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from
__future__
import
print_function
import
numpy
import
sklearn
.
cluster
import
scipy
import
os
from
pyAudioAnalysis
import
audioFeatureExtraction
as
aF
from
pyAudioAnalysis
import
audioTrainTest
as
aT
from
pyAudioAnalysis
import
audioBasicIO
from
scipy
.
spatial
import
distance
import
matplotlib
.
pyplot
as
plt
import
sklearn
.
discriminant_analysis
import
csv
import
os
.
path
import
sklearn
import
sklearn
.
cluster
import
hmmlearn
.
hmm
import
pickle
as
cPickle
import
glob
""" General utility functions """
def
smoothMovingAvg
(
inputSignal
,
windowLen
=
11
):
windowLen
=
int
(
windowLen
)
if
inputSignal
.
ndim
!=
1
:
raise
ValueError
(
""
)
if
inputSignal
.
size
<
windowLen
:
raise
ValueError
(
"Input vector needs to be bigger than window size."
)
if
windowLen
<
3
:
return
inputSignal
s
=
numpy
.
r_
[
2
*
inputSignal
[
0
]
-
inputSignal
[
windowLen
-
1
::
-
1
],
inputSignal
,
2
*
inputSignal
[
-
1
]
-
inputSignal
[
-
1
:
-
windowLen
:
-
1
]]
w
=
numpy
.
ones
(
windowLen
,
'd'
)
y
=
numpy
.
convolve
(
w
/
w
.
sum
(),
s
,
mode
=
'same'
)
return
y
[
windowLen
:
-
windowLen
+
1
]
def
selfSimilarityMatrix
(
featureVectors
):
'''
This function computes the self-similarity matrix for a sequence
of feature vectors.
ARGUMENTS:
- featureVectors: a numpy matrix (nDims x nVectors) whose i-th column
corresponds to the i-th feature vector
RETURNS:
- S: the self-similarity matrix (nVectors x nVectors)
'''
[
nDims
,
nVectors
]
=
featureVectors
.
shape
[
featureVectors2
,
MEAN
,
STD
]
=
aT
.
normalizeFeatures
([
featureVectors
.
T
])
featureVectors2
=
featureVectors2
[
0
].
T
S
=
1.0
-
distance
.
squareform
(
distance
.
pdist
(
featureVectors2
.
T
,
'cosine'
))
return
S
def
flags2segs
(
flags
,
window
):
'''
ARGUMENTS:
- flags: a sequence of class flags (per time window)
- window: window duration (in seconds)
RETURNS:
- segs: a sequence of segment's limits: segs[i,0] is start and
segs[i,1] are start and end point of segment i
- classes: a sequence of class flags: class[i] is the class ID of
the i-th segment
'''
preFlag
=
0
cur_flag
=
0
n_segs
=
0
cur_val
=
flags
[
cur_flag
]
segsList
=
[]
classes
=
[]
while
(
cur_flag
<
len
(
flags
)
-
1
):
stop
=
0
preFlag
=
cur_flag
preVal
=
cur_val
while
(
stop
==
0
):
cur_flag
=
cur_flag
+
1
tempVal
=
flags
[
cur_flag
]
if
((
tempVal
!=
cur_val
)
|
(
cur_flag
==
len
(
flags
)
-
1
)):
# stop
n_segs
=
n_segs
+
1
stop
=
1
cur_seg
=
cur_val
cur_val
=
flags
[
cur_flag
]
segsList
.
append
((
cur_flag
*
window
))
classes
.
append
(
preVal
)
segs
=
numpy
.
zeros
((
len
(
segsList
),
2
))
for
i
in
range
(
len
(
segsList
)):
if
i
>
0
:
segs
[
i
,
0
]
=
segsList
[
i
-
1
]
segs
[
i
,
1
]
=
segsList
[
i
]
return
(
segs
,
classes
)
def
segs2flags
(
seg_start
,
seg_end
,
seg_label
,
win_size
):
'''
This function converts segment endpoints and respective segment
labels to fix-sized class labels.
ARGUMENTS:
- seg_start: segment start points (in seconds)
- seg_end: segment endpoints (in seconds)
- seg_label: segment labels
- win_size: fix-sized window (in seconds)
RETURNS:
- flags: numpy array of class indices
- class_names: list of classnames (strings)
'''
flags
=
[]
class_names
=
list
(
set
(
seg_label
))
curPos
=
win_size
/
2.0
while
curPos
<
seg_end
[
-
1
]:
for
i
in
range
(
len
(
seg_start
)):
if
curPos
>
seg_start
[
i
]
and
curPos
<=
seg_end
[
i
]:
break
flags
.
append
(
class_names
.
index
(
seg_label
[
i
]))
curPos
+=
win_size
return
numpy
.
array
(
flags
),
class_names
def
computePreRec
(
cm
,
class_names
):
'''
This function computes the precision, recall and f1 measures,
given a confusion matrix
'''
n_classes
=
cm
.
shape
[
0
]
if
len
(
class_names
)
!=
n_classes
:
print
(
"Error in computePreRec! Confusion matrix and class_names "
"list must be of the same size!"
)
return
precision
=
[]
recall
=
[]
f1
=
[]
for
i
,
c
in
enumerate
(
class_names
):
precision
.
append
(
cm
[
i
,
i
]
/
numpy
.
sum
(
cm
[:,
i
]))
recall
.
append
(
cm
[
i
,
i
]
/
numpy
.
sum
(
cm
[
i
,:]))
f1
.
append
(
2
*
precision
[
-
1
]
*
recall
[
-
1
]
/
(
precision
[
-
1
]
+
recall
[
-
1
]))
return
recall
,
precision
,
f1
def
readSegmentGT
(
gt_file
):
'''
This function reads a segmentation ground truth file, following a simple CSV format with the following columns:
<segment start>,<segment end>,<class label>
ARGUMENTS:
- gt_file: the path of the CSV segment file
RETURNS:
- seg_start: a numpy array of segments' start positions
- seg_end: a numpy array of segments' ending positions
- seg_label: a list of respective class labels (strings)
'''
f
=
open
(
gt_file
,
'rt'
)
reader
=
csv
.
reader
(
f
,
delimiter
=
','
)
seg_start
=
[]
seg_end
=
[]
seg_label
=
[]
for
row
in
reader
:
if
len
(
row
)
==
3
:
seg_start
.
append
(
float
(
row
[
0
]))
seg_end
.
append
(
float
(
row
[
1
]))
#if row[2]!="other":
# seg_label.append((row[2]))
#else:
# seg_label.append("silence")
seg_label
.
append
((
row
[
2
]))
return
numpy
.
array
(
seg_start
),
numpy
.
array
(
seg_end
),
seg_label
def
plotSegmentationResults
(
flags_ind
,
flags_ind_gt
,
class_names
,
mt_step
,
ONLY_EVALUATE
=
False
):
'''
This function plots statistics on the classification-segmentation results produced either by the fix-sized supervised method or the HMM method.
