diff --git a/examples/tacotron2/synthesize.ipynb b/examples/tacotron2/synthesize.ipynb index 2ede277..f71182f 100644 --- a/examples/tacotron2/synthesize.ipynb +++ b/examples/tacotron2/synthesize.ipynb @@ -21,10 +21,10 @@ "\n", "from parakeet.utils import display\n", "from parakeet.utils import layer_tools\n", - "paddle.set_device(\"gpu:5\")\n", + "paddle.set_device(\"gpu:0\")\n", "\n", "import sys\n", - "sys.path.append(\"/home/chenfeiyu/project/Parakeet_0.2\")\n", + "sys.path.append(\"../..\")\n", "import examples" ] }, @@ -114,34 +114,34 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[checkpoint] Rank 0: loaded model from runs/refactor/checkpoints/step-50000.pdparams\n" + "[checkpoint] Rank 0: loaded model from ../../pretrained/tacotron2/tacotron2_ljspeech_ckpt_0.3_alternative/step-50000.pdparams\n" ] } ], "source": [ "frontend = EnglishCharacter()\n", "model = Tacotron2.from_pretrained(\n", - " synthesizer_config, \"runs/refactor/checkpoints/step-50000\")\n", + " synthesizer_config, \"../../pretrained/tacotron2/tacotron2_ljspeech_ckpt_0.3_alternative/step-50000\")\n", "model.eval()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 36%|███▋ | 363/1000 [00:01<00:02, 266.51it/s]" + " 36%|███▋ | 365/1000 [00:01<00:02, 256.89it/s]\n" ] }, { @@ -150,13 +150,6 @@ "text": [ "content exhausted!\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -171,12 +164,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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+GPgSsADYDPwM+JNqBuWcG4M0mO+QUC6H9fYRtLfRdfZCml7K8NjXl/HY47lkLpEdYNvHPpdLpZnQ/PdUM3tP6QZJFwAPVSck55xzpdJezpfTpuzLZW5zzjlXBWYq+1ULIz6RSHo9cD4wd6AXe6IVCIc/yjnnXCUZtSsgyjVaaisHNCf7lPZi3w+8o5pBOedGIaEwhDAkaG2F9hbWfLKdH194C2vzc/noQ4s55X1PYPn+I44bNkeS9ryJI+0/oRELEjN7EHhQ0jfNbAOApABoNrP9UxWgc84d1QwsqtwTiaTLiRtQhcDXzOymYfZ5F/Dp+Oo8aWbvHu2c5dSR/LWkVklNwNPAs5I+Md7gnXPOTUyl6kgkhcAtwBXAUuAaSUuH7HMycCNwgZm9GvjIWPGV02prqZntl/Qe4CfADcCjwOfLONY5N0HKZCAMsf44RZU5biE7bmng9tfeSrdl6AgKbC/mWNvfyd984Wo+/v7LsL4+TgmfPjKtBSOmsJTJYJEdPmHVKPu7qVfBH8U5wDozWw8g6XbiEd2fLdnnfcAtZrYnvrbtGOuk5TyRZJOOiL8L3GVmedKfsnPOuRnBGPcTyRxJq0pe15WcbgGwsWR9U7Kt1CnAKZIekvRwkgobVTlPJP8AvAQ8CfxC0vHEFe7OOeeqzYDxtdraaWZnT+KKGeBk4GJgIfHf/dea2d7RDhiVmf0d8HclmzZIetMkgnTOjUWCMCR/wWvY8oY6eo/rh0ic/L/6+cCjl0JkWCF/KOcxVw8TDeQ/CoVxXcfGs7+riQqmtjYDi0rWFybbSm0CHkmyTy9K+i1xwbJypJOWM4x8p6SvS/pJsr4UWD7O4J1zzk2UjeM1upXAyZKWSMoBV5OM7F7iX4ifRpA0hzjVtX60k5ZTR/JN4B5gfrL+W8qoxR8gKZT0uKR/TdaXSHpE0jpJ302+Geecc8Mqv35krFZbZlYAPkj8N/054A4ze0bSZyVdmex2D7ArmYfqfuATZrZrtPOWU0cyx8zukHTjQCCSxjOM/MBowa3J+ueAL5jZ7ZK+ClwLfGUc53OutgamVCgZNPEQs8PXS6anVRiCAhQGEARYvgAWxYMqJscFdXUEc2ZzcOk8+ltDdvx+L/m9IY0v5Jj7ZJ7wseeJ+vpGjmukHIgUxzK0ZZabHirYvMnM7gbuHrLtUyXLBnw0eZWlnCeSbkmzSb4VSecB+8o5uaSFwH8AvpasC7gE+H6yy23ErcGcc84Nx6bxWFslPkqcQztR0kPAXMofIuWLwH9jcIiV2cDe5PEKhm96BkDSZO06gHoay7ycc87NQNN4rC0AzOwxSRcBpwICnk9q80cl6e3ADjN7VNLF4w3MzFYAKwBa1eH9VpxzR6+U/wUcsyBJutS/DVic7P8WSZjZzWMcegFwpaS3AfXEdSRfAtolZZKnkuGanjmXbkPrIUZbL5nnPK4LKXDov2ESymRRGPcu3/3OM9n5OmPeaTvo7jvA/q4GTvmrPmzNM/F87Llc3AN9qKF1NiPFPJ451126pLwgKaeO5MfAe4nTUi0lr1GZ2Y1mttDMFhM3Mft5MkHW/QymxpYDd44/bOecO0oMdEgs91UD5dSRLDSz0yt4zeuB2yX9T+Bx4OsVPLdzzs04aR/2rJyC5CeS3mJmP5voRczsAeCBZHk98cBhzk0fYzWtHem9IIyb3JqhTDbujQ6ggHBWGxw7F6KI7pNmsfOtvdQ/00DdXbNoe3En83v3Utj+CkRxSsr6+gjq67HCMKms0uVyUl1D4x/P/m7qpfxHU05B8jDwo2QukjxxhbuZWevohznnnKuI6d5qC7gZeD3wVNJRxTnn3BRSyv/yllOQbASe9kLEzVjDpXaSnuAKQxQGqK0V+vNEPT2HHxqGBJ1zsV17IAyJenpQLgdRhBobUVMD9PVjvX1EJy7A6jJk128jmtvOmmvbOfE1m7lo7loe3r2E096bJdqxE6KIohlKptS1JLWFFPdqH0+rsXL4r3a6lTeGVk2VU5CsBx5IBm08NDZDGc1/nXPOTVrtWmOVq5yC5MXklUtezjnnplLKh0grp2f7Z6YiEOeccyOYrqktSV80s49I+jHDfBtmduUwhzmXHsON0qsABcn2ZCRe1dfBMXMozGkmOFggqs/Q355jzylZ8i1QrDPybRHWXCTYlyHsFVEmrgAt1hnZ/SKqm0+xziCweJC9rEFoIMg294OM/MEsRKK5o4N8PqTtwYDsikZ+ta4N6992RF3FML90Vb1dLqXGP0PilBvtieSfkq///1QE4pxzbnjTttWWmT2aLJ5hZl8qfU/Sh4EHqxmYc865xHQtSEosJx5ssdR7h9nmXM0F9fUQhmA2OHFUZChQ3Cw3ac5LGKKWZqK2Jkxi8yXtHFjWS6bOaGroQ+qlu6uR4v4sQV9A4+aQsC+kd45RrDdUEASQ2ydUEBYa9fsDolz8XpCHTA9ku41Mb0ihXjRvLYBBlGsmu79I7pEnKXZ31/qWOTdpo9WRXAO8G1giqXRO3xZgd7UDc845F5u2qS3g18BWYA7wtyXbu4DV1QzKOedciela2W5mG4ANxMOjOJdOGmyBhUXxnB1maH4nL/3BMfzOFU/zPxfcTQB0RQE9liFvIf2EPHnweJ7oWkRXoY7st9o47XMHKLQ1kNlbRPu7OWb7CwBYoTDy9Sch5V0DXFrMkJ7tzjnnaskLEuecc5OhlD++jlqQJNPsfiuZ2dC52lNpZ8KQcE4HUWcHfXMa2PWaOnrO6aGzYz9dvVnq74UtN57EHz18GVYs+U20KD6+vg41NkB/nmMW7EG9/WS278T680T9/fHUuCNd3zsHuqmU8o/bqAWJmRUlHS8pZ2b9UxWUc865mGx6t9oasB54KGkCfKjRu4/+65xzU2S6ttoq8ULyCoj7kDjnnJtK0/2JZGD0X0mNZtYz1v7OVYsyGdTQQNDWijXUke9sZdtZjRxYZLDgIFGhl2PurqduS0jbizuxrh0Ud+8lioap6wAs3w9dXfHKvv1xc1wrrUsZ5rfX60ZcDaQ9tRWMtYOk10t6FliTrC+T9PdVj8w551zMxvGqgTELEuCLwFuBXQBm9iRwYTWDcs45l7DBCvdyXrVQVj8SM9soHVbZM3yuwLnJCEKCXBZIepMnvdUHBl3c9v5zqL9iB5fNf57O7H5e6p3NDx85m1lPhRxzRxE9tYaoPw8WUUiOLTsVNUL6y7lUmO6pLWCjpPMBk5SV9HHgubEOklQv6TeSnpT0jKSBupYlkh6RtE7SdyX59L3OOTeaGZDa+mPgA8ACYDNwRrI+lj7gEjNblhxzuaTzgM8BXzCzk4A9wLUTCdw5544W0z61ZWY7gXH3bDczAw4kq9nkZcAlxMPTA9wGfBr4ynjP72YWZXMEHe1Ex3VSrAvZ8bpGll39NDfO+ynzM3Fa9Yx7zqD9y7N4/NnFRLv3Eh04wCmsBAXxf8ZsSO/1cQUwTDt9b6Hl0iLlH8XR5iP5MqOEb2Z/OtbJkyFWHgVOAm4h7o+y18wGhlPdRPyk45xzbjjToGf7aKmtVcSFQD1wFrA2eZ0BlFWvYWZFMzsDWAicA5xWbmCSrpO0StKqPH3lHuacczNPyutIRpuP5DYASe8H3jDwFCHpq8Avx3MRM9sr6X7iuU3aJWWS8y0krncZ7pgVwAqAVnWkvDx2zrkqSvlfwHKa/84CWhmcXrc52TYqSXOBfFKINABvJq5ovx94B3A78Xzwd04gbpdSQX394ERQClA2AxIKw3jCqfp6or37DqvDCBob6b7wNF6+yjh36QvMyXWz4YVT2fjpU/jYv0PxQDdYxKmZ1RCIYrEYj8w7UIdhFWi66/UhLqXE9E5tDbgJeFzSNyXdBjwG/K8yjpsH3C9pNbASuNfM/hW4HviopHXAbODrEwvdOeeOEhVMbUm6XNLzSReMG0bZ7/clmaSzxzpnOa22viHpJ8C5SZjXm9m2Mo5bDZw5zPb1xPUlzjnnxlLByvakAdQtxBmiTcBKSXeZ2bND9msBPgw8Us55y50h8RzgjcmyAT8u8zh3tJAIW1qI+vritJMCFERYsYhyOchm0OxZYMb7HnqEixq2klX8QLyzWORdq49l3v/pYP+fh+x5pY8lrAYOH0LB8lWeEkfyFJdLp8p9LM8B1iX/oUfS7cBVwLND9vsfxFURnyjnpOUM2ngTccn0bPL6U0nlpLacc85VQuVSWwuAjSXrR3TBkHQWsMjM/q3c8Mp5InkbcIYlvb2SepLHgT8v9yLOOecmbpxzts+RtKpkfUXSCnbs60gBcDPw3vFcsNzUVjuDrbbaxnMBN8MFIWFbK9FJC+n5qy6uXriSjswB6pXnxOwuioiuKMe2Qjsb8x3sKzRy65svYsXGLYcNlDgniOdPK9Zy8ERPa7k0Gn//kJ1mNlIF+WZgUcn60C4YLcBrgAeSgXqPBe6SdKWZlRZOhymnIPlr4lZb9xO3RLsQGLGm3znnXGVVsPnvSuBkSUuIC5CrGRyyCjPbB8w5dF3pAeDjoxUiUF6rre8kJ/udZFNZrbacc85VSIUKEjMrSPogcA8QArea2TOSPgusMrO7JnLeMQsSSb8H/HzgApLaJf2umf3LRC7ophkJ5XIEJxwHr+ymuGs3CkN63n4W26/pZfmrHqEts4Nf7hG7P7GIO586gejAgThNNDAQ4hEpo41HXIaoOPzAiZOM3dNVbiaoZIdEM7sbuHvItk+NsO/F5ZyznA6Jf5k87gyceC/wl+Wc3DnnXAVM17G2SgxX2JRbSe+cc24yalhAlKucJ5JVkm6WdGLyupl4VGDnnHNVpnG+aqGcJ4sPAZ8Evpus30t5MyS6aU6ZDMpkCObO4eW3z6V3zhwKswqEzXlm/zSk8zv1/GrtGWh/N0QRmf4tFAfqR6D29RO1vr5zlZLyj3I5rba68ea+zjlXM2kf/becVlunAB8HFpfub2aXVC8s55xzh4xz5uipVk5q63vAV4GvcfgYem6mCkIynXMpzp/NgeOa2XNqSPYA1O8S0d4s9bszzH54B/byZop9fYMppCAsL500XLPcSjb99Wa/biaZBlPtllOQFMzsK1WPxDnn3PBmQEHyY0l/AvwIBidPN7PdIx/inHOuUmbCE8ny5GvpuPQGnFD5cFy1KZuDQBDZkfN7JOmlA79/NlsuL3LF6U/zq81LyN3TTue3VmP9eayQBwUULToyfTSZARcrmYrytJabaVL+kS6n1daSqQjEOefc8NL+RDJih0RJ/61k+Z1D3vOJrZxzbiqMZ3iUGhU4o/Vsv7pk+cYh711ehVicc84NJ+UFyWipLY2wPNy6q5XSEXZLm9AqGLbOwgp5guZmJFEsFg+NuqtMlmDxQvJfzdMWbKT3O8fz0p/lmNe1BoUhUaFQcpIR6kIkgro6rBjFdSkDcQAKhEUWfy0919Dvxes3nDuMSH9qa7SCxEZYHm7dOedctaT8L+5oBckySfuJC8SGZJlkvb7qkTnnnANAKX9SH7EgMbNwKgNxE1T6ATtsuYiyOYKGkjI/m4EgxHp6sIF9JRSGKJdFhSLZD9ZTXLOOOdoeN/EFrFiEIESBDqWqrFiEYZoAR729Q+KL02DJqQ59HfN7cc7FDJTyIVLKGUZ+QiQtknS/pGclPSPpw8n2Dkn3SlqbfJ1VrRicc25GSHlle9UKEqAAfMzMlgLnAR+QtJR4JOH7zOxk4D58ZGHnnBuVrPxXLVRtpkMz2wpsTZa7JD0HLACuAi5OdrsNeAC4vlpxzHilLZ0Up54UhiibQS3NcLAXtTRjrU1QjOhdPIuX35rhLRc+wSc7H6QtyNFjeV4pii/uuJSX37c4Pp+VzKGugCCXxQqFuDd80spLYZaoPz/YOsxTU85VR8p/taZkylxJi4EzgUeAzqSQAdgGdE5FDM45Ny3NkNF/J0VSM/AD4CNmtl8lfR3MzKThb5Gk64DrAOpprHaYzjmXXkdzQSIpS1yIfNvMfphs3i5pnpltlTQP2DHcsWa2AlgB0KqOlN/GKSQRtrWy9T+9mr3L8lzy2uc4rXkrV7Wspr6kTN5dzHJv91I2981iX97oyvfTlu3lwfUtHP9148VPhby3542Hp6PUC6wZXD80ZW6RqLd42HbL92P56powZAwAABPNSURBVH6rzrnp0SGxmq22BHwdeM7Mbi556y4GRxReDtxZrRicc25GMCv/VQPVfCK5APjPwFOSnki2/TlwE3CHpGuBDcC7qhiDc85Ne2l/Iqlmq61fMfKYXJdW67rOOTej1LB/SLmmpNWWmzhlMug1p9B1UgsHOwJ65ovsGXvo+Mc88771PFvDLJt75vBzXYoVo8O6jquuDjU3xSuR0d0dcXK0hqg/TzTcJFTefNe5VNIk5oybCl6QOOdcyh21qS3nnHMVYKQ+W+AFSUopm4t7py+azws3Zjj12A1s2tdG75Y2Ov65jcZV6yke6EZhiBXyh6dQk7lJ7GAv6uvDIhtMeSkYY+RE51za+BOJc865yfGCxDnn3ERNhw6JXpCkQTLXhxUKEIT0/ofX8fJVEbdd8jXu3HMW+Y8uo+/BbcxlG3MhTls1NKDXLSVY+zLRgSieH6RUMvDiEVmskabJdc6lUw07GpbLCxLnnEs5fyJxzjk3OV6QuDFFRcwEQUhm0Xya1u/jVTcbf/2n5wOQ0W+JSkZNViZL1NsHK5+iGISD84EMNZAyG2i1lUyTO9wUueNSOgeKc67q/InEOefcxBlQTHdJ4gWJc86lXNqfSKo5Z7tzzrlKqOAw8pIul/S8pHWSbhjm/Y9KelbSakn3STp+rHN6QVJLQYiyOTKLFtL1rnPh3nmceedLvHBNBz3HtxH19hH19MTNgjX4o7JCyTzpI9WPAEEuS9DYSNBQT1BXF8/lHobxuTTSwMxl8PoR56aUrPzXqOeRQuAW4ApgKXCNpKVDdnscONvMTge+D/zNWPF5QeKcc2lm43yN7hxgnZmtN7N+4HbgqsMuZ3a/mfUkqw8DC8c6qdeROOdcisU928eVBZgjaVXJ+opk6nKABcDGkvc2AeeOcq5rgZ+MdUEvSKZSEKJshqC5CSKjcNpx7DuxgX0nBPTPirCvLeTRu/awpPAYli8cSltZX9+ELhf19kJvbyW/A+dcLYxvnNWdZnb2ZC8p6T8BZwMXjbWvFyTOOZdy43wiGc1mYFHJ+sJk2+HXky4D/gK4yMzG/J+s15E451yaVbaOZCVwsqQlknLA1cBdpTtIOhP4B+BKM9tRToj+RFJJ0qH5PpTLEbS2wtxZvPCe2Zx4/gZuOeEOQsG2Yh0hxj1dr+XuLa/GftPJCf/SR/CLJ4iSuUTKETQ1xWkvBVi+v8rfnHOuNio3aKOZFSR9ELgHCIFbzewZSZ8FVpnZXcDngWbge4r/Fr1sZleOdl4vSJxzLuUUVa7JvZndDdw9ZNunSpYvG+85vSBxzrk0M1DKJzX1gsQ559Iu5Z2AvSAZamBk25FGuE3qQRQM1oeggKChnsKrl7B2eR3/39mPs3z2L3mqbyHrejtZf+cb6b55IX9y31uwfCHumQ5gRhPraWL94dco80MT9fSk/gPmnKuAlP+aV63VlqRbJe2Q9HTJtg5J90pam3ydVa3rO+fcTCGzsl+1UM3mv98ELh+y7QbgPjM7GbgvWXfOOTeaCg7aWA1VS22Z2S8kLR6y+Srg4mT5NuAB4PpqxTBhw6W1Bpr2AspmUCZD0N4GxSLRsbPZdXobXceJ1jXwzPdO55OPNiZprALH5x+BqDjOzqll8LSWczOfMd6e7VNuqutIOs1sa7K8DegcaUdJ1wHXAdTTOAWhOedc+ojapazKVbPKdjMzaeRBj5NBxlYAtKoj3XfROeeqyQuSw2yXNM/MtkqaB5TV/X7KqLQlVtK7XAFBLgvZLHbq8Rw8tpFXzszwznc8yIl1z3Nibge/7jmZW587n/r7W5h3x/MUd+2mONk50SH1Hx7n3BRJ+d+CqR5r6y5gebK8HLhziq/vnHPTi4GKVvarFqrZ/Pc7wL8Dp0raJOla4CbgzZLWApcl684550ZzFLfaumaEty6t1jUnLAhRIMI5s7HWZqKWevaf3ELmYMTuV2XoX9bNgjl7MTtAoC4Kj85n5TtO5ZGXmrDCIjDjOJ4CaXIprQEpf4x1zk2l2hUQ5fKe7c45l2aGFyTOOecmyfuROOecmwzvR5JWEgpDCEOUyaD6OmhsoNjeyIHFTWw/DzpO2kvP+ll0/LyR4it1ND+3m2j9y5xgmykWi0c+bqb8h+2cm6ZS/rfl6C1InHNuOjCgghNbVYMXJM45l2reait9kl7jYccs1NpC1NJA37HNdC3MUqyD/lahIpz0z11kNh9gbt8uoq4DUCxSjOzQ/CPOOTdlvCBxzjk3YQYU091sywsS55xLtSQTkmJHTUESNDailmZ6lx3HK2fk4PV7WdS+lygqcrA/z84ts2h+PsfsZwo0/XYX0YZNFPr7h5+XpHQ55Y+czrkZIOV/Z46agsQ556Ylb7XlnHNu0vyJxDnn3KR4QVIlI82rDiiTjb/W16FMBjIZus9bwiunZ7AzuujdI467tRnWFwl37qF9307a2QxhGDfz7c9DVBz+upZMoJzyH6xzbqbwfiTOOecmw4DIW20555ybDH8iqRCJoK4OggA1NaFcln3nLWLHO3p532t/xX9seZIA2FBopSPsoV5F9kY5Qoy8hdy08W1sefgETvpMHlvzHFYsHjkJVblNe1P+Q3XOzTAp/5szfQoS55w7GplhxRHqbFPCCxLnnEs770cyeaec3sO//nQVW4sH2VJooNtyvNDfyZfXtHPMt1u4/9Nnct/62XGpXTqUwGGPg9s5ge2jTzSW8sdH59xRKuV/m6ZFQeKcc0ctM2+15ZxzbpL8iWTyfru6kbcvPBvlcgQN9Vi+gBrqWaRXIL+Z4t59ZZ9LdXUoDFFdHRSLhzohRn19KJMhOtiLAmGFwpHHZpLblRxjkcUdF4MQZTOHb0tamamhAcIA6zlI1NMzZnxh5zEwZxZs3Epx//7hv4dsjgNXnknTD38Tbyj3QyYRNDdjpy0m3LaHwuatgx0vk6mHD33fQRjH09GO6uuJdu7CCoX4fYkD7zyX9l+8SLS/C8sXwKIktTiBD7xEOLtjzHukTGbw/g4IwkP3G7P4ZxGI8NhOyGYobtqKFfIlJwkG058DsQ6cY4opkxn2cza4Q/I9SSiTJZzfifX0UnzllZEPqasjaG0ddZ+BawctLdjx82DtBqLu7nEGX8aApck+QWMjWrII7d5HtGcvhGH8cx6Y12e89z753QpmtVPYum1chwb19US9vUeeMpsjaGrAFs4j6OqmsGFj+bE0Ng7evyoN5Gr+ROKcc27i0t+zvSZT/Um6XNLzktZJuqEWMTjn3LQwMPpvua8amPInEkkhcAvwZmATsFLSXWb27FTH4pxz00LKJ7aSTfEjk6TXA582s7cm6zcCmNlfj3RMqzrsXF06mMcvFlEYxrnXtlbs4EHsYC8EAUFHO7ZvP1aMUBhgvX2YGZYvENTXER08CGYomwOL4nqR/jiHrkBxPUgQEPX2Df7wFAzm4QfyuqV59mS7spm4viDJ+Sqbwwp5grq6Q9cgKsbXUIDl+4Ekb9vXd3jOHuLvsbWZ4q7dR9yToKWFwrITyW3cBWFI4aWNcUwD5xgpV5vcQ2AwPz/Qo18BQS57eA55oF6lvx/r6xu8fn19/D0NyW8HTU0Es9pBorjjFay/P743YRjXVYz0eQtCgob6wTqqYnHwvpdcEzj8Xo1mpIE9U5ImUCaDFYsEDQ3x5w0GP0MDSn+myT2ygfq8kp/HcPVbpfVf8bmG/zwEjY1YX198fFLHZJEdfu2xvo9CIf5ch+Fhn5NDSuuyFKBgcBQJG/hf9NDrjfGzClpaIJ+Pr93QQHTgQDxgq0WHf7ZHq4sZeH/gd7au7tD9D+fOxroOxD+jujqKB7oPrwsd6ZzAI3Yf+223hn1znFqD2XZe5q1l739v/juPmtnZlbh2uWpRR7IAKK3J2gScO3QnSdcB1wHU0zg1kTnnXNp4z/aJM7MVwAqIn0hqHI5zztWOp7aGXHACqS1JXcDzUxPhhM0BdtY6iDJMhzg9xsrwGCtjIjEeb2ZzK3FxST9NYijXTjO7vBLXLlctCpIM8FvgUmAzsBJ4t5k9M8oxq6Y65zde0yFGmB5xeoyV4TFWxnSIsdamPLVlZgVJHwTuAULg1tEKEeecc+lWkzoSM7sbuLsW13bOOVdZNemQOAErah1AGaZDjDA94vQYK8NjrIzpEGNNTXkdiXPOuZllujyROOecS6nUFyRpHZdL0kuSnpL0hKRVybYOSfdKWpt8nTXFMd0qaYekp0u2DRuTYn+X3NfVks6qYYyflrQ5uZdPSHpbyXs3JjE+L6n87r2Ti3GRpPslPSvpGUkfTran5l6OEmPa7mW9pN9IejKJ8zPJ9iWSHkni+a6kXLK9Lllfl7y/uIYxflPSiyX38oxke01+d1LNzFL7Im7V9QJwApADngSW1jquJLaXgDlDtv0NcEOyfAPwuSmO6ULgLODpsWIC3gb8BBBwHvBIDWP8NPDxYfZdmvzM64AlyWchnIIY5wFnJcstxM3Vl6bpXo4SY9rupYDmZDkLPJLcozuAq5PtXwXenyz/CfDVZPlq4Ls1jPGbwDuG2b8mvztpfqX9ieQcYJ2ZrTezfuB24KoaxzSaq4DbkuXbgN+dyoub2S+AoQNzjRTTVcC3LPYw0C5pXo1iHMlVwO1m1mdmLwLriD8TVWVmW83ssWS5C3iOeGif1NzLUWIcSa3upZnZgWQ1m7wMuAT4frJ96L0cuMffBy6VVJExqyYQ40hq8ruTZmkvSIYbl2u0X5apZMDPJD2ajAsG0GlmW5PlbUBnbUI7zEgxpe3efjBJE9xakhKseYxJauVM4v+lpvJeDokRUnYvJYWSngB2APcSPw3tNbOB0SlLYzkUZ/L+PmD2VMdoZgP38q+Se/kFSXVDYxwm/qNS2guSNHuDmZ0FXAF8QNKFpW9a/AycqiZxaYwp8RXgROAMYCvwt7UNJyapGfgB8BEzO2y6yrTcy2FiTN29NLOimZ0BLCR+CjqtxiEdYWiMkl4D3Egc6+8AHcD1NQwx1dJekGwGFpWsL0y21ZyZbU6+7gB+RPwLsn3gETf5uqN2ER4yUkypubdmtj35RY6Af2Qw5VKzGCVlif9Af9vMfphsTtW9HC7GNN7LAWa2F7gfeD1xOmigQ3RpLIfiTN5vA3bVIMbLk/ShmVkf8A1SdC/TJu0FyUrg5KSFR4648u2uGseEpCZJLQPLwFuAp4ljW57sthy4szYRHmakmO4C/kvSAuU8YF9J2mZKDckv/x7xvYQ4xquTljxLgJOB30xBPAK+DjxnZjeXvJWaezlSjCm8l3MltSfLDcQT2j1H/Mf6HcluQ+/lwD1+B/Dz5OlvqmNcU/KfBhHX4ZTey1T87qRGrWv7x3oRt5D4LXFe9S9qHU8S0wnELWCeBJ4ZiIs4l3sfsBb4v0DHFMf1HeJ0Rp44b3vtSDERtzi5JbmvTwFn1zDGf0piWE38SzqvZP+/SGJ8HrhiimJ8A3HaajXwRPJ6W5ru5Sgxpu1eng48nsTzNPCpZPsJxAXZOuB7QF2yvT5ZX5e8f0INY/x5ci+fBv4Pgy27avK7k+aX92x3zjk3KWlPbTnnnEs5L0icc85NihckzjnnJsULEuecc5PiBYlzzrlJ8YLEVZSkYjJS6jPJaKofk1S1z5mkxSoZSXiC5/jzIeu/nlxUo15rsaR3V+v8ztWCFySu0g6a2Rlm9mrijl1XAH9Z45gOKelNXeqwgsTMzq9iCIsBL0jcjOIFiasai4ePuY54EEElA+N9XtLKZCC8PxrYV9L1iud3eVLSTcm2MyQ9nOz7Iw3O//G6ZL8ngQ+UnGPY80u6WNIvJd0FPFsaY3KthuQp6tvJtgMlxz0o6U5J6yXdJOk9iueueErSicl+cyX9ILnuSkkXJNsv0uBcFo8noyHcBLwx2fZnY8T8C0n/pnj+kK9W88nOuUmpdY9If82sF3BgmG17iUfKvQ7478m2OmAV8dwYVwC/BhqT9wZ6jK8GLkqWPwt8sWT7hcny50nmNhnl/BcD3cCScmIeWE+O20s890cd8XhKn0ne+3BJPP9MPIgnwHHEw5YA/Bi4IFluBjLJOf+15FqjxdxL3AM8JB4194i5MfzlrzS8hnvMd65a3gKcLmlgjKU24jGfLgO+YWY9AGa2W1Ib0G5mDyb73gZ8LxkTqd3ieU0gHhLkijHO3w/8xuJ5OMZrpSXjKEl6AfhZsv0p4E3J8mXAUg1Om9GqeFTeh4CbkyedH5rZJh05tcZYMa9Prv0d4mFRvj/0BM7VmhckrqoknQAUiUfKFfAhM7tnyD6VmvZ1pPNfTPxEMhF9JctRyXrE4O9PAJxnZr1Djr1J0r8Rj4H10Ajf52gxDx2/yMczcqnkOVdXNZLmEk+j+r/NzIB7gPcrHv4cSacoHj35XuAPJTUm2zvMbB+wR9Ibk9P9Z+BBi4f53ivpDcn295RccqTzjyU/cMwE/Qz40MCKBuf2PtHMnjKzzxGPZH0a0EU8NW45MZ+jeOTrAPgD4FeTiNG5qvEnEldpDYpnmssCBeLU08Aw518jbrX0WDI09yvA75rZT5M/vqsk9QN3E7ekWg58NSlg1gN/mJznD4FbJRmDqaYRz19GzCuA1ZIeM7P3jLn3kf4UuEXSauLfqV8Afwx8RNKbiJ9eniGe5zsCiklDgW8CXxol5pXA/wZOIh52/UcTiM25qvPRf51LoSS19XEze3utY3FuLJ7acs45Nyn+ROKcc25S/InEOefcpHhB4pxzblK8IHHOOTcpXpA455ybFC9InHPOTYoXJM455ybl/wG2SGZbPnFFOwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -201,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -252,35 +245,35 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[checkpoint] Rank 0: loaded model from /home/chenfeiyu/projects/Parakeet_0.2/examples/waveflow/pretrained/waveflow_ljspeech_ckpt_0.2/step-2000000.pdparams\n" + "[checkpoint] Rank 0: loaded model from ../../pretrained/waveflow/waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams\n" ] } ], "source": [ "vocoder = ConditionalWaveFlow.from_pretrained(\n", " vocoder_config, \n", - " \"/home/chenfeiyu/projects/Parakeet_0.2/examples/waveflow/pretrained/waveflow_ljspeech_ckpt_0.2/step-2000000\")\n", + " \"../../pretrained/waveflow/waveflow_ljspeech_ckpt_0.3/step-2000000\")\n", "layer_tools.recursively_remove_weight_norm(vocoder)\n", "vocoder.eval()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "time: 9.420342922210693s\n" + "time: 9.412613868713379s\n" ] } ], @@ -291,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -299,7 +292,7 @@ "text/html": [ "\n", " \n", " " @@ -308,7 +301,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -316,6 +309,13 @@ "source": [ "ipd.Audio(wav, rate=22050)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/tacotron2_aishell3/aishell3.py b/examples/tacotron2_aishell3/aishell3.py index e9a3126..ab8977a 100644 --- a/examples/tacotron2_aishell3/aishell3.py +++ b/examples/tacotron2_aishell3/aishell3.py @@ -11,7 +11,7 @@ from preprocess_transcription import _phones, _tones voc_phones = Vocab(sorted(list(_phones))) print("vocab_phones:\n", voc_phones) voc_tones = Vocab(sorted(list(_tones))) -print("vocab+tones:\n", voc_tones) +print("vocab_tones:\n", voc_tones) class AiShell3(Dataset): diff --git a/examples/tacotron2_aishell3/train.py b/examples/tacotron2_aishell3/train.py index 4ec0a0a..54a33cf 100644 --- a/examples/tacotron2_aishell3/train.py +++ b/examples/tacotron2_aishell3/train.py @@ -183,8 +183,8 @@ class Experiment(ExperimentBase): config.training.weight_decay), grad_clip=grad_clip) criterion = Tacotron2Loss( - use_stop_token_loss=True, - use_guided_attention_loss=False, + use_stop_token_loss=config.model.use_stop_token, + use_guided_attention_loss=config.model.use_guided_attention_loss, sigma=config.model.guided_attention_loss_sigma) self.model = model self.optimizer = optimizer diff --git a/examples/tacotron2_aishell3/voice_cloning.ipynb b/examples/tacotron2_aishell3/voice_cloning.ipynb index 65e9d43..a6b62bc 100644 --- a/examples/tacotron2_aishell3/voice_cloning.ipynb +++ b/examples/tacotron2_aishell3/voice_cloning.ipynb @@ -13,14 +13,14 @@ "import soundfile as sf\n", "import librosa.display\n", "from parakeet.utils import display\n", - "paddle.set_device(\"gpu:5\")\n", + "paddle.set_device(\"gpu:0\")\n", "import sys\n", - "sys.path.append(\"/home/chenfeiyu/projects/Parakeet_0.2/\")" + "sys.path.append(\"../../\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -36,9 +36,24 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vocab_phones:\n", + " Vocab(size: 68,\n", + "stoi:\n", + "OrderedDict([('', 0), ('', 1), ('', 2), ('', 3), ('$', 4), ('%', 5), ('&r', 6), ('a', 7), ('ai', 8), ('an', 9), ('ang', 10), ('ao', 11), ('b', 12), ('c', 13), ('ch', 14), ('d', 15), ('e', 16), ('ea', 17), ('ei', 18), ('en', 19), ('eng', 20), ('er', 21), ('f', 22), ('g', 23), ('h', 24), ('i', 25), ('ia', 26), ('iai', 27), ('ian', 28), ('iang', 29), ('iao', 30), ('ie', 31), ('ien', 32), ('ieng', 33), ('ii', 34), ('iii', 35), ('io', 36), ('iou', 37), ('j', 38), ('k', 39), ('l', 40), ('m', 41), ('n', 42), ('o', 43), ('ou', 44), ('p', 45), ('q', 46), ('r', 47), ('s', 48), ('sh', 49), ('t', 50), ('u', 51), ('ua', 52), ('uai', 53), ('uan', 54), ('uang', 55), ('uei', 56), ('uen', 57), ('ueng', 58), ('uo', 59), ('v', 60), ('van', 61), ('ve', 62), ('ven', 63), ('veng', 64), ('x', 65), ('z', 66), ('zh', 67)]))\n", + "vocab+tones:\n", + " Vocab(size: 10,\n", + "stoi:\n", + "OrderedDict([('', 0), ('', 1), ('', 2), ('', 3), ('0', 4), ('1', 5), ('2', 6), ('3', 7), ('4', 8), ('5', 9)]))\n" + ] + } + ], "source": [ "from examples.ge2e.audio_processor import SpeakerVerificationPreprocessor\n", "from parakeet.models.lstm_speaker_encoder import LSTMSpeakerEncoder\n", @@ -57,7 +72,7 @@ " min_pad_coverage=0.75, \n", " partial_overlap_ratio=0.5)\n", "speaker_encoder = LSTMSpeakerEncoder(n_mels=40, num_layers=3, hidden_size=256, output_size=256)\n", - "speaker_encoder_params_path = \"/home/chenfeiyu/projects/Parakeet_0.2/examples/ge2e/runs/cn/checkpoints/step-3000000.pdparams\"\n", + "speaker_encoder_params_path = \"../../pretrained/ge2e/ge2e_ckpt_0.3/step-3000000.pdparams\"\n", "speaker_encoder.set_state_dict(paddle.load(speaker_encoder_params_path))\n", "speaker_encoder.eval()\n", "\n", @@ -66,9 +81,8 @@ "from examples.tacotron2_aishell3.chinese_g2p import convert_sentence\n", "from examples.tacotron2_aishell3.aishell3 import voc_phones, voc_tones\n", "\n", - "from yacs.config import CfgNode\n", "synthesizer = Tacotron2(\n", - " vocab_size=70,\n", + " vocab_size=68,\n", " n_tones=10,\n", " d_mels= 80,\n", " d_encoder= 512,\n", @@ -92,14 +106,14 @@ " d_global_condition=256,\n", " use_stop_token=False,\n", ")\n", - "params_path = \"/home/chenfeiyu/projects/Parakeet_0.2/examples/tacotron2_aishell3/runs/debug/checkpoints/step-55000.pdparams\"\n", + "params_path = \"../../pretrained/tacotron2_aishell3/tacotron2_aishell3_ckpt_0.3/step-450000.pdparams\"\n", "synthesizer.set_state_dict(paddle.load(params_path))\n", "synthesizer.eval()\n", "\n", "# vocoder\n", "from parakeet.models import ConditionalWaveFlow\n", "vocoder = ConditionalWaveFlow(upsample_factors=[16, 16], n_flows=8, n_layers=8, n_group=16, channels=128, n_mels=80, kernel_size=[3, 3])\n", - "params_path = \"/home/chenfeiyu/projects/parakeet_examples/waveflow/step-2000000.pdparams\"\n", + "params_path = \"../../pretrained/waveflow/waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams\"\n", "vocoder.set_state_dict(paddle.load(params_path))\n", "vocoder.eval()" ] @@ -111,9 +125,16 @@ "## 生成 speaker encoding" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "首先在当前文件夹下新建文件夹 `ref_audio`,把要作为参考的音频存在在这个文件夹中。格式要求是 wav 格式,采样率会被重采样至 16kHz." + ] + }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -130,13 +151,12 @@ "" ] }, - "execution_count": 18, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# ref_audio_path = \"/home/chenfeiyu/datasets/aishell3/train/wav/SSB0011/SSB00110001.wav\"\n", "ref_name = \"女声2.wav\"\n", "ref_audio_path = f\"./ref_audio/{ref_name}\"\n", "ipd.Audio(ref_audio_path, normalize=True)" @@ -144,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -171,22 +191,31 @@ "## 合成频谱" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "因为 AISHELL-3 数据集中使用 `%` 和 `$` 表示韵律词和韵律短语的边界,它们大约对应着较短和较长的停顿,在文本中可以使用 `%` 和 `$` 来调节韵律。