def __init__(self, classifier_config_path):
clf_cfg = Hparam(classifier_config_path)
cpc_cfg = Hparam(clf_cfg.model.cpc_config_path)
self.device = clf_cfg.train.device
speakers_bank = pickle.load(open('templates/mean_speakers_vecs_dict.pkl', 'rb'))
self.speakers, self.mean_vecs = list(speakers_bank.keys()), torch.stack(list(speakers_bank.values()), dim=0)
model_cpc = CPCModel_NCE(cpc_cfg).to(clf_cfg.train.device)
self.model = SpeakerClassificationModel(model_cpc,
clf_cfg.model.hidden_size,
40,
clf_cfg).to(clf_cfg.train.device)
self.model.load_state_dict(torch.load(clf_cfg.train.checkpoints_dir + '/' + clf_cfg.train.cpc_checkpoint))
self.model.eval()
from tqdm import tqdm
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from tensorboardX import SummaryWriter
from hparams import Hparam
from data.datasets import AudioDataset
from GIL_model.model import GILModel
from GIL_model.freezers import SimultaneousFreezer, IterativeFreezer
config = Hparam('./GIL_model/config.yaml')
gettime = lambda: str(dt.time(dt.now()))[:8]
if not os.path.isdir('./checkpoints'):
os.mkdir('./checkpoints')
if __name__ == "__main__":
writer = SummaryWriter()
print('Extracting data')
dataset = AudioDataset(config.data.path)
train_ixs, test_ixs = dataset.train_test_split_ixs(config.train.test_size)
train_sampler = SubsetRandomSampler(train_ixs)
test_sampler = SubsetRandomSampler(test_ixs)
dataloader_fabric = lambda ds, sampler: DataLoader(
ds, config.train.batch_size, sampler=sampler, drop_last=True)
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from hparams import Hparam
from data.datasets import SpeakersDataset
from CPC_model.model import CPCModel_NCE
from classifier_models.speaker_model import SpeakerClassificationModel
config = Hparam('./classifier_models/config.yaml')
gettime = lambda: str(dt.time(dt.now()))[:8]
if not os.path.isdir('./checkpoints'):
os.mkdir('./checkpoints')
if __name__ == "__main__":
writer = SummaryWriter()
print('Extracting data')
dataset = SpeakersDataset(config.data.path)
train_ixs, test_ixs = dataset.train_test_split_ixs(config.train.test_split)
train_sampler = SubsetRandomSampler(train_ixs)
test_sampler = SubsetRandomSampler(test_ixs)
dataloader_fabric = lambda ds, sampler: DataLoader(
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from hparams import Hparam
from data.datasets import SpeakersDataset
from GIL_model.model import GILModel
from classifier_models.speaker_model import SpeakerClassificationModel
from GIL_model.freezers import SimultaneousFreezer, IterativeFreezer
config = Hparam('./classifier_models/config_gil.yaml')
gettime = lambda: str(dt.time(dt.now()))[:8]
if not os.path.isdir('./checkpoints'):
os.mkdir('./checkpoints')
if __name__ == "__main__":
writer = SummaryWriter()
print('Extracting data')
dataset = SpeakersDataset(config.data.path)
train_ixs, test_ixs = dataset.train_test_split_ixs(config.train.test_size)
train_sampler = SubsetRandomSampler(train_ixs)
test_sampler = SubsetRandomSampler(test_ixs)
dataloader_fabric = lambda ds, sampler: DataLoader(
from datetime import datetime as dt
from tqdm import tqdm
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from tensorboardX import SummaryWriter
from hparams import Hparam
from data.datasets import AudioDataset
from CPC_model.model import CPCModel_NCE
config = Hparam('./CPC_model/config.yaml')
gettime = lambda: str(dt.time(dt.now()))[:8]
if not os.path.isdir('./checkpoints'):
os.mkdir('./checkpoints')
if __name__ == "__main__":
writer = SummaryWriter()
print('Extracting data')
dataset = AudioDataset(config.data.path)
train_ixs, test_ixs = dataset.train_test_split_ixs(config.train.test_size)
train_sampler = SubsetRandomSampler(train_ixs)
test_sampler = SubsetRandomSampler(test_ixs)
dataloader_fabric = lambda ds, sampler: DataLoader(
ds, config.train.batch_size, sampler=sampler, drop_last=True)