* Copy files for editing.
* Use librispeech + gigaspeech with modified conformer.
* Support specifying number of workers for on-the-fly feature extraction.
* Feature extraction code for GigaSpeech.
* Combine XL splits lazily during training.
* Fix warnings in decoding.
* Add decoding code for GigaSpeech.
* Fix decoding the gigaspeech dataset.
We have to use the decoder/joiner networks for the GigaSpeech dataset.
* Disable speed perturbe for XL subset.
* Compute the Nbest oracle WER for RNN-T decoding.
* Minor fixes.
* Minor fixes.
* Add results.
* Update results.
* Update CI.
* Update results.
* Fix style issues.
* Update results.
* Fix style issues.
* update tedlium3-pruned-transducer-stateless-codes
* update README.md
* update README.md
* add fast beam search for decoding
* do a change for RESULTS.md
* do a change for RESULTS.md
* do a fix
* do some changes for pruned RNN-T
* Add modified beam search for pruned rnn-t.
* Fix style issues.
* Update RESULTS.md.
* Fix typos.
* Minor fixes.
* Test the pre-trained model using GitHub actions.
* Let the user install optimized_transducer on her own.
* Fix errors in GitHub CI.
* Add modified transducer for aishell.
* Minor fixes.
* Add extra data in transducer training.
The extra data is from http://www.openslr.org/62/
* Update export.py and pretrained.py
* Update CI to install pretrained models with aishell.
* Update results.
* Update results.
* Update README.
* Use symlinks to avoid copies.
* Disable weight decay.
* Remove input feature batchnorm..
* Replace BatchNorm in the Conformer model with LayerNorm.
* Use tanh in the joint network.
* Remove sos ID.
* Reduce the number of decoder layers from 4 to 2.
* Minor fixes.
* Fix typos.
* Begin to add RNN-T training for librispeech.
* Copy files from conformer_ctc.
Will edit it.
* Use conformer/transformer model as encoder.
* Begin to add training script.
* Add training code.
* Remove long utterances to avoid OOM when a large max_duraiton is used.
* Begin to add decoding script.
* Add decoding script.
* Minor fixes.
* Add beam search.
* Use LSTM layers for the encoder.
Need more tunings.
* Use stateless decoder.
* Minor fixes to make it ready for merge.
* Fix README.
* Update RESULT.md to include RNN-T Conformer.
* Minor fixes.
* Fix tests.
* Minor fixes.
* Minor fixes.
* Fix tests.
* 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.
* Add recipe for the yes_no dataset.
* Refactoring: Remove unused code.
* Add Colab notebook for the yesno dataset.
* Add GitHub actions to run yesno.
* Fix a typo.
* Minor fixes.
* Train more epochs for GitHub actions.
* Minor fixes.
* Minor fixes.
* Fix style issues.
