.07696533203125, "words": [ { "start": 2186.25, "end": 2186.43, "word": " per", "probability": 0.91015625 }, { "start": 2186.43, "end": 2186.69, "word": " cluster", "probability": 0.74609375 }, { "start": 2186.69, "end": 2186.79, "word": ",", "probability": 0.455078125 }, { "start": 2186.79, "end": 2187.05, "word": " basically", "probability": 0.88525390625 }, { "start": 2187.05, "end": 2187.53, "word": ".", "probability": 0.984375 }, { "start": 2187.77, "end": 2188.03, "word": " Use", "probability": 0.607421875 }, { "start": 2188.03, "end": 2188.13, "word": " the", "probability": 0.763671875 }, { "start": 2188.13, "end": 2188.43, "word": " Upcoma", "probability": 0.5419921875 } ] }, { "id": 742, "seek": 214714, "start": 2188.43, "end": 2190.13, "text": " algorithm to iterate through every single cluster", "tokens": [ 50964, 9284, 281, 44497, 807, 633, 2167, 13630, 51064 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, "compression_ratio": 1.8345864661654134, "no_speech_prob": 0.07696533203125, "words": [ { "start": 2188.43, "end": 2188.81, "word": " algorithm", "probability": 0.7255859375 }, { "start": 2188.81, "end": 2189.01, "word": " to", "probability": 0.8935546875 }, { "start": 2189.01, "end": 2189.25, "word": " iterate", "probability": 0.86083984375 }, { "start": 2189.25, "end": 2189.41, "word": " through", "probability": 0.798828125 }, { "start": 2189.41, "end": 2189.59, "word": " every", "probability": 0.7509765625 }, { "start": 2189.59, "end": 2189.77, "word": " single", "probability": 0.8837890625 }, { "start": 2189.77, "end": 2190.13, "word": " cluster", "probability": 0.73046875 } ] }, { "id": 743, "seek": 214714, "start": 2190.13, "end": 2191.91, "text": " doing comparisons between every single", "tokens": [ 51064, 884, 33157, 1296, 633, 2167, 51164 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, "compression_ratio": 1.8345864661654134, "no_speech_prob": 0.07696533203125, "words": [ { "start": 2190.13, "end": 2190.47, "word": " doing", "probability": 0.35205078125 }, { "start": 2190.47, "end": 2190.89, "word": " comparisons", "probability": 0.6796875 }, { "start": 2190.89, "end": 2191.23, "word": " between", "probability": 0.79248046875 }, { "start": 2191.23, "end": 2191.55, "word": " every", "probability": 0.73681640625 }, { "start": 2191.55, "end": 2191.91, "word": " single", "probability": 0.8984375 } ] }, { "id": 744, "seek": 214714, "start": 2191.91, "end": 2194.15, "text": " sequence. You take the two with", "tokens": [ 51164, 8310, 13, 509, 747, 264, 732, 365, 51264 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, "compression_ratio": 1.8345864661654134, "no_speech_prob": 0.07696533203125, "words": [ { "start": 2191.91, "end": 2192.57, "word": " sequence", "probability": 0.8955078125 }, { "start": 2192.57, "end": 2193.05, "word": ".", "probability": 0.98876953125 }, { "start": 2193.27, "end": 2193.47, "word": " You", "probability": 0.8857421875 }, { "start": 2193.47, "end": 2193.65, "word": " take", "probability": 0.8232421875 }, { "start": 2193.65, "end": 2193.77, "word": " the", "probability": 0.80615234375 }, { "start": 2193.77, "end": 2193.95, "word": " two", "probability": 0.8037109375 }, { "start": 2193.95, "end": 2194.15, "word": " with", "probability": 0.806640625 } ] }, { "id": 745, "seek": 214714, "start": 2194.15, "end": 2196.35, "text": " the smallest distance between each other.", "tokens": [ 51264, 264, 16998, 4560, 1296, 1184, 661, 13, 51364 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, "compression_ratio": 1.8345864661654134, "no_speech_prob": 0.07696533203125, "words": [ { "start": 2194.15, "end": 2194.59, "word": " the", "probability": 0.794921875 }, { "start": 2194.59, "end": 2194.87, "word": " smallest", "probability": 0.77734375 }, { "start": 2194.87, "end": 2195.21, "word": " distance", "probability": 0.79638671875 }, { "start": 2195.21, "end": 2195.49, "word": " between", "probability": 0.7890625 }, { "start": 2195.49, "end": 2195.67, "word": " each", "probability": 0.8671875 }, { "start": 2195.67, "end": 2195.87, "word": " other", "probability": 0.7890625 }, { "start": 2195.87, "end": 2196.35, "word": ".", "probability": 0.80078125 } ] }, { "id": 746, "seek": 214714, "start": 2196.37, "end": 2198.25, "text": " You create a new cluster k by uniting", "tokens": [ 51364, 509, 1884, 257, 777, 13630, 350, 538, 517, 1748, 51464 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, 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2201.91, "word": " k", "probability": 0.966796875 }, { "start": 2201.91, "end": 2202.09, "word": " with", "probability": 0.73828125 }, { "start": 2202.09, "end": 2202.31, "word": " child", "probability": 0.6728515625 } ] }, { "id": 749, "seek": 214714, "start": 2202.31, "end": 2204.37, "text": " nodes i and j to build the tree.", "tokens": [ 51664, 13891, 741, 293, 361, 281, 1322, 264, 4230, 13, 51764 ], "temperature": 0.0, "avg_logprob": -0.17355768634722782, "compression_ratio": 1.8345864661654134, "no_speech_prob": 0.07696533203125, "words": [ { "start": 2202.31, "end": 2202.53, "word": " nodes", "probability": 0.796875 }, { "start": 2202.53, "end": 2202.75, "word": " i", "probability": 0.876953125 }, { "start": 2202.75, "end": 2202.89, "word": " and", "probability": 0.888671875 }, { "start": 2202.89, "end": 2203.09, "word": " j", "probability": 0.99755859375 }, { "start": 2203.09, "end": 2203.41, "word": " to", "probability": 0.82275390625 }, { "start": 2203.41, "end": 2203.55, 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"probability": 0.323486328125 } ] }, { "id": 751, "seek": 217512, "start": 2206.41, "end": 2207.79, "text": " to the universal set and you remove", "tokens": [ 50464, 281, 264, 11455, 992, 293, 291, 4159, 50564 ], "temperature": 0.0, "avg_logprob": -0.1759672655709206, "compression_ratio": 1.6755725190839694, "no_speech_prob": 0.00183868408203125, "words": [ { "start": 2206.41, "end": 2206.51, "word": " to", "probability": 0.90234375 }, { "start": 2206.51, "end": 2206.61, "word": " the", "probability": 0.79638671875 }, { "start": 2206.61, "end": 2206.89, "word": " universal", "probability": 0.57373046875 }, { "start": 2206.89, "end": 2207.21, "word": " set", "probability": 0.89697265625 }, { "start": 2207.21, "end": 2207.35, "word": " and", "probability": 0.58544921875 }, { "start": 2207.35, "end": 2207.47, "word": " you", "probability": 0.58203125 }, { "start": 2207.47, "end": 2207.79, "word": " remove", "probability": 0.85009765625 } ] }, { "id": 752, "seek": 217512, "start": 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" going", "probability": 0.86181640625 }, { "start": 2378.95, "end": 2379.21, "word": " to", "probability": 0.89990234375 }, { "start": 2379.21, "end": 2379.93, "word": " translate", "probability": 0.7158203125 }, { "start": 2379.93, "end": 2380.33, "word": " themselves", "probability": 0.75634765625 } ] }, { "id": 837, "seek": 234040, "start": 2380.33, "end": 2381.83, "text": " all the way down to the leaf node.", "tokens": [ 50764, 439, 264, 636, 760, 281, 264, 10871, 9984, 13, 50864 ], "temperature": 0.0, "avg_logprob": -0.178240743610594, "compression_ratio": 1.9844961240310077, "no_speech_prob": 0.016143798828125, "words": [ { "start": 2380.33, "end": 2380.55, "word": " all", "probability": 0.8759765625 }, { "start": 2380.55, "end": 2380.63, "word": " the", "probability": 0.8125 }, { "start": 2380.63, "end": 2380.73, "word": " way", "probability": 0.89306640625 }, { "start": 2380.73, "end": 2380.91, "word": " down", "probability": 0.8125 }, { "start": 2380.91, "end": 2381.05, 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I don't really know if that's kosher", "tokens": [ 50464, 484, 295, 300, 13, 286, 500, 380, 534, 458, 498, 300, 311, 19532, 511, 50564 ], "temperature": 0.0, "avg_logprob": -0.19858156324278378, "compression_ratio": 1.832167832167832, "no_speech_prob": 0.033599853515625, "words": [ { "start": 2402.23, "end": 2402.35, "word": " out", "probability": 0.7666015625 }, { "start": 2402.35, "end": 2402.41, "word": " of", "probability": 0.89111328125 }, { "start": 2402.41, "end": 2402.49, "word": " that", "probability": 0.82373046875 }, { "start": 2402.49, "end": 2402.57, "word": ".", "probability": 0.75048828125 }, { "start": 2402.57, "end": 2402.61, "word": " I", "probability": 0.99755859375 }, { "start": 2402.61, "end": 2402.73, "word": " don't", "probability": 0.94287109375 }, { "start": 2402.73, "end": 2402.91, "word": " really", "probability": 0.7255859375 }, { "start": 2402.91, "end": 2403.03, "word": " know", "probability": 0.810546875 }, { "start": 2403.03, "end": 2403.15, "word": " if", "probability": 0.89111328125 }, { "start": 2403.15, "end": 2403.59, "word": " that's", "probability": 0.9267578125 }, { "start": 2403.59, "end": 2404.05, "word": " kosher", "probability": 0.918212890625 } ] }, { "id": 849, "seek": 236866, "start": 2404.05, "end": 2406.01, "text": " in the bioinformatics world or anything like that, but", "tokens": [ 50564, 294, 264, 12198, 37811, 30292, 1002, 420, 1340, 411, 300, 11, 457, 50664 ], "temperature": 0.0, "avg_logprob": -0.19858156324278378, "compression_ratio": 1.832167832167832, "no_speech_prob": 0.033599853515625, "words": [ { "start": 2404.05, "end": 2404.23, "word": " in", "probability": 0.65771484375 }, { "start": 2404.23, "end": 2404.37, "word": " the", "probability": 0.80419921875 }, { "start": 2404.37, "end": 2404.91, "word": " bioinformatics", "probability": 0.8992513020833334 }, { "start": 2404.91, "end": 2405.13, "word": " world", "probability": 0.884765625 }, { "start": 2405.13, "end": 2405.23, "word": " or", 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0.1732177734375, "words": [ { "start": 2669.64, "end": 2670.0, "word": " that", "probability": 0.1463623046875 }, { "start": 2670.0, "end": 2670.14, "word": " might", "probability": 0.798828125 }, { "start": 2670.14, "end": 2670.3, "word": " be", "probability": 0.87548828125 }, { "start": 2670.3, "end": 2670.56, "word": " actually", "probability": 0.66845703125 }, { "start": 2670.56, "end": 2671.34, "word": " useful", "probability": 0.7666015625 } ] }, { "id": 978, "seek": 261958, "start": 2673.36, "end": 2674.12, "text": " one thing", "tokens": [ 50964, 472, 551, 51064 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2673.36, "end": 2673.74, "word": " one", "probability": 0.001972198486328125 }, { "start": 2673.74, "end": 2674.12, "word": " thing", "probability": 0.8193359375 } ] }, { "id": 979, "seek": 