add vad doc, fix vad state name

runtime/engine/vad/interface/vad_interface.cc

@@ -15,7 +15,6 @@
 
 #include "vad/interface/vad_interface.h"
 #include "common/base/config.h"
-#include "common/base/log.h"
 #include "vad/nnet/vad.h"
 
 
@@ -68,14 +67,14 @@ PPSVadState_t PPSVadFeedForward(PPSHandle_t instance,
                                 int num_element) {
     ppspeech::Vad* model = static_cast<ppspeech::Vad*>(instance);
     if (model == nullptr) {
-        LOG(ERROR) << "instance is null";
-        return PPS_ILLEGAL;
+        printf("instance is null\n");
+        return PPS_VAD_ILLEGAL;
     }
 
     std::vector<float> chunk_in(chunk, chunk + num_element);
     if (!model->ForwardChunk(chunk_in)) {
-        LOG(ERROR) << "forward chunk failed";
-        return PPS_ILLEGAL;
+        printf("forward chunk failed\n");
+        return PPS_VAD_ILLEGAL;
     }
     ppspeech::Vad::State s = model->Postprocess();
     PPSVadState_t ret = (PPSVadState_t)s;

runtime/engine/vad/interface/vad_interface.h

@@ -21,11 +21,12 @@ extern "C" {
 typedef void* PPSHandle_t;
 
 typedef enum {
-    PPS_ILLEGAL = 0,  // error
-    PPS_SIL,          // silence
-    PPS_START,        // start speech
-    PPS_SPEECH,       // in speech
-    PPS_END,          // end speech
+    PPS_VAD_ILLEGAL = 0,  // error
+    PPS_VAD_SIL,          // silence
+    PPS_VAD_START,        // start speech
+    PPS_VAD_SPEECH,       // in speech
+    PPS_VAD_END,          // end speech
+    PPS_VAD_NUMSTATES,    // number of states
 } PPSVadState_t;
 
 PPSHandle_t PPSVadCreateInstance(const char* conf_path);

runtime/engine/vad/nnet/vad.h

@@ -49,6 +49,8 @@ class Vad : public fastdeploy::FastDeployModel {
         const fastdeploy::RuntimeOption& custom_option =
             fastdeploy::RuntimeOption());
 
+    virtual ~Vad() {}
+
     void Init();
 
     void Reset();

runtime/examples/vad/README.md

@@ -1,121 +1,166 @@
-English | [简体中文](README_CN.md)
+# Silero VAD - pre-trained enterprise-grade Voice Activity Detector
 
-# Silero VAD Deployment Example
+This directory provides VAD models on CPU/GPU.
 
-This directory provides examples that `infer_onnx_silero_vad` fast finishes the deployment of VAD models on CPU/GPU.
+![](https://user-images.githubusercontent.com/36505480/198026365-8da383e0-5398-4a12-b7f8-22c2c0059512.png)
 
-Before deployment, two steps require confirmation.
+## Linux
 
-- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../docs/en/build_and_install/download_prebuilt_libraries.md).  
-- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
+### Build Runtime
+```bash
+# cd /path/to/paddlespeech/runtime
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DWITH_ASR=OFF -DWITH_CLS=OFF -DWITH_VAD=ON
+cmake --build build
+```
 
-Taking VAD inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.
+Since VAD using FastDeploy runtime, if you have another FastDeploy Library, you can using this command to build:
 
 ```bash
-mkdir build
-cd build
-# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
-tar xvf fastdeploy-linux-x64-x.x.x.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
-make -j
-
-# Download the VAD model file and test audio. After decompression, place the model and test audio in the infer_onnx_silero_vad.cc peer directory
-wget https://bj.bcebos.com/paddlehub/fastdeploy/silero_vad.tgz
-wget https://bj.bcebos.com/paddlehub/fastdeploy/silero_vad_sample.wav
-
-# inference
-./infer_onnx_silero_vad ../silero_vad.onnx ../silero_vad_sample.wav
+# cd /path/to/paddlespeech/runtime
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DWITH_ASR=OFF -DWITH_CLS=OFF -DWITH_VAD=ON -DFASTDEPLOY_INSTALL_DIR=/workspace/zhanghui/paddle/FastDeploy/build/Linux/x86_64/install
+cmake --build build
 ```
 
-- The above command works for Linux or MacOS. Refer to:
-  - [How to use FastDeploy C++ SDK in Windows](../../../../docs/en/faq/use_sdk_on_windows.md)  for SDK use-pattern in Windows
+`DFASTDEPLOY_INSTALL_DIR` is the directory of FastDeploy Library.
 
