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PaddleSpeech/runtime/examples/codelab/decoder/run.sh

79 lines
1.9 KiB

#!/bin/bash
set +x
set -e
. path.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
pushd ${SPEECHX_ROOT}
bash build.sh
popd
fi
# input
mkdir -p data
data=$PWD/data
ckpt_dir=$data/model
model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
vocb_dir=$ckpt_dir/data/lang_char/
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
# output
exp_dir=./exp
mkdir -p $exp_dir
# 2. download model
if [[ ! -f data/model/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz ]]; then
mkdir -p data/model
pushd data/model
wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
popd
fi
# produce wav scp
if [ ! -f data/wav.scp ]; then
pushd data
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
echo "utt1 " $PWD/zh.wav > wav.scp
popd
fi
# download lm
if [ ! -f $lm ]; then
pushd data
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
popd
fi
feat_wspecifier=$exp_dir/feats.ark
cmvn=$exp_dir/cmvn.ark
export GLOG_logtostderr=1
# dump json cmvn to kaldi
cmvn_json2kaldi_main \
--json_file $ckpt_dir/data/mean_std.json \
--cmvn_write_path $cmvn \
--binary=false
echo "convert json cmvn to kaldi ark."
# generate linear feature as streaming
compute_linear_spectrogram_main \
--wav_rspecifier=scp:$data/wav.scp \
--feature_wspecifier=ark,t:$feat_wspecifier \
--cmvn_file=$cmvn
echo "compute linear spectrogram feature."
# run ctc beam search decoder as streaming
ctc_beam_search_decoder_main \
--result_wspecifier=ark,t:$exp_dir/result.txt \
--feature_rspecifier=ark:$feat_wspecifier \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm