Reduce model initialization time for online speech recognition (#215)

* Reduce model initialization time for online speech recognition

* Fixed Styling

---------

Co-authored-by: w11wo <wilsowong961@gmail.com>

@@ -50,6 +50,8 @@ SherpaOnnxOnlineRecognizer *CreateOnlineRecognizer(
       SHERPA_ONNX_OR(config->model_config.num_threads, 1);
   recognizer_config.model_config.provider =
       SHERPA_ONNX_OR(config->model_config.provider, "cpu");
+  recognizer_config.model_config.model_type =
+      SHERPA_ONNX_OR(config->model_config.model_type, "");
   recognizer_config.model_config.debug =
       SHERPA_ONNX_OR(config->model_config.debug, 0);
 

@@ -53,6 +53,7 @@ SHERPA_ONNX_API typedef struct SherpaOnnxOnlineTransducerModelConfig {
   const char *tokens;
   int32_t num_threads;
   const char *provider;
+  const char *model_type;
   int32_t debug;  // true to print debug information of the model
 } SherpaOnnxOnlineTransducerModelConfig;
 

@@ -22,26 +22,30 @@ void OnlineTransducerModelConfig::Register(ParseOptions *po) {
 
   po->Register("debug", &debug,
                "true to print model information while loading it.");
+  po->Register("model-type", &model_type,
+               "Specify it to reduce model initialization time. "
+               "Valid values are: conformer, lstm, zipformer, zipformer2. "
+               "All other values lead to loading the model twice.");
 }
 
 bool OnlineTransducerModelConfig::Validate() const {
   if (!FileExists(tokens)) {
-    SHERPA_ONNX_LOGE("%s does not exist", tokens.c_str());
+    SHERPA_ONNX_LOGE("tokens: %s does not exist", tokens.c_str());
     return false;
   }
 
   if (!FileExists(encoder_filename)) {
-    SHERPA_ONNX_LOGE("%s does not exist", encoder_filename.c_str());
+    SHERPA_ONNX_LOGE("encoder: %s does not exist", encoder_filename.c_str());
     return false;
   }
 
   if (!FileExists(decoder_filename)) {
-    SHERPA_ONNX_LOGE("%s does not exist", decoder_filename.c_str());
+    SHERPA_ONNX_LOGE("decoder: %s does not exist", decoder_filename.c_str());
     return false;
   }
 
   if (!FileExists(joiner_filename)) {
-    SHERPA_ONNX_LOGE("%s does not exist", joiner_filename.c_str());
+    SHERPA_ONNX_LOGE("joiner: %s does not exist", joiner_filename.c_str());
     return false;
   }
 
@@ -63,6 +67,7 @@ std::string OnlineTransducerModelConfig::ToString() const {
   os << "tokens=\"" << tokens << "\", ";
   os << "num_threads=" << num_threads << ", ";
   os << "provider=\"" << provider << "\", ";
+  os << "model_type=\"" << model_type << "\", ";
   os << "debug=" << (debug ? "True" : "False") << ")";
 
   return os.str();

@@ -19,19 +19,33 @@ struct OnlineTransducerModelConfig {
   bool debug = false;
   std::string provider = "cpu";
 
+  // With the help of this field, we only need to load the model once
+  // instead of twice; and therefore it reduces initialization time.
+  //
+  // Valid values:
+  //  - conformer
+  //  - lstm
+  //  - zipformer
+  //  - zipformer2
+  //
+  // All other values are invalid and lead to loading the model twice.
+  std::string model_type;
+
   OnlineTransducerModelConfig() = default;
   OnlineTransducerModelConfig(const std::string &encoder_filename,
                               const std::string &decoder_filename,
                               const std::string &joiner_filename,
                               const std::string &tokens, int32_t num_threads,
-                              bool debug, const std::string &provider)
+                              bool debug, const std::string &provider,
+                              const std::string &model_type)
       : encoder_filename(encoder_filename),
         decoder_filename(decoder_filename),
         joiner_filename(joiner_filename),
         tokens(tokens),
         num_threads(num_threads),
         debug(debug),
-        provider(provider) {}
+        provider(provider),
+        model_type(model_type) {}
 
   void Register(ParseOptions *po);
   bool Validate() const;

