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Anthony Larcher
sidekit
Commits
2a3b74d2
Commit
2a3b74d2
authored
Mar 31, 2021
by
Anthony Larcher
Browse files
add noise
parent
c58d2d5f
Changes
2
Hide whitespace changes
Inline
Side-by-side
nnet/augmentation.py
View file @
2a3b74d2
...
...
@@ -489,8 +489,9 @@ def data_augmentation(speech,
speech
=
torch
.
nn
.
functional
.
conv1d
(
speech_
[
None
,
...],
rir
[
None
,
...])[
0
]
if
"add_noise"
in
augmentations
:
# Pick a noise sample from the noise_df
noise_row
=
noise_df
.
iloc
[
random
.
randrange
(
noise_df
.
shape
[
0
])]
# Pick a noise type
noise
=
torch
.
zeros_like
(
speech
)
noise_idx
=
random
.
randrange
(
3
)
# speech
if
noise_idx
==
0
:
...
...
nnet/xvector.py
View file @
2a3b74d2
...
...
@@ -202,7 +202,10 @@ def eer(negatives, positives):
def
test_metrics
(
model
,
device
,
speaker_number
,
idmap_test_filename
,
ndx_test_filename
,
key_test_filename
,
data_root_name
,
num_thread
,
mixed_precision
):
"""Compute model metrics
...
...
@@ -221,10 +224,10 @@ def test_metrics(model,
Returns:
[type]: [description]
"""
idmap_test_filename
=
'h5f/idmap_test.h5'
ndx_test_filename
=
'h5f/ndx_test.h5'
key_test_filename
=
'h5f/key_test.h5'
data_root_name
=
'/lium/scratch/larcher/voxceleb1/test/wav'
#
idmap_test_filename = 'h5f/idmap_test.h5'
#
ndx_test_filename = 'h5f/ndx_test.h5'
#
key_test_filename = 'h5f/key_test.h5'
#
data_root_name='/lium/scratch/larcher/voxceleb1/test/wav'
transform_pipeline
=
dict
()
...
...
@@ -837,7 +840,6 @@ def xtrain(speaker_number,
new_model_dict
.
update
(
pretrained_dict
)
model
.
load_state_dict
(
new_model_dict
)
# Freeze required layers
for
name
,
param
in
model
.
named_parameters
():
if
name
.
split
(
"."
)[
0
]
in
freeze_parts
:
...
...
@@ -980,7 +982,7 @@ def xtrain(speaker_number,
elif
opt
==
'rmsprop'
:
_optimizer
=
torch
.
optim
.
RMSprop
_options
=
{
'lr'
:
lr
}
else
:
# opt == 'sgd'
else
:
# opt == 'sgd'
_optimizer
=
torch
.
optim
.
SGD
_options
=
{
'lr'
:
lr
,
'momentum'
:
0.9
}
...
...
@@ -1002,9 +1004,9 @@ def xtrain(speaker_number,
param_list
.
append
({
'params'
:
model
.
module
.
after_speaker_embedding
.
parameters
(),
'weight_decay'
:
model
.
module
.
after_speaker_embedding_weight_decay
})
optimizer
=
_optimizer
(
param_list
,
**
_options
)
scheduler
=
scheduler
=
torch
.
optim
.
lr_scheduler
.
MultiStepLR
(
optimizer
,
milestones
=
[
10000
,
50000
,
100000
],
gamma
=
0.5
)
scheduler
=
torch
.
optim
.
lr_scheduler
.
MultiStepLR
(
optimizer
,
milestones
=
[
10000
,
50000
,
100000
],
gamma
=
0.5
)
if
mixed_precision
:
scaler
=
torch
.
cuda
.
amp
.
GradScaler
()
...
...
@@ -1047,7 +1049,15 @@ def xtrain(speaker_number,
logging
.
critical
(
f
"***
{
time
.
strftime
(
'%H
:
%
M
:
%
S
', time.localtime())
}
Training metrics - Cross validation accuracy =
{
val_acc
}
%, EER =
{
val_eer
*
100
}
%"
)
if
compute_test_eer
:
test_eer
=
test_metrics
(
model
,
device
,
speaker_number
,
num_thread
,
mixed_precision
)
test_eer
=
test_metrics
(
model
,
device
,
idmap_test_filename
=
dataset_params
[
"test_set"
][
"idmap_test_filename"
],
ndx_test_filename
=
dataset_params
[
"test_set"
][
"ndx_test_filename"
],
key_test_filename
=
dataset_params
[
"test_set"
][
"key_test_filename"
],
data_root_name
=
dataset_params
[
"test_set"
][
"data_root_name"
],
num_thread
=
num_thread
,
mixed_precision
=
mixed_precision
)
logging
.
critical
(
f
"***
{
time
.
strftime
(
'%H
:
%
M
:
%
S
', time.localtime())
}
Training metrics - Test EER =
{
test_eer
*
100
}
%"
)
# remember best accuracy and save checkpoint
...
...
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