Fix two scripts

codes/scripts/classify_into_folders.py

@@ -71,7 +71,7 @@ if __name__ == "__main__":
             for k, lbl in enumerate(lbls):
                 lbl = labels[torch.argmax(lbl, dim=0)]
                 src_path = data[path_key][k]
-                output_file.write(f'{src_path}\t{lbl}')
+                output_file.write(f'{src_path}\t{lbl}\n')
                 if output_base_dir is not None:
                     dest = os.path.join(output_base_dir, lbl)
                     os.makedirs(dest, exist_ok=True)

codes/scripts/diffusion/diffusion_noise_surfer.py

@@ -14,6 +14,7 @@ from torchvision.transforms import ToTensor
 import utils
 import utils.options as option
 import utils.util as util
+from data.audio.unsupervised_audio_dataset import load_audio
 from models.tacotron2.taco_utils import load_wav_to_torch
 from trainer.ExtensibleTrainer import ExtensibleTrainer
 from data import create_dataset, create_dataloader
@@ -48,10 +49,8 @@ def forward_pass(model, data, output_dir, spacing, audio_mode):
 def load_image(path, audio_mode):
     # Load test image
     if audio_mode:
-        im, sr = load_wav_to_torch(path)
-        assert sr == 22050
-        im = im.unsqueeze(0)
-        im = im[:, :(im.shape[1]//4096)*4096]
+        im = load_audio(path, 22050)
+        im = im[:, :(im.shape[1]//4096)*4096].unsqueeze(0)
     else:
         im = ToTensor()(Image.open(path)) * 2 - 1
         _, h, w = im.shape
@@ -113,7 +112,7 @@ if __name__ == "__main__":
         if audio_mode:
             data = {
                 'clip': im.to('cuda'),
-                'alt_clips': refs.to('cuda'),
+                'alt_clips': torch.zeros_like(refs[:,0].to('cuda')),
                 'num_alt_clips': torch.tensor([refs.shape[1]], dtype=torch.int32, device='cuda'),
                 'GT_path': opt['image']
             }