You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/speechx/examples/ds2_ol/aishell/run_fbank.sh

177 lines
6.1 KiB

#!/bin/bash
set +x
set -e
. path.sh
nj=40
stage=0
stop_stage=5
. utils/parse_options.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=$data/fbank_model
model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
vocb_dir=$ckpt_dir/data/lang_char/
# output
mkdir -p exp
exp=$PWD/exp
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
aishell_wav_scp=aishell_test.scp
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
if [ ! -d $data/test ]; then
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
mkdir -p $ckpt_dir
pushd $ckpt_dir
wget -c https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/WIP1_asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz
tar xzfv WIP1_asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz
popd
fi
if [ ! -f $lm ]; then
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_fbank
wer=./aishell_wer_fbank
export GLOG_logtostderr=1
cmvn=$data/cmvn_fbank.ark
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# 3. gen linear feat
cmvn_json2kaldi_main --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn --binary=false
./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_fbank_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--feature_wspecifier=ark,scp:$data/split${nj}/JOB/fbank_feat.ark,$data/split${nj}/JOB/fbank_feat.scp \
--cmvn_file=$cmvn \
--streaming_chunk=36
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# recognizer
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.fbank.wolm.log \
ctc_prefix_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/fbank_feat.scp \
--model_path=$model_dir/avg_5.jit.pdmodel \
--param_path=$model_dir/avg_5.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_fbank
cat $data/split${nj}/*/result_fbank > $exp/${label_file}
utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file} > $exp/${wer}
tail -n 7 $exp/${wer}
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# decode with lm
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.fbank.lm.log \
ctc_prefix_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/fbank_feat.scp \
--model_path=$model_dir/avg_5.jit.pdmodel \
--param_path=$model_dir/avg_5.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
--result_wspecifier=ark,t:$data/split${nj}/JOB/fbank_result_lm
cat $data/split${nj}/*/fbank_result_lm > $exp/${label_file}_lm
utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_lm > $exp/${wer}.lm
tail -n 7 $exp/${wer}.lm
fi
wfst=$data/wfst_fbank/
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
mkdir -p $wfst
if [ ! -f $wfst/aishell_graph2.zip ]; then
pushd $wfst
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph2.zip
unzip aishell_graph2.zip
mv aishell_graph2/* $wfst
popd
fi
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# TLG decoder
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.fbank.wfst.log \
tlg_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/fbank_feat.scp \
--model_path=$model_dir/avg_5.jit.pdmodel \
--param_path=$model_dir/avg_5.jit.pdiparams \
--word_symbol_table=$wfst/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--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 ] && [ ${stop_stage} -ge 5 ]; then
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/fbank_recognizer.log \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
--cmvn_file=$cmvn \
--model_path=$model_dir/avg_5.jit.pdmodel \
--use_fbank=true \
--param_path=$model_dir/avg_5.jit.pdiparams \
--word_symbol_table=$wfst/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_fbank_recognizer
cat $data/split${nj}/*/result_fbank_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