lemur/transcript-citations.ipynb

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      "source": [
        "<a href=\"https://www.assemblyai.com/\">\n",
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JEQK5R/ZY0SR4aNdGkh9f0/CUjcJX3au5fm4pd1ENad5jXyYiEXKY3u2k3JRid8hMvo3U+9z/1Dv4H0yP3eK+Fct3AcyJy1G3sCNq8WPU7/O+FSRDKuKPvzQWk8KV8nvQrPON0yuuXT2zk2V7nS/p1QU0UseQFasCPR96GrsPFXVdZ/I1iF2Sxbg/K9RVJkRidQSB8WN7MsRVZewAOrYHtLx2h5QQiTXc5Kjdq98X4C9Ky9m/4/q5nd1ThrKDarFjTCRC1unNTsoOsA/ub8nPxT5+trUU/H9O4bBr0rO3cxO9PUveO6JML8/7NpEMqYi0xYfdOtFoqsBkik6c4R0Ca6T+0F//jC3BGKpEfpqoP7qf90qe3idHK4kQJpJHUyZEuvK+2cOR7QiJhaNYvG8hlYfZtwMoFo6qTCJhGOxa2b8zZhHgFdfPQcZ1+60EzkrukAiph4RIc6bybaTex/6n3sH/aYPB/7Z4Nk5vcuGifZnJd/GinfdUNToBkiGFOPlhnN9uEmGVHY3eCq4Hx6KReqaQaiFuTDTa6+NEAqjDc7I5UfNm8q1Fem4AtUgnIiB0XGVAeqOK4LgFdUjkH8+Bq8e7iB1Ajbkvfy+HWDpqqC6UAf7K/ZK5/sEsevXFAfQPYQHI0ZAQOaGWGql7lZLaNM/g/9cViOLzK5Hf/M/Oz6bjnPaiPXfeeCfLeo9kSCGaZxNhpSjTtXR2X5D9NyJlNFKvIxpxQ8XgFA++nsHUxidCRaks7xKAvQ2Y5GM+lm9ZtCFINrmKtngwstI/rHA9Gdst8EV1GImQxtkKeK6bYSgD/BYA5365niWISRZivzp9eLBaG43Ue5PoayH471G1wJNdg57PbdeKZ+umeVdyGFSfz6aRDHkPjdPXmWfugTc3u6feuSrUkG3/5edeSQQMy0R+Zo5bkL1LAPa1Bm8sAkJN2PQqWvv5BK2acbnjO8EohdYsez+vDKQkEJYB/i+L+2VdCdcGDlDupIyFI6uUDfKq1mE/oE/XrWfw3zItjysQld4pnu9H47tkKjuiZvLTt/O+VacExR//J1v9GgurpOl3z84UKGuk/tDnX51FohTaOvmN24KEMwHD4ZkMn8qJrejJJ4j2UOG1Uq2swTtVv0y0+c/8VMsHwpeLrzvFv1usSioTaEUyqnp8VMsV8vb1nDZf7qJcRftJtahS7iXWZqVaM47Fa90/jrGW49eVcSwTSx9Sx+Tvn702a2zcxRW5qW6N+8sH/PcP6taYd2Gcq8pkpiXsvYJT6I6xUFd5bUydVgoPWarl/fKg97X8bOzyIpxFidD83Pgk58axlLtDvD7PH5XvArdWVOaNnv0bPcs3eUjkx3OXjA3ml+T7+ntx3m8CyRAtZjyUylgj02iiwOUfLI/nN7pEWMkSRhcu39i2BJKAnsvvC57J8J38nuO9o87zocLY5+NUPVGs6NvEZ35Z1syCjrW3Yhd/r/p3Z9X/Xqzmt3PYHphibYatok3a2m5fKZm0qXG8puVDZK1dX5WVYQeOeaUUwqbH8Uz+Wib5652oI4r3ZqxurHqz8Vtcv/nxov35KMGwSnDD7teJlkn4RJtVrnT+GIG9IylXepZJ0FTvv8bj4ms1gZ1oeFLdeq9S1X+vNp08tGvD7jsT4TjK+6XdK2da3i/TOv/H4l5p4x9rec107dopS0t+QTgOKxvktTt7u835qDObE3rdA0NrnG4T6Uflx7t3Srloxms8+3TetyrSwFnj9GiuG8JK2a7OpN87mypwn/n8a69bKShhtTxx9PdfvXcioOfyycPT8uuNYZPNR+So2Dnwunzd05dAma30VLtB9Fl+PN7GBLVIjIy1mSTBjfx3bGVXQWWnQJsB8pmWD/Azz2uhkhgZazPjaPeSD3Xheq8kvb6vzQZFZ1omMhutJ11JjiRajrXX51AdX85/t40F9or3YqJuly+0sS/vATMdw77r25JhsfpppmXS8JnjBtw68l5t7H5Y/P42fwyptFm5WGCx8KOp961yrywXDGzyXlmyF/XJTQcfi/nQj9X89eH2+xWv2fp/JfLxbP66W92t3DTn96gT525dlbngj+XDfsCHPJNDxXhaqUrP3rxX89/hMfVQ8f7N5YCeIXt7ibBSlGnah0TIQjb3bmLUD1HEbin0XrFLwPOhyntXSLlzYCZfbQad3bS8K2SWHw/k4/NAWw8c9nPyY6zlQ4P7ubdP3HLPibaCITPdGkev5orvsV0kxfky1mbGsVwN3QVl6a5NJUKm+XFfMfaNl8spxtp2D1rw8FNajvfjWq6cb1vXe8Zs0kzLe4AtCpic5H5eXt+V+7QFu1P1g10fdv7eU1wzT5wkuNSR96pL98Mus1XP9j6dsQVAxbg1dr+s3CununWv3PS1s+hjRePiYynLBnkJuqF0pT9GIh8hNk73XCjh2duzVJaH83SR+9HRDT4ZEkVbXV6F1AkhN07fj0bqtcU0UscAJPLT5mTTuwme50qWNo3lL1XLSZD9bFJfBJDaDBYsaqzLWaVsUixftiJ4vKlxLII/qZa/q61wTNUez1IEtRTjbPedRO2b6lZQ77paUBnvSX48oPYbm5aJJ9xiVQPc7uXFmE91KwkWMnu2OlMkixpPGG/4vXqUANOhymvkviL55b6DprxXVs6HTSZFLFn2N8KRRLcaqXudL31oKE3j9NvdLx/2frSxcNG+zOS7eNE+p8bCkQw6GRInP0hE4/R1gm6cvp/1wchoDl5LxO4Q9J9nMrzNXWhlLVIv2z1ZNex9T5tpuZJ8pg4oggUWWG0loKuid4h82RPFw/L1Qn58rIV+P2sVD1DP5sfH1N4ukY1e75WSCG0nYcvA3iPRhmpp70uKWNm5a2rPomeMYCy4/7E27uXFNT7RcrxThaW8Zh5rKxCu9t8rdoe8ny0WsDH/0CbnOx1JKI7ZVXcs9kHv+Zz0CYXLbnSezyvPKRAtLICyzy3vHRsl+2W+JT923jwoHMmgkyHRfGssrJRp3ptdIaV5FlEqq4Z8innBGqkL6KHi4SWWn9aCWEUQwvteHXRyNB/vc/Id76eKFcSd6q1SrKC0Zp9tfO7ZRPy8nLRQOsBYIuSTmwqGH6QI+Cx2qqidgE8ru3zW/HxLVMdqT2vB7zqKMbdAs5WEaTPIN/RV8Hadfaqt4H6pCPLbeFvSs80E2ElMtYFrpvJe2Y65thL9BJhuWSTn83HoTKWFDScU2VV3fJ7PLUmISapKr6RYPo7dx2lDvAP816J2n9um8pUUz7uoabDJkDh5IZ/ss/J9rd3RVD2zd+qtWT72nQpYdVO2ffrdu8cC+mksP7MNTDa9AygXAg+SXZCf60VZqs7KX5+tbG0jIeI9r0rkxwJsn+5aQqtUCfi0ERzfSM3tSsKrrfm5Bb8faTv4XVdlzC1InsrfkFfBL5IRRQ+X1lWSnp+WOl+e+PFiB9VGrpkNJETOsfp/wca9U4sFSpUE8iZ2iVjPNHYPHUFxDafyq9bhujjHkeeuEJtgTRWIynzQ6/nNfsDX1ZLinPfu82k/xPN5t3eGuzNk7w5OlDXye9CsN43TKxalsrK93u148ZCNgpxIAHV4nttTtaxYnTmTn9BrkXrVm021XL3deUVCxDvQ51liySb5nnWDOxnkqSoepizQ00Zia6z2tbnKdhFMLcqsdFYx5osdS2onITLE3SHluZBqw/LXYPeiR9Tdkr4WEJ9ow4rrwpJHbVwXoQZWm1LumJqo4yoJ5C+oPYvdlPSWOTLvskGhNpT2utfYTvG2SkI1JZGfTTSSt3P+S/K18b5/IRlsMiTSFrtC1on6tyuklGkzK79Ck8/uEhqpo2/yyfFYy+C+h50N9hrwrgMbcjAgkY+nAtty7t1s1Dto9FH5mIYyjkWw9DH5ry7zSjwdqFIeIpG/MvjdVpmdE9m3Ej6Vr6HtDlkE1Dt2/dvFYDtEUnVLJxIhpX0JEe9dKkONG5T3ymCem4vz4sta7qhra/cSvWWOZyq/MQpqEVelP4bX82lruyAa4l2ytfXEUKWROn0+O2KQyZA4+WGcX16JsEqaPv/h3u6e+OZX753RSL2eSBm7qNA3ng+1m3xg9K7hHGQtUsfXbJPZztTNrqMoa/KIfDUeRC8eEu0B0WMs21ip1TR7zZ+V7wNVq8kQtderpDO7AI5iX0LEO8A3lN0hi+uoa+dCJcj/WXXHk13cGVC5Lrzv4UMMMAWVNK7at6OurYTI0HsuHUmlbJBXvGnRb6KYP4bAXu/D8hFiiaxEfr1T7AdssoewZ4y13KkmrDfIZEg0zybCSlHW/0RBNFdoWwU3Ioq2LtJIHX2RTw5i+a483lgSuQh0z+QrxORoLB9tN95rRLEt3PM8PecUEPBKas26FgxdpxIA9HyY224r+dnirpDFivvQxrtUGXfvIHnoZRHr+lJXV7xXVpBuMmBTsnOuzbJDR1K8V7YwYSY/9kMSDceif02o90pTSYh8Wu0Yyn2zSfaZ7HkPTuQ3d2xMpT9GIh+hzXPt4h3LzybfD+9z3tg5T+yuhoGWyYraXu0WnPm7rTcfa93uHW9PaaReR7Z95/wDXDPoi7H8bKL+6H7e9+5LCk8sHzOFayJfHg+fXhP75xUuC5R6zmPaCiJ4Ng0t2QPoF0Jc5VxVBPjsQdozSL5YUat+uxFADwQ7Z+0zfZPPKmU/pa4/L7Wxw+8TGoZyx1TQ90pT3C+fVTvJvCHcNxtVjI+V+J3JR0gNpT0bhU8ViMpOcK9raaPvRwvnvCExW9PgkiHxx18ayy8w0gt9bZy+H43U65tnNy8L6AfPgFsX7if28Eot0tvF8vGyAlWsiJrJj0cQPVbz7KHoBQWoUkrHKyhuP8CrR8t7KisivZMhT+TvWVBl7VYog+Sp/CQ9Lgu0CPCr41q4xusIop9SZSfNTH6SAZRBWtxbQuoRsk5l51Ab11Gf75te7JzzrNbxaAAlg+wk9Wp8bQv1QusXYokhz3vtt7RZ3uf8on8jpbLWG1wyhMbpNfS4cfp+NFKvxxqpUyoLoSseUGL5mWrDitWb3kmZKwqL170rVdi8dhHZJPzP1bw/kI83FLaZ/MRqh/fc3Mr89GbHc0s9JRYP0+qnIAL8Fd47wA7TRi+OJpVJQk+dL7lzQi/k10Yfq0OU58YN+VrsRCAIeWT23OLZSD1RR7XQH2OmsHj2TjHTjux09DznjVV1SYSVBpUMoXF6TaO3BtNLY9FIPVPw24DbcPrmXewOQejG8tOleqzutUhpEtkLnruIYsFdZbt9Kh/uO0PkXyKrXOncq7KoLa2E7+MCsjbKKTWmhSbDh7H36amQkkYtlR/pczLExvw/qocqCeQ2fj8L5DJHrqlyj5vJR9cbSnv2xwjq866F3imLzzVtWOWc94xBkpitYVg7Q+bzsbBSlGmazu4bVB+NSBmN1OuIRiH2CgAWisbpnoGdqTqi6Fsykx9qkS4F/bBbBIe9JuKxwhErfM/JRyxHLayINLYKcOMPv068Ax0hlkVcJ7RdIca7pMZBUnWj9OdReb5XrZQO3JAyaZyqp4oApM15vMtl2dwwlD4VXWHnn+e4JOr2nN2rP0aIjdM9F99ayTCv+fJRtbGTkcTsGoNKhkQaUSJrjXkW5MT3RHZPvdOXGtLOsu2//NwriYAwJfJjQeWuJVW9Xw+1SPuxQvRF+dgO6PwI/UHB3mi31WVFItlLG7tCQirzcyQt7A4JqflsHUGeD5VxTtWe0IJoVZ7Psvern9KelsfarwxAei78XJT5YY5cX2VXl9e4eAfZj6U4R8bymYcG1Ti9wisxZDrzrN7COW/svOp7accTGUwyJE5+kIiyDevcSL97dqaBWTRSD6+e4kbkN+4rkwmTO4SlmGx67my61sESLFP5SkQt0j5MMFP52FYYSQZ7GnmwB0GLlxUuz+BiiLsAjsq77EMfro9SyAF+01YgJ9gkYgsld+IeBrntF5poACrlstroLcOK7KPx3h3Sxc8yz/4YQTVOb2GnsPf5dRzer6nrJeI2bhDJkCTJFM23xsJKmUZDWBFyoPwmMdjf/Siskfr1nZTJHUITyzdw3bkddUVyZiZfodQi9UpUXezBBDPVcjLucTT9WeEV8O9D0CKV3zh+UA6Ka8fe+1g+OlEbuiUWJPfs/xMrfKGuki21WSrreg+SiF67Hk2sfgkqaHpSRULEApCp/LAi+3g8A8M2Hok6ooX+GDOFxbN3iunqYoiZfCUiMXuoQSRDUv0ozq+vi8Jqu/Ou1NBrnTVSz8+RQfVKOS4aqSNAE/lJix4dXeSd5LXP1RAmWKl82O9+OeSESH7u2q6mkcOx5fDQ4fVG36OAx9ECO/nxguM4es4NE/npUm1oN5WV8J6B8kTh2+lBwNetHF5FH5KI9jvM5KdPfUMGsytkH+/zvG8lBt211Ei9a4u4EvkItXG61y6ZTi6GqJTKmslPJ0vEdcUwymTtzRNhpUzZM+n3zqYasmzeta1z3TQanR9fCTf4h2EpJlj3y0/XeoW8p0jSUIvUd/Xflfw4wxbkVniu9P1ifiSMY6sWJcrkp7P3ZgeewV8bpz6scA46MVYJFLaREOnDteM53veoPwa1K2Qfe+73nCN/gjnFkXk3le5SQ+lFCSP5CLEkZCJfz6ub2tj12adyp43qfTIkTm7kE8jRFWG1bPSkBm50Kujt8+3JsnNv/vLVREAYxvItaXBV3ea9wvNKABMsz+CRPVR9WyRE2pDKjz2U/kN+3Mc4tiqRjyGVyCp5PkyHHtSzF/+0+sEzKWzSAINot6kkjlI1z755rH4Y4n1yoXKOeP7+9A05ohaaStt4jLVhzv0xQiwJaQP/qPx0vX+c3Yc8E7OdKhHXJQPYGfJmIhqnr5Om3/mzIa2gO9DfXb03pZF6PZGyCzRSR9cVk83z8hPCyptr8pWo4w97xRh5TjJjLRMi4/yciwimN885uFWy1b7/qGXJLMbRUSUQ4MUCum2soO+EFq6PPgT1uroq9CjswvE+r2fqD6/36k/VH4NMhhTsevKeI3vuTO8rz6bSix2pHZjfefbHsLnAtxSISv84rx2onU76tlAebvFjFE6fz1b1OhliwVoap68XZSQAStF8UGUVji2Kti5ef51G6ui8WL41e6fquKJU1ky+QqhF6h1AivPDdlhaUuSBMpjOxLNx3qVu7IHhy/nx4/y4yDi68iy95L16vqs8r49Y4epDQ/DSy/Lj3WujbV7vVV/KZIVYSqcxlV0IqfycEY5jJj+JulH68RPyYb0AQ+qD693TIoT+cZ4JwFKXSsR1Rq+TIdPZizbgnquCe2H+rnuT3WDs3vH2VL4riHsi277z5t1jAd2WyFcoyVPv13k+gGBxWxPhRMuEiAXTx1qWz4oIqjeizUBdrGWy08bxkhjHptnDr1cgwAbnuXKchnIUPJNAIfcN8UwgtC2Vr/+qfrCLIpWPP1D42qhTHwqv98E+59gZckQtNJXeaHP74vN6LL8SWV9ReLzmg2amjmuhPJyxuPjGzvuuOqU+27vjQn52kQFbIb8fzwbfOL3i2tUzOw997qdPRdHokrDSfCRrpH51+ngkoGuKyaZnv6hpQCtvplq+F16fh2Ut0pm6aybf82G/WMudIsZ2pTxXHLP83KyeN+9FMYvJMFZrO3gTa9kXyI7rxWGv4cV8HNPK32Mcjy6Wj7Km/yMang/Kh72nH1WY+rbbIZWjnpWX85qjxeqHrq+WbkNZKsvruf+j9jzCvODIymRdIh+P5uMy2dC42A/1Wqx9PaR7uHNiaPEj8uNLCkO5O8TrWdXOu4fz93zK/eiW3iZDkiTTy3s/uiisFo2mwm3yKcu1yG9S1Bv5e5QUjdRnAronke8DazB1li1pk09+bHKcyI/VIp11dYJl5cKK92ATq5vLWrjl50paHLaKu1wJdH1fksTctv1g6JNX+/2L92imzTQCLMdxXPzvxbhpOY43tFxRzTjW53ktModrXsiLy3pRNq1yD/TSp0SI6dOOoKYNqq/SYYprqpyHedzj4uL7UnXi6Ow5y2shl33PRC3HMIrgfyyfZIh986sKi2diyIRWCtASgJ4L9xJxP7pNb8tkpfpRnF9eibBKmj7/4WACem355lfvndFIvR4aqaPDxvKTFr04QuJdDtEWH3Q9WNaVkhCxlhNSC9j+g5ZltX5VHN8u/p31rbicHw/kx335cU+lTFN0SImcIbBftiulPcuHaRtHewCtjuP3dWsc7b9/SgePYzS0caw0y0RYQt0ZYvoW9E3lo28BEgI+hxtqX6XDeN4jYuFIKk2lveJU9gO+uKF5VyI/zysQzomhxY9QAL09S8U5b/ehmfx492cJTm93hkTzbLIcbxyGxumHWzRSH5FMW2fRSH0nnYgHDnRIMcHyXGkSXJ3lYmeE18o3Fd93rG6vSrLXZoHpriZtyuD6YcqdCPbVAhm26jXV+3cj9LZkU2UVZ6ruBhhsHMtdJAexsUqLw8aw3FWSDqj0ViyEJtidIYE1k92kvgXIGfeD9a10XBPs3E/kw+YCg9+FcwzeJcwStb9KfpGEkY9pYLsgTCJfwSSHCt7l4cylDZaI65xe7gz5SPJS/s+IhlVrzLe2nhAOtGykzsPTetn2qZt3scITXTOWb+AmtG3IJe97fqcbqRcBsZA/98pkyQUtt1F/TQfvKil3lNyT9bPpd5d2hxxHmSyxcazuKrFm7XPd2lUy0XIcz/RwF0nIuwyGKg70vCMIWY8NLs899cQKHztDbvFODgWbSN6kSlNpr3t4q6vkK7tiYzXPvnlo1V48E0MmxOSQsXH0bqSeCAu9TIa8tReNxaqzNaIX0tm/4QHhENZIPf9cmQlr5R80VyiVha4oJpue/aJCqz9aNZOvRN1/6LPA8w31TzVRYqWZyiSJBdjfS5D0ITlSPCDbw0Jf5zBlosQSXmWSpEx2TXRAoiswNoD3CCGKFZ4+BvhT+aDHxnAQA7idV3LIPu8+KBxXuVLey4MtzqE8ky9Wvvk5BaJ4zxP5Nk6/psBUysPN5OviwEosH6p3yRBrnD5S5FkepRcyRewKWWOeZbxHNVgj9es7Kate0BWxfFc8TBWoos/JTL5aW2V1HMXukC9oOGLdniCZqxJUDzigbi/4MQ1HmewqEyRlcsTGNQkwyfWnAtrxhlDX68IQ7FA67pYiAJnKzx8IJ+EZj7HFJ4na8wk1zyZ9E4XFLrqx/Fhy6FsKk42neyUHsWNtoXfJEGucnkXRBWG13Xkw2eNNsUbqlMqq5/TNuzodAMWgeJ+Lod87vfudXOp6MDafINtqoVBLnTUh0e27Dqzc1qdCSoxUyicMedFComWZrWe1TI7YDiDbFXdPAOPIQ1iYYoWHAH99JI6GIRUOkspHLBxLCyvl7Qdc8J4vFd/f4pOxfITWON3mgA/Kz0yBqjzfeJfKGgs9LJO1N0+ElaJM0/R7Z1NhvWzO7pA6otGl8RW222GzWmicPs2PlytB4+AOUYu0ZD0n+lgu66hiLSfEFkgvEyP3hZAUyR8Yyt0hM8GuO3vQnurWOCYdHsdYQDsI8AO3Y6HfwVL5IPl/Mt594h6W/xjZ85dXCecQe2PYfNXrPbfz5UshPJOveFY3X5Ef+xltlojrrFPqkTi5oWj+zhVhpXkWXIOljdk99c7V03t3c06tlW2/+ctXExGUwmYl8g2y2Xbqbyt83pP+i/kEa1asbukkKxGRv8ZPajmeZwQTa5kYscPqiV/N36Ov51+zDo+lzeQ/nR//KMaxVK74siPNj0kA44gwxAoPyRDgdqlwEK+eOSRDTsDmLfkcplwp7/FelnMmlx3jRcA5ls9ivVAbpz8sP/aZ/6TC533fSLSMawy6f1SvkiHSm0n+K8XCKjfS756dCbVYI/WHPv/qLApjpfNGjaLo0mSSzSYTgi1oXzHZHMvXOaEOW/FjK/Y7vfrQVlKREDmUnetTLesQdzaYXjwkWxmcf58f/0WM436xujmOsRAilhEC4SNBiNCUfRQ8FqjahOh8Pj+66jg3SuQjxMbpsXzjaqFUKNi0RYk4DTwZ0psyWdY4PZpvjYWVMo08txn2Un7j5j2rJUquv04jdWxMLL8tyDiaYGqRFlvLLSFCyayDxbpVeuliF8suFQ+vNn6WEHlBOEisjo8jguC1chpAe0iGvJ99IKbyEQtN8Cxdfr/8Aug2SX1UzQu1cTp9Zrvj0aE/C/QmGWKN0/Pri0DYOjROPzIaqdeVbd958+6xgM1IhC45H8oEq5IQeVo4TKxlMN16UZzpcELExtEemIn0HyzWchytR0znxhEAAKCqpUbqiRpWzLHOya+yQDCN0ys8G6fjaAa/i6Y/DdT39hJhpUzZMzROPyYaqdcyH+k8jdTRtmKyeUnokkQBTbAsIZIf1nvCdgJyEzvcWMuyYkkXEyLWC0bLVWdfyI/XhcPY1njrs3KRhAgAAOi4slSWF49V8p47IZ4JqXF68d4mYqdUl9j5+cUhPwf0IhnykYdeyh+ARwTC1slGfWgmtBHZfO+asJb1VikaqQNtikU/jy5KFJj8wWKSf/mQlrsMiBIfLNYyIXKlixPoYgWhNcL8mJaNJRnHg92jZZNJG0eafaGOVAAAbEbZSN2D1yr5T6h5Nq8NLa5n88yx0DWJ/Ju1d1YvkiFv/TTKg2ARgbDV0vQ7f/aMcCzf+Ns/uZ75bc3slUjZhcmE2BNaNRG66FKIq02KXSKWEPmsSIocxh5qrJFlZxMixYq5sRjHVWwcJ/nxNRIiqIGSsQCA1lVKZT0lH42uki++j+3CjdU8e075lgJRaZz+sNBFYw1UL5Ih0VZEI541ooxA/klFc5FMqiGKti7SSB1tKSZY9wtdFHQt0vxBY0pSZKVOJ0RMkRSZarnbh3E83FgkRLBGUYYOAIBNsPmbZ7WORM2tkrf51AU1z94Dr4SQp0ToIjtPg+nz2bTgkyEfSV7K/xkRCFtj/u6iDjpOYPeOt6diVVwNNFJHq8ai/miXdTZQXte+pMizWj6IEFBf6nxCxOxLiliT9bJ8FuN4y1jLhIiAA6QC0Ad/KuxncxmvhXyp0Ihid4iVyprJRyM9PlrYCRFaMsTe10eFrrJYeqIBCj4Z8tZeNBaBsJXy+/GMxuknd+3qmZ0sm4eYiW+dNVJ/iFJZcFZMNs8LXZaoJ7VIi6SIBdItoP6Ybu0yGPrNrkyIXO56IL1Iisy0DPyXCa4XxDiWLqqdxFYqhMROCBYDAf3ADsCDUdUgDPZ55Fmto6lG6ol8zAJsnH5O9PbsssUupiEuhjqlgCVJppf3fnRRWC0aTYVGZIqu5XeLS8JK1kg9+9nP7UPvugA/sXy2IKNZYy0bWvdC8RDyhB35xNHuc7aixpJySflXNDyLWstali9I1XHF6sI0P6b5GE61vJckWq7iS8q/puEpE1vP5e/LrHifQmIB+6cFDy8LQB98VDjIB+UjFZpmi1NtruKRwCpL/M50fF47ISxaPVVYGtltA3f2/DPRwBa+BJ0MSfWjOH9OS4QVop30+Q+zm6Eh3/zqvbOHPv/qLKLu4VrRaM+C1CRD4CkRQmC1SK8GGFhdK/+d7B5nhyVGygeo+4ujugpqCIH1e/Ljyfx9eCCksT4gMWLjaGNnn2F/rtvvM0MYR/sdv5YfH5PfQ5HX9309H8/PCgBwmG1bAdzHOdkJsTMkAHbe5uevzSFsd4jHomi7MC4ed0FIpUSWx04Ia5z+dYXnE0LX2f3Pnl0H1SM56DJZ0TybCCtFWebZZGqQ8vf0OWG9aHRpfIWqI/BRTDavCCFINIDElTUXzo9r+fFYflgg+Q+17E9hJbVstXq1HFNfb442ke58uazDFGW0bBztIfhyURbN5spDG8cz8h3HVD5iep4AwEqxCPwfxKuMTyo0zXuHhO32Pu41YhmUiXzMFJBiPjYWLQ1CYOftpaHNoYNNhvzFf/iJaJy+3jwLrsFS5+2eeqc35V58Zdtv/vLVRICPREyuQjK4WqSVoPoT+fHpfQmST2tZOuyg4HrIb1RZLqs3gZayz8i+cbT5s321cXxc/RtHYyUevMbxDfmJBQBYJRYWKj0NXL69KDHYuEoj9VQ+bO4z1vF57ISwc