mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
702ec03ad8
* add hotword for deploy_tools * Support wfst decoder and contextual biasing (#1039) * Support wfst decoder and contextual biasing * Turn on fstbin compilation --------- Co-authored-by: gongbo.gb <gongbo.gb@alibaba-inc.com> * mv funasr/runtime runtime * Fix crash caused by OOV in hotwords list * funasr infer * funasr infer * funasr infer * funasr infer * funasr infer * fix some bugs about fst hotword; support wfst for websocket server and clients; mv runtime out of funasr; modify relative docs * del onnxruntime/include/gflags * update tensor.h * update run_server.sh * update deploy tools * update deploy tools * update websocket-server * update funasr-wss-server * Remove self loop propagation * Update websocket_protocol_zh.md * Update websocket_protocol_zh.md * update hotword protocol * author zhaomingwork: change hotwords for h5 and java * update hotword protocol * catch exception for json_fst_hws * update hotword on message * update onnx benchmark for ngram&hotword * update docs * update funasr-wss-serve * add NONE for LM_DIR * update docs * update run_server.sh * add whats-new * modify whats-new * update whats-new * update whats-new * Support decoder option for beam searching * update benchmark_onnx_cpp * Support decoder option for websocket * fix bug of CompileHotwordEmbedding * update html client * update docs --------- Co-authored-by: gongbo.gb <35997837+aibulamusi@users.noreply.github.com> Co-authored-by: gongbo.gb <gongbo.gb@alibaba-inc.com> Co-authored-by: 游雁 <zhifu.gzf@alibaba-inc.com>
75 lines
2.1 KiB
Bash
75 lines
2.1 KiB
Bash
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split_scps_tool=split_scp.pl
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inference_tool=test_cer.py
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proce_text_tool=proce_text.py
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compute_wer_tool=compute_wer.py
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nj=32
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stage=0
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stop_stage=2
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scp="/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/wav.scp"
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label_text="/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/text"
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export_root="/nfs/zhifu.gzf/export"
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#:<<!
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model_name="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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backend="onnx" # "torch"
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quantize='true' # 'False'
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fallback_op_num_torch=20
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tag=${model_name}/${backend}_quantize_${quantize}_${fallback_op_num_torch}
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!
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output_dir=${export_root}/logs/${tag}/split$nj
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mkdir -p ${output_dir}
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echo ${output_dir}
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ];then
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python -m funasr.export.export_model --model-name ${model_name} --export-dir ${export_root} --type ${backend} --quantize ${quantize} --audio_in ${scp} --fallback-num ${fallback_op_num_torch}
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
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model_dir=${export_root}/${model_name}
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split_scps=""
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for JOB in $(seq ${nj}); do
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split_scps="$split_scps $output_dir/wav.$JOB.scp"
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done
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perl ${split_scps_tool} $scp ${split_scps}
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for JOB in $(seq ${nj}); do
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{
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core_id=`expr $JOB - 1`
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taskset -c ${core_id} python ${inference_tool} --backend ${backend} --model_dir ${model_dir} --wav_file ${output_dir}/wav.$JOB.scp --quantize ${quantize} --output_dir ${output_dir}/${JOB} &> ${output_dir}/log.$JOB.txt
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}&
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done
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wait
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mkdir -p ${output_dir}/1best_recog
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for f in token text; do
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if [ -f "${output_dir}/1/${f}" ]; then
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for JOB in $(seq "${nj}"); do
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cat "${output_dir}/${JOB}/${f}"
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done | sort -k1 >"${output_dir}/1best_recog/${f}"
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fi
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done
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
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echo "Computing WER ..."
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python ${proce_text_tool} ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
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python ${proce_text_tool} ${label_text} ${output_dir}/1best_recog/text.ref
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python ${compute_wer_tool} ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
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tail -n 3 ${output_dir}/1best_recog/text.cer
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fi
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