- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle
deprecations in PyTorch ≥2.3.0
- Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast
with the new utilities across all training and inference scripts
- Update all torch.load calls to include weights_only=False for compatibility with
newer PyTorch versions
* Update RESULTS using vocab size 500, att rate 0.8
* Update README.
* Refactoring.
Since FSAs in an Nbest object are linear in structure, we can
add the scores of a path to compute the total scores.
* Update documentation.
* Change default vocab size from 5000 to 500.
* Use new APIs with k2.RaggedTensor
* Fix style issues.
* Update the installation doc, saying it requires at least k2 v1.7
* Extract framewise alignment information using CTC decoding.
* Print environment information.
Print information about k2, lhotse, PyTorch, and icefall.
* Fix CI.
* Fix CI.
* Compute framewise alignment information of the LibriSpeech dataset.
* Update comments for the time to compute alignments of train-960.
* Preserve cut id in mix cut transformer.
* Minor fixes.
* Add doc about how to extract framewise alignments.
* Add CI to run pre-trained models.
* Minor fixes.
* Install kaldifeat
* Install a CPU version of PyTorch.
* Fix CI errors.
* Disable decoder layers in pretrained.py if it is not used.
* Clone pre-trained model from GitHub.
* Minor fixes.
* Minor fixes.
* Minor fixes.
* Rename lattice_score_scale to nbest_scale.
* Support pure CTC decoding requiring neither a lexicion nor an n-gram LM.
* Fix style issues.
* Fix a typo.
* Minor fixes.
* Refactor decode.py to make it more readable and more modular.
* Fix an error.
Nbest.fsa should always have token IDs as labels and
word IDs as aux_labels.
* Add nbest decoding.
* Compute edit distance with k2.
* Refactor nbest-oracle.
* Add rescore with nbest lists.
* Add whole-lattice rescoring.
* Add rescoring with attention decoder.
* Refactoring.
* Fixes after refactoring.
* Fix a typo.
* Minor fixes.
* Replace [] with () for shapes.
* Use k2 v1.9
* Use Levenshtein graphs/alignment from k2 v1.9
* [doc] Require k2 >= v1.9
* Minor fixes.
* Support computing nbest oracle WER.
* Add scale to all nbest based decoding/rescoring methods.
* Add script to run pretrained models.
* Use torchaudio to extract features.
* Support decoding multiple files at the same time.
Also, use kaldifeat for feature extraction.
* Support decoding with LM rescoring and attention-decoder rescoring.
* Minor fixes.
* Replace scale with lattice-score-scale.
* Add usage example with a provided pretrained model.
