"""
This script contains basic functions used for speaker diarization.
This script has an optional dependency on open source scikit-learn (sklearn) library.
A few scikit-learn 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 scikit-learn (sklearn) is used in this module\n"
err_msg += "Cannot import scikit-learn. \n"
err_msg += "Please follow the below instructions\n"
err_msg += "=============================\n"
err_msg += "Using pip:\n"
err_msg += "pip install scikit-learn\n"
err_msg += "================================ \n"
err_msg += "Using conda:\n"
err_msg += "conda install scikit-learn"
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 = []
sseg = lol[0]
flag = False
for i in range(1, len(lol)):
next_sseg = lol[i]
if is_overlapped(sseg[2], next_sseg[1]) and sseg[3] == next_sseg[3]:
sseg[2] = next_sseg[2]
if i == len(lol) - 1:
flag = True
new_lol.append(sseg)
else:
new_lol.append(sseg)
sseg = next_sseg
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]
for i in range(1, len(lol)):
next_sseg = lol[i]
if is_overlapped(sseg[2], next_sseg[1]):
overlap = sseg[2] - next_sseg[1]
sseg[2] = sseg[2] - (overlap / 2.0)
next_sseg[1] = next_sseg[1] + (overlap / 2.0)
if len(new_lol) == 0:
new_lol.append(sseg)
else:
if new_lol[-1] != sseg:
new_lol.append(sseg)
sseg = next_sseg
else:
if len(new_lol) == 0:
new_lol.append(sseg)
else:
if new_lol[-1] != sseg:
new_lol.append(sseg)
sseg = next_sseg
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):
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):
n_connected_components, _ = connected_components(graph)
return n_connected_components == 1
else:
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]
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
n_diags = np.unique(laplacian.row - laplacian.col).size
if n_diags <= 7:
laplacian = laplacian.todia()
else:
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:
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]]
"""
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):
"""Performs spectral clustering using sklearn on embeddings."""
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
"""
connectivity = kneighbors_graph(
X, n_neighbors=n_neighbors, include_self=True,
)
self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T)
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
>>> pruned_sim_mat = clust.p_pruning(sim_mat, 0.3)
>>> print (np.around(pruned_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_pruned_sim_mat = 0.5 * (pruned_sim_mat + pruned_sim_mat.T)
>>> print (np.around(sym_pruned_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_pruned_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.
"""
sim_mat = self.get_sim_mat(X)
pruned_sim_mat = self.p_pruning(sim_mat, p_val)
sym_pruned_sim_mat = 0.5 * (pruned_sim_mat + pruned_sim_mat.T)
laplacian = self.get_laplacian(sym_pruned_sim_mat)
emb, num_of_spk = self.get_spec_embs(laplacian, k_oracle)
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.
"""
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).
pruned affinity matrix based on p_val.
"""
n_elems = int((1 - pval) * A.shape[0])
for i in range(A.shape[0]):
low_indexes = np.argsort(A[i, :])
low_indexes = low_indexes[0:n_elems]
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 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))
]
)
if lambda_gap_list
else 0
) + 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(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
clust_obj.do_spec_clust(diary_obj.stat1, k_oracle, pval)
labels = clust_obj.labels_
else:
clust_obj = Spec_Cluster(
n_clusters=k,
assign_labels="kmeans",
random_state=1234,
affinity="nearest_neighbors",
)
clust_obj.perform_sc(diary_obj.stat1, n_neighbors)
labels = clust_obj.labels_
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)
lol.sort(key=lambda x: float(x[1]))
lol = merge_ssegs_same_speaker(lol)
lol = distribute_overlap(lol)
write_rttm(lol, out_rttm_file)
def do_kmeans_clustering(
diary_obj, out_rttm_file, rec_id, k_oracle=4, p_val=0.3
):
"""Performs kmeans clustering on embeddings.
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. Used only when number of speakers
are unknown. Note that this is just for experiment. Prefer Spectral clustering
for better clustering results.
"""
if k_oracle is not None:
num_of_spk = k_oracle
else:
clust_obj = Spec_Clust_unorm(min_num_spkrs=2, max_num_spkrs=10)
sim_mat = clust_obj.get_sim_mat(diary_obj.stat1)
pruned_sim_mat = clust_obj.p_pruning(sim_mat, p_val)
sym_pruned_sim_mat = 0.5 * (pruned_sim_mat + pruned_sim_mat.T)
laplacian = clust_obj.get_laplacian(sym_pruned_sim_mat)
_, num_of_spk = clust_obj.get_spec_embs(laplacian, k_oracle)
_, labels, _ = k_means(diary_obj.stat1, num_of_spk)
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)
lol.sort(key=lambda x: float(x[1]))
lol = merge_ssegs_same_speaker(lol)
lol = distribute_overlap(lol)
write_rttm(lol, out_rttm_file)
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 : 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. 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
diary_obj.norm_stat1()
if k_oracle is not None:
num_of_spk = k_oracle
clustering = AgglomerativeClustering(
n_clusters=num_of_spk, affinity="cosine", linkage="ward",
).fit(diary_obj.stat1)
labels = clustering.labels_
else:
clustering = AgglomerativeClustering(
n_clusters=None,
affinity="cosine",
linkage="ward",
distance_threshold=p_val,
).fit(diary_obj.stat1)
labels = clustering.labels_
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)
lol.sort(key=lambda x: float(x[1]))
lol = merge_ssegs_same_speaker(lol)
lol = distribute_overlap(lol)
write_rttm(lol, out_rttm_file)