pychain's Introduction

PyTorch implementation of LF-MMI for End-to-end ASR

End-to-end version of lattice-free MMI is mainly based on these two papers:

"End-to-end speech recognition using lattice-free MMI", Hossein Hadian, Hossein Sameti, Daniel Povey, Sanjeev Khudanpur, Interspeech 2018 (pdf)
"Purely sequence-trained neural networks for ASR based on lattice-free MMI", Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahrmani, Vimal Manohar, Xingyu Na, Yiming Wang and Sanjeev Khudanpur, Interspeech 2016, (pdf) (slides,pptx)

The code is a modification of the version in the kaldi repository with no dependency on the kaldi base.

Installation

Install PyTorch

Setup OpenFst

Download and install OpenFST

OPENFST_PATH shold be set to the root of the OpenFst installation. i.e. $OPENFST_PATH/include and $OPENFST_PATH/openfst should contain the required headers and libraries.

./configure --prefix=`pwd` --enable-static --enable-shared --enable-ngram-fsts CXX="g++" LIBS="-ldl" CPPFLAGS="-D_GLIBCXX_USE_CXX11_ABI=0" CXXFLAGS="-D_GLIBCXX_USE_CXX11_ABI=0"
make
make install

Note that the option -D_GLIBCXX_USE_CXX11_ABI=0 must be compatible with the option used when compiling PyTorch. Details here.

Install the bindings

cd pytorch_binding
python setup.py install

Structure of the directory

/ contains the instructions and makefiles etc.
- src contains C++/CUDA code agnostic of PyTorch
- pytorch_bindings contains the pytorch-based module of the LF-MMI objf function
  - src contains C++ level implementation of the LF-MMI objf function
  - pychain_pytorch contains python level implementation of the LF-MMI objf function. This includes the actual loss-function module, which can be bound the C++ level code.
    - __init__.py the actual implementation of the python module
- openfst_bindings contains the openfst-based module to interact with FSTs

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