Extract feature
link kaldi timit example dirs (local
steps
utils
)
excute run.sh
to extract 40 dim fbank feature
run feature_transform.sh
to get 123 dim feature as described in Graves2013
Train CTC acoustic model
python train_ctc.py --lr 1e-3 --bi --dropout 0.5 --out exp/ctc_bi_lr1e-3 --schedule
Train RNNT joint model
python train_rnnt.py --lr 4e-4 --bi --dropout 0.5 --out exp/rnnt_bi_lr4e-4 --schedule
Decode
python eval.py <path to best model> [--ctc] --bi
Model | PER |
---|---|
CTC | 21.38 |
RNN-T | 20.59 |