use isort (#389)

.github/workflows/ci.yaml

@@ -19,7 +19,7 @@ jobs:
         python-version: [3.7]
         # 1.6 is failed on cpu: https://github.com/kan-bayashi/ParallelWaveGAN/issues/198
         # 1.10 does not support python 3.6
-        pytorch-version: [1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, 1.11.0]
+        pytorch-version: [1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, 1.11.0, 1.12.1, 1.13.1]
     steps:
       - uses: actions/checkout@master
       - uses: actions/setup-python@v2
@@ -45,10 +45,11 @@ jobs:
           export PATH=shellcheck-stable:$PATH
           # not to check the script copied from kaldi
           find egs -name "*.sh" | grep -v path.sh | while read line; do shellcheck -x --shell=bash -P $(dirname $line) ${line}; done
-      - name: Black & Flake8
+      - name: Black & isort & Flake8
         run: |
           source tools/venv/bin/activate
           black --diff --check parallel_wavegan
+          isort --diff --check parallel_wavegan
           flake8 parallel_wavegan
           flake8 --extend-ignore=D test
       - name: Pytest
@@ -62,7 +63,7 @@ jobs:
       max-parallel: 10
       matrix:
         python-version: [3.9]
-        pytorch-version: [1.11.0]
+        pytorch-version: [1.13.1]
         config:
           - "parallel_wavegan.v1.debug.yaml"
           - "melgan.v1.debug.yaml"
@@ -103,8 +104,8 @@ jobs:
     strategy:
       max-parallel: 10
       matrix:
-        python-version: [3.7]
-        pytorch-version: [1.9]
+        python-version: [3.8]
+        pytorch-version: [1.13.1]
         config:
           - "melgan_vae.v3.debug.yaml"
           - "conditioned_melgan_vae.v3.debug.yaml"

README.md

@@ -62,7 +62,7 @@ This repository is tested on Ubuntu 20.04 with a GPU Titan V.
 - sox (you can install via `sudo apt install sox` in ubuntu)
 
 Different cuda version should be working but not explicitly tested.  
-All of the codes are tested on Pytorch 1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, and 1.11.0.
+All of the codes are tested on Pytorch 1.4, 1.5.1, 1.7.1, 1.8.1, 1.9, 1.10.2, 1.11.0, 1.12.1, and 1.13.1.
 
 Pytorch 1.6 works but there are some issues in cpu mode (See #198).
 

egs/csd/voc1/local/dataset_split.py

@@ -2,7 +2,6 @@
 import os
 import shutil
 
-
 UTT_PREFIX = "csd"
 DEV_LIST = ["046"]
 TEST_LIST = ["047", "048", "049", "050"]

egs/kiritan/voc1/local/dataset_split.py

@@ -1,12 +1,11 @@
 #!/usr/bin/env python3
-import os
 import argparse
-import sys
+import os
 import random
 import shutil
+import sys
 from shutil import copyfile
 
-
 UTT_PREFIX = "kiritan"
 DEV_LIST = ["13", "14", "26", "28", "39"]
 TEST_LIST = ["01", "16", "17", "27", "44"]

egs/kising/voc1/local/data_prep.py

@@ -1,5 +1,6 @@
 import argparse
 import os
+
 import librosa
 import numpy as np
 

egs/natsume/voc1/local/dataset_split.py

@@ -2,7 +2,6 @@
 import os
 import shutil
 
-
 UTT_PREFIX = "natsume"
 DEV_LIST = ["9", "22", "38", "43", "44"]
 TEST_LIST = ["2", "13", "24", "25", "27"]

egs/no7singing/voc1/local/dataset_split.py

@@ -1,12 +1,11 @@
 #!/usr/bin/env python3
-import os
 import argparse
-import sys
+import os
 import random
 import shutil
+import sys
 from shutil import copyfile
 
