if args.mfb: transform = transforms.Compose([ concateinputfromMFB(), # truncatedinputfromMFB(), totensor() ]) transform_T = transforms.Compose([ concateinputfromMFB(input_per_file=args.test_input_per_file), # truncatedinputfromMFB(input_per_file=args.test_input_per_file), totensor() ]) file_loader = read_MFB else: transform = transforms.Compose([ truncatedinput(), toMFB(), totensor(), # tonormal() ]) file_loader = read_audio train_dir = ClassificationDataset(voxceleb=voxceleb_dev, dir=args.dataroot, loader=file_loader, transform=transform) test_dir = VoxcelebTestset(dir=args.test_dataroot, pairs_path=args.test_pairs_path, loader=file_loader, transform=transform_T)
transform = transforms.Compose([ # concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, remove_vad=True), varLengthFeat(remove_vad=args.remove_vad), to2tensor(), tonormal() ]) transform_T = transforms.Compose([ # concateinputfromMFB(num_frames=c.NUM_FRAMES_SPECT, input_per_file=args.test_input_per_file, remove_vad=True), varLengthFeat(remove_vad=args.remove_vad), to2tensor(), tonormal() ]) else: transform = transforms.Compose( [truncatedinput(), toMFB(), totensor(), tonormal()]) file_loader = read_audio # pdb.set_trace() file_loader = read_mat train_dir = ScriptTrainDataset(dir=args.train_dir, samples_per_speaker=args.input_per_spks, loader=file_loader, transform=transform, num_valid=args.num_valid) test_dir = ScriptTestDataset(dir=args.test_dir, loader=file_loader, transform=transform_T) if len(test_dir) < args.veri_pairs: