Merge pull request #15 from shahules786/dev-reformat

precommit hooks

.pre-commit-config.yaml

@@ -0,0 +1,43 @@
+
+repos:
+    # # Clean Notebooks
+    # - repo: https://github.com/kynan/nbstripout
+    #   rev: master
+    #   hooks:
+    #     - id: nbstripout
+    # Format Code
+    - repo: https://github.com/ambv/black
+      rev: 22.8.0
+      hooks:
+        - id: black
+
+    # Sort imports
+    - repo: https://github.com/PyCQA/isort
+      rev: 5.10.1
+      hooks:
+      - id: isort
+        args: ["--profile", "black"]
+
+    - repo: https://gitlab.com/pycqa/flake8
+      rev: 5.0.4
+      hooks:
+      - id: flake8
+        args: ['--ignore=E203,E501,F811,E712,W503']
+
+    # Formatting, Whitespace, etc
+    - repo: https://github.com/pre-commit/pre-commit-hooks
+      rev: v3.2.0
+      hooks:
+      - id: trailing-whitespace
+      - id: check-added-large-files
+        args: ['--maxkb=1000']
+      - id: check-ast
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-xml
+      - id: check-yaml
+      - id: debug-statements
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: mixed-line-ending
+        args: ['--fix=no']

enhancer/cli/train.py

@@ -1,11 +1,12 @@
 import os
 from types import MethodType
+
 import hydra
 from hydra.utils import instantiate
 from omegaconf import DictConfig
-from torch.optim.lr_scheduler import ReduceLROnPlateau
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.loggers import MLFlowLogger
+from torch.optim.lr_scheduler import ReduceLROnPlateau
 
 os.environ["HYDRA_FULL_ERROR"] = "1"
 JOB_ID = os.environ.get("SLURM_JOBID", "0")

enhancer/cli/train_config/dataset/DNS-2020.yaml

@@ -10,4 +10,3 @@ files:
   test_clean : clean_test_wav
   train_noisy : clean_test_wav
   test_noisy : clean_test_wav
-

enhancer/cli/train_config/dataset/Vctk.yaml

@@ -10,6 +10,3 @@ files:
   test_clean : clean_testset_wav
   train_noisy : noisy_trainset_28spk_wav
   test_noisy : noisy_testset_wav
-
-
-

enhancer/cli/train_config/hyperparameters/default.yaml

@@ -5,4 +5,3 @@ ReduceLr_patience : 5
 ReduceLr_factor : 0.1
 min_lr : 0.000001
 EarlyStopping_factor : 10
-

enhancer/cli/train_config/model/Demucs.yaml

@@ -14,5 +14,3 @@ encoder_decoder:
 lstm:
   bidirectional: False
   num_layers: 2
-
-  

enhancer/data/dataset.py

@@ -1,16 +1,17 @@
-import multiprocessing
 import math
+import multiprocessing
 import os
-import pytorch_lightning as pl
-from torch.utils.data import IterableDataset, DataLoader, Dataset
-import torch.nn.functional as F
 from typing import Optional
 
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, Dataset, IterableDataset
+
 from enhancer.data.fileprocessor import Fileprocessor
-from enhancer.utils.random import create_unique_rng
-from enhancer.utils.io import Audio
 from enhancer.utils import check_files
 from enhancer.utils.config import Files
+from enhancer.utils.io import Audio
+from enhancer.utils.random import create_unique_rng
 
 
 class TrainDataset(IterableDataset):

enhancer/data/fileprocessor.py

@@ -1,5 +1,6 @@
 import glob
 import os
+
 import numpy as np
 from scipy.io import wavfile
 

enhancer/inference.py

@@ -1,11 +1,12 @@
-import numpy as np
-from scipy.signal import get_window
-from scipy.io import wavfile
+from pathlib import Path
 from typing import Optional, Union
+
+import numpy as np
 import torch
 import torch.nn.functional as F
-from pathlib import Path
 from librosa import load as load_audio
+from scipy.io import wavfile
+from scipy.signal import get_window
 
 from enhancer.utils import Audio
 

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):