-
 UTT_PREFIX = "no7singing"
 DEV_LIST = ["13", "14", "26", "28", "39"]
 TEST_LIST = ["01", "16", "17", "27", "44"]

egs/ofuton_p_utagoe_db/voc1/local/dataset_split.py

@@ -2,7 +2,6 @@
 import os
 import shutil
 
-
 UTT_PREFIX = "ofuton"
 DEV_LIST = [
     "chatsumi",

egs/oniku_kurumi_utagoe_db/voc1/local/dataset_split.py

@@ -2,7 +2,6 @@
 import os
 import shutil
 
-
 UTT_PREFIX = "oniku"
 DEV_LIST = ["chatsumi", "goin_home", "aoimeno_ningyou", "momiji", "tetsudou_shouka"]
 TEST_LIST = ["usagito_kame", "sousyunfu", "romance_anonimo", "momotarou", "furusato"]

egs/opencpop/voc1/local/data_prep.py

@@ -1,5 +1,6 @@
 import argparse
 import os
+
 import librosa
 import numpy as np
 

egs/pjs/voc1/local/dataset_split.py

@@ -2,7 +2,6 @@
 import os
 import shutil
 
-
 UTT_PREFIX = "pjs"
 DEV_LIST = [
     "pjs002",

egs/vctk/voc1/local/preprocess_hubert.py

@@ -12,14 +12,12 @@
 
 import librosa
 import numpy as np
-import soundfile as sf
 import resampy
+import soundfile as sf
 import yaml
-
 from tqdm import tqdm
 
-from parallel_wavegan.datasets import AudioDataset
-from parallel_wavegan.datasets import AudioSCPDataset
+from parallel_wavegan.datasets import AudioDataset, AudioSCPDataset
 from parallel_wavegan.utils import write_hdf5
 
 

egs/vctk/vq1/local/decode_from_text.py

@@ -14,11 +14,9 @@
 import soundfile as sf
 import torch
 import yaml
-
 from tqdm import tqdm
 
 import parallel_wavegan.models
-
 from parallel_wavegan.utils import load_model
 
 

egs/vctk/vq1/local/setup_for_vc.py

@@ -12,12 +12,10 @@
 
 import numpy as np
 import yaml
-
 from tqdm import tqdm
 
 from parallel_wavegan.datasets import AudioDataset
-from parallel_wavegan.utils import read_hdf5
-from parallel_wavegan.utils import write_hdf5
+from parallel_wavegan.utils import read_hdf5, write_hdf5
 
 
 def main():

parallel_wavegan/bin/compute_statistics.py

@@ -12,14 +12,11 @@
 
 import numpy as np
 import yaml
-
 from sklearn.preprocessing import StandardScaler
 from tqdm import tqdm
 
-from parallel_wavegan.datasets import MelDataset
-from parallel_wavegan.datasets import MelSCPDataset
-from parallel_wavegan.utils import read_hdf5
-from parallel_wavegan.utils import write_hdf5
+from parallel_wavegan.datasets import MelDataset, MelSCPDataset
+from parallel_wavegan.utils import read_hdf5, write_hdf5
 
 
 def main():

parallel_wavegan/bin/decode.py

@@ -15,15 +15,15 @@
 import soundfile as sf
 import torch
 import yaml
-
 from tqdm import tqdm
 
-from parallel_wavegan.datasets import AudioDataset
-from parallel_wavegan.datasets import AudioSCPDataset
-from parallel_wavegan.datasets import MelDataset
-from parallel_wavegan.datasets import MelSCPDataset
-from parallel_wavegan.utils import load_model
-from parallel_wavegan.utils import read_hdf5
+from parallel_wavegan.datasets import (
+    AudioDataset,
+    AudioSCPDataset,
+    MelDataset,
+    MelSCPDataset,
+)
+from parallel_wavegan.utils import load_model, read_hdf5
 
 
 def main():

parallel_wavegan/bin/normalize.py

@@ -12,16 +12,16 @@
 
 import numpy as np
 import yaml
-
 from sklearn.preprocessing import StandardScaler
 from tqdm import tqdm
 
-from parallel_wavegan.datasets import AudioMelDataset
-from parallel_wavegan.datasets import AudioMelSCPDataset
-from parallel_wavegan.datasets import MelDataset
-from parallel_wavegan.datasets import MelSCPDataset
-from parallel_wavegan.utils import read_hdf5
-from parallel_wavegan.utils import write_hdf5
+from parallel_wavegan.datasets import (
+    AudioMelDataset,
+    AudioMelSCPDataset,
+    MelDataset,
+    MelSCPDataset,
+)
+from parallel_wavegan.utils import read_hdf5, write_hdf5
 
 
 def main():

parallel_wavegan/bin/preprocess.py

@@ -15,12 +15,10 @@
 import numpy as np
 import soundfile as sf
 import yaml
-
 from scipy.interpolate import interp1d
 from tqdm import tqdm
 
