PaddleSpeech/demos/asr_deployment

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ASR Deployment by SpeechX

Introduction

ASR deployment support U2/U2++/Deepspeech2 asr model using c++, which is good practice in industry deployment.

More info about SpeechX, please see here.

Usage

1. Environment

• python - 3.7
• docker - registry.baidubce.com/paddlepaddle/paddle:2.2.2-gpu-cuda10.2-cudnn7
• os - Ubuntu 16.04.7 LTS
• gcc/g++/gfortran - 8.2.0
• cmake - 3.16.0

More info please see here.

2. Compile SpeechX

Please see here.

3. Usage

For u2++ asr deployment example, please to see here.

First go to speechx/speechx/examples/u2pp_ol/wenetspeech dir.

• Source path.sh

  source path.sh
  

• Download Model, Prepare test data and cmvn

  run.sh --stage 0 --stop_stage 1
  

• Decode with WAV

  # FP32
  ./local/recognizer.sh
  
  # INT8
  ./local/recognizer_quant.sh
  

  Output:

  I1026 16:13:24.683531 48038 u2_recognizer_main.cc:55] utt: BAC009S0916W0495
  I1026 16:13:24.683578 48038 u2_recognizer_main.cc:56] wav dur: 4.17119 sec.
  I1026 16:13:24.683595 48038 u2_recognizer_main.cc:64] wav len (sample): 66739
  I1026 16:13:25.037652 48038 u2_recognizer_main.cc:87] Pratial result: 3 这令
  I1026 16:13:25.043697 48038 u2_recognizer_main.cc:87] Pratial result: 4 这令
  I1026 16:13:25.222124 48038 u2_recognizer_main.cc:87] Pratial result: 5 这令被贷款
  I1026 16:13:25.228385 48038 u2_recognizer_main.cc:87] Pratial result: 6 这令被贷款
  I1026 16:13:25.414669 48038 u2_recognizer_main.cc:87] Pratial result: 7 这令被贷款的员工
  I1026 16:13:25.420714 48038 u2_recognizer_main.cc:87] Pratial result: 8 这令被贷款的员工
  I1026 16:13:25.608129 48038 u2_recognizer_main.cc:87] Pratial result: 9 这令被贷款的员工们请
  I1026 16:13:25.801620 48038 u2_recognizer_main.cc:87] Pratial result: 10 这令被贷款的员工们请食难安
  I1026 16:13:25.804101 48038 feature_cache.h:44] set finished
  I1026 16:13:25.804128 48038 feature_cache.h:51] compute last feats done.
  I1026 16:13:25.948771 48038 u2_recognizer_main.cc:87] Pratial result: 11 这令被贷款的员工们请食难安
  I1026 16:13:26.246963 48038 u2_recognizer_main.cc:113] BAC009S0916W0495 这令被贷款的员工们请食难安
  

Result

CER compute under aishell-test. RTF compute with feature and decoder, which is more end to end. Machine Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz avx512_vnni

FP32

Overall -> 5.75 % N=104765 C=99035 S=5587 D=143 I=294
Mandarin -> 5.75 % N=104762 C=99035 S=5584 D=143 I=294
English -> 0.00 % N=0 C=0 S=0 D=0 I=0
Other -> 100.00 % N=3 C=0 S=3 D=0 I=0

RTF is: 0.315337

INT8

Overall -> 5.83 % N=104765 C=98943 S=5675 D=147 I=286
Mandarin -> 5.83 % N=104762 C=98943 S=5672 D=147 I=286
English -> 0.00 % N=0 C=0 S=0 D=0 I=0
Other -> 100.00 % N=3 C=0 S=3 D=0 I=0

RTF is: 0.269674