Building Python bindings

Dependencies

We require python >= 3.6 with the following packages installed:

Anaconda makes this easy. There are plenty of tutorials on how to set this up.

Aside from the above, the dependencies for Python bindings are a strict subset of the dependencies for the main wav2letter++ build. So if you already have the dependencies to build wav2letter++, you're all set to build python bindings as well.

The following dependencies are required to build python bindings:

KenLM
ATLAS or OpenBLAS
FFTW3
cmake >= 3.5.1, and make
CUDA >= 9.2

Please refer to the previous sections for details on how to install the above dependencies.

The following dependencies are not required to build python bindings:

flashlight
libsndfile
gflags
glog

Build Instructions

Once the dependencies are satisfied, simply run from wav2letter root:

cd bindings/python
pip install -e .

Note that if you encounter errors, you'll probably have to rm -rf build before retrying the install.

Advanced Options

The following environment variables can be used to control various options:

USE_CUDA=0 removes the CUDA dependency, but you won't be able to use ASG criterion with CUDA tensors.
USE_KENLM=0 removes the KenLM dependency, but you won't be able to use the decoder unless you write C++ pybind11 bindings for your own LM.
USE_MKL=1 will use Intel MKL for featurization but this may cause dynamic loading conflicts.
If you do not have torch, you'll only have a raw pointer interface to ASG criterion instead of class ASGLoss(torch.nn.Module).

Build inside docker container

For CUDA backend inside docker container run commands

pip install torch==1.2.0 packaging==19.1
export KENLM_ROOT_DIR=/root/kenlm && \
cd /root/wav2letter/bindings/python && pip install -e .

For CPU backend inside docker container run commands

pip install torch==1.2.0 packaging==19.1
export USE_CUDA=0 && export KENLM_ROOT_DIR=/root/kenlm && \
cd /root/wav2letter/bindings/python && pip install -e .