Support Agglomerative clustering. (#1384)

We use the open-source implementation from
https://github.com/cdalitz/hclust-cpp

@@ -40,6 +40,7 @@ option(SHERPA_ONNX_ENABLE_WASM_VAD_ASR "Whether to enable WASM for VAD+ASR" OFF)
 option(SHERPA_ONNX_ENABLE_WASM_NODEJS "Whether to enable WASM for NodeJS" OFF)
 option(SHERPA_ONNX_ENABLE_BINARY "Whether to build binaries" ON)
 option(SHERPA_ONNX_ENABLE_TTS "Whether to build TTS related code" ON)
+option(SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION "Whether to build speaker diarization related code" ON)
 option(SHERPA_ONNX_LINK_LIBSTDCPP_STATICALLY "True to link libstdc++ statically. Used only when BUILD_SHARED_LIBS is OFF on Linux" ON)
 option(SHERPA_ONNX_USE_PRE_INSTALLED_ONNXRUNTIME_IF_AVAILABLE "True to use pre-installed onnxruntime if available" ON)
 option(SHERPA_ONNX_ENABLE_SANITIZER "Whether to enable ubsan and asan" OFF)
@@ -142,6 +143,7 @@ message(STATUS "SHERPA_ONNX_ENABLE_WASM_VAD_ASR ${SHERPA_ONNX_ENABLE_WASM_VAD_AS
 message(STATUS "SHERPA_ONNX_ENABLE_WASM_NODEJS ${SHERPA_ONNX_ENABLE_WASM_NODEJS}")
 message(STATUS "SHERPA_ONNX_ENABLE_BINARY ${SHERPA_ONNX_ENABLE_BINARY}")
 message(STATUS "SHERPA_ONNX_ENABLE_TTS ${SHERPA_ONNX_ENABLE_TTS}")
+message(STATUS "SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION ${SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION}")
 message(STATUS "SHERPA_ONNX_LINK_LIBSTDCPP_STATICALLY ${SHERPA_ONNX_LINK_LIBSTDCPP_STATICALLY}")
 message(STATUS "SHERPA_ONNX_USE_PRE_INSTALLED_ONNXRUNTIME_IF_AVAILABLE ${SHERPA_ONNX_USE_PRE_INSTALLED_ONNXRUNTIME_IF_AVAILABLE}")
 message(STATUS "SHERPA_ONNX_ENABLE_SANITIZER: ${SHERPA_ONNX_ENABLE_SANITIZER}")
@@ -341,6 +343,10 @@ if(SHERPA_ONNX_ENABLE_TTS)
   include(cppjieba) # For Chinese TTS. It is a header-only C++ library
 endif()
 
+if(SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION)
+  include(hclust-cpp)
+endif()
+
 # if(NOT MSVC AND CMAKE_BUILD_TYPE STREQUAL Debug AND (CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang"))
 if(SHERPA_ONNX_ENABLE_SANITIZER)
   message(WARNING "enable ubsan and asan")

+function(download_hclust_cpp)
+  include(FetchContent)
+
+  # The latest commit as of 2024.09.29
+  set(hclust_cpp_URL  "https://github.com/csukuangfj/hclust-cpp/archive/refs/tags/2024-09-29.tar.gz")
+  set(hclust_cpp_HASH "SHA256=abab51448a3cb54272aae07522970306e0b2cc6479d59d7b19e7aee4d6cedd33")
+
+  # If you don't have access to the Internet,
+  # please pre-download hclust-cpp
+  set(possible_file_locations
+    $ENV{HOME}/Downloads/hclust-cpp-2024-09-29.tar.gz
+    ${CMAKE_SOURCE_DIR}/hclust-cpp-2024-09-29.tar.gz
+    ${CMAKE_BINARY_DIR}/hclust-cpp-2024-09-29.tar.gz
+    /tmp/hclust-cpp-2024-09-29.tar.gz
+    /star-fj/fangjun/download/github/hclust-cpp-2024-09-29.tar.gz
+  )
+
+  foreach(f IN LISTS possible_file_locations)
+    if(EXISTS ${f})
+      set(hclust_cpp_URL  "${f}")
+      file(TO_CMAKE_PATH "${hclust_cpp_URL}" hclust_cpp_URL)
+      message(STATUS "Found local downloaded hclust_cpp: ${hclust_cpp_URL}")
+      break()
+    endif()
+  endforeach()
+
+  FetchContent_Declare(hclust_cpp
+    URL
+      ${hclust_cpp_URL}
+      ${hclust_cpp_URL2}
+    URL_HASH          ${hclust_cpp_HASH}
+  )
+
+  FetchContent_GetProperties(hclust_cpp)
+  if(NOT hclust_cpp_POPULATED)
+    message(STATUS "Downloading hclust_cpp from ${hclust_cpp_URL}")
+    FetchContent_Populate(hclust_cpp)
+  endif()
+
+  message(STATUS "hclust_cpp is downloaded to ${hclust_cpp_SOURCE_DIR}")
+  message(STATUS "hclust_cpp's binary dir is ${hclust_cpp_BINARY_DIR}")
+  include_directories(${hclust_cpp_SOURCE_DIR})
+endfunction()
+
+download_hclust_cpp()

