def __init__(self, cfg):
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.dtype = torch.float32
self.eps = 1e-4
self.stft_module = STFTModule(cfg['stft_params'], self.device)
self.train_data_num = cfg['train_data_num']
self.valid_data_num = cfg['valid_data_num']
self.sample_len = cfg['sample_len']
self.epoch_num = cfg['epoch_num']
self.train_batch_size = cfg['train_batch_size']
self.valid_batch_size = cfg['valid_batch_size']
self.train_dataset = DSD100Dataset(data_num=self.train_data_num,
sample_len=self.sample_len,
folder_type='train')
self.valid_dataset = DSD100Dataset(data_num=self.valid_data_num,
sample_len=self.sample_len,
folder_type='validation')
self.train_data_loader = FastDataLoader(
self.train_dataset, batch_size=self.train_batch_size, shuffle=True)
self.valid_data_loader = FastDataLoader(
self.valid_dataset, batch_size=self.valid_batch_size, shuffle=True)
self.model = UNet().to(self.device)
self.criterion = MSE()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
self.save_path = 'results/model/dsd_unet_config_1/'
def __init__(self, cfg):
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.dtype = torch.float32
self.eps = 1e-8
self.stft_module = STFTModule(cfg['stft_params'], self.device)
self.stft_module_ex1 = STFTModule(cfg['stft_params_ex1'], self.device)
self.stft_module_ex2 = STFTModule(cfg['stft_params_ex2'], self.device)
self.train_data_num = cfg['train_data_num']
self.valid_data_num = cfg['valid_data_num']
self.sample_len = cfg['sample_len']
self.epoch_num = cfg['epoch_num']
self.train_batch_size = cfg['train_batch_size']
self.valid_batch_size = cfg['valid_batch_size']
self.train_full_data_num = cfg['train_full_data_num']
self.valid_full_data_num = cfg['valid_full_data_num']
self.save_path = cfg['save_path']
self.train_dataset = VoicebankDemandDataset(
data_num=self.train_data_num,
full_data_num=self.train_full_data_num,
sample_len=self.sample_len,
folder_type='train',
shuffle=True,
device=self.device,
augmentation=True)
self.valid_dataset = VoicebankDemandDataset(
data_num=self.valid_data_num,
full_data_num=self.valid_full_data_num,
sample_len=self.sample_len,
folder_type='validation',
shuffle=True,
device=self.device,
augmentation=False)
self.train_data_loader = FastDataLoader(
self.train_dataset, batch_size=self.train_batch_size, shuffle=True)
self.valid_data_loader = FastDataLoader(
self.valid_dataset, batch_size=self.valid_batch_size, shuffle=True)
self.model = FeatExtractorBlstm_pp_v3(cfg['dnn_cfg']).to(self.device)
self.criterion = Clip_SDR()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
self.early_stopping = EarlyStopping(patience=10)
def __init__(self, cfg):
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.dtype = torch.float32
self.eps = 1e-4
self.eval_path = cfg['eval_path']
self.model = CNNOpenUnmix_p2(cfg['dnn_cfg']).to(self.device)
self.model.eval()
self.model.load_state_dict(
torch.load(self.eval_path, map_location=self.device))
self.stft_module = STFTModule(cfg['stft_params'], self.device)
self.stft_module_ex2 = STFTModule(cfg['stft_params_ex2'], self.device)
self.test_data_num = cfg['test_data_num']
self.test_batch_size = cfg['test_batch_size']
self.sample_len = cfg['sample_len']
self.test_dataset = VoicebankDemandDataset(data_num=self.test_data_num,
sample_len=self.sample_len,
folder_type='test',
shuffle=False)
self.test_data_loader = FastDataLoader(self.test_dataset,
batch_size=self.test_batch_size,
shuffle=False)
self.stoi_list = np.array([])
self.pesq_list = np.array([])
self.si_sdr_list = np.array([])
self.si_sdr_improve_list = np.array([])
def __init__(self, cfg):
