diff --git a/examples/ami/sd0/local/ami_prepare.py b/examples/ami/sd0/local/ami_prepare.py
index d03810a7..01582dbd 100644
--- a/examples/ami/sd0/local/ami_prepare.py
+++ b/examples/ami/sd0/local/ami_prepare.py
@@ -17,11 +17,8 @@ Data preparation.
 Download: http://groups.inf.ed.ac.uk/ami/download/
 
 Prepares metadata files (JSON) from manual annotations "segments/" using RTTM format (Oracle VAD).
-
-Authors
- * qingenz123@126.com (Qingen ZHAO) 2022
-
 """
+
 import argparse
 import glob
 import json
diff --git a/examples/ami/sd0/local/ami_splits.py b/examples/ami/sd0/local/ami_splits.py
index 010638a3..a8bc5dc8 100644
--- a/examples/ami/sd0/local/ami_splits.py
+++ b/examples/ami/sd0/local/ami_splits.py
@@ -15,10 +15,6 @@
 AMI corpus contained 100 hours of meeting recording.
 This script returns the standard train, dev and eval split for AMI corpus.
 For more information on dataset please refer to http://groups.inf.ed.ac.uk/ami/corpus/datasets.shtml
-
-Authors
- * qingenz123@126.com (Qingen ZHAO) 2022
-
 """
 
 ALLOWED_OPTIONS = ["scenario_only", "full_corpus", "full_corpus_asr"]
diff --git a/examples/ami/sd0/local/dataio.py b/examples/ami/sd0/local/dataio.py
index f7fe8815..4ff76bd5 100644
--- a/examples/ami/sd0/local/dataio.py
+++ b/examples/ami/sd0/local/dataio.py
@@ -13,10 +13,6 @@
 # limitations under the License.
 """
 Data reading and writing.
-
-Authors
- * qingenz123@126.com (Qingen ZHAO) 2022
- 
 """
 import os
 import pickle
diff --git a/paddlespeech/vector/cluster/diarization.py b/paddlespeech/vector/cluster/diarization.py
new file mode 100644
index 00000000..6432acb8
--- /dev/null
+++ b/paddlespeech/vector/cluster/diarization.py
@@ -0,0 +1,1082 @@
+# Copyright (c) 2022 SpeechBrain Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script contains basic functions used for speaker diarization.
+This script has an optional dependency on open source sklearn library.
+A few sklearn functions are modified in this script as per requirement.
+"""
+
+import argparse
+import warnings
+import scipy
+import numpy as np
+from distutils.util import strtobool
+
+from scipy import sparse
+from scipy.sparse.linalg import eigsh
+from scipy.sparse.csgraph import connected_components
+from scipy.sparse.csgraph import laplacian as csgraph_laplacian
+
+import sklearn
+from sklearn.neighbors import kneighbors_graph
+from sklearn.cluster import SpectralClustering
+from sklearn.cluster._kmeans import k_means
+
+
+def _graph_connected_component(graph, node_id):
+    """
+    Find the largest graph connected components that contains one
+    given node.
+
+    Arguments
+    ---------
+    graph : array-like, shape: (n_samples, n_samples)
+        Adjacency matrix of the graph, non-zero weight means an edge
+        between the nodes.
+    node_id : int
+        The index of the query node of the graph.
+
+    Returns
+    -------
+    connected_components_matrix : array-like
+        shape - (n_samples,).
+        An array of bool value indicating the indexes of the nodes belonging
+        to the largest connected components of the given query node.
+    """
+
+    n_node = graph.shape[0]
+    if sparse.issparse(graph):
+        # speed up row-wise access to boolean connection mask
+        graph = graph.tocsr()
+    connected_nodes = np.zeros(n_node, dtype=bool)
+    nodes_to_explore = np.zeros(n_node, dtype=bool)
+    nodes_to_explore[node_id] = True
+    for _ in range(n_node):
+        last_num_component = connected_nodes.sum()
+        np.logical_or(connected_nodes, nodes_to_explore, out=connected_nodes)
+        if last_num_component >= connected_nodes.sum():
+            break
+        indices = np.where(nodes_to_explore)[0]
+        nodes_to_explore.fill(False)
+        for i in indices:
+            if sparse.issparse(graph):
+                neighbors = graph[i].toarray().ravel()
+            else:
+                neighbors = graph[i]
+            np.logical_or(nodes_to_explore, neighbors, out=nodes_to_explore)
+    return connected_nodes
+
+
+def _graph_is_connected(graph):
+    """
+    Return whether the graph is connected (True) or Not (False)
+
+    Arguments
+    ---------
+    graph : array-like or sparse matrix, shape: (n_samples, n_samples)
+        Adjacency matrix of the graph, non-zero weight means an edge between the nodes.
