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.
125 lines
3.9 KiB
125 lines
3.9 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
|
|
mkdir -p exp
|
|
exp=$PWD/exp
|
|
|
|
aishell_wav_scp=aishell_test.scp
|
|
if [ ! -d $data/test ]; then
|
|
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
|
|
unzip -d $data aishell_test.zip
|
|
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
|
|
|
|
model_dir=$PWD/aishell_ds2_online_model
|
|
if [ ! -d $model_dir ]; then
|
|
mkdir -p $model_dir
|
|
wget -P $model_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
|
|
tar xzfv $model_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $model_dir
|
|
fi
|
|
|
|
if [ ! -f $lm ]; then
|
|
pushd $data
|
|
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
|
|
popd
|
|
fi
|
|
|
|
|
|
# 3. make feature
|
|
aishell_online_model=$model_dir/exp/deepspeech2_online/checkpoints
|
|
label_file=./aishell_result
|
|
wer=./aishell_wer
|
|
|
|
nj=40
|
|
export GLOG_logtostderr=1
|
|
|
|
#./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
|
|
|
|
data=$PWD/data
|
|
# 3. gen linear feat
|
|
cmvn=$PWD/cmvn.ark
|
|
cmvn_json2binary_main --json_file=$model_dir/data/mean_std.json --cmvn_write_path=$cmvn
|
|
|
|
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat_log \
|
|
linear_spectrogram_without_db_norm_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 \
|
|
--streaming_chunk=0.36
|
|
|
|
text=$data/test/text
|
|
|
|
# 4. recognizer
|
|
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log \
|
|
offline_decoder_sliding_chunk_main \
|
|
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
|
|
--model_path=$aishell_online_model/avg_1.jit.pdmodel \
|
|
--param_path=$aishell_online_model/avg_1.jit.pdiparams \
|
|
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
|
|
--dict_file=$vocb_dir/vocab.txt \
|
|
--result_wspecifier=ark,t:$data/split${nj}/JOB/result
|
|
|
|
cat $data/split${nj}/*/result > ${label_file}
|
|
utils/compute-wer.py --char=1 --v=1 ${label_file} $text > ${wer}
|
|
|
|
# 4. decode with lm
|
|
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log_lm \
|
|
offline_decoder_sliding_chunk_main \
|
|
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
|
|
--model_path=$aishell_online_model/avg_1.jit.pdmodel \
|
|
--param_path=$aishell_online_model/avg_1.jit.pdiparams \
|
|
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
|
|
--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 > ${label_file}_lm
|
|
utils/compute-wer.py --char=1 --v=1 ${label_file}_lm $text > ${wer}_lm
|
|
|
|
|
|
graph_dir=./aishell_graph
|
|
if [ ! -d $ ]; then
|
|
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
|
|
unzip -d aishell_graph.zip
|
|
fi
|
|
|
|
|
|
# 5. test TLG decoder
|
|
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/log_tlg \
|
|
offline_wfst_decoder_main \
|
|
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
|
|
--model_path=$aishell_online_model/avg_1.jit.pdmodel \
|
|
--param_path=$aishell_online_model/avg_1.jit.pdiparams \
|
|
--word_symbol_table=$graph_dir/words.txt \
|
|
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
|
|
--graph_path=$graph_dir/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 > ${label_file}_tlg
|
|
utils/compute-wer.py --char=1 --v=1 ${label_file}_tlg $text > ${wer}_tlg |