* Fix torch.nn.Embedding error for torch below 1.8.0
* Changes to fbank computation, use lilcom chunky writer
* Add min in q,k,v of attention
* Remove learnable offset, use relu instead.
* Experiments based on SpecAugment change
* Merge specaug change from Mingshuang.
* Use much more aggressive SpecAug setup
* Fix to num_feature_masks bug I introduced; reduce max_frames_mask_fraction 0.4->0.3
* Change p=0.5->0.9, mask_fraction 0.3->0.2
* Change p=0.9 to p=0.8 in SpecAug
* Fix num_time_masks code; revert 0.8 to 0.9
* Change max_frames from 0.2 to 0.15
* Remove ReLU in attention
* Adding diagnostics code...
* Refactor/simplify ConformerEncoder
* First version of rand-combine iterated-training-like idea.
* Improvements to diagnostics (RE those with 1 dim
* Add pelu to this good-performing setup..
* Small bug fixes/imports
* Add baseline for the PeLU expt, keeping only the small normalization-related changes.
* pelu_base->expscale, add 2xExpScale in subsampling, and in feedforward units.
* Double learning rate of exp-scale units
* Combine ExpScale and swish for memory reduction
* Add import
* Fix backprop bug
* Fix bug in diagnostics
* Increase scale on Scale from 4 to 20
* Increase scale from 20 to 50.
* Fix duplicate Swish; replace norm+swish with swish+exp-scale in convolution module
* Reduce scale from 50 to 20
* Add deriv-balancing code
* Double the threshold in brelu; slightly increase max_factor.
* Fix exp dir
* Convert swish nonlinearities to ReLU
* Replace relu with swish-squared.
* Restore ConvolutionModule to state before changes; change all Swish,Swish(Swish) to SwishOffset.
* Replace norm on input layer with scale of 0.1.
* Extensions to diagnostics code
* Update diagnostics
* Add BasicNorm module
* Replace most normalizations with scales (still have norm in conv)
* Change exp dir
* Replace norm in ConvolutionModule with a scaling factor.
* use nonzero threshold in DerivBalancer
* Add min-abs-value 0.2
* Fix dirname
* Change min-abs threshold from 0.2 to 0.5
* Scale up pos_bias_u and pos_bias_v before use.
* Reduce max_factor to 0.01
* Fix q*scaling logic
* Change max_factor in DerivBalancer from 0.025 to 0.01; fix scaling code.
* init 1st conv module to smaller variance
* Change how scales are applied; fix residual bug
* Reduce min_abs from 0.5 to 0.2
* Introduce in_scale=0.5 for SwishExpScale
* Fix scale from 0.5 to 2.0 as I really intended..
* Set scaling on SwishExpScale
* Add identity pre_norm_final for diagnostics.
* Add learnable post-scale for mha
* Fix self.post-scale-mha
* Another rework, use scales on linear/conv
* Change dir name
* Reduce initial scaling of modules
* Bug-fix RE bias
* Cosmetic change
* Reduce initial_scale.
* Replace ExpScaleRelu with DoubleSwish()
* DoubleSwish fix
* Use learnable scales for joiner and decoder
* Add max-abs-value constraint in DerivBalancer
* Add max-abs-value
* Change dir name
* Remove ExpScale in feedforward layes.
* Reduce max-abs limit from 1000 to 100; introduce 2 DerivBalancer modules in conv layer.
* Make DoubleSwish more memory efficient
* Reduce constraints from deriv-balancer in ConvModule.
* Add warmup mode
* Remove max-positive constraint in deriv-balancing; add second DerivBalancer in conv module.
* Add some extra info to diagnostics
* Add deriv-balancer at output of embedding.
* Add more stats.
* Make epsilon in BasicNorm learnable, optionally.
* Draft of 0mean changes..
* Rework of initialization
* Fix typo
* Remove dead code
* Modifying initialization from normal->uniform; add initial_scale when initializing
* bug fix re sqrt
* Remove xscale from pos_embedding
* Remove some dead code.
* Cosmetic changes/renaming things
* Start adding some files..
* Add more files..
* update decode.py file type
* Add remaining files in pruned_transducer_stateless2
* Fix diagnostics-getting code
* Scale down pruned loss in warmup mode
* Reduce warmup scale on pruned loss form 0.1 to 0.01.
* Remove scale_speed, make swish deriv more efficient.
* Cosmetic changes to swish
* Double warm_step
* Fix bug with import
* Change initial std from 0.05 to 0.025.
* Set also scale for embedding to 0.025.
* Remove logging code that broke with newer Lhotse; fix bug with pruned_loss
* Add norm+balancer to VggSubsampling
* Incorporate changes from master into pruned_transducer_stateless2.
* Add max-abs=6, debugged version
* Change 0.025,0.05 to 0.01 in initializations
* Fix balancer code
* Whitespace fix
* Reduce initial pruned_loss scale from 0.01 to 0.0
* Increase warm_step (and valid_interval)
* Change max-abs from 6 to 10
* Change how warmup works.
* Add changes from master to decode.py, train.py
* Simplify the warmup code; max_abs 10->6
* Make warmup work by scaling layer contributions; leave residual layer-drop
* Fix bug
* Fix test mode with random layer dropout
* Add random-number-setting function in dataloader
* Fix/patch how fix_random_seed() is imported.
* Reduce layer-drop prob
* Reduce layer-drop prob after warmup to 1 in 100
* Change power of lr-schedule from -0.5 to -0.333
* Increase model_warm_step to 4k
* Change max-keep-prob to 0.95
* Refactoring and simplifying conformer and frontend
* Rework conformer, remove some code.
