diff --git a/.gitignore b/.gitignore
index 01d4d61..2170dfe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,3 +14,5 @@ venv/
 
 /pretrained/*.safetensors
 /pretrained/*.pth
+
+/scripts/test/
diff --git a/App.bat b/App.bat
index 7867d59..2f2b653 100644
--- a/App.bat
+++ b/App.bat
@@ -1,9 +1,11 @@
 chcp 65001 > NUL
 @echo off
 
+pushd %~dp0
 echo Running app.py...
 venv\Scripts\python app.py
 
 if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
 
+popd
 pause
\ No newline at end of file
diff --git a/Dataset.bat b/Dataset.bat
new file mode 100644
index 0000000..03d3850
--- /dev/null
+++ b/Dataset.bat
@@ -0,0 +1,11 @@
+chcp 65001 > NUL
+@echo off
+
+pushd %~dp0
+echo Running webui_dataset.py...
+venv\Scripts\python webui_dataset.py
+
+if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
+
+popd
+pause
\ No newline at end of file
diff --git a/README.md b/README.md
index 55a2acc..0dc4055 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,6 @@ python initialize.py
 ### 音声合成
 
 `App.bat`をダブルクリックするとWebUIが起動します。
-TODO: デフォルトモデルをいくつかダウンロードするようにする
 
 ディレクトリ構造:
 ```
@@ -74,6 +73,12 @@ model_assets
 - safetensors形式のサポート、デフォルトでsafetensorsを使用するように
 - その他軽微なbugfixやリファクタリング
 
+## TODO
+- [ ] 複数話者学習での音声合成対応（学習は現在でも可能）
+- [ ] 本家のver 2.1, 2.2, 2.3モデルの推論対応？（ver 2.1以外は明らかにめんどいのでたぶんやらない）
+- [ ] `server_fastapi.py`の対応、とくにAPIで使えるようになると嬉しい人が増えるのかもしれない
+- [ ] 音声ファイルだけ与えられた時にスライスしたり書き起こしたりしてデータセットを作れてすぐ学習できる機能をつける？[このライブラリ](https://github.com/litagin02/slice-and-transcribe)を組み込む？
+
 ## Bert-VITS2 v2.1と同じ点
 - [事前学習モデル](https://huggingface.co/litagin/style_bert_vits2_jvnv)は、実質Bert-VITS2 v2.1と同じものを使用しています（不要な重みを削ってsafetensorsに変換したもの）。
 
diff --git a/Style.bat b/Style.bat
index 54c2abb..409cf91 100644
--- a/Style.bat
+++ b/Style.bat
@@ -2,9 +2,11 @@ chcp 65001 > NUL
 
 @echo off
 
+pushd %~dp0
 echo Running webui_style_vectors.py...
 venv\Scripts\python webui_style_vectors.py
 
 if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
 
+popd
 pause
\ No newline at end of file
diff --git a/Train.bat b/Train.bat
index 9a6d4c5..5a93d02 100644
--- a/Train.bat
+++ b/Train.bat
@@ -2,9 +2,12 @@ chcp 65001 > NUL
 
 @echo off
 
+pushd %~dp0
+
 echo Running webui_train.py...
 venv\Scripts\python webui_train.py
 
 if %errorlevel% neq 0 ( pause & popd & exit /b %errorlevel% )
 
+popd
 pause
\ No newline at end of file
diff --git a/app.py b/app.py
index 525bcaa..cf0ffa2 100644
--- a/app.py
+++ b/app.py
@@ -1,5 +1,6 @@
 import argparse
 import os
+import sys
 
 import gradio as gr
 import numpy as np
@@ -248,6 +249,13 @@ initial_text = "こんにちは、初めまして。あなたの名前はなん
 
 example_local = [
     [initial_text, "JP"],
+    [
+        """あなたがそんなこと言うなんて、私はとっても嬉しい。
+あなたがそんなこと言うなんて、私はとっても怒ってる。
+あなたがそんなこと言うなんて、私はとっても驚いてる。
+あなたがそんなこと言うなんて、私はとっても辛い。""",
+        "JP",
+    ],
     [  # ChatGPTに考えてもらった告白セリフ
         """私、ずっと前からあなたのことを見てきました。あなたの笑顔、優しさ、強さに、心惹かれていたんです。
 友達として過ごす中で、あなたのことがだんだんと特別な存在になっていくのがわかりました。
@@ -268,7 +276,7 @@ example_local = [
         "JP",
     ],
     [  # ChatGPTと考えた、感情を表すセリフ
-        """やったー！テストで満点取れたよ！私とっても嬉しいな！
+        """やったー！テストで満点取れた！私とっても嬉しいな！
 どうして私の意見を無視するの？許せない！ムカつく！あんたなんか死ねばいいのに。
 あはははっ！この漫画めっちゃ笑える、見てよこれ、ふふふ、あはは。
 あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しい。""",
@@ -306,19 +314,39 @@ example_hf_spaces = [
 ]
 
 initial_md = """
-# Bert-VITS2 okiba TTS デモ
+# Style-Bert-VITS2 音声合成
 
-[bert_vits2_okiba](https://huggingface.co/litagin/bert_vits2_okiba) のモデルのデモです。
-モデル名は[rvc_okiba](https://huggingface.co/litagin/rvc_okiba)のモデル名と対応しています。
-モデルは随時追加していきます。現在のモデルはすべてBert-VITS2のver 2.1のものです。
+注意: 初期からある[jvnvのモデル](https://huggingface.co/litagin/style_bert_vits2_jvnv)は、[JVNVコーパス（言語音声と非言語音声を持つ日本語感情音声コーパス）](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus)で学習されたモデルです。ライセンスは[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.ja)です。とくに**商用利用は不可**です。
+"""
 
-**定形サンプルは[こちら](https://huggingface.co/litagin/bert_vits2_okiba/blob/main/examples.md)から聴くほうが速いです。**
+how_to_md = """
+下のように`model_assets`ディレクトリの中にモデルファイルたちを置いてください。
+```
+model_assets
+├── your_model
+│   ├── config.json
+│   ├── your_model_file1.safetensors
+│   ├── your_model_file2.safetensors
+│   ├── ...
