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    Hardcoded Neural Network does not work as expected

    Scheduled Pinned Locked Moved Bug Reports
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    • Dan KorneffD
      Dan Korneff
      last edited by

      If anyone wants to start learning about RTneural, here's a snip that contains a model of the ProCo Rat pedal:

      HiseSnippet 20992.3oc6810imccdcmUKp1Rh1dlDj.ja4vqjAnXN6226XXXY8VFgQThPjQSBBLLJ0cQwBpYWDcWTxLF494qPta9ZL24OR4avLqeO+qlc2h7+dQqoRPvfoSfEqpN0oN+2m89400yZ89O6lGc0ye9MO6hG7c9vO+Su5hG7m8vO3ye5se7O7iu75mdwO8GcwCdyG9K+ve9Ue1yt7IW7C97O8xm+7qd7EO3Auw+V94O367MuH92+0+5evkO4xm9nqd425hK9U2b8it5mc8mb8su7699e++2t9IO4mb4iu5Cu9Sdkqt98+oO5lm9Cu4I27Y5Y4Md3wEe5kO52d4u4pe9kbYeiGdwC9S9wO95au4YevsWd6UO+hG7M+A273O+C93a98O8z0+qt94W+qexU7EoK9.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.26qCYo7XuWnCsk93HkKSYbrk1+JjMGU8pqvowg7Ds8VuzVzVQO0itN3l1usaVkIwp7VUzqHsBd9KN+tyRWaPJxH8bjkEuc2XsyWGsWyoV7ZGyoYmTopGDYXT2X8aV2e0st1fpKWqwEVr2ejUN1lM8bu5krwA2w.WlxBoBhPQTr+PXQVZjke4Ibsxa8vom4VtkOFUtT8Xu2Bl1ymlI8.mzle44e+MVGmj8bsQMszF5lwhTIul53hr9NKGi49McJPhCYBSFSGKsqd+sFK4Irun210xbqKq9Xpm2rLqO3HvduJE44NujIukdrMNNmyhNKUVxR97vsXnWeZy7XHic5xy62Yn6ltNEo4p14IZ+FZcPUduayTRwOdrx6eRvpqr6mzakjr6sOhTE0QgfZT3OGiQYsOvQEKSi.TNZxgQqXVQjcCYKPFSakb4HuOZ2YuWkwVs0P1kl6uyJl.EIn7Wn.i0YKS.rccma8ZUGTjivwdCoJ1NcxhfoUH54R1b.Pa90sOqjDj66TwDpYQqEXuSwtISz68rncQy4pMV3pifv2uCIqcdx2sdv0Yq59GjDGEU3IxlfL9m16fqKiRGGJIjgdxMAgUjo.cjUWormJqoFGsZ2TVVNVkDAyt2A.d.0Qrd3NrsO5XEKphCKIqB5s9XYhTRQ8HmEJ+iFI2nykekW7e6W56929N2KIaJaEJWL8mszORKWHREEbohKtqPHFaMdE1PaEk6He3jmyQs6dOqTZhbJTrOlnMzQUYVVwp0Tn70ism+zYzAwOSN0J3bW9XEseSIFlvekhMoXhQJcTwnn9m7rr8wfCRxMgxoubbrO7RkQuBjRtcx5wQ24lIaZ8zpHXjOYEcRs497oTtIYLY7Wa62Glq96qTeZXTTFy6ysWsN+qjYk8YxtWgqseqjhdnIm2Jz.E75gyfgBfI0zaQsZq8flMGJEBclV4XkWojoHGU9noWfJgod9nt2daRIeTvDvP6UU986C3HQTnJPhiAeDKi8gTor+6gsSYVpISi6OrjUzI5dqWl4CSHw5TxQVI5IicoxZ+yblzZ5IEEn100WtXjjaD4+VwyL6TAJi414XzKJ1jFgw2bWdQISqDjKDLwPaP1GEQknRT1RZW0vZESGCOV5TqVMpKyqbtw0tLbrj2yp4NKCiy7fsQTFLyYb4f+.yRxSgRFJMc9tWopN3JKoILBuOKnYUm.00qHR0B99BLn0V8zpz5U9aK8tbercGj.jrQO5ZIwX8fJbjx3SSFUMQarHyJ8JWVQIyPi8.Y4.W7knND6WLjUWEKfBNQqwXVZWgYTdwJ6CEFpBxsZLyHCRxrAqE5YHObamaxZgxDanDUZZO8buQIEnlLvz4bHQ5ZR4UgFoPNHEV8bu8NO5xPfhNQwjPov1ahVGN5J8iEATQob1uMR+kkGKk4aD1Zxj+gNrJO.0UTdHsdahjPIzqjjo3gqtLlse0S2yjBKVwuJaiqCSlr5dRIt0hcGCH6epKZmgbNqjlNvwgo1jYYetKKBUYPmJNt+XnLKlkmELSqHdM0LfJwoHz0t+j1qLcwznGkCYjQICwal8KeSk8u1mTZxQvZePwEkozwjG7l1R6JM4j24wqasDV2dnsp6pdZyr3sTJBlp.K2DZChhjXp0u0dKMxxemxQ2pzi.EhkYoSYHqUXEfU5zK+sqGZOsLipsQjae17fH6yT4B1gbLwT19c0KV0Hn2jNdkLmt3Ht7fJy4soRsX6ctwqQckUp7X1EJlNKp7BSxl2DOnlCLQo.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.IkFqh5Lf4TKHXPVbnYVJgNiYL4yTu8T3aJJujocVTMbYZTAP1kOiZ20WAxNSuHGxxmRBNu2p9XQSbV3fKk22p.kXepq8PDPQqatuGTEwgNSoUttoPdYJ4QzhcE.oqCzsoxXmCVGipMHLEs6rSIlTFTJQfhs+k5QQoiH+97K12uUZLkwFEdfBQXPBAlxQ0oEX5AR1ZxtBYjT.0T6+N4pZaGXWlOzYqAsUPAfLLsYuRg+JzyNdR1uaRgMtFzYx5HRVb+mQ5VgrQlS.2jVeY5xWE7WP+pWGqrAgSfJkD8DQoAmxMiawDw3giiIKMi86nzknDbR5Mjx2JUSlh6Lq5nH.DSaDStdsqTEGxhdT29byzvrLUrmbn3nKfJaO.fzFZYnVldoEdlH6OTBnx1woZnsn78aiIiHazgfhLSpCYlvDTBc8njdqRQYxzbMXltbKOF5kY1rwNqblk+hlh1Rokj2+Pqc15n6nGkFXn.V150P1ZTTpYYEVYwX5XFH5QVH0x2jFtaBpfJwJ2gfKvkrEeX7wfkTZZGgIjLkgLpllV4T7aJ5y8EVTdKnojjM.Mqs41dDomEQmpzAL95XQtpiJMEXcu1sYQ0ItFRbP1Jc8tFKvSJ6KIaa5qKURSeD0Agi9Wi9zPoiJIveFs4pc+jTIcWNpbEwD1butUN3EhpgX12V9pDmqNHiqAU32UwMBfrh07FFUMQxp7aTHaKL.SraCa7GCpOHs+TmYml7nhmCf9BO3t3uU.JGjGmhnfnOL6M5zFXvclB1L6LyrJ4bQ9aI6gsdD0sRwLMoCWxMTdeMzTB1fZUY6Uqeq995YqMDJfIEiPZNTnxiU2jaaBO3.hE.WpYmTl5BHycf7EJ+895BT.3fkRsB1J7GAkEoC4bSmuR6KmsBFTOpS.VshYeeMBkc79Qk.NqSGDthZNpT3zZRgpLsOlFRSPeBa5QPwM02CGpNlP0qCBTwDXp9w.XCE7SbDee82oYRk.