Refactor: style_bert_vits2/text_processing/cleaner.py integrated into style_bert_vits2/text_processing/__init__.py

This was often used in 3 function sets and felt like a wasteful division with few lines.

preprocess_text.py

@@ -9,7 +9,7 @@ from tqdm import tqdm
 
 from config import config
 from style_bert_vits2.logging import logger
-from style_bert_vits2.text_processing.cleaner import clean_text
+from style_bert_vits2.text_processing import clean_text
 from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT
 
 preprocess_text_config = config.preprocess_text_config

style_bert_vits2/models/infer.py

@@ -6,8 +6,7 @@ from style_bert_vits2.logging import logger
 from style_bert_vits2.models import commons
 from style_bert_vits2.models.models import SynthesizerTrn
 from style_bert_vits2.models.models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
-from style_bert_vits2.text_processing import cleaned_text_to_sequence, extract_bert_feature
+from style_bert_vits2.text_processing import clean_text, cleaned_text_to_sequence, extract_bert_feature
-from style_bert_vits2.text_processing.cleaner import clean_text
 from style_bert_vits2.text_processing.symbols import SYMBOLS
 
 

style_bert_vits2/text_processing/__init__.py

@@ -11,28 +11,6 @@ from style_bert_vits2.text_processing.symbols import (
 _symbol_to_id = {s: i for i, s in enumerate(SYMBOLS)}
 
 
-def cleaned_text_to_sequence(cleaned_text: str, tones: list[int], language: Languages) -> tuple[list[int], list[int], list[int]]:
-    """
-    Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
-    Args:
-        cleaned_text (str): string to convert to a sequence
-        tones (list[int]): List of tones
-        language (Languages): Language of the text
-
-    Returns:
-        tuple[list[int], list[int], list[int]]: List of integers corresponding to the symbols in the text
-    """
-
-    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-    tone_start = LANGUAGE_TONE_START_MAP[language]
-    tones = [i + tone_start for i in tones]
-    lang_id = LANGUAGE_ID_MAP[language]
-    lang_ids = [lang_id for i in phones]
-
-    return phones, tones, lang_ids
-
-
 def extract_bert_feature(
     text: str,
     word2ph: list[int],
@@ -66,3 +44,64 @@ def extract_bert_feature(
         raise ValueError(f"Language {language} not supported")
 
     return extract_bert_feature(text, word2ph, device, assist_text, assist_text_weight)
+
+
+def clean_text(
+    text: str,
+    language: Languages,
+    use_jp_extra: bool = True,
+    raise_yomi_error: bool = False,
+) -> tuple[str, list[str], list[int], list[int]]:
+    """
+    テキストをクリーニングし、音素に変換する
+
+    Args:
+        text (str): クリーニングするテキスト
+        language (Languages): テキストの言語
+        use_jp_extra (bool, optional): テキストが日本語の場合に JP-Extra モデルを利用するかどうか。Defaults to True.
+        raise_yomi_error (bool, optional): False の場合、読めない文字が消えたような扱いとして処理される。Defaults to False.
+
+    Returns:
+        tuple[str, list[str], list[int], list[int]]: クリーニングされたテキストと、音素・アクセント・元のテキストの各文字に音素が何個割り当てられるかのリスト
+    """
+
+    # Changed to import inside if condition to avoid unnecessary import
+    if language == Languages.JP:
+        from style_bert_vits2.text_processing.japanese.g2p import g2p
+        from style_bert_vits2.text_processing.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 ...text import english as language_module
+        norm_text = language_module.normalize_text(text)
+        phones, tones, word2ph = language_module.g2p(norm_text)
+    elif language == Languages.ZH:
+        from ...text import chinese as language_module
+        norm_text = language_module.normalize_text(text)
+        phones, tones, word2ph = language_module.g2p(norm_text)
+    else:
+        raise ValueError(f"Language {language} not supported")
+
+    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]]:
+    """
+    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
+
+    Args:
+        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
+
+    Returns:
+        tuple[list[int], list[int], list[int]]: List of integers corresponding to the symbols in the text
+    """
+
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_phones]
+    tone_start = LANGUAGE_TONE_START_MAP[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = LANGUAGE_ID_MAP[language]
+    lang_ids = [lang_id for i in phones]
+
+    return phones, tones, lang_ids

style_bert_vits2/text_processing/cleaner.py

@@ -1,40 +0,0 @@
-from style_bert_vits2.constants import Languages
-
-
-def clean_text(
-    text: str,
-    language: Languages,
-    use_jp_extra: bool = True,
-    raise_yomi_error: bool = False,
-) -> tuple[str, list[str], list[int], list[int]]:
-    """
-    テキストをクリーニングし、音素に変換する
-
-    Args:
-        text (str): クリーニングするテキスト
-        language (Languages): テキストの言語
-        use_jp_extra (bool, optional): テキストが日本語の場合に JP-Extra モデルを利用するかどうか。Defaults to True.
-        raise_yomi_error (bool, optional): False の場合、読めない文字が消えたような扱いとして処理される。Defaults to False.
-
-    Returns:
-        tuple[str, list[str], list[int], list[int]]: クリーニングされたテキストと、音素・アクセント・元のテキストの各文字に音素が何個割り当てられるかのリスト
-    """
-
-    # Changed to import inside if condition to avoid unnecessary import
-    if language == Languages.JP:
-        from style_bert_vits2.text_processing.japanese.g2p import g2p
-        from style_bert_vits2.text_processing.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 ...text import english as language_module
-        norm_text = language_module.normalize_text(text)
-        phones, tones, word2ph = language_module.g2p(norm_text)
-    elif language == Languages.ZH:
-        from ...text import chinese as language_module
-        norm_text = language_module.normalize_text(text)
-        phones, tones, word2ph = language_module.g2p(norm_text)
-    else:
-        raise ValueError(f"Language {language} not supported")
-
-    return norm_text, phones, tones, word2ph