Validate that there are no OOV tokens in BPE-based lexicons. #359
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yaozengwei
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* 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 <[email protected]> * Add LG decoding (#277) * Add LG decoding * Add log weight pushing * Minor fixes * Support computing RNN-T loss with torchaudio (#316) * Update results for torchaudio RNN-T. (#322) * Fix some typos. (#329) * fix fp16 option in example usage (#332) * Support averaging models with weight tying. (#333) * Support specifying iteration number of checkpoints for decoding. (#336) See also #289 * Modified conformer with multi datasets (#312) * 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 results. (#340) * Update results. * Typo fixes. * Validate generated manifest files. (#338) * Validate generated manifest files. (#338) * Save batch to disk on OOM. (#343) * Save batch to disk on OOM. * minor fixes * Fixes after review. * Fix style issues. * Fix decoding for gigaspeech in the libri + giga setup. (#345) * Model average (#344) * First upload of model average codes. * minor fix * update decode file * update .flake8 * rename pruned_transducer_stateless3 to pruned_transducer_stateless4 * change epoch number counter starting from 1 instead of 0 * minor fix of pruned_transducer_stateless4/train.py * refactor the checkpoint.py * minor fix, update docs, and modify the epoch number to count from 1 in the pruned_transducer_stateless4/decode.py * update author info * add docs of the scaling in function average_checkpoints_with_averaged_model * Save batch to disk on exception. (#350) * Bug fix (#352) * Keep model_avg on cpu (#348) * keep model_avg on cpu * explicitly convert model_avg to cpu * minor fix * remove device convertion for model_avg * modify usage of the model device in train.py * change model.device to next(model.parameters()).device for decoding * assert params.start_epoch>0 * assert params.start_epoch>0, params.start_epoch * Do some changes for aishell/ASR/transducer stateless/export.py (#347) * do some changes for aishell/ASR/transducer_stateless/export.py * Support decoding with averaged model when using --iter (#353) * support decoding with averaged model when using --iter * minor fix * monir fix of copyright date * Stringify torch.__version__ before serializing it. (#354) * Run decode.py in GitHub actions. (#356) * Ignore padding frames during RNN-T decoding. (#358) * Ignore padding frames during RNN-T decoding. * Fix outdated decoding code. * Minor fixes. * Support --iter in export.py (#360) * GigaSpeech RNN-T experiments (#318) * Copy RNN-T recipe from librispeech * flake8 * flake8 * Update params * gigaspeech decode * black * Update results * syntax highlight * Update RESULTS.md * typo * Update decoding script for gigaspeech and remove duplicate files. (#361) * Validate that there are no OOV tokens in BPE-based lexicons. (#359) * Validate that there are no OOV tokens in BPE-based lexicons. * Typo fixes. * Decode gigaspeech in GitHub actions (#362) * Add CI for gigaspeech. * Update results for libri+giga multi dataset setup. (#363) * Update results for libri+giga multi dataset setup. * Update GigaSpeech reults (#364) * Update decode.py * Update export.py * Update results * Update README.md * Fix GitHub CI for decoding GigaSpeech dev/test datasets (#366) * modify .flake8 * minor fix * minor fix Co-authored-by: Daniel Povey <[email protected]> Co-authored-by: Wei Kang <[email protected]> Co-authored-by: Mingshuang Luo <[email protected]> Co-authored-by: Fangjun Kuang <[email protected]> Co-authored-by: Guo Liyong <[email protected]> Co-authored-by: Wang, Guanbo <[email protected]> Co-authored-by: whsqkaak <[email protected]> Co-authored-by: pehonnet <[email protected]>
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See #357 (comment)
This PR can avoid the following error mentioned in #357 (comment)
@armusc You may find this PR helpful.