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PaddleSpeech/speechx/examples/ds2_ol/aishell/run.sh

180 lines
6.2 KiB

#!/bin/bash
set -x
set -e
. path.sh
nj=40
stage=0
stop_stage=100
. utils/parse_options.sh
# 1. compile
if [ ! -d ${SPEECHX_BUILD} ]; 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=$ckpt_dir/data/lang_char/
# output
mkdir -p exp
exp=$PWD/exp
aishell_wav_scp=aishell_test.scp
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
if [ ! -d $data/test ]; then
# donwload dataset
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
popd
realpath $data/test/*/*.wav > $data/wavlist
awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
fi
if [ ! -f $ckpt_dir/data/mean_std.json ]; then
# download model
mkdir -p $ckpt_dir
pushd $ckpt_dir
wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
popd
fi
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
if [ ! -f $lm ]; then
# download kenlm bin
pushd $data
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
popd
fi
fi
# 3. make feature
text=$data/test/text
label_file=./aishell_result
wer=./aishell_wer
export GLOG_logtostderr=1
cmvn=$data/cmvn.ark
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# 3. convert cmvn format and compute linear feat
cmvn_json2kaldi_main --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \
compute_linear_spectrogram_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--feature_wspecifier=ark,scp:$data/split${nj}/JOB/feat.ark,$data/split${nj}/JOB/feat.scp \
--cmvn_file=$cmvn \
echo "feature make have finished!!!"
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# decode w/o lm
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \
ctc_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result
cat $data/split${nj}/*/result > $exp/${label_file}
utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file} > $exp/${wer}
echo "ctc-prefix-beam-search-decoder-ol without lm has finished!!!"
echo "please checkout in ${exp}/${wer}"
tail -n 7 $exp/${wer}
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# decode w/ ngram lm with feature input
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \
ctc_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm
cat $data/split${nj}/*/result_lm > $exp/${label_file}_lm
utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_lm > $exp/${wer}.lm
echo "ctc-prefix-beam-search-decoder-ol with lm test has finished!!!"
echo "please checkout in ${exp}/${wer}.lm"
tail -n 7 $exp/${wer}.lm
fi
wfst=$data/wfst/
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
mkdir -p $wfst
if [ ! -f $wfst/aishell_graph.zip ]; then
# download TLG graph
pushd $wfst
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
unzip aishell_graph.zip
mv aishell_graph/* $wfst
popd
fi
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# decoder w/ TLG graph with feature input
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \
ctc_tlg_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$wfst/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--nnet_decoder_chunk=8 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
cat $data/split${nj}/*/result_tlg > $exp/${label_file}_tlg
utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_tlg > $exp/${wer}.tlg
echo "wfst-decoder-ol have finished!!!"
echo "please checkout in ${exp}/${wer}.tlg"
tail -n 7 $exp/${wer}.tlg
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# recognize from wav file w/ TLG graph
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recognizer.log \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--cmvn_file=$cmvn \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$wfst/words.txt \
--nnet_decoder_chunk=8 \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_recognizer
cat $data/split${nj}/*/result_recognizer > $exp/${label_file}_recognizer
utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_recognizer > $exp/${wer}.recognizer
echo "recognizer test have finished!!!"
echo "please checkout in ${exp}/${wer}.recognizer"
tail -n 7 $exp/${wer}.recognizer
fi