+│   └── style_vectors.npy
+└── another_model
+    ├── ...
+```
+各モデルにはファイルたちが必要です：
+- `config.json`：学習時の設定ファイル
+- `*.safetensors`：学習済みモデルファイル（1つ以上が必要、複数可）
+- `style_vectors.npy`：スタイルベクトルファイル
 
-- huggingfaceのcpuで動くので、何故かやたら遅いことが多かったりなんか不安定で動かないときもあるみたいです。
-- huggingface上では最大100文字にしています。
-- Style textの実装あたりで本家の内部コードを改造しているので、このapp.pyをそのまま本家に使っても今のところは動きません。
+上2つは`Train.bat`による学習で自動的に正しい位置に保存されます。`style_vectors.npy`は`Style.bat`を実行して指示に従って生成してください。
 
-現在のところはspeaker_id = 0に固定しています。
+TODO: 現在のところはspeaker_id = 0に固定しており複数話者の合成には対応していません。
+"""
+
+style_md = """
+- プリセットまたは音声ファイルから読み上げの声音・感情・スタイルのようなものを制御できます。
+- デフォルトのNeutralでも、十分に読み上げる文に応じた感情で感情豊かに読み上げられます。このスタイル制御は、それを重み付きで上書きするような感じです。
+- 重みを大きくしすぎると発音が変になったり声にならなかったりと崩壊することがあります。
+- 音声ファイルを入力する場合は、学習データと似た声音の話者（特に同じ性別）でないとよい効果が出ないかもしれません。
 """
 
 
@@ -331,8 +359,8 @@ def make_non_interactive():
 
 
 def gr_util(item):
-    if item == "クラスタから選ぶ":
-        return (gr.update(visible=True), gr.update(visible=False))
+    if item == "プリセットから選ぶ":
+        return (gr.update(visible=True), gr.Audio(visible=False, value=None))
     else:
         return (gr.update(visible=False), gr.update(visible=True))
 
@@ -357,11 +385,16 @@ if __name__ == "__main__":
     examples = example_hf_spaces if is_hf_spaces else example_local
 
     model_names = model_holder.model_names
+    if len(model_names) == 0:
+        logger.error(f"モデルが見つかりませんでした。{model_dir}にモデルを置いてください。")
+        sys.exit(1)
     initial_id = 1 if is_hf_spaces else 0
     initial_pth_files = model_holder.model_files_dict[model_names[initial_id]]
 
     with gr.Blocks(theme="NoCrypt/miku") as app:
         gr.Markdown(initial_md)
+        with gr.Accordion(label="使い方", open=False):
+            gr.Markdown(how_to_md)
         with gr.Row():
             with gr.Column():
                 with gr.Row():
@@ -376,35 +409,17 @@ if __name__ == "__main__":
                             choices=initial_pth_files,
                             value=initial_pth_files[0],
                         )
-                    refresh_button = gr.Button(
-                        "モデル一覧を更新", scale=1, visible=not is_hf_spaces
-                    )
-                    load_button = gr.Button("モデルをロード", scale=1)
+                    refresh_button = gr.Button("更新", scale=1, visible=not is_hf_spaces)
+                    load_button = gr.Button("ロード", scale=1, variant="primary")
                 text_input = gr.TextArea(label="テキスト", value=initial_text)
-                use_style_text = gr.Checkbox(label="Style textを使う", value=False)
-                style_text = gr.Textbox(
-                    label="Style text",
-                    placeholder="どうして私の意見を無視するの？許せない、ムカつく！死ねばいいのに。",
-                    info="このテキストの読み上げと似た声音・感情になりやすくなります。ただ抑揚やテンポ等が犠牲になるかも。",
-                    visible=False,
-                )
-                style_text_weight = gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    value=0.7,
-                    step=0.1,
-                    label="Style textの強さ",
-                    visible=False,
-                )
-                use_style_text.change(
-                    lambda x: (gr.Textbox(visible=x), gr.Slider(visible=x)),
-                    inputs=[use_style_text],
-                    outputs=[style_text, style_text_weight],
-                )
 
                 line_split = gr.Checkbox(label="改行で分けて生成", value=True)
                 split_interval = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.5, step=0.1, label="分けた場合に挟む無音の長さ"
+                    minimum=0.1,
+                    maximum=2,
+                    value=0.5,
+                    step=0.1,
+                    label="分けた場合に挟む無音の長さ（秒）",
                 )
                 language = gr.Dropdown(choices=languages, value="JP", label="Language")
                 with gr.Accordion(label="詳細設定", open=False):
@@ -420,14 +435,36 @@ if __name__ == "__main__":
                     length_scale = gr.Slider(
                         minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
                     )
+                    use_style_text = gr.Checkbox(label="Style textを使う", value=False)
+                    style_text = gr.Textbox(
+                        label="Style text",
+                        placeholder="どうして私の意見を無視するの？許せない、ムカつく！死ねばいいのに。",