+lLCosNlFcTeTQv0cAS5cEmQ375xhtWviAWVrmRfvfsTGjUopF6YJn5TcknK1cmNFJHwyorTRAO28o++JWIlQRJuYNkEVzPjzK4Xu5AKAPsz+r2Ru0hBNmtXFav6wMXCZLrxThO9iVJrDG7jaOVawp+HB2SmPYNPUtosuIZAFajmfHtPD83BlWDv0U2l/Ba9GtB/PNHnf8Uf98rRhli4/RYlLQOyeD6uwTZM8Qap//z3g8vEmJdLdPoccFFfoN/PP9ASoSVrLfK9Z3UJphMBNEkO6fOCyEZZC3Soyj6j5jZQf89/9yJtXywKA/rZ1H2t6g+yHehEPiD+eudDK0meaX/SHrQf688HNpRjt2f6lZ96a6No+0OsR1MTX+Ge84J7H30TLYAQMjss4VkyO0S+XlR8PKE/Eom2zNLoqMtgLVr64KaF2LjdJMIoXl4SM9vQSZDlo3TI48bTb/szult4cQaqX/m86/tWCkoYaXTN++yDPNEQHPs/s+1Fx5bZf4MTTuPp1gRlh7034rm7Rbc+Oi+r4v/q9pnPzsRuyhvc0Cy5Fr534pESTmOcfG12sB9E+Nor8fut1M1yys4ZO9RLADAKn9OE/X32Jvg2eA5FRpXaaQ+k0/QfZHYqNtIvbLYxaMsVIiN08uyuQhLrAE9v4VZJmtvnggrRZmm6ffOpoKfbP6EsB6N1NGgYrJ5UQhRIpJYLvb1J3mk0p/EelpMdatEU2svST6r43prX/P26QEN3G3OsYlxfNihVFYqPx8UAGCVRMzHqrx2yuyEVNooQDY58ayEYomNo1wniXzMFJBizpiIxSkhKpOAGoLgkiFxciN/WBx5bYfrDRqn+6ORel00UkejYrHtNmSDqkW6SUVg/VqRHCn7WEzVXjD94aFMpj1VGrjbtVOOo83x2npz95fvOpFihaWt5kzl437OOwBYa/ClsipBW6/EECUbHVUaqXuV3ix369Z6OfLZFRJq4/SxEKqjJgGDFeDOkDcTkWVcJ6Vxuj8aqdeXJzAv0kgdDSGYHraLBCo3owioP6JlQP0F+bOJdCw0pkyMaPmQaePYRlLkKMGAo/Aq5RqL8w4AVmH35i2J/NAvxJ/NgbyqdSxKPa17bnFuFh5i43SbNz4ohMrGb6wBCCoZYsHUaL41FlbKNGdXSEvmWUSprBrymcSF66+nbMfGiRQTLBqnhy0WO3s2yh6qijJanqUFSonQuCIpkmr5sPIF+SZEPIJm9no9V8wmGiD7jPQ6APQOuzeXn29eQVt7c2dCG56Rn0TrV8nbeTRR80JtnE5vz7At7otD+HwIqoH6dPbidqQ7qRW/zu5oKrRi79Rbs629u21rJjf8lbLtO2/ePc7/QGkxnEQi/xW/T8tvu3UIbAX4ffJVuyHhcRUTOK8fkPWh6Wj+O0yK98mr9Ki9SSfeUZC/RsbxEEUDUftctXvW1+R3znv04fBsom6lsqZDag5cXMsP5Mf9ap71qmGhFdAv9uyaaKAB+8pqfs9yYS8Lrop5kC2umMmvkbpVJZis+XufUPNsbvcthcWrXFiVzUlmGq5ykZJn/DHR8v6YqseCSoZo744LGs5zzbHkn+szGqe3x0plPfS5nz4VRaNLwkrzkc6Pr2RXp49zEePoioeWsXy9kE+qP60By9/nJP/yrHzZooaJfJNONkF8Wj5sR8VMPVAkRP5AfuXnTvRwWCRCvi0flkTwXE3YiiIQMNXynP+yfHxUzbPghddikvPF9x1SYtuuFZuLeuyetGuFZAjQL2Wj3NmQEscVXqv5S7YLl54h7bCHRJvPJfLxaH6dTA66TpybhVvfv2DmMc7lwt77MflhY/F1DVTxbGRzXM9Sh2Xfl4l6LJhkSJJkennvR+wKWSdiV0jbMkXXouUDKFbI36OkaKQ+E3B0sZZBdE+DL3tn/QjswVi+E9myFqnnTrFyxaOHvgUFLbkzlk9QuonvmciH9awIPhliioSI3b/sHumxyjW2h9ymAmbF67UHfAsUJWpe2edkpmHx2BVigYc2egwBaJ+t4J5ouDuiPVbzl2ZCm2xubjudveayiQ4e0zJo3DT77P2KwuKdYDTpkBMhhbJPjmcyxByaBOyLYHqGpPpRnF9eibBKmj7/YVZuteybX713RiP1evLwxwUaqeOYEvnrRWC0AW28D+epVd0Nxaozr0RgzDi3xt5ozzlgrGbZ6/Vqol6r6WlfVFamepVM+K8C0Ef35MfloX1OV3abx/JBv5AWFcFam8t6zYHsBzy6/zqpNAv3KAt1PdCdRZ4JRjPTwBXnu82fvZPYngsLOyGYZEg0zybCSlHGzWFTojlB1DqiaOvi9R0aqeNoismm9+6raUhbkZ1N5T/BSuTY/6VoLu0lVv9M5SfWyaTy8afqH89kiMdn90x+Eg2nn5vXylSzQ6kXoNce1fB6Xy4S5vL1vNAme1i8Jj+JDr5OPFbn2+/iuXu+cc7lwt77MfnxJcGUu0M8HZgE7JMgkiEfSV7K/xl5bP3ulfm7i1IX2IDdO96eathNl2vKtu+cf4BrGUd1Tr4NDg276gpFUqiN92OsMHk0k96oInk0tM+wWD1SWRnpFbhuNFjWwsq2RdPTAa149lqNSSIE6LdB7Q5pYVeImTkvysE+lTnFTD7KEr+3/Vj5NQsPLZnmuSijxHV1uzbKeyfqcbI8iGTIW3vRWP1cidkYGqdvljVSz7I5wdQa5tnNy5TKwhFdlq8b1itDqPJcXVW65PzwncpH0tOgQSofJ51EewXLz/V0HF9UWDznTr1f8ewc2Cub0gLotyHtDvHeFWL3zamwCZ6fWXbevFfi17lZ+DSkoH/lvfBKDC1+jLiu3lNZADWTr4OSgL3R+WSINU6PNLoorEbj9I2zRurCWtZInVJZqKuYYHnvJmJX3T5FcmgmX961SL2C6LH6GTTwCqKf9L1K5aOPtXDthpnKh8eOKO+yFrbi+UrPVzx7B/a8+roA6I4h3CvtmcLul1fkv8iWElmbYwssvOb/9jyaFH9e7D5V87z7v3lJ5IvG6e9n54p3DOO2JGDfdD4ZQuP0OqIdjd5i5daGLRqpZxnlBGo4ffMu75X+6I+x/B9aCPYcrI3PFc/dIan8jIW6TvpQ+rJ82AQ/EepqfBwqZS08507Wb8q7zOJGtFDuJaVfCDAYdq/s687X8n55RstkiKdnKOWzGS2slrcfcKFyjTyo5tnnbmjPpYveEvI1E27TYiP1ahKwV7pfJms+HwsrRVl2LZ3dR7+KDogoJ1BPNLo0vkKpLKxWTDbPy1dQW5FbNlU7jdS9dll4BdFNb1fJODjJOeS508H0ujFgILxLMdnT4pd7Os7eu0JmAjAUdj/5mvpbLst+v/8iX/ZB86SwSd6Npa0UVLmzOFaz7LVPFJBibuXd29N+yFeEg7TVSD1RD3U6GRInNyiRVcM8o/FvV+yeeueqUEO2/eYvX00ErBbnxwX54v55iKKR+ky+PGuReq5oTtS/iaFHGSRz0oSaZw+MvpXKsgeWe+TDMzH6hPP3t1VtvWoQ3EK5l1BLdQA4vsXOib4lj1ssj2Wr+r8lbEwLq+XLeeNYPkJsnO5d8eM6u1RXaqORei8Xj3V8Z8ibiWicvs6N9LtnZ0InLBqps5KulvxD7QqN1LFGIl8pjdPXamOC5bXLYiZfvQkYVJofNq5Iqp2E5wPQIkDSswn+n8tHKgeVshaegXf7IX+jnpSAaancS4ilOgCcnJXL6k3yuPg9bGGVd3ms4Fb195jnavkysebRLDzUagWfkB8bSxYbH6LlRuqJeqazyZBF4/T51lhYKdPIu2kOjiifdDEmNdBIHasUDy/eDy6sel2jSBa1USorUcOKBwrP157kxwM9CRjY9vZYzTtRIqMyyfdMiPRi10AloZWoeTsNJLVW8W6kbsoSMGdCHuvitdvuH89yLwT1gOHqTfK4eP33aXnv95aKxuld4rmYy6MkVHC7MVvoW1Zit9VqbZXK+mLfdod0NhmyaJyuiBJZ6+zOWbXVMdZIfdHUHmvRSB0rJPKfXE2FOtrYHZLIx0y+7AHbqyxRK4qJrde9uKm+LZ5znbLvQtBB8kIiH67lCSplLWbyZbspLIlwT8BjbW/Wk/L/fCSoBwyX3Wf+IT/uC/VeWdlBZ7+H9+K7RSCbHoTd0OJq+SaF2jjdY4dM1dR5MU5ftNFIPVHPekp1t0zW3jwRVooyTdPvnU2F7snmbQQPwzcanaeROvarrDTx9AwPLbW1sT35ktMDt/eDRZwfXws8iB7nh8fiE3tTZjo57wbbxhJa/48CneRXdgt4NdP27NtSsl+ijZ21ZUIkuORXUffeEiHn5SvUUh0AmlPuQAsuIVLZEfJttVNyPRW7zbvGToIvKQyhNk6P5Vs6id5lNbRUbnbxo+TfH6ZVnUyGLBqnRyPv8ijBmyvyLimAYxqdYsV5LVl2jkbqOIAFJL2DPU8LtbTYSD1R86byZ7WoJyEmRIrXPJGfpnYU2PfxXvFkgZMvFwHn0Hg2h20qqbVSZXdIG3NbG+tgEiL2GiuJEO9d8yEFkAD4KhMiF0OZ4xT3ygfUXiKEXSHd1cbcsSkh7sZM5IveZfW1UW7W3N+nUlkd3RlC4/Qa0vQ7f+a9UhLH9HdX701ppF5PpOwCjdSxjwWXPVdo2+SKlSZH08aK7cYbWbeUyDEWiLaESBRQwMC+TOQXXN1p4iGmxRVPZqzlTp+QxtHeoEvF4aWNnSHG3vQvqJ3gRRA7RCq7fiyw550IMewKAVBl9x9LxF7p8mdjJWlsn4V2v2xrp6c9U7QxR8YRtDx3PKkQqxWUJWa90LvsCFosN3u/etRIvXPJEAuK0jh9vSgj0N510Vwkq2qIoq2L11+nkTqWiocs74DPTDiSFhupe9wL2ipbaAkRq03d+X4ERcBgouVr9tLkCqW2VjyZcX78owLYNVCMo90vvyw/s7Ye0ouHuRtq75q1hMj/VAeDfJXAnu1i+b7aefhkVwiAg5RzBksydO6zsbhX2v3cXp/n5+H7frQI2HZZm3PH47LX+KQCUlz/iehd1jVtlBW2e+2FvuwO6VwyZDp7sY3yKMGbv9vKKl2cwO4db08VztbMDcq277x591jAUiz/oA/3z+NpI0DpUYt0pvbuxbaryQKXZ4pgprqkEmD9G/kmQhp9AG1xxVOprDWedHElbGUcbQw9H6LtF5+qfXavSdWOMshXJsA2Pt6V3SA2vnY/ieVvMdbsCgGwQqJil1pX7pWV3SBtJY2rruX3zK8LnbSBueNx2M6ibyks9saO5Yv5yPHYTijvZ96H1ZNG6t0rk7V3Rx5IiFglvkL+uT+jcXr3Xbt6ZifL5pTiqWE+Eo3UUfJuzDVjcnVsbTRSv9+pVFZbK81NnB8/1jLA2okVlJWAgdXRtoCB93Xm8XDXdiPFOD+ezY+vqSPJrco4litgPRNapVZX5hXBi9fz4xG1yxJgdt2W491qoK88v4rxtd/drtM2xreUil0hANaLtUyS2/3y4obvlWVvENsN0nb8yD6nviB0XRur5Y+r7XntiRXXul1rD8pPcO9LF7RYGs7G/371QKeSIUmS5WnGkXd5lPBFo6kQhIwm97Xkt+6ERuooJljeOwOnwrG01H8jkc/KPkvktL1TzwKZ9pA+LoIFra+i3Bc8f7J4Pefky+Uhppjk2/dN1a6xbiUezmx4HKu7BRL528jKvMpqzjaTmKWxDkiKeI35vrG1JOX3i58dqz2LUi8sFABwBLHenxSJWrpXfkrLz+XFDk61z37Jx7lnBqON1fLHFWLQv43enifuOThQbZSGW+zG69qu+ePoVDIk1Y/i/K1NhFXS9PkPkykNxDe/eu+MRur10Egd8q8/SuP0k2sjONl4LdIN7A4pxVomIfYHV10CBtUVk5VVk/9Q/Pw2F5t4XWeLAITaF2u506ccx6Q6jk2P5SHjeLX4+ZYMaWMF7Kb7R5RjfUObMdby/S4bl5856ZhXxvWwsbXVzd7JyoPMKPUC4Jhi3UqKHPj5eNT7ZY17pc1rEm1GWVJwE3NKHFHHG6mHWK3A3tCH5YddISfQYmm4RD0oldWpZEg0zybCSjRODw+N1OtZNFLfoZH6UBUPSmP5mgknNZP/6ioLPHrcC+whelOB1Vi3gqu28tuCng9UAgbRvof/ld9s/9+tBAtsxeQDxff/lZaB3Atqj+tqycrukJk2Z6xl+awyeG3v9z1O41gGfmwcH1W7Dx4brddcKZf1H7XZFZ2JbgX6ymvXViXft+/6XXvo1rjaDpCndesabXtsq+ye+FkBwMnEuv3z0RIW1stj/2dkqPfKUipKCoamjdXyR7WpnmzHVsxpY/knIkmGnExbjdS9Sy67O6WO+Ejykt6eR/cLK823tliFEBhrpH56719csUbhwgrZ9qmbd9mKyJkwRLH8V65z/zwh22GRT4ZtknpJfspapI1O5IrXbkG/Z7VZ54qjfA9n+fGilg/Y9tUCv/Za0xXfI9byfbKvH8yPj1a+7yal8n+IKXcMJNqsWMsxLMfxeuX4Zy3fi1DHcdO7QhYsIZK/fy9oOd5f1ubtv3ZNWhw7OjhpE+vWGHdtHkh5LAAe4uKoLsaw++P14s/pAf+f7cr/r6vPzHbP/CT3zLAUcwlbLW/n36bnV6U0wB2ZFgCfyNczXF+NsGcx753kj+bX1aRYvBSkziRD3tqLxvn7GAsrRC+ks39zXQiKNVL/zOdfnand1blBym+mVyaTbDaZhHtTxbEl8vVCfn5x/2yGra7yTIaYy/kE65mmJ1j595vl33cq/11IR5Fo84H9JrQSWK081Fpy0/s8PIouJDGa0Kla6MV4X9Xyga7NhuJ1xVKQzy/lOFMeC0Ab7B6eKFx2z3yMQG2wytXyXZmnzRSmT8iPjdHTwokU8+ayz6dn/LG8p88UqE6UybLG6SNF3k1zg5eJ2pShmmcZY1eDNVKnVNbwFNtuvYOaXIMNsYSCwq5F+pg2Vy6rr8oa2q0EVvOfswhK5McLQtNu5O/vJvqyHKpIitpronRCc17o2jgDQEeVyWOeJcLWlfHrxO7boyie1S2wHstPiLtlusoGzPt8t8l5430+29SJZIg1Ts+iiFXz6+zOnxOCZI3U8/vFJmteB+P0zbsuC0PTxopq7p/NaqMXksu9oGim/mkt+xGgGanaf7Cz2bf1k2Acm2Pv5b9XN5EAa44lg/+jAADrlIkQkscBqzRSn2nzQm2c7h2vnQmNqDRS944/PqyAG6l3o4H63jwRVooyTdPvnU2FcGVzVpPUEY0uja+Em2HGsXgnwKYBTjq7bir/CdYlr9UmRcm0LwhN2EgN7WKib0HdTxevASdj7+EjXb1XVhqqW7KGhMjx2TVDzXsAWM8+F58iEdIbZc+5Tb+GqQJSaZz+sPwEt1smAG3sDrFEyFiB2ngyJE5u5A84oy7WAO6UeUZpgNDtnnrnqlBDtv3mL19NhEEoJlj3yxf3z4YVuyu831fX+tL57zDV5h+KQrfRAHoRIJ/lx2dFQuQkypWvbez4OjYSIidGIgQYNrt/XhPqKBMhjwi90OJq+VVCLQWVyFeIu2VC0EaprAdDLZXVgZ0hbyYKs/Fgm26k3z07E4JmjdQztv/VMoqiS5MJca2BGMv3M+BG0eMCzWvjgdq1Fml+bkxEQuS4ygD6RpONxcPtVMudPnxwHF1QJUBsvPPDAnp/IRLdR0EiBIDtirXFAySTVys/F0mE9E8bq+VXmSk8NtF+VH6C2y0TghZLwyXyL3fuYqPJEGucHs23xsJKmUYEanoiD+oxlrVEyfXXaaTed0WQ+6J8cc05aamRup0frveCSkKEQHp9nQqgFxN+233JDpGjCbYWev6a7bWPxbVbx49FIgSA9IZu7a67IRyEHiH9t6lkSKiN0717e9I43U8bpeHa6CfjYqPJEGucnr933oGw8NE4vTdopF5Xtn3nzbvHQt/F8t92y/3Tl3dZnVZqkRYJEXYW1FOWxupUoKCyQ8R6iNBUfb3gAz7FmE/EtXsYe0++nx9/QSIEGDy7H1yn3OBK9r58ikRIf7W4Wv4goTZO9+7tORNctFga7tEQS2VttkzW3l4irJQpe4bG6T1DI/Va5iOdp5F67yXyReN0f1P5T7DOtzHBys8V21nwMbFacpVf5ccDmy6NdZhi0m/l26yEEuN4OAv4dC6hdRyVXUH/Wox51aIUSP7+