Apply black formatter

style_bert_vits2/nlp/__init__.py

@@ -74,16 +74,19 @@ def clean_text(
     if language == Languages.JP:
         from style_bert_vits2.nlp.japanese.g2p import g2p
         from style_bert_vits2.nlp.japanese.normalizer import normalize_text
+
         norm_text = normalize_text(text)
         phones, tones, word2ph = g2p(norm_text, use_jp_extra, raise_yomi_error)
     elif language == Languages.EN:
         from style_bert_vits2.nlp.english.g2p import g2p
         from style_bert_vits2.nlp.english.normalizer import normalize_text
+
         norm_text = normalize_text(text)
         phones, tones, word2ph = g2p(norm_text)
     elif language == Languages.ZH:
         from style_bert_vits2.nlp.chinese.g2p import g2p
         from style_bert_vits2.nlp.chinese.normalizer import normalize_text
+
         norm_text = normalize_text(text)
         phones, tones, word2ph = g2p(norm_text)
     else:
@@ -92,7 +95,9 @@ def clean_text(
     return norm_text, phones, tones, word2ph
 
 
-def cleaned_text_to_sequence(cleaned_phones: list[str], tones: list[int], language: Languages) -> tuple[list[int], list[int], list[int]]:
+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
 

style_bert_vits2/nlp/bert_models.py

@@ -30,7 +30,9 @@ from style_bert_vits2.logging import logger
 __loaded_models: dict[Languages, Union[PreTrainedModel, DebertaV2Model]] = {}
 
 # 各言語ごとのロード済みの BERT トークナイザーを格納する辞書
-__loaded_tokenizers: dict[Languages, Union[PreTrainedTokenizer, PreTrainedTokenizerFast, DebertaV2Tokenizer]] = {}
+__loaded_tokenizers: dict[
+    Languages, Union[PreTrainedTokenizer, PreTrainedTokenizerFast, DebertaV2Tokenizer]
+] = {}
 
 
 def load_model(
@@ -63,18 +65,24 @@ def load_model(
 
     # pretrained_model_name_or_path が指定されていない場合はデフォルトのパスを利用
     if pretrained_model_name_or_path is None:
-        assert DEFAULT_BERT_TOKENIZER_PATHS[language].exists(), \
-            f"The default {language} BERT model does not exist on the file system. Please specify the path to the pre-trained model."
+        assert DEFAULT_BERT_TOKENIZER_PATHS[
+            language
+        ].exists(), f"The default {language} BERT model does not exist on the file system. Please specify the path to the pre-trained model."
         pretrained_model_name_or_path = str(DEFAULT_BERT_TOKENIZER_PATHS[language])
 
     # BERT モデルをロードし、辞書に格納して返す
     ## 英語のみ DebertaV2Model でロードする必要がある
     if language == Languages.EN:
-        model = cast(DebertaV2Model, DebertaV2Model.from_pretrained(pretrained_model_name_or_path))
+        model = cast(
+            DebertaV2Model,
+            DebertaV2Model.from_pretrained(pretrained_model_name_or_path),
+        )
     else:
         model = AutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path)
     __loaded_models[language] = model
-    logger.info(f"Loaded the {language} BERT model from {pretrained_model_name_or_path}")
+    logger.info(
+        f"Loaded the {language} BERT model from {pretrained_model_name_or_path}"
+    )
 
     return model
 
@@ -109,8 +117,9 @@ def load_tokenizer(
 
     # pretrained_model_name_or_path が指定されていない場合はデフォルトのパスを利用
     if pretrained_model_name_or_path is None:
-        assert DEFAULT_BERT_TOKENIZER_PATHS[language].exists(), \
-            f"The default {language} BERT tokenizer does not exist on the file system. Please specify the path to the pre-trained model."
+        assert DEFAULT_BERT_TOKENIZER_PATHS[
+            language
+        ].exists(), f"The default {language} BERT tokenizer does not exist on the file system. Please specify the path to the pre-trained model."
         pretrained_model_name_or_path = str(DEFAULT_BERT_TOKENIZER_PATHS[language])
 
     # BERT トークナイザーをロードし、辞書に格納して返す
@@ -120,7 +129,9 @@ def load_tokenizer(
     else:
         tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
     __loaded_tokenizers[language] = tokenizer
-    logger.info(f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}")
+    logger.info(
+        f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}"
+    )
 
     return tokenizer
 

style_bert_vits2/nlp/chinese/g2p.py

@@ -95,7 +95,11 @@ def __g2p(segments: list[str]) -> tuple[list[str], list[int], list[int]]:
                         if pinyin[0] in single_rep_map.keys():
                             pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
 