+
+    Returns
+    -------
+    is_connected : bool
+        True means the graph is fully connected and False means not.
+    """
+
+    if sparse.isspmatrix(graph):
+        # sparse graph, find all the connected components
+        n_connected_components, _ = connected_components(graph)
+        return n_connected_components == 1
+    else:
+        # dense graph, find all connected components start from node 0
+        return _graph_connected_component(graph, 0).sum() == graph.shape[0]
+
+
+def _set_diag(laplacian, value, norm_laplacian):
+    """
+    Set the diagonal of the laplacian matrix and convert it to a sparse
+    format well suited for eigenvalue decomposition.
+
+    Arguments
+    ---------
+    laplacian : array or sparse matrix
+        The graph laplacian.
+    value : float
+        The value of the diagonal.
+    norm_laplacian : bool
+        Whether the value of the diagonal should be changed or not.
+
+    Returns
+    -------
+    laplacian : array or sparse matrix
+        An array of matrix in a form that is well suited to fast eigenvalue
+        decomposition, depending on the bandwidth of the matrix.
+    """
+
+    n_nodes = laplacian.shape[0]
+    # We need all entries in the diagonal to values
+    if not sparse.isspmatrix(laplacian):
+        if norm_laplacian:
+            laplacian.flat[::n_nodes + 1] = value
+    else:
+        laplacian = laplacian.tocoo()
+        if norm_laplacian:
+            diag_idx = laplacian.row == laplacian.col
+            laplacian.data[diag_idx] = value
+        # If the matrix has a small number of diagonals (as in the
+        # case of structured matrices coming from images), the
+        # dia format might be best suited for matvec products:
+        n_diags = np.unique(laplacian.row - laplacian.col).size
+        if n_diags <= 7:
+            # 3 or less outer diagonals on each side
+            laplacian = laplacian.todia()
+        else:
+            # csr has the fastest matvec and is thus best suited to
+            # arpack
+            laplacian = laplacian.tocsr()
+    return laplacian
+
+
+def _deterministic_vector_sign_flip(u):
+    """
+    Modify the sign of vectors for reproducibility. Flips the sign of
+    elements of all the vectors (rows of u) such that the absolute
+    maximum element of each vector is positive.
+
+    Arguments
+    ---------
+    u : ndarray
+        Array with vectors as its rows.
+
+    Returns
+    -------
+    u_flipped : ndarray
+        Array with the sign flipped vectors as its rows. The same shape as `u`.
+    """
+
+    max_abs_rows = np.argmax(np.abs(u), axis=1)
+    signs = np.sign(u[range(u.shape[0]), max_abs_rows])
+    u *= signs[:, np.newaxis]
+    return u
+
+
+def _check_random_state(seed):
+    """
+    Turn seed into a np.random.RandomState instance.
+
+    Arguments
+    ---------
+    seed : None | int | instance of RandomState
+        If seed is None, return the RandomState singleton used by np.random.
+        If seed is an int, return a new RandomState instance seeded with seed.
+        If seed is already a RandomState instance, return it.
+        Otherwise raise ValueError.
+    """
+
+    if seed is None or seed is np.random:
+        return np.random.mtrand._rand
+    if isinstance(seed, numbers.Integral):
+        return np.random.RandomState(seed)
+    if isinstance(seed, np.random.RandomState):
+        return seed
+    raise ValueError("%r cannot be used to seed a np.random.RandomState"
+                     " instance" % seed)
+
+
+def spectral_embedding(
+        adjacency,
+        n_components=8,
+        norm_laplacian=True,
+        drop_first=True, ):
+    """
+    Returns spectral embeddings.
+
+    Arguments
+    ---------
+    adjacency : array-like or sparse graph
+        shape - (n_samples, n_samples)
+        The adjacency matrix of the graph to embed.
+    n_components : int
+        The dimension of the projection subspace.
+    norm_laplacian : bool
+        If True, then compute normalized Laplacian.
+    drop_first : bool
+        Whether to drop the first eigenvector.
+
+    Returns
+    -------
+    embedding : array
+        Spectral embeddings for each sample.
