Add Python API for clustering (#1385)

sherpa-onnx/python/csrc/fast-clustering.cc

@@ -0,0 +1,52 @@
+// sherpa-onnx/python/csrc/fast-clustering.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/fast-clustering.h"
+
+#include <sstream>
+#include <vector>
+
+#include "sherpa-onnx/csrc/fast-clustering.h"
+
+namespace sherpa_onnx {
+
+static void PybindFastClusteringConfig(py::module *m) {
+  using PyClass = FastClusteringConfig;
+  py::class_<PyClass>(*m, "FastClusteringConfig")
+      .def(py::init<int32_t, float>(), py::arg("num_clusters") = -1,
+           py::arg("threshold") = 0.5)
+      .def_readwrite("num_clusters", &PyClass::num_clusters)
+      .def_readwrite("threshold", &PyClass::threshold)
+      .def("__str__", &PyClass::ToString)
+      .def("validate", &PyClass::Validate);
+}
+
+void PybindFastClustering(py::module *m) {
+  PybindFastClusteringConfig(m);
+
+  using PyClass = FastClustering;
+  py::class_<PyClass>(*m, "FastClustering")
+      .def(py::init<const FastClusteringConfig &>(), py::arg("config"))
+      .def(
+          "__call__",
+          [](const PyClass &self,
+             py::array_t<float> features) -> std::vector<int32_t> {
+            int num_dim = features.ndim();
+            if (num_dim != 2) {
+              std::ostringstream os;
+              os << "Expect an array of 2 dimensions. Given dim: " << num_dim
+                 << "\n";
+              throw py::value_error(os.str());
+            }
+
+            int32_t num_rows = features.shape(0);
+            int32_t num_cols = features.shape(1);
+            float *p = features.mutable_data();
+            py::gil_scoped_release release;
+            return self.Cluster(p, num_rows, num_cols);
+          },
+          py::arg("features"));
+}
+
+}  // namespace sherpa_onnx