enhancer/utils/__init__.py

@@ -1,3 +1,3 @@
-from enhancer.utils.utils import check_files
-from enhancer.utils.io import Audio
 from enhancer.utils.config import Files
+from enhancer.utils.io import Audio
+from enhancer.utils.utils import check_files

enhancer/utils/io.py

@@ -1,7 +1,8 @@
 import os
-import librosa
 from pathlib import Path
 from typing import Optional, Union
+
+import librosa
 import numpy as np
 import torch
 import torchaudio

enhancer/utils/random.py

@@ -1,5 +1,6 @@
 import os
 import random
+
 import torch
 
 

enhancer/utils/utils.py

@@ -1,5 +1,6 @@
 import os
 from typing import Optional
+
 from enhancer.utils.config import Files
 
 

requirements.txt

@@ -1,16 +1,16 @@
-joblib==1.2.0
-librosa==0.9.2
-numpy==1.23.3
-hydra-core==1.2.0
-scikit-learn==1.1.2
-scipy==1.9.1
-torch==1.12.1
-tqdm==4.64.1
-mlflow==1.29.0
-protobuf==3.19.6
-boto3==1.24.86
-torchaudio==0.12.1
-huggingface-hu==0.10.0
-pytorch-lightning==1.7.7
-flake8==5.0.4
-black==22.8.0
+black>=22.8.0
+boto3>=1.24.86
+flake8>=5.0.4
+huggingface-hu>=0.10.0
+hydra-core>=1.2.0
+joblib>=1.2.0
+librosa>=0.9.2
+mlflow>=1.29.0
+numpy>=1.23.3
+protobuf>=3.19.6
+pytorch-lightning>=1.7.7
+scikit-learn>=1.1.2
+scipy>=1.9.1
+torch>=1.12.1
+torchaudio>=0.12.1
+tqdm>=4.64.1

tests/loss_function_test.py

@@ -1,6 +1,5 @@
-from asyncio import base_tasks
-import torch
 import pytest
+import torch
 
 from enhancer.loss import mean_absolute_error, mean_squared_error
 

tests/models/demucs_test.py

@@ -1,10 +1,9 @@
 import pytest
 import torch
-from enhancer import data
 
-from enhancer.utils.config import Files
-from enhancer.models import Demucs
 from enhancer.data.dataset import EnhancerDataset
+from enhancer.models import Demucs
+from enhancer.utils.config import Files
 
 
 @pytest.fixture
@@ -41,4 +40,4 @@ def test_forward(batch_size, samples):
 )
 def test_demucs_init(dataset, channels, loss):
     with torch.no_grad():
-        model = Demucs(num_channels=channels, dataset=dataset, loss=loss)
+        _ = Demucs(num_channels=channels, dataset=dataset, loss=loss)

tests/models/test_waveunet.py

@@ -1,10 +1,9 @@
 import pytest
 import torch
-from enhancer import data
 
-from enhancer.utils.config import Files
-from enhancer.models import WaveUnet
 from enhancer.data.dataset import EnhancerDataset
+from enhancer.models import WaveUnet
+from enhancer.utils.config import Files
 
 
 @pytest.fixture
@@ -41,4 +40,4 @@ def test_forward(batch_size, samples):
 )
 def test_demucs_init(dataset, channels, loss):
     with torch.no_grad():
-        model = WaveUnet(num_channels=channels, dataset=dataset, loss=loss)
+        _ = WaveUnet(num_channels=channels, dataset=dataset, loss=loss)

tests/utils_test.py

@@ -1,11 +1,9 @@
-from logging import root
+import numpy as np
 import pytest
 import torch
-import numpy as np
 
-from enhancer.utils.io import Audio
-from enhancer.utils.config import Files
 from enhancer.data.fileprocessor import Fileprocessor
+from enhancer.utils.io import Audio
 
 
 def test_io_channel():
@@ -47,6 +45,6 @@ def test_fileprocessor_names(dataset_name):
 
 def test_fileprocessor_invaliname():
     with pytest.raises(ValueError):
-        fp = Fileprocessor.from_name(
+        _ = Fileprocessor.from_name(
             "undefined", "clean_dir", "noisy_dir", 16000
         ).prepare_matching_dict()