314545f938
* Add speaker embedding extractor API for HarmonyOS * Add ArkTS API for speaker identification
118 lines
3.7 KiB
C++
118 lines
3.7 KiB
C++
// sherpa-onnx/csrc/speaker-embedding-extractor-general-impl.h
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//
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// Copyright (c) 2024 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_SPEAKER_EMBEDDING_EXTRACTOR_GENERAL_IMPL_H_
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#define SHERPA_ONNX_CSRC_SPEAKER_EMBEDDING_EXTRACTOR_GENERAL_IMPL_H_
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#include <algorithm>
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#include <memory>
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#include <utility>
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#include <vector>
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#include "Eigen/Dense"
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#include "sherpa-onnx/csrc/speaker-embedding-extractor-impl.h"
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#include "sherpa-onnx/csrc/speaker-embedding-extractor-model.h"
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namespace sherpa_onnx {
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class SpeakerEmbeddingExtractorGeneralImpl
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: public SpeakerEmbeddingExtractorImpl {
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public:
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explicit SpeakerEmbeddingExtractorGeneralImpl(
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const SpeakerEmbeddingExtractorConfig &config)
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: model_(config) {}
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template <typename Manager>
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SpeakerEmbeddingExtractorGeneralImpl(
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Manager *mgr, const SpeakerEmbeddingExtractorConfig &config)
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: model_(mgr, config) {}
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int32_t Dim() const override { return model_.GetMetaData().output_dim; }
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std::unique_ptr<OnlineStream> CreateStream() const override {
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FeatureExtractorConfig feat_config;
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const auto &meta_data = model_.GetMetaData();
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feat_config.sampling_rate = meta_data.sample_rate;
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feat_config.normalize_samples = meta_data.normalize_samples;
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return std::make_unique<OnlineStream>(feat_config);
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}
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bool IsReady(OnlineStream *s) const override {
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return s->GetNumProcessedFrames() < s->NumFramesReady();
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}
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std::vector<float> Compute(OnlineStream *s) const override {
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int32_t num_frames = s->NumFramesReady() - s->GetNumProcessedFrames();
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if (num_frames <= 0) {
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#if __OHOS__
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SHERPA_ONNX_LOGE(
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"Please make sure IsReady(s) returns true. num_frames: %{public}d",
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num_frames);
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#else
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SHERPA_ONNX_LOGE(
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"Please make sure IsReady(s) returns true. num_frames: %d",
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num_frames);
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#endif
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return {};
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}
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std::vector<float> features =
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s->GetFrames(s->GetNumProcessedFrames(), num_frames);
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s->GetNumProcessedFrames() += num_frames;
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int32_t feat_dim = features.size() / num_frames;
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const auto &meta_data = model_.GetMetaData();
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if (!meta_data.feature_normalize_type.empty()) {
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if (meta_data.feature_normalize_type == "global-mean") {
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SubtractGlobalMean(features.data(), num_frames, feat_dim);
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} else {
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#if __OHOS__
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SHERPA_ONNX_LOGE("Unsupported feature_normalize_type: %{public}s",
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meta_data.feature_normalize_type.c_str());
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#else
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SHERPA_ONNX_LOGE("Unsupported feature_normalize_type: %s",
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meta_data.feature_normalize_type.c_str());
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#endif
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exit(-1);
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}
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}
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auto memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
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std::array<int64_t, 3> x_shape{1, num_frames, feat_dim};
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Ort::Value x =
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Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
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x_shape.data(), x_shape.size());
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Ort::Value embedding = model_.Compute(std::move(x));
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std::vector<int64_t> embedding_shape =
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embedding.GetTensorTypeAndShapeInfo().GetShape();
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std::vector<float> ans(embedding_shape[1]);
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std::copy(embedding.GetTensorData<float>(),
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embedding.GetTensorData<float>() + ans.size(), ans.begin());
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return ans;
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}
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private:
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void SubtractGlobalMean(float *p, int32_t num_frames,
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int32_t feat_dim) const {
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auto m = Eigen::Map<
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Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>(
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p, num_frames, feat_dim);
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m = m.rowwise() - m.colwise().mean();
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}
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private:
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SpeakerEmbeddingExtractorModel model_;
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_SPEAKER_EMBEDDING_EXTRACTOR_GENERAL_IMPL_H_
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