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PaddleSpeech/speechx/examples/wfst/run.sh

59 lines
1.4 KiB

#!/bin/bash
set -eo pipefail
. path.sh
stage=-1
stop_stage=100
corpus=aishell
lmtype=srilm
lexicon= # aishell/resource_aishell/lexicon.txt
text= # aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
source parse_options.sh
if [ ! which fstprint ]; then
pushd $MAIN_ROOT/tools
make kaldi.done
popd
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# 7.1 Prepare dict
unit_file=data/vocab.txt
mkdir -p data/local/dict
cp $unit_file data/local/dict/units.txt
utils/fst/prepare_dict.py \
--unit_file $unit_file \
--in_lexicon ${lexicon} \
--out_lexicon data/local/dict/lexicon.txt
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# 7.2 Train lm
lm=data/local/lm
mkdir -p data/train
mkdir -p $lm
utils/manifest_key_value.py \
--manifest_path data/manifest.train \
--output_path data/train
utils/filter_scp.pl data/train/text \
$text > $lm/text
if [ $lmtype == 'srilm' ];then
local/aishell_train_lms.sh
else
utils/ngram_train.sh --order 3 $lm/text $lm/lm.arpa
fi
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# 7.3 Build decoding TLG
utils/fst/compile_lexicon_token_fst.sh \
data/local/dict data/local/tmp data/local/lang
utils/fst/make_tlg.sh data/local/lm data/local/lang data/lang_test || exit 1;
fi
echo "Aishell build TLG done."
exit 0