def main():
#Train settings
wandb.init(project="vgg_triplet")
global args, best_acc
parser = argparse.ArgumentParser(description='VGG Triplet-Loss Speaker Embedding')
parser.add_argument('--batch-size', type=int, default=16, metavar='N',
help='input batch size for training')
parser.add_argument('--no-cuda', action='store_true',default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed')
parser.add_argument('--log-interval',type=int, default=20, metavar='N',
help='how many batches to wait before logging training score')
parser.add_argument('--margin', type=float, default=1, metavar='M',
help='margin for triplet loss (default: 0.2)')
parser.add_argument('--base-path',type=str,
default='/home/lucas/PycharmProjects/Papers_with_code/data/AMI/triplet_splits',
help='string to triplets')
parser.add_argument('--s-file',default='/home/lucas/PycharmProjects/Papers_with_code/data/AMI/amicorpus_individual/Extracted_Speech/trimmed_sample_list.txt',type=str,
help='name of sample list')
parser.add_argument('--load-path',default='/home/lucas/PycharmProjects/Papers_with_code/data/models/VGG_Triplet'
,type=str, help='path to save models to')
parser.add_argument('--name', default='Compare_Embeddings', type=str,
help='name of experiment')
parser.add_argument('--base-sample', default='/home/lucas/PycharmProjects/Papers_with_code/data/AMI/amicorpus_individual/Extracted_Speech/EN2001d_MEO069.wav', type=str,
help='path to base sample')
parser.add_argument('--test-path', type=str, default='/home/lucas/PycharmProjects/Papers_with_code/data/AMI/pyannote/amicorpus/EN2001a/audio/EN2001a.Mix-Headset.wav'
,help='path to audio file to be used in testing')
args = parser.parse_args()
#wandb.run.name = args.name
#wandb.run.save()
#wandb.config.update(args)
args.cuda = not args.no_cuda and torch.cuda.is_available()
print(args.no_cuda, torch.cuda.is_available())
if args.cuda:
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
torch.manual_seed(args.seed)
kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
ground_truth = Prepare_Track(track_name='EN2001a.Mix-Headset',rttm_name='MixHeadset.train.rttm',path_to_track='/home/lucas/PycharmProjects/Papers_with_code/data/AMI/pyannote/amicorpus/EN2001a/audio', path_to_rttm='/home/lucas/PycharmProjects/Papers_with_code/data/AMI/pyannote/AMI')
true_label_df, true_frame_list, speaker_dict = ground_truth.label_frames(window_size=3, step_size=0.1)
model = VGGVox()
model.to(device)
model.load_state_dict(torch.load(args.load_path))
print("Model loaded from state dict")
cudnn.benchmark = True
#print(get_target_embeddings(num_embeddings=3,label_df=true_label_df,sample_list=args.s_file,model=model))
anchor = get_average_embedding(window_size=3, num_embeddings=5, path=args.base_sample, model=model)
#print(diarize(get_target_embeddings(num_embeddings=3, label_df=true_label_df, sample_list=args.s_file, model=model), frame_list=true_frame_list,label_df=true_label_df, model=model,path=args.test_path,margin=20))
speaker_label = args.base_sample[args.base_sample.rfind('_')+1:args.base_sample.rfind('.')]
