pydrobert-kaldi 0.6.4

Python wrapper for Kaldi

pydrobert-kaldi

Some Kaldi bindings for Python. I started this project because I wanted to seamlessly incorporate Kaldi's I/O mechanism into the gamut of Python-based data science packages (e.g. Theano, Tensorflow, CNTK, PyTorch, etc.). The code base is expanding to wrap more of Kaldi's feature processing and mathematical functions, but is unlikely to include modelling or decoding.

Eventually, I plan on adding hooks for Kaldi audio features and pre-/post- processing. However, I have no plans on porting any code involving modelling or decoding.

This is student-driven code, so don't expect a stable API. I'll try to use semantic versioning, but the best way to keep functionality stable is by forking.

Documentation

• Latest
• v0.6.3

Input/Output

Most I/O can be performed with the pydrobert.kaldi.io.open function:

from pydrobert.kaldi import io
with io.open('scp:foo.scp', 'bm') as f:
     for matrix in f:
         ...

open is a factory function that determines the appropriate underlying stream to open, much like Python's built-in open. The data types we can read (here, a BaseMatrix) are listed in pydrobert.kaldi.io.enums.KaldiDataType. Big data types, like matrices and vectors, are piped into Numpy arrays. Passing an extended filename (e.g. paths to files on discs, '-' for stdin/stdout, 'gzip -c a.ark.gz |', etc.) opens a stream from which data types can be read one-by-one and in the order they were written. Alternatively, prepending the extended filename with 'ark[,[option_a[,option_b...]]:' or 'scp[,...]:' and specifying a data type allows one to open a Kaldi table for iterator-like sequential reading (mode='r'), dict-like random access reading (mode='r+'), or writing (mode='w'). For more information on the open function, consult the docstring.

The submodule pydrobert.kaldi.io.corpus contains useful wrappers around Kaldi I/O to serve up batches of data to, say, a neural network:

train = ShuffledData('scp:feats.scp', 'scp:labels.scp', batch_size=10)
for feat_batch, label_batch in train:
    ...

Logging and CLI

By default, Kaldi error, warning, and critical messages are piped to standard error. The pydrobert.kaldi.logging submodule provides hooks into python's native logging interface: the logging module. The :class:KaldiLogger can handle stack traces from Kaldi C++ code, and there are a variety of decorators to finagle the kaldi logging patterns to python logging patterns, or vice versa.

You'd likely want to explicitly handle logging when creating new kaldi-style commands for command line. pydrobert.kaldi.io.argparse provides :class:KaldiParser, an :class:ArgumentParser tailored to Kaldi inputs/outputs. It is used by a few command-line entry points added by this package. See the Command-Line Interface page for details.

Installation

Prepackaged binaries of tagged versions of pydrobert-kaldi are available for most 64-bit platforms (Windows, Glibc Linux, OSX) and most active Python versions (3.7-3.11) on both conda and PyPI.

To install via conda-forge

   conda install -c conda-forge pydrobert-kaldi

If you only want to rely on Anaconda depenedencies, you can install from the sdrobert channel instead. There is not yet a 3.11 build there.

To install via PyPI

   pip install pydrobert-kaldi

You can also try building the cutting-edge version. To do so, you'll need to first install SWIG 4.0 and an appropriate C++ compiler, then

   pip install git+https://github.com/sdrobert/pydrobert-kaldi.git

The current version does not require a BLAS install, though it likely will in the future as more is wrapped.

License

This code is licensed under Apache 2.0.

Code found under the src/ directory has been primarily copied from Kaldi. setup.py is also strongly influenced by Kaldi's build configuration. Kaldi is also covered by the Apache 2.0 license; its specific license file was copied into src/COPYING_Kaldi_Project to live among its fellows.

How to Cite

Please see the pydrobert page for more details.