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208 lines
9.3 KiB
208 lines
9.3 KiB
#!/bin/bash
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set -e
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stage=-1
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stop_stage=100
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dict_dir=data/lang_char
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# bpemode (unigram or bpe)
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nbpe=8000
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bpemode=bpe
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bpeprefix="${dict_dir}/bpe_${bpemode}_${nbpe}"
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data_dir=./TED_EnZh
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target_dir=data/ted_en_zh
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dumpdir=data/dump
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do_delta=false
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nj=20
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source ${MAIN_ROOT}/utils/parse_options.sh
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TARGET_DIR=${MAIN_ROOT}/dataset
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mkdir -p ${TARGET_DIR}
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mkdir -p data
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mkdir -p ${dict_dir}
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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if [ ! -e ${data_dir} ]; then
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echo "Error: Dataset is not avaiable. Please download and unzip the dataset"
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echo "Download Link: https://pan.baidu.com/s/18L-59wgeS96WkObISrytQQ Passwd: bva0"
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echo "The tree of the directory should be:"
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echo "."
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echo "|-- En-Zh"
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echo "|-- test-segment"
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echo " |-- tst2010"
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echo " |-- ..."
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echo "|-- train-split"
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echo " |-- train-segment"
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echo "|-- README.md"
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exit 1
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fi
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# extract data
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echo "data Extraction"
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python3 local/ted_en_zh.py \
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--tgt-dir=${target_dir} \
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--src-dir=${data_dir}
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fi
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prep_dir=${target_dir}/data_prep
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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### Task dependent. You have to make data the following preparation part by yourself.
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### But you can utilize Kaldi recipes in most cases
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echo "stage 0: Data preparation"
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for set in train dev test; do
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# for set in train; do
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dst=${target_dir}/${set}
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for lang in en zh; do
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if [ ${lang} = 'en' ]; then
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echo "remove punctuation $lang"
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# remove punctuation
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local/remove_punctuation.pl < ${dst}/${lang}.org > ${dst}/${lang}.raw
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else
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cp ${dst}/${lang}.org ${dst}/${lang}.raw
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fi
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paste -d " " ${dst}/.yaml ${dst}/${lang}.raw | sort > ${dst}/text.${lang}
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done
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# error check
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n=$(cat ${dst}/.yaml | wc -l)
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n_en=$(cat ${dst}/en.raw | wc -l)
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n_tgt=$(cat ${dst}/zh.raw | wc -l)
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[ ${n} -ne ${n_en} ] && echo "Warning: expected ${n} data data files, found ${n_en}" && exit 1;
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[ ${n} -ne ${n_tgt} ] && echo "Warning: expected ${n} data data files, found ${n_tgt}" && exit 1;
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echo "done text processing"
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cat ${dst}/wav.scp.org | uniq | sort -k1,1 -u > ${dst}/wav.scp
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cat ${dst}/utt2spk.org | uniq | sort -k1,1 -u > ${dst}/utt2spk
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cat ${dst}/utt2spk | utils/utt2spk_to_spk2utt.pl | sort -k1,1 -u > ${dst}/spk2utt
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rm -rf ${prep_dir}/${set}.en-zh
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mkdir -p ${prep_dir}/${set}.en-zh
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echo "remove duplicate lines..."
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cut -d ' ' -f 1 ${dst}/text.en | sort | uniq -c | sort -n -k1 -r | grep -v '1 ted-en-zh' \
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| sed 's/^[ \t]*//' > ${dst}/duplicate_lines
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cut -d ' ' -f 1 ${dst}/text.en | sort | uniq -c | sort -n -k1 -r | grep '1 ted-en-zh' \
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| cut -d '1' -f 2- | sed 's/^[ \t]*//' > ${dst}/reclist
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reduce_data_dir.sh ${dst} ${dst}/reclist ${prep_dir}/${set}.en-zh
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echo "done wav processing"
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for l in en zh; do
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cp ${dst}/text.${l} ${prep_dir}/${set}.en-zh/text.${l}
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done
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utils/fix_data_dir.sh --utt_extra_files \
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"text.en text.zh" \
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${prep_dir}/${set}.en-zh
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done
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fi
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feat_tr_dir=${dumpdir}/train_sp/delta${do_delta}; mkdir -p ${feat_tr_dir}
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feat_dt_dir=${dumpdir}/dev/delta${do_delta}; mkdir -p ${feat_dt_dir}
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feat_trans_dir=${dumpdir}/test/delta${do_delta}; mkdir -p ${feat_trans_dir}
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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### Task dependent. You have to design training and dev sets by yourself.
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### But you can utilize Kaldi recipes in most cases
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echo "stage 1: Feature Generation"
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fbankdir=data/fbank
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# Generate the fbank features; by default 80-dimensional fbanks with pitch on each frame
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for x in train dev test; do
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steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj ${nj} --write_utt2num_frames true \
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${prep_dir}/${x}.en-zh data/make_fbank/${x} ${fbankdir}
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done
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echo "speed perturbation"
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utils/perturb_data_dir_speed.sh 0.9 ${prep_dir}/train.en-zh ${prep_dir}/temp1.en-zh
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utils/perturb_data_dir_speed.sh 1.0 ${prep_dir}/train.en-zh ${prep_dir}/temp2.en-zh
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utils/perturb_data_dir_speed.sh 1.1 ${prep_dir}/train.en-zh ${prep_dir}/temp3.en-zh
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utils/combine_data.sh --extra-files utt2uniq ${prep_dir}/train_sp.en-zh \
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${prep_dir}/temp1.en-zh ${prep_dir}/temp2.en-zh ${prep_dir}/temp3.en-zh
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rm -r ${prep_dir}/temp*.en-zh
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utils/fix_data_dir.sh ${prep_dir}/train_sp.en-zh
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steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj ${nj} --write_utt2num_frames true \
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${prep_dir}/train_sp.en-zh exp/make_fbank/train_sp.en-zh ${fbankdir}
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for lang in en zh; do
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cat /dev/null > ${prep_dir}/train_sp.en-zh/text.${lang}
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for p in "sp0.9-" "sp1.0-" "sp1.1-"; do
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awk -v p=${p} '{printf("%s %s%s\n", $1, p, $1);}' ${prep_dir}/train.en-zh/utt2spk > ${prep_dir}/train_sp.en-zh/utt_map
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utils/apply_map.pl -f 1 ${prep_dir}/train_sp.en-zh/utt_map < ${prep_dir}/train.en-zh/text.${lang} >>${prep_dir}/train_sp.en-zh/text.${lang}
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done
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done
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for x in train_sp dev test; do
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local/divide_lang.sh ${prep_dir}/${x}.en-zh zh
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done
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for x in train_sp dev; do
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# remove utt having more than 3000 frames
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# remove utt having more than 400 characters
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for lang in zh en; do
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remove_longshortdata.sh --maxframes 3000 --maxchars 400 ${prep_dir}/${x}.en-zh.${lang} ${prep_dir}/${x}.en-zh.${lang}.tmp
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done
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cut -f 1 -d " " ${prep_dir}/${x}.en-zh.en.tmp/text > ${prep_dir}/${x}.en-zh.${lang}.tmp/reclist1
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cut -f 1 -d " " ${prep_dir}/${x}.en-zh.${lang}.tmp/text > ${prep_dir}/${x}.en-zh.${lang}.tmp/reclist2
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comm -12 ${prep_dir}/${x}.en-zh.${lang}.tmp/reclist1 ${prep_dir}/${x}.en-zh.${lang}.tmp/reclist2 > ${prep_dir}/${x}.en-zh.en.tmp/reclist
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for lang in zh en; do
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reduce_data_dir.sh ${prep_dir}/${x}.en-zh.${lang}.tmp ${prep_dir}/${x}.en-zh.en.tmp/reclist ${prep_dir}/${x}.en-zh.${lang}
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utils/fix_data_dir.sh ${prep_dir}/${x}.en-zh.${lang}
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done
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rm -rf ${prep_dir}/${x}.en-zh.*.tmp
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done
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compute-cmvn-stats scp:${prep_dir}/train_sp.en-zh.zh/feats.scp ${prep_dir}/train_sp.en-zh.zh/cmvn.ark
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dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta $do_delta \
