* 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>
* 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.