/EXR4wkArExWed+0+6UlROghsrxf2g66j3W9dxYasegHo3aFXArqE/Jj78tXBE9tNVJPFJiNJUM+8tBL1jj9krBaNnpS6JVsvseks4Z8mp7QSL2/iuD2FfminryzIsg2k69ELdUizX8fCxR8Mj+eFivNq6orzDu926oS5LHElt0DGMdb7L0oe0f05v64b8ztgW/oY27Jrsfy9+UxAcAtb5R/qPRf+pSGXW6wDBSyg25YLOnV5kIB+1nfUkCKZ/WxfHt7Xi+eveDLe85vE/EvhrY7ZGPJkLd+GuWBjSjIRistStPv/BmrE3rmG3/7J9dppF5PpOwCjdR7K5Hv5CqlcXpr2tjt1lotUnsYzg8rtWSldyi3dPsK81QBKII89uA51rKPCDsGbgV8PtbHB8/KmFs5h6GOuY3xs1qOMbuQAez3vnt/pdzg0O6b5eIA2+36GDvohqNSKqvNRSHXAjzH7I16WH7sGrwquCrO91T+8UeL7QfV83djyZBoK/KuPRe8KCNg3lfRXCS5aoiirYs0Uu+fykoTT+wKaUmRdPKe4F9qe7VJpWzWVMNcMVkGVh8IdYV5pY+I7fYZ6i6RQQV8KmNu1+6QVjuXu0FolA7gIDuH3f/3fVZaBYO+3zeriwPoLThMdg60Va0juFJQxTNXLP/SR0HtlglYW6WyxgrIRpIhH0leyv8Z3S+sNH9XNO/qqd073p6q3a2ZgaKRek/Zh+V5+ZoKbeplLdJil8gjWgYILKA8xMBq0IGCYsdAquUE/UMaRqDH2O9o42hzyUEFfCq7RCZajnmfE2H2ez2ZHx9iNwiAFVbuCKx8VlrZrL7uEikXeXyI3SDDVmksPZO/UEtBee/Kn3INtsq7kbpdVOdDKpW1kWTIW3vRWL7lUYKXn0MzGqf317WrZ3aybM7K9RqskfpDlMrqG5tcee74mbEytnVtbHO+sqkJlu1+yQ8LqlqAwB6k+3pTKoPnvQus7gv0WHKrr+NYTYLYOD4+1IfNAxJhfUqKVHdufZaAAoA13qjzl/btEunL7rrq/ZLdcyjZeeFdrSPUUlB2I3hUfjbRxH6wWiwNZxseEgWi9WRIkmT5lTW6KKwWjaZCr+VXAo3Ua7BG6vrZz+kv1BNFMNv7M2AqtKqlRuobr0Wa/562iskCBHZMtZzMhx4oKMsolcHzSZ8Dq0WAfKblGFYD5H0YR5IgBzgkKRLiTq/yPK0G9SjxAmAdu2/UXpleuWdOdOue+brCumdyv0Qddm57z5WeV0CKZ/VE/r09uR7b1UZpOMu6eO8oakzryZBUP4rztygRVoh20uc/TKa057751XtnNFKvJxrtBXNTxVqxfFcM2OSK++dmeJd27Ewt0mKniJXPKneLvKCwAupl4NyCBBYg6H0SZL99AfI/1O27fkIax2qw5w9Jghxu35hbT5FQxrya6PpDgnoAjiHVER1yz+x6Ipn7JWqprJafyc80wJ1I9saM5ceu0YnQqspisJl8PRxKqazWkyHRPJsIK0VZxo6BgcjHmslZHdHo0vhK6It2UZjI10zYiJYaqXeqFmnRU8QedCxIYIkRazTe1eBqNQFir/NDRZBgpgEr+0vYOOrWbpEuB8mrCZDLIthzZIGMeXm9PikSXQBO7mUdU5kUKe6Zdr+0+2ZXdtiV92y7X1o5Iu6XOAo7d74uH8GVgiqesWzx2YPyFdRumZ7xLg23kT6fx3FKLfqL//AT/erXv75fWGmeUT9vKHZPvXP19N7dV4Q1su03f/lqIgLdffAJ+T44ee9OwGrWZ+KL8lPWIp2pY4qVX/b7P5E/TNhE0Mp6nc+Pj+r2El+R/JXXmAUCrDTGi1pOfq8THDhcsUow1bIE2rRSKsAOu3dtahyNjWPZ7PM5xrEZ1THPx3uqWw9x1Wv3vb8uHweN87UBJrg85gZ9XUnT9O/F+7TZ73nYz2nqZx2pTNYqxT1zZscBn5FJ9a/Kx/75zeJzccALAjwS+ENbgehVgjfUUlA2//kD+Z0HIe6W6ROLNduzumfpafv+MzXH5Vxs40HuPfHH/+lCfuI/Laxy48bzZz8kDMZDn3/12SigRkObYiXF/u3//kcPTCat3rbQsPzBKZYjJleb5z3GuZ0QA8H5+2JB1VjL4OqfazkJrdMHpXrTWzUZTLUMDNjqzxeLP6cEzZtTBH4OGsdY62srl+O4bkKf6tY42qrb/yqSWBtRWSFpY22JkTNajnms1eNdZ6xtPFMtx9qOwY5z5X32eDAP8vPiMLxX9RXvVSwH3nNNj3FuY35cSY5U75fr5jl2v6zzuWiHzW3sc/H5/PdpJLkTMs9zXD27Hxwmfw/t/Puxmn8fbXDG+XvotevEhfNnTGkQ51ZXtTTGjX3muH6WqyXWOP2f9374dBZFF4RDZRqN6RcyLH/5uVeSURQ9K6y1u/X2PdeunuHDE0CvFAmkuPif1Qmqrcy6p/JX031/tvvhDknAzatM1rf3HaY6jvYXX973Z8YxMJXxjot/FVf+c/nng8Y6FYEAAAOy4n5ZfjbaX7B74hvFv98pjpTPRXgpzssH8uPbap598w9x/gLd1VqZLGucHpEIWW93Ts3ngbFG6p/5/Gs7VgpKWOn0zbsui4ZbAHqmeFhKhWBVyi1hACrjnQoAcCjul+goz0bhlIICOq69Bup780RYKco0Tb93NhWGJ5s/IaxHI3UAAAAAAHAMld1KD6t5wTVOB4aolWRInNxQFI2uCCvROH24rJG6UMN7jdQBAAAAAACOKpGPUBunA4PS0s6QNxP5NXfqizT97tmZMEjWB8MahAtr5YnVi5MJu0MAAAAAAMCRWImsL6p5FqSYCEDnuSdDLGgZzbfGwkqZ5uwKGbh5FlEqq4Z85nLh+usp/VUAAAAAAEAtRYmsRH6LtZ8XgM5zT4ZMZy/mQcvoorDa7mgqDNreqbdm+ZcdYY1s+86bd48FAAAAAABQD43TAbRQJmvv1AVhpTw5PaNxOhalsjJ2CNUxH+k8jdQBAAAAAMA6LTROvyYAQXBNhiRJZvX9LwmrRewKwVJ+xfABWkOU315opA4AAAAAAGpK5MMap39LAILgmgxJ9aM4D1ueE1ZJ0+c/zG4ALHzzq/fOaKReT6TsAo3UAQAAAADAGp6N04npAQFxTYZE82wirBRlBL5xu2iuZ4S1omjr4vUdGqkDAAAAAICDtdA4nWQIEBC3ZMhHkpfyf0b3CyvN39XjAip273h7Khqp15Bt3zn/APcYAAAAAABwGM/G6TMapwNhcUuGvLUXjeWXde0FGqfjIDRSr2+e3bxMqSwAAAAAALBfC43TpwIQFJdkyKJxukYXhdVonI5D0Ei9HmukTqksAAAAAABwiAvyYY3Tvy4AQXFJhiwap0eLenw4VLSj0Vv0hsCBFo3Us+y6sNbpm3ddFgAAAAAAwO2sRNaj8jETgOD4lMmaz8fCSlGWXUtn99EXAofKP7FJltURjS6Nr1AqCwAAAAAALBUlssbyKeFv3/xLAhCcxpMhcXKDElk1zDPREwIr7Z5656pQQ7b95i9fTQQAAAAAALBku0K+KB80TgcC5bAz5M1ENE5f50b63bMzASssGqmz7bKWfBJyhUbqAAAAAACghV0hUwEIUqPJkEXj9PnWWFgp0+hxATXkH+CcKzXQSB0AAAAAABSJkDPy2xVC43QgYI0mQxaN0xVRImud3flzAmqwRur5NUVvmRpopA4AAAAAwHAViRArj/Vl+VWtmQlAsJotk7U3T4SVokzT9HtnUwF1ZfMnhPVGo/M0UgcAAAAAYFgsCVIkQu7Jjyfz47x80DgdCFxjyZBF4/RodEVYaa7omoAjGJ2iFmUtWXaORuoAAAAAAHRLmaxwOCI7tEyCfCo/vp8fnhVraJwOBC5SQ+LkB0k0P/WssEp64/mzZwQc0UOff/VZ64shrJRl8yf+7b+89/Jk0titDQAAAAAAHFOxY+OClgmLJsX58afF13P54d1H1H6RMf1CgLCdUgMmk0xPffuHY2GlKKOuII4nmusZjUiGrBNFWxevv55O8j/SZwUAAAAAgM2z1YqXFP4CTxqnAz3QSJms6exFy7561ePrjfm7elzAMeze8fZUBPhryLbvvHn3WAAAAAAAAM2wXSETAQheMz1D9u64kCd6vbejBS3LNKNxOo7r2tUzO1k2f0pYaz4SjdQBAAAAAECTnheA4J04GZIkmSKNPJsT9UM0mgo4gfxKuyasZb1VaKQOAAAAAAAaMqVxOtAPJ06GpPpRnEcfE2GVNH3+w6zqx4l886v3zjLRd6aOSNkF62UEAAAAAABwAhZc+JIA9MKJkyHRPJsIK9E4HU1ZNFLHWotG6jsppfsAAAAAAMBJsCsE6JETJUM+kryU/zO6X1hpvrX1hIAGLBupRzRSXyvbvnP+Ae5NAAAAAADguNgVAvTMiZIhb+1F4/xLLKwQvZDO/s11AQ2wRur5Z/FMWGue3bxMqSwAAAAAAHAMFlB4nF0hQL8cOxlijdNHis4LK2WK2BWCRs2zjHOqBmukTqksAAAAAABwDGmeCHlcAHrl2MkQa5yeRdEFYbXd+XMCGmSN1CmVVc/pm3ddFgAAAAAAQH22K2QiAL1z/DJZ84xEyBpRpmn6vbOpgKZlc3aH1BGNLo2vUCoLAAAAAADUYkEEa5r+dQHonWMlQ+LkhiJFl4SV5pmeEuBg99Q7V4Uasu03f/lqIgAAAAAAgPVu5McXBKCXjrkz5M1ENE5f50b63bMzAQ6skXomzYS1RlF0iUb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        "</a>\n",
        "</img>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwoGmHLVtt3C"
      },
      "source": [
        "# Extracting Quotes with Timestamps Using LeMUR + Semantic Search\n",
        "\n",
        "This Colab will demonstrate how to use AssemblyAI's [LeMUR](https://www.assemblyai.com/blog/lemur/) (Leveraging Large Language Models to Understand Recognized Speech) framework to process an audio file and find the best quotes included in it through [Semantic Search](https://www.elastic.co/what-is/semantic-search).\n",
        "\n",
        "## Getting Started\n",
        "\n",
        "Before we begin, make sure you have an AssemblyAI account and an API key. You can [#](https://www.assemblyai.com/dashboard/#) for an AssemblyAI account and get your API key from your [dashboard](https://www.assemblyai.com/app/account).\n",
        "\n",
        "You'll also need to install a few libraries that this code depends on:\n",
        "- The AssemblyAI [Python SDK](https://github.com/AssemblyAI/assemblyai-python-sdk).\n",
        "- [Numpy](https://numpy.org/), a scientific computing library.\n",
        "- [Sciki-Learn](https://scikit-learn.org/stable/), a library for predictive data analysis.\n",
        "- [Sentence-Transformers](https://www.sbert.net/index.html), a framework for state-of-the-art sentence and text embedding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uozPKf7dkTq1"
      },
      "outputs": [],
      "source": [
        "%pip install -U assemblyai numpy scikit-learn sentence-transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then we'll import all of these libraries and set our AssemblyAI API key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import datetime\n",
        "import numpy as np\n",
        "import assemblyai as aai\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "aai.settings.api_key = \"API_KEY_HERE\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we'll use AssemblyAI to transcribe a file and save our transcript for later use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "transcriber = aai.Transcriber()\n",
        "\n",
        "transcript = transcriber.transcribe(\"URL_OR_FILE_PATH_HERE\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can iterate over all of the paragraphs in our transcript and create [embeddings](https://github.com/microsoft/semantic-kernel/blob/main/docs/EMBEDDINGS.md) for them to use as part of our Semantic Search later.\n",
        "\n",
        "We'll be relying on SentenceTransformer's `multi-qa-mpnet-base-dot-v1` model, which has been fine-tuned specifically for Semantic Search, and is their [highest-performing model](https://www.sbert.net/docs/pretrained_models.html) for this task.\n",
        "\n",
        "We'll also be implementing a [sliding window](https://blandthony.medium.com/methods-for-semantic-text-segmentation-prior-to-generating-text-embeddings-vectorization-6442afdb086), which allows us to group sentences together in different combinations to retain their semantic meaning and context while also enabling us to customize the length (and thus duration) of the quotes. By default, we'll group 5 sentences together while having 2 of them overlap when the window moves. This should give us quotes around 30 seconds in length at most."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "embedder = SentenceTransformer(\"multi-qa-mpnet-base-dot-v1\")\n",
