6da85d4662
* [wekws] add online noise and rir argumentation * format * format * update copyright Co-authored-by: menglong.xu <menglong.xu>
136 lines
3.7 KiB
Bash
Executable File
136 lines
3.7 KiB
Bash
Executable File
#!/bin/bash
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# Copyright 2021 Binbin Zhang
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# Menglong Xu
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. ./path.sh
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stage=0
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stop_stage=4
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num_keywords=1
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config=conf/ds_tcn.yaml
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norm_mean=true
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norm_var=true
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gpus="0"
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checkpoint=
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dir=exp/ds_tcn
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num_average=30
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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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noise_lmdb=
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reverb_lmdb=
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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 "Extracte all datasets"
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local/snips_data_extract.sh --dl_dir $download_dir
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "Preparing datasets..."
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mkdir -p dict
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echo "<filler> -1" > dict/words.txt
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echo "Hey_Snips 0" >> dict/words.txt
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for folder in train dev test; do
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mkdir -p data/$folder
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json_path=$download_dir/hey_snips_research_6k_en_train_eval_clean_ter/$folder.json
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local/prepare_data.py $download_dir/hey_snips_research_6k_en_train_eval_clean_ter/audio_files $json_path \
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data/$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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num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
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torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
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wekws/bin/train.py --gpus $gpus \
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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 $num_keywords \
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--min_duration 50 \
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--seed 777 \
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$cmvn_opts \
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${reverb_lmdb:+--reverb_lmdb $reverb_lmdb} \
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${noise_lmdb:+--noise_lmdb $noise_lmdb} \
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${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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echo "Do model average, Compute FRR/FAR ..."
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python wekws/bin/average_model.py \
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--dst_model $score_checkpoint \
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--src_path $dir \
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--num ${num_average} \
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--val_best
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result_dir=$dir/test_$(basename $score_checkpoint)
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mkdir -p $result_dir
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python wekws/bin/score.py \
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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 $result_dir/score.txt \
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--num_workers 8
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first_keyword=0
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last_keyword=$(($num_keywords+$first_keyword-1))
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for keyword in $(seq $first_keyword $last_keyword); do
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python wekws/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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python wekws/bin/plot_det_curve.py \
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--keywords_dict dict/words.txt \
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--stats_dir $result_dir \
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--figure_file $result_dir/det.png \
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--xlim 10 \
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--x_step 2 \
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--ylim 10 \
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--y_step 2
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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jit_model=$(basename $score_checkpoint | sed -e 's:.pt$:.zip:g')
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onnx_model=$(basename $score_checkpoint | sed -e 's:.pt$:.onnx:g')
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python wekws/bin/export_jit.py \
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--config $dir/config.yaml \
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--checkpoint $score_checkpoint \
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--jit_model $dir/$jit_model
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python wekws/bin/export_onnx.py \
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--config $dir/config.yaml \
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--checkpoint $score_checkpoint \
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--onnx_model $dir/$onnx_model
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fi
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