+                        info="このテキストの読み上げと似た声音・感情になりやすくなります。ただ抑揚やテンポ等が犠牲になる傾向があります。",
+                        visible=False,
+                    )
+                    style_text_weight = gr.Slider(
+                        minimum=0,
+                        maximum=1,
+                        value=0.7,
+                        step=0.1,
+                        label="Style textの強さ",
+                        visible=False,
+                    )
+                    use_style_text.change(
+                        lambda x: (gr.Textbox(visible=x), gr.Slider(visible=x)),
+                        inputs=[use_style_text],
+                        outputs=[style_text, style_text_weight],
+                    )
             with gr.Column():
+                with gr.Accordion("スタイルについて詳細", open=False):
+                    gr.Markdown(style_md)
                 style_mode = gr.Radio(
-                    ["クラスタから選ぶ", "音声ファイルを入力"],
+                    ["プリセットから選ぶ", "音声ファイルを入力"],
                     label="スタイルの指定方法",
-                    value="クラスタから選ぶ",
+                    value="プリセットから選ぶ",
                 )
                 style = gr.Dropdown(
-                    label="スタイル（0が平均スタイル）", choices=list(range(7)), value=0
+                    label="スタイル（Neutralが平均スタイル）", choices=["モデルをロードしてください"], value=0
                 )
                 style_weight = gr.Slider(
                     minimum=0,
@@ -442,7 +479,8 @@ if __name__ == "__main__":
                 )
                 text_output = gr.Textbox(label="情報")
                 audio_output = gr.Audio(label="結果")
-                gr.Examples(examples, inputs=[text_input, language], label="テキスト例")
+                with gr.Accordion("テキスト例", open=False):
+                    gr.Examples(examples, inputs=[text_input, language])
 
         tts_button.click(
             tts_fn,
diff --git a/clustering.ipynb b/clustering.ipynb
index 81106ed..bcf58bc 100644
--- a/clustering.ipynb
+++ b/clustering.ipynb
@@ -64,7 +64,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 2,
       "metadata": {},
       "outputs": [
         {
@@ -104,7 +104,7 @@
         "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
         "\n",
         "method = \"k\"\n",
-        "n_clusters = 4\n",
+        "n_clusters = 5\n",
         "\n",
         "if method == \"k\":\n",
         "    model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n",
@@ -143,12 +143,12 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 6,
       "metadata": {},
       "outputs": [
         {
           "data": {
-            "image/png": "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",
+            "image/png": "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",
             "text/plain": [
               "<Figure size 700x700 with 1 Axes>"
             ]
@@ -159,7 +159,6 @@
       ],
       "source": [
         "# TSNEで可視化\n",
-        "from sklearn.manifold import TSNE\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
         "plt.figure(figsize=(7, 7))\n",
@@ -170,7 +169,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -208,7 +207,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 8,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -235,7 +234,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 10,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -257,28 +256,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 正解のメドイドを計算\n",
-        "from sklearn.metrics.pairwise import pairwise_distances\n",
-        "\n",
-        "medoids = []\n",
-        "for i in range(6):\n",
-        "    dist = pairwise_distances(x[y_true == i])\n",
-        "    medoid = np.argmin(np.sum(dist, axis=0))\n",
-        "    medoids.append(x[y_true == i][medoid])\n",
-        "\n",
-        "medoids = np.array(medoids)\n",
-        "\n",
-        "# np.save(\"model_assets/jvnv-F2/xvectors.npy\", np.vstack([mean, medoids]))\n",
-        "# メドイドよりもセントロイドがいいかも？\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 11,
       "metadata": {},
       "outputs": [
         {
diff --git a/config.py b/config.py
index aea1253..286f028 100644
--- a/config.py
+++ b/config.py
@@ -205,7 +205,7 @@ class Config:
             print(
                 "If you have no special needs, please do not modify default_config.yml."
