diff --git a/convert_bert_onnx.py b/convert_bert_onnx.py
index e9cefdd..5e929d5 100644
--- a/convert_bert_onnx.py
+++ b/convert_bert_onnx.py
@@ -1,12 +1,16 @@
 # usage: .venv/bin/python convert_bert_onnx.py --language JP
 # ref: https://github.com/tuna2134/sbv2-api/blob/main/convert/convert_deberta.py
 
+import time
 from argparse import ArgumentParser
 from pathlib import Path
 
 import onnx
 import torch
-from onnxsim import simplify
+from onnxsim import model_info, simplify
+from rich import print
+from rich.rule import Rule
+from rich.style import Style
 from torch import nn
 from transformers.convert_slow_tokenizer import BertConverter
 
@@ -15,6 +19,7 @@ from style_bert_vits2.nlp import bert_models
 
 
 if __name__ == "__main__":
+    start_time = time.time()
     parser = ArgumentParser()
     parser.add_argument("--language", default=Languages.JP)
     args = parser.parse_args()
@@ -25,11 +30,18 @@ if __name__ == "__main__":
     onnx_temp_model_path = Path(pretrained_model_name_or_path) / f"model_temp.onnx"
     onnx_optimized_model_path = Path(pretrained_model_name_or_path) / f"model.onnx"
     tokenizer_json_path = Path(pretrained_model_name_or_path) / "tokenizer.json"
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold cyan]Language:[/bold cyan] {language.name}")
+    print(f"[bold cyan]Pretrained model:[/bold cyan] {pretrained_model_name_or_path}")
+    print(Rule(characters="=", style=Style(color="blue")))
 
     # トークナイザーを Fast Tokenizer 用形式に変換して保存
     tokenizer = bert_models.load_tokenizer(language)
     converter = BertConverter(tokenizer)
     converter.converted().save(str(tokenizer_json_path))
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold green]Tokenizer JSON saved to {tokenizer_json_path}[/bold green]")
+    print(Rule(characters="=", style=Style(color="blue")))
 
     # TODO: JP, ZH は変換できるが、EN は途中で強制終了されてしまい変換できない
     class ONNXBert(nn.Module):
@@ -52,6 +64,10 @@ if __name__ == "__main__":
     inputs = tokenizer("今日はいい天気ですね", return_tensors="pt")
 
     # モデルを ONNX に変換
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold cyan]Exporting ONNX model...[/bold cyan]")
+    print(Rule(characters="=", style=Style(color="blue")))
+    export_start_time = time.time()
     torch.onnx.export(
         model=model,
         args=(inputs["input_ids"], inputs["token_type_ids"], inputs["attention_mask"]),
@@ -64,12 +80,26 @@ if __name__ == "__main__":
             "attention_mask": {1: "batch_size"},
         },
     )
+    print(
+        f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
+    )
 
     # ONNX モデルを最適化
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold cyan]Optimizing ONNX model...[/bold cyan]")
+    print(Rule(characters="=", style=Style(color="blue")))
+    optimize_start_time = time.time()
     onnx_model = onnx.load(onnx_temp_model_path)
     simplified_onnx_model, check = simplify(onnx_model)
     onnx.save(simplified_onnx_model, onnx_optimized_model_path)
-
-    # 最適化前の ONNX モデルを削除
     onnx_temp_model_path.unlink()
-    print(f"ONNX model optimized and saved to {onnx_optimized_model_path}")
+    print(
+        f"[bold green]ONNX model optimized and saved to {onnx_optimized_model_path} ({time.time() - optimize_start_time:.2f}s)[/bold green]"
+    )
+    print(
+        f"[bold]Total Time: {time.time() - start_time:.2f}s / Size: {onnx_optimized_model_path.stat().st_size / 1024 / 1024:.2f}MB[/bold]"
+    )
+    print(Rule(characters="=", style=Style(color="blue")))
+    print("[bold cyan]Optimized model info:[/bold cyan]")
+    model_info.print_simplifying_info(onnx_model, simplified_onnx_model)
+    print(Rule(characters="=", style=Style(color="blue")))
diff --git a/convert_onnx.py b/convert_onnx.py
index 9a8edb2..932364c 100644
--- a/convert_onnx.py
+++ b/convert_onnx.py
@@ -1,13 +1,17 @@
 # usage: .venv/bin/python convert_onnx.py --model model_assets/amitaro/amitaro.safetensors
 # ref: https://github.com/tuna2134/sbv2-api/blob/main/convert/convert_model.py
 
+import time
 from argparse import ArgumentParser
 from pathlib import Path
 from typing import cast
 
 import onnx
 import torch
-from onnxsim import simplify
+from onnxsim import model_info, simplify
+from rich import print
+from rich.rule import Rule
+from rich.style import Style
 
 from style_bert_vits2.constants import (
     DEFAULT_ASSIST_TEXT_WEIGHT,
@@ -23,6 +27,7 @@ from style_bert_vits2.tts_model import TTSModel
 
 
 if __name__ == "__main__":
+    start_time = time.time()
     parser = ArgumentParser()
     parser.add_argument("--model", required=True)
     args = parser.parse_args()
@@ -37,6 +42,11 @@ if __name__ == "__main__":
     assert config_path.exists(), "Config file does not exist"
     assert style_vec_path.exists(), "Style vector file does not exist"
     assert model_path.suffix != ".onnx", "Model file is already ONNX"
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold cyan]Model file:[/bold cyan] {model_path}")
+    print(f"[bold cyan]Config file:[/bold cyan] {config_path}")
+    print(f"[bold cyan]Style vector file:[/bold cyan] {style_vec_path}")
+    print(Rule(characters="=", style=Style(color="blue")))
 
     # PyTorch モデルを読み込む
     device = "cpu"
@@ -104,6 +114,10 @@ if __name__ == "__main__":
     style_vec_tensor = torch.from_numpy(style_vector).to(device).unsqueeze(0)
 
     # モデルを ONNX に変換
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold cyan]Exporting ONNX model...[/bold cyan]")
+    print(Rule(characters="=", style=Style(color="blue")))
+    export_start_time = time.time()
     torch.onnx.export(
         model=tts_model.net_g,
         args=(
@@ -139,12 +153,26 @@ if __name__ == "__main__":
         ],
         output_names=["output"],
     )
+    print(
+        f"[bold green]ONNX model exported to {onnx_temp_model_path} ({time.time() - export_start_time:.2f}s)[/bold green]"
+    )
 
     # ONNX モデルを最適化
+    print(Rule(characters="=", style=Style(color="blue")))
+    print(f"[bold cyan]Optimizing ONNX model...[/bold cyan]")
+    print(Rule(characters="=", style=Style(color="blue")))
+    optimize_start_time = time.time()
     onnx_model = onnx.load(onnx_temp_model_path)
     simplified_onnx_model, check = simplify(onnx_model)
     onnx.save(simplified_onnx_model, onnx_optimized_model_path)
-
-    # 最適化前の ONNX モデルを削除
     onnx_temp_model_path.unlink()
-    print(f"ONNX model optimized and saved to {onnx_optimized_model_path}")
+    print(
+        f"[bold green]ONNX model optimized and saved to {onnx_optimized_model_path} ({time.time() - optimize_start_time:.2f}s)[/bold green]"
+    )
+    print(
+        f"[bold]Total Time: {time.time() - start_time:.2f}s / Size: {onnx_optimized_model_path.stat().st_size / 1024 / 1024:.2f}MB[/bold]"
+    )
+    print(Rule(characters="=", style=Style(color="blue")))
+    print("[bold cyan]Optimized model info:[/bold cyan]")
+    model_info.print_simplifying_info(onnx_model, simplified_onnx_model)
+    print(Rule(characters="=", style=Style(color="blue")))
diff --git a/server_editor.py b/server_editor.py
index 50921c1..a1e95d0 100644
--- a/server_editor.py
+++ b/server_editor.py
@@ -202,9 +202,7 @@ if args.preload_onnx_bert:
     )
     onnx_bert_models.load_tokenizer(Languages.JP)
 
-model_holder = TTSModelHolder(
-    model_dir, device, torch_device_to_onnx_providers(device)
-)
+model_holder = TTSModelHolder(model_dir, device, torch_device_to_onnx_providers(device))
 if len(model_holder.model_names) == 0:
     logger.error(f"Models not found in {model_dir}.")
     sys.exit(1)
diff --git a/style_bert_vits2/nlp/onnx_bert_models.py b/style_bert_vits2/nlp/onnx_bert_models.py
index b14397d..442f2ad 100644
--- a/style_bert_vits2/nlp/onnx_bert_models.py
+++ b/style_bert_vits2/nlp/onnx_bert_models.py
@@ -97,7 +97,9 @@ def load_model(
     sess_options = onnxruntime.SessionOptions()
     # 基本的な最適化のみ有効化
     # ONNX モデルの作成時にすでに onnxsim により最適化されているため、ここでは基本的な最適化のみ有効化する
-    sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    sess_options.graph_optimization_level = (
+        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    )
     # エラー以外のログを出力しない
     # 本来は log_severity_level = 3 だけで効くはずだが、なぜか抑制できないので set_default_logger_severity() も呼び出している
     sess_options.log_severity_level = 3
diff --git a/style_bert_vits2/tts_model.py b/style_bert_vits2/tts_model.py
index 2ea10a7..bd56de7 100644
--- a/style_bert_vits2/tts_model.py
+++ b/style_bert_vits2/tts_model.py
@@ -132,7 +132,7 @@ class TTSModel:
                 hps=self.hyper_parameters,
             )
             logger.info(
-                f"Model loaded successfully from {self.model_path} to \"{self.device}\" device ({time.time() - start_time:.2f}s)"
+                f'Model loaded successfully from {self.model_path} to "{self.device}" device ({time.time() - start_time:.2f}s)'
             )
 
         # ONNX 推論時
@@ -140,7 +140,9 @@ class TTSModel:
             sess_options = onnxruntime.SessionOptions()
             # 基本的な最適化のみ有効化
             # ONNX モデルの作成時にすでに onnxsim により最適化されているため、ここでは基本的な最適化のみ有効化する
-            sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
+            sess_options.graph_optimization_level = (
+                onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
+            )
             # エラー以外のログを出力しない
             # 本来は log_severity_level = 3 だけで効くはずだが、なぜか抑制できないので set_default_logger_severity() も呼び出している
             sess_options.log_severity_level = 3