-## VAD C++ Interface
+### Run Demo
 
-### Vad Class
+After building success, we can do this to run demo under this example dir:
 
-```c++
-Vad::Vad(const std::string& model_file,
-    const fastdeploy::RuntimeOption& custom_option = fastdeploy::RuntimeOption())
+```bash 
+bash run.sh
 ```
 
-**Parameter**
+### Result
 
-> * **model_file**(str): Model file path
-> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
+```bash
+/workspace/zhanghui/PaddleSpeech/runtime/engine/vad/nnet/vad.cc(92)::SetConfig  sr=16000 threshold=0.45 frame_ms=32 min_silence_duration_ms=200 speech_pad_left_ms=0 speech_pad_right_ms=0[INFO] fastdeploy/runtime/runtime.cc(293)::CreateOrtBackend     Runtime initialized with Backend::ORT in Device::CPU./workspace/zhanghui/PaddleSpeech/runtime/engine/vad/nnet/vad.cc(141)::Initialize        init done.
+[SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [STA] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [END] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [STA] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SIL] [SIL] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [END] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [STA] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [END] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [STA] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SIL] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SPE] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [END] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] [SIL] speak start: 0.32 s, end: 2.496 s | speak start: 3.296 s, end: 4.672 s | speak start: 5.408 s, end: 7.936 s | speak start: 8.192 s, end: 10.72 s   
+vad_nnet_main done!
+
+sr = 16000
+frame_ms = 32
+threshold = 0.45
+min_silence_duration_ms = 200
+speech_pad_left_ms = 200
+speech_pad_right_ms = 0
+model_path = ./data/silero_vad/silero_vad.onnx
+param_path = (default)
+num_cpu_thread = 1(default)
+/workspace/zhanghui/PaddleSpeech/runtime/engine/vad/nnet/vad.cc(92)::SetConfig  sr=16000 threshold=0.45 frame_ms=32 min_silence_duration_ms=200 speech_pad_left_ms=200 speech_pad_right_ms=0
+[INFO] fastdeploy/runtime/runtime.cc(293)::CreateOrtBackend     Runtime initialized with Backend::ORT in Device::CPU.
+/workspace/zhanghui/PaddleSpeech/runtime/engine/vad/nnet/vad.cc(141)::Initialize        init done.
+1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 vad_interface_main done!
+```
 
-### setAudioCofig function
+The environment as below:
+
+```text
+Architecture:        x86_64
+CPU op-mode(s):      32-bit, 64-bit
+Byte Order:          Little Endian
+CPU(s):              80
+On-line CPU(s) list: 0-79
+Thread(s) per core:  2
+Core(s) per socket:  20
+Socket(s):           2
+NUMA node(s):        2
+Vendor ID:           GenuineIntel
+CPU family:          6Model:               85Model name:          Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz
+Stepping:            7
+CPU MHz:             2599.998
+BogoMIPS:            5199.99
+Hypervisor vendor:   KVM
+Virtualization type: full
+L1d cache:           32KL1i cache:           32KL2 cache:            1024K
+L3 cache:            33792K
+NUMA node0 CPU(s):   0-39
+NUMA node1 CPU(s):   40-79
+Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc eagerfpu pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 arat umip pku ospke avx512_vnni spec_ctrl arch_capabilities
+```
 
-**Must be called before the `init` function**
+## Android
 
-```c++
-void Vad::setAudioCofig(int sr, int frame_ms, float threshold, int min_silence_duration_ms, int speech_pad_ms);
-```
+When to using on Android, please setup your `NDK` enverment before, then do as below:
 
-**Parameter**
+```bash
+# cd /path/to/paddlespeech/runtime
+bash build_android.sh
+```
 
-> * **sr**(int): sampling rate
-> * **frame_ms**(int): The length of each detection frame, and it is used to calculate the detection window size
-> * **threshold**(float): Result probability judgment threshold
-> * **min_silence_duration_ms**(int): The threshold used to calculate whether it is silence
-> * **speech_pad_ms**(int): Used to calculate the end time of the speech
+## VAD Interface
 
-### init function
+For vad interface please see [](../../engine/vad/interface/).
 
-Used to initialize audio-related parameters.
+### Create Handdle
 
 ```c++
-void Vad::init();
+PPSHandle_t PPSVadCreateInstance(const char* conf_path);
 ```
 
-### loadAudio function
-
-Load audio.
+### Destroy Handdle
 
 ```c++
-void Vad::loadAudio(const std::string& wavPath)
+int PPSVadDestroyInstance(PPSHandle_t instance);
 ```
 
-**Parameter**
+### Reset Vad State
 
-> * **wavPath**(str): Audio file path
+```c++
+int PPSVadReset(PPSHandle_t instance);
+```
 
-### Predict function
+Reset Vad state before processing next `wav`.
 
-Used to start model reasoning.
+### Get Chunk Size
 
 ```c++
-bool Vad::Predict();
+int PPSVadChunkSizeSamples(PPSHandle_t instance);
 ```
 
-### getResult function
+This API will return chunk size in `sample` unit.
+When do forward, we need feed `chunk size` samples, except last chunk.
 
-**Used to obtain reasoning results**
+### Vad Forward
+
+```c++
+PPSVadState_t PPSVadFeedForward(PPSHandle_t instance,
+                                float* chunk,
+                                int num_element);
+```
 
+Vad has below states:
 ```c++
-std::vector<std::map<std::string, float>> Vad::getResult(
-            float removeThreshold = 1.6, float expandHeadThreshold = 0.32, float expandTailThreshold = 0,
-            float mergeThreshold = 0.3);
+typedef enum {
+    PPS_VAD_ILLEGAL = 0,  // error
+    PPS_VAD_SIL,          // silence
+    PPS_VAD_START,        // start speech
+    PPS_VAD_SPEECH,       // in speech
+    PPS_VAD_END,          // end speech
+    PPS_VAD_NUMSTATES,    // number of states
+} PPSVadState_t;
 ```
 
-**Parameter**
+If `PPSVadFeedForward` occur an error will return `PPS_VAD_ILLEGAL` state.
+
+
+## FastDeploy Runtime
+
+For FastDeploy software and hardware requements, and pre-released library please to see [FastDeploy](https://github.com/PaddlePaddle/FastDeploy):
+
+- 1. [FastDeploy Environment Requirements](https://github.com/PaddlePaddle/FastDeploy/docs/en/build_and_install/download_prebuilt_libraries.md).
+- 2. [FastDeploy Precompiled Library](https://github.com/PaddlePaddle/FastDeploy/docs/en/build_and_install/download_prebuilt_libraries.md).
 
-> * **removeThreshold**(float): Discard result fragment threshold; If some recognition results are too short, they will be discarded according to this threshold
-> * **expandHeadThreshold**(float): Offset at the beginning of the segment; The recognized start time may be too close to the voice part, so move forward the start time accordingly
-> * **expandTailThreshold**(float): Offset at the end of the segment; The recognized end time may be too close to the voice part, so the end time is moved back accordingly
-> * **mergeThreshold**(float): Some result segments are very close and can be combined into one, and the vocal segments can be combined accordingly
 
-**The output result format is**`std::vector<std::map<std::string, float>>`
+## Download Pre-trained ONNX Model
 
-> Output a list, each element is a speech fragment
->
-> Each clip can use 'start' to get the start time and 'end' to get the end time
+For developers' testing, model exported by VAD are provided below. Developers can download them directly.
 
-### Tips
+| 模型                                                         | 大小  | 备注                                                         |
+| :----------------------------------------------------------- | :---- | :----------------------------------------------------------- |
+| [silero-vad](https://bj.bcebos.com/paddlehub/fastdeploy/silero_vad.tgz) | 1.8MB | This model file is sourced from [snakers4/silero-vad](https://github.com/snakers4/silero-vad)，MIT License |
 
-1. `The setAudioCofig`function must be called before the `init` function
