move egs from codelab to examples

speechx/CMakeLists.txt

@@ -117,6 +117,7 @@ set(MKLDNN_PATH "${PADDLE_LIB_THIRD_PARTY_PATH}mkldnn")
 include_directories("${MKLDNN_PATH}/include")
 set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
 set(EXTERNAL_LIB "-lrt -ldl -lpthread")
+
 set(DEPS ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
 set(DEPS ${DEPS}
       ${MATH_LIB} ${MKLDNN_LIB}
@@ -137,4 +138,7 @@ set(DEPS ${DEPS}
 #target_link_libraries(lib_name item0 item1)
 #add_dependencies(lib_name depend-target)
 
-add_subdirectory(speechx)
+set(SPEECHX_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/speechx)
+
+add_subdirectory(speechx)
+add_subdirectory(examples)

speechx/build.sh

@@ -16,7 +16,7 @@ if [ ! -d ${boost_SOURCE_DIR} ]; then wget -c https://boostorg.jfrog.io/artifact
   echo -e "\n"
 fi
 
-rm -rf build
+#rm -rf build
 mkdir -p build
 cd build
 

speechx/cmake/external/openblas.cmake

@@ -18,6 +18,8 @@ ExternalProject_Add(
     SOURCE_DIR  ${OpenBLAS_SOURCE_DIR}
     CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=<INSTALL_DIR> 
     CMAKE_GENERATOR "Unix Makefiles")
+
+
 # https://cmake.org/cmake/help/latest/module/ExternalProject.html?highlight=externalproject_get_property#external-project-definition
 ExternalProject_Get_Property(OPENBLAS INSTALL_DIR)
 set(OpenBLAS_INSTALL_PREFIX ${INSTALL_DIR})

speechx/examples/CMakeLists.txt

@@ -0,0 +1,5 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_subdirectory(feat)
+add_subdirectory(nnet)
+add_subdirectory(decoder)

speechx/examples/README.md

@@ -0,0 +1,6 @@
+# Examples
+
+* decoder - offline decoder 
+* feat - mfcc, linear 
+* nnet - ds2 nn
+

speechx/examples/decoder/CMakeLists.txt

@@ -0,0 +1,5 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_executable(offline-decoder-main ${CMAKE_CURRENT_SOURCE_DIR}/offline-decoder-main.cc)
+target_include_directories(offline-decoder-main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(offline-decoder-main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})

speechx/examples/feat/CMakeLists.txt

@@ -0,0 +1,10 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+
+add_executable(mfcc-test ${CMAKE_CURRENT_SOURCE_DIR}/feature-mfcc-test.cc)
+target_include_directories(mfcc-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(mfcc-test kaldi-mfcc)
+
+add_executable(linear-spectrogram-main ${CMAKE_CURRENT_SOURCE_DIR}/linear-spectrogram-main.cc)
+target_include_directories(linear-spectrogram-main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(linear-spectrogram-main frontend kaldi-util kaldi-feat-common gflags glog)

speechx/examples/nnet/CMakeLists.txt

@@ -0,0 +1,5 @@
+cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
+
+add_executable(pp-model-test ${CMAKE_CURRENT_SOURCE_DIR}/pp-model-test.cc)
+target_include_directories(pp-model-test PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+target_link_libraries(pp-model-test PUBLIC nnet gflags ${DEPS})

speechx/examples/nnet/pp-model-test.cc

@@ -8,13 +8,30 @@
 #include <numeric>
 #include <functional>
 
+using std::cout;
+using std::endl;
+
+DEFINE_string(model_path, "avg_1.jit.pdmodel", "xxx.pdmodel");
+DEFINE_string(param_path, "avg_1.jit.pdiparams", "xxx.pdiparams");
+
+
 void produce_data(std::vector<std::vector<float>>* data);
 void model_forward_test();
 
-int main(int argc, char* argv[]) {
-    gflags::ParseCommandLineFlags(&argc, &argv, true);
-    model_forward_test();
-    return 0;
+void produce_data(std::vector<std::vector<float>>* data) {
+  int chunk_size = 35; // chunk_size in frame
+  int col_size = 161; // feat dim
+  cout << "chunk size: " << chunk_size << endl;
+  cout << "feat dim: " << col_size << endl;
+
+  data->reserve(chunk_size);
+  data->back().reserve(col_size);
+  for (int row = 0; row < chunk_size; ++row) {
+    data->push_back(std::vector<float>());
+    for (int col_idx = 0; col_idx < col_size; ++col_idx) {
+      data->back().push_back(0.201);
+    }
+  }
 }
 
 void model_forward_test() {
@@ -23,18 +40,23 @@ void model_forward_test() {
     produce_data(&feats);
     
     std::cout << "2. load the model" << std::endl;;
-    std::string model_graph = "../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdmodel";
-    std::string model_params = "../../../../model/paddle_online_deepspeech/model/avg_1.jit.pdiparams";
+    std::string model_graph = FLAGS_model_path;
+    std::string model_params = FLAGS_param_path;
+    cout << "model path: " << model_graph << endl;
+    cout << "model param path : " << model_params << endl;
+
     paddle_infer::Config config;
     config.SetModel(model_graph, model_params);
     config.SwitchIrOptim(false);
+    cout << "SwitchIrOptim: " << false << endl; 
     config.DisableFCPadding();
+    cout << "DisableFCPadding: " << endl;
     auto predictor = paddle_infer::CreatePredictor(config);
     
     std::cout << "3. feat shape, row=" << feats.size() << ",col=" << feats[0].size() << std::endl;
-    std::vector<float> paddle_input_feature_matrix;
+    std::vector<float> pp_input_mat;
     for(const auto& item : feats) {
-        paddle_input_feature_matrix.insert(paddle_input_feature_matrix.end(), item.begin(), item.end());
+        pp_input_mat.insert(pp_input_mat.end(), item.begin(), item.end());
     }
 
     std::cout << "4. fead the data to model" << std::endl;
@@ -42,13 +64,21 @@ void model_forward_test() {
     int col = feats[0].size();
     std::vector<std::string> input_names = predictor->GetInputNames();
     std::vector<std::string> output_names = predictor->GetOutputNames();
+    for (auto name : input_names){
+       cout << "model input names: " <<  name << endl;
+    }
+    for (auto name : output_names){
+       cout << "model output names: " << name << endl;
+    }
 