It also computes the overall accuracy achieved by the respective method if ground-truth is available.
'''
flags
=
[
class_names
[
int
(
f
)]
for
f
in
flags_ind
]
(
segs
,
classes
)
=
flags2segs
(
flags
,
mt_step
)
min_len
=
min
(
flags_ind
.
shape
[
0
],
flags_ind_gt
.
shape
[
0
])
if
min_len
>
0
:
accuracy
=
numpy
.
sum
(
flags_ind
[
0
:
min_len
]
==
flags_ind_gt
[
0
:
min_len
])
/
float
(
min_len
)
else
:
accuracy
=
-
1
if
not
ONLY_EVALUATE
:
duration
=
segs
[
-
1
,
1
]
s_percentages
=
numpy
.
zeros
((
len
(
class_names
),
1
))
percentages
=
numpy
.
zeros
((
len
(
class_names
),
1
))
av_durations
=
numpy
.
zeros
((
len
(
class_names
),
1
))
for
iSeg
in
range
(
segs
.
shape
[
0
]):
s_percentages
[
class_names
.
index
(
classes
[
iSeg
])]
+=
\
(
segs
[
iSeg
,
1
]
-
segs
[
iSeg
,
0
])
for
i
in
range
(
s_percentages
.
shape
[
0
]):
percentages
[
i
]
=
100.0
*
s_percentages
[
i
]
/
duration
S
=
sum
(
1
for
c
in
classes
if
c
==
class_names
[
i
])
if
S
>
0
:
av_durations
[
i
]
=
s_percentages
[
i
]
/
S
else
:
av_durations
[
i
]
=
0.0
for
i
in
range
(
percentages
.
shape
[
0
]):
print
(
class_names
[
i
],
percentages
[
i
],
av_durations
[
i
])
font
=
{
'size'
:
10
}
plt
.
rc
(
'font'
,
**
font
)
fig
=
plt
.
figure
()
ax1
=
fig
.
add_subplot
(
211
)
ax1
.
set_yticks
(
numpy
.
array
(
range
(
len
(
class_names
))))
ax1
.
axis
((
0
,
duration
,
-
1
,
len
(
class_names
)))
ax1
.
set_yticklabels
(
class_names
)
ax1
.
plot
(
numpy
.
array
(
range
(
len
(
flags_ind
)))
*
mt_step
+
mt_step
/
2.0
,
flags_ind
)
if
flags_ind_gt
.
shape
[
0
]
>
0
:
ax1
.
plot
(
numpy
.
array
(
range
(
len
(
flags_ind_gt
)))
*
mt_step
+
mt_step
/
2.0
,
flags_ind_gt
+
0.05
,
'--r'
)
plt
.
xlabel
(
"time (seconds)"
)
if
accuracy
>=
0
:
plt
.
title
(
'Accuracy = {0:.1f}%'
.
format
(
100.0
*
accuracy
))
ax2
=
fig
.
add_subplot
(
223
)
plt
.
title
(
"Classes percentage durations"
)
ax2
.
axis
((
0
,
len
(
class_names
)
+
1
,
0
,
100
))
ax2
.
set_xticks
(
numpy
.
array
(
range
(
len
(
class_names
)
+
1
)))
ax2
.
set_xticklabels
([
" "
]
+
class_names
)
ax2
.
bar
(
numpy
.
array
(
range
(
len
(
class_names
)))
+
0.5
,
percentages
)
ax3
=
fig
.
add_subplot
(
224
)
plt
.
title
(
"Segment average duration per class"
)
ax3
.
axis
((
0
,
len
(
class_names
)
+
1
,
0
,
av_durations
.
max
()))
ax3
.
set_xticks
(
numpy
.
array
(
range
(
len
(
class_names
)
+
1
)))
ax3
.
set_xticklabels
([
" "
]
+
class_names
)
ax3
.
bar
(
numpy
.
array
(
range
(
len
(
class_names
)))
+
0.5
,
av_durations
)
fig
.
tight_layout
()
plt
.
show
()
return
accuracy
def
evaluateSpeakerDiarization
(
flags
,
flags_gt
):
min_len
=
min
(
flags
.
shape
[
0
],
flags_gt
.
shape
[
0
])
flags
=
flags
[
0
:
min_len
]
flags_gt
=
flags_gt
[
0
:
min_len
]
u_flags
=
numpy
.
unique
(
flags
)
u_flags_gt
=
numpy
.
unique
(
flags_gt
)
# compute contigency table:
c_matrix
=
numpy
.
zeros
((
u_flags
.
shape
[
0
],
u_flags_gt
.
shape
[
0
]))
for
i
in
range
(
min_len
):
c_matrix
[
int
(
numpy
.
nonzero
(
u_flags
==
flags
[
i
])[
0
]),
int
(
numpy
.
nonzero
(
u_flags_gt
==
flags_gt
[
i
])[
0
])]
+=
1.0
Nc
,
Ns
=
c_matrix
.
shape
N_s
=
numpy
.
sum
(
c_matrix
,
axis
=
0
)
N_c
=
numpy
.
sum
(
c_matrix
,
axis
=
1
)
N
=
numpy
.
sum
(
c_matrix
)
purity_clust
=
numpy
.
zeros
((
Nc
, ))
purity_speak
=
numpy
.
zeros
((
Ns
, ))
# compute cluster purity:
for
i
in
range
(
Nc
):
purity_clust
[
i
]
=
numpy
.
max
((
c_matrix
[
i
, :]))
/
(
N_c
[
i
])
for
j
in
range
(
Ns
):
purity_speak
[
j
]
=
numpy
.
max
((
c_matrix
[:,
j
]))
/
(
N_s
[
j
])
purity_cluster_m
=
numpy
.
sum
(
purity_clust
*
N_c
)
/
N
purity_speaker_m
=
numpy
.
sum
(
purity_speak
*
N_s
)
/
N
return
purity_cluster_m
,
purity_speaker_m
def
trainHMM_computeStatistics
(
features
,
labels
):
'''
This function computes the statistics used to train an HMM joint segmentation-classification model
using a sequence of sequential features and respective labels
ARGUMENTS:
- features: a numpy matrix of feature vectors (numOfDimensions x n_wins)
- labels: a numpy array of class indices (n_wins x 1)
RETURNS:
- start_prob: matrix of prior class probabilities (n_classes x 1)
- transmat: transition matrix (n_classes x n_classes)
- means: means matrix (numOfDimensions x 1)
- cov: deviation matrix (numOfDimensions x 1)
'''
u_labels
=
numpy
.
unique
(
labels
)
n_comps
=
len
(
u_labels
)
n_feats
=
features
.
shape
[
0
]
if
features
.
shape
[
1
]
<
labels
.
shape
[
0
]:
print
(
"trainHMM warning: number of short-term feature vectors "
"must be greater or equal to the labels length!"
