* 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.