-                assert pinyin in __PINYIN_TO_SYMBOL_MAP.keys(), (pinyin, seg, raw_pinyin)
+                assert pinyin in __PINYIN_TO_SYMBOL_MAP.keys(), (
+                    pinyin,
+                    seg,
+                    raw_pinyin,
+                )
                 phone = __PINYIN_TO_SYMBOL_MAP[pinyin].split(" ")
                 word2ph.append(len(phone))
 
@@ -125,7 +129,7 @@ if __name__ == "__main__":
     text = normalize_text(text)
     print(text)
     phones, tones, word2ph = g2p(text)
-    bert = extract_bert_feature(text, word2ph, 'cuda')
+    bert = extract_bert_feature(text, word2ph, "cuda")
 
     print(phones, tones, word2ph, bert.shape)
 

style_bert_vits2/nlp/english/normalizer.py

@@ -121,7 +121,9 @@ def __expand_number(m: re.Match[str]) -> str:
         else:
             return __INFLECT.number_to_words(
                 num, andword="", zero="oh", group=2  # type: ignore
-            ).replace(", ", " ")  # type: ignore
+            ).replace(
+                ", ", " "
+            )  # type: ignore
     else:
         return __INFLECT.number_to_words(num, andword="")  # type: ignore
 

style_bert_vits2/nlp/japanese/g2p.py

@@ -10,9 +10,7 @@ from style_bert_vits2.nlp.symbols import PUNCTUATIONS
 
 
 def g2p(
-    norm_text: str,
-    use_jp_extra: bool = True,
-    raise_yomi_error: bool = False
+    norm_text: str, use_jp_extra: bool = True, raise_yomi_error: bool = False
 ) -> tuple[list[str], list[int], list[int]]:
     """
     他で使われるメインの関数。`normalize_text()` で正規化された `norm_text` を受け取り、
@@ -93,8 +91,7 @@ def g2p(
 
 
 def text_to_sep_kata(
-    norm_text: str,
-    raise_yomi_error: bool = False
+    norm_text: str, raise_yomi_error: bool = False
 ) -> tuple[list[str], list[str]]:
     """
     `normalize_text` で正規化済みの `norm_text` を受け取り、それを単語分割し、
@@ -212,7 +209,9 @@ def __g2phone_tone_wo_punct(text: str) -> list[tuple[str, int]]:
     return result
 
 
-def __pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) -> list[str]:
+def __pyopenjtalk_g2p_prosody(
+    text: str, drop_unvoiced_vowels: bool = True
+) -> list[str]:
     """
     ESPnet の実装から引用、変更点無し。「ん」は「N」なことに注意。
     ref: https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
@@ -414,8 +413,7 @@ def __kata_to_phoneme_list(text: str) -> list[str]:
 
 
 def __align_tones(
-    phones_with_punct: list[str],
-    phone_tone_list: list[tuple[str, int]]
+    phones_with_punct: list[str], phone_tone_list: list[tuple[str, int]]
 ) -> list[tuple[str, int]]:
     """
     例: …私は、、そう思う。

style_bert_vits2/nlp/japanese/g2p_utils.py

@@ -34,11 +34,13 @@ def phone_tone2kata_tone(phone_tone: list[tuple[str, int]]) -> list[tuple[str, i
     """
 
     # 子音の集合
-    CONSONANTS = set([
-        consonant
-        for consonant, _ in MORA_KATA_TO_MORA_PHONEMES.values()
-        if consonant is not None
-    ])
+    CONSONANTS = set(
+        [
+            consonant
+            for consonant, _ in MORA_KATA_TO_MORA_PHONEMES.values()
+            if consonant is not None
+        ]
+    )
 
     phone_tone = phone_tone[1:]  # 最初の("_", 0)を無視
     phones = [phone for phone, _ in phone_tone]

style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__init__.py

@@ -25,6 +25,7 @@ def run_frontend(text: str) -> list[dict[str, Any]]:
     else:
         # without worker
         import pyopenjtalk
+
         return pyopenjtalk.run_frontend(text)
 
 
@@ -36,6 +37,7 @@ def make_label(njd_features: Any) -> list[str]:
     else:
         # without worker
         import pyopenjtalk
+
         return pyopenjtalk.make_label(njd_features)
 
