Metadata-Version: 2.1
Name: pydrobert-pytorch
Version: 0.0.2
Summary: PyTorch utilities for ML, specifically speech
Home-page: https://github.com/sdrobert/pydrobert-pytorch
Author: Sean Robertson
Author-email: sdrobert@cs.toronto.edu
License: Apache 2.0
Description: [![Build Status](https://travis-ci.com/sdrobert/pydrobert-pytorch.svg?branch=master)](https://travis-ci.com/sdrobert/pydrobert-pytorch)
        [![Documentation Status](https://readthedocs.org/projects/pydrobert-pytorch/badge/?version=latest)](https://pydrobert-pytorch.readthedocs.io/en/latest/?badge=latest)
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        
        # 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 of `torch.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](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm)
          file formats. Feature data and senone alignments from
          [Kaldi](http://kaldi-asr.org/) can be converted to this format using the
          command line hooks from
          [pydrobert-kaldi](https://github.com/sdrobert/pydrobert-kaldi).
        
        ## Documentation
        
        - [Latest](https://pydrobert-pytorch.readthedocs.io/en/latest/)
        - [Stable](https://pydrobert-pytorch.readthedocs.io/en/stable/)
        
        ## Installation
        
        `pydrobert-pytorch` is available through both Conda and PyPI.
        
        ``` bash
        conda install -c sdrobert pydrobert-pytorch
        pip install pydrobert-pytorch
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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