models

enhancer/models/__init__.py

@@ -1,3 +1,3 @@
 from enhancer.models.demucs import Demucs
-from enhancer.models.waveunet import WaveUnet
 from enhancer.models.model import Model
+from enhancer.models.waveunet import WaveUnet

enhancer/models/demucs.py

@@ -1,11 +1,12 @@
 import logging
-from typing import Optional, Union, List
-from torch import nn
-import torch.nn.functional as F
 import math
+from typing import List, Optional, Union
+
+import torch.nn.functional as F
+from torch import nn
 
-from enhancer.models.model import Model
 from enhancer.data.dataset import EnhancerDataset
+from enhancer.models.model import Model
 from enhancer.utils.io import Audio as audio
 from enhancer.utils.utils import merge_dict
 

enhancer/models/model.py

@@ -1,20 +1,20 @@
-from importlib import import_module
-from huggingface_hub import cached_download, hf_hub_url
-import numpy as np
 import os
-from typing import Optional, Union, List, Text, Dict, Any
-from torch.optim import Adam
-import torch
-import pytorch_lightning as pl
-from pytorch_lightning.utilities.cloud_io import load as pl_load
-from urllib.parse import urlparse
+from importlib import import_module
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Text, Union
+from urllib.parse import urlparse
 
+import numpy as np
+import pytorch_lightning as pl
+import torch
+from huggingface_hub import cached_download, hf_hub_url
+from pytorch_lightning.utilities.cloud_io import load as pl_load
+from torch.optim import Adam
 
 from enhancer import __version__
 from enhancer.data.dataset import EnhancerDataset
-from enhancer.loss import Avergeloss
 from enhancer.inference import Inference
+from enhancer.loss import Avergeloss
 
 CACHE_DIR = ""
 HF_TORCH_WEIGHTS = ""
@@ -293,7 +293,7 @@ class Model(pl.LightningModule):
 
         with torch.no_grad():
             for batch_id in range(0, batch.shape[0], batch_size):
-                batch_data = batch[batch_id: batch_id + batch_size, :, :].to(
+                batch_data = batch[batch_id : batch_id + batch_size, :, :].to(
                     self.device
                 )
                 prediction = self(batch_data)

enhancer/models/waveunet.py

@@ -1,11 +1,12 @@
 import logging
+from typing import List, Optional, Union
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from typing import Optional, Union, List
 
-from enhancer.models.model import Model
 from enhancer.data.dataset import EnhancerDataset
+from enhancer.models.model import Model
 
 
 class WavenetDecoder(nn.Module):