* Reduce 1st conv channels from 64 to 32
* Add another convolutional layer
* Fix padding bug
* Remove dropout in output layer
* Reduce speed of some components
* Initial refactoring to remove unnecessary vocab_size
* Fix RE identity
* Bug-fix
* Add final dropout to conformer
* Remove some un-used code
* Replace nn.Linear with ScaledLinear in simple joiner
* Make 2 projections..
* Reduce initial_speed
* Use initial_speed=0.5
* Reduce initial_speed further from 0.5 to 0.25
* Reduce initial_speed from 0.5 to 0.25
* Change how warmup is applied.
* Bug fix to warmup_scale
* Fix test-mode
* Remove final dropout
* Make layer dropout rate 0.075, was 0.1.
* First draft of model rework
* Various bug fixes
* Change learning speed of simple_lm_proj
* Revert transducer_stateless/ to state in upstream/master
* Fix to joiner to allow different dims
* Some cleanups
* Make training more efficient, avoid redoing some projections.
* Change how warm-step is set
* First draft of new approach to learning rates + init
* Some fixes..
* Change initialization to 0.25
* Fix type of parameter
* Fix weight decay formula by adding 1/1-beta
* Fix weight decay formula by adding 1/1-beta
* Fix checkpoint-writing
* Fix to reading scheudler from optim
* Simplified optimizer, rework somet things..
* Reduce model_warm_step from 4k to 3k
* Fix bug in lambda
* Bug-fix RE sign of target_rms
* Changing initial_speed from 0.25 to 01
* Change some defaults in LR-setting rule.
* Remove initial_speed
* Set new scheduler
* Change exponential part of lrate to be epoch based
* Fix bug
* Set 2n rule..
* Implement 2o schedule
* Make lrate rule more symmetric
* Implement 2p version of learning rate schedule.
* Refactor how learning rate is set.
* Fix import
* Modify init (#301)
* update icefall/__init__.py to import more common functions.
* update icefall/__init__.py
* make imports style consistent.
* exclude black check for icefall/__init__.py in pyproject.toml.
* Minor fixes for logging (#296)
* Minor fixes for logging
* Minor fix
* Fix dir names
* Modify beam search to be efficient with current joienr
* Fix adding learning rate to tensorboard
* Fix docs in optim.py
* Support mix precision training on the reworked model (#305)
* Add mix precision support
* Minor fixes
* Minor fixes
* Minor fixes
* Tedlium3 pruned transducer stateless (#261)
* 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 mix precision support
* Minor fixes
* Minor fixes
* Updating RESULTS.md; fix in beam_search.py
* Fix rebase
* Code style check for librispeech pruned transducer stateless2 (#308)
* Update results for tedlium3 pruned RNN-T (#307)
* Update README.md
* Fix CI errors. (#310)
* Add more results
* Fix tensorboard log location
* Add one more epoch of full expt
* fix comments
* Add results for mixed precision with max-duration 300
* Changes for pretrained.py (tedlium3 pruned RNN-T) (#311)
* GigaSpeech recipe (#120)
* initial commit
* support download, data prep, and fbank
* on-the-fly feature extraction by default
* support BPE based lang
* support HLG for BPE
* small fix
* small fix
* chunked feature extraction by default
* Compute features for GigaSpeech by splitting the manifest.
* Fixes after review.
* Split manifests into 2000 pieces.
* set audio duration mismatch tolerance to 0.01
* small fix
* add conformer training recipe
* Add conformer.py without pre-commit checking
* lazy loading and use SingleCutSampler
* DynamicBucketingSampler
* use KaldifeatFbank to compute fbank for musan
* use pretrained language model and lexicon
* use 3gram to decode, 4gram to rescore
* Add decode.py
* Update .flake8
* Delete compute_fbank_gigaspeech.py
* Use BucketingSampler for valid and test dataloader
* Update params in train.py
* Use bpe_500
* update params in decode.py
* Decrease num_paths while CUDA OOM
* Added README
* Update RESULTS
* black
* Decrease num_paths while CUDA OOM
* Decode with post-processing
* Update results
* Remove lazy_load option
* Use default `storage_type`
* Keep the original tolerance
* Use split-lazy
* black
* Update pretrained model
Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
* Add LG decoding (#277)
* Add LG decoding
* Add log weight pushing
* Minor fixes
* Support computing RNN-T loss with torchaudio (#316)
* Support modified beam search decoding for streaming inference with Emformer model.
* Formatted imports.
* Update results for torchaudio RNN-T. (#322)
* Fixed streaming decoding codes for emformer model.
* Fixed docs.
* Sorted imports for transducer_emformer/streaming_feature_extractor.py
* Minor fix for transducer_emformer/streaming_feature_extractor.py
Co-authored-by: pkufool <wkang@pku.org.cn>
Co-authored-by: Daniel Povey <dpovey@gmail.com>
Co-authored-by: Mingshuang Luo <37799481+luomingshuang@users.noreply.github.com>
Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
Co-authored-by: Guo Liyong <guonwpu@qq.com>
Co-authored-by: Wang, Guanbo <wgb14@outlook.com>
* 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.
* Add recipe for the yes_no dataset.
* Refactoring: Remove unused code.
* Add Colab notebook for the yesno dataset.
* Add GitHub actions to run yesno.
* Fix a typo.
* Minor fixes.
* Train more epochs for GitHub actions.
* Minor fixes.
* Minor fixes.
* Fix style issues.
