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177 lines
6.0 KiB
177 lines
6.0 KiB
#!/bin/bash
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set +x
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set -e
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. path.sh
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nj=40
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stage=0
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stop_stage=100
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. utils/parse_options.sh
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# 1. compile
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if [ ! -d ${SPEECHX_EXAMPLES} ]; then
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pushd ${SPEECHX_ROOT}
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bash build.sh
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popd
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fi
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# input
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mkdir -p data
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data=$PWD/data
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ckpt_dir=$data/model
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model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
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vocb_dir=$ckpt_dir/data/lang_char/
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# output
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mkdir -p exp
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exp=$PWD/exp
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aishell_wav_scp=aishell_test.scp
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
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if [ ! -d $data/test ]; then
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pushd $data
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wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
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unzip aishell_test.zip
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popd
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realpath $data/test/*/*.wav > $data/wavlist
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awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
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paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
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fi
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if [ ! -f $ckpt_dir/data/mean_std.json ]; then
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mkdir -p $ckpt_dir
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pushd $ckpt_dir
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wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
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tar xzfv asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
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popd
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fi
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lm=$data/zh_giga.no_cna_cmn.prune01244.klm
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if [ ! -f $lm ]; then
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pushd $data
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wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
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popd
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fi
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fi
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# 3. make feature
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text=$data/test/text
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label_file=./aishell_result
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wer=./aishell_wer
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export GLOG_logtostderr=1
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cmvn=$data/cmvn.ark
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# 3. gen linear feat
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cmvn_json2kaldi_main --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
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./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
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utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \
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compute_linear_spectrogram_main \
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--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
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--feature_wspecifier=ark,scp:$data/split${nj}/JOB/feat.ark,$data/split${nj}/JOB/feat.scp \
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--cmvn_file=$cmvn \
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echo "feature make have finished!!!"
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# recognizer
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utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \
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ctc_prefix_beam_search_decoder_main \
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--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
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--model_path=$model_dir/avg_1.jit.pdmodel \
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--param_path=$model_dir/avg_1.jit.pdiparams \
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--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
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--nnet_decoder_chunk=8 \
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--dict_file=$vocb_dir/vocab.txt \
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--result_wspecifier=ark,t:$data/split${nj}/JOB/result
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cat $data/split${nj}/*/result > $exp/${label_file}
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utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file} > $exp/${wer}
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echo "ctc-prefix-beam-search-decoder-ol without lm has finished!!!"
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echo "please checkout in ${exp}/${wer}"
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tail -n 7 $exp/${wer}
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# decode with lm
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utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \
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ctc_prefix_beam_search_decoder_main \
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--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
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--model_path=$model_dir/avg_1.jit.pdmodel \
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--param_path=$model_dir/avg_1.jit.pdiparams \
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--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
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--nnet_decoder_chunk=8 \
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--dict_file=$vocb_dir/vocab.txt \
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--lm_path=$lm \
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--result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm
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cat $data/split${nj}/*/result_lm > $exp/${label_file}_lm
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utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_lm > $exp/${wer}.lm
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echo "ctc-prefix-beam-search-decoder-ol with lm test has finished!!!"
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echo "please checkout in ${exp}/${wer}.lm"
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tail -n 7 $exp/${wer}.lm
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fi
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wfst=$data/wfst/
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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mkdir -p $wfst
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if [ ! -f $wfst/aishell_graph.zip ]; then
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pushd $wfst
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wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
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unzip aishell_graph.zip
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mv aishell_graph/* $wfst
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popd
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fi
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# TLG decoder
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utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \
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tlg_decoder_main \
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--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
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--model_path=$model_dir/avg_1.jit.pdmodel \
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--param_path=$model_dir/avg_1.jit.pdiparams \
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--word_symbol_table=$wfst/words.txt \
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--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
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--graph_path=$wfst/TLG.fst --max_active=7500 \
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--nnet_decoder_chunk=8 \
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--acoustic_scale=1.2 \
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--result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
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cat $data/split${nj}/*/result_tlg > $exp/${label_file}_tlg
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utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_tlg > $exp/${wer}.tlg
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echo "wfst-decoder-ol have finished!!!"
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echo "please checkout in ${exp}/${wer}.tlg"
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tail -n 7 $exp/${wer}.tlg
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fi
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# TLG decoder
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utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recognizer.log \
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recognizer_main \
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--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
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--cmvn_file=$cmvn \
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--model_path=$model_dir/avg_1.jit.pdmodel \
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--param_path=$model_dir/avg_1.jit.pdiparams \
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--word_symbol_table=$wfst/words.txt \
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--nnet_decoder_chunk=8 \
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--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
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--graph_path=$wfst/TLG.fst --max_active=7500 \
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--acoustic_scale=1.2 \
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--result_wspecifier=ark,t:$data/split${nj}/JOB/result_recognizer
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cat $data/split${nj}/*/result_recognizer > $exp/${label_file}_recognizer
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utils/compute-wer.py --char=1 --v=1 $text $exp/${label_file}_recognizer > $exp/${wer}.recognizer
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echo "recognizer test have finished!!!"
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echo "please checkout in ${exp}/${wer}.recognizer"
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tail -n 7 $exp/${wer}.recognizer
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fi
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