+
+    Example
+    -------
+    >>> import numpy as np
+    >>> import diarization as diar
+    >>> affinity = np.array([[1, 1, 1, 0.5, 0, 0, 0, 0, 0, 0.5],
+    ... [1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+    ... [1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+    ... [0.5, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+    ... [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+    ... [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+    ... [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+    ... [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+    ... [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+    ... [0.5, 0, 0, 0, 0, 0, 1, 1, 1, 1]])
+    >>> embs = diar.spectral_embedding(affinity, 3)
+    >>> # Notice similar embeddings
+    >>> print(np.around(embs , decimals=3))
+    [[ 0.075  0.244  0.285]
+     [ 0.083  0.356 -0.203]
+     [ 0.083  0.356 -0.203]
+     [ 0.26  -0.149  0.154]
+     [ 0.29  -0.218 -0.11 ]
+     [ 0.29  -0.218 -0.11 ]
+     [-0.198 -0.084 -0.122]
+     [-0.198 -0.084 -0.122]
+     [-0.198 -0.084 -0.122]
+     [-0.167 -0.044  0.316]]
+    """
+
+    # Whether to drop the first eigenvector
+    if drop_first:
+        n_components = n_components + 1
+
+    if not _graph_is_connected(adjacency):
+        warnings.warn("Graph is not fully connected, spectral embedding"
+                      " may not work as expected.")
+
+    laplacian, dd = csgraph_laplacian(
+        adjacency, normed=norm_laplacian, return_diag=True)
+
+    laplacian = _set_diag(laplacian, 1, norm_laplacian)
+
+    laplacian *= -1
+
+    vals, diffusion_map = eigsh(
+        laplacian,
+        k=n_components,
+        sigma=1.0,
+        which="LM", )
+
+    embedding = diffusion_map.T[n_components::-1]
+
+    if norm_laplacian:
+        embedding = embedding / dd
+
+    embedding = _deterministic_vector_sign_flip(embedding)
+    if drop_first:
+        return embedding[1:n_components].T
+    else:
+        return embedding[:n_components].T
+
+
+def spectral_clustering(
+        affinity,
+        n_clusters=8,
+        n_components=None,
+        random_state=None,
+        n_init=10, ):
+    """
+    Performs spectral clustering.
+
+    Arguments
+    ---------
+    affinity : matrix
+        Affinity matrix.
+    n_clusters : int
+        Number of clusters for kmeans.
+    n_components : int
+        Number of components to retain while estimating spectral embeddings.
+    random_state : int
+        A pseudo random number generator used by kmeans.
+     n_init : int
+        Number of time the k-means algorithm will be run with different centroid seeds.
+
+    Returns
+    -------
+    labels : array
+        Cluster label for each sample.
+
+    Example
+    -------
+    >>> import numpy as np
+    >>> diarization as diar
+    >>> affinity = np.array([[1, 1, 1, 0.5, 0, 0, 0, 0, 0, 0.5],
+    ... [1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+    ... [1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+    ... [0.5, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+    ... [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+    ... [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+    ... [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+    ... [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+    ... [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
+    ... [0.5, 0, 0, 0, 0, 0, 1, 1, 1, 1]])
+    >>> labs = diar.spectral_clustering(affinity, 3)
+    >>> # print (labs) # [2 2 2 1 1 1 0 0 0 0]
+    """
+
+    random_state = _check_random_state(random_state)
+    n_components = n_clusters if n_components is None else n_components
+
+    maps = spectral_embedding(
+        affinity,
+        n_components=n_components,
+        drop_first=False, )
+
+    _, labels, _ = k_means(
+        maps, n_clusters, random_state=random_state, n_init=n_init)
+
+    return labels
+
+
+class EmbeddingMeta:
+    """
+    A utility class to pack deep embeddings and meta-information in one object.
+
+    Arguments
+    ---------
+    segset : list
+        List of session IDs as an array of strings.
+    stats : tensor
+        An ndarray of float64. Each line contains embedding
+        from the corresponding session.
+    """
+
+    def __init__(
+            self,
+            segset=None,
+            stats=None, ):
+
+        if segset is None:
+            self.segset = numpy.empty(0, dtype="|O")
+            self.stats = numpy.array([], dtype=np.float64)
+        else:
+            self.segset = segset
+            self.stats = stats
+
+    def norm_stats(self):
+        """
+        Divide all first-order statistics by their Euclidean norm.
+        """
+
+        vect_norm = np.clip(np.linalg.norm(self.stats, axis=1), 1e-08, np.inf)
+        self.stats = (self.stats.transpose() / vect_norm).transpose()
+
+
+class SpecClustUnorm:
+    """
+    This class implements the spectral clustering with unnormalized affinity matrix.
+    Useful when affinity matrix is based on cosine similarities.
+
+    Reference
+    ---------
+    Von Luxburg, U. A tutorial on spectral clustering. Stat Comput 17, 395–416 (2007).
