Commit b8bc4faa authored by Ho Yin Chan's avatar Ho Yin Chan
Browse files

trunk:egs/hkust/s5b add nnet results with wider decoding beam width

git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@2985 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
parent 9285758b
......@@ -40,7 +40,9 @@ nnet_8m_6l/decode_eval_iter260/cer_10:%CER 26.61 [ 2012 / 7562, 419 ins, 555 del
nnet_8m_6l/decode_eval_iter270/cer_10:%CER 25.72 [ 1945 / 7562, 405 ins, 533 del, 1007 sub ]
nnet_8m_6l/decode_eval_iter280/cer_10:%CER 27.43 [ 2074 / 7562, 424 ins, 605 del, 1045 sub ]
nnet_8m_6l/decode_eval_iter290/cer_10:%CER 26.37 [ 1994 / 7562, 410 ins, 572 del, 1012 sub ]
nnet_8m_6l/decode_eval/cer_10:%CER 25.55 [ 1932 / 7562, 405 ins, 549 del, 978 sub ] # 6 layers neural network
nnet_8m_6l/decode_wide_eval/cer_10:%CER 24.13 [ 1825 / 7562, 384 ins, 535 del, 906 sub ] # wider decoding beam width and lattice beam
tri5a_pretrain-dbn_dnn/decode/cer_10:%CER 20.48 [ 1549 / 7562, 383 ins, 468 del, 698 sub ] # pretrained RBM, cross entropy trained DNN
tri5a_pretrain-dbn_dnn_smbr/decode_it1/cer_10:%CER 18.73 [ 1416 / 7562, 306 ins, 453 del, 657 sub ] # sMBR trained DNN
......@@ -91,7 +93,9 @@ nnet_8m_6l/decode_eval_closelm_iter260/cer_10:%CER 20.97 [ 1586 / 7562, 347 ins,
nnet_8m_6l/decode_eval_closelm_iter270/cer_10:%CER 20.50 [ 1550 / 7562, 348 ins, 465 del, 737 sub ]
nnet_8m_6l/decode_eval_closelm_iter280/cer_10:%CER 21.44 [ 1621 / 7562, 354 ins, 520 del, 747 sub ]
nnet_8m_6l/decode_eval_closelm_iter290/cer_10:%CER 20.40 [ 1543 / 7562, 323 ins, 492 del, 728 sub ]
nnet_8m_6l/decode_eval_closelm/cer_10:%CER 20.68 [ 1564 / 7562, 351 ins, 483 del, 730 sub ]
nnet_8m_6l/decode_wide_eval_closelm/cer_10:%CER 17.87 [ 1351 / 7562, 343 ins, 453 del, 555 sub ]
tri5a_pretrain-dbn_dnn/decode_closelm/cer_10:%CER 16.54 [ 1251 / 7562, 346 ins, 413 del, 492 sub ]
tri5a_pretrain-dbn_dnn_smbr/decode_closelm_it1/cer_10:%CER 15.31 [ 1158 / 7562, 280 ins, 410 del, 468 sub ]
......
beam=18.0 # beam for decoding.
lat_beam=10.0 # lattice beam for decoding
first_beam=10.0 # beam for 1st-pass decoding in SAT.
......@@ -139,6 +139,10 @@ steps/decode_nnet_cpu.sh --cmd "$decode_cmd" --nj 2 --iter $n --config conf/deco
steps/decode_nnet_cpu.sh --cmd "$decode_cmd" --nj 2 --iter $n --config conf/decode.config --transform-dir exp/tri5a/decode_eval_closelm exp/tri5a/graph_closelm data/eval exp/nnet_8m_6l/decode_eval_closelm_iter${n} &
done
# wider beam width and lattice beam decoding
steps/decode_nnet_cpu.sh --cmd "$decode_cmd" --nj 2 --config conf/decode_wide.config --transform-dir exp/tri5a/decode_eval exp/tri5a/graph data/eval exp/nnet_8m_6l/decode_wide_eval
steps/decode_nnet_cpu.sh --cmd "$decode_cmd" --nj 2 --config conf/decode_wide.config --transform-dir exp/tri5a/decode_eval_closelm exp/tri5a/graph_closelm data/eval exp/nnet_8m_6l/decode_wide_eval_closelm
# GPU based DNN traing, this was run on CentOS 6.4 with CUDA 5.0
# 6 layers DNN pretrained with restricted boltzmann machine, frame level cross entropy training, sequence discriminative training with sMBR criterion
local/run_dnn.sh
......@@ -199,6 +203,9 @@ for n in 290 280 270 260 250 240 230 220 210 200 150 100 50; do
local/ext/score.sh data/eval exp/tri5a/graph_closelm exp/nnet_8m_6l/decode_eval_closelm_iter${n};
done
local/ext/score.sh data/eval exp/tri5a/graph exp/nnet_8m_6l/decode_wide_eval
local/ext/score.sh data/eval exp/tri5a/graph_closelm exp/nnet_8m_6l/decode_wide_eval_closelm
local/ext/score.sh data/eval exp/tri5a/graph exp/tri5a_pretrain-dbn_dnn/decode
local/ext/score.sh data/eval exp/tri5a/graph_closelm exp/tri5a_pretrain-dbn_dnn/decode_closelm
......
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