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7 Commits

Author SHA1 Message Date
Fangjun Kuang
21096e99d8
Update result for the librispeech recipe using vocab size 500 and att rate 0.8 (#113) 
* 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.
 2021-11-10 14:32:52 +08:00
Fangjun Kuang
4890e27b45
Extract framewise alignment information using CTC decoding (#39) 
* 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.
 2021-10-18 14:24:33 +08:00
Fangjun Kuang
707d7017a7
Support pure ctc decoding requiring neither a lexicon nor an n-gram LM (#58) 
* 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.
 2021-09-26 14:21:49 +08:00
Fangjun Kuang
7f8e3a673a
Add commands for reproducing. (#40) 
* Add commands for reproducing.

* Use --bucketing-sampler by default.
 2021-09-09 13:50:31 +08:00
Fangjun Kuang
abadc71415
Use new APIs with k2.RaggedTensor (#38) 
* Use new APIs with k2.RaggedTensor

* Fix style issues.

* Update the installation doc, saying it requires at least k2 v1.7

* Use k2 v1.7
 2021-09-08 14:55:30 +08:00
pkufool
f4223ee110
Add TDNN-LSTM-CTC Results (#25) 
* Add tdnn-lstm pretrained model and results

* Add docs for TDNN-LSTM-CTC

* Minor fix

* Fix typo

* Fix style checking
 2021-08-24 21:09:27 +08:00
pkufool
ef233486ae
The training script produce WER of 2.57% on librispeech test-clean (#13) 
* Add grad_clip and weight-decay, small fix of dataloader and masking

* Add RESULTS.md
 2021-08-20 10:08:08 +08:00