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echo `date` | |
infname=$1 | |
outfname=$2 | |
src_lang=$3 | |
tgt_lang=$4 | |
exp_dir=$5 | |
ref_fname=$6 | |
SRC_PREFIX='SRC' | |
TGT_PREFIX='TGT' | |
#`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 scripts/preprocess_translate.py $infname $outfname.norm $src_lang true` | |
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} \ | |
--vocabulary $exp_dir/vocab/vocab.$SRC_PREFIX \ | |
--vocabulary-threshold 5 \ | |
< $outfname.norm \ | |
> $outfname._bpe | |
# not needed for joint training | |
# echo "Adding language tags" | |
python scripts/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 \ | |
--user-dir model_configs \ | |
--input $src_input_bpe_fname > $tgt_output_fname.log 2>&1 | |
echo "Extracting translations, script conversion and detokenization" | |
# this part reverses the transliteration from devnagiri script to target lang and then detokenizes it. | |
python scripts/postprocess_translate.py $tgt_output_fname.log $tgt_output_fname $input_size $tgt_lang true | |
# This block is now moved to compute_bleu.sh for release with more documentation. | |
# if [ $src_lang == 'en' ]; then | |
# # indicnlp tokenize the output files before evaluation | |
# input_size=`python scripts/preprocess_translate.py $ref_fname $ref_fname.tok $tgt_lang` | |
# input_size=`python scripts/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" | |