             )
-            sys.exit(0)
+            # sys.exit(0)
         with open(file=config_path, mode="r", encoding="utf-8") as file:
             yaml_config: Dict[str, any] = yaml.safe_load(file.read())
             model_name: str = yaml_config["model_name"]
diff --git a/initialize.py b/initialize.py
index 1199a34..25f4cca 100644
--- a/initialize.py
+++ b/initialize.py
@@ -36,6 +36,34 @@ def download_pretrained_models():
             )
 
 
+def download_jvnv_models():
+    files = [
+        "jvnv-F1/config.json",
+        "jvnv-F1/jvnv-F1.safetensors",
+        "jvnv-F1/style_vectors.npy",
+        "jvnv-F2/config.json",
+        "jvnv-F2/jvnv-F2.safetensors",
+        "jvnv-F2/style_vectors.npy",
+        "jvnv-M1/config.json",
+        "jvnv-M1/jvnv-M1.safetensors",
+        "jvnv-M1/style_vectors.npy",
+        # "jvnv-M2/config.json",
+        # "jvnv-M2/jvnv-M2.safetensors",
+        # "jvnv-M2/style_vectors.npy",
+    ]
+    for file in files:
+        if not Path(f"model_assets/{file}").exists():
+            logger.info(f"Downloading {file}")
+            hf_hub_download(
+                "litagin/style_bert_vits2_jvnv",
+                file,
+                local_dir="model_assets",
+                local_dir_use_symlinks=False,
+            )
+
+
 download_bert_models()
 
 download_pretrained_models()
+
+download_jvnv_models()
diff --git a/inputs/.gitignore b/inputs/.gitignore
new file mode 100644
index 0000000..005717e
--- /dev/null
+++ b/inputs/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
diff --git a/requirements.txt b/requirements.txt
index fa086f1..8fc8d7d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 cmudict
 cn2an
+faster-whisper>=0.10.0
 g2p_en
 GPUtil
 gradio
diff --git a/resample.py b/resample.py
index e09ddef..c8326ca 100644
--- a/resample.py
+++ b/resample.py
@@ -65,7 +65,9 @@ if __name__ == "__main__":
                 twople = (spk_dir, filename, args)
                 tasks.append(twople)
 
-    for _ in tqdm(pool.imap_unordered(process, tasks), file=sys.stdout):
+    for _ in tqdm(
+        pool.imap_unordered(process, tasks), file=sys.stdout, total=len(tasks)
+    ):
         pass
 
     pool.close()
diff --git a/safetensors.ipynb b/safetensors.ipynb
index 86ced5d..73f04fc 100644
--- a/safetensors.ipynb
+++ b/safetensors.ipynb
@@ -58,6 +58,76 @@
    "source": [
     "save_file(new_dict, f\"pretrained/{model_name.replace('.pth', '.safetensors')}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glob import glob\n",
+    "root_dir = \"model_assets\"\n",
+    "\n",
+    "safetensors_files = glob(f\"{root_dir}/**/*.safetensors\", recursive=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 推論に不要なenc_qを消し忘れていたのを削除\n",
+    "\n",
+    "from safetensors import safe_open\n",
+    "from safetensors.torch import save_file\n",
+    "\n",
+    "for path in safetensors_files:\n",
+    "    print(path)\n",
+    "    tensors = {}\n",
+    "    with safe_open(path, framework=\"pt\", device=\"cpu\") as f:\n",
+    "        for key in f.keys():\n",
+    "            if key.startswith(\"enc_q\"):\n",
+    "                print(key)\n",
+    "                continue\n",
+    "            tensors[key] = f.get_tensor(key)\n",
+    "    save_file(tensors, path.replace(\".safetensors\", \".new.safetensors\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "enc_p.xvec_proj.weight enc_p.style_proj.weight\n",
+      "enc_p.xvec_proj.bias enc_p.style_proj.bias\n"
+     ]
+    }
+   ],
+   "source": [
+    "# pthファイルを推論用safetensorsに変換\n",
+    "from safetensors.torch import save_file\n",
+    "from safetensors import safe_open\n",
+    "import torch\n",
+    "\n",
+    "pth_path = \"model_assets/jvnv-F1/release_7000.pth\"\n",
+    "pth_weight = torch.load(pth_path, map_location=torch.device(\"cpu\"))\n",
+    "new_dict = {}\n",
+    "for key in pth_weight[\"model\"]:\n",
+    "    if key.startswith(\"enc_p.xvec_proj.\"):  # 前のモデルの名残\n",
+    "        print(key, key.replace(\"enc_p.xvec_proj.\", \"enc_p.style_proj.\"))\n",
+    "        new_dict[key.replace(\"enc_p.xvec_proj.\", \"enc_p.style_proj.\")] = pth_weight[\"model\"][key].clone().contiguous()  # よく分からないおまじないをしないとエラーになる\n",
+    "    elif not key.startswith(\"enc_q\"):\n",
+    "        new_dict[key] = pth_weight[\"model\"][key]\n",
+    "    else:\n",
+    "        continue\n",
+    "new_dict[\"iteration\"] = torch.LongTensor([pth_weight[\"iteration\"]])\n",
+    "save_file(new_dict, pth_path.replace(\".pth\", \".pth.safetensors\"))\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/slice.py b/slice.py
new file mode 100644
index 0000000..2f33c30
--- /dev/null
+++ b/slice.py
@@ -0,0 +1,119 @@
+import argparse
+import os
+import shutil
+import sys
+
+import torch
+from pydub import AudioSegment
+from tqdm import tqdm
+
+vad_model, utils = torch.hub.load(
+    repo_or_dir="snakers4/silero-vad",
+    model="silero_vad",
+    onnx=True,
+)
+
+(get_speech_timestamps, _, read_audio, *_) = utils
+
+
+def get_stamps(audio_file, min_silence_dur_ms=700, min_sec=2):
+    """
+    min_silence_dur_ms:
+        このミリ秒数以上を無音だと判断する。
+        逆に、この秒数以下の無音区間では区切られない。
+        小さくすると、音声がぶつ切りに小さくなりすぎ、
+        大きくすると音声一つ一つが長くなりすぎる。
+        データセットによってたぶん要調整。
+    min_sec:
+        この秒数より小さい発話は無視する。TTSのためには2秒未満は切り捨てたほうがいいかも。
+    """
+
+    sampling_rate = 16000  # 16kHzか8kHzのみ対応
+
+    wav = read_audio(audio_file, sampling_rate=sampling_rate)
+    speech_timestamps = get_speech_timestamps(
+        wav,
+        vad_model,
+        sampling_rate=sampling_rate,
+        min_silence_duration_ms=min_silence_dur_ms,
+        min_speech_duration_ms=min_sec * 1000,
+    )
+
+    return speech_timestamps
+
+
+def split_wav(
+    audio_file, target_dir="raw", max_sec=12, min_silence_dur_ms=700, min_sec=2
+):
+    margin = 200  # ミリ秒単位で、音声の前後に余裕を持たせる
+    upper_bound_ms = max_sec * 1000  # これ以上の長さの音声は無視する
+
+    speech_timestamps = get_stamps(
+        audio_file, min_silence_dur_ms=min_silence_dur_ms, min_sec=min_sec
+    )
+
+    # WAVファイルを読み込む
+    audio = AudioSegment.from_wav(audio_file)
+
+    # リサンプリング（44100Hz）
+    audio = audio.set_frame_rate(44100)
+
+    # ステレオをモノラルに変換
+    audio = audio.set_channels(1)
+
+    total_ms = len(audio)
+
+    file_name = os.path.basename(audio_file).split(".")[0]
+    os.makedirs(target_dir, exist_ok=True)
+
+    total_time_ms = 0
+
+    # タイムスタンプに従って分割し、ファイルに保存
+    for i, ts in enumerate(speech_timestamps):
+        start_ms = max(ts["start"] / 16 - margin, 0)
+        end_ms = min(ts["end"] / 16 + margin, total_ms)
+        if end_ms - start_ms > upper_bound_ms:
+            continue
+        segment = audio[start_ms:end_ms]
+        segment.export(os.path.join(target_dir, f"{file_name}-{i}.wav"), format="wav")
+        total_time_ms += end_ms - start_ms
+
+    return total_time_ms / 1000
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--max_sec", "-M", type=int, default=12)
+    parser.add_argument("--min_sec", "-m", type=int, default=2)
+    parser.add_argument("--min_silence_dur_ms", "-s", type=int, default=700)
+    parser.add_argument("--input_dir", "-i", type=str, default="inputs")
+    parser.add_argument("--output_dir", "-t", type=str, default="raw")
+    args = parser.parse_args()
+
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    min_sec = args.min_sec
+    max_sec = args.max_sec
+    min_silence_dur_ms = args.min_silence_dur_ms
+
+    wav_files = [
+        os.path.join(input_dir, f)
+        for f in os.listdir(input_dir)
+        if f.lower().endswith(".wav")
+    ]
+    if os.path.exists(output_dir):  # ディレクトリを削除
+        print(f"{output_dir}フォルダが存在するので、削除します。")
+        shutil.rmtree(output_dir)
+
+    total_sec = 0
+    for wav_file in tqdm(wav_files, file=sys.stdout):
+        time_sec = split_wav(
+            wav_file,
+            output_dir,
+            max_sec=max_sec,
+            min_sec=min_sec,
+            min_silence_dur_ms=min_silence_dur_ms,
+        )
+        total_sec += time_sec
+
+    print(f"Done! Total time: {total_sec / 60:.2f} min.")