+    https://doi.org/10.1007/s11222-007-9033-z
+
+    Example
+    -------
+    >>> import diarization as diar
+    >>> clust = diar.SpecClustUnorm(min_num_spkrs=2, max_num_spkrs=10)
+    >>> emb = [[ 2.1, 3.1, 4.1, 4.2, 3.1],
+    ... [ 2.2, 3.1, 4.2, 4.2, 3.2],
+    ... [ 2.0, 3.0, 4.0, 4.1, 3.0],
+    ... [ 8.0, 7.0, 7.0, 8.1, 9.0],
+    ... [ 8.1, 7.1, 7.2, 8.1, 9.2],
+    ... [ 8.3, 7.4, 7.0, 8.4, 9.0],
+    ... [ 0.3, 0.4, 0.4, 0.5, 0.8],
+    ... [ 0.4, 0.3, 0.6, 0.7, 0.8],
+    ... [ 0.2, 0.3, 0.2, 0.3, 0.7],
+    ... [ 0.3, 0.4, 0.4, 0.4, 0.7],]
+    >>> # Estimating similarity matrix
+    >>> sim_mat = clust.get_sim_mat(emb)
+    >>> print (np.around(sim_mat[5:,5:], decimals=3))
+    [[1.    0.957 0.961 0.904 0.966]
+     [0.957 1.    0.977 0.982 0.997]
+     [0.961 0.977 1.    0.928 0.972]
+     [0.904 0.982 0.928 1.    0.976]
+     [0.966 0.997 0.972 0.976 1.   ]]
+    >>> # Prunning
+    >>> prunned_sim_mat = clust.p_pruning(sim_mat, 0.3)
+    >>> print (np.around(prunned_sim_mat[5:,5:], decimals=3))
+    [[1.    0.    0.    0.    0.   ]
+     [0.    1.    0.    0.982 0.997]
+     [0.    0.977 1.    0.    0.972]
+     [0.    0.982 0.    1.    0.976]
+     [0.    0.997 0.    0.976 1.   ]]
+    >>> # Symmetrization
+    >>> sym_prund_sim_mat = 0.5 * (prunned_sim_mat + prunned_sim_mat.T)
+    >>> print (np.around(sym_prund_sim_mat[5:,5:], decimals=3))
+    [[1.    0.    0.    0.    0.   ]
+     [0.    1.    0.489 0.982 0.997]
+     [0.    0.489 1.    0.    0.486]
+     [0.    0.982 0.    1.    0.976]
+     [0.    0.997 0.486 0.976 1.   ]]
+    >>> # Laplacian
+    >>> laplacian = clust.get_laplacian(sym_prund_sim_mat)
+    >>> print (np.around(laplacian[5:,5:], decimals=3))
+    [[ 1.999  0.     0.     0.     0.   ]
+     [ 0.     2.468 -0.489 -0.982 -0.997]
+     [ 0.    -0.489  0.975  0.    -0.486]
+     [ 0.    -0.982  0.     1.958 -0.976]
+     [ 0.    -0.997 -0.486 -0.976  2.458]]
+    >>> # Spectral Embeddings
+    >>> spec_emb, num_of_spk = clust.get_spec_embs(laplacian, 3)
+    >>> print(num_of_spk)
+    3
+    >>> # Clustering
+    >>> clust.cluster_embs(spec_emb, num_of_spk)
+    >>> # print (clust.labels_) # [0 0 0 2 2 2 1 1 1 1]
+    >>> # Complete spectral clustering
+    >>> clust.do_spec_clust(emb, k_oracle=3, p_val=0.3)
+    >>> # print(clust.labels_) # [0 0 0 2 2 2 1 1 1 1]
+    """
+
+    def __init__(self, min_num_spkrs=2, max_num_spkrs=10):
+
+        self.min_num_spkrs = min_num_spkrs
+        self.max_num_spkrs = max_num_spkrs
+
+    def do_spec_clust(self, X, k_oracle, p_val):
+        """
+        Function for spectral clustering.
+
+        Arguments
+        ---------
+        X : array
+            (n_samples, n_features).
+            Embeddings extracted from the model.
+        k_oracle : int
+            Number of speakers (when oracle number of speakers).
+        p_val : float
+            p percent value to prune the affinity matrix.
+        """
+
+        # Similarity matrix computation
+        sim_mat = self.get_sim_mat(X)
+
+        # Refining similarity matrix with p_val
+        prunned_sim_mat = self.p_pruning(sim_mat, p_val)
+
+        # Symmetrization
+        sym_prund_sim_mat = 0.5 * (prunned_sim_mat + prunned_sim_mat.T)
+
+        # Laplacian calculation
+        laplacian = self.get_laplacian(sym_prund_sim_mat)
+
+        # Get Spectral Embeddings
+        emb, num_of_spk = self.get_spec_embs(laplacian, k_oracle)
+
+        # Perform clustering
+        self.cluster_embs(emb, num_of_spk)
+
+    def get_sim_mat(self, X):
+        """
+        Returns the similarity matrix based on cosine similarities.
+
+        Arguments
+        ---------
+        X : array
+            (n_samples, n_features).
