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[speechx] more doc for speechx (#2702)

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* doc for ds2 websocket
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Hui Zhang 2 years ago committed by GitHub
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2
speechx/examples/custom_asr/README.md
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@ -1,4 +1,4 @@
# customized Auto Speech Recognition
# Customized ASR
## introduction
These scripts are tutorials to show you how build your own decoding graph.

1
speechx/examples/ds2_ol/README.md
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@ -4,3 +4,4 @@
* `websocket` - Streaming ASR with websocket for deepspeech2_aishell.
* `aishell` - Streaming Decoding under aishell dataset, for local WER test.
* `onnx` - Example to convert deepspeech2 to onnx format.

70
speechx/examples/ds2_ol/aishell/README.md
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@ -1,12 +1,57 @@
# Aishell - Deepspeech2 Streaming
## How to run
> We recommend using U2/U2++ model instead of DS2, please see [here](../../u2pp_ol/wenetspeech/).
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
```bash
. path.sh
```
SpeechX bins is under `echo $SPEECHX_BUILD`, more info please see `path.sh`.
## Recognize with linear feature
```bash
bash run.sh
```
## Results
`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:
```text
# 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:
```text
key path/to/wav/file
```
Then specify `--wav_rspecifier=` param for `recognizer_main` bin. For other flags meaning, please see `help`:
```bash
recognizer_main --help
```
For the exmaple to using `recognizer_main` please see `run.sh`.
### CTC Prefix Beam Search w/o LM
@ -25,7 +70,7 @@ 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 WFST
### CTC TLG WFST
LM: [aishell train](http://paddlespeech.bj.bcebos.com/speechx/examples/ds2_ol/aishell/aishell_graph.zip)
--acoustic_scale=1.2
@ -43,8 +88,11 @@ 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
```
## fbank
```
## Recognize with fbank feature
This script is same to `run.sh`, but using fbank feature.
```bash
bash run_fbank.sh
```
@ -66,7 +114,7 @@ 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 WFST
### CTC TLG WFST
LM: [aishell train](https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph2.zip)
```
@ -75,7 +123,11 @@ 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 graph
```
bash run_build_tlg.sh
## 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
bash ./local/run_build_tlg.sh
```

12
speechx/examples/ds2_ol/aishell/run_build_tlg.sh → speechx/examples/ds2_ol/aishell/local/run_build_tlg.sh
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@ -22,6 +22,7 @@ mkdir -p $data
if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
if [ ! -f $data/speech.ngram.zh.tar.gz ];then
# download ngram
pushd $data
wget -c http://paddlespeech.bj.bcebos.com/speechx/examples/ngram/zh/speech.ngram.zh.tar.gz
tar xvzf speech.ngram.zh.tar.gz
@ -29,6 +30,7 @@ if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
fi
if [ ! -f $ckpt_dir/data/mean_std.json ]; then
# download model
mkdir -p $ckpt_dir
pushd $ckpt_dir
wget -c https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/WIP1_asr0_deepspeech2_online_wenetspeech_ckpt_1.0.0a.model.tar.gz
@ -43,6 +45,7 @@ if [ ! -f $unit ]; then
fi
if ! which ngram-count; then
# need srilm install
pushd $MAIN_ROOT/tools
make srilm.done
popd
@ -71,7 +74,7 @@ lm=data/local/lm
mkdir -p $lm
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# Train lm
# Train ngram lm
cp $text $lm/text
local/aishell_train_lms.sh
echo "build LM done."
@ -94,8 +97,8 @@ cmvn=$data/cmvn_fbank.ark
wfst=$data/lang_test
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
if [ ! -d $data/test ]; then
# download test dataset
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
@ -107,7 +110,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
fi
./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
# convert cmvn format
cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
fi
@ -116,7 +120,7 @@ label_file=aishell_result
export GLOG_logtostderr=1
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# TLG decoder
# recognize w/ TLG graph
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/check_tlg.log \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \

24
speechx/examples/ds2_ol/aishell/run.sh
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@ -32,6 +32,7 @@ exp=$PWD/exp
aishell_wav_scp=aishell_test.scp
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
if [ ! -d $data/test ]; then
# donwload dataset
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
@ -43,6 +44,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
fi
if [ ! -f $ckpt_dir/data/mean_std.json ]; then
# download model
mkdir -p $ckpt_dir
pushd $ckpt_dir
wget -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
@ -52,6 +54,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
lm=$data/zh_giga.no_cna_cmn.prune01244.klm
if [ ! -f $lm ]; then
# download kenlm bin
pushd $data
wget -c https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
popd
@ -68,7 +71,7 @@ export GLOG_logtostderr=1
cmvn=$data/cmvn.ark
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# 3. gen linear feat
# 3. convert cmvn format and compute linear feat
cmvn_json2kaldi_main --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
@ -82,14 +85,14 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# recognizer
# decode w/o lm
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wolm.log \
ctc_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--nnet_decoder_chunk=8 \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result
@ -101,14 +104,14 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# decode with lm
# decode w/ ngram lm with feature input
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.lm.log \
ctc_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--nnet_decoder_chunk=8 \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_lm
@ -124,6 +127,7 @@ wfst=$data/wfst/
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
mkdir -p $wfst
if [ ! -f $wfst/aishell_graph.zip ]; then
# download TLG graph
pushd $wfst
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
unzip aishell_graph.zip
@ -133,7 +137,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# TLG decoder
# decoder w/ TLG graph with feature input
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.wfst.log \
ctc_tlg_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
@ -142,7 +146,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--word_symbol_table=$wfst/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--nnet_decoder_chunk=8 \
--nnet_decoder_chunk=8 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
@ -154,7 +158,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# TLG decoder
# recognize from wav file w/ TLG graph
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recognizer.log \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
@ -162,7 +166,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$wfst/words.txt \
--nnet_decoder_chunk=8 \
--nnet_decoder_chunk=8 \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
@ -173,4 +177,4 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
echo "recognizer test have finished!!!"
echo "please checkout in ${exp}/${wer}.recognizer"
tail -n 7 $exp/${wer}.recognizer
fi
fi

21
speechx/examples/ds2_ol/aishell/run_fbank.sh
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@ -68,7 +68,7 @@ export GLOG_logtostderr=1
cmvn=$data/cmvn_fbank.ark
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# 3. gen linear feat
# 3. convert cmvn format and compute fbank feat
cmvn_json2kaldi_main --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn --binary=false
./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
@ -82,7 +82,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# recognizer
# decode w/ lm by feature
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.fbank.wolm.log \
ctc_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/fbank_feat.scp \
@ -90,7 +90,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
--param_path=$model_dir/avg_5.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_fbank
@ -100,15 +100,15 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# decode with lm
# decode with ngram lm by feature
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.fbank.lm.log \
ctc_beam_search_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/fbank_feat.scp \
--model_path=$model_dir/avg_5.jit.pdmodel \
--param_path=$model_dir/avg_5.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
--result_wspecifier=ark,t:$data/split${nj}/JOB/fbank_result_lm
@ -131,7 +131,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# TLG decoder
# decode w/ TLG graph by feature
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/recog.fbank.wfst.log \
ctc_tlg_decoder_main \
--feature_rspecifier=scp:$data/split${nj}/JOB/fbank_feat.scp \
@ -139,8 +139,8 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--param_path=$model_dir/avg_5.jit.pdiparams \
--word_symbol_table=$wfst/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_tlg
@ -153,6 +153,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# recgonize w/ TLG graph by wav
utils/run.pl JOB=1:$nj $data/split${nj}/JOB/fbank_recognizer.log \
recognizer_main \
--wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
@ -163,7 +164,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
--word_symbol_table=$wfst/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--model_cache_shapes="5-1-2048,5-1-2048" \
--nnet_decoder_chunk=8 \
--nnet_decoder_chunk=8 \
--graph_path=$wfst/TLG.fst --max_active=7500 \
--acoustic_scale=1.2 \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result_fbank_recognizer

