DeepSpeech/bin/run-tc-ldc93s1_new.sh

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#!/bin/sh
set -xe
ldc93s1_dir="./data/ldc93s1-tc"
ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
epoch_count=$1
if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
python -u bin/import_ldc93s1.py ${ldc93s1_dir}
fi;
# Force only one visible device because we have a single-sample dataset
# and when trying to run on multiple devices (like GPUs), this will break
export CUDA_VISIBLE_DEVICES=0
python -u DeepSpeech.py --noshow_progressbar --noearly_stop \
--train_files ${ldc93s1_csv} --train_batch_size 1 \
--feature_cache '/tmp/ldc93s1_cache' \
--dev_files ${ldc93s1_csv} --dev_batch_size 1 \
--test_files ${ldc93s1_csv} --test_batch_size 1 \
--n_hidden 100 --epochs $epoch_count \
--max_to_keep 1 --checkpoint_dir '/tmp/ckpt' \
--learning_rate 0.001 --dropout_rate 0.05 --export_dir '/tmp/train' \
--lm_binary_path 'data/smoke_test/vocab.pruned.lm' \
--lm_trie_path 'data/smoke_test/vocab.trie'