 
@@ -45,6 +47,7 @@ def mecab_dict_index(path: str, out_path: str, dn_mecab: Optional[str] = None) -
     else:
         # without worker
         import pyopenjtalk
+
         pyopenjtalk.mecab_dict_index(path, out_path, dn_mecab)
 
 
@@ -54,6 +57,7 @@ def update_global_jtalk_with_user_dict(path: str) -> None:
     else:
         # without worker
         import pyopenjtalk
+
         pyopenjtalk.update_global_jtalk_with_user_dict(path)
 
 
@@ -63,6 +67,7 @@ def unset_user_dict() -> None:
     else:
         # without worker
         import pyopenjtalk
+
         pyopenjtalk.unset_user_dict()
 
 
@@ -102,7 +107,12 @@ def initialize_worker(port: int = WORKER_PORT) -> None:
         else:
             # align with Windows behavior
             # start_new_session is same as specifying setsid in preexec_fn
-            subprocess.Popen(args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, start_new_session=True)
+            subprocess.Popen(
+                args,
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                start_new_session=True,
+            )
 
         # wait until server listening
         count = 0

style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_client.py

@@ -2,12 +2,15 @@ import socket
 from typing import Any, cast
 
 from style_bert_vits2.logging import logger
-from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_common import RequestType, receive_data, send_data
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_common import (
+    RequestType,
+    receive_data,
+    send_data,
+)
 
 
 class WorkerClient:
-    """ pyopenjtalk worker client """
-
+    """pyopenjtalk worker client"""
 
     def __init__(self, port: int) -> None:
         sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
@@ -16,19 +19,15 @@ class WorkerClient:
         sock.connect((socket.gethostname(), port))
         self.sock = sock
 
-
     def __enter__(self) -> "WorkerClient":
         return self
 
-
     def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
         self.close()
 
-
     def close(self) -> None:
         self.sock.close()
 
-
     def dispatch_pyopenjtalk(self, func: str, *args: Any, **kwargs: Any) -> Any:
         data = {
             "request-type": RequestType.PYOPENJTALK,
@@ -43,7 +42,6 @@ class WorkerClient:
         logger.trace(f"client received response: {response}")
         return response.get("return")
 
-
     def status(self) -> int:
         data = {"request-type": RequestType.STATUS}
         logger.trace(f"client sends request: {data}")
@@ -53,7 +51,6 @@ class WorkerClient:
         logger.trace(f"client received response: {response}")
         return cast(int, response.get("client-count"))
 
-
     def quit_server(self) -> None:
         data = {"request-type": RequestType.QUIT_SERVER}
         logger.trace(f"client sends request: {data}")

style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_server.py

@@ -26,14 +26,12 @@ PYOPENJTALK_FUNC_DICT = {
 
 
 class WorkerServer:
-    """ pyopenjtalk worker server """
-
+    """pyopenjtalk worker server"""
 
     def __init__(self) -> None:
         self.client_count: int = 0
         self.quit: bool = False
 
-
     def handle_request(self, request: dict[str, Any]) -> dict[str, Any]:
         request_type = None
         try:
@@ -70,7 +68,6 @@ class WorkerServer:
 
         return response
 
-
     def start_server(self, port: int, no_client_timeout: int = 30) -> None:
         logger.info("start pyopenjtalk worker server")
         with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server_socket:

style_bert_vits2/nlp/japanese/user_dict/__init__.py

@@ -18,7 +18,11 @@ 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.part_of_speech_data import (
+    MAX_PRIORITY,
+    MIN_PRIORITY,
+    part_of_speech_data,
+)
 
 # root_dir = engine_root()
 # save_dir = get_save_dir()
@@ -26,9 +30,13 @@ from style_bert_vits2.nlp.japanese.user_dict.part_of_speech_data import MAX_PRIO
 # if not save_dir.is_dir():
 #     save_dir.mkdir(parents=True)
 
-default_dict_path = DEFAULT_USER_DICT_DIR / "default.csv"  # VOICEVOXデフォルト辞書ファイルのパス
+default_dict_path = (
+    DEFAULT_USER_DICT_DIR / "default.csv"
+)  # VOICEVOXデフォルト辞書ファイルのパス
 user_dict_path = DEFAULT_USER_DICT_DIR / "user_dict.json"  # ユーザー辞書ファイルのパス
-compiled_dict_path = DEFAULT_USER_DICT_DIR / "user.dic"  # コンパイル済み辞書ファイルのパス
+compiled_dict_path = (
+    DEFAULT_USER_DICT_DIR / "user.dic"
+)  # コンパイル済み辞書ファイルのパス
 
 
 # # 同時書き込みの制御