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icefall/egs/librispeech/ASR
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Fangjun Kuang 2332ba312d
Begin to use multiple datasets in training (#213) 
* Begin to use multiple datasets.

* Finish preparing training datasets.

* Minor fixes

* Copy files.

* Finish training code.

* Display losses for gigaspeech and librispeech separately.

* Fix decode.py

* Make the probability to select a batch from GigaSpeech configurable.

* Update results.

* Minor fixes.
2022-02-21 15:27:27 +08:00
..
conformer_ctc
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
conformer_mmi
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
local
Begin to use multiple datasets in training (#213)
2022-02-21 15:27:27 +08:00
pruned_transducer_stateless
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
streaming_conformer_ctc
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
tdnn_lstm_ctc
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
transducer
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
transducer_lstm
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
transducer_stateless
Reset seed at the beginning of each epoch. (#221)
2022-02-21 15:16:39 +08:00
transducer_stateless_multi_datasets
Begin to use multiple datasets in training (#213)
2022-02-21 15:27:27 +08:00
prepare_giga_speech.sh
Begin to use multiple datasets in training (#213)
2022-02-21 15:27:27 +08:00
prepare.sh
Add MMI training with word pieces as modelling unit. (#6)
2021-10-18 15:20:32 +08:00
README.md
Begin to use multiple datasets in training (#213)
2022-02-21 15:27:27 +08:00
RESULTS-100hours.md
Begin to use multiple datasets in training (#213)
2022-02-21 15:27:27 +08:00
RESULTS.md
Use k2 pruned transducer loss to train conformer-transducer model (#194)
2022-02-17 13:33:54 +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 Comment
transducer Conformer LSTM
transducer_stateless Conformer Embedding + Conv1d
transducer_lstm LSTM LSTM
transducer_stateless_multi_datasets Conformer Embedding + Conv1d Using data from GigaSpeech as extra training data

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.