)
labels
=
labels
[
0
:
features
.
shape
[
1
]]
# compute prior probabilities:
start_prob
=
numpy
.
zeros
((
n_comps
,))
for
i
,
u
in
enumerate
(
u_labels
):
start_prob
[
i
]
=
numpy
.
count_nonzero
(
labels
==
u
)
# normalize prior probabilities
start_prob
=
start_prob
/
start_prob
.
sum
()
# compute transition matrix:
transmat
=
numpy
.
zeros
((
n_comps
,
n_comps
))
for
i
in
range
(
labels
.
shape
[
0
]
-
1
):
transmat
[
int
(
labels
[
i
]),
int
(
labels
[
i
+
1
])]
+=
1
# normalize rows of transition matrix:
for
i
in
range
(
n_comps
):
transmat
[
i
, :]
/=
transmat
[
i
, :].
sum
()
means
=
numpy
.
zeros
((
n_comps
,
n_feats
))
for
i
in
range
(
n_comps
):
means
[
i
, :]
=
numpy
.
matrix
(
features
[:,
numpy
.
nonzero
(
labels
==
u_labels
[
i
])[
0
]].
mean
(
axis
=
1
))
cov
=
numpy
.
zeros
((
n_comps
,
n_feats
))
for
i
in
range
(
n_comps
):
#cov[i,:,:] = numpy.cov(features[:,numpy.nonzero(labels==u_labels[i])[0]]) # use this lines if HMM using full gaussian distributions are to be used!
cov
[
i
, :]
=
numpy
.
std
(
features
[:,
numpy
.
nonzero
(
labels
==
u_labels
[
i
])[
0
]],
axis
=
1
)
return
start_prob
,
transmat
,
means
,
cov
def
trainHMM_fromFile
(
wav_file
,
gt_file
,
hmm_model_name
,
mt_win
,
mt_step
):
'''
This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wav_file: the path of the audio filename
- gt_file: the path of the ground truth filename
(a csv file of the form <segment start in seconds>,<segment end in seconds>,<segment label> in each row
- hmm_model_name: the name of the HMM model to be stored
- mt_win: mid-term window size
- mt_step: mid-term window step
RETURNS:
- hmm: an object to the resulting HMM
- class_names: a list of class_names
After training, hmm, class_names, along with the mt_win and mt_step values are stored in the hmm_model_name file
'''
[
seg_start
,
seg_end
,
seg_labs
]
=
readSegmentGT
(
gt_file
)
flags
,
class_names
=
segs2flags
(
seg_start
,
seg_end
,
seg_labs
,
mt_step
)
[
fs
,
x
]
=
audioBasicIO
.
readAudioFile
(
wav_file
)
[
F
,
_
,
_
]
=
aF
.
mtFeatureExtraction
(
x
,
fs
,
mt_win
*
fs
,
mt_step
*
fs
,
round
(
fs
*
0.050
),
round
(
fs
*
0.050
))
start_prob
,
transmat
,
means
,
cov
=
trainHMM_computeStatistics
(
F
,
flags
)
hmm
=
hmmlearn
.
hmm
.
GaussianHMM
(
start_prob
.
shape
[
0
],
"diag"
)
hmm
.
startprob_
=
start_prob
hmm
.
transmat_
=
transmat
hmm
.
means_
=
means
hmm
.
covars_
=
cov
fo
=
open
(
hmm_model_name
,
"wb"
)
cPickle
.
dump
(
hmm
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
cPickle
.
dump
(
class_names
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
cPickle
.
dump
(
mt_win
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
cPickle
.
dump
(
mt_step
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
fo
.
close
()
return
hmm
,
class_names
def
trainHMM_fromDir
(
dirPath
,
hmm_model_name
,
mt_win
,
mt_step
):
'''
This function trains a HMM model for segmentation-classification using
a where WAV files and .segment (ground-truth files) are stored
ARGUMENTS:
- dirPath: the path of the data diretory
- hmm_model_name: the name of the HMM model to be stored
- mt_win: mid-term window size
- mt_step: mid-term window step
RETURNS:
- hmm: an object to the resulting HMM
- class_names: a list of class_names
After training, hmm, class_names, along with the mt_win
and mt_step values are stored in the hmm_model_name file
'''
flags_all
=
numpy
.
array
([])
classes_all
=
[]
for
i
,
f
in
enumerate
(
glob
.
glob
(
dirPath
+
os
.
sep
+
'*.wav'
)):
# for each WAV file
wav_file
=
f
gt_file
=
f
.
replace
(
'.wav'
,
'.segments'
)
if
not
os
.
path
.
isfile
(
gt_file
):
continue
[
seg_start
,
seg_end
,
seg_labs
]
=
readSegmentGT
(
gt_file
)
flags
,
class_names
=
segs2flags
(
seg_start
,
seg_end
,
seg_labs
,
mt_step
)
for
c
in
class_names
:
# update class names:
if
c
not
in
classes_all
:
classes_all
.
append
(
c
)
[
fs
,
x
]
=
audioBasicIO
.
readAudioFile
(
wav_file
)
[
F
,
_
,
_
]
=
aF
.
mtFeatureExtraction
(
x
,
fs
,
mt_win
*
fs
,
mt_step
*
fs
,
round
(
fs
*
0.050
),
round
(
fs
*
0.050
))
lenF
=
F
.
shape
[
1
]
lenL
=
len
(
flags
)
min_sm
=
min
(
lenF
,
lenL
)
F
=
F
[:,
0
:
min_sm
]
flags
=
flags
[
0
:
min_sm
]
flagsNew
=
[]
for
j
,
fl
in
enumerate
(
flags
):
# append features and labels
flagsNew
.
append
(
classes_all
.
index
(
class_names
[
flags
[
j
]]))
flags_all
=
numpy
.
append
(
flags_all
,
numpy
.
array
(
flagsNew
))
if
i
==
0
:
f_all
=
F
else
:
f_all
=
numpy
.
concatenate
((
f_all
,
F
),
axis
=
1
)
start_prob
,
transmat
,
means
,
cov
=
trainHMM_computeStatistics
(
f_all
,
flags_all
)
# compute HMM statistics
hmm
=
hmmlearn
.
hmm
.
