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DATA_PATH=/pfs/dlnel/public/dataset/speech/libri
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#setted by user
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TRAIN_MANI=${DATA_PATH}/manifest_pcloud.train
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#setted by user
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DEV_MANI=${DATA_PATH}/manifest_pcloud.dev
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#setted by user
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TRAIN_TAR=${DATA_PATH}/data.train.tar
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#setted by user
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DEV_TAR=${DATA_PATH}/data.dev.tar
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#setted by user
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VOCAB_PATH=${DATA_PATH}/eng_vocab.txt
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#setted by user
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MEAN_STD_FILE=${DATA_PATH}/mean_std.npz
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tar -xzvf deepspeech.tar.gz
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rm -rf ./cloud/data/*
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# split train data for each pcloud node
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python ./cloud/pcloud_split_data.py \
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--in_manifest_path=$TRAIN_MANI \
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--data_tar_path=$TRAIN_TAR \
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--out_manifest_path='./cloud/data/train.mani'
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# split dev data for each pcloud node
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python pcloud_split_data.py \
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--in_manifest_path=$DEV_MANI \
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--data_tar_path=$DEV_TAR \
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--out_manifest_path='./cloud/data/dev.mani'
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python train.py \
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--use_gpu=1 \
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--trainer_count=4 \
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--batch_size=256 \
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--mean_std_filepath=$MEAN_STD_FILE \
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--train_manifest_path='./cloud/data/train.mani' \
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--dev_manifest_path='./cloud/data/dev.mani' \
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--vocab_filepath=$VOCAB_PATH \
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