diff --git a/default_style.py b/default_style.py
index 763e291..67b6fc3 100644
--- a/default_style.py
+++ b/default_style.py
@@ -1,6 +1,6 @@
 import os
-from style_bert_vits2.logging import logger
 from style_bert_vits2.constants import DEFAULT_STYLE
+from style_bert_vits2.logging import logger
 
 import numpy as np
 import json
diff --git a/gen_yaml.py b/gen_yaml.py
index 91301ac..76df206 100644
--- a/gen_yaml.py
+++ b/gen_yaml.py
@@ -1,7 +1,8 @@
+import argparse
 import os
 import shutil
 import yaml
-import argparse
+
 
 parser = argparse.ArgumentParser(
     description="config.ymlの生成。あらかじめ前準備をしたデータをバッチファイルなどで連続で学習する時にtrain_ms.pyより前に使用する。"
diff --git a/style_bert_vits2/models/monotonic_alignment.py b/style_bert_vits2/models/monotonic_alignment.py
index 0f393c1..b499ad0 100644
--- a/style_bert_vits2/models/monotonic_alignment.py
+++ b/style_bert_vits2/models/monotonic_alignment.py
@@ -28,7 +28,7 @@ def maximum_path(neg_cent: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 
     t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
     t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
+    __maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
 
     return torch.from_numpy(path).to(device=device, dtype=dtype)
 
@@ -43,7 +43,7 @@ def maximum_path(neg_cent: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
     nopython = True,
     nogil = True,
 )  # type: ignore
-def maximum_path_jit(paths: Any, values: Any, t_ys: Any, t_xs: Any) -> None:
+def __maximum_path_jit(paths: Any, values: Any, t_ys: Any, t_xs: Any) -> None:
     """
     与えられたパス、値、およびターゲットの y と x 座標を使用して JIT で最大パスを計算する
 
diff --git a/style_bert_vits2/text_processing/__init__.py b/style_bert_vits2/text_processing/__init__.py
index 5e77df6..4719525 100644
--- a/style_bert_vits2/text_processing/__init__.py
+++ b/style_bert_vits2/text_processing/__init__.py
@@ -8,7 +8,7 @@ from style_bert_vits2.text_processing.symbols import (
 )
 
 
-_symbol_to_id = {s: i for i, s in enumerate(SYMBOLS)}
+__symbol_to_id = {s: i for i, s in enumerate(SYMBOLS)}
 
 
 def extract_bert_feature(
@@ -97,7 +97,7 @@ def cleaned_text_to_sequence(cleaned_phones: list[str], tones: list[int], langua
         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]
+    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]
diff --git a/style_bert_vits2/text_processing/bert_models.py b/style_bert_vits2/text_processing/bert_models.py
index 9132082..e8ef4b4 100644
--- a/style_bert_vits2/text_processing/bert_models.py
+++ b/style_bert_vits2/text_processing/bert_models.py
@@ -5,11 +5,13 @@ Style-Bert-VITS2 の学習・推論に必要な各言語ごとの BERT モデル
 場合によっては多重にロードされて非効率なほか、BERT モデルのロード元のパスがハードコードされているためライブラリ化ができない。
 
 そこで、ライブラリの利用前に、音声合成に利用する言語の BERT モデルだけを「明示的に」ロードできるようにした。
-一度 load_tokenizer() で当該言語の BERT モデルがロードされていれば、ライブラリ内部のどこからでもロード済みのモデル/トークナイザーを取得できる。
+一度 load_model/tokenizer() で当該言語の BERT モデルがロードされていれば、ライブラリ内部のどこからでもロード済みのモデル/トークナイザーを取得できる。
 """
 
+import gc
 from typing import cast
 
+import torch
 from transformers import (
     AutoModelForMaskedLM,
     AutoTokenizer,
@@ -25,10 +27,10 @@ from style_bert_vits2.logging import logger
 
 
 # 各言語ごとのロード済みの BERT モデルを格納する辞書
-loaded_models: dict[Languages, PreTrainedModel | DebertaV2Model] = {}
+__loaded_models: dict[Languages, 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, PreTrainedTokenizer | PreTrainedTokenizerFast | DebertaV2Tokenizer] = {}
+__loaded_tokenizers: dict[Languages, PreTrainedTokenizer | PreTrainedTokenizerFast | DebertaV2Tokenizer] = {}
 
 
 def load_model(
@@ -56,8 +58,8 @@ def load_model(
     """
 
     # すでにロード済みの場合はそのまま返す
-    if language in loaded_models:
-        return loaded_models[language]
+    if language in __loaded_models:
+        return __loaded_models[language]
 
     # 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:
@@ -71,7 +73,7 @@ def load_model(
         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
+    __loaded_models[language] = model
     logger.info(f"Loaded the {language} BERT model from {pretrained_model_name_or_path}")
 
     return model
@@ -102,8 +104,8 @@ def load_tokenizer(
     """
 
     # すでにロード済みの場合はそのまま返す
-    if language in loaded_tokenizers:
-        return loaded_tokenizers[language]
+    if language in __loaded_tokenizers:
+        return __loaded_tokenizers[language]
 
     # 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:
@@ -117,7 +119,59 @@ def load_tokenizer(
         tokenizer = DebertaV2Tokenizer.from_pretrained(pretrained_model_name_or_path)
     else:
         tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
-    loaded_tokenizers[language] = tokenizer
+    __loaded_tokenizers[language] = tokenizer
     logger.info(f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}")
 
     return tokenizer
+
+
+def unload_model(language: Languages) -> None:
+    """
+    指定された言語の BERT モデルをアンロードする
+
+    Args:
+        language (Languages): アンロードする BERT モデルの言語
+    """
+
+    if language in __loaded_models:
+        del __loaded_models[language]
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        logger.info(f"Unloaded the {language} BERT model")
+
+
+def unload_tokenizer(language: Languages) -> None:
+    """
+    指定された言語の BERT トークナイザーをアンロードする
+
+    Args:
+        language (Languages): アンロードする BERT トークナイザーの言語
+    """
+
+    if language in __loaded_tokenizers:
+        del __loaded_tokenizers[language]
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        logger.info(f"Unloaded the {language} BERT tokenizer")
+
+
+def unload_all_models() -> None:
+    """
+    すべての BERT モデルをアンロードする
+    """
+
+    for language in list(__loaded_models.keys()):
+        unload_model(language)
+    logger.info("Unloaded all BERT models")
+
+
+def unload_all_tokenizers() -> None:
+    """
+    すべての BERT トークナイザーをアンロードする
+    """
+
+    for language in list(__loaded_tokenizers.keys()):
+        unload_tokenizer(language)
+    logger.info("Unloaded all BERT tokenizers")
diff --git a/style_bert_vits2/text_processing/chinese/bert_feature.py b/style_bert_vits2/text_processing/chinese/bert_feature.py
index 25024cb..3178565 100644
--- a/style_bert_vits2/text_processing/chinese/bert_feature.py
+++ b/style_bert_vits2/text_processing/chinese/bert_feature.py
@@ -7,7 +7,7 @@ from style_bert_vits2.constants import Languages
 from style_bert_vits2.text_processing import bert_models
 
 
-models: dict[torch.device | str, PreTrainedModel] = {}
+__models: dict[torch.device | str, PreTrainedModel] = {}
 
 
 def extract_bert_feature(
@@ -41,8 +41,8 @@ def extract_bert_feature(
         device = "cuda"
     if device == "cuda" and not torch.cuda.is_available():
         device = "cpu"
-    if device not in models.keys():
-        models[device] = bert_models.load_model(Languages.ZH).to(device)  # type: ignore
+    if device not in __models.keys():
+        __models[device] = bert_models.load_model(Languages.ZH).to(device)  # type: ignore
 