GaussianHMM
(
start_prob
.
shape
[
0
],
"diag"
)
# train HMM
hmm
.
startprob_
=
start_prob
hmm
.
transmat_
=
transmat
hmm
.
means_
=
means
hmm
.
covars_
=
cov
fo
=
open
(
hmm_model_name
,
"wb"
)
# save HMM model
cPickle
.
dump
(
hmm
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
cPickle
.
dump
(
classes_all
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
cPickle
.
dump
(
mt_win
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
cPickle
.
dump
(
mt_step
,
fo
,
protocol
=
cPickle
.
HIGHEST_PROTOCOL
)
fo
.
close
()
return
hmm
,
classes_all
def
hmmSegmentation
(
wav_file_name
,
hmm_model_name
,
plot_res
=
False
,
gt_file_name
=
""
):
[
fs
,
x
]
=
audioBasicIO
.
readAudioFile
(
wav_file_name
)
try
:
fo
=
open
(
hmm_model_name
,
"rb"
)
except
IOError
:
print
(
"didn't find file"
)
return
try
:
hmm
=
cPickle
.
load
(
fo
)
classes_all
=
cPickle
.
load
(
fo
)
mt_win
=
cPickle
.
load
(
fo
)
mt_step
=
cPickle
.
load
(
fo
)
except
:
fo
.
close
()
fo
.
close
()
[
Features
,
_
,
_
]
=
aF
.
mtFeatureExtraction
(
x
,
fs
,
mt_win
*
fs
,
mt_step
*
fs
,
round
(
fs
*
0.050
),
round
(
fs
*
0.050
))
flags_ind
=
hmm
.
predict
(
Features
.
T
)
# apply model
if
os
.
path
.
isfile
(
gt_file_name
):
[
seg_start
,
seg_end
,
seg_labs
]
=
readSegmentGT
(
gt_file_name
)
flags_gt
,
class_names_gt
=
segs2flags
(
seg_start
,
seg_end
,
seg_labs
,
mt_step
)
flagsGTNew
=
[]
for
j
,
fl
in
enumerate
(
flags_gt
):
# "align" labels with GT
if
class_names_gt
[
flags_gt
[
j
]]
in
classes_all
:
flagsGTNew
.
append
(
classes_all
.
index
(
class_names_gt
[
flags_gt
[
j
]]))
else
:
flagsGTNew
.
append
(
-
1
)
cm
=
numpy
.
zeros
((
len
(
classes_all
),
len
(
classes_all
)))
flags_ind_gt
=
numpy
.
array
(
flagsGTNew
)
for
i
in
range
(
min
(
flags_ind
.
shape
[
0
],
flags_ind_gt
.
shape
[
0
])):
cm
[
int
(
flags_ind_gt
[
i
]),
int
(
flags_ind
[
i
])]
+=
1
else
:
flags_ind_gt
=
numpy
.
array
([])
acc
=
plotSegmentationResults
(
flags_ind
,
flags_ind_gt
,
classes_all
,
mt_step
,
not
plot_res
)
if
acc
>=
0
:
print
(
"Overall Accuracy: {0:.2f}"
.
format
(
acc
))
return
(
flags_ind
,
class_names_gt
,
acc
,
cm
)
else
:
return
(
flags_ind
,
classes_all
,
-
1
,
-
1
)
def
mtFileClassification
(
input_file
,
model_name
,
model_type
,
plot_results
=
False
,
gt_file
=
""
):
'''
This function performs mid-term classification of an audio stream.
Towards this end, supervised knowledge is used, i.e. a pre-trained classifier.
ARGUMENTS:
- input_file: path of the input WAV file
- model_name: name of the classification model
- model_type: svm or knn depending on the classifier type
- plot_results: True if results are to be plotted using
matplotlib along with a set of statistics
RETURNS:
- segs: a sequence of segment's endpoints: segs[i] is the
endpoint of the i-th segment (in seconds)
- classes: a sequence of class flags: class[i] is the
class ID of the i-th segment
'''
if
not
os
.
path
.
isfile
(
model_name
):
print
(
"mtFileClassificationError: input model_type not found!"
)
return
(
-
1
,
-
1
,
-
1
,
-
1
)
# Load classifier:
if
model_type
==
"knn"
:
[
classifier
,
MEAN
,
STD
,
class_names
,
mt_win
,
mt_step
,
st_win
,
st_step
,
compute_beat
]
=
\
aT
.
load_model_knn
(
model_name
)
else
:
[
classifier
,
MEAN
,
STD
,
class_names
,
mt_win
,
mt_step
,
st_win
,
st_step
,
compute_beat
]
=
aT
.
load_model
(
model_name
)
if
compute_beat
:
print
(
"Model "
+
model_name
+
" contains long-term music features "
"(beat etc) and cannot be used in "
"segmentation"
)
return
(
-
1
,
-
1
,
-
1
,
-
1
)
[
fs
,
x
]
=
audioBasicIO
.
readAudioFile
(
input_file
)
# load input file
if
fs
==
-
1
:
# could not read file
return
(
-
1
,
-
1
,
-
1
,
-
1
)
x
=
audioBasicIO
.
stereo2mono
(
x
)
# convert stereo (if) to mono
duration
=
len
(
x
)
/
fs
# mid-term feature extraction:
[
mt_feats
,
_
,
_
]
=
aF
.
mtFeatureExtraction
(
x
,
fs
,
mt_win
*
fs
,
mt_step
*
fs
,
round
(
fs
*
st_win
),
round
(
fs
*
st_step
))
flags
=
[]
Ps
=
[]
flags_ind
=
[]
for
i
in
range
(
mt_feats
.
shape
[
1
]):
# for each feature vector (i.e. for each fix-sized segment):
cur_fv
=
(
mt_feats
[:,
i
]
-
MEAN
)
/
STD
# normalize current feature vector
[
res
,
P
]
=
aT
.
classifierWrapper
(
classifier
,
model_type
,
cur_fv
)
# classify vector
flags_ind
.
append
(
res
)
flags
.
append
(
class_names
[
int
(
res
)])
# update class label matrix
Ps
.
append
(
numpy
.
max
(
P
))
# update probability matrix
flags_ind
=
numpy
.