261958, "start": 2674.12, "end": 2675.9, "text": " that I want to say about this though is", "tokens": [ 51064, 300, 286, 528, 281, 584, 466, 341, 1673, 307, 51164 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2674.12, "end": 2674.54, "word": " that", "probability": 0.818359375 }, { "start": 2674.54, "end": 2674.66, "word": " I", "probability": 0.984375 }, { "start": 2674.66, "end": 2674.8, "word": " want", "probability": 0.697265625 }, { "start": 2674.8, "end": 2674.88, "word": " to", "probability": 0.90283203125 }, { "start": 2674.88, "end": 2675.06, "word": " say", "probability": 0.89306640625 }, { "start": 2675.06, "end": 2675.22, "word": " about", "probability": 0.81298828125 }, { "start": 2675.22, "end": 2675.42, "word": " this", "probability": 0.87939453125 }, { "start": 2675.42, "end": 2675.62, "word": " though", "probability": 0.697265625 }, { "start": 2675.62, "end": 2675.9, "word": " is", "probability": 0.865234375 } ] }, { "id": 980, "seek": 261958, "start": 2676.62, "end": 2677.48, "text": " if you'll notice", "tokens": [ 51164, 498, 291, 603, 3449, 51264 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2676.62, "end": 2677.0, "word": " if", "probability": 0.7998046875 }, { "start": 2677.0, "end": 2677.14, "word": " you'll", "probability": 0.880615234375 }, { "start": 2677.14, "end": 2677.48, "word": " notice", "probability": 0.85107421875 } ] }, { "id": 981, "seek": 261958, "start": 2678.26, "end": 2679.84, "text": " with these consensus sequences, I'm just", "tokens": [ 51264, 365, 613, 19115, 22978, 11, 286, 478, 445, 51364 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2678.26, "end": 2678.64, "word": " with", "probability": 0.67431640625 }, { "start": 2678.64, "end": 2678.78, "word": " these", "probability": 0.6611328125 }, { "start": 2678.78, "end": 2679.1, "word": " consensus", "probability": 0.8818359375 }, { "start": 2679.1, "end": 2679.46, "word": " sequences", "probability": 0.8955078125 }, { "start": 2679.46, "end": 2679.6, "word": ",", "probability": 0.27001953125 }, { "start": 2679.6, "end": 2679.72, "word": " I'm", "probability": 0.98876953125 }, { "start": 2679.72, "end": 2679.84, "word": " just", "probability": 0.88623046875 } ] }, { "id": 982, "seek": 261958, "start": 2679.84, "end": 2681.84, "text": " assuming that this is", "tokens": [ 51364, 11926, 300, 341, 307, 51464 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2679.84, "end": 2680.24, "word": " assuming", "probability": 0.767578125 }, { "start": 2680.24, "end": 2681.2, "word": " that", "probability": 0.86572265625 }, { "start": 2681.2, "end": 2681.66, "word": " this", "probability": 0.87939453125 }, { "start": 2681.66, "end": 2681.84, "word": " is", "probability": 0.896484375 } ] }, { "id": 983, "seek": 261958, "start": 2681.84, "end": 2683.16, "text": " going to be variable length and", "tokens": [ 51464, 516, 281, 312, 7006, 4641, 293, 51564 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2681.84, "end": 2682.18, "word": " going", "probability": 0.6376953125 }, { "start": 2682.18, "end": 2682.18, "word": " to", "probability": 0.904296875 }, { "start": 2682.18, "end": 2682.3, "word": " be", "probability": 0.89013671875 }, { "start": 2682.3, "end": 2682.58, "word": " variable", "probability": 0.68994140625 }, { "start": 2682.58, "end": 2682.92, "word": " length", "probability": 0.80029296875 }, { "start": 2682.92, "end": 2683.16, "word": " and", "probability": 0.8525390625 } ] }, { "id": 984, "seek": 261958, "start": 2683.67, "end": 2685.5, "text": " this is not going to mean anything. So basically", "tokens": [ 51564, 341, 307, 406, 516, 281, 914, 1340, 13, 407, 1936, 51664 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2683.67, "end": 2683.86, "word": " this", "probability": 0.84228515625 }, { "start": 2683.86, "end": 2684.04, "word": " is", "probability": 0.8623046875 }, { "start": 2684.04, "end": 2684.28, "word": " not", "probability": 0.890625 }, { "start": 2684.28, "end": 2684.38, "word": " going", "probability": 0.82275390625 }, { "start": 2684.38, "end": 2684.46, "word": " to", "probability": 0.90283203125 }, { "start": 2684.46, "end": 2684.6, "word": " mean", "probability": 0.92138671875 }, { "start": 2684.6, "end": 2684.94, "word": " anything", "probability": 0.84521484375 }, { "start": 2684.94, "end": 2685.12, "word": ".", "probability": 0.10882568359375 }, { "start": 2685.12, "end": 2685.2, "word": " So", "probability": 0.75244140625 }, { "start": 2685.2, "end": 2685.5, "word": " basically", "probability": 0.75 } ] }, { "id": 985, "seek": 261958, "start": 2685.5, "end": 2687.42, "text": " what's happening here is I'm losing information", "tokens": [ 51664, 437, 311, 2737, 510, 307, 286, 478, 7027, 1589, 51764 ], "temperature": 0.0, "avg_logprob": -0.18397177611627885, "compression_ratio": 1.6911764705882353, "no_speech_prob": 0.1732177734375, "words": [ { "start": 2685.5, "end": 2686.0, "word": " what's", "probability": 0.827880859375 }, { "start": 2686.0, "end": 2686.22, "word": " happening", "probability": 0.74267578125 }, { "start": 2686.22, "end": 2686.36, "word": " here", "probability": 0.79541015625 }, { "start": 2686.36, "end": 2686.44, "word": " is", "probability": 0.8603515625 }, { "start": 2686.44, "end": 2686.6, "word": " I'm", "probability": 0.95849609375 }, { "start": 2686.6, "end": 2686.9, "word": " losing", "probability": 0.814453125 }, { "start": 2686.9, "end": 2687.42, "word": " information", "probability": 0.74169921875 } ] }, { "id": 986, "seek": 264760, "start": 2687.42, "end": 2689.58, "text": " there's a C here and two gaps", "tokens": [ 50364, 456, 311, 257, 383, 510, 293, 732, 15031, 50464 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2687.42, "end": 2688.3, "word": " there's", "probability": 0.2919921875 }, { "start": 2688.36, "end": 2688.58, "word": " a C", "probability": 0.8369140625 }, { "start": 2688.58, "end": 2688.84, "word": " here", "probability": 0.78564453125 }, { "start": 2688.84, "end": 2688.96, "word": " and", "probability": 0.8115234375 }, { "start": 2688.96, "end": 2689.16, "word": " two", "probability": 0.8017578125 }, { "start": 2689.16, "end": 2689.58, "word": " gaps", "probability": 0.8291015625 } ] }, { "id": 987, "seek": 264760, "start": 2689.58, "end": 2691.52, "text": " there's got to be a better way to", "tokens": [ 50464, 456, 311, 658, 281, 312, 257, 1101, 636, 281, 50564 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2689.58, "end": 2690.52, "word": " there's", "probability": 0.54754638671875 }, { "start": 2690.52, "end": 2690.92, "word": " got", "probability": 0.3662109375 }, { "start": 2690.92, "end": 2690.96, "word": " to", "probability": 0.8369140625 }, { "start": 2690.96, "end": 2691.04, "word": " be", "probability": 0.90185546875 }, { "start": 2691.14, "end": 2691.24, "word": " a better", "probability": 0.82177734375 }, { "start": 2691.24, "end": 2691.42, "word": " way", "probability": 0.87841796875 }, { "start": 2691.42, "end": 2691.52, "word": " to", "probability": 0.904296875 } ] }, { "id": 988, "seek": 264760, "start": 2691.52, "end": 2693.16, "text": " represent these consensus sequences", "tokens": [ 50564, 2906, 613, 19115, 22978, 50664 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2691.52, "end": 2691.84, "word": " represent", "probability": 0.71337890625 }, { "start": 2691.84, "end": 2692.16, "word": " these", "probability": 0.68017578125 }, { "start": 2692.16, "end": 2692.58, "word": " consensus", "probability": 0.845703125 }, { "start": 2692.58, "end": 2693.16, "word": " sequences", "probability": 0.91796875 } ] }, { "id": 989, "seek": 264760, "start": 2693.16, "end": 2695.54, "text": " there's something called sequence logos", "tokens": [ 50664, 456, 311, 746, 1219, 8310, 40654, 50764 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2693.16, "end": 2694.04, "word": " there's", "probability": 0.696533203125 }, { "start": 2694.04, "end": 2694.18, "word": " something", "probability": 0.7412109375 }, { "start": 2694.18, "end": 2694.34, "word": " called", "probability": 0.791015625 }, { "start": 2694.34, "end": 2695.02, "word": " sequence", "probability": 0.7412109375 }, { "start": 2695.02, "end": 2695.54, "word": " logos", "probability": 0.7412109375 } ] }, { "id": 990, "seek": 264760, "start": 2695.54, "end": 2697.3, "text": " and basically what it is", "tokens": [ 50764, 293, 1936, 437, 309, 307, 50864 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2695.54, "end": 2696.42, "word": " and", "probability": 0.76318359375 }, { "start": 2696.42, "end": 2696.7, "word": " basically", "probability": 0.8583984375 }, { "start": 2696.7, "end": 2696.88, "word": " what", "probability": 0.84521484375 }, { "start": 2696.88, "end": 2697.0, "word": " it", "probability": 0.8525390625 }, { "start": 2697.0, "end": 2697.3, "word": " is", "probability": 0.90380859375 } ] }, { "id": 991, "seek": 264760, "start": 2697.3, "end": 2699.02, "text": " it's kind of just like a stacked histogram", "tokens": [ 50864, 309, 311, 733, 295, 445, 411, 257, 28867, 49816, 50964 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2697.3, "end": 2697.82, "word": " it's", "probability": 0.7213134765625 }, { "start": 2697.82, "end": 2697.98, "word": " kind", "probability": 0.66357421875 }, { "start": 2697.98, "end": 2698.04, "word": " of", "probability": 0.8974609375 }, { "start": 2698.04, "end": 2698.16, "word": " just", "probability": 0.7353515625 }, { "start": 2698.16, "end": 2698.28, "word": " like", "probability": 0.82470703125 }, { "start": 2698.38, "end": 2698.64, "word": " a stacked", "probability": 0.7431640625 }, { "start": 2698.64, "end": 2699.02, "word": " histogram", "probability": 0.708984375 } ] }, { "id": 992, "seek": 264760, "start": 2699.02, "end": 2701.08, "text": " of each offset", "tokens": [ 50964, 295, 1184, 18687, 51064 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2699.02, "end": 2699.52, "word": " of", "probability": 0.890625 }, { "start": 2699.52, "end": 2700.18, "word": " each", "probability": 0.84033203125 }, { "start": 2700.18, "end": 2701.08, "word": " offset", "probability": 0.6201171875 } ] }, { "id": 993, "seek": 264760, "start": 2701.08, "end": 2703.6, "text": " so let me find a good example", "tokens": [ 51064, 370, 718, 385, 915, 257, 665, 1365, 51164 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2701.08, "end": 2702.02, "word": " so", "probability": 0.63037109375 }, { "start": 2702.02, "end": 2702.72, "word": " let", "probability": 0.615234375 }, { "start": 