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#!/usr/bin/env zsh |
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lg=$1 |
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text_path=$2 |
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target_dir=$3 |
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min_phones=$4 |
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phonemizer=$5 |
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lid_path=$6 |
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if [ -z "$lid_path" ]; then |
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lid_path="lid.187.bin" |
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fi |
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ph_lg=${lg:l} |
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if test "$lg" = 'fr'; then |
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ph_lg='fr-fr' |
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elif test "$lg" = 'en'; then |
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ph_lg='en-us' |
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elif test "$lg" = 'pt'; then |
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ph_lg='pt-br' |
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fi |
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ESPEAK_PATH='' |
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if test "$phonemizer" = 'espeak'; then |
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ESPEAK_PATH=$(which espeak) |
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elif test "$phonemizer" = 'espeak-ng'; then |
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ESPEAK_PATH=$(which espeak-ng) |
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elif test "$phonemizer" = 'G2P'; then |
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ESPEAK_PATH='' |
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else |
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echo "Unknown phonemizer $phonemizer. Valid options are espeak, espean-ng and G2P" |
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exit 1 |
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fi |
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echo $lg |
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echo $ph_lg |
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echo $text_path |
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echo $target_dir |
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echo "min phone seen threshold is $min_phones" |
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mkdir -p $target_dir |
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python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py --lang $lg --fasttext-model $lid_path < $text_path | grep -v '\-\-\-' >! $target_dir/lm.upper.lid.txt |
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python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/lm.upper.lid.txt --only-source --destdir $target_dir --thresholdsrc 2 --padding-factor 1 --dict-only |
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cut -f1 -d' ' $target_dir/dict.txt | grep -v -x '[[:punct:]]*' | grep -Pv '\d\d\d\d\d+' >! $target_dir/words.txt |
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if [ -z "$ESPEAK_PATH" ]; then |
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python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py --compact < $target_dir/words.txt > $target_dir/phones.txt |
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else |
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one=$(echo "1" | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -p ' ' -w '' -l $ph_lg --language-switch remove-flags) |
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sed 's/$/ 1/' $target_dir/words.txt | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -o $target_dir/phones.txt -p ' ' -w '' -l $ph_lg -j 70 --language-switch remove-flags |
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echo "one is ${one}" |
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sed -i "s/${one}$//" $target_dir/phones.txt |
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fi |
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paste $target_dir/words.txt $target_dir/phones.txt >! $target_dir/lexicon.lst |
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python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones.txt --only-source --destdir $target_dir/phones --thresholdsrc $min_phones --padding-factor 1 --dict-only |
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python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py -d $target_dir/phones/dict.txt < $target_dir/lexicon.lst >! $target_dir/lexicon_filtered.lst |
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python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py -s 0.25 --surround --lexicon $target_dir/lexicon_filtered.lst < $target_dir/lm.upper.lid.txt >! $target_dir/phones/lm.phones.filtered.txt |
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cp $target_dir/phones/dict.txt $target_dir/phones/dict.phn.txt |
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echo "<SIL> 0" >> $target_dir/phones/dict.phn.txt |
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python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones/lm.phones.filtered.txt --workers 70 --only-source --destdir $target_dir/phones --srcdict $target_dir/phones/dict.phn.txt |
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$KENLM_ROOT/lmplz -o 4 < $target_dir/lm.upper.lid.txt --discount_fallback --prune 0 0 0 3 >! $target_dir/kenlm.wrd.o40003.arpa |
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$KENLM_ROOT/build_binary $target_dir/kenlm.wrd.o40003.arpa $target_dir/kenlm.wrd.o40003.bin |
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lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words_sil lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn "blank_symbol='<SIL>'" |
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lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn |
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$KENLM_ROOT/lmplz -o 4 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.04.arpa |
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$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.04.arpa $target_dir/phones/lm.phones.filtered.04.bin |
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$KENLM_ROOT/lmplz -o 6 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.06.arpa |
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$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.06.arpa $target_dir/phones/lm.phones.filtered.06.bin |
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lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_phn_sil lm_arpa=$target_dir/phones/lm.phones.filtered.06.arpa data_dir=$target_dir/phones in_labels=phn "blank_symbol='<SIL>'" |
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