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egs/gp/s5/run.sh
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#!/bin/bash -u
# Copyright 2012 Arnab Ghoshal
#
# Copyright 2016 by Idiap Research Institute, http://www.idiap.ch
#
# See the file COPYING for the licence associated with this software.
#
# Author(s):
# Bogdan Vlasenko, February 2016
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# This script shows the steps needed to build a recognizer for certain languages
# of the GlobalPhone corpus.
# !!! NOTE: The current recipe assumes that you have pre-built LMs.
echo "This shell script may run as-is on your system, but it is recommended
that you run the commands one by one by copying and pasting into the shell."
#exit 1;
[ -f cmd.sh ] && source ./cmd.sh || echo "cmd.sh not found. Jobs may not execute properly."
# CHECKING FOR AND INSTALLING REQUIRED TOOLS:
# This recipe requires shorten (3.6.1) and sox (14.3.2).
# If they are not found, the local/gp_install.sh script will install them.
#local/gp_check_tools.sh $PWD path.sh || exit 1;
. ./path.sh || { echo "Cannot source path.sh"; exit 1; }
# Set the locations of the GlobalPhone corpus and language models
GP_CORPUS=/idiap/resource/database/GLOBALPHONE
GP_LM=$PWD/language_models
# Set the languages that will actually be processed
export GP_LANGUAGES="FR GE RU"
# The following data preparation step actually converts the audio files from
# shorten to WAV to take out the empty files and those with compression errors.
local/gp_data_prep.sh --config-dir=$PWD/conf --corpus-dir=$GP_CORPUS --languages="$GP_LANGUAGES" || exit 1;
local/gp_dict_prep.sh --config-dir $PWD/conf $GP_CORPUS $GP_LANGUAGES || exit 1;
for L in $GP_LANGUAGES; do
utils/prepare_lang.sh --position-dependent-phones true \
data/$L/local/dict "<unk>" data/$L/local/lang_tmp data/$L/lang \
>& data/$L/prepare_lang.log || exit 1;
done
# Convert the different available language models to FSTs, and create separate
# decoding configurations for each.
for L in $GP_LANGUAGES; do
local/gp_format_lm.sh --filter-vocab-sri true $GP_LM $L &
done
wait
# Now make MFCC features.
for L in $GP_LANGUAGES; do
mfccdir=mfcc/$L
for x in train dev eval; do
(
steps/make_mfcc.sh --nj 6 --cmd "$train_cmd" data/$L/$x \
exp/$L/make_mfcc/$x $mfccdir;
steps/compute_cmvn_stats.sh data/$L/$x exp/$L/make_mfcc/$x $mfccdir;
) &
done
done
wait;
for L in $GP_LANGUAGES; do
mkdir -p exp/$L/mono;
steps/train_mono.sh --nj 10 --cmd "$train_cmd" \
data/$L/train data/$L/lang exp/$L/mono >& exp/$L/mono/train.log &
done
wait;
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/mono/graph_${lm_suffix}
mkdir -p $graph_dir
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/mono \
$graph_dir
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/dev \
exp/$L/mono/decode_dev_${lm_suffix}
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/eval \
exp/$L/mono/decode_eval_${lm_suffix}
) &
done
done
# Train tri1, which is first triphone pass
for L in $GP_LANGUAGES; do
(
mkdir -p exp/$L/mono_ali
steps/align_si.sh --nj 10 --cmd "$train_cmd" \
data/$L/train data/$L/lang exp/$L/mono exp/$L/mono_ali \
>& exp/$L/mono_ali/align.log
num_states=$(grep "^$L" conf/tri.conf | cut -f2)
num_gauss=$(grep "^$L" conf/tri.conf | cut -f3)
mkdir -p exp/$L/tri1
steps/train_deltas.sh --cmd "$train_cmd" \
--cluster-thresh 100 $num_states $num_gauss data/$L/train data/$L/lang \
exp/$L/mono_ali exp/$L/tri1 >& exp/$L/tri1/train.log
) &
done
wait;
# Decode tri1
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/tri1/graph_${lm_suffix}
mkdir -p $graph_dir
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/tri1 \
$graph_dir
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/dev \
exp/$L/tri1/decode_dev_${lm_suffix}
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/eval \
exp/$L/tri1/decode_eval_${lm_suffix}
) &
done
done
# Train tri2a, which is deltas + delta-deltas
for L in $GP_LANGUAGES; do
(
mkdir -p exp/$L/tri1_ali
steps/align_si.sh --nj 10 --cmd "$train_cmd" \
data/$L/train data/$L/lang exp/$L/tri1 exp/$L/tri1_ali \
>& exp/$L/tri1_ali/tri1_ali.log
num_states=$(grep "^$L" conf/tri.conf | cut -f2)
num_gauss=$(grep "^$L" conf/tri.conf | cut -f3)
mkdir -p exp/$L/tri2a
steps/train_deltas.sh --cmd "$train_cmd" \
--cluster-thresh 100 $num_states $num_gauss data/$L/train data/$L/lang \
exp/$L/tri1_ali exp/$L/tri2a >& exp/$L/tri2a/train.log
) &
done
wait;
# Decode tri2a
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/tri2a/graph_${lm_suffix}
mkdir -p $graph_dir
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/tri2a \
$graph_dir
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/dev \
exp/$L/tri2a/decode_dev_${lm_suffix}
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/eval \
exp/$L/tri2a/decode_eval_${lm_suffix}
) &
done
done
# Train tri2b, which is LDA+MLLT
for L in $GP_LANGUAGES; do
(
num_states=$(grep "^$L" conf/tri.conf | cut -f2)
num_gauss=$(grep "^$L" conf/tri.conf | cut -f3)
mkdir -p exp/$L/tri2b
steps/train_lda_mllt.sh --cmd "$train_cmd" \
--splice-opts "--left-context=3 --right-context=3" $num_states $num_gauss data/$L/train \
data/$L/lang exp/$L/tri1_ali exp/$L/tri2b >& exp/$L/tri2b/tri2_ali.log
) &
done
wait;
# for L in $GP_LANGUAGES; do
# mode=4
# # Doing this only for the LMs whose vocabs were limited using SRILM, since the
# # other approach didn't yield LMs for all languages.
# steps/lmrescore.sh --mode $mode --cmd "$decode_cmd" \
# data/$L/lang_test_tgpr_sri data/$L/lang_test_tg_sri data/$L/dev \
# exp/$L/tri2a/decode_dev_tgpr_sri exp/$L/tri2a/decode_dev_tg_sri$mode
# done
# Decode tri2b
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/tri2b/graph_${lm_suffix}
mkdir -p $graph_dir
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/tri2b \
$graph_dir
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/dev \
exp/$L/tri2b/decode_dev_${lm_suffix}
steps/decode.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/eval \
exp/$L/tri2b/decode_eval_${lm_suffix}
) &
done
done
wait;
# Train tri3b, which is LDA+MLLT+SAT.
for L in $GP_LANGUAGES; do
(
mkdir -p exp/$L/tri2b_ali
steps/align_si.sh --nj 10 --cmd "$train_cmd" \
--use-graphs true data/$L/train data/$L/lang exp/$L/tri2b exp/$L/tri2b_ali \
>& exp/$L/tri2b_ali/align.log
num_states=$(grep "^$L" conf/tri.conf | cut -f2)
num_gauss=$(grep "^$L" conf/tri.conf | cut -f3)
mkdir -p exp/$L/tri3b
steps/train_sat.sh --cmd "$train_cmd" \
--cluster-thresh 100 $num_states $num_gauss data/$L/train data/$L/lang \
exp/$L/tri2b_ali exp/$L/tri3b >& exp/$L/tri3b/train.log
) &
done
wait;
# Decode 3b
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/tri3b/graph_${lm_suffix}
mkdir -p $graph_dir
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/tri3b \
$graph_dir
mkdir -p exp/$L/tri3b/decode_dev_${lm_suffix}
steps/decode_fmllr.sh --nj 5 --cmd "$decode_cmd" \
$graph_dir data/$L/dev exp/$L/tri3b/decode_dev_${lm_suffix}
steps/decode_fmllr.sh --nj 5 --cmd "$decode_cmd" \
$graph_dir data/$L/eval exp/$L/tri3b/decode_eval_${lm_suffix}
) &
done
done
wait;