     style_res_mean = None
     with torch.no_grad():
@@ -50,13 +50,13 @@ def extract_bert_feature(
         inputs = tokenizer(text, return_tensors="pt")
         for i in inputs:
             inputs[i] = inputs[i].to(device)  # type: ignore
-        res = models[device](**inputs, output_hidden_states=True)
+        res = __models[device](**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
         if assist_text:
             style_inputs = tokenizer(assist_text, return_tensors="pt")
             for i in style_inputs:
                 style_inputs[i] = style_inputs[i].to(device)  # type: ignore
-            style_res = models[device](**style_inputs, output_hidden_states=True)
+            style_res = __models[device](**style_inputs, output_hidden_states=True)
             style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
             style_res_mean = style_res.mean(0)
 
diff --git a/style_bert_vits2/text_processing/english/bert_feature.py b/style_bert_vits2/text_processing/english/bert_feature.py
index ec556c2..b29d531 100644
--- a/style_bert_vits2/text_processing/english/bert_feature.py
+++ b/style_bert_vits2/text_processing/english/bert_feature.py
@@ -7,7 +7,7 @@ from style_bert_vits2.constants import Languages
 from style_bert_vits2.text_processing import bert_models
 
 
-models: dict[torch.device | str, PreTrainedModel] = {}
+__models: dict[torch.device | str, PreTrainedModel] = {}
 
 
 def extract_bert_feature(
@@ -41,8 +41,8 @@ def extract_bert_feature(
         device = "cuda"
     if device == "cuda" and not torch.cuda.is_available():
         device = "cpu"
-    if device not in models.keys():
-        models[device] = bert_models.load_model(Languages.EN).to(device)  # type: ignore
+    if device not in __models.keys():
+        __models[device] = bert_models.load_model(Languages.EN).to(device)  # type: ignore
 
     style_res_mean = None
     with torch.no_grad():
@@ -50,13 +50,13 @@ def extract_bert_feature(
         inputs = tokenizer(text, return_tensors="pt")
         for i in inputs:
             inputs[i] = inputs[i].to(device)  # type: ignore
-        res = models[device](**inputs, output_hidden_states=True)
+        res = __models[device](**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
         if assist_text:
             style_inputs = tokenizer(assist_text, return_tensors="pt")
             for i in style_inputs:
                 style_inputs[i] = style_inputs[i].to(device)  # type: ignore
-            style_res = models[device](**style_inputs, output_hidden_states=True)
+            style_res = __models[device](**style_inputs, output_hidden_states=True)
             style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
             style_res_mean = style_res.mean(0)
 
diff --git a/style_bert_vits2/text_processing/japanese/bert_feature.py b/style_bert_vits2/text_processing/japanese/bert_feature.py
index 3ff9d7b..d1809fe 100644
--- a/style_bert_vits2/text_processing/japanese/bert_feature.py
+++ b/style_bert_vits2/text_processing/japanese/bert_feature.py
@@ -8,7 +8,7 @@ from style_bert_vits2.text_processing import bert_models
 from style_bert_vits2.text_processing.japanese.g2p import text_to_sep_kata
 
 
-models: dict[torch.device | str, PreTrainedModel] = {}
+__models: dict[torch.device | str, PreTrainedModel] = {}
 
 
 def extract_bert_feature(
@@ -48,8 +48,8 @@ def extract_bert_feature(
         device = "cuda"
     if device == "cuda" and not torch.cuda.is_available():
         device = "cpu"
-    if device not in models.keys():
-        models[device] = bert_models.load_model(Languages.JP).to(device)  # type: ignore
+    if device not in __models.keys():
+        __models[device] = bert_models.load_model(Languages.JP).to(device)  # type: ignore
 
     style_res_mean = None
     with torch.no_grad():
@@ -57,13 +57,13 @@ def extract_bert_feature(
         inputs = tokenizer(text, return_tensors="pt")
         for i in inputs:
             inputs[i] = inputs[i].to(device)  # type: ignore
-        res = models[device](**inputs, output_hidden_states=True)
+        res = __models[device](**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
         if assist_text:
             style_inputs = tokenizer(assist_text, return_tensors="pt")
             for i in style_inputs:
                 style_inputs[i] = style_inputs[i].to(device)  # type: ignore
-            style_res = models[device](**style_inputs, output_hidden_states=True)
+            style_res = __models[device](**style_inputs, output_hidden_states=True)
             style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
             style_res_mean = style_res.mean(0)