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#!/bin/bash
echo `date`
infname=$1
outfname=$2
src_lang=$3
tgt_lang=$4
exp_dir=$5
ref_fname=$6
if [ $src_lang == 'en' ]; then
SRC_PREFIX='TGT'
TGT_PREFIX='SRC'
else
SRC_PREFIX='SRC'
TGT_PREFIX='TGT'
fi
#`dirname $0`/env.sh
SUBWORD_NMT_DIR='subword-nmt'
model_dir=$exp_dir/model
data_bin_dir=$exp_dir/final_bin
### normalization and script conversion
echo "Applying normalization and script conversion"
input_size=`python preprocess_translate.py $infname $outfname.norm $src_lang`
echo "Number of sentences in input: $input_size"
### apply BPE to input file
echo "Applying BPE"
python $SUBWORD_NMT_DIR/subword_nmt/apply_bpe.py \
-c $exp_dir/vocab/bpe_codes.32k.${SRC_PREFIX}_${TGT_PREFIX} \
--vocabulary $exp_dir/vocab/vocab.$SRC_PREFIX \
--vocabulary-threshold 5 \
< $outfname.norm \
> $outfname.bpe
# not needed for joint training
# echo "Adding language tags"
# python add_tags_translate.py $outfname._bpe $outfname.bpe $src_lang $tgt_lang
### run decoder
echo "Decoding"
src_input_bpe_fname=$outfname.bpe
tgt_output_fname=$outfname
fairseq-interactive $data_bin_dir \
-s $SRC_PREFIX -t $TGT_PREFIX \
--distributed-world-size 1 \
--path $model_dir/checkpoint_best.pt \
--batch-size 64 --buffer-size 2500 --beam 5 --remove-bpe \
--skip-invalid-size-inputs-valid-test \
--input $src_input_bpe_fname > $tgt_output_fname.log 2>&1
echo "Extracting translations, script conversion and detokenization"
python postprocess_translate.py $tgt_output_fname.log $tgt_output_fname $input_size $tgt_lang
if [ $src_lang == 'en' ]; then
# indicnlp tokenize the output files before evaluation
input_size=`python preprocess_translate.py $ref_fname $ref_fname.tok $tgt_lang`
input_size=`python preprocess_translate.py $tgt_output_fname $tgt_output_fname.tok $tgt_lang`
sacrebleu --tokenize none $ref_fname.tok < $tgt_output_fname.tok
else
# indic to en models
sacrebleu $ref_fname < $tgt_output_fname
fi
echo `date`
echo "Translation completed"