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.dtype = torch.float32
self.eps = 1e-4
self.eval_path = cfg['eval_path']
self.model = UNet_pp().to(self.device)
self.model.eval()
self.model.load_state_dict(
torch.load(self.eval_path, map_location=self.device))
self.stft_module = STFTModule(cfg['stft_params'], self.device)
self.stft_module_ex1 = STFTModule(cfg['stft_params_ex1'], self.device)
self.stft_module_ex2 = STFTModule(cfg['stft_params_ex2'], self.device)
self.test_data_num = cfg['test_data_num']
self.test_batch_size = cfg['test_batch_size']
self.sample_len = cfg['sample_len']
self.test_dataset = DSD100Dataset(data_num=self.test_data_num,
sample_len=self.sample_len,
folder_type='test',
device=self.device,
shuffle=False)
self.test_data_loader = FastDataLoader(self.test_dataset,
batch_size=self.test_batch_size,
shuffle=False)
self.sdr_list = np.array([])
self.sar_list = np.array([])
self.sir_list = np.array([])
def __init__(self, cfg):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.dtype= torch.float32
self.eps = 1e-8
self.stft_module = STFTModule(cfg['stft_params'], self.device)
self.train_data_num = cfg['train_data_num']
self.valid_data_num = cfg['valid_data_num']
self.sample_len = cfg['sample_len']
self.epoch_num = cfg['epoch_num']
self.train_batch_size = cfg['train_batch_size']
self.valid_batch_size = cfg['valid_batch_size']
self.train_full_data_num = cfg['train_full_data_num']
self.valid_full_data_num = cfg['valid_full_data_num']
self.train_dataset = DSD100Dataset(data_num=self.train_data_num,
full_data_num=self.train_full_data_num,
sample_len=self.sample_len,
folder_type='Dev',
shuffle=True,
device=self.device,
augmentation=True)
self.valid_dataset =DSD100Dataset(data_num=self.valid_data_num,
full_data_num=self.valid_full_data_num,
sample_len=self.sample_len,
folder_type='Validation',
shuffle=True,
device=self.device,
augmentation=False)
self.train_data_loader = FastDataLoader(self.train_dataset,
batch_size=self.train_batch_size,
shuffle=True)
self.valid_data_loader = FastDataLoader(self.valid_dataset,
batch_size=self.valid_batch_size,
shuffle=True)
self.model = OpenUnmix(cfg['dnn_cfg']).to(self.device)
self.criterion = MSE()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
self.save_path = cfg['save_path']
self.early_stopping = EarlyStopping(patience=10)
def __init__(self, cfg):
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.dtype = torch.float32
self.eps = 1e-8
self.stft_module = STFTModule(cfg['stft_params'], self.device)
self.train_data_num = cfg['train_data_num']
self.valid_data_num = cfg['valid_data_num']
self.sample_len = cfg['sample_len']
self.epoch_num = cfg['epoch_num']
self.train_batch_size = cfg['train_batch_size']
self.valid_batch_size = cfg['valid_batch_size']
self.train_full_data_num = cfg['train_full_data_num']
self.valid_full_data_num = cfg['valid_full_data_num']
self.train_dataset = VoicebankDemandDataset(
data_num=self.train_data_num,
full_data_num=self.train_full_data_num,
sample_len=self.sample_len,
folder_type='train')
self.valid_dataset = VoicebankDemandDataset(
data_num=self.valid_data_num,
full_data_num=self.valid_full_data_num,
sample_len=self.sample_len,
folder_type='validation')
self.train_data_loader = FastDataLoader(
self.train_dataset, batch_size=self.train_batch_size, shuffle=True)
self.valid_data_loader = FastDataLoader(
self.valid_dataset, batch_size=self.valid_batch_size, shuffle=True)
self.model = DemandUNet().to(self.device)
self.criterion = PSA()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
self.save_path = cfg['save_path']
self.early_stopping = EarlyStopping(patience=10)