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PyKaldi is a Python wrapper for Kaldi exposing nearly all of Kaldi's C++ API to Python code. It aims to bridge the gap between Kaldi and all the nice things Python has to offer including its mature ecosystem of high quality software for scientific computing, machine learning, interactive data exploration and visualization.

PyKaldi is more than a collection of bindings into Kaldi libraries. It is a scripting layer providing first class support for essential Kaldi and OpenFst types in Python. PyKaldi vector and matrix types are tightly integrated with NumPy. They can be seamlessly converted to NumPy arrays and vice versa without copying the underlying memory buffers. PyKaldi FST types, including Kaldi style lattices, are first class citizens in Python. The API for the user facing FST types and operations is almost entirely defined in Python mimicking the API exposed by pywrapfst, the official Python wrapper for OpenFst.

You can read more about the design and technical details of PyKaldi in our paper.

Features

  • Near-complete coverage of Kaldi's C++ API

  • First class support for Kaldi and OpenFst types in Python

  • Extensible design

  • Open license

  • Extensive documentation

  • Thorough testing

  • Example scripts

  • Support for both Python 2.7 and 3.5+

Overview

About PyKaldi

PyKaldi harnesses the power of CLIF to wrap Kaldi C++ libraries using simple API descriptions. The CPython extension modules generated by CLIF can be imported in Python to interact with Kaldi. While CLIF is great for exposing the existing C++ API in Python, the wrappers do not always expose a "Pythonic" API that is easy to use from Python. To address this concern, PyKaldi extends the raw CLIF wrappers in Python (and sometimes in C++) to provide a more "Pythonic" API. Below figure illustrates where PyKaldi fits in the Kaldi software architecture.

Architecture

PyKaldi has a modular design which makes it easy to maintain and extend. Source files are organized in a directory tree that is a replica of the Kaldi source tree. Each directory defines a subpackage and contains only the wrapper code written for the associated Kaldi library. The wrapper code consists of:

  • CLIF C++ API descriptions defining the types and functions to be wrapped and their Python API,

  • C++ headers defining the shims for Kaldi code that is not compliant with the Google C++ style expected by CLIF,

  • Python modules grouping together related extension modules generated with CLIF and extending the raw CLIF wrappers to provide a more "Pythonic" API.

Coverage Status

The following table shows the status of each PyKaldi package (we currently do not plan to add support for nnet, nnet2 and online) along the following dimensions:

  • Wrapped?: If there are enough CLIF files to make the package usable in Python.
  • Pythonic?: If the package API has a "Pythonic" look-and-feel.
  • Documentation?: If there is documentation beyond what is automatically generated by CLIF. Single checkmark indicates that there is not much additional documentation (if any). Three checkmarks indicates that package documentation is complete (or near complete).
  • Tests?: If there are any tests for the package.
Package Wrapped? Pythonic? Documentation? Tests?
base ✔ ✔ ✔ ✔ ✔
chain ✔ ✔
cudamatrix ✔ ✔ ✔
decoder ✔ ✔ ✔ ✔ ✔
feat ✔ ✔ ✔
fstext ✔ ✔ ✔ ✔ ✔
gmm ✔ ✔ ✔ ✔ ✔
hmm ✔ ✔ ✔ ✔
itf ✔ ✔
ivector ✔ ✔
kws ✔ ✔ ✔ ✔ ✔
lat ✔ ✔ ✔ ✔ ✔
lm ✔ ✔ ✔ ✔ ✔
matrix ✔ ✔ ✔ ✔ ✔ ✔
nnet3 ✔ ✔ ✔
online2 ✔ ✔
rnnlm ✔ ✔ ✔ ✔ ✔
sgmm2 ✔ ✔
tfrnnlm ✔ ✔ ✔ ✔ ✔
transform ✔ ✔ ✔
tree ✔ ✔
util ✔ ✔ ✔ ✔ ✔ ✔

Getting Started

Some places to help you get started:

Installation

Conda

To install PyKaldi with CUDA support:

conda install -c pykaldi pykaldi

To install PyKaldi without CUDA support (CPU only):

conda install -c pykaldi pykaldi-cpu

Note that PyKaldi package does not provide Kaldi executables. If you would like to use Kaldi executables along with PyKaldi, e.g. as part of read/write specifiers, you need to install Kaldi separately.

Docker

If you would like to use PyKaldi inside a Docker container, follow the instructions in the docker folder.

From Source

To install PyKaldi from source, follow the steps given below.

Step 1: Clone PyKaldi Repository and Create a New Python Environment

git clone https://github.com/pykaldi/pykaldi.git
cd pykaldi

Although it is not required, we recommend installing PyKaldi and all of its Python dependencies inside a new isolated Python environment. If you do not want to create a new Python environment, you can skip the rest of this step.

