42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from data.util import is_wav_file, find_files_of_type
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from models.audio_resnet import resnet34, resnet50
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from models.tacotron2.taco_utils import load_wav_to_torch
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from scripts.byol.byol_extract_wrapped_model import extract_byol_model_from_state_dict
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if __name__ == '__main__':
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window = 48000
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root_path = 'D:\\tmp\\clips'
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paths = find_files_of_type('img', root_path, qualifier=is_wav_file)[0]
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clips = []
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for path in paths:
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clip, sr = load_wav_to_torch(os.path.join(root_path, path))
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if len(clip.shape) > 1:
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clip = clip[:,0]
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clip = clip[:window].unsqueeze(0)
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clip = clip / 32768.0 # Normalize
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#clip = clip + torch.rand_like(clip) * .03 # Noise (this is how the model was trained)
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assert sr == 24000
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clips.append(clip)
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clips = torch.stack(clips, dim=0)
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resnet = resnet50()
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sd = torch.load('../experiments/train_byol_audio_clips/models/8000_generator.pth')
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sd = extract_byol_model_from_state_dict(sd)
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resnet.load_state_dict(sd)
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embedding = resnet(clips, return_pool=True)
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for i, path in enumerate(paths):
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print(f'Using a baseline of {path}..')
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for j, cpath in enumerate(paths):
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if i == j:
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continue
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l2 = F.mse_loss(embedding[j], embedding[i])
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print(f'Compared to {cpath}: {l2}')
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