@@ -77,6 +77,22 @@ static ModelType GetModelType(char *model_data, size_t model_data_length,
 
 std::unique_ptr<OnlineTransducerModel> OnlineTransducerModel::Create(
     const OnlineTransducerModelConfig &config) {
+  if (!config.model_type.empty()) {
+    const auto &model_type = config.model_type;
+    if (model_type == "conformer") {
+      return std::make_unique<OnlineConformerTransducerModel>(config);
+    } else if (model_type == "lstm") {
+      return std::make_unique<OnlineLstmTransducerModel>(config);
+    } else if (model_type == "zipformer") {
+      return std::make_unique<OnlineZipformerTransducerModel>(config);
+    } else if (model_type == "zipformer2") {
+      return std::make_unique<OnlineZipformer2TransducerModel>(config);
+    } else {
+      SHERPA_ONNX_LOGE(
+          "Invalid model_type: %s. Trying to load the model to get its type",
+          model_type.c_str());
+    }
+  }
   ModelType model_type = ModelType::kUnkown;
 
   {
@@ -140,6 +156,23 @@ Ort::Value OnlineTransducerModel::BuildDecoderInput(
 #if __ANDROID_API__ >= 9
 std::unique_ptr<OnlineTransducerModel> OnlineTransducerModel::Create(
     AAssetManager *mgr, const OnlineTransducerModelConfig &config) {
+  if (!config.model_type.empty()) {
+    const auto &model_type = config.model_type;
+    if (model_type == "conformer") {
+      return std::make_unique<OnlineConformerTransducerModel>(mgr, config);
+    } else if (model_type == "lstm") {
+      return std::make_unique<OnlineLstmTransducerModel>(mgr, config);
+    } else if (model_type == "zipformer") {
+      return std::make_unique<OnlineZipformerTransducerModel>(mgr, config);
+    } else if (model_type == "zipformer2") {
+      return std::make_unique<OnlineZipformer2TransducerModel>(mgr, config);
+    } else {
+      SHERPA_ONNX_LOGE(
+          "Invalid model_type: %s. Trying to load the model to get its type",
+          model_type.c_str());
+    }
+  }
+
   auto buffer = ReadFile(mgr, config.encoder_filename);
   auto model_type = GetModelType(buffer.data(), buffer.size(), config.debug);
 

@@ -15,11 +15,11 @@ void PybindOnlineTransducerModelConfig(py::module *m) {
   py::class_<PyClass>(*m, "OnlineTransducerModelConfig")
       .def(py::init<const std::string &, const std::string &,
                     const std::string &, const std::string &, int32_t, bool,
-                    const std::string &>(),
+                    const std::string &, const std::string &>(),
            py::arg("encoder_filename"), py::arg("decoder_filename"),
            py::arg("joiner_filename"), py::arg("tokens"),
            py::arg("num_threads"), py::arg("debug") = false,
-           py::arg("provider") = "cpu")
+           py::arg("provider") = "cpu", py::arg("model_type") = "")
       .def_readwrite("encoder_filename", &PyClass::encoder_filename)
       .def_readwrite("decoder_filename", &PyClass::decoder_filename)
       .def_readwrite("joiner_filename", &PyClass::joiner_filename)
@@ -27,6 +27,7 @@ void PybindOnlineTransducerModelConfig(py::module *m) {
       .def_readwrite("num_threads", &PyClass::num_threads)
       .def_readwrite("debug", &PyClass::debug)
       .def_readwrite("provider", &PyClass::provider)
+      .def_readwrite("model_type", &PyClass::model_type)
       .def("__str__", &PyClass::ToString);
 }
 

@@ -41,6 +41,7 @@ class OnlineRecognizer(object):
         max_active_paths: int = 4,
         context_score: float = 1.5,
         provider: str = "cpu",
+        model_type: str = "",
     ):
         """
         Please refer to
@@ -90,6 +91,9 @@ class OnlineRecognizer(object):
             the maximum number of active paths during beam search.
           provider:
             onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
+          model_type:
+            Online transducer model type. Valid values are: conformer, lstm,
+            zipformer, zipformer2. All other values lead to loading the model twice.
         """
         _assert_file_exists(tokens)
         _assert_file_exists(encoder)
@@ -105,6 +109,7 @@ class OnlineRecognizer(object):
             tokens=tokens,
             num_threads=num_threads,
             provider=provider,
+            model_type=model_type,
         )
 
         feat_config = FeatureExtractorConfig(