We are using multiple machines to do various experiments. It makes
life easier to know which experiment is running on which machine
if we also log the IP and hostname of the machine.
* 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.
* Update index.rst (AS->ASR)
* Update conformer_ctc.rst (pretraind->pretrained)
* Fix some spelling errors.
* Fix some spelling errors.
* Use LossRecord to record and print loss in the training process
* Change the name "LossRecord" to "MetricsTracker"
* 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.
