PaddleSpeech/speechx/examples/ds2_ol/aishell

Aishell - Deepspeech2 Streaming

We recommend using U2/U2++ model instead of DS2, please see here.

A C++ deployment example for using the deepspeech2 model to recognize wav and compute CER. We using AISHELL-1 as test data.

Source path.sh

. path.sh

SpeechX bins is under echo $SPEECHX_BUILD, more info please see path.sh.

Recognize with linear feature

bash run.sh

run.sh has multi stage, for details please see run.sh:

1. donwload dataset, model and lm
2. convert cmvn format and compute feature
3. decode w/o lm by feature
4. decode w/ ngram lm by feature
5. decode w/ TLG graph by feature
6. recognize w/ TLG graph by wav input

Recognize with .scp file for wav

This sciprt using recognizer_main to recognize wav file.

The input is scp file which look like this:

# head data/split1/1/aishell_test.scp 
BAC009S0764W0121        /workspace/PaddleSpeech/speechx/examples/u2pp_ol/wenetspeech/data/test/S0764/BAC009S0764W0121.wav
BAC009S0764W0122        /workspace/PaddleSpeech/speechx/examples/u2pp_ol/wenetspeech/data/test/S0764/BAC009S0764W0122.wav
...
BAC009S0764W0125        /workspace/PaddleSpeech/speechx/examples/u2pp_ol/wenetspeech/data/test/S0764/BAC009S0764W0125.wav

If you want to recognize one wav, you can make scp file like this:

key  path/to/wav/file

Then specify --wav_rspecifier= param for recognizer_main bin. For other flags meaning, please see help:

recognizer_main --help

For the exmaple to using recognizer_main please see run.sh.

CTC Prefix Beam Search w/o LM

Overall -> 16.14 % N=104612 C=88190 S=16110 D=312 I=465
Mandarin -> 16.14 % N=104612 C=88190 S=16110 D=312 I=465
Other -> 0.00 % N=0 C=0 S=0 D=0 I=0

CTC Prefix Beam Search w/ LM

LM: zh_giga.no_cna_cmn.prune01244.klm

Overall -> 7.86 % N=104768 C=96865 S=7573 D=330 I=327
Mandarin -> 7.86 % N=104768 C=96865 S=7573 D=330 I=327
Other -> 0.00 % N=0 C=0 S=0 D=0 I=0

CTC TLG WFST

LM: aishell train --acoustic_scale=1.2

Overall -> 11.14 % N=103017 C=93363 S=9583 D=71 I=1819
Mandarin -> 11.14 % N=103017 C=93363 S=9583 D=71 I=1818
Other -> 0.00 % N=0 C=0 S=0 D=0 I=1

LM: wenetspeech --acoustic_scale=1.5

Overall -> 10.93 % N=104765 C=93410 S=9780 D=1575 I=95
Mandarin -> 10.93 % N=104762 C=93410 S=9779 D=1573 I=95
Other -> 100.00 % N=3 C=0 S=1 D=2 I=0

Recognize with fbank feature

This script is same to run.sh, but using fbank feature.

bash run_fbank.sh

CTC Prefix Beam Search w/o LM

Overall -> 10.44 % N=104765 C=94194 S=10174 D=397 I=369
Mandarin -> 10.44 % N=104762 C=94194 S=10171 D=397 I=369
Other -> 100.00 % N=3 C=0 S=3 D=0 I=0

CTC Prefix Beam Search w/ LM

LM: zh_giga.no_cna_cmn.prune01244.klm

Overall -> 5.82 % N=104765 C=99386 S=4944 D=435 I=720
Mandarin -> 5.82 % N=104762 C=99386 S=4941 D=435 I=720
English -> 0.00 % N=0 C=0 S=0 D=0 I=0

CTC TLG WFST

LM: aishell train

Overall -> 9.58 % N=104765 C=94817 S=4326 D=5622 I=84
Mandarin -> 9.57 % N=104762 C=94817 S=4325 D=5620 I=84
Other -> 100.00 % N=3 C=0 S=1 D=2 I=0

Build TLG WFST graph

The script is for building TLG wfst graph, depending on srilm, please make sure it is installed. For more information please see the script below.

 bash ./local/run_build_tlg.sh