Releases: sdrobert/pydrobert-pytorch
Refactored for Python 3, breaking backwards compatibility
A considerable amount of refactoring occurred for this build, chiefly to get
rid of Python 2.7 support. While the functionality did not change much for this
version, we have switched from a pkgutil
-style pydrobert
namespace to
PEP-420-style namespaces. As a result, this package is not
backwards-compatible with previous pydrobert
packages! Make sure that if any
of the following are installed, they exceed the following version thresholds:
pydrobert-param >0.2.0
pydrobert-kaldi >0.5.3
pydrobert-speech >0.1.0
Miscellaneous other stuff:
- Type hints everywhere
- Shifted python source to
src/
- Black-formatted remaining source
- Removed
future
dependency - Shifted most of the configuration to
setup.cfg
, leaving only a shell
insetup.py
to remain compatible with Conda builds - Added
pyproject.toml
for PEP 517. tox.ini
for TOX testing- Switched to AppVeyor for CI
- Added changelog :D
Bug fix from v0.2.0
This release is essentially identical to v0.2.0, modulo a bug fix.
Too many bits-and-bobs to count. I should keep a change log. Main features seem to be
- Optuna hooks for data loaders (see pydrobert-param)
- N-gram language modelling on the GPU. Can read in ARPA-LM files. Good for shallow fusion AM+LM.
- SpecAugment layer for, well, SpecAugment
- A variety of fixes and new features to my data loaders.
This is the last release that will support Python 2.7.
SpecAugment, Optuna, N-gram language modelling, and bug fixes
EDIT: Overnight, pytorch released a version that caused an error. Do not use this version! I'll update and release v0.2.1.
Too many bits-and-bobs to count. I should keep a change log. Main features seem to be
- Optuna hooks for data loaders (see pydrobert-param)
- N-gram language modelling on the GPU. Can read in ARPA-LM files. Good for shallow fusion AM+LM.
- SpecAugment layer for, well, SpecAugment
- A variety of fixes and new features to my data loaders.
More features! Attention, edit-distance based objectives, and more
Welp, the previous version should've been 0.1.0, but now we're at 0.1.0.
Besides bug fixes, this version includes
- The layers submodule, which currently includes attention mechansims for seq2seq. This also includes transformer network attention
- Convert from torch data dirs to NIST TRN and CTM, and back again
- Edit-distance based losses and reward functions, including Optimal Character Distillation
- Cleaner documentation on website, with tutorials
Lots of new stuff
Welp, I guess I never published the v0.0.1 tag... I just released it. What's new? Probably
- pydrobert.torch.data has been cleaned up. There are lots of tools for transducing data. Plus there are examples.
- pydrobert.torch.estimators has been added. The functions therein can be used for discretely sampling data. More examples.
- miscellaneous bug fixes
Enjoy