        "\n",
        "embeddings = {}\n",
        "sentences = transcript.get_sentences()\n",
        "\n",
        "def sliding_window(elements, distance, stride):\n",
        "    idx = 0\n",
        "    results = []\n",
        "    while idx + distance < len(elements):\n",
        "        results.append(elements[idx:idx + distance])\n",
        "        idx += (distance - stride)\n",
        "    return results\n",
        "\n",
        "# Sliding window to determine length of sentence groups. Tune based on desired quote length and duration.\n",
        "sentence_groups = sliding_window(sentences, 5, 2)\n",
        "\n",
        "for sentence_group in sentence_groups:\n",
        "    sentence = {\n",
        "        \"text\": \" \".join([sentence.text for sentence in sentence_group]),\n",
        "        \"start\": sentence_group[0].start,\n",
        "        \"end\": sentence_group[-1].end,\n",
        "    }\n",
        "    embeddings[(sentence[\"start\"], sentence[\"end\"], transcript.id, sentence[\"text\"])] = embedder.encode(sentence[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can query LeMUR to provide the type of quotes we want. In this case, let's prompt LeMUR to find the best 3 quotes out of a video that we transcribed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "questions = [\n",
        "    aai.LemurQuestion(\n",
        "        question=\"What are the 3 best quotes from this video?\",\n",
        "        context=\"Please provide exactly 3 quotes.\",\n",
        "    )\n",
        "]\n",
        "\n",
        "qa_results = transcript.lemur.question(questions, final_model=aai.LemurModel.claude3_5_sonnet).response"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can take the embeddings from the transcript text, as well as the embeddings from LeMUR's output, and use them in our [k-nearest neighbors](https://www.ibm.com/topics/knn) algorithm to determine their similarity. The most similar quotes to what LeMUR identified will be surfaced as our 3 best quotes, along with their timestamps and confidence scores.\n",
        "\n",
        "We'll be relying on [cosine similarity](https://github.com/microsoft/semantic-kernel/blob/main/docs/COSINE_SIMILARITY.md) rather than the default Euclidean distance metric since it takes into account both the magnitude and direction of our vectors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Embed the output from LeMUR for use in our comparison.\n",
        "lemur_embedding = embedder.encode(qa_results[0].answer)\n",
        "\n",
        "# Vectorize our initial transcript embeddings.\n",
        "np_embeddings = np.array(list(embeddings.values()))\n",
        "metadata = list(embeddings.keys())\n",
        "\n",
        "# Find the top 3 most similar quotes to what LeMUR surfaced.\n",
        "knn = NearestNeighbors(n_neighbors=3, metric=\"cosine\")\n",
        "knn.fit(np_embeddings)\n",
        "distances, indices = knn.kneighbors([lemur_embedding])\n",
        "\n",
        "matches = []\n",
        "for distance, index in zip(distances[0], indices[0]):\n",
        "    result_metadata = metadata[index]\n",
        "    matches.append(\n",
        "        {\n",
        "            \"start_timestamp\": result_metadata[0],\n",
        "            \"end_timestamp\": result_metadata[1],\n",
        "            \"transcript_id\": result_metadata[2],\n",
        "            \"text\": result_metadata[3],\n",
        "            \"confidence\": 1 - distance,\n",
        "        }\n",
        "    )\n",
        "\n",
        "for index, m in enumerate(matches):\n",
        "    print('QUOTE #{}: \"{}\"'.format(index + 1, m['text']))\n",
        "    print('START TIMESTAMP:', str(datetime.timedelta(seconds=m['start_timestamp']/1000)))\n",
        "    print('END TIMESTAMP:', str(datetime.timedelta(seconds=m['end_timestamp']/1000)))\n",
        "    print('CONFIDENCE:', m['confidence'])\n",
        "    print()"
      ]
    }
  ],
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