* add ScaledLSTM
* add RNNEncoderLayer and RNNEncoder classes in lstm.py
* add RNN and Conv2dSubsampling classes in lstm.py
* hardcode bidirectional=False
* link from pruned_transducer_stateless2
* link scaling.py pruned_transducer_stateless2
* copy from pruned_transducer_stateless2
* modify decode.py pretrained.py test_model.py train.py
* copy streaming decoding files from pruned_transducer_stateless2
* modify streaming decoding files
* simplified code in ScaledLSTM
* flat weights after scaling
* pruned2 -> pruned4
* link __init__.py
* fix style
* remove add_model_arguments
* modify .flake8
* fix style
* fix scale value in scaling.py
* add random combiner for training deeper model
* add using proj_size
* add scaling converter for ScaledLSTM
* support jit trace
* add using averaged model in export.py
* modify test_model.py, test if the model can be successfully exported by jit.trace
* modify pretrained.py
* support streaming decoding
* fix model.py
* Add cut_id to recognition results
* Add cut_id to recognition results
* do not pad in Conv subsampling module; add tail padding during decoding.
* update RESULTS.md
* minor fix
* fix doc
* update README.md
* minor change, filter infinite loss
* remove the condition of raise error
* modify type hint for the return value in model.py
* minor change
* modify RESULTS.md
Co-authored-by: pkufool <wkang.pku@gmail.com>
* init files
* use average value as memory vector for each chunk
* change tail padding length from right_context_length to chunk_length
* correct the files, ln -> cp
* fix bug in conv_emformer_transducer_stateless2/emformer.py
* fix doc in conv_emformer_transducer_stateless/emformer.py
* refactor init states for stream
* modify .flake8
* fix bug about memory mask when memory_size==0
* add @torch.jit.export for init_states function
* update RESULTS.md
* minor change
* update README.md
* modify doc
* replace torch.div() with <<
* fix bug, >> -> <<
* use i&i-1 to judge if it is a power of 2
* minor fix
* fix error in RESULTS.md
* copy files from existing branch
* add rule in .flake8
* monir style fix
* fix typos
* add tail padding
* refactor, use fixed-length cache for batch decoding
* copy from streaming branch
* copy from streaming branch
* modify emformer states stack and unstack, streaming decoding, to be continued
* refactor Stream class
* remane streaming_feature_extractor.py
* refactor streaming decoding
* test states stack and unstack
* fix bugs, no grad, and num_proccessed_frames
* add modify_beam_search, fast_beam_search
* support torch.jit.export
* use torch.div
* copy from pruned_transducer_stateless4
* modify export.py
* add author info
* delete other test functions
* minor fix
* modify doc
* fix style
* minor fix doc
* minor fix
* minor fix doc
* update RESULTS.md
* fix typo
* add info
* fix typo
* fix doc
* add test function for conv module, and minor fix.
* add copyright info
* minor change of test_emformer.py
* fix doc of stack and unstack, test case with batch_size=1
* update README.md
* Copy files for editing.
* Add random combine from #229.
* Minor fixes.
* Pass model parameters from the command line.
* Fix warnings.
* Fix warnings.
* Update readme.
* Rename to avoid conflicts.
* Update results.
* Add CI for pruned_transducer_stateless5
* Typo fixes.
* Remove random combiner.
* Update decode.py and train.py to use periodically averaged models.
* Minor fixes.
* Revert to use random combiner.
* Update results.
* Minor fixes.
* 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.
* 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.
* Begin to use multiple datasets.
* Finish preparing training datasets.
* Minor fixes
* Copy files.
* Finish training code.
* Display losses for gigaspeech and librispeech separately.
* Fix decode.py
* Make the probability to select a batch from GigaSpeech configurable.
* Update results.
* Minor fixes.
