109 lines
2.8 KiB
Bash
Executable File
109 lines
2.8 KiB
Bash
Executable File
#!/bin/bash
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# Copyright 2021 Binbin Zhang
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# Jingyong Hou
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. ./path.sh
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stage=-1
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stop_stage=4
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num_keywords=11
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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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gpus="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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# your data dir
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download_dir=./data/local
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speech_command_dir=$download_dir/speech_commands_v1
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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 extract all datasets"
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local/data_download.sh --dl_dir $download_dir
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python local/split_dataset.py $download_dir/speech_commands_v1
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "Start preparing Kaldi format files"
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for x in train test valid;
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do
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data=data/$x
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mkdir -p $data
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# make wav.scp utt2spk text file
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find $speech_command_dir/$x -name *.wav | grep -v "_background_noise_" > $data/wav.list
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python local/prepare_speech_command.py --wav_list=$data/wav.list --data_dir=$data
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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 $config \
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--in_scp data/train/wav.scp \
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--out_cmvn data/train/global_cmvn
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for x in train valid test; do
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tools/wav_to_duration.sh --nj 8 data/$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/valid/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 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 ${num_average} \
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--val_best
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# Testing
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result_dir=$dir/test_$(basename $score_checkpoint)
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mkdir -p $result_dir
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python wekws/bin/compute_accuracy.py --gpu 3 \
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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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--num_workers 8 \
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--checkpoint $score_checkpoint
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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python wekws/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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