run-ci-ldc93s1_singleshotinference.sh

#!/bin/sh

set -xe

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

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 -m coqui_stt_training.train \
  --alphabet_config_path "data/alphabet.txt" \
  --show_progressbar false --early_stop false \
  --train_files ${ldc93s1_csv} --train_batch_size 1 \
  --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
  --test_files ${ldc93s1_csv} --test_batch_size 1 \
  --n_hidden 100 --epochs 1 \
  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt' --checkpoint_secs 0 \
  --learning_rate 0.001 --dropout_rate 0.05 \
  --scorer_path 'data/smoke_test/pruned_lm.scorer'

python -m coqui_stt_training.training_graph_inference \
  --n_hidden 100 \
  --checkpoint_dir '/tmp/ckpt' \
  --scorer_path 'data/smoke_test/pruned_lm.scorer' \
  --one_shot_infer 'data/smoke_test/LDC93S1.wav'

python -m coqui_stt_training.training_graph_inference_flashlight \
  --n_hidden 100 \
  --checkpoint_dir '/tmp/ckpt' \
  --scorer_path 'data/smoke_test/pruned_lm.scorer' \
  --vocab_file 'data/smoke_test/vocab.pruned.txt' \
  --one_shot_infer 'data/smoke_test/LDC93S1.wav'