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53 lines
1.5 KiB
53 lines
1.5 KiB
#!/bin/bash
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set -e
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data=data
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exp=exp
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nj=20
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. utils/parse_options.sh
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mkdir -p $exp
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ckpt_dir=./data/model
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model_dir=$ckpt_dir/asr1_chunk_conformer_u2pp_wenetspeech_static_1.3.0.model/
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aishell_wav_scp=aishell_test.scp
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text=$data/test/text
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./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
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lang_dir=./data/lang_test/
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graph=$lang_dir/TLG.fst
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word_table=$lang_dir/words.txt
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if [ ! -f $graph ]; then
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# download ngram, if you want to make graph by yourself, please refer local/run_build_tlg.sh
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mkdir -p $lang_dir
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pushd $lang_dir
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wget -c https://paddlespeech.bj.bcebos.com/speechx/examples/ngram/zh/tlg.zip
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unzip tlg.zip
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popd
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fi
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utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recognizer_wfst.log \
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recognizer_main \
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--use_fbank=true \
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--num_bins=80 \
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--cmvn_file=$model_dir/mean_std.json \
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--model_path=$model_dir/export.jit \
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--graph_path=$lang_dir/TLG.fst \
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--word_symbol_table=$word_table \
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--nnet_decoder_chunk=16 \
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--receptive_field_length=7 \
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--subsampling_rate=4 \
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--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
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--rescoring_weight=0.0 \
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--acoustic_scale=2 \
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--result_wspecifier=ark,t:$data/split${nj}/JOB/result_recognizer_wfst.ark
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cat $data/split${nj}/*/result_recognizer_wfst.ark > $exp/aishell_recognizer_wfst
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utils/compute-wer.py --char=1 --v=1 $text $exp/aishell_recognizer_wfst > $exp/aishell.recognizer_wfst.err
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echo "recognizer test have finished!!!"
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echo "please checkout in $exp/aishell.recognizer_wfst.err"
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tail -n 7 $exp/aishell.recognizer_wfst.err
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