+            Embeddings extracted from the model.
+
+        Returns
+        -------
+        M : array
+            (n_samples, n_samples).
+            Similarity matrix with cosine similarities between each pair of embedding.
+        """
+
+        # Cosine similarities
+        M = sklearn.metrics.pairwise.cosine_similarity(X, X)
+        return M
+
+    def p_pruning(self, A, pval):
+        """
+        Refine the affinity matrix by zeroing less similar values.
+
+        Arguments
+        ---------
+        A : array
+            (n_samples, n_samples).
+            Affinity matrix.
+        pval : float
+            p-value to be retained in each row of the affinity matrix.
+
+        Returns
+        -------
+        A : array
+            (n_samples, n_samples).
+            Prunned affinity matrix based on p_val.
+        """
+
+        n_elems = int((1 - pval) * A.shape[0])
+
+        # For each row in a affinity matrix
+        for i in range(A.shape[0]):
+            low_indexes = np.argsort(A[i, :])
+            low_indexes = low_indexes[0:n_elems]
+
+            # Replace smaller similarity values by 0s
+            A[i, low_indexes] = 0
+
+        return A
+
+    def get_laplacian(self, M):
+        """
+        Returns the un-normalized laplacian for the given affinity matrix.
+
+        Arguments
+        ---------
+        M : array
+            (n_samples, n_samples)
+            Affinity matrix.
+
+        Returns
+        -------
+        L : array
+            (n_samples, n_samples)
+            Laplacian matrix.
+        """
+
+        M[np.diag_indices(M.shape[0])] = 0
+        D = np.sum(np.abs(M), axis=1)
+        D = np.diag(D)
+        L = D - M
+        return L
+
+    def get_spec_embs(self, L, k_oracle=4):
+        """
+        Returns spectral embeddings and estimates the number of speakers
+        using maximum Eigen gap.
+
+        Arguments
+        ---------
+        L : array (n_samples, n_samples)
+            Laplacian matrix.
+        k_oracle : int
+            Number of speakers when the condition is oracle number of speakers,
+            else None.
+
+        Returns
+        -------
+        emb : array (n_samples, n_components)
+            Spectral embedding for each sample with n Eigen components.
+        num_of_spk : int
+            Estimated number of speakers. If the condition is set to the oracle
+            number of speakers then returns k_oracle.
+        """
+
+        lambdas, eig_vecs = scipy.linalg.eigh(L)
+
+        # if params["oracle_n_spkrs"] is True:
+        if k_oracle is not None:
+            num_of_spk = k_oracle
+        else:
+            lambda_gap_list = self.get_eigen_gaps(lambdas[1:self.max_num_spkrs])
+
+            num_of_spk = (np.argmax(
+                lambda_gap_list[:min(self.max_num_spkrs, len(lambda_gap_list))])
+                          + 2)
+
+            if num_of_spk < self.min_num_spkrs:
+                num_of_spk = self.min_num_spkrs
+
+        emb = eig_vecs[:, 0:num_of_spk]
+
+        return emb, num_of_spk
+
+    def cluster_embs(self, emb, k):
+        """
+        Clusters the embeddings using kmeans.
+
+        Arguments
+        ---------
+        emb : array (n_samples, n_components)
+            Spectral embedding for each sample with n Eigen components.
+        k : int
+            Number of clusters to kmeans.
+
+        Returns
+        -------
+        self.labels_ : self
+            Labels for each sample embedding.
+        """
+        _, self.labels_, _ = k_means(emb, k)
+
+    def get_eigen_gaps(self, eig_vals):
+        """
+        Returns the difference (gaps) between the Eigen values.
+
+        Arguments
+        ---------
+        eig_vals : list
+            List of eigen values
+
+        Returns
+        -------
+        eig_vals_gap_list : list
+            List of differences (gaps) between adjacent Eigen values.
+        """
+
+        eig_vals_gap_list = []
+        for i in range(len(eig_vals) - 1):
+            gap = float(eig_vals[i + 1]) - float(eig_vals[i])
+            eig_vals_gap_list.append(gap)
+
+        return eig_vals_gap_list
+
+
+class SpecCluster(SpectralClustering):
+    def perform_sc(self, X, n_neighbors=10):
+        """
+        Performs spectral clustering using sklearn on embeddings.
+
+        Arguments
+        ---------
+        X : array (n_samples, n_features)
+            Embeddings to be clustered.
+        n_neighbors : int
+            Number of neighbors in estimating affinity matrix.
+        """
+
+        # Computation of affinity matrix
+        connectivity = kneighbors_graph(
+            X,
+            n_neighbors=n_neighbors,
+            include_self=True, )
+        self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)
+
+        # Perform spectral clustering on affinity matrix
+        self.labels_ = spectral_clustering(
+            self.affinity_matrix_,
+            n_clusters=self.n_clusters, )
+        return self
+
+
+def is_overlapped(end1, start2):
+    """
+    Returns True if segments are overlapping.