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${prep_dir}/train_sp.en-zh.zh/feats.scp ${prep_dir}/train_sp.en-zh.zh/cmvn.ark ${prep_dir}/dump_feats/train_sp.en-zh.zh ${feat_tr_dir}
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dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta $do_delta \
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${prep_dir}/dev.en-zh.zh/feats.scp ${prep_dir}/train_sp.en-zh.zh/cmvn.ark ${prep_dir}/dump_feats/dev.en-zh.zh ${feat_dt_dir}
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dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta $do_delta \
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${prep_dir}/test.en-zh.zh/feats.scp ${prep_dir}/train_sp.en-zh.zh/cmvn.ark ${prep_dir}/dump_feats/test.en-zh.zh ${feat_trans_dir}
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fi
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dict=${dict_dir}/ted_en_zh_${bpemode}${nbpe}.txt
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nlsyms=${dict_dir}/ted_en_zh_non_lang_syms.txt
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bpemodel=${dict_dir}/ted_en_zh_${bpemode}${nbpe}
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary and Json Data Preparation"
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echo "make a joint source and target dictionary"
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echo "<unk> 1" > ${dict} # <unk> must be 1, 0 will be used for "blank" in CTC
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offset=$(wc -l < ${dict})
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grep sp1.0 ${prep_dir}/train_sp.en-zh.*/text | cut -f 2- -d' ' | grep -v -e '^\s*$' > ${dict_dir}/input.txt
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spm_train --input=${dict_dir}/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000 --character_coverage=1.0
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spm_encode --model=${bpemodel}.model --output_format=piece < ${dict_dir}/input.txt | tr ' ' '\n' | sort | uniq | awk -v offset=${offset} '{print $0 " " NR+offset}' >> ${dict}
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wc -l ${dict}
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echo "make json files"
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data2json.sh --nj ${nj} --feat ${feat_tr_dir}/feats.scp --text ${prep_dir}/train_sp.en-zh.zh/text --bpecode ${bpemodel}.model --lang zh \
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${prep_dir}/train_sp.en-zh.zh ${dict} > ${feat_tr_dir}/data_${bpemode}${nbpe}.json
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data2json.sh --feat ${feat_dt_dir}/feats.scp --text ${prep_dir}/dev.en-zh.zh/text --bpecode ${bpemodel}.model --lang zh \
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${prep_dir}/dev.en-zh.zh ${dict} > ${feat_dt_dir}/data_${bpemode}${nbpe}.json
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data2json.sh --feat ${feat_trans_dir}/feats.scp --text ${prep_dir}/test.en-zh.zh/text --bpecode ${bpemodel}.model --lang zh \
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${prep_dir}/test.en-zh.zh ${dict} > ${feat_trans_dir}/data_${bpemode}${nbpe}.json
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echo "update json (add source references)"
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# update json (add source references)
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for x in train_sp dev; do
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feat_dir=${dumpdir}/${x}/delta${do_delta}
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data_dir=${prep_dir}/$(echo ${x} | cut -f 1 -d ".").en-zh.en
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update_json.sh --text ${data_dir}/text --bpecode ${bpemodel}.model \
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${feat_dir}/data_${bpemode}${nbpe}.json ${data_dir} ${dict}
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done
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Format the Json Data"
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python3 local/espnet_json_to_manifest.py --json-file ${feat_tr_dir}/data_${bpemode}${nbpe}.json --manifest-file data/manifest.train
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python3 local/espnet_json_to_manifest.py --json-file ${feat_dt_dir}/data_${bpemode}${nbpe}.json --manifest-file data/manifest.dev
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python3 local/espnet_json_to_manifest.py --json-file ${feat_trans_dir}/data_${bpemode}${nbpe}.json --manifest-file data/manifest.test
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fi
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echo "Ted En-Zh Data preparation done."
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exit 0
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