@@ -160,6 +160,13 @@ if(SHERPA_ONNX_ENABLE_TTS)
   )
 endif()
 
+if(SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION)
+  list(APPEND sources
+    fast-clustering-config.cc
+    fast-clustering.cc
+  )
+endif()
+
 if(SHERPA_ONNX_ENABLE_CHECK)
   list(APPEND sources log.cc)
 endif()
@@ -523,6 +530,12 @@ if(SHERPA_ONNX_ENABLE_TESTS)
     )
   endif()
 
+  if(SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION)
+    list(APPEND sherpa_onnx_test_srcs
+      fast-clustering-test.cc
+    )
+  endif()
+
   list(APPEND sherpa_onnx_test_srcs
     speaker-embedding-manager-test.cc
   )

+// sherpa-onnx/csrc/fast-clustering-config.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/fast-clustering-config.h"
+
+#include <sstream>
+#include <string>
+
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+std::string FastClusteringConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "FastClusteringConfig(";
+  os << "num_clusters=" << num_clusters << ", ";
+  os << "threshold=" << threshold << ")";
+
+  return os.str();
+}
+
+void FastClusteringConfig::Register(ParseOptions *po) {
+  std::string prefix = "ctc";
+  ParseOptions p(prefix, po);
+
+  p.Register("num-clusters", &num_clusters,
+             "Number of cluster. If greater than 0, then --cluster-thresold is "
+             "ignored");
+
+  p.Register("cluster-threshold", &threshold,
+             "If --num-clusters is not specified, then it specifies the "
+             "distance threshold for clustering.");
+}
+
+bool FastClusteringConfig::Validate() const {
+  if (num_clusters < 1 && threshold < 0) {
+    SHERPA_ONNX_LOGE("Please provide either num_clusters or threshold");
+    return false;
+  }
+
+  return true;
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/fast-clustering-config.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_FAST_CLUSTERING_CONFIG_H_
+#define SHERPA_ONNX_CSRC_FAST_CLUSTERING_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+
+namespace sherpa_onnx {
+
+struct FastClusteringConfig {
+  // If greater than 0, then threshold is ignored
+  int32_t num_clusters = -1;
+
+  // distance threshold
+  float threshold = 0.5;
+
+  std::string ToString() const;
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+};
+
+}  // namespace sherpa_onnx
+#endif  // SHERPA_ONNX_CSRC_FAST_CLUSTERING_CONFIG_H_

