run-ci-ldc93s1_new_sdb.sh

#!/bin/sh

set -xe

ldc93s1_dir="./data/smoke_test"
ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
ldc93s1_sdb="${ldc93s1_dir}/ldc93s1.sdb"

epoch_count=$1
audio_sample_rate=$2

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;

if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
    echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
    python -u bin/data_set_tool.py ${ldc93s1_csv} ${ldc93s1_sdb}
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 train.py --alphabet_config_path "data/alphabet.txt" \
  --show_progressbar false --early_stop false \
  --train_files ${ldc93s1_sdb} --train_batch_size 1 \
  --dev_files ${ldc93s1_sdb} --dev_batch_size 1 \
  --test_files ${ldc93s1_sdb} --test_batch_size 1 \
  --n_hidden 100 --epochs $epoch_count \
  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt_sdb' \
  --learning_rate 0.001 --dropout_rate 0.05  --export_dir '/tmp/train_sdb' \
  --scorer_path 'data/smoke_test/pruned_lm.scorer' \
  --audio_sample_rate ${audio_sample_rate}