78
speechx/examples/ds2_ol/websocket/README.md
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@ -0,0 +1,78 @@
# Streaming DeepSpeech2 Server with WebSocket
This example is about using `websocket` as streaming deepspeech2 server. For deepspeech2 model training please see [here](../../../../examples/aishell/asr0/).
The websocket protocal is same to [PaddleSpeech Server](../../../../demos/streaming_asr_server/),
for detail of implementation please see [here](../../../speechx/protocol/websocket/).
## Source path.sh
```bash
. path.sh
```
SpeechX bins is under `echo $SPEECHX_BUILD`, more info please see `path.sh`.
## Start WebSocket Server
```bash
bash websoket_server.sh
```
The output is like below:
```text
I1130 02:19:32.029882 12856 cmvn_json2kaldi_main.cc:39] cmvn josn path: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/model/data/mean_std.json
I1130 02:19:32.032230 12856 cmvn_json2kaldi_main.cc:73] nframe: 907497
I1130 02:19:32.032564 12856 cmvn_json2kaldi_main.cc:85] cmvn stats have write into: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/cmvn.ark
I1130 02:19:32.032579 12856 cmvn_json2kaldi_main.cc:86] Binary: 1
I1130 02:19:32.798342 12937 feature_pipeline.h:53] cmvn file: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/cmvn.ark
I1130 02:19:32.798542 12937 feature_pipeline.h:58] dither: 0
I1130 02:19:32.798583 12937 feature_pipeline.h:60] frame shift ms: 10
I1130 02:19:32.798588 12937 feature_pipeline.h:62] feature type: linear
I1130 02:19:32.798596 12937 feature_pipeline.h:80] frame length ms: 20
I1130 02:19:32.798601 12937 feature_pipeline.h:88] subsampling rate: 4
I1130 02:19:32.798606 12937 feature_pipeline.h:90] nnet receptive filed length: 7
I1130 02:19:32.798611 12937 feature_pipeline.h:92] nnet chunk size: 1
I1130 02:19:32.798615 12937 feature_pipeline.h:94] frontend fill zeros: 0
I1130 02:19:32.798630 12937 nnet_itf.h:52] subsampling rate: 4
I1130 02:19:32.798635 12937 nnet_itf.h:54] model path: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/model/exp/deepspeech2_online/checkpoints//avg_1.jit.pdmodel
I1130 02:19:32.798640 12937 nnet_itf.h:57] param path: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/model/exp/deepspeech2_online/checkpoints//avg_1.jit.pdiparams
I1130 02:19:32.798643 12937 nnet_itf.h:59] DS2 param:
I1130 02:19:32.798647 12937 nnet_itf.h:61] cache names: chunk_state_h_box,chunk_state_c_box
I1130 02:19:32.798652 12937 nnet_itf.h:63] cache shape: 5-1-1024,5-1-1024
I1130 02:19:32.798656 12937 nnet_itf.h:65] input names: audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box
I1130 02:19:32.798660 12937 nnet_itf.h:67] output names: softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0
I1130 02:19:32.798664 12937 ctc_tlg_decoder.h:41] fst path: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/wfst//TLG.fst
I1130 02:19:32.798669 12937 ctc_tlg_decoder.h:42] fst symbole table: /workspace/zhanghui/PaddleSpeech/speechx/examples/ds2_ol/websocket/data/wfst//words.txt
I1130 02:19:32.798673 12937 ctc_tlg_decoder.h:47] LatticeFasterDecoder max active: 7500
I1130 02:19:32.798677 12937 ctc_tlg_decoder.h:49] LatticeFasterDecoder beam: 15
I1130 02:19:32.798681 12937 ctc_tlg_decoder.h:50] LatticeFasterDecoder lattice_beam: 7.5
I1130 02:19:32.798708 12937 websocket_server_main.cc:37] Listening at port 8082
```
## Start WebSocket Client
```bash
bash websocket_client.sh
```
This script using AISHELL-1 test data to call websocket server.
The input is specific by `--wav_rspecifier=scp:$data/$aishell_wav_scp`.
The `scp` file which look like this:
```text
# 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:
```text
key path/to/wav/file
```

9
speechx/examples/u2pp_ol/wenetspeech/README.md
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@ -6,13 +6,14 @@ This example will demonstrate how to using the u2/u2++ model to recognize `wav`
## Testing with Aishell Test Data
### Source `path.sh` first
## Source path.sh
```bash
source path.sh
```bash
. path.sh
```
All bins are under `echo $SPEECHX_BUILD` dir.
SpeechX bins is under `echo $SPEECHX_BUILD`, more info please see `path.sh`.
### Download dataset and model

5
speechx/examples/u2pp_ol/wenetspeech/run.sh
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@ -85,3 +85,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# decode with wav input
./loca/recognizer.sh
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# decode with wav input with quanted model
./loca/recognizer_quant.sh
fi

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