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[egs] Update WSJ run.sh: comment out outdated things, add run_tdnn.sh. (#2723)
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egs/wsj/s5/run.sh

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@@ -320,41 +320,48 @@ if [ $stage -le 6 ]; then
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fi
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fi
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if [ $stage -le 7 ]; then
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# Caution: this part needs a GPU.
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local/chain/run_tdnn.sh
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fi
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exit 0;
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### Caution: the parts of the script below this statement are not run by default.
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###
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# Below are some commented-out commands that demonstrate how to run various other things--
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# mainly outdated methods.
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# Train and test MMI, and boosted MMI, on tri4b (LDA+MLLT+SAT on
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# all the data). Use 30 jobs.
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steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
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data/train_si284 data/lang exp/tri4b exp/tri4b_ali_si284 || exit 1;
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local/run_mmi_tri4b.sh
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# These demonstrate how to build a sytem usable for online-decoding with the nnet2 setup.
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# (see local/run_nnet2.sh for other, non-online nnet2 setups).
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local/online/run_nnet2.sh
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local/online/run_nnet2_baseline.sh
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local/online/run_nnet2_discriminative.sh
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# Demonstration of RNNLM rescoring on TDNN models. We comment this out by
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# default.
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# Note: there isn't much use for this these days.
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#steps/align_fmllr.sh --nj 30 --cmd "$train_cmd" \
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# data/train_si284 data/lang exp/tri4b exp/tri4b_ali_si284 || exit 1;
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#local/run_mmi_tri4b.sh
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# The following are the old nnet2 recipes.
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#local/online/run_nnet2.sh
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#local/online/run_nnet2_baseline.sh
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#local/online/run_nnet2_discriminative.sh
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# The following is the
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# Demonstration of RNNLM rescoring on nnet2 TDNN models. This is
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# outdated now.
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# local/run_rnnlms.sh
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#local/run_nnet2.sh
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# You probably want to run the sgmm2 recipe as it's generally a bit better:
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local/run_sgmm2.sh
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# The SGMM2 recipe. This is better than GMMs but you probably just want the neural net.
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# local/run_sgmm2.sh
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# We demonstrate MAP adaptation of GMMs to gender-dependent systems here. This also serves
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# as a generic way to demonstrate MAP adaptation to different domains.
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# local/run_gender_dep.sh
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# You probably want to run the hybrid recipe as it is complementary:
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local/nnet/run_dnn.sh
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# This is the old "nnet1" neural net.
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#local/nnet/run_dnn.sh
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# The following demonstrate how to re-segment long audios.
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# local/run_segmentation_long_utts.sh

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