2702.72, "end": 2702.92, "word": " me", "probability": 0.8505859375 }, { "start": 2702.92, "end": 2703.06, "word": " find", "probability": 0.75244140625 }, { "start": 2703.16, "end": 2703.26, "word": " a good", "probability": 0.87158203125 }, { "start": 2703.26, "end": 2703.6, "word": " example", "probability": 0.9248046875 } ] }, { "id": 994, "seek": 264760, "start": 2703.6, "end": 2705.7, "text": " so right here after the host", "tokens": [ 51164, 370, 558, 510, 934, 264, 3975, 51264 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2703.6, "end": 2704.08, "word": " so", "probability": 0.7021484375 }, { "start": 2704.08, "end": 2704.4, "word": " right", "probability": 0.85009765625 }, { "start": 2704.4, "end": 2704.64, "word": " here", "probability": 0.81396484375 }, { "start": 2704.64, "end": 2704.92, "word": " after", "probability": 0.62744140625 }, { "start": 2704.92, "end": 2705.36, "word": " the", "probability": 0.80615234375 }, { "start": 2705.36, "end": 2705.7, "word": " host", "probability": 0.88037109375 } ] }, { "id": 995, "seek": 264760, "start": 2705.7, "end": 2707.8, "text": " you have two W's followed by a", "tokens": [ 51264, 291, 362, 732, 343, 311, 6263, 538, 257, 51364 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2705.7, "end": 2706.38, "word": " you", "probability": 0.8095703125 }, { "start": 2706.38, "end": 2706.5, "word": " have", "probability": 0.876953125 }, { "start": 2706.5, "end": 2706.7, "word": " two", "probability": 0.802734375 }, { "start": 2706.7, "end": 2707.02, "word": " W's", "probability": 0.7373046875 }, { "start": 2707.02, "end": 2707.22, "word": " followed", "probability": 0.8203125 }, { "start": 2707.22, "end": 2707.38, "word": " by", "probability": 0.90625 }, { "start": 2707.54, "end": 2707.8, "word": " a gap", "probability": 0.89599609375 } ] }, { "id": 996, "seek": 264760, "start": 2707.8, "end": 2709.78, "text": " gap. Basically when you stack that", "tokens": [ 51364, 7417, 13, 8537, 562, 291, 8630, 300, 51464 ], "temperature": 0.0, "avg_logprob": -0.18763020361463228, "compression_ratio": 1.7309236947791165, "no_speech_prob": 0.05499267578125, "words": [ { "start": 2707.8, "end": 2708.3, "word": ".", "probability": 0.0272674560546875 }, { "start": 2708.6, "end": 2708.82, "word": " Basically", "probability": 0.724609375 }, { "start": 2708.82, "end": 2709.04, "word": " when", "probability": 0.7470703125 }, { "start": 2709.04, "end": 2709.14, "word": " you", "probability": 0.89306640625 }, { "start": 2709.14, "end": 2709.34, "word": " stack", "probability": 0.9248046875 }, { "start": 2709.34, "end": 2709.48, "word": " that", "probability": 0.619140625 }, { "start": 2709.48, "end": 2709.78, "word": " histogram", "probability": 0.72412109375 } ] }, { "id": 997, "seek": 264760, "start": 2709.78, "end": 2711.62, "text": " histogram up, usually", "tokens": [ 51464, 49816, 493, 11, 2673, 51564 ], 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2801.18, "word": " and", "probability": 0.6552734375 }, { "start": 2801.18, "end": 2801.36, "word": " fields", "probability": 0.30029296875 }, { "start": 2801.36, "end": 2801.58, "word": " end", "probability": 0.82763671875 }, { "start": 2801.58, "end": 2801.8, "word": " based", "probability": 0.74072265625 }, { "start": 2801.8, "end": 2801.92, "word": " on", "probability": 0.90576171875 }, { "start": 2801.92, "end": 2802.06, "word": " the", "probability": 0.79833984375 }, { "start": 2802.06, "end": 2802.76, "word": " mutation", "probability": 0.837890625 }, { "start": 2802.76, "end": 2803.06, "word": " rate", "probability": 0.68359375 } ] }, { "id": 1036, "seek": 273176, "start": 2803.06, "end": 2805.24, "text": " so for instance", "tokens": [ 51364, 370, 337, 5197, 51464 ], "temperature": 0.0, "avg_logprob": -0.20410156822098152, "compression_ratio": 1.7083333333333333, "no_speech_prob": 0.00263214111328125, "words": [ { "start": 2803.06, "end": 2803.38, "word": " so", "probability": 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0.00263214111328125, "words": [ { "start": 2809.36, "end": 2809.8, "word": " for", "probability": 0.89306640625 }, { "start": 2809.8, "end": 2810.02, "word": " each", "probability": 0.8642578125 }, { "start": 2810.02, "end": 2810.34, "word": " offset", "probability": 0.8173828125 }, { "start": 2810.34, "end": 2810.88, "word": " and", "probability": 0.8779296875 } ] }, { "id": 1040, "seek": 276032, "start": 2810.88, "end": 2812.98, "text": " using that we can kind of understand", "tokens": [ 50364, 1228, 300, 321, 393, 733, 295, 1223, 50464 ], "temperature": 0.0, "avg_logprob": -0.1951822920391957, "compression_ratio": 1.7228464419475655, "no_speech_prob": 0.035675048828125, "words": [ { "start": 2810.88, "end": 2811.4, "word": " using", "probability": 0.038604736328125 }, { "start": 2811.4, "end": 2811.76, "word": " that", "probability": 0.8828125 }, { "start": 2811.76, "end": 2811.96, "word": " we", "probability": 0.6513671875 }, { "start": 2811.96, "end": 2812.12, "word": " can", 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thing I wanted to say about this particular tool", "tokens": [ 51464, 551, 286, 1415, 281, 584, 466, 341, 1729, 2290, 51564 ], "temperature": 0.0, "avg_logprob": -0.2073412761092186, "compression_ratio": 1.6956521739130435, "no_speech_prob": 0.01132965087890625, "words": [ { "start": 3007.84, "end": 3007.92, "word": " thing", "probability": 0.81298828125 }, { "start": 3007.92, "end": 3008.0, "word": " I", "probability": 0.9931640625 }, { "start": 3008.0, "end": 3008.1, "word": " wanted", "probability": 0.7216796875 }, { "start": 3008.1, "end": 3008.2, "word": " to", "probability": 0.900390625 }, { "start": 3008.2, "end": 3008.46, "word": " say", "probability": 0.892578125 }, { "start": 3008.46, "end": 3008.82, "word": " about", "probability": 0.81591796875 }, { "start": 3008.82, "end": 3009.14, "word": " this", "probability": 0.88720703125 }, { "start": 3009.14, "end": 3009.4, "word": " particular", "probability": 0.8203125 }, { "start": 3009.4, "end": 3009.7, "word": " tool", "probability": 0.89892578125 } ] }, { "id": 1132, "seek": 292512, "start": 3009.7, "end": 3011.74, "text": " is it will never be fully automated", "tokens": [ 51564, 307, 309, 486, 1128, 312, 4498, 18473, 51664 ], "temperature": 0.0, "avg_logprob": -0.2073412761092186, "compression_ratio": 1.6956521739130435, "no_speech_prob": 0.01132965087890625, "words": [ { "start": 3009.7, "end": 3009.94, "word": " is", "probability": 0.7568359375 }, { "start": 3009.94, "end": 3010.48, "word": " it", "probability": 0.81201171875 }, { "start": 3010.48, "end": 3010.6, "word": " will", "probability": 0.80908203125 }, { "start": 3010.6, "end": 3010.74, "word": " never", "probability": 0.85546875 }, { "start": 3010.74, "end": 3010.9, "word": " be", "probability": 0.90185546875 }, { "start": 3010.9, "end": 3011.28, "word": " fully", "probability": 0.708984375 }, { "start": 3011.28, "end": 3011.74, "word": " automated", "probability": 0.8896484375 } ] }, { "id": 1133, "seek": 292512, "start": 3011.74, "end": 3013.28, "text": " if accuracy is in mind but", "tokens": [ 51664, 498, 14170, 307, 294, 1575, 457, 51764 ], "temperature": 0.0, "avg_logprob": -0.2073412761092186, "compression_ratio": 1.6956521739130435, "no_speech_prob": 0.01132965087890625, "words": [ { "start": 3011.74, "end": 3011.96, "word": " if", "probability": 0.89208984375 }, { "start": 3011.96, "end": 3012.3, "word": " accuracy", "probability": 0.75732421875 }, { "start": 3012.3, "end": 3012.54, "word": " is", "probability": 0.8798828125 }, { "start": 3012.54, "end": 3012.66, "word": " in", "probability": 0.90185546875 }, { "start": 3012.66, "end": 3012.94, "word": " mind", "probability": 0.8037109375 }, { "start": 3012.94, "end": 3013.28, "word": " but", "probability": 0.42919921875 } ] }, { "id": 1134, "seek": 295290, "start": 3013.28, "end": 3014.94, "text": " if you are doing protocol fuzzing", "tokens": [ 50364, 498, 291, 366, 884, 10336, 283, 3334, 8781, 50464 ], "temperature": 0.0, "avg_logprob": 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0.6845703125 }, { "start": 3022.82, "end": 3022.94, "word": " of", "probability": 0.8955078125 }, { "start": 3022.94, "end": 3023.28, "word": " entropy", "probability": 0.6953125 } ] }, { "id": 1139, "seek": 295290, "start": 3023.28, "end": 3025.76, "text": " in each sequence.", "tokens": [ 50864, 294, 1184, 8310, 13, 50964 ], "temperature": 0.0, "avg_logprob": -0.21448864245956595, "compression_ratio": 1.668103448275862, "no_speech_prob": 0.04400634765625, "words": [ { "start": 3023.28, "end": 3024.12, "word": " in", "probability": 0.8974609375 }, { "start": 3024.12, "end": 3024.4, "word": " each", "probability": 0.8603515625 }, { "start": 3024.4, "end": 3025.28, "word": " sequence", "probability": 0.87646484375 }, { "start": 3025.28, "end": 3025.76, "word": ".", "probability": 0.47119140625 } ] }, { "id": 1140, "seek": 295290, "start": 3028.96, "end": 3030.32, "text": " Let me get this.", "tokens": [ 51064, 961, 385, 483, 341, 13, 51164 ], "temperature": 0.0, "avg_logprob": 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3037.96, "end": 3039.94, "text": " So basically what this can be used for is", "tokens": [ 51264, 407, 1936, 437, 341, 393, 312, 1143, 337, 307, 51364 ], "temperature": 0.0, "avg_logprob": -0.21448864245956595, "compression_ratio": 1.668103448275862, "no_speech_prob": 0.04400634765625, "words": [ { "start": 3037.96, "end": 3038.4, "word": " So", "probability": 0.80712890625 }, { "start": 3038.4, "end": 3038.82, "word": " basically", "probability": 0.7880859375 }, { "start": 3038.82, "end": 3039.0, "word": " what", "probability": 0.86572265625 }, { "start": 3039.0, "end": 3039.14, "word": " this", "probability": 0.87158203125 }, { "start": 3039.14, "end": 3039.22, "word": " can", "probability": 0.86962890625 }, { "start": 3039.22, "end": 3039.32, "word": " be", "probability": 0.896484375 }, { "start": 3039.32, "end": 3039.5, "word": " used", "probability": 0.80712890625 }, { "start": 3039.5, "end": 3039.74, "word": " for", "probability": 0.8935546875 }, { "start": 3039.74, "end": 3039.94, "word": " is", "probability": 0.88134765625 } ] }, { "id": 1143, "seek": 295290, "start": 3039.94, "end": 3042.3, "text": " understanding unknown protocols, some kind of", "tokens": [ 51364, 3701, 9841, 20618, 11, 512, 733, 295, 51464 ], "temperature": 0.0, "avg_logprob": -0.21448864245956595, "compression_ratio": 1.668103448275862, "no_speech_prob": 