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icefall/egs/wenetspeech/ASR
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Wei Kang 219bba1310
zipformer wenetspeech (#1130) 
* copy files

* update train.py

* small fixes

* Add decode.py

* Fix dataloader in decode.py

* add blank penalty

* Add blank-penalty to other decoding method

* Minor fixes

* add zipformer2 recipe

* Minor fixes

* Remove pruned7

* export and test models

* Replace bpe with tokens in export.py and pretrain.py

* Minor fixes

* Minor fixes

* Minor fixes

* Fix export

* Update results

* Fix zipformer-ctc

* Fix ci

* Fix ci

* Fix CI

* Fix CI

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Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
2023-06-26 09:33:18 +08:00
..
local
Shallow fusion for Aishell (#954)
2023-04-03 16:20:29 +08:00
pruned_transducer_stateless2
zipformer wenetspeech (#1130)
2023-06-26 09:33:18 +08:00
pruned_transducer_stateless5
zipformer wenetspeech (#1130)
2023-06-26 09:33:18 +08:00
zipformer
zipformer wenetspeech (#1130)
2023-06-26 09:33:18 +08:00
finetune.sh
Add finetuning script for aishell (#974)
2023-03-30 17:08:46 +08:00
prepare.sh
Shallow fusion for Aishell (#954)
2023-04-03 16:20:29 +08:00
README.md
[WIP] Pruned-transducer-stateless5-for-WenetSpeech (offline and streaming) (#447)
2022-07-28 12:54:27 +08:00
RESULTS.md
zipformer wenetspeech (#1130)
2023-06-26 09:33:18 +08:00
shared
[Ready to merge] Pruned Transducer Stateless2 for WenetSpeech (char-based) (#349)
2022-05-23 17:13:01 +08:00

README.md

Introduction

This recipe includes some different ASR models trained with WenetSpeech.

./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
pruned_transducer_stateless2 Conformer(modified) Embedding + Conv1d Using k2 pruned RNN-T loss
pruned_transducer_stateless5 Conformer(modified) Embedding + Conv1d Using k2 pruned RNN-T loss

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.