+// sherpa-onnx/csrc/fast-clustering-test.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/fast-clustering.h"
+
+#include <vector>
+
+#include "gtest/gtest.h"
+
+namespace sherpa_onnx {
+
+TEST(FastClustering, TestTwoClusters) {
+  std::vector<float> features = {
+      // point 0
+      0.1,
+      0.1,
+      // point 2
+      0.4,
+      -0.5,
+      // point 3
+      0.6,
+      -0.7,
+      // point 1
+      0.2,
+      0.3,
+  };
+
+  FastClusteringConfig config;
+  config.num_clusters = 2;
+
+  FastClustering clustering(config);
+  auto labels = clustering.Cluster(features.data(), 4, 2);
+  int32_t k = 0;
+  for (auto i : labels) {
+    std::cout << "point " << k << ": label " << i << "\n";
+    ++k;
+  }
+}
+
+TEST(FastClustering, TestClusteringWithThreshold) {
+  std::vector<float> features = {
+      // point 0
+      0.1,
+      0.1,
+      // point 2
+      0.4,
+      -0.5,
+      // point 3
+      0.6,
+      -0.7,
+      // point 1
+      0.2,
+      0.3,
+  };
+
+  FastClusteringConfig config;
+  config.threshold = 0.5;
+
+  FastClustering clustering(config);
+  auto labels = clustering.Cluster(features.data(), 4, 2);
+  int32_t k = 0;
+  for (auto i : labels) {
+    std::cout << "point " << k << ": label " << i << "\n";
+    ++k;
+  }
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/fast-clustering.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/fast-clustering.h"
+
+#include <vector>
+
+#include "Eigen/Dense"
+#include "fastcluster-all-in-one.h"  // NOLINT
+
+namespace sherpa_onnx {
+
+class FastClustering::Impl {
+ public:
+  explicit Impl(const FastClusteringConfig &config) : config_(config) {}
+
+  std::vector<int32_t> Cluster(float *features, int32_t num_rows,
+                               int32_t num_cols) {
+    if (num_rows <= 0) {
+      return {};
+    }
+
+    if (num_rows == 1) {
+      return {0};
+    }
+
+    Eigen::Map<
+        Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+        m(features, num_rows, num_cols);
+    m.rowwise().normalize();
+
+    std::vector<double> distance((num_rows * (num_rows - 1)) / 2);
+
+    int32_t k = 0;
+    for (int32_t i = 0; i != num_rows; ++i) {
+      auto v = m.row(i);
+      for (int32_t j = i + 1; j != num_rows; ++j) {
+        double cosine_similarity = v.dot(m.row(j));
+        double consine_dissimilarity = 1 - cosine_similarity;
+
+        if (consine_dissimilarity < 0) {
+          consine_dissimilarity = 0;
+        }
+
+        distance[k] = consine_dissimilarity;
+        ++k;
+      }
+    }
+
+    std::vector<int32_t> merge(2 * (num_rows - 1));
+    std::vector<double> height(num_rows - 1);
+
+    fastclustercpp::hclust_fast(num_rows, distance.data(),
+                                fastclustercpp::HCLUST_METHOD_SINGLE,
+                                merge.data(), height.data());
+
+    std::vector<int32_t> labels(num_rows);
+    if (config_.num_clusters > 0) {
+      fastclustercpp::cutree_k(num_rows, merge.data(), config_.num_clusters,
+                               labels.data());
+    } else {
+      fastclustercpp::cutree_cdist(num_rows, merge.data(), height.data(),
+                                   config_.threshold, labels.data());
+    }
+
+    return labels;
+  }
+
+ private:
+  FastClusteringConfig config_;
+};
+
+FastClustering::FastClustering(const FastClusteringConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+FastClustering::~FastClustering() = default;
+
+std::vector<int32_t> FastClustering::Cluster(float *features, int32_t num_rows,
+                                             int32_t num_cols) {
+  return impl_->Cluster(features, num_rows, num_cols);
+}
+}  // namespace sherpa_onnx

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

@@ -4,8 +4,6 @@
 
 #include "sherpa-onnx/csrc/offline-stream.h"
 
-#include <math.h>
-
 #include <algorithm>
 #include <cassert>
 #include <cmath>

@@ -8,16 +8,16 @@
 namespace sherpa_onnx {
 
 struct OnlineCNNBiLSTMModelMetaData {
-  int32_t comma_id;
-  int32_t period_id;
-  int32_t quest_id;
+  int32_t comma_id = -1;
+  int32_t period_id = -1;
+  int32_t quest_id = -1;
 
-  int32_t upper_id;
-  int32_t cap_id;
-  int32_t mix_case_id;
+  int32_t upper_id = -1;
+  int32_t cap_id = -1;
+  int32_t mix_case_id = -1;
 
-  int32_t num_cases;
-  int32_t num_punctuations;
+  int32_t num_cases = -1;
+  int32_t num_punctuations = -1;
 };
 
 }  // namespace sherpa_onnx

@@ -169,7 +169,7 @@ static std::vector<int64_t> CoquiPhonemesToIds(
   return ans;
 }
 
-void InitEspeak(const std::string &data_dir) {
+static void InitEspeak(const std::string &data_dir) {
   static std::once_flag init_flag;
   std::call_once(init_flag, [data_dir]() {
     int32_t result =

@@ -41,7 +41,7 @@
 namespace sherpa_onnx {
 
 template <class I>
-I Gcd(I m, I n) {
+static I Gcd(I m, I n) {
   // this function is copied from kaldi/src/base/kaldi-math.h
   if (m == 0 || n == 0) {
     if (m == 0 && n == 0) {  // gcd not defined, as all integers are divisors.
@@ -65,7 +65,7 @@ I Gcd(I m, I n) {
 /// Returns the least common multiple of two integers.  Will
 /// crash unless the inputs are positive.
 template <class I>
-I Lcm(I m, I n) {
+static I Lcm(I m, I n) {
   // This function is copied from kaldi/src/base/kaldi-math.h
   assert(m > 0 && n > 0);
   I gcd = Gcd(m, n);