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103 lines
3.1 KiB
103 lines
3.1 KiB
#!/bin/bash
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# Copyright 2021 Mobvoi Inc(Author: Di Wu, Binbin Zhang)
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# NPU, ASLP Group (Author: Qijie Shao)
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#
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# Modified from wenet(https://github.com/wenet-e2e/wenet)
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stage=-1
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stop_stage=100
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# Use your own data path. You need to download the WenetSpeech dataset by yourself.
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wenetspeech_data_dir=./wenetspeech
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# Make sure you have 1.2T for ${shards_dir}
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shards_dir=./wenetspeech_shards
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#wenetspeech training set
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set=L
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train_set=train_`echo $set | tr 'A-Z' 'a-z'`
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dev_set=dev
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test_sets="test_net test_meeting"
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cmvn=true
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cmvn_sampling_divisor=20 # 20 means 5% of the training data to estimate cmvn
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. ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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set -u
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set -o pipefail
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mkdir -p data
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TARGET_DIR=${MAIN_ROOT}/dataset
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mkdir -p ${TARGET_DIR}
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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# download data
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echo "Please follow https://github.com/wenet-e2e/WenetSpeech to download the data."
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exit 0;
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "Data preparation"
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local/wenetspeech_data_prep.sh \
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--train-subset $set \
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$wenetspeech_data_dir \
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data || exit 1;
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fi
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dict=data/lang_char/vocab.txt
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "Make a dictionary"
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echo "dictionary: ${dict}"
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mkdir -p $(dirname $dict)
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echo "<blank>" > ${dict} # 0 will be used for "blank" in CTC
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echo "<unk>" >> ${dict} # <unk> must be 1
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echo "▁" >> ${dict} # ▁ is for space
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utils/text2token.py -s 1 -n 1 --space "▁" data/${train_set}/text \
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| cut -f 2- -d" " | tr " " "\n" \
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| sort | uniq | grep -a -v -e '^\s*$' \
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| grep -v "▁" \
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| awk '{print $0}' >> ${dict} \
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|| exit 1;
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echo "<eos>" >> $dict
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "Compute cmvn"
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# Here we use all the training data, you can sample some some data to save time
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# BUG!!! We should use the segmented data for CMVN
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if $cmvn; then
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full_size=`cat data/${train_set}/wav.scp | wc -l`
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sampling_size=$((full_size / cmvn_sampling_divisor))
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shuf -n $sampling_size data/$train_set/wav.scp \
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> data/$train_set/wav.scp.sampled
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python3 utils/compute_cmvn_stats.py \
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--num_workers 16 \
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--train_config $train_config \
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--in_scp data/$train_set/wav.scp.sampled \
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--out_cmvn data/$train_set/mean_std.json \
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|| exit 1;
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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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echo "Making shards, please wait..."
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RED='\033[0;31m'
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NOCOLOR='\033[0m'
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echo -e "It requires ${RED}1.2T ${NOCOLOR}space for $shards_dir, please make sure you have enough space"
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echo -e "It takes about ${RED}12 ${NOCOLOR}hours with 32 threads"
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for x in $dev_set $test_sets ${train_set}; do
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dst=$shards_dir/$x
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mkdir -p $dst
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utils/make_filted_shard_list.py --resample 16000 --num_utts_per_shard 1000 \
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--do_filter --num_node 1 --num_gpus_per_node 8 \
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--num_threads 32 --segments data/$x/segments \
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data/$x/wav.scp data/$x/text \
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$(realpath $dst) data/$x/data.list
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done
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fi
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echo "Wenetspeech data preparation done."
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exit 0
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