128 lines
3.3 KiB
Bash
Executable File
128 lines
3.3 KiB
Bash
Executable File
#!/bin/bash
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# Copyright 2021 Binbin Zhang
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. ./path.sh
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export CUDA_VISIBLE_DEVICES="0"
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stage=2
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stop_stage=2
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num_keywords=2
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config=conf/mdtc.yaml
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norm_mean=false
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norm_var=false
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gpu_id=0
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checkpoint=
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dir=exp/mdtc
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num_average=10
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score_checkpoint=$dir/avg_${num_average}.pt
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download_dir=./data/local # your data dir
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. tools/parse_options.sh || exit 1;
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set -euo pipefail
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "Download and extracte all datasets"
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local/mobvoi_data_download.sh --dl_dir $download_dir
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "Preparing datasets..."
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mkdir -p dict
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echo "<filler> -1" > dict/words.txt
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echo "Hi_Xiaowen 0" >> dict/words.txt
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echo "Nihao_Wenwen 1" >> dict/words.txt
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for folder in train dev test; do
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mkdir -p data/$folder
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for prefix in p n; do
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mkdir -p data/${prefix}_$folder
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json_path=$download_dir/mobvoi_hotword_dataset_resources/${prefix}_$folder.json
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local/prepare_data.py $download_dir/mobvoi_hotword_dataset $json_path \
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data/${prefix}_$folder
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done
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cat data/p_$folder/wav.scp data/n_$folder/wav.scp > data/$folder/wav.scp
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cat data/p_$folder/text data/n_$folder/text > data/$folder/text
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rm -rf data/p_$folder data/n_$folder
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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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echo "Compute CMVN and Format datasets"
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tools/compute_cmvn_stats.py --num_workers 16 --train_config $config \
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--in_scp data/train/wav.scp \
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--out_cmvn data/train/global_cmvn
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for x in train dev test; do
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tools/wav_to_duration.sh --nj 8 data/$x/wav.scp data/$x/wav.dur
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tools/make_list.py data/$x/wav.scp data/$x/text \
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data/$x/wav.dur data/$x/data.list
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done
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "Start training ..."
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mkdir -p $dir
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cmvn_opts=
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$norm_mean && cmvn_opts="--cmvn_file data/train/global_cmvn"
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$norm_var && cmvn_opts="$cmvn_opts --norm_var"
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python kws/bin/train.py --gpu $gpu_id \
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--config $config \
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--train_data data/train/data.list \
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--cv_data data/dev/data.list \
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--model_dir $dir \
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--num_workers 8 \
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--num_keywords $num_keywords \
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--min_duration 50 \
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$cmvn_opts \
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${checkpoint:+--checkpoint $checkpoint}
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# Do model average
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python kws/bin/average_model.py \
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--dst_model $score_checkpoint \
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--src_path $dir \
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--num ${num_average} \
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--val_best
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# Compute posterior score
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result_dir=$dir/test_$(basename $score_checkpoint)
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mkdir -p $result_dir
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python kws/bin/score.py --gpu 1 \
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--config $dir/config.yaml \
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--test_data data/test/data.list \
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--batch_size 256 \
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--checkpoint $score_checkpoint \
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--score_file $result_dir/score.txt
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# Compute detection error tradeoff
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result_dir=$dir/test_$(basename $score_checkpoint)
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for keyword in 0 1; do
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python kws/bin/compute_det.py \
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--keyword $keyword \
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--test_data data/test/data.list \
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--score_file $result_dir/score.txt \
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--stats_file $result_dir/stats.${keyword}.txt
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done
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fi
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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python kws/bin/export_jit.py --config $dir/config.yaml \
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--checkpoint $score_checkpoint \
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--output_file $dir/final.zip \
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--output_quant_file $dir/final.quant.zip
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fi
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