PyTorch utilities for ML, specifically speech
Project description
pydrobert-pytorch
PyTorch utilities for Machine Learning. This is an eclectic mix of utilities that I've used in my various projects, but have been tailored to be as generic as possible.
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.
Overview
Functionality is split by submodule. They include
pydrobert.torch.estimators
: Implements a number of popular gradient estimators in ML literature. Useful for RL tasks, or anything that needs discrete samples.pydrobert.torch.training
: Utilities that should be useful to most model training loops, even the most esoteric.TrainingStateController
can be used to persist model and optimizer states across runs, and manage non-determinism.pydrobert.torch.data
: Primarily serves as a means to manipulate speech data. It contains subclasses oftorch.utils.data.DataLoader
for both random and sequential access of speech data, as well as examples of how to use them.pydrobert.torch.data
also contains functions for transducing back and forth between tensors and transcriptions. In particular, this package comes with command line hooks for converting to and from NIST sclite file formats. Feature data and senone alignments from Kaldi can be converted to this format using the command line hooks from pydrobert-kaldi.
Documentation
Installation
pydrobert-pytorch
is available through both Conda and PyPI.
conda install -c sdrobert pydrobert-pytorch
pip install pydrobert-pytorch
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