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egs/timit/s5/run.sh
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#!/bin/bash
#
# Copyright 2013 Bagher BabaAli,
# 2014-2017 Brno University of Technology (Author: Karel Vesely)
#
# TIMIT, description of the database:
# http://perso.limsi.fr/lamel/TIMIT_NISTIR4930.pdf
#
# Hon and Lee paper on TIMIT, 1988, introduces mapping to 48 training phonemes,
# then re-mapping to 39 phonemes for scoring:
# http://repository.cmu.edu/cgi/viewcontent.cgi?article=2768&context=compsci
#
. ./cmd.sh
[ -f path.sh ] && . ./path.sh
set -e
# Acoustic model parameters
numLeavesTri1=2500
numGaussTri1=15000
numLeavesMLLT=2500
numGaussMLLT=15000
numLeavesSAT=2500
numGaussSAT=15000
numGaussUBM=400
numLeavesSGMM=7000
numGaussSGMM=9000
feats_nj=10
train_nj=30
decode_nj=5
echo ============================================================================
echo " Data & Lexicon & Language Preparation "
echo ============================================================================
#timit=/export/corpora5/LDC/LDC93S1/timit/TIMIT # @JHU
timit=/mnt/matylda2/data/TIMIT/timit # @BUT
local/timit_data_prep.sh $timit || exit 1
local/timit_prepare_dict.sh
# Caution below: we remove optional silence by setting "--sil-prob 0.0",
# in TIMIT the silence appears also as a word in the dictionary and is scored.
utils/prepare_lang.sh --sil-prob 0.0 --position-dependent-phones false --num-sil-states 3 \
data/local/dict "sil" data/local/lang_tmp data/lang
local/timit_format_data.sh
echo ============================================================================
echo " MFCC Feature Extration & CMVN for Training and Test set "
echo ============================================================================
# Now make MFCC features.
mfccdir=mfcc
for x in train dev test; do
steps/make_mfcc.sh --cmd "$train_cmd" --nj $feats_nj data/$x exp/make_mfcc/$x $mfccdir
steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
done
echo ============================================================================
echo " MonoPhone Training & Decoding "
echo ============================================================================
steps/train_mono.sh --nj "$train_nj" --cmd "$train_cmd" data/train data/lang exp/mono
utils/mkgraph.sh data/lang_test_bg exp/mono exp/mono/graph
steps/decode.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/mono/graph data/dev exp/mono/decode_dev
steps/decode.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/mono/graph data/test exp/mono/decode_test
echo ============================================================================
echo " tri1 : Deltas + Delta-Deltas Training & Decoding "
echo ============================================================================
steps/align_si.sh --boost-silence 1.25 --nj "$train_nj" --cmd "$train_cmd" \
data/train data/lang exp/mono exp/mono_ali
# Train tri1, which is deltas + delta-deltas, on train data.
steps/train_deltas.sh --cmd "$train_cmd" \
$numLeavesTri1 $numGaussTri1 data/train data/lang exp/mono_ali exp/tri1
utils/mkgraph.sh data/lang_test_bg exp/tri1 exp/tri1/graph
steps/decode.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/tri1/graph data/dev exp/tri1/decode_dev
steps/decode.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/tri1/graph data/test exp/tri1/decode_test
echo ============================================================================
echo " tri2 : LDA + MLLT Training & Decoding "
echo ============================================================================
steps/align_si.sh --nj "$train_nj" --cmd "$train_cmd" \
data/train data/lang exp/tri1 exp/tri1_ali
steps/train_lda_mllt.sh --cmd "$train_cmd" \
--splice-opts "--left-context=3 --right-context=3" \
$numLeavesMLLT $numGaussMLLT data/train data/lang exp/tri1_ali exp/tri2
utils/mkgraph.sh data/lang_test_bg exp/tri2 exp/tri2/graph
steps/decode.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/tri2/graph data/dev exp/tri2/decode_dev
steps/decode.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/tri2/graph data/test exp/tri2/decode_test
echo ============================================================================
echo " tri3 : LDA + MLLT + SAT Training & Decoding "
echo ============================================================================
# Align tri2 system with train data.
steps/align_si.sh --nj "$train_nj" --cmd "$train_cmd" \
--use-graphs true data/train data/lang exp/tri2 exp/tri2_ali
# From tri2 system, train tri3 which is LDA + MLLT + SAT.
steps/train_sat.sh --cmd "$train_cmd" \
$numLeavesSAT $numGaussSAT data/train data/lang exp/tri2_ali exp/tri3
utils/mkgraph.sh data/lang_test_bg exp/tri3 exp/tri3/graph
steps/decode_fmllr.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/tri3/graph data/dev exp/tri3/decode_dev
steps/decode_fmllr.sh --nj "$decode_nj" --cmd "$decode_cmd" \
exp/tri3/graph data/test exp/tri3/decode_test
echo ============================================================================
echo " SGMM2 Training & Decoding "
echo ============================================================================
steps/align_fmllr.sh --nj "$train_nj" --cmd "$train_cmd" \
data/train data/lang exp/tri3 exp/tri3_ali
exit 0 # From this point you can run Karel's DNN : local/nnet/run_dnn.sh
steps/train_ubm.sh --cmd "$train_cmd" \
$numGaussUBM data/train data/lang exp/tri3_ali exp/ubm4
steps/train_sgmm2.sh --cmd "$train_cmd" $numLeavesSGMM $numGaussSGMM \
data/train data/lang exp/tri3_ali exp/ubm4/final.ubm exp/sgmm2_4
utils/mkgraph.sh data/lang_test_bg exp/sgmm2_4 exp/sgmm2_4/graph
steps/decode_sgmm2.sh --nj "$decode_nj" --cmd "$decode_cmd"\
--transform-dir exp/tri3/decode_dev exp/sgmm2_4/graph data/dev \
exp/sgmm2_4/decode_dev
steps/decode_sgmm2.sh --nj "$decode_nj" --cmd "$decode_cmd"\
--transform-dir exp/tri3/decode_test exp/sgmm2_4/graph data/test \
exp/sgmm2_4/decode_test
echo ============================================================================
echo " MMI + SGMM2 Training & Decoding "
echo ============================================================================
steps/align_sgmm2.sh --nj "$train_nj" --cmd "$train_cmd" \
--transform-dir exp/tri3_ali --use-graphs true --use-gselect true \
data/train data/lang exp/sgmm2_4 exp/sgmm2_4_ali
steps/make_denlats_sgmm2.sh --nj "$train_nj" --sub-split "$train_nj" \
--acwt 0.2 --lattice-beam 10.0 --beam 18.0 \
--cmd "$decode_cmd" --transform-dir exp/tri3_ali \
data/train data/lang exp/sgmm2_4_ali exp/sgmm2_4_denlats
steps/train_mmi_sgmm2.sh --acwt 0.2 --cmd "$decode_cmd" \
--transform-dir exp/tri3_ali --boost 0.1 --drop-frames true \
data/train data/lang exp/sgmm2_4_ali exp/sgmm2_4_denlats exp/sgmm2_4_mmi_b0.1
for iter in 1 2 3 4; do
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \
--transform-dir exp/tri3/decode_dev data/lang_test_bg data/dev \
exp/sgmm2_4/decode_dev exp/sgmm2_4_mmi_b0.1/decode_dev_it$iter
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \
--transform-dir exp/tri3/decode_test data/lang_test_bg data/test \
exp/sgmm2_4/decode_test exp/sgmm2_4_mmi_b0.1/decode_test_it$iter
done
echo ============================================================================
echo " DNN Hybrid Training & Decoding "
echo ============================================================================
# DNN hybrid system training parameters
dnn_mem_reqs="--mem 1G"
dnn_extra_opts="--num_epochs 20 --num-epochs-extra 10 --add-layers-period 1 --shrink-interval 3"
steps/nnet2/train_tanh.sh --mix-up 5000 --initial-learning-rate 0.015 \
--final-learning-rate 0.002 --num-hidden-layers 2 \
--num-jobs-nnet "$train_nj" --cmd "$train_cmd" "${dnn_train_extra_opts[@]}" \
data/train data/lang exp/tri3_ali exp/tri4_nnet
[ ! -d exp/tri4_nnet/decode_dev ] && mkdir -p exp/tri4_nnet/decode_dev
decode_extra_opts=(--num-threads 6)
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj "$decode_nj" "${decode_extra_opts[@]}" \
--transform-dir exp/tri3/decode_dev exp/tri3/graph data/dev \
exp/tri4_nnet/decode_dev | tee exp/tri4_nnet/decode_dev/decode.log
[ ! -d exp/tri4_nnet/decode_test ] && mkdir -p exp/tri4_nnet/decode_test
steps/nnet2/decode.sh --cmd "$decode_cmd" --nj "$decode_nj" "${decode_extra_opts[@]}" \
--transform-dir exp/tri3/decode_test exp/tri3/graph data/test \
exp/tri4_nnet/decode_test | tee exp/tri4_nnet/decode_test/decode.log
echo ============================================================================
echo " System Combination (DNN+SGMM) "
echo ============================================================================
for iter in 1 2 3 4; do
local/score_combine.sh --cmd "$decode_cmd" \
data/dev data/lang_test_bg exp/tri4_nnet/decode_dev \
exp/sgmm2_4_mmi_b0.1/decode_dev_it$iter exp/combine_2/decode_dev_it$iter
local/score_combine.sh --cmd "$decode_cmd" \
data/test data/lang_test_bg exp/tri4_nnet/decode_test \
exp/sgmm2_4_mmi_b0.1/decode_test_it$iter exp/combine_2/decode_test_it$iter
done
echo ============================================================================
echo " DNN Hybrid Training & Decoding (Karel's recipe) "
echo ============================================================================
local/nnet/run_dnn.sh
#local/nnet/run_autoencoder.sh : an example, not used to build any system,
echo ============================================================================
echo " Getting Results [see RESULTS file] "
echo ============================================================================
bash RESULTS dev
bash RESULTS test
echo ============================================================================
echo "Finished successfully on" `date`
echo ============================================================================
exit 0
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