diff --git a/tools/log.py b/tools/log.py
index 7fcccf2..85526cb 100644
--- a/tools/log.py
+++ b/tools/log.py
@@ -10,7 +10,7 @@ logger.remove()
 
 # 自定义格式并添加到标准输出
 log_format = (
-    "<g>{time:MM-DD HH:mm:ss}</g> <lvl>{level:<9}</lvl>| {file}:{line} | {message}"
+    "<g>{time:MM-DD HH:mm:ss}</g> |<lvl>{level:^8}</lvl>| {file}:{line} | {message}"
 )
 
 logger.add(sys.stdout, format=log_format, backtrace=True, diagnose=True)
diff --git a/train_ms.py b/train_ms.py
index fc977b0..00d3e36 100644
--- a/train_ms.py
+++ b/train_ms.py
@@ -352,6 +352,7 @@ def run():
             scheduler_dur_disc.step()
 
         if epoch == hps.train.epochs:
+            # Save the final models
             utils.save_checkpoint(
                 net_g,
                 optim_g,
@@ -379,8 +380,9 @@ def run():
                 epoch,
                 os.path.join(
                     out_dir,
-                    f"{config.model_name}.safetensors",
+                    f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
                 ),
+                for_infer=True,
             )
 
 
@@ -652,6 +654,7 @@ def train_and_evaluate(
                         config.model_name,
                         f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
                     ),
+                    for_infer=True,
                 )
 
         global_step += 1
diff --git a/transcribe.py b/transcribe.py
new file mode 100644
index 0000000..4f2b955
--- /dev/null
+++ b/transcribe.py
@@ -0,0 +1,51 @@
+import argparse
+import os
+import sys
+
+from faster_whisper import WhisperModel
+from tqdm import tqdm
+
+
+def transcribe(wav_path, initial_prompt=None):
+    segments, _ = model.transcribe(
+        wav_path, beam_size=5, language="ja", initial_prompt=initial_prompt
+    )
+    texts = [segment.text for segment in segments]
+    return "".join(texts)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_dir", type=str, default="raw")
+    parser.add_argument("--output_file", type=str, default="esd.list")
+    parser.add_argument(
+        "--initial_prompt", type=str, default="こんにちは。元気、ですかー？私は……ちゃんと元気だよ！"
+    )
+    parser.add_argument("--speaker_name", type=str, default=None, required=True)
+    parser.add_argument("--model", type=str, default="large-v3")
+
+    args = parser.parse_args()
+
+    speaker_name = args.speaker_name
+
+    input_dir = args.input_dir
+    output_file = args.output_file
+    initial_prompt = args.initial_prompt
+
+    model = WhisperModel("large-v3", device="cuda", compute_type="bfloat16")
+
+    wav_files = [
+        os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith(".wav")
+    ]
+    if os.path.exists(output_file):
+        print(f"{output_file}が存在するので、バックアップを{output_file}.bakに作成します。")
+        if os.path.exists(output_file + ".bak"):
+            print(f"{output_file}.bakも存在するので、削除します。")
+            os.remove(output_file + ".bak")
+        os.rename(output_file, output_file + ".bak")
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        for wav_file in tqdm(wav_files, file=sys.stdout):
+            file_name = os.path.basename(wav_file)
+            text = transcribe(wav_file, initial_prompt=initial_prompt)
+            f.write(f"{file_name}|{speaker_name}|JP|{text}\n")
diff --git a/utils.py b/utils.py
index bb73e21..47e3792 100644
--- a/utils.py
+++ b/utils.py
@@ -73,7 +73,7 @@ def load_checkpoint(
             # For upgrading from the old version
             if "ja_bert_proj" in k:
                 v = torch.zeros_like(v)
-                logger.warn(
+                logger.warning(
                     f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
                 )
             elif "enc_q" in k and for_infer:
@@ -88,9 +88,7 @@ def load_checkpoint(
     else:
         model.load_state_dict(new_state_dict, strict=False)
 
-    logger.info(
-        "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
-    )
+    logger.info("Loaded '{}' (iteration {})".format(checkpoint_path, iteration))
 
     return model, optimizer, learning_rate, iteration
 
@@ -165,9 +163,9 @@ def load_safetensors(checkpoint_path, model, for_infer=False):
             continue
         logger.warning(f"Unexpected key: {key}")
     if iteration is None:
-        logger.info(f"Loaded safetensors '{checkpoint_path}'")
+        logger.info(f"Loaded '{checkpoint_path}'")
     else:
-        logger.info(f"Loaded safetensors '{checkpoint_path}' (iteration {iteration})")
+        logger.info(f"Loaded '{checkpoint_path}' (iteration {iteration})")
     return model, iteration
 
 
@@ -382,7 +380,7 @@ def get_hparams_from_file(config_path):
 def check_git_hash(model_dir):
     source_dir = os.path.dirname(os.path.realpath(__file__))
     if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
+        logger.warning(
             "{} is not a git repository, therefore hash value comparison will be ignored.".format(
                 source_dir
             )
@@ -395,7 +393,7 @@ def check_git_hash(model_dir):
     if os.path.exists(path):
         saved_hash = open(path).read()
         if saved_hash != cur_hash:
-            logger.warn(
+            logger.warning(
                 "git hash values are different. {}(saved) != {}(current)".format(
                     saved_hash[:8], cur_hash[:8]
                 )
diff --git a/webui_dataset.py b/webui_dataset.py
new file mode 100644
index 0000000..08f5ddb
--- /dev/null
+++ b/webui_dataset.py
@@ -0,0 +1,96 @@
+import os
+import subprocess
+import sys
+
+import gradio as gr
+
+python = sys.executable
+
+
+def subprocess_wrapper(cmd):
+    return subprocess.run(
+        cmd,
+        stdout=sys.stdout,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+
+
+def do_slice(model_name):
+    input_dir = "inputs"
+    output_dir = os.path.join("Data", model_name, "raw")
+    result = subprocess_wrapper(
+        [
+            python,
+            "slice.py",
+            "--input_dir",
+            input_dir,
+            "--output_dir",
+            output_dir,
+        ]
+    )
+    return "ターミナルを見て結果を確認してください。"
+
+
+def do_transcribe(model_name):
+    input_dir = os.path.join("Data", model_name, "raw")
+    output_file = os.path.join("Data", model_name, "esd.list")
+    result = subprocess_wrapper(
+        [
+            python,
+            "transcribe.py",
+            "--input_dir",
+            input_dir,
+            "--output_file",
+            output_file,
+            "--speaker_name",
+            model_name,
+        ]
+    )
+    if result.stderr:
+        return f"{result.stderr}"
+    return "音声の文字起こしが完了しました。"
+
+
+initial_md = """
+# 学習用データセット作成ツール
+
+Style-Bert-VITS2の学習用データセットを作成するためのツールです。与えられた音声からちょうどいい長さの発話区間を切り取りスライスし、それぞれの音声に対して文字起こしを行います。
+
+## 必要なもの
+学習したい音声が入ったwavファイルいくつか。
+合計時間がある程度はあったほうがいいかも、10分とかでも大丈夫だったとの報告あり。単一ファイルでも良いし複数ファイルでもよい。
+
+## 使い方
+1. `inputs`フォルダ直下にwavファイルをすべて入れる
+2. `モデル名`を入力して、`音声のスライス`ボタンを押す
+3. 完了したら、`音声の文字起こし`ボタンを押す
+
+細かいパラメータ調整とかがしたい人は、`slice.py`と`transcribe.py`を眺めて直接実行してください。
+
+また、出来上がった音声ファイルたちは`Data/{モデル名}/raw`に、書き起こしファイルは`Data/{モデル名}/esd.list`に保存されます。
+
+**ffmpeg のインストールが別途必要のよう**です、「Couldn't find ffmpeg」とか怒られたら、「Windows ffmpeg インストール」等でググって別途インストールしてください。
+"""
+
+with gr.Blocks(theme="NoCrypt/miku") as app:
+    gr.Markdown(initial_md)
+    model_name = gr.Textbox(label="モデル名を入力してください（話者名としても使われます）。")
+    with gr.Row():
+        slice_button = gr.Button("音声のスライス")
+        result1 = gr.Textbox(label="結果")
+    with gr.Row():
+        transcribe_button = gr.Button("2. 音声の文字起こし")
+        result2 = gr.Textbox(label="結果")
+    slice_button.click(
+        do_slice,
+        inputs=[model_name],
+        outputs=[result1],
+    )
+    transcribe_button.click(
+        do_transcribe,
+        inputs=[model_name],
+        outputs=[result2],
+    )
+
+app.launch(inbrowser=True)
diff --git a/webui_style_vectors.py b/webui_style_vectors.py
index ed8a986..b8696e3 100644
--- a/webui_style_vectors.py
+++ b/webui_style_vectors.py
@@ -112,6 +112,32 @@ def do_clustering_gradio(n_clusters=4, method="KMeans"):
     )
 
 
+def save_only_mean(model_name):
+    global mean
+    if len(x) == 0:
+        return "Error: スタイルベクトルを読み込んでください。"
+    result_dir = os.path.join(config.out_dir, model_name)
+    os.makedirs(result_dir, exist_ok=True)
+    mean = np.mean(x, axis=0)
+    style_vectors = np.stack([mean])
+    style_vector_path = os.path.join(result_dir, "style_vectors.npy")
+    if os.path.exists(style_vector_path):
+        return f"{style_vector_path}が既に存在します。削除するか別の名前にバックアップしてください。"
+    np.save(style_vector_path, style_vectors)
+
+    # config.jsonの更新
+    config_path = os.path.join(result_dir, "config.json")
+    if not os.path.exists(config_path):
+        return f"{config_path}が存在しません。"
+    with open(config_path, "r") as f:
+        json_dict = json.load(f)
