Format import using isort --profile black --gitignore --lai 2 .

style_bert_vits2/models/attentions.py

@@ -1,6 +1,6 @@
+import math
 from typing import Any, Optional
 
-import math
 import torch
 from torch import nn
 from torch.nn import functional as F

style_bert_vits2/models/commons.py

@@ -3,9 +3,10 @@
 コードと完全に一致している保証はない。あくまで参考程度とすること。
 """
 
+from typing import Any, Optional, Union
+
 import torch
 from torch.nn import functional as F
-from typing import Any, Optional, Union
 
 
 def init_weights(m: torch.nn.Module, mean: float = 0.0, std: float = 0.01) -> None:

style_bert_vits2/models/infer.py

@@ -1,12 +1,11 @@
-from typing import Any, cast, Optional, Union
+from typing import Any, Optional, Union, cast
 
 import torch
 from numpy.typing import NDArray
 
 from style_bert_vits2.constants import Languages
 from style_bert_vits2.logging import logger
-from style_bert_vits2.models import commons
-from style_bert_vits2.models import utils
+from style_bert_vits2.models import commons, utils
 from style_bert_vits2.models.hyper_parameters import HyperParameters
 from style_bert_vits2.models.models import SynthesizerTrn
 from style_bert_vits2.models.models_jp_extra import (

style_bert_vits2/models/models.py

@@ -7,10 +7,7 @@ from torch.nn import Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
 
-from style_bert_vits2.models import attentions
-from style_bert_vits2.models import commons
-from style_bert_vits2.models import modules
-from style_bert_vits2.models import monotonic_alignment
+from style_bert_vits2.models import attentions, commons, modules, monotonic_alignment
 from style_bert_vits2.nlp.symbols import NUM_LANGUAGES, NUM_TONES, SYMBOLS
 
 

style_bert_vits2/models/models_jp_extra.py

@@ -7,10 +7,7 @@ from torch.nn import Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
 
-from style_bert_vits2.models import attentions
-from style_bert_vits2.models import commons
-from style_bert_vits2.models import modules
-from style_bert_vits2.models import monotonic_alignment
+from style_bert_vits2.models import attentions, commons, modules, monotonic_alignment
 from style_bert_vits2.nlp.symbols import NUM_LANGUAGES, NUM_TONES, SYMBOLS
 
 

style_bert_vits2/models/monotonic_alignment.py

@@ -3,10 +3,11 @@
 コードと完全に一致している保証はない。あくまで参考程度とすること。
 """
 
+from typing import Any
+
 import numba
 import torch
-from numpy import int32, float32, zeros
-from typing import Any
+from numpy import float32, int32, zeros
 
 
 def maximum_path(neg_cent: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:

style_bert_vits2/models/utils/__init__.py

@@ -4,7 +4,7 @@ import os
 import re
 import subprocess
 from pathlib import Path
-from typing import Any, Optional, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 import numpy as np
 import torch
@@ -15,6 +15,7 @@ from style_bert_vits2.logging import logger
 from style_bert_vits2.models.utils import checkpoints  # type: ignore
 from style_bert_vits2.models.utils import safetensors  # type: ignore
 
+
 if TYPE_CHECKING:
     # tensorboard はライブラリとしてインストールされている場合は依存関係に含まれないため、型チェック時のみインポートする
     from torch.utils.tensorboard import SummaryWriter

style_bert_vits2/nlp/__init__.py

@@ -1,4 +1,4 @@
-from typing import Optional, TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 from style_bert_vits2.constants import Languages
 from style_bert_vits2.nlp.symbols import (
@@ -7,6 +7,7 @@ from style_bert_vits2.nlp.symbols import (
     SYMBOLS,
 )
 
+
 # __init__.py は配下のモジュールをインポートした時点で実行される
 # PyTorch のインポートは重いので、型チェック時以外はインポートしない
 if TYPE_CHECKING:
@@ -99,10 +100,10 @@ def cleaned_text_to_sequence(
     cleaned_phones: list[str], tones: list[int], language: Languages
 ) -> tuple[list[int], list[int], list[int]]:
     """
-    テキスト文字列を、テキスト内の記号に対応する一連の ID に変換する
+    音素リスト・アクセントリスト・言語を、テキスト内の対応する ID に変換する
 
     Args:
-        cleaned_phones (list[str]): clean_text() でクリーニングされた音素のリスト (?)
+        cleaned_phones (list[str]): clean_text() でクリーニングされた音素のリスト
         tones (list[int]): 各音素のアクセント
         language (Languages): テキストの言語
 

style_bert_vits2/nlp/bert_models.py

@@ -9,7 +9,7 @@ Style-Bert-VITS2 の学習・推論に必要な各言語ごとの BERT モデル
 """
 
 import gc
-from typing import cast, Optional, Union
+from typing import Optional, Union, cast
 
 import torch
 from transformers import (

style_bert_vits2/nlp/chinese/g2p.py

@@ -2,7 +2,7 @@ import re
 from pathlib import Path
 
 import jieba.posseg as psg
-from pypinyin import lazy_pinyin, Style
+from pypinyin import Style, lazy_pinyin
 
 from style_bert_vits2.nlp.chinese.tone_sandhi import ToneSandhi
 from style_bert_vits2.nlp.symbols import PUNCTUATIONS

style_bert_vits2/nlp/chinese/tone_sandhi.py

@@ -13,8 +13,7 @@
 # limitations under the License.
 
 import jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
+from pypinyin import Style, lazy_pinyin
 
 
 class ToneSandhi:

style_bert_vits2/nlp/japanese/normalizer.py

@@ -1,5 +1,6 @@
 import re
 import unicodedata
+
 from num2words import num2words
 
 from style_bert_vits2.nlp.symbols import PUNCTUATIONS

style_bert_vits2/nlp/japanese/user_dict/__init__.py

@@ -17,12 +17,13 @@ from fastapi import HTTPException
 
 from style_bert_vits2.constants import DEFAULT_USER_DICT_DIR
 from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
-from style_bert_vits2.nlp.japanese.user_dict.word_model import UserDictWord, WordTypes
 from style_bert_vits2.nlp.japanese.user_dict.part_of_speech_data import (
     MAX_PRIORITY,
     MIN_PRIORITY,
     part_of_speech_data,
 )
+from style_bert_vits2.nlp.japanese.user_dict.word_model import UserDictWord, WordTypes
+
 
 # root_dir = engine_root()
 # save_dir = get_save_dir()

style_bert_vits2/nlp/japanese/user_dict/part_of_speech_data.py

@@ -14,6 +14,7 @@ from style_bert_vits2.nlp.japanese.user_dict.word_model import (
     WordTypes,
 )
 
+
 MIN_PRIORITY = USER_DICT_MIN_PRIORITY
 MAX_PRIORITY = USER_DICT_MAX_PRIORITY
 

style_bert_vits2/nlp/japanese/user_dict/word_model.py

@@ -11,6 +11,7 @@ from typing import List, Optional
 
 from pydantic import BaseModel, Field, validator
 
+
 USER_DICT_MIN_PRIORITY = 0
 USER_DICT_MAX_PRIORITY = 10