You can use any tool you like for creating a new Python environment. Here we use virtualenv, but you can use another tool like conda if you prefer that. Make sure you activate the new Python environment before continuing with the rest of the installation.

virtualenv env
source env/bin/activate

Step 2: Install Dependencies

On Ubuntu 16.04, running the following commands will install system packages needed for building PyKaldi from source.

sudo apt-get install autoconf automake cmake curl g++ git graphviz \
    libatlas3-base libtool make pkg-config subversion unzip wget zlib1g-dev

pip install --upgrade pip
pip install --upgrade setuptools
pip install numpy pyparsing
pip install ninja  # not required but strongly recommended

In addition to above listed system packages, we also need PyKaldi compatible installations of the following software:

  • Google Protobuf v3.2 or later. Both the C++ library and the Python package must be installed.

  • PyKaldi compatible fork of CLIF. To streamline PyKaldi development, we made some changes to CLIF codebase. We are hoping to upstream these changes over time.

  • PyKaldi compatible fork of Kaldi. To comply with CLIF requirements we had to make some changes to Kaldi codebase. We are hoping to upstream these changes over time.

You can use the scripts in the tools directory to install or update these software locally. Make sure you check the output of these scripts. If you do not see "Done installing {protobuf,CLIF,Kaldi}." printed at the very end, it means that installation has failed for some reason.

cd tools
./check_dependencies.sh  # checks if system dependencies are installed
./install_protobuf.sh    # installs both the C++ library and the Python package
./install_clif.sh        # installs both the C++ library and the Python package
./install_kaldi.sh       # installs the C++ library
cd ..

Step 3: Install PyKaldi

If Kaldi is installed inside the tools directory and all Python dependencies (numpy, pyparsing, pyclif, protobuf) are installed in the active Python environment, you can install PyKaldi with the following command.

python setup.py install

Once installed, you can run PyKaldi tests with the following command.

python setup.py test

FAQ

How do I prevent PyKaldi install command from exhausting the system memory?

By default, PyKaldi install command uses all available (logical) processors to accelerate the build process. If the size of the system memory is relatively small compared to the number of processors, the parallel compilation/linking jobs might end up exhausting the system memory and result in swapping. You can limit the number of parallel jobs used for building PyKaldi as follows:

MAKE_NUM_JOBS=2 python setup.py install

How do I build PyKaldi on MacOS?

At the moment, PyKaldi installation scripts do not work on non-Linux platforms. It should not be too hard to get them to work on MacOS but we haven't yet looked into it.

How do I build PyKaldi on Windows?

We have no idea what is needed to build PyKaldi on Windows. We have no intention of adding Windows support in the foreseeable future.

How do I build PyKaldi using a different Kaldi installation?

At the moment, PyKaldi is not compatible with the upstream Kaldi repository. You need to build it against our Kaldi fork.

If you already have a compatible Kaldi installation on your system, you do not need to install a new one inside the pykaldi/tools directory. Instead, you can simply set the following environment variable before running the PyKaldi installation command.

export KALDI_DIR=<directory where Kaldi is installed, e.g. "$HOME/tools/kaldi">

How do I build PyKaldi using a different CLIF installation?

At the moment, PyKaldi is not compatible with the upstream CLIF repository. You need to build it using our CLIF fork.

If you already have a compatible CLIF installation on your system, you do not need to install a new one inside the pykaldi/tools directory. Instead, you can simply set the following environment variables before running the PyKaldi installation command.

export PYCLIF=<path to pyclif executable, e.g. "$HOME/anaconda3/envs/clif/bin/pyclif">
export CLIF_MATCHER=<path to clif-matcher executable, e.g. "$HOME/anaconda3/envs/clif/clang/bin/clif-matcher">

How do I update Protobuf, CLIF or Kaldi used by PyKaldi?

While the need for updating Protobuf and CLIF should not come up very often, you might want or need to update Kaldi installation used for building PyKaldi. Rerunning the relevant install script in tools directory should update the existing installation. If this does not work, please open an issue.

How do I build PyKaldi with Tensorflow RNNLM support?

PyKaldi tfrnnlm package is built automatically along with the rest of PyKaldi if kaldi-tensorflow-rnnlm library can be found among Kaldi libraries. After building Kaldi, go to KALDI_DIR/src/tfrnnlm/ directory and follow the instructions given in the Makefile. Make sure the symbolic link for the kaldi-tensorflow-rnnlm library is added to the KALDI_DIR/src/lib/ directory.

Citing

If you use PyKaldi for research, please cite our paper as follows:

@inproceedings{pykaldi,
  title = {PyKaldi: A Python Wrapper for Kaldi},
  author = {Doğan Can and Victor R. Martinez and Pavlos Papadopoulos and
            Shrikanth S. Narayanan},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP),
             2018 IEEE International Conference on},
  year = {2018},
  organization = {IEEE}
}

Contributing

We appreciate all contributions! If you find a bug, feel free to open an issue or a pull request. If you would like to add, extend or request a feature, please open an issue for discussion.