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855536d355
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation Paper: https://arxiv.org/abs/2506.02157
176 lines
5.6 KiB
Bash
176 lines
5.6 KiB
Bash
#!/usr/bin/env bash
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# Copyright 2023 Johns Hopkins University (Amir Hussein)
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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set -eou pipefail
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nj=20
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stage=0
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stop_stage=7
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# We assume dl_dir (download dir) contains the following
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# directories and files.
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#
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# - $dl_dir/cts
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#
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# You can download the data from
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#
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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#
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dl_dir=cts
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. shared/parse_options.sh || exit 1
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# vocab size for sentence piece models.
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# It will generate data/lang_bpe_xxx,
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# data/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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5000
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)
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st_vocab_sizes=(
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4000
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)
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "dl_dir: $dl_dir"
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# Download callhome_spanish, fisher_spanish iwslt22_ta and HKUST from LDC
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#
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# you can create a symlink
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#
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# ln -sfv /path/to/data $dl_dir/data
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# If you have pre-downloaded it to /path/to/musan,
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# you can create a symlink
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#
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# ln -sfv /path/to/musan $dl_dir/
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#
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if [ ! -d $dl_dir/musan ]; then
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lhotse download musan $dl_dir
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fi
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fi
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fbank=data/fbank
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manifests=data/manifests
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mkdir -p $manifests
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sets="hkust iwslt-ta callhome-sp fisher-sp"
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Prepare telephone manifest"
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# We assume that you have downloaded callhome_spanish, fisher_spanish iwslt22_ta and hkust to $dl_dir/
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for set in $sets; do
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log "Prepare $set manifests"
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if [[ "$set" == "iwslt-ta" ]]; then
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if [ ! -d "iwslt22-dialect" ]; then
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echo "Splits directory (iwslt22-dialect) does not exist"
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echo "Run: git clone https://github.com/kevinduh/iwslt22-dialect"
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exit 1
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else
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lhotse prepare "$set" "$dl_dir/$set" iwslt22-dialect "$manifests"
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fi
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else
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lhotse prepare "$set" "$dl_dir/$set" "$manifests"
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# validate recordings and supervisions
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fi
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# python local/cuts_validate.py \
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# --sup "${manifests}/supervisions.jsonl.gz" \
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# --rec "${manifests}/recordings.jsonl.gz" \
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# --savecut "${manifests}/cuts_${set}.jsonl.gz"
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done
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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if [ ! -f ${manifests}/cut_train.jsonl.gz ]; then
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log "Combining conversational data to create train, dev sets"
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# combine train
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lhotse combine $manifests/iwslt-ta_supervisions_train.jsonl.gz $manifests/hkust_supervisions_train.jsonl.gz $manifests/fisher-sp_supervisions_train.jsonl.gz ${manifests}/cts_supervisions_train.jsonl.gz
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lhotse combine $manifests/iwslt-ta_recordings_train.jsonl.gz $manifests/hkust_recordings_train.jsonl.gz $manifests/fisher-sp_recordings_train.jsonl.gz ${manifests}/cts_recordings_train.jsonl.gz
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# python local/cuts_validate.py --sup $manifests/cts_supervisions_train.jsonl.gz --rec ${manifests}/cts_recordings_train.jsonl.gz
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# combine dev
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lhotse combine $manifests/iwslt-ta_supervisions_dev1.jsonl.gz $manifests/hkust_supervisions_dev1.jsonl.gz $manifests/fisher-sp_supervisions_dev.jsonl.gz ${manifests}/cts_supervisions_dev.jsonl.gz
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lhotse combine $manifests/iwslt-ta_recordings_dev1.jsonl.gz $manifests/fisher-spanish_recordings_dev.jsonl.gz $manifests/hkust_recordings_dev1.jsonl.gz $manifests/fisher-sp_recordings_dev.jsonl.gz ${manifests}/cts_recordings_dev.jsonl.gz
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# python local/cuts_validate.py --sup ${manifests}/cts_supervisions_dev.jsonl.gz --rec ${manifests}/cts_recordings_dev.jsonl.gz
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Prepare musan manifest"
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# We assume that you have downloaded the musan corpus
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# to data/musan
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if [ ! -f ${manifests}/musan_recordings_speech.jsonl.gz ]; then
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mkdir -p $manifests
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lhotse prepare musan $dl_dir/musan $manifests
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute fbank features"
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mkdir -p ${fbank}
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./local/compute_fbank_gpu.py
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./local/compute_fbank_gpu.py --test
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Compute fbank for musan"
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./local/compute_fbank_musan.py
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p ${lang_dir}
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cp data/lang_phone/words.txt $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate text for BPE training from data/fbank/cuts_train.jsonl.gz"
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python local/prepare_transcripts.py --cut ${fbank}/cuts_train.jsonl.gz --langdir ${lang_dir}
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fi
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript $lang_dir/transcript_words.txt
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done
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Prepare BPE ST based lang"
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for vocab_size in ${st_vocab_sizes[@]}; do
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lang_dir=data/lang_st_bpe_${vocab_size}
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mkdir -p ${lang_dir}
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if [ ! -f $lang_dir/st_words.txt ]; then
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log "Generate text for BPE training from data/fbank/cuts_train.jsonl.gz"
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python local/prepare_st_transcripts.py --cut ${fbank}/cuts_train.jsonl.gz --langdir ${lang_dir}
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fi
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript $lang_dir/st_words.txt
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done
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fi
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