Muskits - apply isort

egs2/TEMPLATE/svs1/pyscripts/audio/format_xml_scp.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python3
 import argparse
 import logging
-from pathlib import Path
 import os
+from pathlib import Path
 
 from espnet.utils.cli_utils import get_commandline_args
 

egs2/TEMPLATE/svs1/pyscripts/utils/evaluate_semitone.py

@@ -11,6 +11,7 @@
 import logging
 import multiprocessing as mp
 import os
+from math import log2, pow
 from typing import Dict, List, Tuple
 
 import librosa
@@ -20,7 +21,6 @@
 import soundfile as sf
 from fastdtw import fastdtw
 from scipy import spatial
-from math import log2, pow
 
 
 def _Hz2Semitone(freq):

egs2/TEMPLATE/svs1/pyscripts/utils/prep_segments_from_xml.py

@@ -3,6 +3,7 @@
 import math
 import os
 import sys
+
 import music21 as m21
 
 """Divide songs into segments according to structured musicXML."""

egs2/ofuton_p_utagoe_db/svs1/local/dataset_split.py

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

egs2/ofuton_p_utagoe_db/svs1/local/prep_segments_from_xml.py

@@ -3,6 +3,7 @@
 import math
 import os
 import sys
+
 import music21 as m21
 
 """Divide songs into segments according to structured musicXML."""

espnet2/bin/svs_inference.py

@@ -4,10 +4,10 @@
 
 import argparse
 import logging
-from pathlib import Path
 import shutil
 import sys
 import time
+from pathlib import Path
 from typing import Any, Dict, Optional, Sequence, Tuple, Union
 
 import matplotlib
@@ -16,24 +16,23 @@
 import torch
 from typeguard import check_argument_types
 
-from espnet.utils.cli_utils import get_commandline_args
 from espnet2.fileio.npy_scp import NpyScpWriter
+from espnet2.svs.naive_rnn.naive_rnn import NaiveRNN
+from espnet2.svs.naive_rnn.naive_rnn_dp import NaiveRNNDP
 from espnet2.tasks.svs import SVSTask
 from espnet2.torch_utils.device_funcs import to_device
 from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
-
-from espnet2.svs.naive_rnn.naive_rnn import NaiveRNN
-from espnet2.svs.naive_rnn.naive_rnn_dp import NaiveRNNDP
-
-# from espnet2.svs.glu_transformer.glu_transformer import GLU_Transformer
-# from espnet2.svs.xiaoice.XiaoiceSing import XiaoiceSing
-# from espnet2.svs.xiaoice.XiaoiceSing import XiaoiceSing_noDP
-
 from espnet2.utils import config_argparse
 from espnet2.utils.get_default_kwargs import get_default_kwargs
 from espnet2.utils.griffin_lim import Spectrogram2Waveform
 from espnet2.utils.nested_dict_action import NestedDictAction
 from espnet2.utils.types import str2bool, str2triple_str, str_or_none
+from espnet.utils.cli_utils import get_commandline_args
+
+# from espnet2.svs.glu_transformer.glu_transformer import GLU_Transformer
+# from espnet2.svs.xiaoice.XiaoiceSing import XiaoiceSing
+# from espnet2.svs.xiaoice.XiaoiceSing import XiaoiceSing_noDP
+
 
 
 class SingingGenerate:

espnet2/fileio/xml_scp.py

@@ -3,9 +3,9 @@
 from pathlib import Path
 from typing import Union
 
+import music21 as m21
 import numpy as np
 from typeguard import check_argument_types
-import music21 as m21
 
 from espnet2.fileio.read_text import read_2column_text
 

espnet2/svs/espnet_model.py

@@ -4,23 +4,23 @@
 
 """Singing-voice-synthesis ESPnet model."""
 
+import logging
 from contextlib import contextmanager
 from distutils.version import LooseVersion
-import logging
 from typing import Dict, Optional, Tuple
 
 import torch
-
 from typeguard import check_argument_types
 
 from espnet2.layers.abs_normalize import AbsNormalize
 from espnet2.layers.inversible_interface import InversibleInterface
-from espnet2.train.abs_espnet_model import AbsESPnetModel
 from espnet2.svs.abs_svs import AbsSVS
+from espnet2.svs.feats_extract.score_feats_extract import (
+    FrameScoreFeats,
+    SyllableScoreFeats,
+)
+from espnet2.train.abs_espnet_model import AbsESPnetModel
 from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
-
-from espnet2.svs.feats_extract.score_feats_extract import FrameScoreFeats
-from espnet2.svs.feats_extract.score_feats_extract import SyllableScoreFeats
 from espnet.nets.pytorch_backend.nets_utils import pad_list
 
 if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):

espnet2/svs/feats_extract/score_feats_extract.py

@@ -5,9 +5,9 @@
 import torch
 from typeguard import check_argument_types
 
-from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
 from espnet2.layers.stft import Stft
 from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
+from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
 
 
 def ListsToTensor(xs):

espnet2/svs/naive_rnn/naive_rnn.py

@@ -3,23 +3,22 @@
 
 """Transformer-SVS related modules."""
 