-2. The sampling rate of the input audio file must be consistent with that set in the code
 
-- [Model Description](../)
-- [How to switch the model inference backend engine](../../../../docs/en/faq/how_to_change_backend.md)
+## Reference
+* https://github.com/snakers4/silero-vad
+* https://github.com/PaddlePaddle/FastDeploy/blob/develop/examples/audio/silero-vad/README.md

runtime/examples/vad/README_CN.md

@@ -1,119 +0,0 @@
-[English](README.md) | 简体中文
-# Silero VAD 部署示例
-
-本目录下提供`infer_onnx_silero_vad`快速完成 Silero VAD 模型在CPU/GPU。
-
-在部署前，需确认以下两个步骤
-
-- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
-- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
-
-以Linux上 VAD 推理为例，在本目录执行如下命令即可完成编译测试。
-
-```bash
-mkdir build
-cd build
-# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
-tar xvf fastdeploy-linux-x64-x.x.x.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
-make -j
-
-# 下载 VAD 模型文件和测试音频，解压后将模型和测试音频放置在与 infer_onnx_silero_vad.cc 同级目录下
-wget https://bj.bcebos.com/paddlehub/fastdeploy/silero_vad.tgz
-wget https://bj.bcebos.com/paddlehub/fastdeploy/silero_vad_sample.wav
-
-# 推理
-./infer_onnx_silero_vad ../silero_vad.onnx ../silero_vad_sample.wav
-```
-
-以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
-- [如何在Windows中使用FastDeploy C++ SDK](../../../../docs/cn/faq/use_sdk_on_windows.md)
-
-## VAD C++ 接口
-### Vad 类
-
-```c++
-Vad::Vad(const std::string& model_file,
-    const fastdeploy::RuntimeOption& custom_option = fastdeploy::RuntimeOption())
-```
-
-**参数**
-
-> * **model_file**(str): 模型文件路径
-> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
-
-### setAudioCofig 函数
-
-**必须在`init`函数前调用**
-
-```c++
-void Vad::setAudioCofig(int sr, int frame_ms, float threshold, int min_silence_duration_ms, int speech_pad_ms);
-```
-
-**参数**
-
-> * **sr**(int): 采样率
-> * **frame_ms**(int): 每次检测帧长，用于计算检测窗口大小
-> * **threshold**(float): 结果概率判断阈值
-> * **min_silence_duration_ms**(int): 用于计算判断是否是 silence 的阈值
-> * **speech_pad_ms**(int): 用于计算 speach 结束时刻
-
-### init 函数
-
-用于初始化音频相关参数
-
-```c++
-void Vad::init();
-```
-
-### loadAudio 函数
-
-加载音频
-
-```c++
-void Vad::loadAudio(const std::string& wavPath)
-```
-
-**参数**
-
-> * **wavPath**(str): 音频文件路径
-
-### Predict 函数
-
-用于开始模型推理
-
-```c++
-bool Vad::Predict();
-```
-
-### getResult 函数
-
-**用于获取推理结果**
-
-```c++
-std::vector<std::map<std::string, float>> Vad::getResult(
-            float removeThreshold = 1.6, float expandHeadThreshold = 0.32, float expandTailThreshold = 0,
-            float mergeThreshold = 0.3);
-```
-
-**参数**
-
-> * **removeThreshold**(float): 丢弃结果片段阈值；部分识别结果太短则根据此阈值丢弃
-> * **expandHeadThreshold**(float): 结果片段开始时刻偏移；识别到的开始时刻可能过于贴近发声部分，因此据此前移开始时刻
-> * **expandTailThreshold**(float): 结果片段结束时刻偏移；识别到的结束时刻可能过于贴近发声部分，因此据此后移结束时刻
-> * **mergeThreshold**(float): 有的结果片段十分靠近，可以合并成一个，据此合并发声片段
-
-**输出结果格式为**`std::vector<std::map<std::string, float>>`
-
-> 输出一个列表，每个元素是一个讲话片段
->
-> 每个片段可以用 'start' 获取到开始时刻，用 'end' 获取到结束时刻
-
-### 提示
-
-1. `setAudioCofig`函数必须在`init`函数前调用
-2. 输入的音频文件的采样率必须与代码中设置的保持一致
-
-- [模型介绍](../)
-- [如何切换模型推理后端引擎](../../../../docs/cn/faq/how_to_change_backend.md)

runtime/examples/vad/run.sh

@@ -15,8 +15,8 @@ exp=exp
 mkdir -p $exp $data
 
 # 1. compile
-if [ ! -d ${SPEECHX_BUILD} ]; then
-    pushd ${SPEECHX_ROOT} 
+if [ ! -d ${ENGINE_BUILD} ]; then
+    pushd ${ENGINE_ROOT} 
     bash build.sh
 
     # build for android armv8/armv7
@@ -24,8 +24,6 @@ if [ ! -d ${SPEECHX_BUILD} ]; then
     popd
 fi
 
-ckpt_dir=$data/silero_vad
-wav=$data/silero_vad_sample.wav
 
 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ];then
     ./local/download.sh