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* 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>
Symbolic link
1 line
40 B
Python
Symbolic link
1 line
40 B
Python
../pruned_transducer_stateless2/optim.py |