"""

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.

Reference

---------

This code is written using the following:

- Von Luxburg, U. A tutorial on spectral clustering. Stat Comput 17, 395–416 (2007).

https://doi.org/10.1007/s11222-007-9033-z

- https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/cluster/_spectral.py

- https://github.com/tango4j/Auto-Tuning-Spectral-Clustering/blob/master/spectral_opt.py

Authors

* Nauman Dawalatabad 2020

"""

import csv

import numbers

import warnings

import scipy

import pytest

import numpy as np

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

np.random.seed(1234)

pytest.importorskip("sklearn")

try:

import sklearn

from sklearn.neighbors import kneighbors_graph

from sklearn.cluster import SpectralClustering

from sklearn.cluster._kmeans import k_means

except ImportError:

err_msg = "The optional dependency sklearn is used in this module\n"

err_msg += "Cannot import sklearn. \n"

err_msg += "Please follow the below instructions\n"

err_msg += "=============================\n"

err_msg += "Using pip:\n"

err_msg += "pip install sklearn\n"

err_msg += "================================ \n"

err_msg += "Using conda:\n"

err_msg += "conda install sklearn"

raise ImportError(err_msg)

def read_rttm(rttm_file_path):

"""Reads and returns RTTM in list format.

Arguments

---------

rttm_file_path : str

Path to the RTTM file to be read.

Returns

-------

rttm : list

List containing rows of RTTM file.

"""

rttm = []

with open(rttm_file_path, "r") as f:

for line in f:

entry = line[:-1]

rttm.append(entry)

return rttm

def write_ders_file(ref_rttm, DER, out_der_file):

"""Write the final DERs for individual recording.

Arguments

---------

ref_rttm : str

Reference RTTM file.

DER : array

Array containing DER values of each recording.

out_der_file : str

File to write the DERs.

"""

rttm = read_rttm(ref_rttm)

spkr_info = list(filter(lambda x: x.startswith("SPKR-INFO"), rttm))

rec_id_list = []

count = 0

with open(out_der_file, "w") as f:

for row in spkr_info:

a = row.split(" ")

rec_id = a[1]

if rec_id not in rec_id_list:

r = [rec_id, str(round(DER[count], 2))]

rec_id_list.append(rec_id)

line_str = " ".join(r)

f.write("%s\n" % line_str)

count += 1

r = ["OVERALL ", str(round(DER[count], 2))]

line_str = " ".join(r)

f.write("%s\n" % line_str)

def prepare_subset_csv(full_diary_csv, rec_id, out_csv_file):

"""Prepares csv for a given recording ID.

Arguments

---------

full_diary_csv : csv

Full csv containing all the recordings

rec_id : str

The recording ID for which csv has to be prepared

out_csv_file : str

Path of the output csv file.

"""

out_csv_head = [full_diary_csv[0]]

entry = []

for row in full_diary_csv:

if row[0].startswith(rec_id):

entry.append(row)

out_csv = out_csv_head + entry

with open(out_csv_file, mode="w") as csv_file:

csv_writer = csv.writer(

csv_file, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL

)

for r in out_csv:

csv_writer.writerow(r)

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

-------

>>> from speechbrain.processing 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, sseg_start, sseg_end, spkr_id].

Returns

-------

new_lol : list of list

new_lol contains adjacent segments merged from the same speaker ID.

Example

-------

>>> from speechbrain.processing 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, sseg_start, sseg_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

-------

>>> from speechbrain.processing 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, sseg_start, sseg_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 _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 get_oracle_num_spkrs(rec_id, spkr_info):

"""

Returns actual number of speakers in a recording from the ground-truth.

This can be used when the condition is oracle number of speakers.

Arguments

---------

rec_id : str

Recording ID for which the number of speakers have to be obtained.

spkr_info : list

Header of the RTTM file. Starting with `SPKR-INFO`.

Example

-------

>>> from speechbrain.processing import diarization as diar

>>> spkr_info = ['SPKR-INFO ES2011a 0 <NA> <NA> <NA> unknown ES2011a.A <NA> <NA>',

... 'SPKR-INFO ES2011a 0 <NA> <NA> <NA> unknown ES2011a.B <NA> <NA>',

... 'SPKR-INFO ES2011a 0 <NA> <NA> <NA> unknown ES2011a.C <NA> <NA>',

... 'SPKR-INFO ES2011a 0 <NA> <NA> <NA> unknown ES2011a.D <NA> <NA>',

... 'SPKR-INFO ES2011b 0 <NA> <NA> <NA> unknown ES2011b.A <NA> <NA>',

... 'SPKR-INFO ES2011b 0 <NA> <NA> <NA> unknown ES2011b.B <NA> <NA>',

... 'SPKR-INFO ES2011b 0 <NA> <NA> <NA> unknown ES2011b.C <NA> <NA>']

>>> diar.get_oracle_num_spkrs('ES2011a', spkr_info)

4

>>> diar.get_oracle_num_spkrs('ES2011b', spkr_info)

3

"""

num_spkrs = 0

for line in spkr_info:

if rec_id in line:

# Since rec_id is prefix for each speaker

num_spkrs += 1

return num_spkrs

def spectral_embedding_sb(

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

>>> from speechbrain.processing 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_sb(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_sb(

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

>>> from speechbrain.processing 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]])

>>> labs = diar.spectral_clustering_sb(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_sb(

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 Spec_Cluster(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.

Reference

---------

https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/cluster/_spectral.py

"""

# 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_sb(

self.affinity_matrix_, n_clusters=self.n_clusters,

)

return self

#####################

class Spec_Clust_unorm:

"""

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

-------

>>> from speechbrain.processing import diarization as diar

>>> clust = diar.Spec_Clust_unorm(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.getEigenGaps(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 getEigenGaps(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(float(eig_vals[i + 1]) - float(eig_vals[i]))

eig_vals_gap_list.append(gap)

return eig_vals_gap_list

#####################

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 : StatObject_SB type

Contains embeddings in diary_obj.stat1 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 = Spec_Clust_unorm(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.stat1, k_oracle, pval)

labels = clust_obj.labels_

else: