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-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.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_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_pp().to(self.device) self.criterion = MSE() self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3) self.save_path = 'results/model/dsd_unet_pp_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-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)
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.inst_num = cfg['inst_num'] 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 = SlakhDataset(inst_num=self.inst_num, data_num=self.train_data_num, sample_len=self.sample_len, folder_type='train') self.valid_dataset = SlakhDataset(inst_num=self.inst_num, data_num=self.valid_data_num, sample_len=self.sample_len, folder_type='validation') self.train_data_loader = torch.utils.data.DataLoader(self.train_dataset, batch_size=self.train_batch_size, shuffle=True) self.valid_data_loader = torch.utils.data.DataLoader(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/inst_num_{0}/'.format(self.inst_num)