array
(
flags_ind
)
# 1-window smoothing
for
i
in
range
(
1
,
len
(
flags_ind
)
-
1
):
if
flags_ind
[
i
-
1
]
==
flags_ind
[
i
+
1
]:
flags_ind
[
i
]
=
flags_ind
[
i
+
1
]
# convert fix-sized flags to segments and classes
(
segs
,
classes
)
=
flags2segs
(
flags
,
mt_step
)
segs
[
-
1
]
=
len
(
x
)
/
float
(
fs
)
# Load grount-truth:
if
os
.
path
.
isfile
(
gt_file
):
[
seg_start_gt
,
seg_end_gt
,
seg_l_gt
]
=
readSegmentGT
(
gt_file
)
flags_gt
,
class_names_gt
=
segs2flags
(
seg_start_gt
,
seg_end_gt
,
seg_l_gt
,
mt_step
)
flags_ind_gt
=
[]
for
j
,
fl
in
enumerate
(
flags_gt
):
# "align" labels with GT
if
class_names_gt
[
flags_gt
[
j
]]
in
class_names
:
flags_ind_gt
.
append
(
class_names
.
index
(
class_names_gt
[
flags_gt
[
j
]]))
else
:
flags_ind_gt
.
append
(
-
1
)
flags_ind_gt
=
numpy
.
array
(
flags_ind_gt
)
cm
=
numpy
.
zeros
((
len
(
class_names_gt
),
len
(
class_names_gt
)))
for
i
in
range
(
min
(
flags_ind
.
shape
[
0
],
flags_ind_gt
.
shape
[
0
])):
cm
[
int
(
flags_ind_gt
[
i
]),
int
(
flags_ind
[
i
])]
+=
1
else
:
cm
=
[]
flags_ind_gt
=
numpy
.
array
([])
acc
=
plotSegmentationResults
(
flags_ind
,
flags_ind_gt
,
class_names
,
mt_step
,
not
plot_results
)
if
acc
>=
0
:
print
(
"Overall Accuracy: {0:.3f}"
.
format
(
acc
) )
return
(
flags_ind
,
class_names_gt
,
acc
,
cm
)
else
:
return
(
flags_ind
,
class_names
,
acc
,
cm
)
def
evaluateSegmentationClassificationDir
(
dir_name
,
model_name
,
method_name
):
flags_all
=
numpy
.
array
([])
classes_all
=
[]
accuracies
=
[]
for
i
,
f
in
enumerate
(
glob
.
glob
(
dir_name
+
os
.
sep
+
'*.wav'
)):
# for each WAV file
wav_file
=
f
print
(
wav_file
)
gt_file
=
f
.
replace
(
'.wav'
,
'.segments'
)
# open for annotated file
if
method_name
.
lower
()
in
[
"svm"
,
"svm_rbf"
,
"knn"
,
"randomforest"
,
"gradientboosting"
,
"extratrees"
]:
flags_ind
,
class_names
,
acc
,
cm_t
=
\
mtFileClassification
(
wav_file
,
model_name
,
method_name
,
False
,
gt_file
)
else
:
flags_ind
,
class_names
,
acc
,
cm_t
=
hmmSegmentation
(
wav_file
,
model_name
,
False
,
gt_file
)
if
acc
>
-
1
:
if
i
==
0
:
cm
=
numpy
.
copy
(
cm_t
)
else
:
cm
=
cm
+
cm_t
accuracies
.
append
(
acc
)
print
(
cm_t
,
class_names
)
print
(
cm
)
[
rec
,
pre
,
f1
]
=
computePreRec
(
cm_t
,
class_names
)
cm
=
cm
/
numpy
.
sum
(
cm
)
[
rec
,
pre
,
f1
]
=
computePreRec
(
cm
,
class_names
)
print
(
" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - "
)
print
(
"Average Accuracy: {0:.1f}"
.
format
(
100.0
*
numpy
.
array
(
accuracies
).
mean
()))
print
(
"Average recall: {0:.1f}"
.
format
(
100.0
*
numpy
.
array
(
rec
).
mean
()))
print
(
"Average precision: {0:.1f}"
.
format
(
100.0
*
numpy
.
array
(
pre
).
mean
()))
print
(
"Average f1: {0:.1f}"
.
format
(
100.0
*
numpy
.
array
(
f1
).
mean
()))
print
(
"Median Accuracy: {0:.1f}"
.
format
(
100.0
*
numpy
.
median
(
numpy
.
array
(
accuracies
))))
print
(
"Min Accuracy: {0:.1f}"
.
format
(
100.0
*
numpy
.
array
(
accuracies
).
min
()))
print
(
"Max Accuracy: {0:.1f}"
.
format
(
100.0
*
numpy
.
array
(
accuracies
).
max
()))
def
silenceRemoval
(
x
,
fs
,
st_win
,
st_step
,
smoothWindow
=
0.5
,
weight
=
0.5
,
plot
=
False
):
'''
Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- fs: sampling freq
- st_win, st_step: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- weight: (optinal) weight factor (0 < weight < 1) the higher, the more strict
- plot: (optinal) True if results are to be plotted
RETURNS:
- seg_limits: list of segment limits in seconds (e.g [[0.1, 0.9], [1.4, 3.0]] means that
the resulting segments are (0.1 - 0.9) seconds and (1.4, 3.0) seconds
'''
if
weight
>=
1
:
weight
=
0.99
if
weight
<=
0
:
weight
=
0.01
# Step 1: feature extraction
x
=
audioBasicIO
.
stereo2mono
(
x
)
st_feats
,
_
=
aF
.
stFeatureExtraction
(
x
,
fs
,
st_win
*
fs
,
st_step
*
fs
)
# Step 2: train binary svm classifier of low vs high energy frames
# keep only the energy short-term sequence (2nd feature)
st_energy
=
st_feats
[
1
, :]
en
=
numpy
.
sort
(
st_energy
)
# number of 10% of the total short-term windows
l1
=
int
(
len
(
en
)
/
10
)
# compute "lower" 10% energy threshold
t1
=
numpy
.
mean
(
en
[
0
:
l1
])
+
0.000000000000001
# compute "higher" 10% energy threshold
t2
=
numpy
.
mean
(
en
[
-
l1
:
-
1
])
+
0.000000000000001
# get all features that correspond to low energy
class1
=
st_feats
[:,
numpy
.
where
(
st_energy
<=
t1
)[
0
]]
# get all features that correspond to high energy
class2
=
st_feats
[:,
numpy
.
where
(
st_energy
>=
t2
)[
0
]]
# form the binary classification task and ...
faets_s
=
[
class1
.
T
,
class2
.
