* shuffled full/partial librispeech data
* fixed the code style issue
* Shuffled full librispeech data off-line
* Fixed style, addressed comments, and removed redandunt codes
* Used the suggested version of black
* Propagated the changes to other folders for librispeech (except
conformer_mmi and streaming_conformer_ctc)
* print out timestamps during decoding
* add word-level alignments
* support to compute mean symbol delay with word-level alignments
* print variance of symbol delay
* update doc
* support to compute delay for pruned_transducer_stateless4
* fix bug
* add doc
* 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>
non stable training in some scenarios. The clamping range is set to (-10,2).
Note that this change may cause unexpected effect if you resume
training from a model that is trained without clamping.
* 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>
* Sort result to make it more convenient to compare decoding results
* Add cut_id to recognition results
* add cut_id to results for all recipes
* Fix torch.jit.script
* Fix comments
* Minor fixes
* Fix torch.jit.tracing for Pytorch version before v1.9.0
* WIP: Support exporting to ONNX format
* Minor fixes.
* Combine encoder/decoder/joiner into a single file.
* Revert merging three onnx models into a single one.
It's quite time consuming to extract a sub-graph from the combined
model. For instance, it takes more than one hour to extract
the encoder model.
* Update CI to test ONNX models.
* Decode with exported models.
* Fix typos.
* Add more doc.
* Remove ncnn as it is not fully tested yet.
* Fix as_strided for streaming conformer.