+h.mLCHrkmslh8Qoqj8c8WGMjmdsNC.OjMWSl3DVPKBRJf529npnRQZEICb9StG5wT65p.zk5p6NUoDw0IOclpRWoa6M0ovVIL8ohxzAAB7P0UHLoJ.hctG2FJsW.UDvrMYwsQI1DGKZyzvzw.EBZgVHloI9tF.qXuTXAJMtnWKEmc.siXFgUAfg12.wJf6n1K5tqmaClyxAxE0FZvnYeeoCHhDspoX9aJw819HSTJxJjj.OmYZFroawfejdoBTnnQQ6eLJMxApfKqzzTdvCYskMQzCelLDSUJO5TQIsGMqS.ltdNoX.x1I.ItVMcVPKWx7LuOFwdu8kRboczJ0I8Xqys6yBRgtzTNYM5ZbcKNLXiDERL.DeTug01Z+HahxyMspUI7s0F5IeDxaF.dDjn1G95nKerijbqQiDMk2hBhTAlGJ5jkaGcixF.fGU1oJ3ts1jZfLxL.zTaSJNjvB1Lo77M4Ij.e1WjxFnhD.zuXLi1tuqFEtSeHkisjqwqZKGkj.nkK+wi8mUNXbSHlwF1RMgDzwHPYIG7J5jwtTSSuqNlP80J5IG7et8imdOz.qPKp0lou7oIXJW4p2oB4lDdkwb42QAWpP8SDi49ifKhgADvImwKGNqvXQmYUHScQLglTxbBQl45ATHb8e6XLo7jo.CP66PafjTskVgyI6iFj6Jibr+LKC50gxQvLtP.jRkxc3s2hdjhreEy1VBj83K.fRqQ1Dz9+xXucTlXuor9qM9G66p7ozZj+OEy3g9EVyj4XkxjgqkCVSiciZfjYllqDPKZXlXtB0krCdWT5Gy6qDG0o0QA3.NlLsDl59efGM8RLCTTGJ108nMVwvbbvj.ModsIyMmy2UlQxCdsWMsQ5ftjon909jQLkVcSkYn6M3bgQFJs0lN8YQgXRyT4jvzLybU84rOiX1nnvtb25TCyTTxf49W5.3+9hjH0RxdCoLZjIldIhvLcXl9R.2mrLtNMslU25bEXxwrsIeg.i1817NXXOZfyUFgOWsIilXRJmZ0n6h8+fyW3CeICZtdP.BOxju2hdBtG5wT.tJNrnGvl1tpyeDRbixPPGtcvZaI2wqAQq.BZ2uIU9MYaJCrPIkZtHoST5+CJnE0ZvDSiV6XvgXTSWGMyPFUi4HIw3FpTA12B+HUVkkJ9tjEASixvzKotovzq19HwD.MIOOkZs1vlbFmRfC1FsObnjIc8zUly01iB.ly0lEFb0BnoWGCF6m0oLcWoEilaK0JlYWklfyXksZU4Kce3+D5i1gvXmDows+hKiwhvUTRmyUeeW0nTGEsmCr7NRNKBCZdNyIYSlQalouWWDivNn5T9QM4rHWs0zf5MhY8jYZrU3+xKqheSlyylCsKlryn94Z6+vflxkbornXi5YW+B6GdOkgmhPoRmFWMeu90GtJcjzEHMMDEPkNYVHvrt6jhhOHAZ.0yQd+P+ps6MFlKJ09X3Z.BMzf9U20xVFLHaFnxJkNjBH1sFG0xf9zAfJpLlVtQqQVzIOHJxdYu+vACENlAnoFUSc9ThE5EGENWgUqfNcwktX1PlTDEZ1jAeZMPJcIZHtbrzMmuo98EEVfbJa5VcO2.y1xIaK6liq5TQFPRY5AXteqQocB+53mntcGZjLTXyO0acRzwU4bYuWwXJGDLoSlw2PIvNntqxQKS1fqwh5eJB1Tfc7r4AoC170SPMSUAMYgnkYvllLSC9I2aI3f4seRaEi1O35+gb.obHi5aiEOS8TAvkx0irp6JcGnbQqxZwaE3evTfMR0KAcODyj218FAUOzjuXYs0fg3DY3QgZUvTZUouegSFhnOF8vV99xXRVxJ8.J3XotJlA+BxdfLTYzsKVnC.fohyILK688EPgt6JynLNdEfKa2hubvqRB13.H1tusof3BPBddF3AeqktFCiGvEQOMq8AeQ5Dz66DnzVuIuuxgLCFeJPPEAv9RNvAMFJEYkERoBNGMQO.CiHCiQIeFlXMhvaI.BPJ6QdXhoih2yctoGGSwFab56TG3gWTbM4sGsZSum0mxrqWWZmOlMj+dZhbY5nQBc4LWtx8c2fzrDNHVT0roLPOLnaQthitncp.5loSozYTZB.QtRNPQTTVMxkVT1+koCjLlnrH2F5guXVoasRLIYAafXPgNUhaxvO.jRYxNK1o0A3bnWkckmhYdlYxRSLAsin7+FXQLX.WWY4i63v0w4L4gANXxT.EGbAUbCMnoEZ0TpZ3nHhVToyWobDokgba.KlDWGwoQEg2e1Jc.MHP87oFM66daggjrAXH2GlKMSfVMKahc.2xzEwAU1lh8DDSxXt8Qlp82XNOz41Z0P7KYFIbYYhziqVXs.nFoG7GgW.SsjVv7QJrR.J6z31j5BoXuzaQvpWYOR6kQ5JnLiR4q216y8.xjgZEKiFohoEjJ7uXFLZLQ3CSPArIhtllGTuQCcBnjL6JCyocb.qLBu.lV.54bqGyXZFVAv4W4dv2OICFlzFtB9eNJlsmoblJW1BfjxfBa.oBSP.KGoSEKaerkLVX33l586ZlGUVTusiRxbTLr1fBAdvPJKyR4wxAtxFEfPtR3WvDOUh2IDvC8ez.giAtSxELKWMSiEAGQM4WyX90WlksfN+fVJVrE0E.3fSTxfAUnJaxpRgbob.ovaUNor2lObOBTjFCQSYenkGzaDYg6KFtOyjrAa5AzyqwzZrOdfC5vkLLulw7+ru.DSbPoqES3i8e9n4bv0ccY1nQgeMcemFjn.ifV0BdKzMr1JP.Jjp76XbmjWM53MrICC9ichvJrwWmpzuV2r8nvFHxPmdoIanlYWexz0dnbS6ksi0TBCRyUghUL.9GFbFovJUHALXZz6CC8VoXnRLqp.Y95djhAll6c.RXNXqCSb4GmJ1KXlyTVClNUJhbAJxvLYg5O+AdpBPzh698mtoEdfEZEThxk2P1DLuJz5fdLSQK2VTlC7N.OuPTtl4PDlTR14Z.bTlz2gicVht0zHiWEU79XKYbhBlN4X0N5lhZy.mxy7hsnirI545X.+VAVWqtRfqrGAs7cPg4w59J2Qf7qNTQh+YldL2fGQ5tJ5kDYRerbnfNSi8SfoDFpHSsHY5nGsX.C2lQSjOuNLUHePZLP2h4Ix4GXfNCxuX4HssnR+LywU1cah3moTB3DNXnYsT5DU7gptBH4SikCKSJGhUNZoMAPObSmLSt9QCXF.8gXXH0ghCTonrfWF1eu4Ve.Aop+uPnE.lBSCrljmP39xFo4AXHGnv.f1cPaLA0pAIslXbQllxZImVU5uITckqWWAcLNNf9KSmJhtgCGpm.3xRYhLbCyrNI.oHdvfZ1bkuPNwGiSkYS1dxFFBtfExQTLZGfZ6SL0.IEkgDSc8BRFFKzDMLRYLtqfzyT+W4Q.93zLBCGPIP7VuDC.mqaWLxaAyigoACkvA5YzBsrlcX1cHO9ZOwDxAhWglB4zo6l5bRgLec8BphILchpR2JMiTMwcOfEBHqnggIbl0iQ6.eWf8IGrivMaz0YJImClySJhItVjQohoSu4LznJ4LPFWFFaJvgr1fnyg0HaG2fNxPtynmV.3CFKdkfNWOHSmwb43rLlbbsCU1c0AVyr7ePsSzZgBlV+NCCodC7Xw3n9XhCOSkOKAikMho11fDffQWfAeYHFoLdtVwPB3ZKkxbQ1mZN1WUN7kSKlwbPZv9WiC.8FbuRUQ7MLLydp1NHNAfra0DNVhY97.N8dQuYWF3NbnnjILccXeZnTUJzdA+4G7733xBlflF37D5A1MI3LBaUlAJRYsalMG3vhgdajYHPlq8nvEjIoyq3CGNIu3KPeAKHEhGxgIYcBb.2xjoIR08jyKEfRAd.EfWA6oNmKL13v4ovLNNXb1xf5Nl53AXnzToFP2tVRnKKPXqFloJnLVnnHk1pAKv8f0UZMP.UZ4x3BPz0w7O8CzX8.Nm3DBJmTDlsl0UzozJT4RlheX5+8QLvHJUtAb3+d2xoXx0k8fYLjUK6fR2G3VFFmF1tL6RDV96YN.mkijglQhwpMSgfOBkGXeUlY6l7MulC9m408PIwQEt50bMa1abPzO.SD4OuZRaNGP9l4MCPPYF3FfW.6hHPBkUogygn4XvsM5t2gjpMgVoyoZKM8axHHFAW+VpPW3Pr168YoXHRASjyHjA5uu2HXGT8AFrhIHXqaPuILC..YUFTsvJOSt8nJGwrLL2Oa4sns1MDcgRtLc.qMJF.vXBU4vDqzhfGkKVnpdavfcpfMzQR3J2zifns1vE4ClrBSO.mXFRd4YxsX5hLHTMwLi.HoNFlzNgzIA5bkjN15D3BXxfEKFvNENtJi9oCjj0SA4MahaDNZpMQqCTNjcSDXJSc.nJoltboXkSwFiAL4nRgxQWq5SVE3pbP1PtLg.+p.ZyT.Kyoq6hInUAYN5fZ.sG6EvoTP2P5+Kj1TwzmEFeelOU.sUStCSF1pYAwXojQR3t2PfgvlAf4NslCtsbtuSjWeTgbmFdTBVbGdESVnU3ltgGdPJNDmNbthIbWF7MDxfXR2c8aUgvPlVsf890yuYCUARPCEaAuFCybofX5PJskI7QhKnM4IdQtYfai0vAnUHmZxFmAhpXncwbm4yDPERQhalTytqaK85QLXVlAXTuEA0tTogQ2L5MTg0dPjVLY21wmu.5wTNkn+Ti847w1hX8fLRlN1wk93TigCSgU2ptguFsLPohzBUaXY.zcANdUuqyxNh4nXFBPMPUKwIXYS5AM7QFpaQOOLrgCGRVcFsNXWroiCrWSlMF41BZP8nZbHAdW3jH0.BaqaixaAKALXN0HbOmutnp5A25wjPXxaQO1JVVV+PiS1e5RFFKT36AyEhLVt8pgo2BkIPN0gyZLyvBCIFPdYkc78VllJOhtbyrhYjxfYvhVSZQB3OyLtD.C2dPB7yb2Plz.LMbMuVv9tsjY+QNWBhgiQxHsubCmXsL3TLny7AosZr+x.X.MmAe+Xh0qH+bYF7EN8VZNhILiB.UomQsso8Ey3Az0GiSFPFxLEKifG.6HlSTMO2f8.kSvvUfGCljbWPYLtIL1zYp.Sw4gFrqCOcIydEqpioyfL9rLJz52XXvFbmp0jPzi56krmTHlGAW6ehbALmBpALjnfkEhC2X+MgZkzXFAVl1WHeJmpe1hA8qV6N.oivjAbZvWiYJBGETVvH.enfNCcWkhoiDpm.vS3XpwFsjl1nmQ8TbyjJTzBv0F5TrmbZ5z.gaT9kPJGRtAmPlGUbdyfd9cE6BJ2R6S0acHFDkEy8FNWwBCykEpPPos2aDBNSPMmXFyIjdMDPhBy0OvzbYkYN38H.0hRGXkcZTYCYcBZlBSqlPkQDKnnyybBAPp5X3rDLdBmdcBMWgDofvmUh9xb1vMIWLamJABF1alxh8oCiElCbAzspoDMGAw.EJCfIRxodhGSBgiNYbfao8JyHGsAsCLI2Td38nHjDmYCMvRPKNNLixgWD4CFEggijofXKpn9dJDt84KBqyAM+Q0tZoCW2.jwN8Tz6D0Bhmv90ZJJ0Au4g0CbjpHQIvjxC5cZEC9Sfxl5.mxBvxwza+Bi3bJjhMPEgAE40fB7T7oT2lV0QrDCZ.0nFDX7wduhxMDnNedPZiSWUHIZ1XbD.AHFTlBgBsv5Nrc49TzJPc7nFIDKoi3aWAUDGZ2Sd3XMiIjs.gvdRyZ2utEX9P2RTJIL0rOKJrIsBtRep+m8mqTjOX1sAMEYFdODSIHXInEAj4MiLBMAOkPsRvvYtPSAWHfoK8eLLEOJUiBfFDHmdo3RyAhqGw0.BdolmtFzncmAzBn8cllNRdYZurbY.hmLgKgrtQwrgJTIAImcW1d1fq9X1cL7BB5YQv74Jti1dKXP0sTqDtVkkcxzHcftceAhSHGQCs+i7NzCTPX.kJA8.8p1AH1JP1rYbaAGKAQomLbUAibIDdKCIBkSaZIWoNEvcFRMminCoAoDfxL3hOm29BCdYm20c1T6vOWEYnMna1iPALLDACZiUuEONKq9UAbu6f37YHUFtrUyP..vMnGIq3egHLw.2lNA2n8o80YoH5hNM+2DaEPVlgV9qQ+bhDaBcrNpZuYL.BdwMCmjoPNbi8THF5.SdFbf8ZPV5cQitwxDVz2y7C3ChhQo3i4.iB8XO9XgONQitkQuQ0oMPYHEDFiC4jStEsF7Tn+MfHHzmgiW7gwtwZJQtMllBElwqBHohcI48.VfgKEbGRwqzy8dKBvBSqPD4iAKt53DbpeKMh8zrqseqDC9gBPtyt08xf.Bd+DIaZAnVL8EDhPXTgrQnh1IaenH9HPWWfYXW7ZJfwLMfB1nr4TGRzh5vcKhEV1pUACxrlxo.Fzb5f.puph9OXklZ5di0cNhAA.DWWBRDwnQ8qPPCnJg0poPCXRhOgsriGGBtTg.GOIVHtyIAUYP42Y1MKldWGzPfhGFtqrX3PpVP7J7DP5aMC40rXeOLELCIQ03ERmWgfwnaN0pis0ZQ6BlcJb.rwggc8XrvYvCKnrAFvqRFjsJRSMfxvoA4mpSJBLHiU+xPuqLSGPnivcMFVC.M9kQCeDdhLMqRw0AWzRZY5HqSFdh.yancJNFhnGy9AOFS3MCCHIAQ6TGmFZeWwIDIxKUEHskzqbfAjozpnPAny5582b5t2H.HrEMPrVyMJ4LfAvmJztijQpIBoYov3NbXnU9P1smmDdiAbLoYtyIWH4SC3aP4NLHrC9SKJtTqD7.79I1phn3BJpzY2joHrAzbQz1Tj2to1j1ARMenrDjkpq23jrWYFDBkqKof9FHoK4weA52GFjuP4H.LYMhlNa.USsFJ58HTRTCGRQFKfrg.24rSc+5mrGn2hvWoPkOlys.nXTP8JfdxjgyjoQ.rEnbVLa7zSg1ssnQ9PsoUKKwizDA4aUbDNHwowX.HeVZky06oA0xADhkwVpSplXLGfnRQcMalJkhJWyvicRX5bcC4HpiChSZYTbCBID+yJj.jQ0zCGniR3cQ89V46YfFBE2cgPxnfdQ+s2WXjNwzkvv6XczVVH3Py7kOeXJTCTmPyJ.K6PMA08NvkArLHN+HGLrpwyYnPkjiPnM.FgPGRrlJnqn.gGObTxQLS3GHt2L4vNdrENcrPgRUT5zYg8Y2fnfBNVfq8bp4gNfPF3T.pR2vnIgjeQxP7V2VzXY5B8TfAJ+vPUiSTuKL2BFwNpN8CsCL1gj+ykkSDjoXzAuSCMz2cXwlZtvnHhNYiLSXFnbv3ZDK1dlMJJYbTnVFHLXs9ioC2FgpeMgNaMH0rGjzabiaN49hTZ5vCwLnJGlKtG4UEphALPjA.UPB+5Uhdz2OlfgEIHozLcPp.poMI8xgDvTQdzcrcfRdDsAoSvwSpfm64fgcC1+Idku+4nfZthnPxBhwXNjsXNxeDvwuxNSRg9Nm0d4kipOZPmVx1QKgfg6.+.