T
]
# normalize and train the respective svm probabilistic model
# (ONSET vs SILENCE)
[
faets_s_norm
,
means_s
,
stds_s
]
=
aT
.
normalizeFeatures
(
faets_s
)
svm
=
aT
.
trainSVM
(
faets_s_norm
,
1.0
)
# Step 3: compute onset probability based on the trained svm
prob_on_set
=
[]
for
i
in
range
(
st_feats
.
shape
[
1
]):
# for each frame
cur_fv
=
(
st_feats
[:,
i
]
-
means_s
)
/
stds_s
# get svm probability (that it belongs to the ONSET class)
prob_on_set
.
append
(
svm
.
predict_proba
(
cur_fv
.
reshape
(
1
,
-
1
))[
0
][
1
])
prob_on_set
=
numpy
.
array
(
prob_on_set
)
# smooth probability:
prob_on_set
=
smoothMovingAvg
(
prob_on_set
,
smoothWindow
/
st_step
)
# Step 4A: detect onset frame indices:
prog_on_set_sort
=
numpy
.
sort
(
prob_on_set
)
# find probability Threshold as a weighted average
# of top 10% and lower 10% of the values
Nt
=
int
(
prog_on_set_sort
.
shape
[
0
]
/
10
)
T
=
(
numpy
.
mean
((
1
-
weight
)
*
prog_on_set_sort
[
0
:
Nt
])
+
weight
*
numpy
.
mean
(
prog_on_set_sort
[
-
Nt
::]))
max_idx
=
numpy
.
where
(
prob_on_set
>
T
)[
0
]
# get the indices of the frames that satisfy the thresholding
i
=
0
time_clusters
=
[]
seg_limits
=
[]
# Step 4B: group frame indices to onset segments
while
i
<
len
(
max_idx
):
# for each of the detected onset indices
cur_cluster
=
[
max_idx
[
i
]]
if
i
==
len
(
max_idx
)
-
1
:
break
while
max_idx
[
i
+
1
]
-
cur_cluster
[
-
1
]
<=
2
:
cur_cluster
.
append
(
max_idx
[
i
+
1
])
i
+=
1
if
i
==
len
(
max_idx
)
-
1
:
break
i
+=
1
time_clusters
.
append
(
cur_cluster
)
seg_limits
.
append
([
cur_cluster
[
0
]
*
st_step
,
cur_cluster
[
-
1
]
*
st_step
])
# Step 5: Post process: remove very small segments:
min_dur
=
0.2
seg_limits_2
=
[]
for
s
in
seg_limits
:
if
s
[
1
]
-
s
[
0
]
>
min_dur
:
seg_limits_2
.
append
(
s
)
seg_limits
=
seg_limits_2
if
plot
:
timeX
=
numpy
.
arange
(
0
,
x
.
shape
[
0
]
/
float
(
fs
),
1.0
/
fs
)
plt
.
subplot
(
2
,
1
,
1
)
plt
.
plot
(
timeX
,
x
)
for
s
in
seg_limits
:
plt
.
axvline
(
x
=
s
[
0
])
plt
.
axvline
(
x
=
s
[
1
])
plt
.
subplot
(
2
,
1
,
2
)
plt
.
plot
(
numpy
.
arange
(
0
,
prob_on_set
.
shape
[
0
]
*
st_step
,
st_step
),
prob_on_set
)
plt
.
title
(
'Signal'
)
for
s
in
seg_limits
:
plt
.
axvline
(
x
=
s
[
0
])
plt
.
axvline
(
x
=
s
[
1
])
plt
.
title
(
'svm Probability'
)
plt
.
show
()
return
seg_limits
def
speakerDiarization
(
filename
,
n_speakers
,
mt_size
=
2.0
,
mt_step
=
0.2
,
st_win
=
0.05
,
lda_dim
=
35
,
plot_res
=
False
):
'''
ARGUMENTS:
- filename: the name of the WAV file to be analyzed
- n_speakers the number of speakers (clusters) in the recording (<=0 for unknown)
- mt_size (opt) mid-term window size
- mt_step (opt) mid-term window step
- st_win (opt) short-term window size
- lda_dim (opt) LDA dimension (0 for no LDA)
- plot_res (opt) 0 for not plotting the results 1 for plottingy
'''
[
fs
,
x
]
=
audioBasicIO
.
readAudioFile
(
filename
)
x
=
audioBasicIO
.
stereo2mono
(
x
)
duration
=
len
(
x
)
/
fs
[
classifier_1
,
MEAN1
,
STD1
,
classNames1
,
mtWin1
,
mtStep1
,
stWin1
,
stStep1
,
computeBEAT1
]
=
aT
.
load_model_knn
(
os
.
path
.
join
(
os
.
path
.
dirname
(
os
.
path
.
realpath
(
__file__
)),
"data"
,
"knnSpeakerAll"
))
[
classifier_2
,
MEAN2
,
STD2
,
classNames2
,
mtWin2
,
mtStep2
,
stWin2
,
stStep2
,
computeBEAT2
]
=
aT
.
load_model_knn
(
os
.
path
.
join
(
os
.
path
.
dirname
(
os
.
path
.
realpath
(
__file__
)),
"data"
,
"knnSpeakerFemaleMale"
))
[
mt_feats
,
st_feats
,
_
]
=
aF
.
mtFeatureExtraction
(
x
,
fs
,
mt_size
*
fs
,
mt_step
*
fs
,
round
(
fs
*
st_win
),
round
(
fs
*
st_win
*
0.5
))
MidTermFeatures2
=
numpy
.
zeros
((
mt_feats
.
shape
[
0
]
+
len
(
classNames1
)
+
len
(
classNames2
),
mt_feats
.
shape
[
1
]))
for
i
in
range
(
mt_feats
.
shape
[
1
]):
cur_f1
=
(
mt_feats
[:,
i
]
-
MEAN1
)
/
STD1
cur_f2
=
(
mt_feats
[:,
i
]
-
MEAN2
)
/
STD2
[
res
,
P1
]
=
aT
.
classifierWrapper
(
classifier_1
,
"knn"
,
cur_f1
)
[
res
,
P2
]
=
aT
.
classifierWrapper
(
classifier_2
,
"knn"
,
cur_f2
)
MidTermFeatures2
[
0
:
mt_feats
.
shape
[
0
],
i
]
=
mt_feats
[:,
i
]
MidTermFeatures2
[
mt_feats
.
shape
[
0
]:
mt_feats
.
shape
[
0
]
+
len
(
classNames1
),
i
]
=
P1
+
0.0001
MidTermFeatures2
[
mt_feats
.
shape
[
0
]
+
len
(
classNames1
)::,
i
]
=
P2
+
0.0001
mt_feats
=
MidTermFeatures2
# TODO
iFeaturesSelect
=
[
8
,
9
,
10
,
11
,
12
,
13
,
14
,
15
,
16
,
17
,
18
,
19
,
20
,
41
,
42
,
43
,
44
,
45
,
46
,
47
,
48
,
49
,
50
,
51
,
52
,
53
]
mt_feats
=
mt_feats
[
iFeaturesSelect
, :]
(
mt_feats_norm
,
MEAN
,
STD
)
=
aT
.
normalizeFeatures
([
mt_feats
.
