Encode hotwords in C++ side (#828)

* Encode hotwords in C++ side

sherpa-onnx/python/csrc/offline-model-config.cc

@@ -36,7 +36,8 @@ void PybindOfflineModelConfig(py::module *m) {
                     const OfflineTdnnModelConfig &,
                     const OfflineZipformerCtcModelConfig &,
                     const OfflineWenetCtcModelConfig &, const std::string &,
-                    int32_t, bool, const std::string &, const std::string &>(),
+                    int32_t, bool, const std::string &, const std::string &,
+                    const std::string &, const std::string &>(),
            py::arg("transducer") = OfflineTransducerModelConfig(),
            py::arg("paraformer") = OfflineParaformerModelConfig(),
            py::arg("nemo_ctc") = OfflineNemoEncDecCtcModelConfig(),
@@ -45,7 +46,8 @@ void PybindOfflineModelConfig(py::module *m) {
            py::arg("zipformer_ctc") = OfflineZipformerCtcModelConfig(),
            py::arg("wenet_ctc") = OfflineWenetCtcModelConfig(),
            py::arg("tokens"), py::arg("num_threads"), py::arg("debug") = false,
-           py::arg("provider") = "cpu", py::arg("model_type") = "")
+           py::arg("provider") = "cpu", py::arg("model_type") = "",
+           py::arg("modeling_unit") = "cjkchar", py::arg("bpe_vocab") = "")
       .def_readwrite("transducer", &PyClass::transducer)
       .def_readwrite("paraformer", &PyClass::paraformer)
       .def_readwrite("nemo_ctc", &PyClass::nemo_ctc)
@@ -58,6 +60,8 @@ void PybindOfflineModelConfig(py::module *m) {
       .def_readwrite("debug", &PyClass::debug)
       .def_readwrite("provider", &PyClass::provider)
       .def_readwrite("model_type", &PyClass::model_type)
+      .def_readwrite("modeling_unit", &PyClass::modeling_unit)
+      .def_readwrite("bpe_vocab", &PyClass::bpe_vocab)
       .def("validate", &PyClass::Validate)
       .def("__str__", &PyClass::ToString);
 }

sherpa-onnx/python/csrc/online-model-config.cc

@@ -32,6 +32,7 @@ void PybindOnlineModelConfig(py::module *m) {
                     const OnlineZipformer2CtcModelConfig &,
                     const OnlineNeMoCtcModelConfig &, const std::string &,
                     int32_t, int32_t, bool, const std::string &,
+                    const std::string &, const std::string &,
                     const std::string &>(),
            py::arg("transducer") = OnlineTransducerModelConfig(),
            py::arg("paraformer") = OnlineParaformerModelConfig(),
@@ -40,7 +41,8 @@ void PybindOnlineModelConfig(py::module *m) {
            py::arg("nemo_ctc") = OnlineNeMoCtcModelConfig(), py::arg("tokens"),
            py::arg("num_threads"), py::arg("warm_up") = 0,
            py::arg("debug") = false, py::arg("provider") = "cpu",
-           py::arg("model_type") = "")
+           py::arg("model_type") = "", py::arg("modeling_unit") = "",
+           py::arg("bpe_vocab") = "")
       .def_readwrite("transducer", &PyClass::transducer)
       .def_readwrite("paraformer", &PyClass::paraformer)
       .def_readwrite("wenet_ctc", &PyClass::wenet_ctc)
@@ -51,6 +53,8 @@ void PybindOnlineModelConfig(py::module *m) {
       .def_readwrite("debug", &PyClass::debug)
       .def_readwrite("provider", &PyClass::provider)
       .def_readwrite("model_type", &PyClass::model_type)
+      .def_readwrite("modeling_unit", &PyClass::modeling_unit)
+      .def_readwrite("bpe_vocab", &PyClass::bpe_vocab)
       .def("validate", &PyClass::Validate)
       .def("__str__", &PyClass::ToString);
 }

sherpa-onnx/python/sherpa_onnx/offline_recognizer.py

@@ -49,6 +49,8 @@ class OfflineRecognizer(object):
         hotwords_file: str = "",
         hotwords_score: float = 1.5,
         blank_penalty: float = 0.0,
+        modeling_unit: str = "cjkchar",
+        bpe_vocab: str = "",
         debug: bool = False,
         provider: str = "cpu",
         model_type: str = "transducer",
@@ -91,6 +93,16 @@ class OfflineRecognizer(object):
             hotwords_file is given with modified_beam_search as decoding method.
           blank_penalty:
             The penalty applied on blank symbol during decoding.
+          modeling_unit:
+            The modeling unit of the model, commonly used units are bpe, cjkchar,
+            cjkchar+bpe, etc. Currently, it is needed only when hotwords are
+            provided, we need it to encode the hotwords into token sequence.
+            and the modeling unit is bpe or cjkchar+bpe.
+          bpe_vocab:
+            The vocabulary generated by google's sentencepiece program.
+            It is a file has two columns, one is the token, the other is
+            the log probability, you can get it from the directory where
+            your bpe model is generated. Only used when hotwords provided
           debug:
             True to show debug messages.
           provider:
@@ -107,6 +119,8 @@ class OfflineRecognizer(object):
             num_threads=num_threads,
             debug=debug,
             provider=provider,
+            modeling_unit=modeling_unit,
+            bpe_vocab=bpe_vocab,
             model_type=model_type,
         )
 

sherpa-onnx/python/sherpa_onnx/online_recognizer.py

@@ -58,6 +58,8 @@ class OnlineRecognizer(object):
         hotwords_file: str = "",
         provider: str = "cpu",
         model_type: str = "",
+        modeling_unit: str = "cjkchar",
+        bpe_vocab: str = "",
         lm: str = "",
         lm_scale: float = 0.1,
         temperature_scale: float = 2.0,
@@ -136,6 +138,16 @@ class OnlineRecognizer(object):
           model_type:
             Online transducer model type. Valid values are: conformer, lstm,
             zipformer, zipformer2. All other values lead to loading the model twice.
+          modeling_unit:
+            The modeling unit of the model, commonly used units are bpe, cjkchar,
+            cjkchar+bpe, etc. Currently, it is needed only when hotwords are
+            provided, we need it to encode the hotwords into token sequence.
+          bpe_vocab:
+            The vocabulary generated by google's sentencepiece program.
+            It is a file has two columns, one is the token, the other is
+            the log probability, you can get it from the directory where
+            your bpe model is generated. Only used when hotwords provided
+            and the modeling unit is bpe or cjkchar+bpe.
         """
         self = cls.__new__(cls)
         _assert_file_exists(tokens)
@@ -157,6 +169,8 @@ class OnlineRecognizer(object):
             num_threads=num_threads,
             provider=provider,
             model_type=model_type,
+            modeling_unit=modeling_unit,
+            bpe_vocab=bpe_vocab,
             debug=debug,
         )