\n", + "\n", + "值得的注意的是,句子的有效字符集仅包含汉字和 `%`, `$`, 因此输入的句子只能包含这些字符。" + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['v', 'ien', 'd', 'e', 'b', 'iao', 'x', 'ian', 'x', 'ieng', 'sh', 'iii', '%', 'z', 'ai', 'uei', 'l', 'ai', '%', 'j', 'iang', 'b', 'ian', 'd', 'e', 've', 'l', 'ai', 've', 'zh', 'ueng', 'iao', '$']\n", - "['3', '1', '0', '5', '0', '3', '0', '4', '0', '2', '0', '4', '0', '0', '4', '4', '0', '2', '0', '0', '1', '0', '4', '0', '2', '4', '0', '2', '4', '0', '4', '4', '0']\n" + "['m', 'ei', 'd', 'ang', 'n', 'i', 'j', 've', 'd', 'e', '%', 'x', 'iang', 'iao', 'p', 'i', 'p', 'ieng', 'sh', 'en', 'm', 'e', 'r', 'en', 'd', 'e', 'sh', 'iii', 'h', 'ou', '$', 'n', 'i', 'q', 'ie', 'iao', 'j', 'i', 'zh', 'e', '%', 'zh', 'e', 'g', 'e', 'sh', 'iii', 'j', 'ie', 'sh', 'ang', 'd', 'e', 'r', 'en', '%', 'b', 'ieng', 'f', 'ei', 'd', 'ou', 'j', 'v', 'b', 'ei', 'n', 'i', 'b', 'ieng', 'iou', 'd', 'e', 't', 'iao', 'j', 'ian', '$']\n", + "['0', '3', '0', '1', '0', '3', '0', '2', '0', '5', '0', '0', '3', '4', '0', '1', '0', '2', '0', '2', '0', '5', '0', '2', '0', '5', '0', '2', '0', '4', '0', '0', '3', '0', '4', '4', '0', '4', '0', '5', '0', '0', '4', '0', '4', '0', '4', '0', '4', '0', '4', '0', '5', '0', '2', '0', '0', '4', '0', '1', '0', '1', '0', '4', '0', '4', '0', '3', '0', '3', '3', '0', '5', '0', '2', '0', '4', '0']\n" ] } ], "source": [ - "sentence = \"语音的表现形式%在未来%将变得越来越重要$\"\n", + "sentence = \"每当你觉得%想要批评什么人的时候$你切要记着%这个世界上的人%并非都具备你禀有的条件$\"\n", "phones, tones = convert_sentence(sentence)\n", "print(phones)\n", "print(tones)\n", @@ -201,14 +230,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 35%|███▍ | 349/1000 [00:01<00:02, 233.91it/s]" + " 74%|███████▍ | 741/1000 [00:02<00:01, 249.49it/s]\n" ] }, { @@ -218,28 +247,11 @@ "content exhausted!\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, { "data": { + 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\n", "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -251,7 +263,7 @@ "source": [ "outputs = synthesizer.infer(phones, tones=tones, global_condition=utterance_embeds)\n", "mel_input = paddle.transpose(outputs[\"mel_outputs_postnet\"], [0, 2, 1])\n", - "fig = display(outputs[\"alignments\"][0].numpy().T)" + "fig = display.plot_alignment(outputs[\"alignments\"][0].numpy().T)" ] }, { @@ -261,31 +273,38 @@ "## 合成语音" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "合成的语音会保存在 `syn_audio` 目录下,使用和 reference 相同的文件名。" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "time: 12.234672784805298s\n" + "time: 23.468628406524658s\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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ilnAaXS9ZF2+JSaeSlqsc/p9eZoElI97GtG/9uyGrsyEKQ0MPLU9zgEXeGwJmDjylHaNwlJF0BrLX0YqxdKFVir4dHHAw5FSa8ssA9SbeUxzm+0rZikikTHTZFCuiulVWBKiZ8ciHe2tIL1pT283kJumsr33NWeHf3fXMp+W+P+OVRz/K/E3V1ewrU9mObNCzyklD4Mcf/a8PlqDXbe+i683B4r5VTQiS0Dh5e+5qfL7U35KMp1mGBSPf8J6d7JWNDtIMdhQqnsL9adxcvL+wtnGJepYY1ZAhSFDCyzP2pgGd/2TtMFs3aYzvNAXRe99d5HicFU4zkbDoWmFd/zwBPUa+E9l10kROGgJj0osb/zfpawCA06DOLnEG8OYXNWcjA3Wrp0WpsGnH1S/NwrXaqN5LBbWSEW8HrsHrxdXkF913vX13JS595gtPsyqVnaiehPfr52cqPKv368bF5EXrHPfbzYjc3Ddeo6isCGprf/OCuu+qqppx6sNTI3fVjZ/zve3alipy0hDMdsku9ss6h9wELzekVRjclpgEr/QF41ytnby7shp/nbAQHyyu8FTUxS68MAjbDB2ysRaFikVBhn0+wsXP2EefqHBlmKnYusvR+NiGtPqwunFViwuyrmT3PcxZuQnzVm3xJcEShOvGfolHP6xb60QlOWkIVOPU2W8LODoz31pRqHYCmWzSrTv3oPdtEwO3NUncBvkH3PJOTcH1qQ7RLToqZ17GthlLdP7L4DcPc+4gd0SPke/ggAjcGX1ut0/vecTm73Vqv3ld7rY3vbscK7buirUCYNQmak9VNUpGvO24VuklYGRXZVVggyqGwIBd4Xkn98/DQfMETN/XqHfs/aR2neHE+Wtc5RGI9o5QD3J4mINi1l9Kkm9ijsowZuVWGaSadyqIpw8S3aR3Alahut586s69q905lm/YbumWc3LVmV2RL36+HM9/Vu7aQqDuorQbYV1pZTZrHVTzO5xV0nXKnAYpXhQT+v5lEm55Yx4+WLTWMeTXirw2BOYprbnwvE5ltfrpdpCQUCM7dld58k0XEPm+lp/okagTxfwsvN6vrffYsV7xOsxP/v5hzetaiVk+2mzndnhy6lJPax5/e2chXi3LLNhu+NFej8jNFXnZszPw+BRn98Mxo6wzr79cvgkPWPzvnVyqn1mEjHoVhKysYl9db3nIwkbXuETHORlt8/Mxfen6OoM3/Vt+tWyF7WDU6X+ps2NPFcZ+sRyXPluGi572Vy8irw2BVz/9vz6wn+qbNXri4t8uD63O3JWbMdOnfryf6JGoo15Vnv+28eFlD1Rzp01oYVU1Y5OH+/PxKUtr7gWnEaXbbTp50TpfgnNmFqyuu2A67Vt7xdo7/hc8pJLBvpLX1jiEINth7KztRuMff50xZu9qM3OrgYi5fK4eyGI08rqU/T8/WFJnMOrV1RMkF8NIXhsCpxvViNNIK6gd8NPBWRmshybvdUlt/HG3o///S4+L59/VRAQlY9ysCDtzMvJlTMqUfv57z04rt9xexew7tHfoQ3s7nZv/qz5c1wlzCG1QvAQ1MPv7H182pix4gzTuGD+/zuztnXm1I+H+Obmum9hKNwoAvjcYp+9+sJ+B65IiXy53fobNobt+yWtD8PeJ3moLO40+gvoH/RgCN5fHYXdNquX/N7sUvEoLnPiPjzCjfIMvH6wKlUmdmcs2YEaEZRm/DzAyDELYKngAsLTiRxQWeLu3rG4ls+T0KgXF3uPAy3NRzdHnaph5dlo57p1Yex3Prl/4cNE6z0mqc1ZscsyjiCIKzIq8NgReWbjavmMM+kUZi3+7+YL9CsCFCZ/dsmMP7vDhQnnaZ/H3aUt+sF1X+MVjn+Gsf39W2+fqc0JgtQ7AzJYSzEFgZtcFenPdiijYsbvKs3sQcM8FiBOndSUv7dxdWZ2IS9ZcDdHKTu/cU4VLnp2BXrc5a1fpRJ0f4BUxBCaufnEW3vaR/PT5d8FGsEZ5CrcR+5fLN/ny24cJRb3tzfn4YZv3Iig7fWajnj/6c4yZVl6n0MrXBtVIYyKS37/Eqsbvu/PW1FrYVUFQWZLLnvWuQOkkM9HrtncdI83MqKgYpgonzZ1pS9x93Y9//K3j3+PmXlKVAGZli078x0e+zrHIQccJcHZL66jIshZDYOLtr1bj6peiqWlghxd5iScNxiJKuV+/N1UQm/Pu/DXoe+ck3PN2ZsGwqppxy+vWceS/HO3P92n1cG5VmD+h26igYcN+Rub/neWxRpOH76Bpg2LbfVFmKlvN0P4zY4XFkRledtin88PWXY4Z7m5uMD8DHTPGdYJ6hXW7TydtLCO6F+DB953vI2MwgZ3nwO778+OtEEOQAry4Eow3r9uI52MPMcSqwj6DqECu1R4WXaJ7y449rro0XvlocTwukGUBBNr8ssZjjoaX78ApBDrK2cI8izoOYZP6dlvM+oy4DU78ujONGA3VLJcF3KBy4Xa8/ZW1p8JuKclLadKacwRpUFpIWrEzTuyiS4zo/kYvawSq6gP4caPpmBdtVRkBwHm0qQL9jktPXBVQ7mH9w0mPX+WCv5mLnv4Cwx/5tFYYZIHHRXA73AyfW6SZufaGH/78uvdorIuf8RfLb0WR4X+10cZNZPfv8BO0oMQQENEQIlpMREuIaITF/huIaAERzSWiyUTU2bCviohmaz/jzZ91IqgvPEhccRS8ErDTsostrtRGSlM9lJmMoiMLapjNAnxhcBulqcKqmlmSWdZfBFyrAqJv95wVm9DtzxNq3ocdwLkZArf8z088rEOowCnk02uOqvE+s/ur7bSOVm70PmsNbQiIqBDAIwCGAugN4Dwi6m067EsApcx8CIBxAO4z7NvBzH21n9Pgg6C6Gpt2uPsIg2iz