- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle
deprecations in PyTorch ≥2.3.0
- Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast
with the new utilities across all training and inference scripts
- Update all torch.load calls to include weights_only=False for compatibility with
newer PyTorch versions
- some AudioTransform classes produce audio signals out of range [-1,+1]
- Resample produced 1.0079
- The range [-10,+10] was chosen to still be able to reliably
distinguish from the [-32k,+32k] signal...
- this is related to : https://github.com/lhotse-speech/lhotse/issues/1254
* 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)
* 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
* add stats about duration and padding proportion
* add for utt_duration
* add stats for other recipes
* add stats for other 2 recipes
* modify doc
* minor change
* support streaming in conformer
* Add more documents
* support streaming on pruned_transducer_stateless2; add delay penalty; fixes for decode states
* Minor fixes
* streaming for pruned_transducer_stateless4
* Fix conv cache error, support async streaming decoding
* Fix style
* Fix style
* Fix style
* Add torch.jit.export
* mask the initial cache
* Cutting off invalid frames of encoder_embed output
* fix relative positional encoding in streaming decoding for compution saving
* Minor fixes
* Minor fixes
* Minor fixes
* Minor fixes
* Minor fixes
* Fix jit export for torch 1.6
* Minor fixes for streaming decoding
* Minor fixes on decode stream
* move model parameters to train.py
* make states in forward streaming optional
* update pretrain to support streaming model
* update results.md
* update tensorboard and pre-models
* fix typo
* Fix tests
* remove unused arguments
* add streaming decoding ci
* Minor fix
* Minor fix
* disable right context by default
* Add fast_beam_search_nbest.
* Fix CI errors.
* Fix CI errors.
* More fixes.
* Small fixes.
* Support using log_add in LG decoding with fast_beam_search.
* Support LG decoding in pruned_transducer_stateless
* Support LG for pruned_transducer_stateless2.
* Support LG for fast beam search.
* Minor fixes.
* Use jsonl for cutsets in the librispeech recipe.
* Use lazy cutset for all recipes.
* More fixes to use lazy CutSet.
* Remove force=True from logging to support Python < 3.8
* Minor fixes.
* Fix style issues.
* 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
