if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
TIMIT_lst = TIMITDataProvider.load_speaker_list(
ds_dir + 'datasets/TIMIT/traininglist_100/testlist_400.txt')
dp = TIMITDataProvider(
# data_dir=top_dir + "/test/TIMIT_mini", cache_directory=top_dir + "/test/cache",
data_dir=top_dir + "/TIMIT",
cache_directory=top_dir + "/test/cache",
min_cluster_count=1,
max_cluster_count=15,
return_1d_audio_data=False,
test_classes=TIMIT_lst[:200],
validate_classes=TIMIT_lst[200:],
concat_audio_files_of_speaker=True,
# Create at least two 1s snippets per speaker and create also some hints
window_width=[(100, 100), (200, 200), (300, 300), (400, 400)],
minimum_snippets_per_cluster=[(100, 100), (100, 100)],
split_audio_pieces_longer_than_and_create_hints=100)
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
TIMIT_lst = TIMITDataProvider.load_speaker_list(
ds_dir + 'datasets/TIMIT/traininglist_100/testlist_20.txt')
dp = TIMITDataProvider(
# data_dir=top_dir + "/test/TIMIT_mini", cache_directory=top_dir + "/test/cache",
data_dir=top_dir + "/TIMIT",
cache_directory=top_dir + "/test/cache",
min_cluster_count=1,
max_cluster_count=5,
return_1d_audio_data=False,
test_classes=TIMIT_lst,
validate_classes=TIMIT_lst,
concat_audio_files_of_speaker=True)
en = CnnEmbedding(output_size=256,
cnn_layers_per_block=1,
block_feature_counts=[32, 64],
fc_layer_feature_counts=[],
hidden_activation=LeakyReLU(),
# found, but some of them may be empty
from sys import platform
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.data.image.facescrub_data_provider import FaceScrubDataProvider
from impl.data.image.birds200_data_provider import Birds200DataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT_gpulab/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
rnd = Random()
rnd.seed(1729)
TIMIT_lst = TIMITDataProvider.load_speaker_list(
ds_dir + 'datasets/TIMIT/traininglist_100/testlist_200.txt')
rnd.shuffle(TIMIT_lst)
p = Augmentor.Pipeline()
p.random_distortion(probability=1,
grid_width=4,
grid_height=4,
magnitude=8)
p.flip_left_right(probability=0.5)
# p.flip_top_bottom(probability=0.5)
# p.rotate90(probability=0.5)
# p.rotate270(probability=0.5)
dp = FaceScrubDataProvider(
top_dir + '/facescrub_128x128',
min_cluster_count=1,
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.image.mnist_data_provider import MNISTDataProvider
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
speaker_list = TIMITDataProvider.load_speaker_list(ds_dir + 'datasets/TIMIT/traininglist_100/testlist_20.txt')[:10]
dp = TIMITDataProvider(
# data_dir=top_dir + "/test/TIMIT_mini", cache_directory=top_dir + "/test/cache",
data_dir=top_dir + "/TIMIT", cache_directory=top_dir + "/test/cache",
min_cluster_count=10,
max_cluster_count=10,
return_1d_audio_data=False,
train_classes=speaker_list,
test_classes=speaker_list,
validate_classes=speaker_list,
concat_audio_files_of_speaker=True
)
en = CnnEmbedding(
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
TIMIT_lst = TIMITDataProvider.load_speaker_list(ds_dir + 'datasets/TIMIT/traininglist_100/testlist_200.txt')
# Shuffle the speakers
random.seed(1)
random.shuffle(TIMIT_lst)
# Only use the first 20 speakers
TIMIT_lst = TIMIT_lst[:20]
dp = TIMITDataProvider(
data_dir=top_dir + "/TIMIT", cache_directory=top_dir + "/test/cache",
# data_dir=top_dir + "/test/TIMIT_mini", cache_directory=top_dir + "/test/cache",
return_1d_audio_data=False,
train_classes=TIMIT_lst,
test_classes=TIMIT_lst,
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
TIMIT20_lst = [
'MTDB0', 'FCMG0', 'MABW0', 'MWEM0', 'MTLS0', 'MMAM0', 'MTJU0', 'FECD0',
'FVMH0', 'MDCD0', 'MJPG0', 'MRSP0', 'MRFK0', 'FCAU0', 'MRCG0', 'MRKM0',
'MPRT0', 'MCTT0', 'FEME0', 'MCRE0'
]
dp = TIMITDataProvider(
# data_dir=top_dir + "/test/TIMIT_mini", cache_directory=top_dir + "/test/cache",
data_dir=top_dir + "/TIMIT",
cache_directory=top_dir + "/test/cache",
min_cluster_count=1,
max_cluster_count=5,
return_1d_audio_data=False,
test_classes=TIMIT20_lst,
validate_classes=TIMIT20_lst,
concat_audio_files_of_speaker=True)
en = CnnEmbedding(output_size=32,
cnn_layers_per_block=1,
block_feature_counts=[32, 64],
fc_layer_feature_counts=[],
hidden_activation=LeakyReLU(),
final_activation='tanh',
batch_norm_for_init_layer=True)
c_nn = ClusterNNTry00_V16(dp,
20,
en,
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
TIMIT_lst = TIMITDataProvider.load_speaker_list(
ds_dir + 'datasets/TIMIT/traininglist_100/testlist_200.txt')
dp = TIMITDataProvider(
# data_dir=top_dir + "/test/TIMIT_mini", cache_directory=top_dir + "/test/cache",
data_dir=top_dir + "/TIMIT",
cache_directory=top_dir + "/test/cache",
min_cluster_count=1,
max_cluster_count=5,
return_1d_audio_data=True,
test_classes=TIMIT_lst,
validate_classes=TIMIT_lst,
concat_audio_files_of_speaker=True,
minimum_snippets_per_cluster=[(200, 200), (100, 100)],
window_width=[(100, 200)])
en = CnnEmbedding(output_size=256,
cnn_layers_per_block=1,
block_feature_counts=[64, 128, 256],
