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#!/usr/bin/env bash
: <<'END'
sh run.sh --stage 0 --stop_stage 1 --system_version windows --file_folder_name file_dir --final_model_name sound-4-ch32 \
--filename_patterns "E:/Users/tianx/HuggingDatasets/vm_sound_classification/data/wav_finished/wav_finished/en-US/wav_finished/*/*.wav \
E:/Users/tianx/HuggingDatasets/vm_sound_classification/data/wav_finished/id-ID/wav_finished/*/*.wav" \
--label_plan 4
sh run.sh --stage 2 --stop_stage 2 --system_version windows --file_folder_name file_dir --final_model_name sound-2-ch32 \
--filename_patterns "E:/Users/tianx/HuggingDatasets/vm_sound_classification/data/wav_finished/wav_finished/en-US/wav_finished/*/*.wav \
E:/Users/tianx/HuggingDatasets/vm_sound_classification/data/wav_finished/id-ID/wav_finished/*/*.wav" \
--label_plan 4
sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch32 \
--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
--label_plan 3 \
--config_file "yaml/conv2d-classifier-3-ch4.yaml"
sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch32 \
--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ms-MY/wav_finished/*/*.wav" \
--label_plan 2-voicemail \
--config_file "yaml/conv2d-classifier-2-ch32.yaml"
END
# params
system_version="windows";
verbose=true;
stage=0 # start from 0 if you need to start from data preparation
stop_stage=9
work_dir="$(pwd)"
file_folder_name=file_folder_name
final_model_name=final_model_name
filename_patterns="/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav"
label_plan=4
config_file="yaml/conv2d-classifier-2-ch4.yaml"
pretrained_model=null
nohup_name=nohup.out
country=en-US
# model params
batch_size=64
max_epochs=200
save_top_k=10
patience=5
# parse options
while true; do
[ -z "${1:-}" ] && break; # break if there are no arguments
case "$1" in
--*) name=$(echo "$1" | sed s/^--// | sed s/-/_/g);
eval '[ -z "${'"$name"'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
old_value="(eval echo \\$$name)";
if [ "${old_value}" == "true" ] || [ "${old_value}" == "false" ]; then
was_bool=true;
else
was_bool=false;
fi
# Set the variable to the right value-- the escaped quotes make it work if
# the option had spaces, like --cmd "queue.pl -sync y"
eval "${name}=\"$2\"";
# Check that Boolean-valued arguments are really Boolean.
if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
exit 1;
fi
shift 2;
;;
*) break;
esac
done
file_dir="${work_dir}/${file_folder_name}"
final_model_dir="${work_dir}/../../trained_models/${final_model_name}";
dataset="${file_dir}/dataset.xlsx"
train_dataset="${file_dir}/train.xlsx"
valid_dataset="${file_dir}/valid.xlsx"
evaluation_file="${file_dir}/evaluation.xlsx"
vocabulary_dir="${file_dir}/vocabulary"
$verbose && echo "system_version: ${system_version}"
$verbose && echo "file_folder_name: ${file_folder_name}"
if [ $system_version == "windows" ]; then
alias python3='D:/Users/tianx/PycharmProjects/virtualenv/vm_sound_classification/Scripts/python.exe'
elif [ $system_version == "centos" ] || [ $system_version == "ubuntu" ]; then
#source /data/local/bin/vm_sound_classification/bin/activate
alias python3='/data/local/bin/vm_sound_classification/bin/python3'
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
$verbose && echo "stage 0: prepare data"
cd "${work_dir}" || exit 1
python3 step_1_prepare_data.py \
--file_dir "${file_dir}" \
--filename_patterns "${filename_patterns}" \
--train_dataset "${train_dataset}" \
--valid_dataset "${valid_dataset}" \
--label_plan "${label_plan}" \
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
$verbose && echo "stage 1: make vocabulary"
cd "${work_dir}" || exit 1
python3 step_2_make_vocabulary.py \
--vocabulary_dir "${vocabulary_dir}" \
--train_dataset "${train_dataset}" \
--valid_dataset "${valid_dataset}" \
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
$verbose && echo "stage 2: train model"
cd "${work_dir}" || exit 1
python3 step_3_train_model.py \
--vocabulary_dir "${vocabulary_dir}" \
--train_dataset "${train_dataset}" \
--valid_dataset "${valid_dataset}" \
--serialization_dir "${file_dir}" \
--config_file "${config_file}" \
--pretrained_model "${pretrained_model}" \
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
$verbose && echo "stage 3: test model"
cd "${work_dir}" || exit 1
python3 step_4_evaluation_model.py \
--dataset "${dataset}" \
--vocabulary_dir "${vocabulary_dir}" \
--model_dir "${file_dir}/best" \
--output_file "${evaluation_file}" \
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
$verbose && echo "stage 4: export model"
cd "${work_dir}" || exit 1
python3 step_5_export_models.py \
--vocabulary_dir "${vocabulary_dir}" \
--model_dir "${file_dir}/best" \
--serialization_dir "${file_dir}" \
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
$verbose && echo "stage 5: collect files"
cd "${work_dir}" || exit 1
mkdir -p ${final_model_dir}
cp "${file_dir}/best"/* "${final_model_dir}"
cp -r "${file_dir}/vocabulary" "${final_model_dir}"
cp "${file_dir}/evaluation.xlsx" "${final_model_dir}/evaluation.xlsx"
cp "${file_dir}/trace_model.zip" "${final_model_dir}/trace_model.zip"
cp "${file_dir}/trace_quant_model.zip" "${final_model_dir}/trace_quant_model.zip"
cp "${file_dir}/script_model.zip" "${final_model_dir}/script_model.zip"
cp "${file_dir}/script_quant_model.zip" "${final_model_dir}/script_quant_model.zip"
cd "${final_model_dir}/.." || exit 1;
if [ -e "${final_model_name}.zip" ]; then
rm -rf "${final_model_name}_backup.zip"
mv "${final_model_name}.zip" "${final_model_name}_backup.zip"
fi
zip -r "${final_model_name}.zip" "${final_model_name}"
rm -rf "${final_model_name}"
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
$verbose && echo "stage 6: clear file_dir"
cd "${work_dir}" || exit 1
rm -rf "${file_dir}";
fi
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