Commit b9dd2052 authored by Félix Michaud's avatar Félix Michaud

spencer comparaison

parent 35171878
num_mix2
batch0
save_per_batchs500
out_threshold0.5
audLen24000
audRate48000
stft_frame1022
stft_hop256
beta10.9
modetrain
path./Article/training3
augment
species['crow', 'eastern_wood_pewee']
name_classes['crow', 'eastern_wood_pewee']
lr_sound0.001
batch_size16
devicecuda:0
starting_training_time1569422085.0147443
nb_classes2
lr_sounds1e-05
_augmentUnet5 output for 2 species_base de donnees9species_for 3s training audio_3alls en noise avec pitch shifting et time stretch(0.7-1.3), -10,0 SNR pour natural noise et 3,50 pour gaussian noise
This diff is collapsed.
num_mix2
batch0
save_per_batchs500
out_threshold0.5
audLen24000
audRate48000
stft_frame1022
stft_hop256
beta10.9
modetrain
path./Article/training4
augment
species['crow', 'eastern_wood_pewee']
name_classes['crow', 'eastern_wood_pewee']
lr_sound0.001
batch_size16
devicecuda:0
starting_training_time1569422126.3140655
nb_classes2
lr_sounds1e-05
_augmentUnet5 output for 2 species_base de donnees9species_for 3s training audio_4alls en noise avec pitch shifting et time stretch(0.7-1.3), -10,0 SNR pour natural noise et 3,50 pour gaussian noise
This diff is collapsed.
num_mix2
batch0
save_per_batchs500
out_threshold0.5
audLen24000
audRate48000
stft_frame1022
stft_hop256
beta10.9
modetrain
path./Article/training5
augment
species['crow', 'eastern_wood_pewee']
name_classes['crow', 'eastern_wood_pewee']
lr_sound0.001
batch_size16
devicecuda:0
starting_training_time1569422239.9374652
nb_classes2
lr_sounds1e-05
_augmentUnet5 output for 2 species_base de donnees9species_for 3s training audio_5alls en noise avec pitch shifting et time stretch(0.7-1.3), -10,0 SNR pour natural noise et 3,50 pour gaussian noise
This diff is collapsed.
This diff is collapsed.
......@@ -143,8 +143,10 @@ if __name__ == '__main__':
args.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
args.starting_training_time = time.time()
args.save_per_batchs = 500
args.nb_classes = 3
args.name_classes = ['crow', 'eastern_wood_pewee', 'flicker']
#nb of class to train the net on
args.nb_classes = 2
#names of the species the net is training on
args.name_classes = ['crow', 'eastern_wood_pewee']
args.mode = 'train'
args.lr_sounds = 1e-5
#model definition
......@@ -153,8 +155,9 @@ if __name__ == '__main__':
net = net.to(args.device)
# Set up optimizer
optimizer = create_optimizer(net, args)
args.path = "./Article/shift_stretch12"
args._augment = 'Unet5_3masks output for 3 species_base de donnees9species_for 3s training audio_2calls en noise avec pitch shifting et time stretch(0.8-1.2), -10,0 SNR pour natural noise et 3,50 pour gaussian noise'
#path to the repertory where to save everything
args.path = "./Article/spencer_modif"
args._augment = 'Unet5 output for 2 species_base de donnees10species_for 3s training audio_3calls en noise avec pitch shifting et time stretch(0.7-1.3), -10,0 SNR pour natural noise et 3,50 pour gaussian noise avec comparaison mag et mag noise pour mask'
###########################################################
################### TRAINING ##############################
###########################################################
......@@ -162,10 +165,14 @@ if __name__ == '__main__':
#OverWrite the Files for loss saving and time saving
fichierLoss = open(args.path+"/loss_times.csv", "w")
fichierLoss.close()
#Dataset loading
root = './data_sound/trainset9/'
#Dataset loading of the bird calls for n different species
#the diversity in the bird call used as noise will make the task more
# complicated for the network
root = './data_sound/trainset10/'
ext = '.wav'
train_classes = Dataset(root, name_classes=args.name_classes, nb_class_noise =2, path_background="./data_sound/noises/")
#path_background is the repertory for the noise we put in the
#background of all the bird calls
train_classes = Dataset(root, name_classes=args.name_classes, nb_class_noise =3, path_background="./data_sound/noises/")
loader_train = torch.utils.data.DataLoader(
train_classes,
batch_size = args.batch_size,
......
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