PyTorch utilities for Machine Learning. This is an eclectic mix of utilities
that I've used in my various projects. There is a definite leaning towards
speech, specifically end-to-end ASR. The primary benefit pydrobert-pytorch
has over other packages is modularity: you can pick and choose the
functionality you desire without subscribing to an entire ecosystem. You can
find out more about what the package offers in the documentation links below.
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 pinning the version in the requirements or by forking.
pydrobert-pytorch is available through both Conda and PyPI.
conda install -c sdrobert pydrobert-pytorch
pip install pydrobert-pytorchPlease see the pydrobert page for more details on how to cite this package.
Implementations of
pydrobert.torch._img.{polyharmonic_spline,sparse_image_warp} are based off
Tensorflow's codebase, which is Apache 2.0 licensed.
Implementations of
pydrobert.torch._compat.{broadcast_shapes,TorchVersion,one_hot} were directly
taken from the PyTorch codebase. A number of methods and functions in
pydrobert.torch._straight_through modify PyTorch code (see the file for more
info). PyTorch has a BSD-style license which can be found in the file
LICENSE_pytorch.txt.
The implementation of pydrobert.torch._compat.check_methods was taken
directly from the CPython codebase, Copyright 2007 Google with additional
notices at https://docs.python.org/3/copyright.html?highlight=copyright.
The file pydrobert.torch._textgrid,py was taken with some minor modifications
from
nltk_contrib.
It is Apache 2.0-licensed, with the specific license text saved to
LICENSE_nltk.txt.