44 lines
1.3 KiB
C++
44 lines
1.3 KiB
C++
// sherpa-onnx/csrc/fast-clustering.h
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//
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// Copyright (c) 2024 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_FAST_CLUSTERING_H_
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#define SHERPA_ONNX_CSRC_FAST_CLUSTERING_H_
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#include <memory>
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#include <vector>
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#include "sherpa-onnx/csrc/fast-clustering-config.h"
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namespace sherpa_onnx {
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class FastClustering {
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public:
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explicit FastClustering(const FastClusteringConfig &config);
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~FastClustering();
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/**
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* @param features Pointer to a 2-D feature matrix in row major. Each row
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* is a feature frame. It is changed in-place. We will
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* convert each feature frame to a normalized vector.
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* That is, the L2-norm of each vector will be equal to 1.
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* It uses cosine dissimilarity,
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* which is 1 - (cosine similarity)
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* @param num_rows Number of feature frames
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* @param num-cols The feature dimension.
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*
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* @return Return a vector of size num_rows. ans[i] contains the label
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* for the i-th feature frame, i.e., the i-th row of the feature
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* matrix.
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*/
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std::vector<int32_t> Cluster(float *features, int32_t num_rows,
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int32_t num_cols) const;
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private:
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class Impl;
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std::unique_ptr<Impl> impl_;
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_FAST_CLUSTERING_H_
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