## Train sgmm2b, which is SGMM on top of LDA+MLLT+SAT features.
for L in $GP_LANGUAGES; do
(
mkdir -p exp/$L/tri3b_ali
steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
data/$L/train data/$L/lang exp/$L/tri3b exp/$L/tri3b_ali
num_states=$(grep "^$L" conf/sgmm.conf | cut -f2)
num_substates=$(grep "^$L" conf/sgmm.conf | cut -f3)
mkdir -p exp/$L/ubm4a
steps/train_ubm.sh --cmd "$train_cmd" \
600 data/$L/train data/$L/lang exp/$L/tri3b_ali exp/$L/ubm4a
mkdir -p exp/$L/sgmm2_4a
steps/train_sgmm2.sh --cmd "$train_cmd" \
$num_states $num_substates data/$L/train data/$L/lang exp/$L/tri3b_ali \
exp/$L/ubm4a/final.ubm exp/$L/sgmm2_4a
) &
done
wait;
## Decode sgmm2_4a
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/sgmm2_4a/graph_${lm_suffix}
mkdir -p $graph_dir
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/sgmm2_4a \
$graph_dir
steps/decode_sgmm2.sh --use-fmllr true --nj 5 --cmd "$decode_cmd" \
--transform-dir exp/$L/tri3b/decode_dev_${lm_suffix} $graph_dir data/$L/dev \
exp/$L/sgmm2_4a/decode_dev_${lm_suffix}
steps/decode_sgmm2.sh --use-fmllr true --nj 5 --cmd "$decode_cmd" \
--transform-dir exp/$L/tri3b/decode_eval_${lm_suffix} $graph_dir data/$L/eval \
exp/$L/sgmm2_4a/decode_eval_${lm_suffix}
)
done
done
wait;
# Now we'll align the SGMM system to prepare for discriminative training MMI
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
mkdir -p exp/$L/sgmm2_4a_ali
steps/align_sgmm2.sh --nj 10 --cmd "$train_cmd" \
--transform-dir exp/$L/tri3b_ali --use-graphs true --use-gselect true data/$L/train \
data/$L/lang exp/$L/sgmm2_4a exp/$L/sgmm2_4a_ali
mkdir -p exp/$L/sgmm2_4a_denlats
steps/make_denlats_sgmm2.sh --nj 10 --sub-split 10 --cmd "$decode_cmd" \
--transform-dir exp/$L/tri3b_ali data/$L/train data/$L/lang \
exp/$L/sgmm2_4a_ali exp/$L/sgmm2_4a_denlats
mkdir -p exp/$L/sgmm2_4a_mmi_b0.1
steps/train_mmi_sgmm2.sh --cmd "$decode_cmd" \
--transform-dir exp/$L/tri3b_ali --boost 0.1 data/$L/train data/$L/lang \
exp/$L/sgmm2_4a_ali exp/$L/sgmm2_4a_denlats exp/$L/sgmm2_4a_mmi_b0.1
) &
done
done
wait;
# decode sgmm2_4a-mmi_b0.1
for L in $GP_LANGUAGES; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/sgmm2_4a/graph_${lm_suffix}
for iter in 1 2 3 4; do
for test in dev eval; do
steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" \
--iter $iter --transform-dir exp/$L/tri3b/decode_${test}_${lm_suffix} data/$L/lang_test_${lm_suffix} \
data/$L/${test} exp/$L/sgmm2_4a/decode_${test}_${lm_suffix} \
exp/$L/sgmm2_4a_mmi_b0.1/decode_${test}_${lm_suffix}_it$iter
done
done
) &
done
done
wait;
# SGMMs starting from non-SAT triphone system, both with and without
# speaker vectors.
for L in $GP_LANGUAGES; do
(
mkdir -p exp/$L/ubm2a
steps/train_ubm.sh --cmd "$train_cmd" \
400 data/$L/train data/$L/lang exp/$L/tri1_ali exp/$L/ubm2a \
>& exp/$L/ubm2a/train.log
num_states=$(grep "^$L" conf/sgmm.conf | cut -f2)
num_substates=$(grep "^$L" conf/sgmm.conf | cut -f3)
mkdir -p exp/$L/sgmm2a
steps/train_sgmm2.sh --cmd "$train_cmd" --cluster-thresh 100 --spk-dim 0 \
$num_states $num_substates data/$L/train data/$L/lang exp/$L/tri1_ali \
exp/$L/ubm2a/final.ubm exp/$L/sgmm2a >& exp/$L/sgmm2a/train.log
mkdir -p exp/$L/sgmm2b
steps/train_sgmm2.sh --cmd "$train_cmd" --cluster-thresh 100 \
$num_states $num_gauss data/$L/train data/$L/lang exp/$L/tri1_ali \
exp/$L/ubm2a/final.ubm exp/$L/sgmm2b >& exp/$L/sgmm2b/train.log
) &
done
wait
for L in $GP_LANGUAGES; do
# Need separate decoding graphs for models with and without speaker vectors,
# since the trees may be different.
for sgmm in sgmm2a sgmm2b; do
for lm_suffix in tgpr_sri; do
(
graph_dir=exp/$L/$sgmm/graph_${lm_suffix}
mkdir -p $graph_dir
$highmem_cmd $graph_dir/mkgraph.log \
utils/mkgraph.sh data/$L/lang_test_${lm_suffix} exp/$L/$sgmm $graph_dir
steps/decode_sgmm2.sh --nj 5 --cmd "$decode_cmd" $graph_dir data/$L/dev \
exp/$L/$sgmm/decode_dev_${lm_suffix}
) &
done # loop over LMs
done # loop over model with and without speaker vecs
done # loop over languages
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