offline-stream.cc
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// sherpa-onnx/python/csrc/offline-stream.cc
//
// Copyright (c) 2023 by manyeyes
#include "sherpa-onnx/python/csrc/offline-stream.h"
#include "sherpa-onnx/csrc/offline-stream.h"
namespace sherpa_onnx {
constexpr const char *kAcceptWaveformUsage = R"(
Process audio samples.
Args:
sample_rate:
Sample rate of the input samples. If it is different from the one
expected by the model, we will do resampling inside.
waveform:
A 1-D float32 tensor containing audio samples. It must be normalized
to the range [-1, 1].
)";
static void PybindOfflineRecognitionResult(py::module *m) { // NOLINT
using PyClass = OfflineRecognitionResult;
py::class_<PyClass>(*m, "OfflineRecognitionResult")
.def_property_readonly("text",
[](const PyClass &self) { return self.text; })
.def_property_readonly("tokens",
[](const PyClass &self) { return self.tokens; })
.def_property_readonly(
"timestamps", [](const PyClass &self) { return self.timestamps; });
}
static void PybindOfflineFeatureExtractorConfig(py::module *m) {
using PyClass = OfflineFeatureExtractorConfig;
py::class_<PyClass>(*m, "OfflineFeatureExtractorConfig")
.def(py::init<int32_t, int32_t>(), py::arg("sampling_rate") = 16000,
py::arg("feature_dim") = 80)
.def_readwrite("sampling_rate", &PyClass::sampling_rate)
.def_readwrite("feature_dim", &PyClass::feature_dim)
.def("__str__", &PyClass::ToString);
}
void PybindOfflineStream(py::module *m) {
PybindOfflineFeatureExtractorConfig(m);
PybindOfflineRecognitionResult(m);
using PyClass = OfflineStream;
py::class_<PyClass>(*m, "OfflineStream")
.def(
"accept_waveform",
[](PyClass &self, float sample_rate, py::array_t<float> waveform) {
self.AcceptWaveform(sample_rate, waveform.data(), waveform.size());
},
py::arg("sample_rate"), py::arg("waveform"), kAcceptWaveformUsage)
.def_property_readonly("result", &PyClass::GetResult);
}
} // namespace sherpa_onnx