+
+    Arguments
+    ---------
+    end1 : float
+        End time of the first segment.
+    start2 : float
+        Start time of the second segment.
+
+    Returns
+    -------
+    overlapped : bool
+        True of segments overlapped else False.
+
+    Example
+    -------
+    >>> import diarization as diar
+    >>> diar.is_overlapped(5.5, 3.4)
+    True
+    >>> diar.is_overlapped(5.5, 6.4)
+    False
+    """
+
+    if start2 > end1:
+        return False
+    else:
+        return True
+
+
+def merge_ssegs_same_speaker(lol):
+    """
+    Merge adjacent sub-segs from the same speaker.
+
+    Arguments
+    ---------
+    lol : list of list
+        Each list contains [rec_id, seg_start, seg_end, spkr_id].
+
+    Returns
+    -------
+    new_lol : list of list
+        new_lol contains adjacent segments merged from the same speaker ID.
+
+    Example
+    -------
+    >>> import diarization as diar
+    >>> lol=[['r1', 5.5, 7.0, 's1'],
+    ... ['r1', 6.5, 9.0, 's1'],
+    ... ['r1', 8.0, 11.0, 's1'],
+    ... ['r1', 11.5, 13.0, 's2'],
+    ... ['r1', 14.0, 15.0, 's2'],
+    ... ['r1', 14.5, 15.0, 's1']]
+    >>> diar.merge_ssegs_same_speaker(lol)
+    [['r1', 5.5, 11.0, 's1'], ['r1', 11.5, 13.0, 's2'], ['r1', 14.0, 15.0, 's2'], ['r1', 14.5, 15.0, 's1']]
+    """
+
+    new_lol = []
+
+    # Start from the first sub-seg
+    sseg = lol[0]
+    flag = False
+    for i in range(1, len(lol)):
+        next_sseg = lol[i]
+
+        # IF sub-segments overlap AND has same speaker THEN merge
+        if is_overlapped(sseg[2], next_sseg[1]) and sseg[3] == next_sseg[3]:
+            sseg[2] = next_sseg[2]  # just update the end time
+            # This is important. For the last sseg, if it is the same speaker the merge
+            # Make sure we don't append the last segment once more. Hence, set FLAG=True
+            if i == len(lol) - 1:
+                flag = True
+                new_lol.append(sseg)
+        else:
+            new_lol.append(sseg)
+            sseg = next_sseg
+
+    # Add last segment only when it was skipped earlier.
+    if flag is False:
+        new_lol.append(lol[-1])
+
+    return new_lol
+
+
+def distribute_overlap(lol):
+    """
+    Distributes the overlapped speech equally among the adjacent segments
+    with different speakers.
+
+    Arguments
+    ---------
+    lol : list of list
+        It has each list structure as [rec_id, seg_start, seg_end, spkr_id].
+
+    Returns
+    -------
+    new_lol : list of list
+        It contains the overlapped part equally divided among the adjacent
+        segments with different speaker IDs.
+
+    Example
+    -------
+    >>> import diarization as diar
+    >>> lol = [['r1', 5.5, 9.0, 's1'],
+    ... ['r1', 8.0, 11.0, 's2'],
+    ... ['r1', 11.5, 13.0, 's2'],
+    ... ['r1', 12.0, 15.0, 's1']]
+    >>> diar.distribute_overlap(lol)
+    [['r1', 5.5, 8.5, 's1'], ['r1', 8.5, 11.0, 's2'], ['r1', 11.5, 12.5, 's2'], ['r1', 12.5, 15.0, 's1']]
+    """
+
+    new_lol = []
+    sseg = lol[0]
+
+    # Add first sub-segment here to avoid error at: "if new_lol[-1] != sseg:" when new_lol is empty
+    # new_lol.append(sseg)
+
+    for i in range(1, len(lol)):
+        next_sseg = lol[i]
+        # No need to check if they are different speakers.
+        # Because if segments are overlapped then they always have different speakers.
+        # This is because similar speaker's adjacent sub-segments are already merged by "merge_ssegs_same_speaker()"
+
+        if is_overlapped(sseg[2], next_sseg[1]):
+
+            # Get overlap duration.
+            # Now this overlap will be divided equally between adjacent segments.
+            overlap = sseg[2] - next_sseg[1]
+
+            # Update end time of old seg
+            sseg[2] = sseg[2] - (overlap / 2.0)
+
+            # Update start time of next seg
+            next_sseg[1] = next_sseg[1] + (overlap / 2.0)
+
+            if len(new_lol) == 0:
+                # For first sub-segment entry
+                new_lol.append(sseg)
+            else:
+                # To avoid duplicate entries
+                if new_lol[-1] != sseg:
+                    new_lol.append(sseg)
+
+            # Current sub-segment is next sub-segment
+            sseg = next_sseg
+
+        else:
+            # For the first sseg
+            if len(new_lol) == 0:
+                new_lol.append(sseg)
+            else:
+                # To avoid duplicate entries
+                if new_lol[-1] != sseg:
+                    new_lol.append(sseg)
+
+            # Update the current sub-segment
+            sseg = next_sseg
+
+    # Add the remaining last sub-segment
+    new_lol.append(next_sseg)
+
+    return new_lol
+
+
+def write_rttm(segs_list, out_rttm_file):
+    """
+    Writes the segment list in RTTM format (A standard NIST format).
+
+    Arguments
+    ---------
+    segs_list : list of list
+        Each list contains [rec_id, seg_start, seg_end, spkr_id].
+    out_rttm_file : str
+        Path of the output RTTM file.
+    """
+
+    rttm = []
+    rec_id = segs_list[0][0]
+
+    for seg in segs_list:
+        new_row = [
+            "SPEAKER",
+            rec_id,
+            "0",
+            str(round(seg[1], 4)),
+            str(round(seg[2] - seg[1], 4)),
+            "<NA>",
+            "<NA>",
+            seg[3],
+            "<NA>",
+            "<NA>",
+        ]
+        rttm.append(new_row)
+
+    with open(out_rttm_file, "w") as f:
+        for row in rttm:
+            line_str = " ".join(row)
+            f.write("%s\n" % line_str)
+
+
+def do_AHC(diary_obj, out_rttm_file, rec_id, k_oracle=4, p_val=0.3):
+    """
+    Performs Agglomerative Hierarchical Clustering on embeddings.
+
+    Arguments
+    ---------
+    diary_obj : EmbeddingMeta type
+        Contains embeddings in diary_obj.stats and segment IDs in diary_obj.segset.
+    out_rttm_file : str
+        Path of the output RTTM file.
+    rec_id : str
+        Recording ID for the recording under processing.
+    k : int
+        Number of speaker (None, if it has to be estimated).
+    pval : float
+        `pval` for prunning affinity matrix. Used only when number of speakers
+        are unknown. Note that this is just for experiment. Prefer Spectral clustering
+        for better clustering results.
+    """
+
+    from sklearn.cluster import AgglomerativeClustering
+
+    # p_val is the threshold_val (for AHC)
+    diary_obj.norm_stats()
+
+    # processing
+    if k_oracle is not None:
+        num_of_spk = k_oracle
+
+        clustering = AgglomerativeClustering(
+            n_clusters=num_of_spk,
+            affinity="cosine",
+            linkage="average", ).fit(diary_obj.stats)
+        labels = clustering.labels_
+
+    else:
+        # Estimate num of using max eigen gap with `cos` affinity matrix.
+        # This is just for experimentation.
+        clustering = AgglomerativeClustering(
+            n_clusters=None,
+            affinity="cosine",
+            linkage="average",
+            distance_threshold=p_val, ).fit(diary_obj.stats)
+        labels = clustering.labels_
+
+    # Convert labels to speaker boundaries
+    subseg_ids = diary_obj.segset
+    lol = []
+
+    for i in range(labels.shape[0]):
+        spkr_id = rec_id + "_" + str(labels[i])
+
+        sub_seg = subseg_ids[i]
+
+        splitted = sub_seg.rsplit("_", 2)
+        rec_id = str(splitted[0])
+        sseg_start = float(splitted[1])
+        sseg_end = float(splitted[2])
+
+        a = [rec_id, sseg_start, sseg_end, spkr_id]
+        lol.append(a)
+
+    # Sorting based on start time of sub-segment
+    lol.sort(key=lambda x: float(x[1]))
+
+    # Merge and split in 2 simple steps: (i) Merge sseg of same speakers then (ii) split different speakers
+    # Step 1: Merge adjacent sub-segments that belong to same speaker (or cluster)
+    lol = merge_ssegs_same_speaker(lol)
+
+    # Step 2: Distribute duration of adjacent overlapping sub-segments belonging to different speakers (or cluster)
+    # Taking mid-point as the splitting time location.
+    lol = distribute_overlap(lol)
+
+    # logger.info("Completed diarizing " + rec_id)
+    write_rttm(lol, out_rttm_file)
+
+
+def do_spec_clustering(diary_obj, out_rttm_file, rec_id, k, pval, affinity_type,
+                       n_neighbors):
+    """
+    Performs spectral clustering on embeddings. This function calls specific
+    clustering algorithms as per affinity.