0.04400634765625, "words": [ { "start": 3039.94, "end": 3040.36, "word": " understanding", "probability": 0.611328125 }, { "start": 3040.36, "end": 3040.6, "word": " unknown", "probability": 0.638671875 }, { "start": 3040.6, "end": 3041.16, "word": " protocols", "probability": 0.7763671875 }, { "start": 3041.16, "end": 3041.7, "word": ",", "probability": 0.431884765625 }, { "start": 3041.7, "end": 3041.86, "word": " some", "probability": 0.81787109375 }, { "start": 3041.86, "end": 3042.1, "word": " kind", "probability": 0.830078125 }, { "start": 3042.1, "end": 3042.3, "word": " of", "probability": 0.8984375 } ] }, { "id": 1144, 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3347.99, "word": " that", "probability": 0.841796875 }, { "start": 3347.99, "end": 3348.19, "word": " Meet", "probability": 0.59765625 }, { "start": 3348.19, "end": 3348.35, "word": " the", "probability": 0.69970703125 }, { "start": 3348.35, "end": 3348.59, "word": " Fed", "probability": 0.9296875 }, { "start": 3348.59, "end": 3348.75, "word": " will", "probability": 0.77099609375 }, { "start": 3348.75, "end": 3348.89, "word": " be", "probability": 0.89453125 }, { "start": 3348.89, "end": 3349.15, "word": " at...", "probability": 0.41790771484375 } ] } ], "text": " All right. DEF CON 13 is proud to present Marshall Betto with the Protocol Informatics Project. Saturday morning at DEF CON. God damn, why am I working Saturday morning at DEF CON? All right. Marshall, take it away. All right, so this project is called the Protocol Informatics Project. It's basically a play on words from bioinformatics, which is kind of my main inspiration for this project. Yeah, it's not that loud. Should I talk closer? Okay, there we go. Thanks. So, my name is Marshall Betto. I'm a research scientist at McAfee. However, this is pretty much all independent research. My company's never even seen this stuff. So, I think that's why it's worthy, as opposed to something that's out there trying to make money or something like that. Let's start. Well, the purpose of this project is to basically automate network protocol analysis. So, I just added this slide the other night just to kind of say, this is what this thing does. You input a PCAP file, and output is going to be a packet format. So, this is my little XML markup of this particular protocol format, which is just kind of something that's made up, which is basically a two-byte link field followed by an ASCII string or something like that. My application, what it does is it goes through, does a series of comparisons and checks, and uses sequence alignment algorithms to understand the general structure of the format. So, if you were to analyze this markup, basically what it's doing is it's describing this protocol, saying that it operates on UDP 435364, something I just pretty much made up. It's a block-based protocol, so you open up a block, you insert the link to that particular block, which is going to be a short, big in the end, followed by a variable type, which is going to be a plain text string. Right now, the default value for that is going to be hello. And then I close it all out. So, the general idea is, you take a protocol you've never seen before in your life, you're trying to reverse engineer a World of Warcraft, for all I know. You input the PCAP file, and out comes the structure. So, that's the general idea behind it. So, the objective of protocol analysis is, first, to understand exactly the format of every single packet. I mean, that's a pretty simple concept in general, but it does get more complicated as you have to understand the actual state and the actual handshake and things of that sort. So, there's really no good way of doing this at this current time. I mean, it's pretty much a manual process. People sit down with E3O or something like that and break out a piece of paper and just start writing down offsets and trying to use their human intuition to be able to understand what this particular field does and where it's located in the packet. So, this information is generally important to understand proprietary protocols, which are actually kind of becoming a little bit more rare, in my opinion. Of all the security products that I've been looking at and things like that, everybody seems to be using SOAP over SSL nowadays. So, maybe this stuff isn't as applicable in those situations. But, if you're going back and looking at legacy protocols, like some SCADA networks and things like that, this stuff is going to be a lot more useful because there's really not that much documentation for that sort of stuff. So, it's also important for extending the functionality of software. If you guys want to write an open source client for a particular service, this project can help out in that regard. And lastly, and my favorite, is finding vulnerabilities in unknown or badly documented protocols using fuzzing. So, what I basically do is I feed it my PCAP file, it analyzes the structure, pops out the XML markup, then I feed that into my fuzzer and it starts going to work. So, what protocol informatics does, which is just the name of my program basically, it heuristically identifies the fields in data format. So, it's never going to be perfect, but it's as close to automated as you're ever going to get. So, nowadays the problems with protocol analysis in general is it's a manual process. Like I was mentioning earlier, you just pretty much do it by hand using ethereal and fields are identified largely using human intuition. So, I guess I was talking to Spoon about this a little earlier and he was basically explaining to me that, well, all these protocols are written by humans so you can basically use your human intuition to be able to understand how they were thinking and things like that. However, computers have never really been able to mimic human behavior that well. I mean, if you're looking at pattern recognition and things like that, people are moving over neural networks and other hidden markup models and other things of that sort in order to try to mimic that intuition in a computer program. I guess the main challenges with protocol analysis these days is kind of just understanding general structure when it comes to different types of protocols. For example, a protocol that's all binary, usually quite hard to analyze because there's really no points of reference for us. When you're going through it, you're just kind of looking at the bytes and trying to find patterns and things like that. Typically, it doesn't seem like that's going to be pretty much a tedious, painstaking process. So, this is really an attempt to automate that. I think the crux of protocol analysis in general is variable length fields, long strings in the middle of packets and stuff like that. You don't necessarily know, if it's not plain text data, where it starts and where it ends and what each byte represents and things of that sort. Typically, this is all going to be differential analysis. You're just going to be going through, doing comparison, comparison, comparison, trying to kind of build a profile as you go along. Fortunately, however, biologists already fixed this problem back in the 1970s when they were trying to analyze genetic structure, DNA, amino acids, and all that sort of stuff. This is all pretty much... I'm not taking credit for any of these algorithms I'm presenting today. All I'm basically doing is reapplying it to our realm, which is computer security, computer science, and things like that. So, bioinformatics. What is bioinformatics? It's basically information theory applied to biology. So, it's a lot of probability, Shannon entropy, all sorts of things like that. And it's mainly used to identify genes in the genetic structure of organisms. So, Human Genome Project, the majority of the algorithms that are used are mainly sequence alignment algorithms. What they're going out there and trying to find is where the beginning of these genes start and where they end, what the function of these genes are, and what the structure of these genes are, how they fold, and all sorts of things like that. So, the workflow for how biologists do this, they have these large databases just full of genetic data. The human genome's in there, they have mice and every other species that they've sequenced. And they put this all in a database and then they can start running queries on it. So, saying, like, I know that this gene exists in, you know, human. It will go through and it can understand common ancestors and things like that, based on mutation rates and all sorts of stuff like that. So, if you want to put it in simple computational terms, it basically is just processing a large amount of structured but complex data. It's just too much for the human eye to just recognize. So, you have to put it in a database, you have to run these algorithms on it and things like that, if you really want to understand the structure. So, yeah, the objective is to identify genes, determine structure, and the function. Easier said than done, naturally, but there's a lot of analogies to protocol analysis in this case. So, one application of bioinformatics is allowing biologists to map phenotypes to genotypes. Being able to say, blue eyes maps of this particular gene, attached earlobes maps of this particular gene, and things like that. Protocol analysis and bioinformatics have a lot of things in common. They're both operating in large sequences of data. And my favorite is just the analogy of biology's phenotype-to-genotype mapping is equivalent to protocol analysis' mapping of offsets to field locations. We're basically just trying to understand the function of a particular field in a byte stream. We don't know where it exists, but it's between this offset and this offset. Fortunately for us, what actually makes this easier is, when it comes to genes and things like that, it's not all... it doesn't have a defined start and a defined end. You can have... I think eye color, for instance, isn't just like one single gene. It's spread out throughout the genome. So, you have to do a bit more complicated analysis to understand, here's a block where it starts, then it skips this many symbols, and then it restarts over here. So, fortunately for us, it's really not like that. We have fields. Here's your start offset, here's your end offset. So, what I did was I created an application that was meant to understand the structured, complex data found in