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icefall/egs/librispeech/ASR/README.md
LIyong.Guo c4ee2bc0af
[Ready to merge]stateless6: states4 + hubert distillation. (#387) 
* a copy of stateless4 as base

* distillation with hubert

* fix typo

* example usage

* usage

* Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* fix comment

* add results of 100hours

* Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* check fairseq and quantization

* a short intro to distillation framework

* Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* add intro of statless6 in README

* fix type error of dst_manifest_dir

* Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* make export.py call stateless6/train.py instead of stateless2/train.py

* update results by stateless6

* adjust results format

* fix typo

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
2022-05-28 12:37:50 +08:00

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Introduction

Please refer to https://icefall.readthedocs.io/en/latest/recipes/librispeech/index.html for how to run models in this recipe.

./RESULTS.md contains the latest results.

Transducers

There are various folders containing the name transducer in this folder. The following table lists the differences among them.

Encoder Decoder Comment
transducer Conformer LSTM
transducer_stateless Conformer Embedding + Conv1d Using optimized_transducer from computing RNN-T loss
transducer_stateless2 Conformer Embedding + Conv1d Using torchaudio for computing RNN-T loss
transducer_lstm LSTM LSTM
transducer_stateless_multi_datasets Conformer Embedding + Conv1d Using data from GigaSpeech as extra training data
pruned_transducer_stateless Conformer Embedding + Conv1d Using k2 pruned RNN-T loss
pruned_transducer_stateless2 Conformer(modified) Embedding + Conv1d Using k2 pruned RNN-T loss
pruned_transducer_stateless3 Conformer(modified) Embedding + Conv1d Using k2 pruned RNN-T loss + using GigaSpeech as extra training data
pruned_transducer_stateless4 Conformer(modified) Embedding + Conv1d same as pruned_transducer_stateless2 + save averaged models periodically during training
pruned_transducer_stateless5 Conformer(modified) Embedding + Conv1d same as pruned_transducer_stateless4 + more layers + random combiner
pruned_transducer_stateless6 Conformer(modified) Embedding + Conv1d same as pruned_transducer_stateless4 + distillation with hubert

The decoder in transducer_stateless is modified from the paper Rnn-Transducer with Stateless Prediction Network. We place an additional Conv1d layer right after the input embedding layer.