+import logging
+import random
 from typing import Dict, Optional, Sequence, Tuple
+
 import torch
 import torch.nn.functional as F
-
-import logging
+from torch.distributions import Beta
 from typeguard import check_argument_types
 
+from espnet2.svs.abs_svs import AbsSVS
+from espnet2.torch_utils.device_funcs import force_gatherable
+from espnet2.torch_utils.initialize import initialize
 from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
-from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder as EncoderPrenet
 from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
+from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder as EncoderPrenet
 from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
-from espnet2.torch_utils.device_funcs import force_gatherable
-from espnet2.torch_utils.initialize import initialize
-from espnet2.svs.abs_svs import AbsSVS
-
-import random
-from torch.distributions import Beta
 
 Beta_distribution = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
 

espnet2/svs/naive_rnn/naive_rnn_dp.py

@@ -3,29 +3,27 @@
 
 """Transformer-SVS related modules."""
 
+import logging
+import random
 from typing import Dict, Optional, Sequence, Tuple
+
 import torch
 import torch.nn.functional as F
-
-import logging
+from torch.distributions import Beta
 from typeguard import check_argument_types
 
+from espnet2.svs.abs_svs import AbsSVS
+from espnet2.torch_utils.device_funcs import force_gatherable
+from espnet2.torch_utils.initialize import initialize
 from espnet.nets.pytorch_backend.e2e_tts_fastspeech import (
     FeedForwardTransformerLoss as FastSpeechLoss,
 )
 from espnet.nets.pytorch_backend.fastspeech.duration_predictor import DurationPredictor
 from espnet.nets.pytorch_backend.fastspeech.length_regulator import LengthRegulator
-
 from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, make_pad_mask
-from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder as EncoderPrenet
 from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
+from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder as EncoderPrenet
 from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
-from espnet2.torch_utils.device_funcs import force_gatherable
-from espnet2.torch_utils.initialize import initialize
-from espnet2.svs.abs_svs import AbsSVS
-
-import random
-from torch.distributions import Beta
 
 
 class NaiveRNNDP(AbsSVS):

espnet2/tasks/svs.py

@@ -12,22 +12,24 @@
 
 from espnet2.layers.abs_normalize import AbsNormalize
 from espnet2.layers.global_mvn import GlobalMVN
+from espnet2.svs.abs_svs import AbsSVS
+from espnet2.svs.espnet_model import ESPnetSVSModel
+from espnet2.svs.feats_extract.score_feats_extract import (
+    FrameScoreFeats,
+    SyllableScoreFeats,
+)
+from espnet2.svs.naive_rnn.naive_rnn import NaiveRNN
+from espnet2.svs.naive_rnn.naive_rnn_dp import NaiveRNNDP
 from espnet2.tasks.abs_task import AbsTask
 from espnet2.train.class_choices import ClassChoices
 from espnet2.train.collate_fn import CommonCollateFn
 from espnet2.train.preprocessor import SVSPreprocessor
 from espnet2.train.trainer import Trainer
-from espnet2.svs.abs_svs import AbsSVS
-from espnet2.svs.espnet_model import ESPnetSVSModel
 from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
 from espnet2.tts.feats_extract.dio import Dio
-from espnet2.svs.feats_extract.score_feats_extract import FrameScoreFeats
-from espnet2.svs.feats_extract.score_feats_extract import SyllableScoreFeats
 from espnet2.tts.feats_extract.energy import Energy
 from espnet2.tts.feats_extract.log_mel_fbank import LogMelFbank
 from espnet2.tts.feats_extract.log_spectrogram import LogSpectrogram
-from espnet2.svs.naive_rnn.naive_rnn import NaiveRNN
-from espnet2.svs.naive_rnn.naive_rnn_dp import NaiveRNNDP
 
 # TODO(Yuning): Models to be added
 # from espnet2.svs.encoder_decoder.transformer.transformer import Transformer
@@ -41,7 +43,6 @@
 from espnet2.utils.nested_dict_action import NestedDictAction
 from espnet2.utils.types import int_or_none, str2bool, str_or_none
 
-
 feats_extractor_choices = ClassChoices(
     "feats_extract",
     classes=dict(fbank=LogMelFbank, spectrogram=LogSpectrogram),

espnet2/train/preprocessor.py

@@ -5,9 +5,9 @@
 from typing import Collection, Dict, Iterable, List, Union
 
 import numpy as np
+import pyopenjtalk
 import scipy.signal
 import soundfile
-import pyopenjtalk
 from typeguard import check_argument_types, check_return_type
 
 from espnet2.text.build_tokenizer import build_tokenizer

tools/check_install.py

@@ -6,9 +6,9 @@
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 
 import importlib
+import os
 import shutil
 import sys
-import os
 
 from packaging.version import parse