Hui Zhang
3 years ago
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([简体中文](./README_cn.md)|English)
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# Customized Auto Speech Recognition
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## introduction
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In some cases, we need to recognize the specific rare words with high accuracy. eg: address recognition in navigation apps. customized ASR can slove those issues.
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this demo is customized for expense account, which need to recognize rare address.
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* G with slot: 打车到 "address_slot"。
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* this is address slot wfst, you can add the address which want to recognize.
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* after replace operation, G = fstreplace(G_with_slot, address_slot), we will get the customized graph.
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## Usage
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### 1. Installation
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install paddle:2.2.2 docker.
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```
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sudo nvidia-docker pull registry.baidubce.com/paddlepaddle/paddle:2.2.2
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sudo nvidia-docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
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```
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### 2. demo
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* run websocket_server.sh. This script will download resources and libs, and launch the service.
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```
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bash websocket_server.sh
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```
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this script run in two steps:
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1. download the resources.tar.gz, those direcotries will be found in resource directory.
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model: acustic model
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graph: the decoder graph (TLG.fst)
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lib: some libs
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bin: binary
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data: audio and wav.scp
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2. websocket_server_main launch the service.
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some params:
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port: the service port
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graph_path: the decoder graph path
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model_path: acustic model path
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please refer other params in those files:
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PaddleSpeech/speechx/speechx/decoder/param.h
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PaddleSpeech/speechx/examples/ds2_ol/websocket/websocket_server_main.cc
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* In other terminal, run script websocket_client.sh, the client will send data and get the results.
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```
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bash websocket_client.sh
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```
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websocket_client_main will launch the client, the wav_scp is the wav set, port is the server service port.
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* result:
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In the log of client, you will see the message below:
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```
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0513 10:58:13.827821 41768 recognizer_test_main.cc:56] wav len (sample): 70208
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I0513 10:58:13.884493 41768 feature_cache.h:52] set finished
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I0513 10:58:24.247171 41768 paddle_nnet.h:76] Tensor neml: 10240
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I0513 10:58:24.247249 41768 paddle_nnet.h:76] Tensor neml: 10240
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LOG ([5.5.544~2-f21d7]:main():decoder/recognizer_test_main.cc:90) the result of case_10 is 五月十二日二十二点三十六分加班打车回家四十一元
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```
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export LD_LIBRARY_PATH=$PWD/resource/lib
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export PATH=$PATH:$PWD/resource/bin
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sudo nvidia-docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
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#!/bin/bash
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set +x
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set -e
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. path.sh
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# input
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data=$PWD/data
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# output
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wav_scp=wav.scp
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export GLOG_logtostderr=1
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# websocket client
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websocket_client_main \
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--wav_rspecifier=scp:$data/$wav_scp \
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--streaming_chunk=0.36 \
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--port=8881
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#!/bin/bash
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set +x
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set -e
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export GLOG_logtostderr=1
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. path.sh
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#test websocket server
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model_dir=./resource/model
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graph_dir=./resource/graph
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cmvn=./data/cmvn.ark
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#paddle_asr_online/resource.tar.gz
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if [ ! -f $cmvn ]; then
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wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/resource.tar.gz
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tar xzfv resource.tar.gz
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ln -s ./resource/data .
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fi
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websocket_server_main \
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--cmvn_file=$cmvn \
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--streaming_chunk=0.1 \
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--use_fbank=true \
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--model_path=$model_dir/avg_10.jit.pdmodel \
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--param_path=$model_dir/avg_10.jit.pdiparams \
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--model_cache_shapes="5-1-2048,5-1-2048" \
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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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--word_symbol_table=$graph_dir/words.txt \
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--graph_path=$graph_dir/TLG.fst --max_active=7500 \
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--port=8881 \
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--acoustic_scale=12
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