Fix two scripts
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df45a9dec2
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@ -71,7 +71,7 @@ if __name__ == "__main__":
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for k, lbl in enumerate(lbls):
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lbl = labels[torch.argmax(lbl, dim=0)]
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src_path = data[path_key][k]
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output_file.write(f'{src_path}\t{lbl}')
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output_file.write(f'{src_path}\t{lbl}\n')
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if output_base_dir is not None:
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dest = os.path.join(output_base_dir, lbl)
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os.makedirs(dest, exist_ok=True)
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@ -14,6 +14,7 @@ from torchvision.transforms import ToTensor
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import utils
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import utils.options as option
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import utils.util as util
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from data.audio.unsupervised_audio_dataset import load_audio
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from models.tacotron2.taco_utils import load_wav_to_torch
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from trainer.ExtensibleTrainer import ExtensibleTrainer
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from data import create_dataset, create_dataloader
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@ -48,10 +49,8 @@ def forward_pass(model, data, output_dir, spacing, audio_mode):
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def load_image(path, audio_mode):
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# Load test image
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if audio_mode:
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im, sr = load_wav_to_torch(path)
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assert sr == 22050
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im = im.unsqueeze(0)
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im = im[:, :(im.shape[1]//4096)*4096]
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im = load_audio(path, 22050)
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im = im[:, :(im.shape[1]//4096)*4096].unsqueeze(0)
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else:
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im = ToTensor()(Image.open(path)) * 2 - 1
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_, h, w = im.shape
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@ -113,7 +112,7 @@ if __name__ == "__main__":
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if audio_mode:
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data = {
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'clip': im.to('cuda'),
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'alt_clips': refs.to('cuda'),
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'alt_clips': torch.zeros_like(refs[:,0].to('cuda')),
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'num_alt_clips': torch.tensor([refs.shape[1]], dtype=torch.int32, device='cuda'),
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'GT_path': opt['image']
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}
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