voxceleb = read_voxceleb_structure(args.dataroot) if args.makemfb: for datum in voxceleb.iterrows(): mk_MFB((args.dataroot +'/voxceleb1_wav/' + datum[1]['filename']+'.wav')) if args.mfb: transform = transforms.Compose([ truncatedinputfromMFB(), totensor() ]) file_loader = read_MFB else: transform = transforms.Compose([ truncatedinput(), toMFB(), totensor(), #tonormal() ]) file_loader = read_audio voxceleb_dev = voxceleb.loc[lambda voxceleb: voxceleb.subset == 'dev'] train_dir = DeepSpeakerDataset(voxceleb = voxceleb_dev, dir=args.dataroot,n_triplets=args.n_triplets,loader = file_loader,transform=transform) test_dir = VoxcelebTestset(dir=args.dataroot,pairs_path=args.test_pairs_path,loader = file_loader, transform=transform)
print('==> Started IF') num_features = c.IF_FEATURES print('==> Started converting wav to npy') parallel_function(mk_if, [datum['file_path'] for datum in voxceleb_test], num_threads) print('===> Converting test set is done') if not args.test_only: parallel_function(mk_if, [datum['file_path'] for datum in voxceleb_dev], num_threads) print('===> Converting dev set is done') print("==> Complete converting") # Data transform_train = transforms.Compose([ totensor(permute=False), truncatedinput(c.NUM_FRAMES), ]) transform_test = transforms.Compose([ totensor(permute=False), truncatedinput(c.NUM_FRAMES), ]) file_loader = read_npy train_dir = DeepSpeakerDataset(voxceleb=voxceleb_dev, dir=args.dataroot, n_triplets=args.n_triplets, loader=file_loader, transform=transform_train) test_dir = VoxcelebTestset(dir=args.dataroot, pairs_path=args.test_pairs_path, loader=file_loader, transform=transform_test)