+
+    Arguments
+    ---------
+    diary_obj : EmbeddingMeta type
+        Contains embeddings in diary_obj.stats and segment IDs in diary_obj.segset.
+    out_rttm_file : str
+        Path of the output RTTM file.
+    rec_id : str
+        Recording ID for the recording under processing.
+    k : int
+        Number of speaker (None, if it has to be estimated).
+    pval : float
+        `pval` for prunning affinity matrix.
+    affinity_type : str
+        Type of similarity to be used to get affinity matrix (cos or nn).
+    """
+
+    if affinity_type == "cos":
+        clust_obj = SpecClustUnorm(min_num_spkrs=2, max_num_spkrs=10)
+        k_oracle = k  # use it only when oracle num of speakers
+        clust_obj.do_spec_clust(diary_obj.stats, k_oracle, pval)
+        labels = clust_obj.labels_
+    else:
+        clust_obj = SpecCluster(
+            n_clusters=k,
+            assign_labels="kmeans",
+            random_state=1234,
+            affinity="nearest_neighbors", )
+        clust_obj.perform_sc(diary_obj.stats, n_neighbors)
+        labels = clust_obj.labels_
+
+    # Convert labels to speaker boundaries
+    subseg_ids = diary_obj.segset
+    lol = []
+
+    for i in range(labels.shape[0]):
+        spkr_id = rec_id + "_" + str(labels[i])
+
+        sub_seg = subseg_ids[i]
+
+        splitted = sub_seg.rsplit("_", 2)
+        rec_id = str(splitted[0])
+        sseg_start = float(splitted[1])
+        sseg_end = float(splitted[2])
+
+        a = [rec_id, sseg_start, sseg_end, spkr_id]
+        lol.append(a)
+
+    # Sorting based on start time of sub-segment
+    lol.sort(key=lambda x: float(x[1]))
+
+    # Merge and split in 2 simple steps: (i) Merge sseg of same speakers then (ii) split different speakers
+    # Step 1: Merge adjacent sub-segments that belong to same speaker (or cluster)
+    lol = merge_ssegs_same_speaker(lol)
+
+    # Step 2: Distribute duration of adjacent overlapping sub-segments belonging to different speakers (or cluster)
+    # Taking mid-point as the splitting time location.
+    lol = distribute_overlap(lol)
+
+    # logger.info("Completed diarizing " + rec_id)
+    write_rttm(lol, out_rttm_file)
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(
+        prog='python diarization.py --backend AHC', description='diarizing')
+    parser.add_argument(
+        '--sys_rttm_dir',
+        required=False,
+        help='Directory to store system RTTM files')
+    parser.add_argument(
+        '--ref_rttm_dir',
+        required=False,
+        help='Directory to store reference RTTM files')
+    parser.add_argument(
+        '--backend', default="AHC", help='type of backend, AHC or SC or kmeans')
+    parser.add_argument(
+        '--oracle_n_spkrs',
+        default=True,
+        type=strtobool,
+        help='Oracle num of speakers')
+    parser.add_argument(
+        '--mic_type',
+        default="Mix-Headset",
+        help='Type of microphone to be used')
+    parser.add_argument(
+        '--affinity', default="cos", help='affinity matrix, cos or nn')
+    parser.add_argument(
+        '--max_subseg_dur',
+        default=3.0,
+        type=float,
+        help='Duration in seconds of a subsegments to be prepared from larger segments'
+    )
+    parser.add_argument(
+        '--overlap',
+        default=1.5,
+        type=float,
+        help='Overlap duration in seconds between adjacent subsegments')
+
+    args = parser.parse_args()
+
+    pval = 0.3
+    rec_id = "utt0001"
+    n_neighbors = 10
+    out_rttm_file = "./out.rttm"
+
+    embeddings = np.empty(shape=[0, 32], dtype=np.float64)
+    segset = []
+
+    for i in range(10):
+        seg = [rec_id + "_" + str(i) + "_" + str(i + 1)]
+        segset = segset + seg
+        emb = np.random.rand(1, 32)
+        embeddings = np.concatenate((embeddings, emb), axis=0)
+
+    segset = np.array(segset, dtype="|O")
+    stat_obj = EmbeddingMeta(segset, embeddings)
+    if args.oracle_n_spkrs is True:
+        num_spkrs = 2
+
+    if args.backend == "SC":
+        print("begin SC ")
+        do_spec_clustering(
+            stat_obj,
+            out_rttm_file,
+            rec_id,
+            num_spkrs,
+            pval,
+            args.affinity,
+            n_neighbors, )
+    if args.backend == "AHC":
+        print("begin AHC ")
+        do_AHC(stat_obj, out_rttm_file, rec_id, num_spkrs, pval)