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icefall/egs/librispeech/ASR
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Fangjun Kuang 5b6699a835
Minor fixes to the RNN-T Conformer model (#152) 
* 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.
2021-12-23 13:54:25 +08:00
..
conformer_ctc
RNN-T Conformer training for LibriSpeech (#143)
2021-12-18 07:42:51 +08:00
conformer_mmi
Set fsa.properties to None after changing its labels in-place. (#121)
2021-11-16 23:11:30 +08:00
local
RNN-T Conformer training for LibriSpeech (#143)
2021-12-18 07:42:51 +08:00
streaming_conformer_ctc
Draft streaming decoding (#89)
2021-11-24 19:35:18 +08:00
tdnn_lstm_ctc
Associate a cut with token alignment (without repeats) (#125)
2021-11-29 18:50:54 +08:00
transducer
Minor fixes to the RNN-T Conformer model (#152)
2021-12-23 13:54:25 +08:00
transducer_lstm
Increase the size of the context in the RNN-T decoder. (#153)
2021-12-23 07:55:02 +08:00
transducer_stateless
Increase the size of the context in the RNN-T decoder. (#153)
2021-12-23 07:55:02 +08:00
prepare.sh
Add MMI training with word pieces as modelling unit. (#6)
2021-10-18 15:20:32 +08:00
README.md
Increase the size of the context in the RNN-T decoder. (#153)
2021-12-23 07:55:02 +08:00
RESULTS.md
Minor fixes to the RNN-T Conformer model (#152)
2021-12-23 13:54:25 +08:00
shared
Refactoring (#4)
2021-08-04 14:53:02 +08:00

README.md

Introduction

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

Transducers

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

Encoder Decoder
transducer Conformer LSTM
transducer_stateless Conformer Embedding + Conv1d
transducer_lstm LSTM LSTM

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.