++Pf5AMR5Y59CiqxarhI4ELciiEJo2N0.9w.RFSETlTg4Q.xOvkkYJ9TzIbiAaXzAZmHITPrfB010zlhRjLQ8DWN5zAzSBWCnFG4kn0cU9T26NVQiwJ7vvXQYkDKCV1D4OH6qpZ3HpfV5IaelNpUPBPIRsP4frv0D44So8hSK4.0MNlSzZ2F3Iah2q8mtPqcqH1EGfNECAJOYljOBlOe3hnWlBzdCZEOAnOM0mDNpqybRUgSoc5bDMLIxLrx7.3jVVTIsgdRXDHsLZPv9AT4ZZ7zzznyV4DIdPwQLCPJHPBDLh4lhIKAH0kkLfEzPmY5aavMyUDXYTQzpCpKfxpYve+UCGbw.9K6h.krZfGZya6JUHchS7Q3XzzDIvqGjZBP3d4HxPzmRHvyFM4vTxpNg4x.Zvc1wlGJblLlYNw6nFgEjdT2qnmeqokLvzFznBJP9u1g.NeBOf0.I4EmHmmCosEzy1C4Ry8JOQ005f7RTZQiNc2nonjaHogu2RcB4rBT2fBFUMpa3ASiIEVhp0Zh7ngtWNlQkCbrgL7Rdvi.LNNl4+pfbkTIuWPUee+bnwmeJHRkr6aFicP3FDwQzO249RggVHoSI5cWlbKJlI.FU+lAM5fgD2XhYA7W0qD5Aw9abCd8FxSBBvp5H.Hkk9fAnRKCybyoHXL9YP70GL7saKYVBZl8..1hdaLcrOh1+F5TBEpZeCPNwMtHPALH9xfpgiDOXp9QmBnI+tZj1.eMnuCcHNr1dbKpnzHbd4cSQHOMRm2fvuVmDQ0lo5qL4uCjdNciYXLMxH3hAyhZj1sDY3AgXRv4kkqo8Q+DnY1czggf6JLuTvrUlQlYBgot+fBJKMv84DpDLr3jbTxnk9E5YiI4sAC1o7fCS663KHkdiLGF4J.SKmrDSKb0MrIcCQ3bZ.jdmQmKliDsBtO5DZNF8tm1OyP2sOcuwLnyZTVpiTx0xDvD+pgoQlysrw8M58B4tQBCN4KBpKuFc5WgHObX.jXFolRwbEaPYdMHY4IYblnfUtASp.htCAXt3lyBbHSlMzu2ilC5hXgFPlQydBf2XV9.jACv0Zv29MiXqjg5hCIlCYt8dKYRpSKMZQVq47hAdxLYY813zv2tWQTOAS5fMcgtpsyTHz5FTeKiRRuYBYCdRIzF6AfM2P+g.PevdGMaU6vMMrHlzIlbektUo3T3pNQVUok0Mi1.bZROF.ubHuEyMtBm9N5wyx3qA77YNtOfrFPJq6VgbIlr.XQ.5OpatIGyN7L.PiukcZwCGWaXNiRisbjhAfhmIRCpQ1.0EnFcvfJp9UwUpgLyEBJJPA8N0AoHpQCvtohHblrrbAB3NPtjViZ9.hxofoClDWGPUKYFqLkdZBjTZNHJyy.5M4I.BxqaR6j5vByUrhQLyo3WHNF55JfOYGvvXpxh5RCsI1bzcLQLETwakQC0pABKX6.Neo2JFNepQSHfVVQiJ6F9JjRZBqbUxZmc027W5Xc.IKXsv8gxpLEvT8AYmYfEODDckQHMEpqPwUsCfh3hFOUbpNOvEDJzGEzZTMwLgnlC9zpLUskCGFSRDPOhfvxvqmvXFqVkNXdLYxBLBmB7+4XMF4fQybe.Q9HfWG34wT6ezwCD4AJC1dDJ78xuanA0vy70XZO1ycFT7N.H2QfHd2DySciQzboKsGYm.xufEcvWKx1tozALHszaOPeW2ILp0v.lr0L51tfq0AhKsQlKcSkR6Xs.t8B.XzM0fBpmHC7MWvBn6aDXeFTOHUtHaF7Fj9P3t6.6eYCJsWL5fJJSfpTu3nl7UL+aPuuSbXYPHD06OFyIFMQCcgFSWRvMaLs7FIjT1a0FSF2.F9Qiw49o19B00nsc6KitrrnK+HDM6wV1sSV4x4AxFZ8NzRYdLfcATtjYlNxlSn.CX4zY9pgT5MP.IGLzFcNcQONL8OEx9S16YBpnjel4hMT8CR+svKRi5kpsyjuNy.GvVr6xpf0sxfYDywKGJk1ZH01mPUmKO7YnTB4fp1x6G.BznVrYPShYWpQ23zBbJz6QXPCSgcg4GxPQyHDUYC+0vL2rB3REBn3V5xE79KOxw7rSygbjEcLvYMX7ugi4MIGxHMkixWC9biPs0VCzyrfMobZj7f1tRGg.a4lQbEYJjJufin8QPyv2M01zfqfV6GSAFm3BEFAd+.iG2a.ZE3lBihRGVRtlfSKQAFcnbhJdFS1G5zcGMPrnysfsi8A1QYqgG7PfcZFnHBObDScYi3dVNmazkQlbpf7LFFnnfF7Ps1XFYZNldhQHfPni1a3vshxNXvrlSU+Ln8OyNeXHzJhDdwjVCjpAD8WEV8zkAIgQsfRDfB+RFFMm5CQ89ALXIPpv9n9AfF74Dz8YK3B9dzJQhfSxNJ.f19FSN9jj6SF5rhmTzZHFQLpnoQ+TxP6+Po3b0tAMAkwfsed5cI3IkAD6L8qCI3zwYuLDrCjJCJdfgXc0QqRzqGYkt3HaX8R4T5RvsEJ9NimY4qJivIxPqlFl3kPtWBicLqUcaqEFAeGWOk.tgrDAE358dL7dU3OXGaMuz8GCBAFCMb7KkrkrNY96MmcoBrcDBw0jQ4xTQG3MrFsDIvcYwQkNDeEC+k93QPNlFqQqEv.R35na4AK5SUHuZIC506Hg6T0aPr5x0EZzucvxChipi5pYjekYZpsuh6crepWXFq.SqPaQf838PNHH+mLPnmpqVM5BDyVAN4TdNGaSaIQYokoQDxEZjoCz3KDcrNs.FNow.aPJY1fdNgT9tLUCKR6TajVLncH.E66svgbvP47Y3NSlrTkYIv7bMfvnhsv7RLwJbHI50XLG1WjlHfZnT1CHPImNXfBcyNCpOR1Y+.tBfRlx.WYZFG86eQiHfi0Mpn.8Ul1xr.UVGNwjjZRThB3Av+yUSvaA6qQvGxbvQw7LGdUnGl.iQmPbRMGw8BR43zpHzUFSsECZdjW9vnYSQMDC5BbOW2Q8OKrOMCvoVl2JxdKcU6PQRNYRnMaSglW0ZV8HJkgCnPof0iWA0X6TPmAXCFRAFrRXxcmt.xr1tf6LxkpooVoXxZ.zWGtwKEc2isQEfR+vf6xLL9JJxo7Asp1YjD79FzjUfh9CSetSQyIkSYferBsMYUIHlPlZGDuzbpXM3akFXy1ilQPUY33GYBauMJqtiRfqXqAhBTwbl2GPVjGhx3BRPeZxOCFGAyXAio2coFEsLtbhdz3bqovzJ4SfdyLXqsCyruW4sNrncFFV2H3gnulXQsTMCd0IgqkvpyHTOzcs6urJQ3ggFthQz0XDSuji3umHLZ8tKdof5RgiQg6qLAtUBxRjHTgfFLXjg51lH1mUn7a6yCXNH0cRGVI5jb8gSNgHVI8xFD6zrqG7+KXo0w9BNbTBEQPIBvr3H6MlkiBg9PkooMQFpXmn0ZyPauTh7lgWBtCgoNLTfamrc.c7jwpAYY2byuALKVPDR.8LyJGJ3vhI9.LEWcRULfiufZzFL6rgs+fxMgBF6.gcWt9MPJwQLr7Jph1zIn8ZuWFIYWuGMd.NxQVYArdXZPccOm.kV5kXO3x97drKE.C.NgrcTc9kOhlRBUttNZFRQXw4ouVJ+K0YnfFenUOsqauaV4QApPU2SGkudZN1ksQX5dnE08NfFTspB.t5vB1QF.Zc65z0hZ0.HJP9VDYBr5oUrhZAgpqTZmvVXFuDGHcsfEDp2wgY9ajEQZEubroGjoatu.aXvnWHZx6WLlAoeBf8PjTJVdGZgKdFS+kA45L6V5UBpJdlrKbCAmdNICqf5TyMig.HNgNC6pd8kWl9OSX4f0QHlJWmsIGYlEJzrbXmGCn8ffI6ckn.5Db01GBZ..C9KcKyQJlo.5+jdOCt5xM8YfyvBvtYTlFYngICV9cHwhx5vhKQZuG5iHnzwQHHUNtB8eQ+6Kld.P4jHvXFYFj0z8O1PGqJH2Rv9DUmSVvfG7Y6Dofce0DQFqWjujxTtc3HARFD0JJAFn01X6.RoWm+TT2TUroQNKj2JrFTONBkkyPRc.M.BOutbzkd3HjmAze9tYzFB09oP5rGwnU2brnbL78ZaJndzs6.oVqdD3FYJu.FLlTHJd8pQqFGGolknRqAuqPiIWVsgHiZvRAj0lI2TRRJm.upYjxrY4SVkV46FUee8iosBLpBTZPsCzQ8CA6.gDfwXOser2QKnVLKR.BupgVuP5dBIWGtlesmvQXLTaIPeWMTYu8lZXbTgY56ouFfLL52O5C+XYlmwP+DX5DQ3TA0pFpAOHi64H31qkcXtzVelicxBA4fz0UWhpih1B8KZl