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enginex-mr_series-sherpa-onnx/sherpa-onnx/csrc/offline-speaker-segmentation-pyannote-model.h
2024-12-10 16:03:03 +08:00

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// sherpa-onnx/csrc/offline-speaker-segmentation-pyannote-model.h
//
// Copyright (c) 2024 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_SPEAKER_SEGMENTATION_PYANNOTE_MODEL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_SPEAKER_SEGMENTATION_PYANNOTE_MODEL_H_
#include <memory>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/offline-speaker-segmentation-model-config.h"
#include "sherpa-onnx/csrc/offline-speaker-segmentation-pyannote-model-meta-data.h"
namespace sherpa_onnx {
class OfflineSpeakerSegmentationPyannoteModel {
public:
explicit OfflineSpeakerSegmentationPyannoteModel(
const OfflineSpeakerSegmentationModelConfig &config);
template <typename Manager>
OfflineSpeakerSegmentationPyannoteModel(
Manager *mgr, const OfflineSpeakerSegmentationModelConfig &config);
~OfflineSpeakerSegmentationPyannoteModel();
const OfflineSpeakerSegmentationPyannoteModelMetaData &GetModelMetaData()
const;
/**
* @param x A 3-D float tensor of shape (batch_size, 1, num_samples)
* @return Return a float tensor of
* shape (batch_size, num_frames, num_speakers). Note that
* num_speakers here uses powerset encoding.
*/
Ort::Value Forward(Ort::Value x) const;
private:
class Impl;
std::unique_ptr<Impl> impl_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_SPEAKER_SEGMENTATION_PYANNOTE_MODEL_H_