Hui Zhang
3 years ago
committed by
GitHub
commit
c119810664
@ -0,0 +1,64 @@
|
||||
([简体中文](./README_cn.md)|English)
|
||||
|
||||
# Customized Auto Speech Recognition
|
||||
|
||||
## introduction
|
||||
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.
|
||||
|
||||
this demo is customized for expense account, which need to recognize rare address.
|
||||
|
||||
* G with slot: 打车到 "address_slot"。
|
||||
|
||||
|
||||
* this is address slot wfst, you can add the address which want to recognize.
|
||||
|
||||
|
||||
* after replace operation, G = fstreplace(G_with_slot, address_slot), we will get the customized graph.
|
||||
|
||||
|
||||
## Usage
|
||||
### 1. Installation
|
||||
install paddle:2.2.2 docker.
|
||||
```
|
||||
sudo nvidia-docker pull registry.baidubce.com/paddlepaddle/paddle:2.2.2
|
||||
|
||||
sudo nvidia-docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
|
||||
```
|
||||
|
||||
### 2. demo
|
||||
* run websocket_server.sh. This script will download resources and libs, and launch the service.
|
||||
```
|
||||
bash websocket_server.sh
|
||||
```
|
||||
this script run in two steps:
|
||||
1. download the resources.tar.gz, those direcotries will be found in resource directory.
|
||||
model: acustic model
|
||||
graph: the decoder graph (TLG.fst)
|
||||
lib: some libs
|
||||
bin: binary
|
||||
data: audio and wav.scp
|
||||
|
||||
2. websocket_server_main launch the service.
|
||||
some params:
|
||||
port: the service port
|
||||
graph_path: the decoder graph path
|
||||
model_path: acustic model path
|
||||
please refer other params in those files:
|
||||
PaddleSpeech/speechx/speechx/decoder/param.h
|
||||
PaddleSpeech/speechx/examples/ds2_ol/websocket/websocket_server_main.cc
|
||||
|
||||
* In other terminal, run script websocket_client.sh, the client will send data and get the results.
|
||||
```
|
||||
bash websocket_client.sh
|
||||
```
|
||||
websocket_client_main will launch the client, the wav_scp is the wav set, port is the server service port.
|
||||
|
||||
* result:
|
||||
In the log of client, you will see the message below:
|
||||
```
|
||||
0513 10:58:13.827821 41768 recognizer_test_main.cc:56] wav len (sample): 70208
|
||||
I0513 10:58:13.884493 41768 feature_cache.h:52] set finished
|
||||
I0513 10:58:24.247171 41768 paddle_nnet.h:76] Tensor neml: 10240
|
||||
I0513 10:58:24.247249 41768 paddle_nnet.h:76] Tensor neml: 10240
|
||||
LOG ([5.5.544~2-f21d7]:main():decoder/recognizer_test_main.cc:90) the result of case_10 is 五月十二日二十二点三十六分加班打车回家四十一元
|
||||
```
|
||||
@ -0,0 +1,2 @@
|
||||
export LD_LIBRARY_PATH=$PWD/resource/lib
|
||||
export PATH=$PATH:$PWD/resource/bin
|
||||
@ -0,0 +1 @@
|
||||
sudo nvidia-docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
|
||||
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
set +x
|
||||
set -e
|
||||
|
||||
. path.sh
|
||||
# input
|
||||
data=$PWD/data
|
||||
|
||||
# output
|
||||
wav_scp=wav.scp
|
||||
|
||||
export GLOG_logtostderr=1
|
||||
|
||||
# websocket client
|
||||
websocket_client_main \
|
||||
--wav_rspecifier=scp:$data/$wav_scp \
|
||||
--streaming_chunk=0.36 \
|
||||
--port=8881
|
||||
@ -0,0 +1,33 @@
|
||||
#!/bin/bash
|
||||
set +x
|
||||
set -e
|
||||
|
||||
export GLOG_logtostderr=1
|
||||
|
||||
. path.sh
|
||||
#test websocket server
|
||||
|
||||
model_dir=./resource/model
|
||||
graph_dir=./resource/graph
|
||||
cmvn=./data/cmvn.ark
|
||||
|
||||
|
||||
#paddle_asr_online/resource.tar.gz
|
||||
if [ ! -f $cmvn ]; then
|
||||
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/resource.tar.gz
|
||||
tar xzfv resource.tar.gz
|
||||
ln -s ./resource/data .
|
||||
fi
|
||||
|
||||
websocket_server_main \
|
||||
--cmvn_file=$cmvn \
|
||||
--streaming_chunk=0.1 \
|
||||
--use_fbank=true \
|
||||
--model_path=$model_dir/avg_10.jit.pdmodel \
|
||||
--param_path=$model_dir/avg_10.jit.pdiparams \
|
||||
--model_cache_shapes="5-1-2048,5-1-2048" \
|
||||
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
|
||||
--word_symbol_table=$graph_dir/words.txt \
|
||||
--graph_path=$graph_dir/TLG.fst --max_active=7500 \
|
||||
--port=8881 \
|
||||
--acoustic_scale=12
|
||||
Loading…
Reference in new issue