T
])
mt_feats_norm
=
mt_feats_norm
[
0
].
T
n_wins
=
mt_feats
.
shape
[
1
]
# remove outliers:
dist_all
=
numpy
.
sum
(
distance
.
squareform
(
distance
.
pdist
(
mt_feats_norm
.
T
)),
axis
=
0
)
m_dist_all
=
numpy
.
mean
(
dist_all
)
i_non_outliers
=
numpy
.
nonzero
(
dist_all
<
1.2
*
m_dist_all
)[
0
]
# TODO: Combine energy threshold for outlier removal:
#EnergyMin = numpy.min(mt_feats[1,:])
#EnergyMean = numpy.mean(mt_feats[1,:])
#Thres = (1.5*EnergyMin + 0.5*EnergyMean) / 2.0
#i_non_outliers = numpy.nonzero(mt_feats[1,:] > Thres)[0]
#print i_non_outliers
perOutLier
=
(
100.0
*
(
n_wins
-
i_non_outliers
.
shape
[
0
]))
/
n_wins
mt_feats_norm_or
=
mt_feats_norm
mt_feats_norm
=
mt_feats_norm
[:,
i_non_outliers
]
# LDA dimensionality reduction:
if
lda_dim
>
0
:
#[mt_feats_to_red, _, _] = aF.mtFeatureExtraction(x, fs, mt_size * fs, st_win * fs, round(fs*st_win), round(fs*st_win));
# extract mid-term features with minimum step:
mt_win_ratio
=
int
(
round
(
mt_size
/
st_win
))
mt_step_ratio
=
int
(
round
(
st_win
/
st_win
))
mt_feats_to_red
=
[]
num_of_features
=
len
(
st_feats
)
num_of_stats
=
2
#for i in range(num_of_stats * num_of_features + 1):
for
i
in
range
(
num_of_stats
*
num_of_features
):
mt_feats_to_red
.
append
([])
for
i
in
range
(
num_of_features
):
# for each of the short-term features:
curPos
=
0
N
=
len
(
st_feats
[
i
])
while
(
curPos
<
N
):
N1
=
curPos
N2
=
curPos
+
mt_win_ratio
if
N2
>
N
:
N2
=
N
curStFeatures
=
st_feats
[
i
][
N1
:
N2
]
mt_feats_to_red
[
i
].
append
(
numpy
.
mean
(
curStFeatures
))
mt_feats_to_red
[
i
+
num_of_features
].
append
(
numpy
.
std
(
curStFeatures
))
curPos
+=
mt_step_ratio
mt_feats_to_red
=
numpy
.
array
(
mt_feats_to_red
)
mt_feats_to_red_2
=
numpy
.
zeros
((
mt_feats_to_red
.
shape
[
0
]
+
len
(
classNames1
)
+
len
(
classNames2
),
mt_feats_to_red
.
shape
[
1
]))
for
i
in
range
(
mt_feats_to_red
.
shape
[
1
]):
cur_f1
=
(
mt_feats_to_red
[:,
i
]
-
MEAN1
)
/
STD1
cur_f2
=
(
mt_feats_to_red
[:,
i
]
-
MEAN2
)
/
STD2
[
res
,
P1
]
=
aT
.
classifierWrapper
(
classifier_1
,
"knn"
,
cur_f1
)
[
res
,
P2
]
=
aT
.
classifierWrapper
(
classifier_2
,
"knn"
,
cur_f2
)
mt_feats_to_red_2
[
0
:
mt_feats_to_red
.
shape
[
0
],
i
]
=
mt_feats_to_red
[:,
i
]
mt_feats_to_red_2
[
mt_feats_to_red
.
shape
[
0
]:
mt_feats_to_red
.
shape
[
0
]
+
len
(
classNames1
),
i
]
=
P1
+
0.0001
mt_feats_to_red_2
[
mt_feats_to_red
.
shape
[
0
]
+
len
(
classNames1
)::,
i
]
=
P2
+
0.0001
mt_feats_to_red
=
mt_feats_to_red_2
mt_feats_to_red
=
mt_feats_to_red
[
iFeaturesSelect
, :]
#mt_feats_to_red += numpy.random.rand(mt_feats_to_red.shape[0], mt_feats_to_red.shape[1]) * 0.0000010
(
mt_feats_to_red
,
MEAN
,
STD
)
=
aT
.
normalizeFeatures
([
mt_feats_to_red
.
T
])
mt_feats_to_red
=
mt_feats_to_red
[
0
].
T
#dist_all = numpy.sum(distance.squareform(distance.pdist(mt_feats_to_red.T)), axis=0)
#m_dist_all = numpy.mean(dist_all)
#iNonOutLiers2 = numpy.nonzero(dist_all < 3.0*m_dist_all)[0]
#mt_feats_to_red = mt_feats_to_red[:, iNonOutLiers2]
Labels
=
numpy
.
zeros
((
mt_feats_to_red
.
shape
[
1
], ));
LDAstep
=
1.0
LDAstepRatio
=
LDAstep
/
st_win
#print LDAstep, LDAstepRatio
for
i
in
range
(
Labels
.
shape
[
0
]):
Labels
[
i
]
=
int
(
i
*
st_win
/
LDAstepRatio
);
clf
=
sklearn
.
discriminant_analysis
.
LinearDiscriminantAnalysis
(
n_components
=
lda_dim
)
clf
.
fit
(
mt_feats_to_red
.
T
,
Labels
)
mt_feats_norm
=
(
clf
.
transform
(
mt_feats_norm
.
T
)).
T
if
n_speakers
<=
0
:
s_range
=
range
(
2
,
10
)
else
:
s_range
=
[
n_speakers
]
clsAll
=
[]
sil_all
=
[]
centersAll
=
[]
for
iSpeakers
in
s_range
:
k_means
=
sklearn
.
cluster
.
KMeans
(
n_clusters
=
iSpeakers
)
k_means
.
fit
(
mt_feats_norm
.
