LoadAudio(), MapLabels(class_map=class_map), MixUp(p=args.p_mixup), STFT(n_fft=config.data.n_fft, hop_size=config.data.hop_size), DropFields(("audio", "filename", "sr")), RenameFields({"stft": "signal"}) ]), clean_transform=Compose([ LoadAudio(), MapLabels(class_map=class_map), ])), shuffle=True, drop_last=True, batch_size=config.train.batch_size, collate_fn=make_collate_fn({"signal": math.log(STFT.eps)}), **loader_kwargs) valid_loader = torch.utils.data.DataLoader( SoundDataset(audio_files=[ os.path.join(args.train_data_dir, fname) for fname in train_df.fname.values[valid] ], labels=[ item.split(",") for item in train_df.labels.values[valid] ], transform=Compose([ LoadAudio(), MapLabels(class_map=class_map), STFT(n_fft=config.data.n_fft,
transform=Compose([ LoadAudio(), SampleLongAudio(max_length=args.max_audio_length), MixUp(p=args.p_mixup), AudioAugmentation(p=args.p_aug), audio_transform, DropFields(("audio", "sr")), ]), clean_transform=Compose([ LoadAudio(), SampleLongAudio(max_length=args.max_audio_length) ])), shuffle=True, drop_last=True, batch_size=config.train.batch_size, collate_fn=make_collate_fn( {"signal": audio_transform.padding_value}), **loader_kwargs) valid_loader = torch.utils.data.DataLoader( AntispoofDataset(audio_files=[ os.path.join(args.train_data_dir, fname) for fname in train_df.fname.values[valid] ], labels=train_df.labels.values[valid], transform=Compose([ LoadAudio(), audio_transform, DropFields(("audio", "sr")), ])), shuffle=False, batch_size=config.train.batch_size,