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egs/vystadial_cz/s5b/run.sh
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#!/bin/bash
# Change this location to somewhere where you want to put the data.
data=$HOME/vystadial_cz
# Load training parameters
. ./env_voip_cs.sh
. ./cmd.sh
. ./path.sh
stage=0
. utils/parse_options.sh
set -euo pipefail
mkdir -p $data
if [ $stage -le 0 ]; then
local/download_cs_data.sh $data || exit 1;
fi
lm="build3"
if [ $stage -le 1 ]; then
local/data_split.sh --every_n 1 $data data "$lm" "dev test"
local/create_LMs.sh data/local data/train/trans.txt \
data/test/trans.txt data/local/lm "$lm"
gzip data/local/lm/$lm
local/prepare_cs_transcription.sh data/local data/local/dict
local/create_phone_lists.sh data/local/dict
utils/prepare_lang.sh data/local/dict '_SIL_' data/local/lang data/lang
utils/format_lm.sh data/lang data/local/lm/$lm.gz data/local/dict/lexicon.txt data/lang_test
for part in dev test train; do
mv data/$part/trans.txt data/$part/text
done
fi
if [ $stage -le 2 ]; then
mfccdir=mfcc
for part in dev train; do
steps/make_mfcc.sh --cmd "$train_cmd" --nj 10 data/$part exp/make_mfcc/$part $mfccdir
steps/compute_cmvn_stats.sh data/$part exp/make_mfcc/$part $mfccdir
done
# Get the shortest 10000 utterances first because those are more likely
# to have accurate alignments.
utils/subset_data_dir.sh --shortest data/train 10000 data/train_10kshort
fi
# train a monophone system
if [ $stage -le 3 ]; then
steps/train_mono.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \
data/train_10kshort data/lang exp/mono
(
utils/mkgraph.sh data/lang_test \
exp/mono exp/mono/graph
for test in dev; do
steps/decode.sh --nj 10 --cmd "$decode_cmd" exp/mono/graph \
data/$test exp/mono/decode_$test
done
)&
steps/align_si.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \
data/train data/lang exp/mono exp/mono_ali_train
fi
# train a first delta + delta-delta triphone system on all utterances
if [ $stage -le 4 ]; then
steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
2000 10000 data/train data/lang exp/mono_ali_train exp/tri1
# decode using the tri1 model
(
utils/mkgraph.sh data/lang_test \
exp/tri1 exp/tri1/graph
for test in dev; do
steps/decode.sh --nj 10 --cmd "$decode_cmd" exp/tri1/graph \
data/$test exp/tri1/decode_$test
done
)&
steps/align_si.sh --nj 10 --cmd "$train_cmd" \
data/train data/lang exp/tri1 exp/tri1_ali_train
fi
# train an LDA+MLLT system.
if [ $stage -le 5 ]; then
steps/train_lda_mllt.sh --cmd "$train_cmd" \
--splice-opts "--left-context=3 --right-context=3" 2500 15000 \
data/train data/lang exp/tri1_ali_train exp/tri2b
# decode using the LDA+MLLT model
(
utils/mkgraph.sh data/lang_test \
exp/tri2b exp/tri2b/graph
for test in dev; do
steps/decode.sh --nj 10 --cmd "$decode_cmd" exp/tri2b/graph \
data/$test exp/tri2b/decode_$test
done
)&
# Align utts using the tri2b model
steps/align_si.sh --nj 10 --cmd "$train_cmd" --use-graphs true \
data/train data/lang exp/tri2b exp/tri2b_ali_train
fi
# Train tri3b, which is LDA+MLLT+SAT
if [ $stage -le 6 ]; then
steps/train_sat.sh --cmd "$train_cmd" 2500 15000 \
data/train data/lang exp/tri2b_ali_train exp/tri3b
# decode using the tri3b model
(
utils/mkgraph.sh data/lang_test \
exp/tri3b exp/tri3b/graph
for test in dev; do
steps/decode_fmllr.sh --nj 10 --cmd "$decode_cmd" \
exp/tri3b/graph data/$test \
exp/tri3b/decode_$test
done
)&
fi
# Now we compute the pronunciation and silence probabilities from training data,
# and re-create the lang directory.
if [ $stage -le 7 ]; then
steps/get_prons.sh --cmd "$train_cmd" \
data/train data/lang exp/tri3b
utils/dict_dir_add_pronprobs.sh --max-normalize true \
data/local/dict \
exp/tri3b/pron_counts_nowb.txt exp/tri3b/sil_counts_nowb.txt \
exp/tri3b/pron_bigram_counts_nowb.txt data/local/dict_sp
utils/prepare_lang.sh data/local/dict_sp "_SIL_" data/local/lang_tmp data/lang_sp
utils/format_lm.sh data/lang_sp data/local/lm/$lm.gz data/local/dict_sp/lexicon.txt data/lang_sp_test
steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
data/train data/lang_sp exp/tri3b exp/tri3b_ali_train_sp
fi
if [ $stage -le 8 ]; then
# Test the tri3b system with the silprobs and pron-probs.
# decode using the tri3b model
utils/mkgraph.sh data/lang_sp_test \
exp/tri3b exp/tri3b/graph_sp
for test in dev; do
steps/decode_fmllr.sh --nj 10 --cmd "$decode_cmd" \
exp/tri3b/graph_sp data/$test \
exp/tri3b/decode_sp_$test
done
fi
# Train a chain model
if [ $stage -le 9 ]; then
local/chain/run_tdnn.sh --stage 0
fi
# Don't finish until all background decoding jobs are finished.
wait
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