* add the zipformer codes, copied from branch from_dan_scaled_adam_exp1119
* support model export with torch.jit.script
* update RESULTS.md
* support exporting streaming model with torch.jit.script
* add results of streaming models, with some minor changes
* update README.md
* add CI test
* update k2 version in requirements-ci.txt
* update pyproject.toml
* add other decoding methods for wenetspeech
* changes for RESULTS.md
* add ngram-lm-scale=0.35 results
* set ngram-lm-scale=0.35 as default
* Update README.md
* add nbest-scale for flie name
* pruned-rnnt5-for-wenetspeech
* style check
* style check
* add streaming conformer
* add streaming decode
* changes codes for fast_beam_search and export cpu jit
* add modified-beam-search for streaming decoding
* add modified-beam-search for streaming decoding
* change for streaming_beam_search.py
* add README.md and RESULTS.md
* change for style_check.yml
* do some changes
* do some changes for export.py
* add some decode commands for usage
* add streaming results on README.md
* add pruned transducer stateless5 recipe for tal_csasr
* do some changes for merging
* change for conformer.py
* add wer and cer for Chinese and English respectively
* fix a error for conformer.py
* pruned-transducer-stateless5 recipe for aishell4
* pruned-transducer-stateless5 recipe for aishell4
* do some changes and text normalize
* do some changes
* add text normalize
* combine the training data and decode without webdataset
* update codes for merging
* Do a change for READMD.md
* add pruned-rnnt2 recipe for alimeeting dataset
* update code for merging
* change LilcomHdf5Writer to ChunkedLilcomHdf5Writer
* change for test.yml
* change for test.yml
* change for test.yml
* change for workflow yml
* change for yml
* change for yml
* change for README.md
* change for yml
* solve the conflicts
* solve the conflicts
* add char-based pruned-rnnt2 for wenetspeech
* style check
* style check
* change for export.py
* do some changes
* do some changes
* a small change for .flake8
* solve the conflicts
* 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.
