Add runtime support for wespeaker models (#516)

sherpa-onnx/csrc/speaker-embedding-extractor-wespeaker-impl.h

@@ -0,0 +1,79 @@
+// sherpa-onnx/csrc/speaker-embedding-extractor-wespeaker-impl.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_SPEAKER_EMBEDDING_EXTRACTOR_WESPEAKER_IMPL_H_
+#define SHERPA_ONNX_CSRC_SPEAKER_EMBEDDING_EXTRACTOR_WESPEAKER_IMPL_H_
+#include <algorithm>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/speaker-embedding-extractor-impl.h"
+#include "sherpa-onnx/csrc/speaker-embedding-extractor-wespeaker-model.h"
+
+namespace sherpa_onnx {
+
+class SpeakerEmbeddingExtractorWeSpeakerImpl
+    : public SpeakerEmbeddingExtractorImpl {
+ public:
+  explicit SpeakerEmbeddingExtractorWeSpeakerImpl(
+      const SpeakerEmbeddingExtractorConfig &config)
+      : model_(config) {}
+
+  int32_t Dim() const override { return model_.GetMetaData().output_dim; }
+
+  std::unique_ptr<OnlineStream> CreateStream() const override {
+    FeatureExtractorConfig feat_config;
+    auto meta_data = model_.GetMetaData();
+    feat_config.sampling_rate = meta_data.sample_rate;
+    feat_config.normalize_samples = meta_data.normalize_features;
+
+    return std::make_unique<OnlineStream>(feat_config);
+  }
+
+  bool IsReady(OnlineStream *s) const override {
+    return s->GetNumProcessedFrames() < s->NumFramesReady();
+  }
+
+  std::vector<float> Compute(OnlineStream *s) const override {
+    int32_t num_frames = s->NumFramesReady() - s->GetNumProcessedFrames();
+    if (num_frames <= 0) {
+      SHERPA_ONNX_LOGE(
+          "Please make sure IsReady(s) returns true. num_frames: %d",
+          num_frames);
+      return {};
+    }
+
+    std::vector<float> features =
+        s->GetFrames(s->GetNumProcessedFrames(), num_frames);
+
+    s->GetNumProcessedFrames() += num_frames;
+
+    int32_t feat_dim = features.size() / num_frames;
+
+    auto memory_info =
+        Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
+
+    std::array<int64_t, 3> x_shape{1, num_frames, feat_dim};
+    Ort::Value x =
+        Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
+                                 x_shape.data(), x_shape.size());
+    Ort::Value embedding = model_.Compute(std::move(x));
+    std::vector<int64_t> embedding_shape =
+        embedding.GetTensorTypeAndShapeInfo().GetShape();
+
+    std::vector<float> ans(embedding_shape[1]);
+    std::copy(embedding.GetTensorData<float>(),
+              embedding.GetTensorData<float>() + ans.size(), ans.begin());
+
+    return ans;
+  }
+
+ private:
+  SpeakerEmbeddingExtractorWeSpeakerModel model_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_SPEAKER_EMBEDDING_EXTRACTOR_WESPEAKER_IMPL_H_