result = compare_embeddings(frame_list=true_frame_list, label_df=true_label_df, model=model, path=args.test_path,anchor=anchor,speaker_label=speaker_label,
margin=0.45)
def main():
#Train settings
wandb.init(project="vgg_triplet")
global args, best_acc
parser = argparse.ArgumentParser(
description='VGG Triplet-Loss Speaker Embedding')
parser.add_argument('--batch-size',
type=int,
default=32,
metavar='N',
help='input batch size for training')
parser.add_argument('--test-batch-size',
type=int,
default=32,
metavar='N',
help='input batch size for testing')
parser.add_argument('--epochs',
type=int,
default=50,
metavar='N',
help='number of epochs for training')
parser.add_argument('--lr',
type=float,
default=0.0001,
metavar='LR',
help='learning rate')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='enables CUDA training')
parser.add_argument('--seed',
type=int,
default=2,
metavar='S',
help='random seed')
parser.add_argument(
'--log-interval',
type=int,
default=20,
metavar='N',
help='how many batches to wait before logging training score')
parser.add_argument('--margin',
type=float,
default=2,
metavar='M',
help='margin for triplet loss (default: 0.2)')
parser.add_argument('--resume',
default='',
type=str,
help='path to latest checkpoint (default: None)')
parser.add_argument('--name',
default='TripletNet_RMSprop',
type=str,
help='name of experiment')
parser.add_argument(
'--base-path',
type=str,
default=
'/home/lucas/PycharmProjects/Papers_with_code/data/AMI/amicorpus_individual/Extracted_Speech',
help='string to triplets')
parser.add_argument('--ap-file',
default='anchor_pairs.txt',
type=str,
help='name of file with anchor-positive pairs')
parser.add_argument('--s-file',
default='trimmed_sample_list.txt',
type=str,
help='name of sample list')
parser.add_argument(
'--save-path',
default=
'/home/lucas/PycharmProjects/Papers_with_code/data/models/VGG_Triplet',
type=str,
help='path to save models to')
parser.add_argument('--save', type=bool, default=True, help='save model?')
parser.add_argument('--load',
type=bool,
default=False,
help='load model from latest checkpoint')
args = parser.parse_args()
wandb.run.name = args.name
wandb.run.save()
wandb.config.update(args)
args.cuda = not args.no_cuda and torch.cuda.is_available()
print(args.no_cuda)
print(torch.cuda.is_available())
if args.cuda:
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
torch.manual_seed(args.seed)
kwargs = {'num_workers': 2, 'pin_memory': True} if args.cuda else {}
#train_loader = torch.utils.data.DataLoader(TripletLoader(base_path=args.base_path,anchor_positive_pairs=args.ap_file,sample_list=args.s_file,train=True),
# batch_size=args.batch_size,shuffle=True,**kwargs)
#test_loader = torch.utils.data.DataLoader(TripletLoader(base_path=args.base_path,anchor_positive_pairs=args.ap_file,sample_list=args.s_file,train=False),
# batch_size=args.test_batch_size,shuffle=True,**kwargs)
#single_train_loader = torch.utils.data.DataLoader(Spectrogram_Loader(base_path=args.base_path, anchor_positive_pairs=args.ap_file, sample_list=args.s_file, train=True), batch_size=args.batch_size, shuffle=True, **kwargs)
#single_test_loader = torch.utils.data.DataLoader(Spectrogram_Loader(base_path=args.base_path, anchor_positive_pairs=args.ap_file, sample_list=args.s_file, train=False), batch_size=args.test_batch_size, shuffle=True, **kwargs)
train_time_loader = torch.utils.data.DataLoader(Triplet_Time_Loader(
path=os.path.join(args.base_path, args.s_file), train=True),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_time_loader = torch.utils.data.DataLoader(
Triplet_Time_Loader(path=os.path.join(args.base_path, args.s_file),
train=False),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
#global plotter
#plotter = VisdomLinePlotter(env_name=args.name)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=7)
self.conv2 = nn.Conv2d(16, 16, kernel_size=7)
self.bn_1 = nn.BatchNorm2d(16)
self.conv3 = nn.Conv2d(16, 32, kernel_size=7)
self.conv4 = nn.Conv2d(32, 32, kernel_size=7)
self.bn_2 = nn.BatchNorm2d(32)
self.conv2_drop = nn.Dropout2d(p=0.2)
self.fc1 = nn.Linear(448, 256)
self.fc2 = nn.Linear(256, 256)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(F.max_pool2d(self.bn_1(self.conv2(x)), 7))
x = F.relu(self.conv3(x))
x = F.relu(
F.max_pool2d(self.conv2_drop(self.bn_2(self.conv4(x))), 7))
#print("SIZE ",x.size())
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
return self.fc2(x)
model = VGGVox()
if args.cuda:
model.to(device)
if args.load:
model.load_state_dict(torch.load(args.save_path))
print("Model loaded from state dict")
#tnet = TripletNet(model)
#if args.cuda:
# tnet.to(device)
wandb.watch(model)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
#criterion = torch.nn.MarginRankingLoss(margin = args.margin)
criterion = nn.TripletMarginLoss(margin=args.margin, p=2)
#optimizer = optim.Adam(tnet.parameters(),lr=args.lr)
#optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
optimizer = optim.RMSprop(model.parameters(),
lr=args.lr,
alpha=0.8,
momentum=args.momentum)
#n_parameters = sum([p.data.nelement() for p in tnet.parameters()])
#print(' + NUmber of params: {}'.format(n_parameters))
for epoch in range(1, args.epochs + 1):
start_time = time.time()
train_batch(train_time_loader, model, optimizer, epoch)
test_batch(test_time_loader, model, epoch)
duration = time.time() - start_time
print("Done training epoch {} in {:.4f}".format(epoch, duration))
#for epoch in range(1, args.epochs + 1):
# test_batch(single_train_loader, model, epoch)
if args.save:
torch.save(model.state_dict(), args.save_path)
print("Model Saved")
def main():
global args
parser = argparse.ArgumentParser(
description='VGG Embeddings Clustering Script')
parser.add_argument('--batch_size',
type=int,
default=16,
metavar='N',
help='input batch size for embeddings')
parser.add_argument(
'--s-file',
default=
'/home/lucas/PycharmProjects/Papers_with_code/data/AMI/amicorpus_individual/Extracted_Speech/trimmed_sample_list.txt',
type=str,
help='path to sample list')
parser.add_argument(
'--load-path',
default=
'/home/lucas/PycharmProjects/Papers_with_code/data/models/VGG_Triplet',
type=str,
help='path to load models from')
parser.add_argument('--name',
default='Cluster Embeddings',
type=str,
help='name of experiment')
parser.add_argument(
'--speakers',
type=list,
default=['FTD019UID', 'MTD017PM', 'MTD018ID', 'MTD020ME'],
metavar='N',
help='List of speaker labels to extract embeddings from')
#default=['FEO065', 'FEE066','MEE067','MEE068','MEO069']
#default=['FTD019UID','MTD017PM','MTD018ID','MTD020ME']
args = parser.parse_args()
#No point in running this on CPU
device = torch.device("cuda:0")
DataLoaders = []
kwargs = {'num_workers': 2, 'pin_memory': True}
#Create a List of DataLoaders, seperate DataLoader for each speaker
for speaker in args.speakers:
DataLoaders.append(
torch.utils.data.DataLoader(Single_Speaker_Loader(path=args.s_file,
speaker=speaker),
batch_size=args.batch_size,
shuffle=False,
**kwargs))
model = VGGVox()
model.to(device)
model.load_state_dict(torch.load(args.load_path))
cudnn.benchmark = True
embeddingList = get_embeddings(DataLoaders=DataLoaders, model=model)
tensor_length = [len(embedding[0]) for embedding in embeddingList]
tensor = torch.empty(
sum([len(embedding[0]) for embedding in embeddingList]), 256)
print(tensor.size())
embeddings = [embedding[0] for embedding in embeddingList]
embeddings = torch.cat(embeddings, dim=0)
#labels = zip([embedding[1] for embedding in embeddingList])
labels = []
for listing in [embedding[1] for embedding in embeddingList]:
for item in listing:
labels.append(item)
#labels =[item for item in [list for list in [embedding[1] for embedding in embeddingList]]]
coords = t_SNE(embeddings, speaker_labels=labels, num_dimensions=2)
#scatter_plot(coords = t_SNE(embeddings,speaker_labels=labels, num_dimensions=3), dimensions=3)
coverage_and_purity(df=coords, method='k-means')
def main():
global args
#wandb.login()
wandb.init(project="vgg_triplet")
config = wandb.config
parser = argparse.ArgumentParser(
description="VGGVox CNN with Spectrograms for Speaker Verification")
parser.add_argument('--test-batch-size',
type=int,
default=40,
metavar='N',
help='input batch size for training')
parser.add_argument('--train-batch-size',
type=int,
default=40,
metavar='N',
help='input batch size for testing')
parser.add_argument('--epochs',
type=int,
default=200,
metavar='N',
help='number of epochs')
parser.add_argument('--lr',
type=float,
default=0.0001,
metavar='LR',
help='learning rate')
parser.add_argument('--seed',
type=int,
default=1234,
metavar='S',
help='random seed')
parser.add_argument('--margin',
type=float,
default=8,
metavar='M',
help='margin for triplet loss (default: 0.2)')
parser.add_argument('--name',
default='VGG_Spectogram_Triplet',
type=str,
help='name of network')
parser.add_argument(
'--train-set',
default=
'/home/lucas/PycharmProjects/Data/pyannote/Extracted_Speech/trimmed_sample_list_train.txt',
type=str,
help='path to train samples')
parser.add_argument(
'--test-set',
default=
'/home/lucas/PycharmProjects/Data/pyannote/Extracted_Speech/trimmed_sample_list_test.txt',
type=str,
help='path to test samples')
parser.add_argument(
'--valid-set',
default=
'/home/lucas/PycharmProjects/Data/pyannote/Extracted_Speech/trimmed_sample_list_valid.txt',
type=str,
help='path to validation samples')
parser.add_argument(
'--model-path',
default=
'/home/lucas/PycharmProjects/MetricEmbeddingNet/models/VGG_Spectrogram_Triplet.pt',
type=str,
help='path to where models are saved/loaded')
parser.add_argument('--save-model',
type=bool,
default=True,
help='save model?')
parser.add_argument('--load-model',
type=bool,
default=False,
help='load model?')
parser.add_argument('--melspectrogram',
type=bool,
default=False,
help='use melspectrogram?')
args = parser.parse_args()
wandb.config.update(args)
torch.manual_seed(config.seed)
random.seed(config.seed)
np.random.seed(config.seed)
torch.backends.cudnn.deterministic = True
device = torch.device('cuda:0')
kwargs = {'num_workers': 6, 'pin_memory': True}
train_loader = data.DataLoader(Spectrogram_Loader(filename=args.train_set,
mel=False),
batch_size=config.train_batch_size,
shuffle=True,
**kwargs)
test_loader = data.DataLoader(Spectrogram_Loader(filename=args.test_set,
mel=False),
batch_size=config.test_batch_size,
shuffle=True,
**kwargs)
valid_loader = data.DataLoader(Spectrogram_Loader(filename=args.valid_set,
mel=False),
batch_size=config.test_batch_size,
shuffle=True,
**kwargs)
model = VGGVox()
model.to(device)
if args.load_model:
try:
model.load_state_dict(torch.load(args.model_path))
except:
print("Could not load model {} not found".format(args.model_path))
#nn.init.xavier_uniform(model.parameters())
#optimizer = optim.Adam(model.parameters(), lr = config.lr)
optimizer = optim.RMSprop(model.parameters(),
lr=0.001,
alpha=0.8,
momentum=0.5)
#optimizer = optim.Adam(model.parameters(), lr=0.0001, eps=1e-3, amsgrad=True)
wandb.watch(model)
for epoch in range(1, config.epochs + 1):
start_time = time.time()
train_loss, train_acc = train(train_loader=train_loader,
model=model,
optimizer=optimizer,
device=device,
epoch=epoch)
test_loss, test_acc = test(data_loader=test_loader,
model=model,
device=device,
epoch=epoch)
#valid_loss, valid_acc = test(data_loader=valid_loader, model=model, device=device, epoch=epoch)
print('Finished epoch {} in {:.2f} '.format(
epoch, (time.time() - start_time)))
wandb.log({
'Train Loss': train_loss,
'Train Accuracy': train_acc,
'Test Loss': test_loss,
'Test Accuracy': test_acc
}) # 'Validation Loss': valid_loss, 'Validation Accuracy': valid_acc})
if config.save_model and (epoch % 20 == 0):
torch.save(model.state_dict(), config.model_path)
print("Model saved after {} epochs".format(epoch))
plot_validation_set(valid_loader=valid_loader,
model=model,
epoch=epoch)
print("Validation plot saved")