+9f0gPIvCVkX+5rdqg7XIa6zKLyTBGYRWo7c0ho8ngR0S2glL2n2aL1Jz59PInDLqwxhzWY3pSbUjEzvoLViX36kKH5l6V5P96cBA6v2x4X7nZlJFbPgHXPRzhWYMr.pKXjGsXCRhaVv6RtwM.SLcq0jAoRLrCy6xic2vz8LlXZwf2jEGBJkeM.UTFu9c27MPqUfmJNJg5EOM34eBQGBsVTLjkD0MgVxf56Ry72GNCiVTCVDZEx7poQOxgEEp5TOmLXRochzs5nMWPlaFjpRFMqPnjFkpqfAJHS3BMXbGLc3X2LHkj.R56EY7SjsihaT2cEnoRFxARTlEJkkNz.OsneueB43b1HBI5OpahqPdM4PKQJE30aukwE8ujonJl.TS+GFfsHFCAvQy9vplLaET3I3wvxvnSRzY.xSWuyg4+MM+k8aknWctNUzwCeBAbiQraeDGGg5tQAiw9uQxEUbLZqIHWoAcVYXXVDmHEaGFORltl2BQlSm.lfp0gorqAAKCkP64Pw9INT+.McmdgXjNBj7hIcSNse5mOITYyS18Yxg.ugF.TpjDP+35AaR6DLvv28.hXhXG2uYldMAecCAR6BQBxzFdQoFfBvji.mRYjCTzknd4lx1Qix6YFDnNfqyvIOGLL8uPSkLFcAW9TNBvQLwP6B3Po6B2xTf6gbtMWAy8nksXTr22imDR02AJzEzYjQp9fSTfX6olDfdcWKQKmjkNBlOcXLQqf5nZ6A0T0MTzUEZtB3PIadNa+502.g+Mnv1rYVBhwzQl4xsVrydeLw.vwVfhX.htSU.Jz8kJHCnZ55PBxum9KArkL3rGVWIDV46XA+pi7WRzaSxvJcXTQ8DECePLzHfJYmNVWH8eZ4KLgA7tkC35893jlQvvNY7TrB9BWIfS8ZKlJ9S0sUbDS5VQwX7Pg+Aor24z8xTAEbwBVbQJYT3GlZ5NgKg34Fz9t0HstonP2TlEFBIWGNQ2Olc8ZAP2avdDb2VLfIo.flltcc27hR.Dsdyk68A1PYJqBxHyPalTTjCxMENcwrxgx6DkErQ5o6SONgLGUCDfLKFF1rBe5CcUBaRTM.LDBZggCKTFwrASBHJXgFho..2GwQTRqCxRAkQTFy2u+DVoHdcnc+Lpv2aSF4AkXAHMA6VdjlV.TxXXSelPJOLSZauQtzJJIF4ktSsYBUK7.gJClG1xItn9TEXp90gi6bPvb04T4Oo.m33zGa5ZTvhDLBIUKe0bDvdNEr8ooyCc1tI2gxeXae0kOUCXlVATzOpjhSCEkMFD4SNIheHSrAsDz0WB090z24ShURCcYW9gHiRGzmaPA3QswoHQcSiHKvdOGrMYkbPG.kvhlJBkHC81sOxJ4kqtBEBtasOlyAgHjfxj0Boo.hJCxB1lVgybGGlvHrLYryF9AWRlj53Em3zCkIxD4ARB.SI4hW4FzV1fHsxUXmVzUUGs0njj60XxSV6Ilv6JdWdDiBjVH0yiChgMJnICCDsLyAelZzQ1.K3NiHmDaI.vKCH09JoT.Mec5FCBIWywX2vRPnyswrjXfbrhuLBjNDAMSigRA64bPtB5Ptczi6HDKH7FEnyDCfKQiJgHgdgAaiMUEY0jOekV10PFXMifpCBBdwnKnoTn2LfFW8JLuOd5D.+XVhYCYrryNQnDWj2IL7heNuqzA7NvQoLLHLrhhPOARvwD93lUepjYNpfGXTtapePvz94CuXVGRmBsnBUbdYfhBlMlHbh3WZKXsSAyZGkph.BMb1JvmD+DGnqf4halLZn71SFS.Rzww65Lo9HvlCf8oiM4fDCUTabd0OCZjI6LjdxSRvS01o3L.CdxzUa5SFR+Tuy3hw3Olb9xAe4CFPqrVYRlbnxAszxrJnfbVtAOTGUI72Uvv7cWQAH.DDV9I0eznOzYN.v7n1o3bt.kwYeYgsFGuldRAuf3honlkXrMcoxgZqimYJnc0pH3CjgAJimrpZJDD875fXxnzsSCHqBDG2GgzrPwRLleCwBlReSklpoCmxwRybRnYIKzVuriNLB++DYc0n6EGTbNH8MnCltgRO4rNZ7cMTJu8CPDjhVcw5L3fJYUxbNEnyVfQktiA+.hiQGqkgJGIGkkMZ5nDE+KiWcqnVqqEV6hdyXp0kRFtDo4y4QWbjIH6aksOpUYrMwQQyTYGZOJ4i5l7D37oTKbFrrMPuGMepEU4YOw6DXBYFEyXh8IBQ7dSQIGLcMy.xDy4zEiEzNEGYqLN0NZ1FknErIR2hbI0w7vQph.UgpUJDZvlZKRVrZXHpPvslQgdUH3oQ0pufDcpLqWkgxILWgIpBDVXc2Kv6lykqPqvM3c8+OA+RXXLBHC6CpEitsMSG7Pv.QndgRQ5MS8PoVFn6KYnj2toCkn1hA43DjZtyj.K1gzq.9TFla8AkalQeAwVqrLEpCsbGGtAWrm6l1ovAFn2cP1+gsuiJtZLTNNNw0bl7A.tNUHB2IvI1.2VEHDhvTvoYZyc1T9zAcoOQ8PlVq0LnrGLJt.RPCI.EwIyDlkPqj5l.qGLvc.mrScgYeWcZLhpY.0ucRwQ3NQYRYZbY4aXH48VDGIiwgrvu1qFHs.+AKXQ3tiXmQK5AoSCxAc+0J+DHFfv+IoU2IMPQW7KjsXFsHy33Rt7qD5ag2fI2xLPBjRe0fIapNPxPbaJrCk879XYYf8o8qZc.fLlMu+nV1KjUFlinkSYTzGwFMuNPSukP3CE2sCusLMgKAe9f3OBwcMxF89AZMjn0GJsR4HvA9LZyPv4Z8PuVqNkQg9rES03xkkM08qVXNvxfREKfqfksg0dowXohCvNqBvHoFLj+p3nhNHoPkqnR.b+vveBxsmFALPwZYO8xoOhnVlkYTUHCVtRITjbftsd1KtfOfA6U7+A.0WTIDCO.gVOPDHgXBk2knMyGKv7f8F4h6sRtCt2YV2PjMLsE.AmCYXVoxofDllQ4lj9PMUz9ZsX6J0ek5pyjKyHL6RuEbqBAqvvZsLM1gyfz2wIVTatQBBR0L5F6fnjaNkgFgLBU.fpJWbZsSJ3pbfTbPUSNfSSiawdmbrPkkMs5ZFgbTyySPzvz2nvyBMIzHtpQESv9zB15uB2FXT.o95HXSChnsYlo7B8bfQySK4FswUtsjII.2L6VsCX4.EnYDXgArt4nQLriQAd.e5FuhLxqCjgrAQ7suAjTHYnstlbd0W1abJmhzxXv+FNIRAMhDB4JEvzxzPkAt3VgxMoXO1ZySwPLw4LPCDz7s2yEYwAj3nZ0CCyTJGsIlVXFC8zdOygnmBN4oL7Fw+MloEJpMTnQ69iBdNNMamiDjelgiBY9NYX44fJjPU2rrAOuvb6CZmpUqp0fIN4IRuNlCCCaEvHtE67Uf8caANwADDdvcTxksRFK7zBg7hPV4TW.nscphAbdmShRymFS3X7Olly2MLiCkio8+8dwTwsDrLMnrfirVNEGAOr.Uni7IXZ7RvrCUj+zwI0p17QrS4kF.AKcN20+LZRzHnuTF8aSjrMHJjiXV..dbl1zrHA.k5IEjeXF5hFrNKZDWnkgFwH.zdj.uQz2aW+lKn05znnET4kQOqOIrpAidqsraeIB38nOuZQI21mZXCY5CVzlV0OVNt9JnFGXhzDs9duwNPPnNxlfU5L7dOj1tRJaEZhqb0ZFjIDmKFInPZk1emgvuGzz.YeTlmxlBovTcASZp68b+AknmKji.T71vUo4AEfsA4sx1YydHFNkVH2tTtHGBqoT8npqDm9dmULB4bttx+iq8BCZqLDh.D4TaOHBykP9MPmHYGhAvZPgS.yhSjJ8zndBGon1wsthH.BBaOMJb.ZCYQtPjUlBuSCZwNdFhxsazrl5oVPjZHPVkpSS8voBV4XjBMCY8BDAchMaASP6qZPLX3qCXeJjMCSwLo7ELv.inON6esP+GnJengIyrIsvN0OOsHANqVqSOb3D3DngObrn.5EkrFv4Pro6nM6AC00KEUdm0YFW+EZo3n3XYdLKWh5Rz5GNZFHvfwjRWPkDMAQv3VvQf4jpJ0LhYM0PTuIgQYqi99PWozHGoEB7g1iZnbD3jUsgiY3qlMz4LCwA0QQGzQQNrTNxXdZP.RJRoihi35HhG34CT5C24PnN3JIAoLgJgHluu9EmXRgIgaHu2qsz.ZGlirEgcWcvmivdPSAlfyslUojz6iCnoJDxAyD.CJwPXyP9UJFmrxjQCZqg4M0fByLxsdIHiWJLmoTsTswRvtRXq1D4JBFhdOCjPIdaCveI7AZy8ji4NVze.NwNn+MHdTGFOKkXnAfEVTTz5biICRszU6LnyPz7cmSKnw7HD5AEBcOkgNGnulTxrB3tcOpWPjryA7MlJvFSeWW0PjhGPccN.VDjNNR6cVg9rxGlLNkIWpaGSxsdpu25HIopbppOvvcF0egIZAMEqBOOs1mVOvdf9stTvqorozSUT1OlDydOFlOiQWTZWn4O.i2gov+AgpqadGIFzcXg15AKQmB3HmcZPF35L3TKZTvvgyZEGJElAzxOcRPYnMjElcmSLr698nT3DnxsD3Qx.1azfIPJLLahoN.T0DDh4Jpna0wekTqc.JGS0X1AQRXNMjrIsFuP7zLLtFCVlhPIwxxzPtiY5vtV0Bx+eezcrimn96.3k8TTBJXZGN3kqNahSJA3b..iInenxzUSDZQvjQTLynwYJpKSfYhA1GH.Uy1nY.fII5+WSuV12+sJ5EK57F5W9QZ5fsGzjRBQ8kVKaZEoxSYhVNFJW1dWrzzW3GAHuf79f4CAbIQ+ll.VXC+5cRtUjmP1MumQZjgwEcKryC89JRByQokKp5DL5uaLZmnQk5sRj3axv0fCxqAhCtB7YctiYuCU8sPK6KlNQBEoW95ftjSTJArW0ZAzDxlJolngrE3ksE7.libWlmTDW5X9.xi7cLLDWfpIjVfQduNOm.eAL4IDGZYO5fSPHfTMP.SIBx496L91.CC8RLUUFISF16HF1NH6CCOuxfL1Y1syfxDyLyD3bf2JHfgYmayhxCCIatJCXZSkCZ0DqAUslYynjcceVlixTbuCJrz9B4bDylJJrLjXukaNgJXoqam3icC850Km3MInwaWsnw8st3B8MGS0l4mf9TfvBfDpXl26LRtHhmaCVVxPpPMvMSLJ6XPvjXChrNSVbf1KGADgxpgGqBT82dUD86EJfb.tO8Nj4Ss5.eArVrxvoCI3YRXAQP.7+y+jkLyrVHyFcT5pIvN6vb0Ltxglj.oYe33FJziMRFBdcH7yYZwY7ZGbSKeRl9PF.TFqXjXQ1b5ZDuEgiYfedNL3CB7F2QAtAZ+6qGNx5mrHAWXvr.aVo6kXzgzoKxrvAPH1DACvw.jZFb4L3ASAORfDi5JatyrSslCTjYlT8BLti7dmCcXM4GCAnBuiPnLlkxxIjcoP4qUTr75wQ++xyUGjRAxbmlrHWGA+6JaXECEySV2nsS5bXKzH28CBI.j.p2cPgOMhNDL0xAzOZfJg6OPsxfBK2EwPebXxrAP6VYj5SYPfQ2z1HkVSJZ4qWO3P83pfxrxcI2ajVvjL5qMbPDL8gSfH6g7rbnnqVyokDVfyVUzXLn.IyfKWXljThEJ7CkcQ0b5dfFSAcKu.7JlhjNgKegoyX.eMrNRAdigCUPls5ffgaIzUefL1MQDjLppwogTQ4OsN.AMtravXGi5uNa2qty2nU3wvhUfalMdgNo1PHP6cXOOShx5uNCPBJoap6lbtRO536frk2+FjJrQIk5zLFmdLSPcPs1Pqo5U9gy4lrNB.k.Zlk8412vgbRaOKdVXYAYNPM9X5dSNgMirwNnZHGMCE92GknYofpvpcZWyn1EncCHE308c.gItoDZ0TTR7zdayXOT6LOPX.Nbj+.86d.ZU3Ovd30.m3h6AniL7JuuZY0.xxLar5yoY79IHdE0EipY1Pzfk9AwjS8hOb0NIfr.yD.3+BN.yIs3JZtEeJQ.qL5P9oAHP15j0fUxPOCzJzJzBMX6Z4nSqXvvSsvLdd+RGSMHHrjgTaXPaJTxI4nDEkYb3pa9.Fdd.4uerLjLHQdtZz4MXpaOCktBJVCwPu.UAYo.LDdOfn8fAZ2oWmDKvLHet1zgxgEX1Gdvi3bylJ9zYPugQbJzkXiWPDyIsapwDvuLUW63HjDaH9JEQU5vNlDAukWhCWIijCiadFt4.DyCGoin8oUpdwHT7JyPR.7u3rcHFtFDFSxqHQ8HtlbrwMXyo.pjKHjx19TlhonQuRTjqn+S58ui22XBWNNwUD5sigtr4vchYvGxY1HAC3FrDfhSl1qKWv4sI0xbR.PKmN0CuuAP3gQqgOmc7+.0wO5rM4.XJeALlmLmxfRffqXHeWXXbDS6nzAltmRg3.FSz8xikaJxm.qKn6iTALXs8pqfXkIBMMO9FNyag9sOn97KO2UBT7I9bEKsR8ranHzIiqGjURF0dy33pOQrXGAJZZNgFJzlTF1vE8YzonMfG.fjL.XASI64csLYtgTJ1vefQRCZM.qN.PBxA2TMyPLGOPKzgT0MFTUL+JSY7hqP21u1ovLkYrjL+NPn7btLhYBGqAAhV26TrDB9Kh3aGL6d+Q8NKFLGfhUHpMlDPH1iLnboS3al5qkinQA6e60OWpAVI3JxTBntb3BSQGPfJDRgnikrchIXSZXXF3Qp887pDDn4LXTWjyt8GXGxAqdCFVdMrnFU9ZBR5BtiwTRvCHUaf7WBtpxDKA0tDXjyhsREyN9.vocUh+f1q6ZaR4NRlLpmZcOzYZARUjAWHb48OzDIHnyfxPSJCNd9iw+Ke.lLqF91AAHDAFJit3ZRJDc7.9rV9AYWm63JbmlBPIAM8.D92mv4A5g6LqTafkUchqL0QZwneBleFlXMq.wLYDf9WVn7R62kp0iFxSOxnwxpDJEhhczBJKXr+VSJrxkUfob3A.yPOnL8HlzfKZNbwgEv2jJXzidfaTlBHcM3dQhFqYDvNV6H1jIDLSypL0vo7sPwp1C6zHvJPrKCCXGNowjR6JHh0FSuxQtsbBoGH4C.rALNmtDJkw4.Uzw06xJZBXNB97rNcJ2LSRDTWIcGczMOEZGGeznijGtRagVyLgMvALVtJtjfxS3..oJrbQRipFCYpLltfv3EdPrQZ4XwDPubhCoBwEFdri7Ys5tHGWHYn.qii.Zeto+jjrRHC8xSmiiO3YEl3kVQGtRMbyObSx7twOxTMEh73DymvXPa1TC8xPMiA9oECHNY.vgGAg7M0tEST5k.XB5PnLA2RslSg.PyDYL4FlY6NEiTp1fPsAJYGst.e1xfi.98aK2n8Ekabg.UzAqPFOLfGYJd1DtSn3v7I8heh5RovAU.9SGp2Rftcs+lLybXJBZ6ErBxHSWcCzCgwhPOqOscSSnKfPOPECDlkw.4ALWC1YfOeN12y0LhVd.WpQxM0rPkVU5zeGo+a3nGSF.azvNNFVbQzGTYnBdrMcDGBCaYEHrP3a6MOdJOKvvOhmhg07NgqIEyQHJzDc+9Pw.d6y.CFxrmolsJP2A5rEhpTfZbyXg0BWrv0HolQsSyf2.zQLDI9dwMIAMnxh9f4edsbytEQRirh.dFN5F34PSSFjJ9Wigwh7gFgHWo21YSLdj045fzYQEUaNKoYpLGGqA+7FjtP68oiuPy1vjDF5MfIOkTTCQT43LoR9leMRt7qNwR4ynB7sP66jWoyxEAeuz6RebAiggrSb15BiBuEi3HRW9LeVdSHB+mZ9f2Gpn94rNGaNCxKT13Hm814.7idFYh.RKFvczrs0YuiM3LqR+D19OqjG.hmTbsjQihpjzpN6MDDOpM3LlVHYEm+IbfZwhNDzCF4ndtUmBcopUhoIABQ5rqhkVzqmwKXOmyegIp6XiPUFsypEa2c7FHOzfEIFmsvfeuLbbRPRSC5K5YgMOsfeET93hIc9rVDCYqqRfC.YYF7myccTeyDj3HVXOKMMvryEzPzodIrNWaOhYkIPgZE9FqbV.TgqqPeaynseLocm40W9cYbIf41ox6mkaSSXjVICUOQpA0yc3K8tvFsPT0HyCswY+7Nwq.0rFPne1XRRuKUIlTzo0Cy95rWGRGhdkUN8mue1GO.QLdQoEums3J51AXlohJv1Gmkx0RHhCLXQbpaVJaLKzSznEbOOlmCh6gke3kPXztFExXd9kuI43gJfdzVm+sAjOLge1C0e9bmjzwiBzfDR0S5rU9JXi.DvvCc9HDCoyuom7IQhFAfa41F6kLziUvOEShync1yQPfP3AGDJP0UN61TbCgklZBilm89wHAD+KC8kbVr1DSiB58WiwoKyrqe1Ozxlu9yhTKAUpe1EG.qObSsBYHSxim2RchJTEg8k1QycALpfP4gYl1fxzu2oRSEFpavlZmK1onmlfm.XtZ33ty16XhCBnnyP8qC8Dhy4MXxbyJapob.Xzy5SlgqZA0XvnJrN+iHMWDbjlkG77liJIBEEVuAuimcdYi44aEXAkGyy2NineuPQI4f3qym8rLX5oDLJeP.E4ytuQgzLh.xJq5Y6DVTCgDn5g5khZSc16GLuKtsoK2iy4A6NdRGzSB6CTYRyO6J3DP1qrMKniRm89AmZhZlAcY1Wm6Cr7PzCAFF4SEJW+7VafrUA7prvbVRXIZILyykxGPwntFmUdnAsZ.wnZnlIkyJK555zlZfwBBOz3r34j8pDoPFNP.s.47u5BLjxjFBk7b1I++DySC7NxbYkylIjVq0YSvVEgdTNqrRECnPHgwToaHC4448Qw.p1NI0DnjFme+eElaRAfCPS6k5Fudv47X7nbZxIO6FLjzTH1wfYhOR4y+9KjZGc.f7Rxm+8B0yIPBQGiMm8HZgkvP383s2YykLBwsBKvC00hPH+l6yq4ke0+4WdG+GdsK4su8y+zqd6+Mu0a+3qd5yu5se8+vu8kO51q+cWd6027Ttj+ve5y+3Kie2ubdRO8ydxSdmule2zatIMr292e00+lO91m+U9W4+3eLJwAEMSYaTQxeXrhum5zXG+tibjpJyhZ6pu2Q6diTdzSsNNfUJpR+w8nxVBqPPdx5nNhvx8WeWyHpMCkeECPBCG68ysNgj0.xQjuLlWmw79i4ilAN9OP2.WxJ981hLIiCD6SLb0G2aHjN5dYTzclYtYZcuciY7cYpfy.9TBv3d6NOwTGqwAVdl2eRrSOKmtSEXRnUSiw8H.BXBqPLHzBACHy81ctx7jJG1cFrUE9+80hLzvaAcUKzgs6sC0Aj2PgCmvAlLHi2aqD.BkL8zSgHCXhx2a2YXqWEI3Dny2XKx82YDFv8fp5GK8ne+cDYLfMalHSSAt.tuqeYT8UXWGpY.AHqPm95GGwad5q+O+W9lu4+5+0+u7Ve3O9m+A+he4O4m8K9e+sduewO5G+ydq+p6q+8lu4it4oO+1252c4ydqa+n25u5s9wO82b8Su5cezyt5xau5me0m8rKexO+pa+827re628s+PE7xMO6m7ja9828sd6+h+R8Dd6G8tO4lKe7K+ou2MO9pm7ceue1mv+6egthO5yd5iH7l25lm9yu41q9EO869W7l+Cu424M0my+vezG8Qek+re3MO81mcySdxUO6q7G+gW+Im4mb2u328oe1m7qu5Yui9f9jO6pu3Bu3Aeye7iu91ad1Gbq9397KdvC+A273O+C93a98O8hG7FOLcwu55me8u9IWc5K9fadxM7ecbgVzt8pmd6qbg27ze5Su91ewmd0ce8E+vO95m732+Y27nqdtVVd9E56b5W5BcA28To+KBL7hG7+zC+fG8rq+za+fmb8iu5YWb8iu3AeqG9Se5m9Y2dQ7.ewC9NeyKdw+9Z7K+se3u3yt8+W7a+ie5k7o9O329+5e8E+69o+nKu8R8Q3A28oQeB+zqd1sWyh2C9QW86t9QW8gw8867vezUO+2d6MeZ7w8S9zadJe1evel8C7e+K+C9e36+4u7Kt96+IW+3G+jqd+ad907x8k+j7+3yu8pO8Ct9+zKdV++7+x+k+u9q+jqekK4+v+3mb4e+qtN77O6i9nq024gO7sd7O3B1od5Q9QW+qu5IW7ou381OUOX+Y28r9V+j+8oK9zKe1kexU2d0c+j3Q9mc0uS+NW705S4W7l4kOMe+O6+A+i4e9cOye4OmeK+dnW4w4ouxmy+wm88+C9zDav9Z9D8l2c2+ke3EW7Emwzg4Sa89m8PYA5ydxk5b8O7iuTqO+zeDey+s5+7te.qq+fO+Su74O+Jc2dva7SYurVu+7W8Y4AeiW21vabdaCGuhsguhS9eMeD+m+v2+5aezG+U+L9M9JdF04v+awy3ab5Y7O+g+3O5it5Q29xGvu4C+I+6es0s6s+7O3Ae40n27tW+5uI+wescCu5Cwou8EO3+iG9RC+e5ytRaXt5Cu48exke9284W9Iep1rnmw24s90O4lG8aYa2W1awca99AbEe2G8wW9zmd0Sd9eLNU9S9Zupj1+R4Wdymc60O827dWd6yhSw+7O6S9fa9rm8nq9g28zou2C9FXU+zWevWy9hO3pm933K9+V+6teXhu9A28CSu3G9R2RWbmW830wc+25H9o09ezy+zW7s9UW8rmGFl9VOTAR8tGW727D4zGqBWe21V8RI9du+MO4y+zO9lmd8i3ac5Jdwi9eymbymgIjSO++ud4y+vKu9IrO+C9rmKunO9W7zOPW7SezUw9rG7yCSWeqexkORKre96e4seLmWvOr1bd0yd2G8EaResm0WtOgU3etrf7GbA7Z48egokme5UB+s9g50xm8rWrbpG8OIL38MdntD94rH8Fmdn9S3u5exC+M7.7ZOe+o546YW8twO3q5A4teGyetuwq73oe6u3qzu+6c8S+UmbQ+se32Kcv6h26x+969VOj2NW7AegQ1+kO7DbX3egRGk5Z22u8pe+om4Kdv+hG1d2ZP2xn8yUPk+Em9ngkyK9a9rau4SzF5GeW.Ne0OHm9q9xmhu8C4u4+zdP3Bn6xEcAvVnqwQJdR9Se3G7I2byserNRbwc2++jGl4tew+8eY4O6g+xqd9U2d519Z27Kdkfht3hWYSxe9Ce9Mezs+c+5XuvquW4e0qrW9Uunup8Mu9swsa8UeXzloSekbh7M3I5+4WthRLzu3yw2TqpewGiWbsJZmm+q3N8nKexKtPc38EmFdvq7A8a+vmFoN75eF+yen1+7wu6c+nupOYewum8Lwq9g5A+AOneqGFYf7hmw+EO7Kk3xqreI788pewq7w3aEGPS+S6T8K9k9++X8+e5i0m+02cmUdYlA+29G7W6dE4l8zqhfUd9Eu1WpscrsjPnuy4y6+pgUe2VhK9C+z75I.7+P9w4Em5bed9l744URe37awe3CS5qu6KHTmUe907g40MP+9udlKegMiK1ZD6LVa+iIA7+6RXjexkO5Y2728nSQFyGnuc7czYimFlE+NO783qeqzWtlDJg2q+6dzid8a0W5WL+G6uX4O1ew5er+hs+X+E6+w9KN9i8Wb5+EeO8024kfs4Wbw689+3XS3CdvoiQQVXW7+CvggePC
      

