This PR integrates LODR (Level-Ordered Deterministic Rescoring) support from Icefall into both online and offline recognizers, enabling LODR for LM shallow fusion and LM rescore. - Extended OnlineLMConfig and OfflineLMConfig to include lodr_fst, lodr_scale, and lodr_backoff_id. - Implemented LodrFst and LodrStateCost classes and wired them into RNN LM scoring in both online and offline code paths. - Updated Python bindings, CLI entry points, examples, and CI test scripts to accept and exercise the new LODR options.
36 lines
1.3 KiB
C++
36 lines
1.3 KiB
C++
// sherpa-onnx/python/csrc/online-lm-config.cc
|
|
//
|
|
// Copyright (c) 2023 Xiaomi Corporation
|
|
|
|
#include "sherpa-onnx/python/csrc/online-lm-config.h"
|
|
|
|
#include <string>
|
|
|
|
#include "sherpa-onnx//csrc/online-lm-config.h"
|
|
|
|
namespace sherpa_onnx {
|
|
|
|
void PybindOnlineLMConfig(py::module *m) {
|
|
using PyClass = OnlineLMConfig;
|
|
py::class_<PyClass>(*m, "OnlineLMConfig")
|
|
.def(py::init<const std::string &, float, int32_t,
|
|
const std::string &, bool, const std::string &,
|
|
float, int>(),
|
|
py::arg("model") = "", py::arg("scale") = 0.5f,
|
|
py::arg("lm_num_threads") = 1, py::arg("lm_provider") = "cpu",
|
|
py::arg("shallow_fusion") = true, py::arg("lodr_fst") = "",
|
|
py::arg("lodr_scale") = 0.0f, py::arg("lodr_backoff_id") = -1)
|
|
.def_readwrite("model", &PyClass::model)
|
|
.def_readwrite("scale", &PyClass::scale)
|
|
.def_readwrite("lm_provider", &PyClass::lm_provider)
|
|
.def_readwrite("lm_num_threads", &PyClass::lm_num_threads)
|
|
.def_readwrite("shallow_fusion", &PyClass::shallow_fusion)
|
|
.def_readwrite("lodr_fst", &PyClass::lodr_fst)
|
|
.def_readwrite("lodr_scale", &PyClass::lodr_scale)
|
|
.def_readwrite("lodr_backoff_id", &PyClass::lodr_backoff_id)
|
|
|
|
.def("__str__", &PyClass::ToString);
|
|
}
|
|
|
|
} // namespace sherpa_onnx
|