++Wm/wYrUm7X2U1f6p4X8ZqWNLQ+hJ/UjscDVsAyo7rS1UgfozivWM26jvyrdWZ6HLz+ZfBkMC/yBSqZ8JV/4Tcjm1zkx42GYvRUNfekap76xFu7vAx27VxQfp4jFTOC/gCWMPNSZt4N4GUAw40HMPOHzKz3rNMBdFRw3cBZp14+FzQayG875n/vvyiHXXz5Eg+Fzf/w6hwA9gXWw1AeUKLaHJanGqtIoiAwc2rdkWO/qDu79DqY+cd73vJp4sROesMLxq/o7rcXKmiNelYEyOuwu/Xsuns/wykVhqADAONduFLbZsdlAIzyiA2IqIyIphPR6XYfIqIrtePKKioyi6FOU0S3xdDNLqOKuB74Yf/8xPdn6hdZf21+OlSVf5++2OhlkdqM1QP/+qxwUtFR6R/ZZYmmFa/rQG4hjKrw872EKUWZDWUsveSjmKlmRnU149Y35uHN2e4RZX5saayLxUR0AYBSAH83bO7MzKUAzgfwIBF1s/osMz/BzKXMXNqmTRsAzhLQbtPdFT78Z2nDTg0xKbbu3INVm3a4Zu4acwV0rKa+YWu7nvHotJrXYUaWZqrZXfAiahE+P3y02L9hjhJVAQpuxFFLOwnWbNmJx6Z8i+enL6tV/8L2eB/JaioMwSoAnQzvO2rbakFEgwGMBHAaM9f00sy8Svu9FMBHAA7zeuGzH//Mdp+bxXSrd/uQhW5IWvingwgeAJR2buF6DpWDpmq2V6Y0cvIDH9fZZiWW9YaWVxGkJjFQOxz3DQ8jJz+4BRr0vNVbRikA3yNCv6StVu4hd7zn+dgwt6dTRcG0EGQd7PEpS61lcWxO9aGPgYAKQzADQA8i6kJE9QCcC6BW9A8RHQbgcWSMwDrD9hZEVF973RrAMQA8373LHMLmVm2yf2CHPjQV610y9qyiQrIFL1NjldPnMFEn99j4cF/8fFmg0FSdZZr0sNvCoh/WbtmJ8570XgZVCE6Y2zOqIk9x46wmu7f3V1HdLbQhYOZKANcAmAhgIYBXmHk+Ed1JRHoU0N8B7APgVVOYaC8AZUQ0B8CHAEYxs5JhjFsyhVtGXzbj5bZQOSM49WH/6xxufLS4Au8tCK5oefzfP1LXGI0x08qzeoAgpIcPPGSYO+V3GAsheVEmcKMo9BkAMPMEABNM224zvB5s87lpAA5W0QbBH2lZULPzGy9es7VOFasgqIxEVZ0pKtgTZpap39vrIsqP2LmnCg2KCyM5t1e+XqvW/ZXVmcX5yvEHtHHc33O/Jq7nSIcZsHfvqTACgNrEuRk+C/w4oUIoLJcJU1Xs0yWZfJotO6Mx3Cpkxr2gIrLv+en2GeVGxBC4YFc6LkncfIKTFzpPO/0oYGY7z3l8EOJmW0ydSdrw0rnNW7U5kJihGadzhInu+n7TjjrquSrR1wa82IFKl0isW9+wFnM0I4bAhbChjFHgVkPZrb7p5EXrUJhQic24WZrS2P8vvvNWHU8FfuvXRskkD+s+23ZVKhkNN6pn776xCmX2yvBHPlViqOzQDYGXR/SZT8uVXFMMgQspcaXXonE956Wds0rdE7dv8ThSyGZUF6tXiUppbCd27qnClACJflGxxcNM6NGPvg393G3bVYlj77WvQRG2I0/LMOqeCWoyp8UQOFBdzak0BP06t7CVngXsM4+N7EhR4lNUzIypRrFfVIT7eeU3L8yM7Vpe8DLS3/BjeAN++iOfOu6fEjLZLo4JdZx9jxgCB5b+8GNqomuAvYUm3AyUV2XDXOfasV8m3QRL/j0l/IjXK36SiuLgxnHuQovzVm1xlZJu27S+4343l+68ABpfRoLWKk8rYggcePLjpfhVivyrE7TCFFUOUgeVVdWpiQhKmih19sOwfP321IrXFYWsFaCKuSudO+p96oeLfPeyVuHEtz9Ev3ao6hbxUnhIDIED/ymLtsiJX3T9JKcvduP2PantZIQMVczo1mafpJuRaj51qRkQp3vNiiWK4/iN/LA1E5H08TfOszmvuRY3jpvjeowYgixi4epMpINTjVkib3VfheQoJMLiEFErUZKWIYTbGpaXqmxRUhXhYOturZa3m3vre4+5KF4qFmatIcjHUe9rmjyz002Y1nBJYS9FhZTTEicqmLfKvY53kkSpZ6Rn27utT3rNdHfSZNPJWkOQ9NQwSZzuj4c/8N7BpMUfnG/YlRYUsoedIeoeu1FIhNWbd7guR//x1WAVDq3IWkOQKwqDfjn14amOHYmXesU6+fo/FOyJY4DVv6Rl5NeIGlXx+1Z8s24bjvrbB+jR1lkqxkmd1C9iCLKMtE+ZBcGNL1JWWCmtqEz6dDMaWWsIqqoZ9QrFtSEIguDGr55yDoPPWkOwdstO7FZUlFwQBCGXWbB6i6NAXdYagkJZ6BQEQfBM95Hv2O5TYgiIaAgRLSaiJUQ0wmJ/fSL6j7b/cyIqMey7Wdu+mIhO8XrNHburQmcXCoIg5BPFrffvZbU9tCEgokIAjwAYCqA3gPOIqLfpsMsAbGTm7gAeAHCv9tneyNQ47gNgCIBHtfO5sqeqOhbhJ0EQhFyBiuo1stquYkbQH8ASZl7KzLsBvAxguOmY4QDGaK/HARhEmerLwwG8zMy7mPk7AEu087myYuMOVMoagSAIQmhU+FY6ADBqGqwEcKTdMcxcSUSbAbTStk83fbaD1UWI6EoAVwJAYdM2uC6lypKCIAhppXrXdktt9qxZLGbmJ5i5lJlLD+jcHl1bN066SYIgCFlFQf1GLSy3Kzj3KgCdDO87atssjyGiIgDNAKz3+Nk6NKpXhP87+1A0aSCLxYIgCGFRYQhmAOhBRF2IqB4yi7/jTceMB3CR9vpMAB9wRjVuPIBztaiiLgB6APBUAKCooCA9UomCIAhZTOghtebzvwbARACFAJ5m5vlEdCeAMmYeD+ApAM8T0RIAG5AxFtCOewXAAgCVAK5mZk9qTg3rFWJPnspMCIIgBGH3miWWtUspG+WcS0tL+fn/fYChD01NuimCIAhZwezbTkKLxvVnMnOpeV/WLBabad+sYdJNEARByApaNCpG80b1bPdnrSEoLCQUi+icIAiCK69edZTj/uw1BETYIwllgpB1HNu9ddJNyAquH9xD2bm67+tc2yBrDUFRns4GykcNw/n997fd76fqWP2irP36hYiIQ8vxE5fC9NnA5cd2iezcPfbdB6/8+ijH2uSqydqeIJ/LLBY4fGv/vuBwz+fZVWkvSytEx+l92yfdBFvItb/Jp8oAACAASURBVECiAACdI05o7d+lpevz+fB5hym7XtYaAspDxbkju2RK/BU6/O19928eV3OEgDSsV4ifH2appJI8KXmserVrmnQTHIlyIFqlRXIWuPRxHVt4C5hpvU9912Oy1hDkIyf1bgsgk1ltBzNwzhGdbPcLybOnijE8pbOClNgBNHGRmC9pZSmiGRtOg7GwXDcwszbQpolzB96ysX0UkJFe7ZzXBwAxBI70TtmopGnDYgBAPQfffpMGRa4jCSFZCMBXKzcn3YxU49Z5Je0RaN88uvD1ntrffuoh7RyP69zKm3vqX+f1cz1GDIEDD57bF89d6kkVOxZO6bMfgEx1NrvHoEFxYWpGdYI1LRvXQ0FK17gqU5Ktf/yBbRz31ysM13Ud3KFZop/3gqoBXbNGxe7XUnKlHKVzq0apGl0302YEhQXkWJQnRU1OlPt+cUjSTbDkukE9YvuODukYfYflh1FnHOx6TLOGxa6L1ovXbnU9hxOlJZYinJ7hHBM6E0PgQP2iwlR2qovWbIXTwC1NxitJTujpPKpMisb1i2KLznn64iNiuY5XvNQaP3C/JqEXK9669ljH/af3DbdYH4cyT5yPsRgCF9LYp67bstNx//PTl7me458KQ8/SSuvG7tESSRGXZ6j1PvU9LyrGQbEHl85vT+gW+jqdWjZyNQZhSMt84Irj1OQziCFwoVOLZKMTrPDyMDnRsLgQO/d4EnnNatLqhwfcfeAqmXXrSbFdy41hLgugANCkQbGS+ZJTUEVJiDyAxy88HFGKdTZpkHFrebnE9YMPUHJNMQQudGqZPkPgNr2+zCXrccGdp6hsTqq542e9k26CJfWLCpNuQiJ4GcQc1ql5pt5ISJweE7c1BCf6dmqOFg4CbmHpoEUkefFGNHYJsx12sLvhBcQQZCVTvq5w3D9ruWVZ0hqSDr0z0rZptO4bleM2lUlOXaTUqi0FBRTqvuinJVU2KI7G2O5TvyiW2aaK5/SRX7qHjgJiCPKWtCwo243MTurdFo3rhX+QVc7g2zVroO5kgiNeY+St0EfJHSNy67qNwuOgU0u1eQyhDAERtSSiSUT0jfa7TkwWEfUlos+IaD4RzSWicwz7niWi74hotvbTN0x7jLhNiS4YYC/clu146eRVDmievrhOnQvP2EXP9NqvCc45Ivh39PKVAwJ/1o60ZgML2ccJHtaInLKX96m/dwClKw6EIeyMYASAyczcA8Bk7b2Z7QB+xcx9AAwB8CARGQVxbmTmvtrPbD8XdxoxOk293/ndcTiwrXPmYtiElSTx0smrnBEc3CG4vtEFR3W23H794ANwdLdWgc87oGvms8UKFVZ7tWuKN64+Rtn5BHvC3J5eQlSTxsvzt5/HGWiTBuFnKGGfkuEAxmivxwA43XwAM3/NzN9or78HsA6AkpCJ1x0eSrfR2wEuhuAvw/sEalMcXDjAuvPUKV+/3fUcKj1DRMCbHjrIxyz8leeU1tVFGtyrLQoKCIMVjHTOLu0Y+hxG+rR3XieYNmKg53MtvntI2OY4EqVUchDKbhns+dgwt2erFIcN6wSJOhp2SDuc179T3c/bnMrPmlZYQ9CWmVdrr9cAcHxyiag/gHoAvjVsvkdzGT1ARLbfIBFdSURlRFRWUZFZLO3goPfhpgXiZgjChmhGiVsHWbF1V0wtybBP/SIc2qk5Wriksg+1cNdZjd7CCop9MXJQzWvV0TluHZQfDZqoI4fOdahbkQSNfKz5hFna+WWOun2P6NwCt/+sDzq3auQpGqhvJ+8zddfejojeJ6J5Fj/DjcdxxkTZfn9E1A7A8wAuYWZdaPtmAD0BHAGgJYCb7D7PzE8wcykzl7Zp00Y7p327nRZ0iMKFj6nk/rMP9f2Z3TY65Zce430EqDJySI/OCOKrtDIEFx1dEqo9+zaJZlG3W5t9IjlvVHj1kDQojmfQ46SaayaMumc21CohIsz7i78wbiJCg+JCTLnxRFw7yL16mZ9/oesdwMyDmfkgi583AazVOni9o19n8wc0BfA2gJHMPN1w7tWcYReAZwD4UngL4+d2C/+KQ3m0gIAz+vl3XVTZ6Eu0b+7eAf76J10BAN3aqA9fDBpbfckxJbXepzURLKPxlM62Hd65rnaO1xDVv/7cXf8nbsLcA8Z+4Yx+6az70KRBEfZJQfSRTtihwHgAF2mvLwLwpvkAIqoH4HUAzzHzONM+3YgQMusL8/xcPKghaFTs/gX0dvEFq+Dy47oG+pydRK+uTurETUN6AgD2jyBR7g8nHxjoc04ZoCq470z14nNWce7/uyY6SQM3fnN8XVkGr0Yr7pmOl4gZJ9xcTMZ+4f6zlQUiKuU6DyN6M3Zfp53R9LMOEfYJHAXgJCL6BsBg7T2IqJSIRmvHnA3gJwAutggTfZGIvgLwFYDWAO72c/HigHWL90+4qIXOn3/aK9Dn3OKYB3RtabsvytF20A69W2t1HZGXakwqOKKk7v/44ASVPgf12jfwZ+PQIlp4596F8bBx+G6JYm5Jyf1iquLn9Dx4rRduXC+ze3LNM2qdtk29u0hDGQJmXs/Mg5i5h+ZC2qBtL2Pmy7XXLzBzsSFEtCZMlJkHMvPBmqvpAmbe5uf6aZ2mR4GX6BddDqP7vu4dq6oQO68p7Ebam8LizGsLYXRcLlckwmWH/l9Li+gYkJFLd3sWznAojRmli+KR8/uhfNQwNDSM4qtD1jxwu3XdlF1bhogq+r0PbZ8pN54Q+Do6VYZnwS64wG57Qx+Z1ekNjckjvHxhxrJ1bqOJ4R4kdv0s3DkRZIaxjxb3fKgW1aAyUzOsvLBXmscQbOBVZsHLwmprh7KHhQFn1l44zGL07XU0bIdbRJ/bLXnm4cFDin99/F537kEdnN3H7ZqFz/6tqtprCE63MeZ2A6fzj/QePSWGwIKLQ0at+OVXNklVRq4wrCfo6oRpIMjEYnjfDnjnd8fV5B7UKyqwlQx+8lfBs5Z1VNaX1U8VdD3Ez9rM8Qd49KV7+PPWb9ttu69phPeTVTitU1RYfwuXm5kGxYVo6pBE5VbrN4w8g9EtFaamhD6Dc5vpXz2we81rO1eTnWH000+IITBRPmoY7jjNezJZ0JJ1PQzumxtOdp5uHtqpOZr7iMgJo6/z1EX+Ol6/neyIoT1x1uEd6yS72CVq+dUbsjJMPzu0PcZeoVZyIqhf/YM/HO/52IE97cNxZ916Eo7q6j3zOuoFeT90c3BdeonyuXZgd8fRttuz0ms/NYEg1RYP2nyfIaEDezqv7bgpIADhJLV10nN3pBinUNLmHuqBWqFH7wDuiUV+Z+5W03GvFBcW4K7TD/J8vJ1EhB1XHd8N+1osYhERbh7aEyOG9qz9kPv8263OXa+oAEeFkKswQkSuMt5OI84iH4mKTja2ZeN6GOtDT+mIkKUZVeI0A/mpx3oFYUpFBg2YOMvkUrIacDWuX4SLjy7xvD4Q5WzMD3ltCLwudFpZfp2go28/A+lLXBLFxl9zDD784wk1781TRa8Ce89ecgSO7d7a10izpUJd9l8f3w1XHd8tMq2YJorWItzWV9or8A13bdPYNl/EjNV/yxyiGUc4tAq8hIQT4ikVaaRji4Z1ZGfs+oU7Tuvjqp6q/5VHd2+Nnx5sH/Ydl25SXhuCK37iLY7faXHLyUg44ccQWCUGXWWIGz+kY3PH5KGD2ntzX51w4L7aaCk9MTEqa/sOUDQrcMPPf89uHaCQyFXXyMzzl+3Nx3z8wsNrtynir3RwiPBVI14imAoK/P2P7/Yxw7Xjk5sG1hkAmJP4TvSRH2EMcz7tUHtdtAP3y7iG3OQibj01XAGmvDYEXvMQfjfYPvkj7pGJzqU2scNmDunYDAN9PqR+wnKjjuBVef4/nRJsgdc3Pu4Jc4etU1hAjlpaOj85oE3NzLanwfftV8eoXbMGjvknbrSxkPXouZ+9fztMrV0C+QoxDuIq/eaeoTWv7SQ49OijQzs1R/moYXjmkrrCCDNNQnu629W4ZqP/KZ1bNcLrvz261vG68XF7DtyqErqR14bA7Ps3fwk6Ru1vM3uqrHV/3Ag70t23aQNP6pLM/q/V1cfiU9QFbvwY2l+6hMv18LDw5od3rz+u5nVQSRK75KifH9bBk0F+7tL+uEGLYGra0H407ZaENW3EQNeR8/hrrBVmD2zbBH+xCLBwMgRnHl5XddYYmumEXx9/kDBOo3t11BnOmelOCWqtTAmOFw7ojPJRw2pt0z2AL1x2JA7b33otx2u48km92+K6QT1wm88ZQnrELhLA/KDZfQlOXHpsF5Qtcy4NaX3x2m9/P/gAPPD+19aH2tz3t5zaG7e4fOHVzDUzn09uOtFb03x07h1bqK2UFAarbN8oMWorGWeXYRYydQ7p6H8U6zQLcNMd8vKd27WpbbMGllFJTuc050j02785RhgCKJzo0qqxrwFC2Mzpn9m4blQ5A47t3hoA0Mwh8KSDh+fsiz8PQuP6RYHycvJ6RuAVp2moH9lhI+ZHpHH9aCSJj+3RGs0b1cPkPxwfSem+qLO73U4/+7aTcFyPzIPkOe4+Aoyd5G9P7O5wpDeIgmnyTxsxEEv/+tPQ1zfzkSEYwYydy82p/eYQz5euGOD5XopbIsauVarcws0aFaN81DDHCCIvs/p9mzYInJyZk4ZA9Ur7PiErAFktgMVVAU0fZWWbhLJXmjeqh3+clZHybuph+uzV/eAFo7vF6BpppUi7x84F8qhDQfL2zRtGoiflFKtuK2nioxlRFZo3EySZzO7/qbu+Tu7tLvYYhouPLsFZigssmclJQzDr1pM8H3uqh7jlPg5RN14WrawqM5nj2utH9CA4jbKuObE7/qgls+3rko0JAN/9LfhI08lfHBRdoqJt0wYoHzXM0wBApVa9XtPizz/tGat0tlvmrGr0qlh22HXibutHSZT9nPJHb+5RLzSuX4TyUcOU5ajYccdpfQK5Cv2Qk4bAT9GZB87piyk3nuC7SISOl9mh1YNi7qDPPcL5YYuCP55yIM7WSkU29JDBS0SBJR+ev+zIQJ9zoksAF0HAtX1bhvTZD786qqTWNhUuA6cuNCqTYzdocPp7jPbPvGDsNgvVZxK/PaGuhLYbgV0gHgy27mJceOcQTLjuOJejc4OcNAR+KC4sQOdWjQMpMP4iQFEZp3bYoTKW3g6vndcgQ0r8dQO9+8LrB6yC5ZS57Za0Y8WRXdQuKP/7wsMjcWs4RThFtSzjJ1pMZ8qNe0fY5nh4t0RG/ZkLMtq9+kT/xsMrYy7NhIE2rFeoJBHvT0NiClsOQV5HDYWldZN6ieURuHHx0SW+OgxzoYzSzi0so6GMI6obAgqv+aGngy5MEBXJrhFUZjOj4pZwinTpFMGiP2A/I3DStd/XEP1j/ru9iJ59MXIQ2gSoIRF1vWeVHK5FI8Zx7wUl72cEYciM1J0f+7DVmIJyx2l9cPvPvIvnmcP5vGZde0WlAqhOpwBV1uxmEbNvc15XeujcdFS6eujcvpZ6Sl64cIA/XSid0/raZ74aO+QgdST2bdIgWOSZz494WQuMiiM1yZYongFVhJoREFFLAP8BUAKgHMDZzFxnGElEVchUIQOA5cx8mra9C4CXAbQCMBPAhcxsr5ebMs49ohPW/7jL8ZiHzjks9HXCyOa6ocd/m59h1TetypoDUeCmWOmlxoNOmMI6bgRxh+kEjX7zeicUp0Dh1E6e+m9nHIwb48ost+Cpi0qxr0X2dVoI+82NADCZmXsAmKy9t2KHoTrZaYbt9wJ4gJm7A9gI4LKQ7cEdPwunuWHH/WcfWmdb80bFNR3oIDs5WR/9qV38d5T1B/QO0Nx19bMohp7rmDM+g2Lnzhp31VGBz6mHyLppzjhRabFS/tUdJ7t+zs6smTW4mjYoxmgtmGDEUG/JYU7s5zDzsXus7GoBN2lQHMqIhmVQr7aJljJ1I6whGA5gjPZ6DDIF6D2hFawfCEAvaO/r83b0CVgfwI0z+nXEM5ccUWsbgWoeEhWJVXGGIJoxi+c5+ajfvPoY33UL8gm7CCyvI3Kr/23Q+txGrEo0qh5kDNbKjjYJmXsDAP06x1NbWAhvCNoy82rt9RoAdpU0GhBRGRFNJyK9s28FYBMzV2rvVwKwnX8T0ZXaOcoqKipCNjsYJeYRBRldKtbjphS7BWs4qmsrS6XL8lHD8HOtPJ5Rh+bQTs0xqJd90ZRs41uXTNyRP+0VU0syWIVdqkhA9FK60E5OwYrHLrBObLv79INw6sHez2PHhQNKbPfZDbwOUKwnlS+43l1E9D4RzbP4GW48jjOOUbtZZGdmLgVwPoAHich37BczP8HMpcxc2qaN/QLsgS6JSxf5LKRixKzX0qR+UY0/+PDO8ercqGTslQNs/ZeHaNPZCwIuNKrAS7JbGNwS0YIsnLsVr3HCqo87uc9+NaU9g+Ilv8atItwNJ+2tpmdXQe2CAZ0ddXO84pTbYveNHRpx4lWu4moImHkwMx9k8fMmgLVE1A4AtN/rbM6xSvu9FMBHAA4DsB5AcyLS55AdAazy+weYNcDdKv5cP9i5LKQbj5y/dxRUUEA1yoFDDqqdZq7Hv2fBhMCRAV1boZuPsLcoKmHVmYkBGJzyGYlZu75JgyJbrSdzPoaVUS4soJpM6iRxqjesGqdF92yYaWcTYeeb4wFcpL2+CMCb5gOIqAUR1ddetwZwDIAF2gziQwBnOn3ejSd/VeorqqZRSHE3s7a57ls3qy+mpQRdWHq1a4rJfzjB8/FnWcgLh+U3FslDo7NsjeKNq4+xTVr8pWm25SXLOymaNiiqVVQlSpxir+ySLIMmLuY7Yf9rowCcRETfABisvQcRlRLRaO2YXgDKiGgOMh3/KGZeoO27CcANRLQEmTWDp/w2oKiwACOGePfhqkpEmXj9TwDszY40lmzs2KIhihQs7mUjUfzddiGB2YRTBIxTwtawQ9opE7ELinFgTkR44fL+uN6hWFMU1/VKXOJ1uUaoJ4yZ1wMYZLG9DMDl2utpAA62+fxSAHXL+vhk2CHtcPVLYc/iD30t4tBOzfHFyEG1RnG/PaE7Rn+yFED0Ms35gFNHGRZb5UzFBM2jeOicvqiKMX3d6nY1uwZ77tfUMeNbHdH+3Y+c3w9XvzSr1rb+Mde0SAs5M48qHzWsThx4XNmEZp/u+UfuH/gedqp2lK9EUUdB5/0bjo/s3CooKiyIVU7ByuYkNZhxsn+t9gk/S9qvWV0X1+DeamovZxs5Ywjiwmlkd7HFQpr+CA13SNM3YhXrLUSDOSEqSe5SUGA91+jqoF7aK2BpUDdyZW3PL+l5EiIgiolls4bFthmoLRxkChqleAFQJXEMHlXp2H92cx2vZmJcOKAzXohAqtsvafJkhi0x6YbVTCfJpM4kyWlDEDe/Pr4r3rr22Frb9HttQFdvxSvS9CAGYehB0bvjwsgsGIm6o9F5/MLDPR13bI/WoYr/5ANnGyp1zRhZt+CTH/pa5BykPSw5KnLaEHQLoK8ehgbFhTjIJHGh+zm9aq5nuR3IiqiNU/rE+7Cf0sd7KcOkgwvSKquuY8zPCFupraCA6lQzjGtwkDZy2hCcaCcEFwPXDeqBpg2K0NBnx5jy5zAQrRUs7Knk2O6tURJzAfRswY/ERBKoznDP147fTPYHaKeU0w/rgNMP8y5dnMtUp8y6XXhUCS40lZeMCqf8gaQxykXotG9eOznzgXPqqu4miZ8sd8E7OT0jMLP47iFJN8GVbHcNAcBLV0S36Bl1cpW53GJYWqR4xNmhuXtG/uk+6jBEgVnwLwrXmZXgYr6RV4Ygm8rbZTNHd2td673KQi1XKq6cZkYvXK6Ct649NtVSGObRvxVJr1mcXapessTM23lSoN6JnHYNRZmRGoZjunuLIMoVVLqG3NRlw3JoJ3X1LMyBA2kjK/T+fdihBqIzFJic/s95GfEkQUG2x4j6RIWMg36OEw7Mz8xPVRjzWVTUOIiaRvUKHd2BRs2jfvvnX1U9VaT/TsgRjIuGv+jX0fa4y4+L1vWRBE94jKN3ojgLOq1swLhAnLTbxwvFhQWYeetJWHTXEEy3SAA0ysqHcet5WS/JZeTpigmjKJ1TNJGdVHE200qBbPFdw/soaIng1vk3DinTHhUNiguxXzNrV6+eZHZFiEHUuUd0UpaomI3kXq+T5aio9ZqLlJa0VFZc3on9muX3yNCuUl2aue/MQ3HfmeHCXK8d1APX2hS+zwdkRpAyOrVslLrY7XwiF2dkRlRGcAm5gxiCFNKn/d5ok44t8nuEKghC9IQyBETUkogmEdE32u86y/ZEdCIRzTb87CSi07V9zxLRd4Z9fcO0x4mpfzoxqlMrxxj2+vGN2dNuIf2c5lEOXcgvws4IRgCYzMw9AEzW3teCmT9k5r7M3BfAQADbAbxnOORGfT8zzw7ZHls6tcwebZlmDfdqome7LG5cFcAEb9QvTOdisJAsYQ3BcABjtNdjAJzucvyZAN5h5u0hryuknK6aJszD5x2WcEsEI2mNChKSJawhaMvMq7XXawC46fueC2Csads9RDSXiB4gIts4QyK6kojKiKisoqIiRJOT5fwj90+6CbHQU8sAPrBttJnAgj+KJB9DsMA1RIKI3gdgJag+0viGmZmIbEMSiKgdMkXsJxo234yMAakH4AkANwG40+rzzPyEdgxKS0t9hT6kKRKkURbo9avgmhN7oLo6+11bgpAPuPaQzGxbBoiI1hJRO2ZerXX06xxOdTaA15l5j+Hc+mxiFxE9A+CPHtvti7NK7TN5hWjo3b4p/q0gozhpbv9Z76SbEAlpGhwJyRP2bhgP4CIAo7Tfbzocex4yM4AaDEaEkFlfmBeyPXX4dMTAyKWL/VASc9U0IRyN6+Vmh9lNFvEFA2Hv8lEAXiGiywAsQ2bUDyIqBXAVM1+uvS8B0AnAFNPnXySiNshoDM4GcFXI9tQhTRoiX989FMWF4ioRkqWogNC9jRgCYS+hDAEzrwdQRwmKmcsAXG54Xw6gjsAOMw8Mc/1so16R94W6lo3rYcOPuyNsjeCFohw03F/dcUpO/l1CcHJz3psD/Ob4bqjYtivpZuQ95iI7uYBRAFEQADEEqeWKiCtxCd6wU7wUhFxCgooFQRDyHDEEgiAIeY4YAkEQhDxHDIEgCEKeI4ZAEIRaDOq5LwDglD5u0mFCriCGQBCEWvxF6kPnHWIIBMGGXu2aJt0EQYgFMQSCYMMbVx+ddBMEIRbEEAiCDfWLJANXyA/EEAiCYEkBiR5RviCGQBCEWrRvllHsvfy4Lgm3RIgLMQSCINRCryrXsFikyPIFMQSCIAh5jhgCQRCEPCeUISCis4hoPhFVa1XJ7I4bQkSLiWgJEY0wbO9CRJ9r2/9DROmpKSnkLeOuOirpJghCrISdEcwDcAaAj+0OIKJCAI8AGAqgN4DziEivCH4vgAeYuTuAjQAuC9keQQhNaUlLlI8alnQzBCE2QhkCZl7IzItdDusPYAkzL2Xm3QBeBjBcK1g/EMA47bgxyBSwFwQhBezbtH7STRBiIo41gg4AVhjer9S2tQKwiZkrTdstIaIriaiMiMoqKioia6wgCED5qGFovY8YgnzBNT6MiN4HsJ/FrpHM/Kb6JlnDzE8AeAIASktLOa7rCoIg5DquhoCZB4e8xioAnQzvO2rb1gNoTkRF2qxA3y4IgiDESByuoRkAemgRQvUAnAtgPDMzgA8BnKkddxGA2GYYgiAIQoaw4aM/J6KVAI4C8DYRTdS2tyeiCQCgjfavATARwEIArzDzfO0UNwG4gYiWILNm8FSY9giCIAj+oczAPLsoLS3lsrKypJshCIKQVRDRTGauk/MlmcWCIAh5jhgCQRCEPEcMgSAIQp6TlWsERLQVgFtGc1K0BvBD0o2wQdoWjLS2La3tAqRtQYm6bZ2ZuY15Y7YKji+2WvBIA0RUJm3zj7TNP2ltFyBtC0pSbRPXkCAIQp4jhkAQBCHPyVZD8ETSDXBA2hYMaZt/0touQNoWlETalpWLxYIgCII6snVGIAiCIChCDIEgCEKek1WGwK72cRogoqeJaB0RzUu6LUaIqBMRfUhEC7T60r9Luk06RNSAiL4gojla2/6SdJvMEFEhEX1JRG8l3RYjRFRORF8R0WwiSpXwFhE1J6JxRLSIiBYSUSqKQBPRgdr/S//ZQkTXJ90uHSL6vfYczCOisUTUILZrZ8sagVb7+GsAJyFTzWwGgPOYeUGiDdMgop8A2AbgOWY+KOn26BBROwDtmHkWETUBMBPA6Wn4v2nlShsz8zYiKgbwCYDfMfP0hJtWAxHdAKAUQFNmPjXp9ugQUTmAUmZOXWIUEY0BMJWZR2vS842YeVPS7TKi9SerABzJzMtS0J4OyNz/vZl5BxG9AmACMz8bx/WzaUZgWfs44TbVwMwfA9iQdDvMMPNqZp6lvd6KjBS4bUnQOOEM27S3xdpPakYmRNQRwDAAo5NuS7ZARM0A/ASapDwz706bEdAYBODbNBgBA0UAGhJREYBGAL6P68LZZAjsah8LHiGiEgCHAfg82ZbsRXO9zAawDsAkZk5N2wA8COBPAKqTbogFDOA9IppJRFcm3RgDXQBUAHhGc6mNJqLGSTfKgnMBjE26ETrMvArAPwAsB7AawGZmfi+u62eTIRBCQET7AHgNwPXMvCXp9ugwcxUz90WmVGl/IkqFW42ITgWwjplnJt0WG45l5n4AhgK4WnNNpoEiAP0APMbMhwH4EUDa1vPqATgNwKtJt0WHiFog4+HoAqA9gMZEdEFc188mQ2BX+1hwQfO/vwbgRWb+b9LtsUJzH3wIYEjSbdE4BsBpmi/+ZQADieiFZJu0F20ECWZeB+B1ZFynaWAlgJWGmd04ZAxDmhgKYBYzr026IQYGA/iOmSuYeQ+A/wI4Oq6LZ5MhsKx9nHCbUo+2IPsUgIXMfH/S7TFCRG2IqLn2uiEygQCLP2f4AwAAAihJREFUkm1VBma+mZk7MnMJMvfaB8wc2wjNCSJqrC38Q3O7nAwgFdFqzLwGwAoiOlDbNAhA4oEJJs5DitxCGssBDCCiRtozOwiZ9bxYyBr1UWauJCK99nEhgKcNtY8Th4jGAjgBQGutjvPtzJyGGszHALgQwFeaLx4A/szMExJsk047AGO0CI4CZOpZpypMM6W0BfB6pr9AEYCXmPndZJtUi2sBvKgN2JYCuCTh9tSgGc6TAPw66bYYYebPiWgcgFkAKgF8iRjlJrImfFQQBEGIhmxyDQmCIAgRIIZAEAQhzxFDIAiCkOeIIRAEQchzxBAIgiDkOWIIBMEBImplUKtcQ0SrtNfbiOjRpNsnCCqQ8FFB8AgR3QFgGzP/I+m2CIJKZEYgCAEgohP0GgVEdAcRjSGiqUS0jIjOIKL7tHoB72oSHyCiw4loiiYUN1GTCBeExBFDIAhq6AZgIDJiZi8A+JCZDwawA8AwzRg8DOBMZj4cwNMA7kmqsYJgJGskJgQh5bzDzHuI6CtkJFB0yYevAJQAOBDAQQAmadIQhcjIDQtC4oghEAQ17AIAZq4moj28d/GtGpnnjADMZ+ZUlG0UBCPiGhKEeFgMoI1ev5eIiomoT8JtEgQAYggEIRa08qpnAriXiOYAmI0Y9eYFwQkJHxUEQchzZEYgCIKQ54ghEARByHPEEAiCIOQ5YggEQRDyHDEEgiAIeY4YAkEQhDxHDIEgCEKe8/9B8BUgD3zTMAAAAABJRU5ErkJggg==\n", 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" ] @@ -306,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -314,7 +333,7 @@ "text/html": [ "\n", " \n", " " @@ -323,7 +342,7 @@ "" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -356,7 +375,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/parakeet/models/tacotron2.py b/parakeet/models/tacotron2.py index 7759f25..d9980f6 100644 --- a/parakeet/models/tacotron2.py +++ b/parakeet/models/tacotron2.py @@ -205,8 +205,8 @@ class Tacotron2Encoder(nn.Layer): Parameters ---------- - x: Tensor [shape=(B, T)] - Batch of the sequencees of padded character ids. + x: Tensor [shape=(B, T, C)] + Input embeddings. text_lens: Tensor [shape=(B,)], optional Batch of lengths of each text input batch. Defaults to None. @@ -502,7 +502,7 @@ class Tacotron2Decoder(nn.Layer): if int(paddle.argmax(alignment[0])) == encoder_steps - 1: if first_hit_end is None: first_hit_end = i - elif i > (first_hit_end + 10): + elif i > (first_hit_end + 20): print("content exhausted!") break if len(mel_outputs) == max_decoder_steps: