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#! /usr/bin/env bash
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TRAIN_MANIFEST=$1
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DEV_MANIFEST=$2
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MODEL_PATH=$3
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NUM_GPU=$4
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BATCH_SIZE=$5
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IS_LOCAL=$6
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python ./cloud/split_data.py \
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--in_manifest_path=${TRAIN_MANIFEST} \
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--out_manifest_path='/local.manifest.train'
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python ./cloud/split_data.py \
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--in_manifest_path=${DEV_MANIFEST} \
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--out_manifest_path='/local.manifest.dev'
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mkdir ./logs
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python -u train.py \
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--batch_size=${BATCH_SIZE} \
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--trainer_count=${NUM_GPU} \
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--num_passes=200 \
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--num_proc_data=${NUM_GPU} \
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--num_conv_layers=2 \
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--num_rnn_layers=3 \
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--rnn_layer_size=2048 \
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--num_iter_print=100 \
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--learning_rate=5e-4 \
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--max_duration=27.0 \
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--min_duration=0.0 \
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--use_sortagrad=True \
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--use_gru=False \
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--use_gpu=True \
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--is_local=${IS_LOCAL} \
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--share_rnn_weights=True \
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--train_manifest='/local.manifest.train' \
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--dev_manifest='/local.manifest.dev' \
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--mean_std_path='data/librispeech/mean_std.npz' \
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--vocab_path='data/librispeech/vocab.txt' \
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--output_model_dir='./checkpoints' \
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--output_model_dir=${MODEL_PATH} \
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--augment_conf_path='conf/augmentation.config' \
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--specgram_type='linear' \
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--shuffle_method='batch_shuffle_clipped' \
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2>&1 | tee ./logs/train.log
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