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        "author": "Joan Puigcerver",
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            "Development Status :: 4 - Beta",
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            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# torch-baidu-ctc\n\n[![Build Status](https://travis-ci.org/jpuigcerver/pytorch-baidu-ctc.svg?branch=master)](https://travis-ci.org/jpuigcerver/pytorch-baidu-ctc)\n\nPytorch bindings for Baidu's Warp-CTC. These bindings were inspired by\n[SeanNaren's](https://github.com/SeanNaren/warp-ctc) but these include some bug fixes,\nand offer some additional features.\n\n```python\nimport torch\nfrom torch_baidu_ctc import ctc_loss, CTCLoss\n\n# Activations. Shape T x N x D.\n# T -> max number of frames/timesteps\n# N -> minibatch size\n# D -> number of output labels (including the CTC blank)\nx = torch.rand(10, 3, 6)\n# Target labels\ny = torch.tensor(\n  [\n    # 1st sample\n    1, 1, 2, 5, 2,\n    # 2nd\n    1, 5, 2,\n    # 3rd\n    4, 4, 2, 3,\n  ],\n  dtype=torch.int,\n)\n# Activations lengths\nxs = torch.tensor([10, 6, 9], dtype=torch.int)\n# Target lengths\nys = torch.tensor([5, 3, 4], dtype=torch.int)\n\n# By default, the costs (negative log-likelihood) of all samples are summed.\n# This is equivalent to:\n#   ctc_loss(x, y, xs, ys, average_frames=False, reduction=\"sum\")\nloss1 = ctc_loss(x, y, xs, ys)\n\n# You can also average the cost of each sample among the number of frames.\n# The averaged costs are then summed.\nloss2 = ctc_loss(x, y, xs, ys, average_frames=True)\n\n# Instead of summing the costs of each sample, you can perform\n# other `reductions`: \"none\", \"sum\", or \"mean\"\n#\n# Return an array with the loss of each individual sample\nlosses = ctc_loss(x, y, xs, ys, reduction=\"none\")\n#\n# Compute the mean of the individual losses\nloss3 = ctc_loss(x, y, xs, ys, reduction=\"mean\")\n#\n# First, normalize loss by number of frames, later average losses\nloss4 = ctc_loss(x, y, xs, ys, average_frames=True, reduction=\"mean\")\n\n\n# Finally, there's also a nn.Module to use this loss.\nctc = CTCLoss(average_frames=True, reduction=\"mean\", blank=0)\nloss4_2 = ctc(x, y, xs, ys)\n\n# Note: the `blank` option is also available for `ctc_loss`.\n# By default it is 0.\n```\n\n## Requirements\n\n- C++11 compiler (tested with GCC 4.9).\n- Python: 2.7, 3.5, 3.6, 3.7 (tested with versions 2.7, 3.5 and 3.6).\n- [PyTorch](http://pytorch.org/) >= 1.1.0 (tested with version 1.1.0).\n- For GPU support: [CUDA Toolkit](https://developer.nvidia.com/cuda-zone).\n\n## Installation\n\nThe installation process should be pretty straightforward assuming that you\nhave correctly installed the required libraries and tools.\n\nThe setup process compiles the package from source, and will compile with\nCUDA support if this is available for PyTorch.\n\n### From Pypi (recommended)\n\n```bash\npip install torch-baidu-ctc\n```\n\n### From GitHub\n\n```bash\ngit clone --recursive https://github.com/jpuigcerver/pytorch-baidu-ctc.git\ncd pytorch-baidu-ctc\npython setup.py build\npython setup.py install\n```\n\n### AVX512 related issues\n\nSome compiling problems may arise when using CUDA and newer host compilers\nwith AVX512 instructions. Please, install GCC 4.9 and use it as the host\ncompiler for NVCC. You can simply set the `CC` and `CXX` environment variables\nbefore the build/install commands:\n\n```bash\nCC=gcc-4.9 CXX=g++-4.9 pip install torch-baidu-ctc\n```\n\nor (if you are using the GitHub source code):\n\n```bash\nCC=gcc-4.9 CXX=g++-4.9 python setup.py build\n```\n\n## Testing\n\nYou can test the library once installed using `unittest`. In particular,\nrun the following commands:\n\n```bash\npython -m unittest torch_baidu_ctc.test\n```\n\nAll tests should pass (CUDA tests are only executed if supported).\n\n\n",
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