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* 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.
20 lines
380 B
Markdown
20 lines
380 B
Markdown
## Introduction
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The encoder consists of LSTM layers in this folder. You can use the
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following command to start the training:
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```bash
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cd egs/librispeech/ASR
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export CUDA_VISIBLE_DEVICES="0,1,2"
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./transducer_lstm/train.py \
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--world-size 3 \
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--num-epochs 30 \
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--start-epoch 0 \
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--exp-dir transducer_lstm/exp \
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--full-libri 1 \
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--max-duration 300 \
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--lr-factor 3
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```
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