b233d46552
* add ctcloss training scripts. * update compute_det_ctc * fix typo. * add fsmn model, can use pretrained kws model from modelscope. * Add streaming detection of CTC model. Add CTC model onnx export. Add CTC model's result in README; For now CTC model runtime is not supported yet. * QA run.sh, maxpooling training scripts is compatible. Ready to PR. * Add a streaming kws demo, support fsmn online forward * fix typo. * Align Stream FSMN and Non-Stream FSMN, both in feature extraction and model forward. * fix repeat activation, add a interval restrict. * fix timestamp when subsampling!=1. * fix flake8, update training script and README, give pretrained ckpt. * fix quickcheck and flake8 * Add realtime CTC-KWS demo in README. --------- Co-authored-by: dujing <dujing@xmov.ai>
136 lines
3.8 KiB
Bash
Executable File
136 lines
3.8 KiB
Bash
Executable File
#!/bin/bash
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# Copyright 2021 Binbin Zhang(binbzha@qq.com)
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. ./path.sh
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stage=$1
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stop_stage=$2
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num_keywords=2
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config=conf/ds_tcn.yaml
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norm_mean=true
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norm_var=true
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gpus="0,1"
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checkpoint=
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dir=exp/ds_tcn
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num_average=30
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score_checkpoint=$dir/avg_${num_average}.pt
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download_dir=./data/local # your data dir
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. tools/parse_options.sh || exit 1;
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window_shift=50
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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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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 $num_keywords \
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--min_duration 50 \
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--seed 666 \
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$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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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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--gpu 0 \
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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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for keyword in 0 1; 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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--window_shift $window_shift \
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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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# plot det curve
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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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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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