+    // input
     std::unique_ptr<paddle_infer::Tensor> input_tensor = 
         predictor->GetInputHandle(input_names[0]);
     std::vector<int> INPUT_SHAPE = {1, row, col};
     input_tensor->Reshape(INPUT_SHAPE);
-    input_tensor->CopyFromCpu(paddle_input_feature_matrix.data());
+    input_tensor->CopyFromCpu(pp_input_mat.data());
     
+    // input length
     std::unique_ptr<paddle_infer::Tensor> input_len = predictor->GetInputHandle(input_names[1]);
     std::vector<int> input_len_size = {1};
     input_len->Reshape(input_len_size);
@@ -56,6 +86,7 @@ void model_forward_test() {
     audio_len.push_back(row);
     input_len->CopyFromCpu(audio_len.data());
 
+    // state_h
     std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box = predictor->GetInputHandle(input_names[2]);
     std::vector<int> chunk_state_h_box_shape = {3, 1, 1024};
     chunk_state_h_box->Reshape(chunk_state_h_box_shape);
@@ -64,6 +95,7 @@ void model_forward_test() {
     std::vector<float> chunk_state_h_box_data(chunk_state_h_box_size, 0.0f);
     chunk_state_h_box->CopyFromCpu(chunk_state_h_box_data.data());
 
+    // state_c
     std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box = predictor->GetInputHandle(input_names[3]);
     std::vector<int> chunk_state_c_box_shape = {3, 1, 1024};
     chunk_state_c_box->Reshape(chunk_state_c_box_shape);
@@ -72,8 +104,10 @@ void model_forward_test() {
     std::vector<float> chunk_state_c_box_data(chunk_state_c_box_size, 0.0f);
     chunk_state_c_box->CopyFromCpu(chunk_state_c_box_data.data());
 
+    // run
     bool success = predictor->Run();
 
+    // state_h out
     std::unique_ptr<paddle_infer::Tensor> h_out = predictor->GetOutputHandle(output_names[2]);
     std::vector<int> h_out_shape = h_out->shape();
     int h_out_size = std::accumulate(h_out_shape.begin(), h_out_shape.end(),
@@ -81,6 +115,7 @@ void model_forward_test() {
     std::vector<float> h_out_data(h_out_size);
     h_out->CopyToCpu(h_out_data.data());
 
+    // stage_c out
     std::unique_ptr<paddle_infer::Tensor> c_out = predictor->GetOutputHandle(output_names[3]);
     std::vector<int> c_out_shape = c_out->shape();
     int c_out_size = std::accumulate(c_out_shape.begin(), c_out_shape.end(),
@@ -88,6 +123,7 @@ void model_forward_test() {
     std::vector<float> c_out_data(c_out_size);
     c_out->CopyToCpu(c_out_data.data());
 
+    // output tensor
     std::unique_ptr<paddle_infer::Tensor> output_tensor =
         predictor->GetOutputHandle(output_names[0]);
     std::vector<int> output_shape = output_tensor->shape();
@@ -99,6 +135,7 @@ void model_forward_test() {
     row = output_shape[1];
     col = output_shape[2];
 
+    // probs
     std::vector<std::vector<float>> probs;
     probs.reserve(row);
     for (int i = 0; i < row; i++) {
@@ -120,15 +157,8 @@ void model_forward_test() {
     }
 }
 
-void produce_data(std::vector<std::vector<float>>* data) {
-  int chunk_size = 35;
-  int col_size = 161;
-  data->reserve(chunk_size);
-  data->back().reserve(col_size);
-  for (int row = 0; row < chunk_size; ++row) {
-    data->push_back(std::vector<float>());
-    for (int col_idx = 0; col_idx < col_size; ++col_idx) {
-      data->back().push_back(0.201);
-    }
-  }
+int main(int argc, char* argv[]) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    model_forward_test();
+    return 0;
 }

speechx/speechx/CMakeLists.txt

@@ -30,16 +30,4 @@ include_directories(
 ${CMAKE_CURRENT_SOURCE_DIR}
 ${CMAKE_CURRENT_SOURCE_DIR}/decoder
 )
-add_subdirectory(decoder)
-
-add_executable(mfcc-test codelab/feat_test/feature-mfcc-test.cc)
-target_link_libraries(mfcc-test kaldi-mfcc)
-
-add_executable(linear_spectrogram_main codelab/feat_test/linear_spectrogram_main.cc)
-target_link_libraries(linear_spectrogram_main frontend kaldi-util kaldi-feat-common gflags glog)
-
-add_executable(offline_decoder_main codelab/decoder_test/offline_decoder_main.cc)
-target_link_libraries(offline_decoder_main PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
-
-add_executable(model_test codelab/nnet_test/model_test.cc)
-target_link_libraries(model_test PUBLIC nnet gflags ${DEPS})
+add_subdirectory(decoder)

speechx/speechx/codelab/README.md

@@ -1,4 +0,0 @@
-# codelab
-
-This directory is here for testing some funcitons temporaril.
-