-from parallel_wavegan.datasets import AudioDataset
-from parallel_wavegan.datasets import AudioSCPDataset
+from parallel_wavegan.datasets import AudioDataset, AudioSCPDataset
 from parallel_wavegan.utils import write_hdf5
 
 

parallel_wavegan/bin/train.py

@@ -10,33 +10,34 @@
 import logging
 import os
 import sys
-
 from collections import defaultdict
 
 import matplotlib
 import numpy as np
 import soundfile as sf
 import torch
 import yaml
-
 from tensorboardX import SummaryWriter
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 import parallel_wavegan
 import parallel_wavegan.models
 import parallel_wavegan.optimizers
-
-from parallel_wavegan.datasets import AudioDataset
-from parallel_wavegan.datasets import AudioMelDataset
-from parallel_wavegan.datasets import AudioMelSCPDataset
-from parallel_wavegan.datasets import AudioSCPDataset
+from parallel_wavegan.datasets import (
+    AudioDataset,
+    AudioMelDataset,
+    AudioMelSCPDataset,
+    AudioSCPDataset,
+)
 from parallel_wavegan.layers import PQMF
-from parallel_wavegan.losses import DiscriminatorAdversarialLoss
-from parallel_wavegan.losses import FeatureMatchLoss
-from parallel_wavegan.losses import GeneratorAdversarialLoss
-from parallel_wavegan.losses import MelSpectrogramLoss
-from parallel_wavegan.losses import MultiResolutionSTFTLoss
+from parallel_wavegan.losses import (
+    DiscriminatorAdversarialLoss,
+    FeatureMatchLoss,
+    GeneratorAdversarialLoss,
+    MelSpectrogramLoss,
+    MultiResolutionSTFTLoss,
+)
 from parallel_wavegan.utils import read_hdf5
 
 # set to avoid matplotlib error in CLI environment

parallel_wavegan/datasets/audio_mel_dataset.py

@@ -7,15 +7,12 @@
 
 import logging
 import os
-
 from multiprocessing import Manager
 
 import numpy as np
-
 from torch.utils.data import Dataset
 
-from parallel_wavegan.utils import find_files
-from parallel_wavegan.utils import read_hdf5
+from parallel_wavegan.utils import find_files, read_hdf5
 
 
 class AudioMelDataset(Dataset):

parallel_wavegan/datasets/scp_dataset.py

@@ -6,16 +6,13 @@
 """Dataset modules based on kaldi-style scp files."""
 
 import logging
-
 from multiprocessing import Manager
 
 import kaldiio
 import numpy as np
-
 from torch.utils.data import Dataset
 
-from parallel_wavegan.utils import HDF5ScpLoader
-from parallel_wavegan.utils import NpyScpLoader
+from parallel_wavegan.utils import HDF5ScpLoader, NpyScpLoader
 
 
 def _get_feats_scp_loader(feats_scp):

parallel_wavegan/distributed/launch.py

@@ -9,9 +9,7 @@
 import os
 import subprocess
 import sys
-
-from argparse import ArgumentParser
-from argparse import REMAINDER
+from argparse import REMAINDER, ArgumentParser
 
 
 def parse_args():

parallel_wavegan/functions/vector_quantizer.py

@@ -10,7 +10,6 @@
 """
 
 import torch
-
 from torch.autograd import Function
 
 

parallel_wavegan/layers/pqmf.py

@@ -8,7 +8,6 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-
 from scipy.signal import kaiser
 
 

parallel_wavegan/layers/vector_quantize_codebook.py

@@ -11,8 +11,7 @@
 
 import torch
 
-from parallel_wavegan.functions import vector_quantize
-from parallel_wavegan.functions import vector_quantize_straight_through
+from parallel_wavegan.functions import vector_quantize, vector_quantize_straight_through
 
 
 class VQCodebook(torch.nn.Module):

parallel_wavegan/losses/mel_loss.py

@@ -9,7 +9,6 @@
 import torch
 import torch.nn.functional as F
 
-
 is_pytorch_17plus = LooseVersion(torch.__version__) >= LooseVersion("1.7")
 
 

parallel_wavegan/losses/stft_loss.py

@@ -5,11 +5,11 @@
 
 """STFT-based Loss modules."""
 
+from distutils.version import LooseVersion
+
 import torch
 import torch.nn.functional as F
 
-from distutils.version import LooseVersion
-
 is_pytorch_17plus = LooseVersion(torch.__version__) >= LooseVersion("1.7")