our protocols. So, the core algorithms that are used in bioinformatics in general are mainly sequence alignment algorithms. There's two of the most classic algorithms, the Niederman-Wunsch algorithm and the Smith-Waterman. I'll get into each particular detail as we go on, but basically, the Niederman-Wunsch algorithm is a global sequence alignment algorithm. It takes into account every single symbol in the genetic sequence and tries to align it to a different sequence. It's just a pairwise alignment. Smith-Waterman, on the other hand, is a local sequence alignment algorithm. It's going to find the most common substring within the two sequences that are most similar. There's ways to improve sequence alignment, and that's basically by using similarity matrices. I'll get into that a little later. All these algorithms are operations on matrices. There's a lot you can do with different weighting and things like that, and assigning scores, and how the matrix is traversed is all dependent on the scores in the cells. There's actually a lot of really neat things that they figured out that they can do using this. Then what we get into is phylogenetic tree building. This is kind of cool, because this is how biologists are able to build a tree of life, not based on fossil record, but instead based on genetic information. How similar are these two sequences, and being able to go all the way up through the evolutionary tree to find common ancestors and things like that. I go over the two tree building algorithms, Upkema and the neighbor joining tree, which I'm sure many people have heard of that. And lastly, I get into multiple sequence alignment, and that's where there's a lot of challenges involved with aligning more than one sequence, just due to computational problems. I go over the progressive and tree-based multiple sequence alignment algorithms. And this is a lot of information, so if I am going too fast, just put your hands up or something like that. The fundamental purpose of sequence alignment is really simple. It's take two sequences, regardless of their length, and insert gaps into either sequence to make them the same length. So, right here I have an example of a DNA alignment. There's four base amino acids in DNA. There's thymine, guanine, cytosine, and adenine. This is kind of just a really simple example of inserting gaps into the first sequence to align it to the second sequence. So, as you can see, there's three gaps that were actually inserted into the first sequence, which would represent a deletion, evolutionary-wise. The Niedermann-Wunsch algorithm, which I mentioned earlier, is a dynamic programming algorithm, meaning that it operates on matrices. I think it was the first sequence alignment algorithm ever written. I think it was 1973 when it first came out. Basically, it performs a global alignment on a pair of sequences, and what that means is that every symbol in the sequence that goes into the algorithm comes out of the algorithm. Gaps are inserted. There's no substring searching or anything like that. It's going to try to insert gaps anywhere it can to make the sequences line up to each other as best as possible to maximize the alignment, maximize the score of the alignment. And it's mainly used for analyzing closely related structures, and the reason for that is if you were trying to analyze the human genome and the mouse genome and using a global sequence alignment algorithm, there's going to be so many gaps that are inserted that it's not even going to be relevant at that point. It's mainly used for maybe comparing two different types of humans' DNA to understand a brown-eyed person versus a blue-eyed person versus an attached earlobes person versus a dangling. Dynamic programming is a common concept in computer science. It's not coding, per se. It's just a terminology that's used to solve problems by using matrices. So the idea is to break the problem up into subproblems. So you have a matrix. You perform a computation on each cell in the matrix, and as the algorithm progresses, it uses information that was previously gained in the other cells to progress until the end of the process. So the results of the previous computations are saved and used by the remaining subproblems. And Niedermann-Wunsch is a dynamic programming algorithm, as is most of the sequence alignment algorithms out there. So how Niedermann-Wunsch works, it's actually a pretty simple little operation that it does. The first sequence is placed in the topmost row, and the second sequence is placed in the leftmost column. And basically there's this little equation right here that basically is used to assess each cell in the matrix, starting at position 1, 1. There's three steps for these sequence alignment algorithms. The first step is to assign a similarity score. So basically what you do is, you go through and you score every single similarity between the character in sequence 1 and the character in sequence 2. The second step is you assess all possible pathways through the matrix, left, up, and diagonal from the particular cell that you're in, assigning the current cell with the value of the maximum scoring pathway using this particular equation, which I have a matrix on the next slide that will make it a bit easier to understand. The third step is constructing a pathway from the highest scoring cell back to the beginning of the matrix. And what this represents is the insertion of gaps in each sequence. And lastly... Okay, actually, let me get back to the equation. What you're going to do is, when you're in cell IJ, you're going to look at your diagonal cell, which is I-1, J-1, plus S-IJ, which S-IJ is going to be the similarity score between the offset I and offset J in each sequence. It's also going to look at the left... It's going to look at the left cell from the current position, and it's going to add a gap penalty because if you're going left or if you're going up, you're actually inserting a gap into the sequence. And that's not necessarily a good thing because it kind of makes it a bit irrelevant if there's too many gaps. So there's something called the gap penalty that will basically dock the alignment score at the end. So you'll be able to understand what the relative... You'll be able to really understand how important this alignment is and what it really represents. If it has a really low score, then you know that it's probably really not that significant. So this is the visualization of the concept. I use HTTP just because everybody understands it. It's a text-based protocol. It's really simple. You actually probably wouldn't ever have to use this program to do text-based protocols just because they're so easy. But for example's sake, I use it just because it's small enough to fit into a PowerPoint presentation. So the first sequence is placed in the topmost row, which is git slash index.html HTTP 1.0. And the second sequence is git slash HTTP 1.0. So as mentioned earlier, you just kind of iterate through the matrix and you perform the scoring based on the similarity between each character. So right here, the Gs match, so you score with one and so on. So you go out and you build up the matrix. One thing to be mentioned, though, is Needleman-Wunsch, kind of like the default runtime environment for it, is to have a match score of 1, a gap penalty of 0, and a mismatch score of 0. So in some cases, you can actually make this a bit more complex by having different scoring mechanisms. G might have a stronger alignment, so you have a higher score in that case, and things like that. And when I talk about similarity matrices a little later on, you'll kind of understand what I'm talking about. It's probably a little too early for that. So what you do is you start in cell 1-1, and you look at your left cell, your diagonal cell, and your upper cell. And you apply this little equation right here, which is going to take the maximum value of the diagonal, or the left, or the upper. For the diagonal cell, you actually add the score of that position. So in this case, it's going to look at diagonal and add 1, because the G's match. There's no match here, there's no match here, so the maximum is going to be the diagonal plus 1. So it's going to leave it a 1. And the way that these matrices are traversed, they're traversed left to right, and then they move downward. The next step, move over one position. This time, it's going to look left, it's going to look diagonal, it's going to look up. It's going to take the maximum. In this case, before, the cell was 0, so this S-I-J in this case is going to be 0. It realizes that the leftmost cell in this case is going to be the maximum, so it places that particular value in this cell. It's a really simple operation, actually. It just goes cell by cell and it builds up. This is kind of like the completed matrix. I do this all in hex down here just to make it all line up and look pretty on the screen. Basically, this is kind of how it looks after you perform all the scoring of the matrix. The next step at this point is to actually traverse back to the beginning of the matrix and understand what your alignment is going to be. In this case, typically, the maximum cell is always going to be your bottom right. What we do is we start in the bottom right cell in this case because it had the highest value. We look at the neighboring cells and we choose the cell with the maximum value and traverse there. We start here at E and we notice that our neighbors are all Ds. Basically, that means that we can go anywhere because they're all equal. We can go left, we can go up, we can go diagonal. I personally like to just go diagonal just because it just makes the most sense. Basically, it signifies that there's not going to be any gap that's inserted into any of the sequences, which may mean that there's actual match there. This is kind of the evolution at this current point. Starting down here, we look, we go diagonal because it's D. At this point in D, we look around. All of our neighbors are C. We want to go diagonal again by default. My mouse is disappearing. The same deal here. All B, so we go diagonal. All the neighbors are A. Go back to A. Neighbors are 9, etc., etc. All until we reach this point up here at this 5. And we look at our neighboring cells again and we realize that there's a 5 to our left, a 4 to our diagonal, and a 4 to our up. We want to have the maximized alignment so