Refactor: run "hatch run style:fmt"

pyproject.toml

@@ -75,18 +75,18 @@ cov = [
 [tool.hatch.envs.style]
 detached = true
 dependencies = [
-  "black",
-  "isort",
+    "black",
+    "isort",
 ]
 [tool.hatch.envs.style.scripts]
 check = [
-  "black --check --diff .",
-  "isort --check-only --diff --profile black --gitignore --lai 2 .",
+    "black --check --diff .",
+    "isort --check-only --diff --profile black --gitignore --lai 2 .",
 ]
 fmt = [
-  "black .",
-  "isort --profile black --gitignore --lai 2 .",
-  "check",
+    "black .",
+    "isort --profile black --gitignore --lai 2 .",
+    "check",
 ]
 
 [[tool.hatch.envs.test.matrix]]

style_bert_vits2/nlp/bert_models.py

@@ -80,10 +80,14 @@ def load_model(
     if language == Languages.EN:
         model = cast(
             DebertaV2Model,
-            DebertaV2Model.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision),
+            DebertaV2Model.from_pretrained(
+                pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision
+            ),
         )
     else:
-        model = AutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision)
+        model = AutoModelForMaskedLM.from_pretrained(
+            pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision
+        )
     __loaded_models[language] = model
     logger.info(
         f"Loaded the {language} BERT model from {pretrained_model_name_or_path}"
@@ -135,9 +139,17 @@ def load_tokenizer(
     # BERT トークナイザーをロードし、辞書に格納して返す
     ## 英語のみ DebertaV2Tokenizer でロードする必要がある
     if language == Languages.EN:
-        tokenizer = DebertaV2Tokenizer.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision)
+        tokenizer = DebertaV2Tokenizer.from_pretrained(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
     else:
-        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision)
+        tokenizer = AutoTokenizer.from_pretrained(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
     __loaded_tokenizers[language] = tokenizer
     logger.info(
         f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}"

style_bert_vits2/nlp/chinese/g2p.py

@@ -10,8 +10,7 @@ from style_bert_vits2.nlp.symbols import PUNCTUATIONS
 
 with open(Path(__file__).parent / "opencpop-strict.txt", "r", encoding="utf-8") as f:
     __PINYIN_TO_SYMBOL_MAP = {
-        line.split("\t")[0]: line.strip().split("\t")[1]
-        for line in f.readlines()
+        line.split("\t")[0]: line.strip().split("\t")[1] for line in f.readlines()
     }