WarpCTC.txt

https://github.com/baidu-research/warp-ctc/tree/master/tensorflow_binding

TensorFlow binding for WarpCTC

This package provides TensorFlow kernels that Wrap the WarpCTC library;). Kernels are provided for both the CTCLoss op already in TensorFlow, as well as a new WarpCTC op provided in this package. The WarpCTC op has an interface that more closely matches the native WarpCTC interface than TensorFlow's CTCLoss op. Note that the CTCLoss op expects the reserved blank label to be the largest value while the WarpCTC op takes the reserved blank label value as an attribute which defaults to 0.
Installation

To build the kernels it is necessary to have the TensorFlow source code available, since TensorFlow doesn't currently install the necessary headers to handle the SparseTensor that the CTCLoss op uses to input the labels. You can retrieve the TensorFlow source from github.com:

git clone https://github.com/tensorflow/tensorflow.git

Tell the build scripts where you have the TensorFlow source tree by setting the TENSORFLOW_SRC_PATH environment variable:

export TENSORFLOW_SRC_PATH=/path/to/tensorflow

WARP_CTC_PATH should be set to the location of a built WarpCTC (i.e. libwarpctc.so). This defaults to ../build, so from within a new warp-ctc clone you could build WarpCTC like this:

mkdir build; cd build
cmake ..
make

Otherwise, set WARP_CTC_PATH to wherever you have libwarpctc.so installed. If you have a GPU, you should also make sure that CUDA_HOME is set to the home cuda directory (i.e. where include/cuda.h and lib/libcudart.so live).

You should now be able to use setup.py to install the package into your current Python environment:

python setup.py install

You can run a few unit tests with setup.py as well if you want:

python setup.py test

Using the kernels

First import the module:

import warpctc_tensorflow

The GPU kernel for the existing CTCLoss op is registered and ready to use. If you want to use WarpCTC as the CPU kernel for the CTCLoss op you can use the ("experimental") _kernel_label_map function to tell TensorFlow to use WarpCTC kernels instead of the default CPU kernel:

with tf.get_default_graph()._kernel_label_map({"CTCLoss": "WarpCTC"}):
    ...
    loss = tf.nn.ctc_loss(inputs, labels, seq_lens)

Note that proprocess_collapse_repeated must be False and ctc_merge_repeated must be True (their default values) as these options are not currently supported.

The WarpCTC op is available via the warpctc_tensorflow.ctc function:

costs = warpctc_tensorflow.ctc(activations, flat_labels, label_lengths, input_lengths)

The activations input is a 3 dimensional Tensor and all the others are single dimension Tensors. See the main WarpCTC documentation for more information.