* parse timestamps and texts for BPE-based models
* parse timestamps (frame indexes) and texts for other cases
* add test functions
* add parse_fsa_timestamps_and_texts function, test in conformer_ctc3/decode.py
* calculate symbol delay for (start, end) timestamps
* Add utility for shallow fusion
* test batch size == 1 without shallow fusion
* Use shallow fusion for modified-beam-search
* Modified beam search with ngram rescoring
* Fix code according to review
Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
* 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.
* 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.