T
)
cls
=
k_means
.
labels_
means
=
k_means
.
cluster_centers_
# Y = distance.squareform(distance.pdist(mt_feats_norm.T))
clsAll
.
append
(
cls
)
centersAll
.
append
(
means
)
sil_1
=
[];
sil_2
=
[]
for
c
in
range
(
iSpeakers
):
# for each speaker (i.e. for each extracted cluster)
clust_per_cent
=
numpy
.
nonzero
(
cls
==
c
)[
0
].
shape
[
0
]
/
\
float
(
len
(
cls
))
if
clust_per_cent
<
0.020
:
sil_1
.
append
(
0.0
)
sil_2
.
append
(
0.0
)
else
:
# get subset of feature vectors
mt_feats_norm_temp
=
mt_feats_norm
[:,
cls
==
c
]
# compute average distance between samples
# that belong to the cluster (a values)
Yt
=
distance
.
pdist
(
mt_feats_norm_temp
.
T
)
sil_1
.
append
(
numpy
.
mean
(
Yt
)
*
clust_per_cent
)
silBs
=
[]
for
c2
in
range
(
iSpeakers
):
# compute distances from samples of other clusters
if
c2
!=
c
:
clust_per_cent_2
=
numpy
.
nonzero
(
cls
==
c2
)[
0
].
shape
[
0
]
/
\
float
(
len
(
cls
))
MidTermFeaturesNormTemp2
=
mt_feats_norm
[:,
cls
==
c2
]
Yt
=
distance
.
cdist
(
mt_feats_norm_temp
.
T
,
MidTermFeaturesNormTemp2
.
T
)
silBs
.
append
(
numpy
.
mean
(
Yt
)
*
(
clust_per_cent
+
clust_per_cent_2
)
/
2.0
)
silBs
=
numpy
.
array
(
silBs
)
# ... and keep the minimum value (i.e.
# the distance from the "nearest" cluster)
sil_2
.
append
(
min
(
silBs
))
sil_1
=
numpy
.
array
(
sil_1
);
sil_2
=
numpy
.
array
(
sil_2
);
sil
=
[]
for
c
in
range
(
iSpeakers
):
# for each cluster (speaker) compute silhouette
sil
.
append
( (
sil_2
[
c
]
-
sil_1
[
c
])
/
(
max
(
sil_2
[
c
],
sil_1
[
c
])
+
0.00001
))
# keep the AVERAGE SILLOUETTE
sil_all
.
append
(
numpy
.
mean
(
sil
))
imax
=
numpy
.
argmax
(
sil_all
)
# optimal number of clusters
nSpeakersFinal
=
s_range
[
imax
]
# generate the final set of cluster labels
# (important: need to retrieve the outlier windows:
# this is achieved by giving them the value of their
# nearest non-outlier window)
cls
=
numpy
.
zeros
((
n_wins
,))
for
i
in
range
(
n_wins
):
j
=
numpy
.
argmin
(
numpy
.
abs
(
i
-
i_non_outliers
))
cls
[
i
]
=
clsAll
[
imax
][
j
]
# Post-process method 1: hmm smoothing
for
i
in
range
(
1
):
# hmm training
start_prob
,
transmat
,
means
,
cov
=
\
trainHMM_computeStatistics
(
mt_feats_norm_or
,
cls
)
hmm
=
hmmlearn
.
hmm
.
GaussianHMM
(
start_prob
.
shape
[
0
],
"diag"
)
hmm
.
startprob_
=
start_prob
hmm
.
transmat_
=
transmat
hmm
.
means_
=
means
;
hmm
.
covars_
=
cov
cls
=
hmm
.
predict
(
mt_feats_norm_or
.
T
)
# Post-process method 2: median filtering:
cls
=
scipy
.
signal
.
medfilt
(
cls
,
13
)
cls
=
scipy
.
signal
.
medfilt
(
cls
,
11
)
sil
=
sil_all
[
imax
]
class_names
=
[
"speaker{0:d}"
.
format
(
c
)
for
c
in
range
(
nSpeakersFinal
)];
# load ground-truth if available
gt_file
=
filename
.
replace
(
'.wav'
,
'.segments'
)
# if groundturh exists
if
os
.
path
.
isfile
(
gt_file
):
[
seg_start
,
seg_end
,
seg_labs
]
=
readSegmentGT
(
gt_file
)
flags_gt
,
class_names_gt
=
segs2flags
(
seg_start
,
seg_end
,
seg_labs
,
mt_step
)
if
plot_res
:
fig
=
plt
.
figure
()
if
n_speakers
>
0
:
ax1
=
fig
.
add_subplot
(
111
)
else
:
ax1
=
fig
.
add_subplot
(
211
)
ax1
.
set_yticks
(
numpy
.
array
(
range
(
len
(
class_names
))))
ax1
.
axis
((
0
,
duration
,
-
1
,
len
(
class_names
)))
ax1
.
set_yticklabels
(
class_names
)
ax1
.
plot
(
numpy
.
array
(
range
(
len
(
cls
)))
*
mt_step
+
mt_step
/
2.0
,
cls
)
if
os
.
path
.
isfile
(
gt_file
):
if
plot_res
:
ax1
.
plot
(
numpy
.
array
(
range
(
len
(
flags_gt
)))
*
mt_step
+
mt_step
/
2.0
,
flags_gt
,
'r'
)
purity_cluster_m
,
purity_speaker_m
=
\
evaluateSpeakerDiarization
(
cls
,
flags_gt
)
print
(
"{0:.1f}
\t
{1:.1f}"
.
format
(
100
*
purity_cluster_m
,
100
*
purity_speaker_m
))
if
plot_res
:
plt
.
title
(
"Cluster purity: {0:.1f}% - "
"Speaker purity: {1:.1f}%"
.
format
(
100
*
purity_cluster_m
,
100
*
purity_speaker_m
))
if
plot_res
:
plt
.
xlabel
(
"time (seconds)"
)
#print s_range, sil_all
if
n_speakers
<=
0
:
plt
.
subplot
(
212
)
plt
.
plot
(
s_range
,
sil_all
)
plt
.
xlabel
(
"number of clusters"
);
plt
.
ylabel
(
"average clustering's sillouette"
);
plt
.
show
()
return
cls
def
speakerDiarizationEvaluateScript
(
folder_name
,
ldas
):
'''
This function prints the cluster purity and speaker purity for
each WAV file stored in a provided directory (.SEGMENT files
are needed as ground-truth)
ARGUMENTS:
- folder_name: the full path of the folder where the WAV and
SEGMENT (ground-truth) files are stored
- ldas: a list of LDA dimensions (0 for no LDA)
'''
types
=
(
'*.wav'
, )
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