we're going to take the path that will result in the maximum score. So at this point, we're going to start going left. This is the completed traverse all the way back to the beginning of the matrix. So at this point... Goddamn. So at this point, we're looking around. We notice that 5 to our left is the maximum, so we're going to go there. Same thing. We're surrounded by two 4s and a 5 on the left, so we go left, etc., etc., all the way until we hit back here. And all of them are equal again, so we decide that we're going to go diagonal. And then it goes all the way back to the root. So what this means at this point is there are going to be gaps inserted into one of these sequences. So what did this do? We computed a path through the matrix, and now we can apply the rules of Needham and Woons to obtain the two aligned sequences. Basically, here's the rule. Any time that you traverse left on the matrix, a gap is inserted into a sequence. So when you go left, it's going to insert a gap into sequence 1 at that particular offset. If you ever go up, you're going to insert a gap at that offset in the first sequence. So, here's the results of that particular alignment. On the bottom down here, kind of a lame example with HTTP, I know, but it illustrates the point. There's gaps that are inserted right here under index.html because that particular sequence did not have that file in there, and everything else matches up. So what this allows us to do, really, is to understand the beginning and ending of the boundaries in the packet. So, if you wanted to analyze this really simple example, you can say that, okay, these two sequences had git slash in common. We can consider that a keyword. Index.html was a file name. It's a variable length field. You can put any file in there that you want. And it was subject to deletion in the second sequence. So what we can assume at this point is that that is a variable length field because it was subject to deletion, followed by the keyword HTTP 1.0. So, if you wanted to actually describe this protocol in a fuzzer, you could say, okay, we're going to send a git space slash and we're going to send a whole bunch of random data in between, followed by HTTP 1.0. Pretty simple. This leads us into explaining the local sequence alignment algorithm, which is used kind of for a different purpose. So, this is called Smith-Waterman. I think it was written in 1979, I think. I might be wrong about that. It's basically a derivative of Needleman-Wunsch. It just has some different rules, basically. What the point that it's trying to do is find the maximum local alignment between the two sequences, which is going to be a subsequence between the two. It's going to find the subset in each sequence that makes the most sense. And that may be subject to gaps and it might not necessarily. So, this is especially useful in determining similarity between distance sequences because you could look at a chimpanzee genome and a human genome and use local sequence alignment algorithms and you'll actually get a lot more data out of it because the alignments will actually mean a lot more in the end. So, local alignment has a strong scoring mechanism, whereas global is weak because of the number of gaps that may be inserted into the global alignment. So, local alignment algorithms are used a lot to kind of search the database to understand which species are the most similar to others and things like that. So, how Smith-Waterman works, it's a derivative of Needleman-Wunsch. The only difference is you kind of start it with some different initial values. Also, this is kind of like a default run. You can also use similarity matrices and things like that to modify how this works. But by default, it has a match score of 2. So, if G and G match, we assign that a 2. It has a gap penalty of negative 2. Any time that we move up or any time that we move left, we're going to subtract negative 2 from the alignment score. The lower the alignment score, the less significant it is. Any mismatch, so G and E don't match, it's going to have a mismatch score of negative 1. The difference is, when you're in the matrix, you start in the cell that has the highest score, and you're going to start traversing back. The difference is, when the score hits 0 in the matrix, you're going to return immediately. And at that point, those particular offsets in the matrix are the ones that are saved and used as a representative local alignment sequence. I actually didn't do a matrix and stuff like that. It was kind of short notice and things like that. But basically, if you want to align these two sequences, same thing as before, the score's going to end up being 18, and it's going to find the substring space, HTTP slash 1.0, as being the most common local alignment in those particular sequences. So that's going to be the most similar difference between the two. Smith-Waterman is actually used a lot to determine the distance between two sequences. So if you want to find the distance between sequence 1 and sequence 2, you're going to take the Smith-Waterman score of sequence 1 running against itself, minus the Smith-Waterman score of sequence 1 aligned with sequence 2. So in this case, it's 48 minus 18, which is 30. So the distance is going to be 30. Just pretty simple. Basically, here's how it's used. In my program, it's not necessarily used to determine the protocol fields. I use it just as a classifier to allow me to build kind of like a relationship matrix and understand which packets in the tcpdump file are the most similar to each other. Because if you're looking at a complex protocol like isocamp or smb or something like that, they have multiple different message types. This is actually going to be really helpful because it's going to allow us to cluster the different types of messages within that particular protocol. It's used to calculate the relative distance between sequences for clustering purposes and tree building, which we'll get into later. In bioinformatics, Smith-Waterman is used for multiple sequence alignment because DNA has no start and end whereas a packet does. That's also another big advantage that we have. We know where the packet starts, we know where the packet ends. We also have fields that start here and end there. There's no fields that are going to span over multiple blocks and jump in over each other and stuff like that, like it does in DNA. To improve these alignment algorithms, we use what's called a similarity matrix. Basically, we replace our scoring mechanism. Before, I was showing you that a match score would equal 1 in Niedermann-Wunsch, a match score in Smith-Waterman would equal 2, and things like that. Similarity matrices, on the other hand, are tables based on the probability that this sequence is allowed to mutate into another sequence. In evolution, it is possible for different amino acids to mutate into others, but not other ones. It basically allows biologists to say, adenine can mutate into thymine but not cytosine, and things like that. By the way, I'm not a biologist, I don't actually know if that is true, but it's just an example. Similarity matrices are pretty different, I guess, because it's kind of based on preference. There's similarity matrices out there that are used only when comparing really distant sequences, and there's similarity matrices that are used when comparing really close sequences. The applications to protocol analysis is really good, though, because we have data types. We have plaintext data, we have binary data, and things like that. We can assume that binary data is acceptable for binary data to mutate into other binary data, and plaintext data to mutate into other plaintext data. Especially if you're using a protocol that usually uses English words, you can actually have a similarity matrix that accepts I before E and other kind of common linguistic rules like that. So, the protocol informatics similarity matrices, our alphabet is 256 bytes long, so we have a 256 by 256 matrix, and each mutation, each cell contains mutation probabilities between every character, so we might say that it's acceptable for A to mutate into E, but not into hex FF and things like that. So, this is actually kind of some outdated stuff. There's actually a lot easier ways of doing this. So, what this allows us to do is obtain a bit more optimized alignments. This way, if we use these similarity matrices that are specially tailored for different types of network protocols, if you have one that's mixed, has plaintext in it and binary data in it, you could use a particular similarity matrix in there. And basically what this does is it allows when gaps are inserted into the sequences, the alignment is still going to hold true where plaintext data is only mutating into other plaintext data, binary data is only mutating into other binary data. You're not going to have any mismatches in between. So, I guess the kind of role of similarity matrices though is that they're going to really have to be tweaked. And you could have probabilistic models, you can do some Markov chains and stuff like that to initially build your stuff, but it's also kind of subjective. I can say that I know that a plaintext character is commonly followed by a null, so it's able to mutate into a null character and stuff like that. That's very acceptable. So, bioinformatics scientists spend years perfecting their version of similarity matrices. I don't know how many revisions there are of Blozeman and the PAM matrices, but there's quite a lot, and they're each used for kind of different purposes. I kind of actually had a problem trying to represent this stuff and kind of allow others to understand kind of what I'm talking about. And this isn't actually that great, but this is just a kind of stupid example. This superscript B just stands for binary. So let's just assume it's like 0x01, 0x02, 0x03, etc. So if you wanted to align these protocols, alignment without proper scoring might actually insert gaps into incorrect places and assume that F might be able to mutate into 0x01 and 0x04 and stuff like that, which isn't necessarily a correct alignment. If you use a similarity matrix with decent data that keeps track of mutations between different types of data types, you'll start getting better alignments where all the ASCII data is aligned to each other, all the binary data is aligned to each other, and things like that. So it's basically just a tool to help us signify our alignments a bit better. So I kind of explained so far how to do a pairwise sequence alignment, which isn't that useful. Just have a look at two HTTP requests and say, okay, here's the variable link field. This is where you put file names. It's really not that big of a deal. But it's really not that pairwise sequence alignment isn't really that useful. You really have to start getting the multiple sequence alignment if you really want to get significant results. Multiple sequence alignment is basically the act of aligning more than two sequences. It uses Niedermann-Munch as the alignment algorithm and uses Smith-Waterman as a scoring algorithm. The problem with this is there's computation issues. So Niedermann-Munch, as I showed before, it operates on a n by n, or excuse me, n by m matrix where n is the length of sequence one and m is the length of sequence two. If you want to start aligning three sequences, you can start building hypercubes and things like that and traversing through hypercubes. The problem is the operation is n to the nth power where n is the length of sequences and n is the number of sequences. This isn't really going to be feasible for us. So I have my little illustration of my hypercube being traversed. It can be done, but I've been trying to align sequences that are up to 1500 bytes in length and it takes a really, really, really long time. What we use is a heuristic sequence alignment algorithm. I'm basically sacrificing accuracy for time. The objective is to align every sequence to each other in a reasonable amount of time, but the results are never, ever going to be perfect. The way that this is done is by building these polygenetic trees. Polygenetic trees are basically what you guys would think of a tree of life. You start with common ancestors and you kind of move down and you can start understanding the similarities between certain species and things like that. It's the same thing with packets. We know that in SMB all the SMB authentication packets are going to be more similar than a data transfer packet and things like that. Typically, these are just binary trees which makes it quite simple, actually. It does have an interesting parallel in protocol analysis because protocols mimic evolution by changing their fields. The kind of goofy thing that I have here is what came first, git slash index.html or git slash? Obviously, git slash is going to be the common ancestor. Here's my little rip-off of a tree of life of lizards. As you can see, on the right-hand side, these guys look a lot more similar in structure than to this C. tigris guy over here with the long tail. So, basically, similar situation, apply it to packets. So, to create polygenetic trees, there's an algorithm called Upcoma which is the unweighted paired group method using arithmetic averages. It's pretty simple, actually. This little equation right here just signifies how the distance between two clusters is taken. Basically, the dpq function is going to determine the distance between each sequence in cluster i and cluster j. And it's basically just taking the mean of that and returning that back as the distance of i and j. In this case, the dpq is going to be the Smith-Waterman score and the d ij is the distance between two clusters i and j. So, this is just the operation that's used each step of the way when building the tree. So, to build the tree, each sequence is put into its individual cluster cell, one packet per cluster, basically. Use the Upcoma algorithm to iterate through every single cluster doing comparisons between every single sequence. You take the two with the smallest distance between each other. You create a new cluster k by uniting those two sequences. You define a new node k with child nodes i and j to build the tree. You add the cluster k to the universal set and you remove the clusters i and j. So, basically at the very end, you should only have one cluster. And you should have a tree build at that point. Here is a phylogenetic tree of the SMB protocol. This is just doing null sessions and stuff like that. As you can see, there's actually a pretty large disparity between the differences between different types of packets. The edge length signifies the distance. So, it's kind of more than a pretty picture. I'm sorry I can't actually I don't have any cool interface where I can zoom in and tell you exactly what those packets were and stuff like that at this current time, but from the analysis that I did each one of these clusters up here represented one of the SMB message types in the protocol. So, this is actually going to help us say we want to analyze the SMB authentication packet. Okay, it's this cluster over here, for instance. So, we start at this root node, and then we're going to start doing our progressive alignment over here. So, the tree is your guide. Basically, what this tree is going to be used for is to align the sequences to each other. As I was talking about the heuristic alignment earlier, what we're basically going to have to do is what's called progressive sequence alignment, and what that means is we're going to align two sequences to each other, and then we're going to pick one of the alignments and use that as a representative sequence for those two sequences. Then we're going to align another sequence to that representative sequence. And as gaps are inserted, they are moved down towards the root, excuse me, down towards the leaf nodes on the graph. So, the problem with this is if gaps are inserted into the tree in errant places, that will be propagated all the way down to the leaf nodes, which can result in insignificant alignment. It probably should be a more tree building should probably be a bit more of a manual process where while you're doing the analysis, you kind of look at it and you say, this does look acceptable to me, I'm going to accept this alignment to be the representative sequence, so gaps aren't inserted into errant places basically. Opposed to the exponential hypercube traversal, Upcoma performs in comparisons where n is equal to the depth of the tree minus the root. That's very feasible for us. We can actually align sequences in our lifetime which is very helpful, I think. So, multiple sequence alignment. Basically, the rule as I was explaining earlier about progressive alignment is once a gap, always a gap. Basically, what you're going to do is you're going to align two sequences to each other, place a representative sequence in the root node of that and and as you align sequences to those representative sequences, those gaps are going to translate themselves all the way down to the leaf node. Basically, the rule for doing the tree traversal, you start in the root and if the root is null you start going left and then you go right. If the left is not null and the right is not null which signifies that there are sequences on those two leaf nodes, you align those two sequences you choose a sequence with the least number of gaps inserted and that's kind of just I got the best results out of that. I don't really know if that's kosher in the bioinformatics world or anything like that, but from my experience, that resulted in decent alignment. In this case we aligned sequence one to sequence two sequence one is chosen to be the representative because there were not as many gaps inserted so it kind of lowers the margin of error of error and gaps propagating all the way down through all the alignment. So, what we're going to do is we're going to place sequence one in the root node of these two leaf nodes and we're going to keep track of the edits in the edge. So, the edits in this case would be I don't have the list of the offsets right in front of my face but there's going to be edits that are inserted into sequence two to insert those gaps. Pretty tree, not really. So, you start in the root node, the parent node right up here like I was saying earlier if the root is null, you go left then right the root is null in this case, there's no value being stored there, so we're going to go left. Same thing here, we're going to go our child nodes are not null, so what we're going to do is we're going to align sequence one and we're going to align sequence two. We're going to choose a representative sequence which is going to be one, we call it one prime at this point we keep track of the edits in the edge. So, the edits between sequence one and two are stored in the left edge and the edits for sequence two are stored in the right edge. We just traverse our way back up to the root start moving right now we align the representative sequence which is one prime with three, store those edits in the edge, and now what we need to do is we actually need to propagate those gaps that are inserted via the edits all the way down to the leaf node. If we want to align all the sequences to each other, we basically add up all the edits so sequence one aligned which is over here, we're going to add the edits of one prime three to one two to add the edits the edits in this case are going to be the gaps by the way. If we're going to propagate those gaps down the tree to sequence two, we're going to take one prime three add it to the edits of two one and lastly for sequence three, all we need to do is add the edits of three and the edits of sequence three and one prime in this case. As explained. So, another kind of hokey example of three sequences being aligned this time a bit more verbose usage of HTTP I guess so basically in this case right down here, this is what's called our consensus sequence what we basically do is we start in offset zero and we go down and we say these are all a bunch of G's so we're going to put a G down here etc etc once we start getting over here we start noticing that there's gaps inserted so that might possibly mean that that is a variable link field so in this case I'm just representing that by the question mark down there and all is the same you have a space here, this all matches up just fine, followed by host followed by more unknowns dot three three spaces for .com, .net etc, followed by the user agent block followed by the browser type followed by accept text X amount so, when we're analyzing this consensus sequence, basically what we can say is okay, here's the structure of this particular format we have a get space slash, followed by a variable link string, followed by HTTP 1.0 I'm not counting new lines or anything like that at this time, followed by host, followed by another variable link sequence, followed by a dot, followed by another variable link sequence, followed by user agent variable link sequence, accept text X amount. So what we could do at this point is feed this little structure into our fuzzer and just let it start going this is a completely automated process I didn't really have to do anything in this case unfortunately I don't have any good mixed protocol where it's half binary, half plain text for me to show you right now but I'll put up some examples on the website and kind of like walk you through using the tool and stuff like that that might be actually useful one thing that I want to say about this though is if you'll notice with these consensus sequences, I'm just assuming that this is going to be variable length and this is not going to mean anything. So basically what's happening here is I'm losing information there's a C here and two gaps there's got to be a better way to represent these consensus sequences there's something called sequence logos and basically what it is it's kind of just like a stacked histogram of each offset so let me find a good example so right here after the host you have two W's followed by a gap. Basically when you stack that histogram up, usually it's a W but sometimes it contains a gap and things like that so in that case you don't really lose information and you're actually able to use a little more of your human intuition to be able to understand the alignment a bit better than just ruling it out just because there are gaps or because there are mismatches and things like that so basically here's how we're going to analyze the consensus sequence in this case. We could do statistical analysis on each column by building histograms so we build our consensus sequence like I showed you on the previous page and we look at the mutation rates and the offset comparison between each offset. So basically what we can do is we can say this has a mutation rate of let me try to find a good example what we can do is if you look at this block right here of all these question marks almost every single one of these has a similar mutation rate they have a rate of change that is pretty constant. So if we wanted to analyze this like a binary protocol it's a lot more difficult to look at just on the screen we pretty much base our understanding where the fields begin and fields end based on the mutation rate so for instance what we can do is look at the rates of mutation for each offset and using that we can kind of understand what the particular field might be used for. One thing I wanted to mention though is beware of junk data. In the last example if there were any posts in there it would result in kind of a bad alignment and it would actually be a little more difficult to understand what the general structure was. What I was talking about earlier with the clustering algorithms, if you cluster things correctly and you choose the root nodes in each cluster you'll be able to get decent alignments out of it instead of trying to align really distant sequences where lots of gaps are going to be inserted. Classification is your friend the better that you cluster the packets, the better your sequence alignment is going to end up being. This was kind of an experimental phase about a year ago when I was working on this but I kind of already implemented this in code so it's really not that experimental anymore. I just haven't really had a chance to work on the slides. At first what I was basically doing was I was separating the dynamic data, data that was changing versus the static data. So in the case of HTTP I was able to understand just the different blocks and stuff like that and understanding the variable length fields just because it was dynamic data it was changing. To understand integer fields this actually worked out pretty good. You can build just like an engram frequency table for 1, 2, and 4 byte window sizes and you just kind of slide yourself over the consensus sequence at that point and you can use some logic in your program to try to discern what the particular function is of that field. So for example if two consecutive bytes that are integers mutate at the same rate the chances are that they are part of the same field and maybe they are checksum or something like that. They are changing every single packet. The second example if in two consecutive bytes the least significant byte increments faster than the greater significant byte it might be a 16-bit sequence identifier field or something of that sort. So this is all also implemented stuff fortunately. But what I was going to basically do is build a protocol profile based on each consensus and allowing each sequence individually to break down the structure. So for example I would take a SMB login authentication packet consensus sequence I would take a SMB data transfer packet and then I would align those two consensus sequences to understand the general structure of the packet format in general. They are still going to be pretty deviant but you will be able to understand the specific blocks in between. Like on block based protocols like ISACAMP and SMB and things like that that is actually going to be quite helpful I would think. The biggest challenge for me this far is being able to present the data in a readable format where the user can actually understand the results. So I have all this stuff kind of implemented like on a command line tool which kind of made it difficult so I'm just kind of using ANSI color and stuff like that to kind of print all this stuff out. And the last thing I wanted to say about this particular tool is it will never be fully automated if accuracy is in mind but if you are doing protocol fuzzing you don't really have to be accurate necessarily so it's kind of okay to have it's kind of okay I guess to have that kind of entropy in each sequence. Let me get this. Sorry. So basically what this can be used for is understanding unknown protocols, some kind of like worm for all we know communicates with its brothers and sisters all over the internet using some binary format that we've never seen before. This also allows us to fuzz network protocols more efficiently because we don't necessarily have to write protocol specs for them. If it's a common protocol I'd probably say just go read the RFC or something like that and just look at the ethereal samples because there's so many of them. But if you're looking at some black box protocol you've never ever seen before in your life this is going to be a tool that would probably definitely be useful. Also, one thing I kind of forgot to mention but this can also be used for file formats. There's really no difference between a packet format and a file format so if you wanted to understand some proprietary file format, same situation. The only thing that you need to do is just have a lot of samples that are different. And also if any of you guys have any ideas about how this stuff can be used I'm interested in hearing it. My conclusion is this stuff will never be automated 100% like I was describing the tree building earlier. It's good if you wanted to be involved in each step of the process so you know which representative sequence is used and which gaps are inserted because as gaps propagate down to the root nodes there can be errant gaps that make the alignment quite useless and things like that. Basically right now I have a Python proof of concept for this. I have some C++ code that I'm working on. It's actually going to be pretty good. I should have it probably finished in about a month or two. The Python code right now, I use Pyrex I don't know if you guys have ever heard that, to do all the hard, computationally expensive algorithms like the Neiman Woons matrix operations and stuff like that. I don't know if you guys have ever seen this tool. It's called Orange. It's pretty cool. It's basically like a widget based visual programming interface that kind of allows you to play with different data mining algorithms. So you can kind of link together everything. In this case, what you'd be able to do with protocol informatics is be able to say okay, I want to use this particular classification algorithm for clustering. I want to use this particular sequence alignment algorithm. I want to use this particular tree building algorithm. Because there's actually four different types of tree building algorithms. There's the upcomma, there's neighbor joining, there's multiple parsimony, things like that. There's a multitude of opportunity to implement all sorts of things and kind of just figure out what works. I guess the whole moral of the story with what I'm trying to say is I know it's kind of difficult for people in this industry to kind of look outside their science that they're working on and kind of understand that people might have already kind of solved everything back in the day. So this is just kind of a good example. This stuff was figured out in the 70s and it's 2005 now and we found a use for it. I'm taking questions now. Feel free to walk up to get a microphone up here or something like that. Thanks for coming. Questions? I can't hear you. Do you want to come up here and try to use a mic? I don't know. Here you go. Yeah, I'm working on almost exactly the same thing with mutating web things, but I just walked in on the end of your lecture and I was talking to this guy who's also doing tree algorithms, but he's doing it with granularity based on the levels of complexity of the search. Have you looked at any of that stuff for your sorting and tree methods? Yeah, that's basically a maximum parsimony tree building. That's where every step of the tree, you're going to look at the complexity of what it would take for these sequences to mutate to reach this particular result. And usually, evolution chooses the path of least resistance. So, in maximum parsimony trees, you have two different choices. You're going to choose the choice that's the most simple. Anyone else? Okay. Well, this was a lot of information. I'm sorry if I didn't have any other ways to represent it in a kind of clear and concise manner, but this is the website for this project and I encourage you all to visit the site and download the tool and just give me feedback if you got it. So, thanks. Just want to make an announcement that Meet the Fed will be at..." }