      I'm still working out the details of the training scripts for my own models. It's mostly working, but seems to stop training before my desired threshold of tolerance is reached.
      I'll post more when the script is working.

      Dan Korneff - Producer / Mixer / Audio Nerd

      C ustkU 2 Replies Last reply Reply Quote 5
      • C
        ccbl @Dan Korneff
        last edited by ccbl

        @Dan-Korneff I'm not sure if the tolerance refers to an absolute loss/ESR value, or whether it actually refers to a threshold of progress between Epochs.

        Also, depending on the size of the model, there's basically a hard limit to how close the model can be to the original. The Rat is quite harmonically rich, so it will be on the harder end to model.

        1 Reply Last reply Reply Quote 0
        • ustkU
          ustk @Dan Korneff
          last edited by ustk

          @Dan-Korneff Would it be CPU greedy to run several small models at the same time?
          I'm thinking about modelling key component of a circuit. Not for reconstructing it though, just for having those models here and there in a DSP...

          In fact my question is, since a single component/sub-circuit has an easier behaviour than a full circuit, are the resultant models lighter to run, at least if you run only one of those?

          Can't help pressing F5 in the forum...

          Dan KorneffD C 2 Replies Last reply Reply Quote 0
          • Dan KorneffD
            Dan Korneff @ustk
            last edited by

            @ustk I think it should be doable. My plan is to do a Grey Box approach. Use ML for some items, and then analog modeling for others.
            Test out the snippet above and see how much CPU it uses.
            I'm still in the "trying to make this work" phase so I haven't gotten into measuring or optimizing yet.

            Dan Korneff - Producer / Mixer / Audio Nerd

            C 1 Reply Last reply Reply Quote 1
            • C
              ccbl @ustk
              last edited by

              @ustk so basically every NN model uses a number of weights. This results in a certain number of parameters being able to be tweaked by the AI essentially. The more parameters the more CPU is required for processing.

              With simpler circuits, or even say individual components, you could probably use very small models. I am actually planning to use the same approach as you, using NNs just for the non linnear stuff. I'm hoping to get it all working at higher sample rates though to reduce aliasing which would stack up quickly if you are chaining NNs together.

              1 Reply Last reply Reply Quote 1
              • C
                ccbl @Dan Korneff
                last edited by

                @Dan-Korneff are there any flags I need to enable when compiling HISE to get the RT_Neural stuff compiling into a plugin properly?

                Dan KorneffD 1 Reply Last reply Reply Quote 0
                • Dan KorneffD
                  Dan Korneff @ccbl
                  last edited by

                  @ccbl I didn't add any flags and it exported correctly.

                  Dan Korneff - Producer / Mixer / Audio Nerd

                  JulesVJ 1 Reply Last reply Reply Quote 1
                  • JulesVJ
                    JulesV @Dan Korneff
                    last edited by

                    @Dan-Korneff Thanks for sharing this.

                    What is the method to train your models?

                    Dan KorneffD 1 Reply Last reply Reply Quote 0
                    • Dan KorneffD
                      Dan Korneff @JulesV
                      last edited by

                      @JulesV https://forum.hise.audio/post/86374

                      Dan Korneff - Producer / Mixer / Audio Nerd

                      C 1 Reply Last reply Reply Quote 1
                      • C
                        ccbl @Dan Korneff
                        last edited by

                        @Dan-Korneff I followed all the steps in your Gitlab to get the Aida-X trainer up and running, but when I get to the actual training part, it reads all the configs and starts the training process but then fails with

                        "RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.
                        

                        I tried with both 24bit and 32bit float input files.

                        Dan KorneffD 1 Reply Last reply Reply Quote 0
                        • Dan KorneffD
                          Dan Korneff @ccbl
                          last edited by

                          @ccbl I'm still working on the scripts, so they aren't 100% yet. Feel free to dig around and see if you can trace the issue.

                          Dan Korneff - Producer / Mixer / Audio Nerd

                          C T 2 Replies Last reply Reply Quote 0
                          • C
                            ccbl @Dan Korneff
                            last edited by

                            This post is deleted!
                            1 Reply Last reply Reply Quote 0
                            • T
                              tomekslesicki @Dan Korneff
                              last edited by

                              @Dan-Korneff I'm also getting the

                               CUDNN_STATUS_NOT_SUPPORTED
                              

                              error when training with CUDA. With CUDA disabled, the traning goes as expected but is very slow (as expected ;-))

                              Are you using CUDA to train the model? If so, how are you setting the enviorment?

                              Like:

                              conda env config vars set CUBLAS_WORKSPACE_CONFIG=:16:8
                              conda activate base
                              

                              ...or some other way?

                              Thank you!

                              Dan KorneffD 1 Reply Last reply Reply Quote 0
                              • Dan KorneffD
                                Dan Korneff @tomekslesicki
                                last edited by

                                @tomekslesicki same is happening here. Looks like there's an incompatibility with the latest CUDA driver. I'll have to tweak that

                                Dan Korneff - Producer / Mixer / Audio Nerd

                                T 1 Reply Last reply Reply Quote 0
                                • T
                                  tomekslesicki @Dan Korneff
                                  last edited by

                                  @Dan-Korneff the solution is to install CUDA Toolkit 11.8 and install pytorch 2.3.0 instead of the current version. Here's the install prompt:

                                  conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
                                  
                                  1 Reply Last reply Reply Quote 1
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