#! /usr/bin/env bash

# download language model
cd ${MAIN_ROOT}/models/lm > /dev/null
bash download_lm_ch.sh
if [ $? -ne 0 ]; then
    exit 1
fi
cd - > /dev/null


# download well-trained model
cd ${MAIN_ROOT}/models/aishell > /dev/null
bash download_model.sh
if [ $? -ne 0 ]; then
    exit 1
fi
cd - > /dev/null


# evaluate model
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python3 -u ${MAIN_ROOT}/test.py \
--batch_size=128 \
--beam_size=300 \
--num_proc_bsearch=8 \
--num_conv_layers=2 \
--num_rnn_layers=3 \
--rnn_layer_size=1024 \
--alpha=2.6 \
--beta=5.0 \
--cutoff_prob=0.99 \
--cutoff_top_n=40 \
--use_gru=True \
--use_gpu=True \
--share_rnn_weights=False \
--test_manifest="data/manifest.test" \
--mean_std_path="${MAIN_ROOT}/models/aishell/mean_std.npz" \
--vocab_path="${MAIN_ROOT}/models/aishell/vocab.txt" \
--model_path="${MAIN_ROOT}/models/aishell" \
--lang_model_path="${MAIN_ROOT}/models/lm/zh_giga.no_cna_cmn.prune01244.klm" \
--decoding_method="ctc_beam_search" \
--error_rate_type="cer" \
--specgram_type="linear"

if [ $? -ne 0 ]; then
    echo "Failed in evaluation!"
    exit 1
fi


exit 0