+    json_dict["data"]["num_styles"] = 1
+    json_dict["data"]["style2id"] = {"Neutral": 0}
+    with open(config_path, "w") as f:
+        json.dump(json_dict, f, indent=2)
+    return f"成功!\n{style_vector_path}に保存し{config_path}を更新しました。"
+
+
 def save_style_vectors(model_name, style_names: str):
     """centerとcentroidsを保存する"""
     result_dir = os.path.join(config.out_dir, model_name)
@@ -120,7 +146,7 @@ def save_style_vectors(model_name, style_names: str):
     style_vector_path = os.path.join(result_dir, "style_vectors.npy")
     if os.path.exists(style_vector_path):
         return f"{style_vector_path}が既に存在します。削除するか別の名前にバックアップしてください。"
-    np.save(os.path.join(result_dir, "style_vectors.npy"), style_vectors)
+    np.save(style_vector_path, style_vectors)
 
     # config.jsonの更新
     config_path = os.path.join(result_dir, "config.json")
@@ -142,11 +168,72 @@ def save_style_vectors(model_name, style_names: str):
     return f"成功!\n{style_vector_path}に保存し{config_path}を更新しました。"
 
 
-md1 = """
+def save_style_vectors_from_files(model_name, audio_files_text, style_names_text):
+    """音声ファイルからスタイルベクトルを作成して保存する"""
+    global mean
+    if len(x) == 0:
+        return "Error: スタイルベクトルを読み込んでください。"
+    mean = np.mean(x, axis=0)
+
+    result_dir = os.path.join(config.out_dir, model_name)
+    os.makedirs(result_dir, exist_ok=True)
+    audio_files = audio_files_text.split(",")
+    style_names = style_names_text.split(",")
+    if len(audio_files) != len(style_names):
+        return f"音声ファイルとスタイル名の数が合いません。`,`で正しく{len(style_names)}個に区切られているか確認してください: {audio_files_text}と{style_names_text}"
+    audio_files = [name.strip() for name in audio_files]
+    style_names = [name.strip() for name in style_names]
+    style_vectors = [mean]
+
+    wavs_dir = os.path.join("Data", model_name, "wavs")
+    for audio_file in audio_files:
+        path = os.path.join(wavs_dir, audio_file)
+        if not os.path.exists(path):
+            return f"{path}が存在しません。"
+        style_vectors.append(np.load(f"{path}.npy"))
+    style_vectors = np.stack(style_vectors)
+    style_vector_path = os.path.join(result_dir, "style_vectors.npy")
+    if os.path.exists(style_vector_path):
+        return f"{style_vector_path}が既に存在します。削除するか別の名前にバックアップしてください。"
+    np.save(style_vector_path, style_vectors)
+
+    # config.jsonの更新
+    config_path = os.path.join(result_dir, "config.json")
+    if not os.path.exists(config_path):
+        return f"{config_path}が存在しません。"
+    style_name_list = ["Neutral"]
+    style_name_list = style_name_list + style_names
+    assert len(style_name_list) == len(style_vectors)
+
+    with open(config_path, "r") as f:
+        json_dict = json.load(f)
+    json_dict["data"]["num_styles"] = len(style_name_list)
+    style_dict = {name: i for i, name in enumerate(style_name_list)}
+    json_dict["data"]["style2id"] = style_dict
+
+    with open(config_path, "w") as f:
+        json.dump(json_dict, f, indent=2)
+    return f"成功!\n{style_vector_path}に保存し{config_path}を更新しました。"
+
+
+initial_md = """
 # Style Bert-VITS2 スタイルベクトルの作成
 
-スタイルを使って音声合成するには、音声ファイルをスタイル別に分け、その各スタイルの特徴を抽出して保存する必要があります。
+Style-Bert-VITS2で音声合成するには、スタイルベクトルのファイル`style_vectors.npy`が必要です。これをモデルごとに作成する必要があります。
+このプロセスは学習とは全く関係がないので、何回でも独立して繰り返して試せます。また学習中にもたぶん軽いので動くはずです。
 
+## 方法
+
+どうやってスタイルベクトルファイルを作るかはいくつか方法があります。
+- 方法1: めんどくさいから平均スタイルのみを使う（使えるスタイルは標準のNeutralのみ）
+- 方法2: 音声ファイルを自動でスタイル別に分け、その各スタイルの平均を取って保存
+- 方法3: スタイルを代表する音声ファイルを手動で選んで、その音声のスタイルベクトルを保存
+- 方法4: 自分でもっと頑張ってこだわって作る（JVNVコーパスなど、もともとスタイルラベル等が利用可能な場合はこれがよいかも）
+
+基本的には方法2を使うことを、めんどくさかったりあまり感情に幅がないデータセットなら方法1をおすすめします。
+"""
+
+method2 = """
 学習の時に取り出したスタイルベクトルを読み込んで、可視化を見ながらスタイルを分けていきます。
 
 手順:
@@ -155,7 +242,6 @@ md1 = """
 3. スタイル分けを行って結果を確認
 4. スタイルの名前を決めて保存
 
-このプロセスは学習とは関係がないので、何回でも独立して繰り返して試せます。また学習中にもたぶん軽いので動くはずです。
 
 詳細: スタイルベクトル(256次元)たちを適当なアルゴリズムでクラスタリングして、各クラスタの中心のベクトル（と全体の平均ベクトル）を保存します。
 
@@ -163,62 +249,98 @@ md1 = """
 """
 
 with gr.Blocks(theme="NoCrypt/miku") as app:
-    gr.Markdown(md1)
+    gr.Markdown(initial_md)
     with gr.Row():
         model_name = gr.Textbox("your_model_name", label="モデル名")
         load_button = gr.Button("スタイルベクトルを読み込む", variant="primary")
     output = gr.Plot(label="音声スタイルの可視化")
     load_button.click(load, inputs=[model_name], outputs=[output])
-    n_clusters = gr.Slider(
-        minimum=2,
-        maximum=10,
-        step=1,
-        value=4,
-        label="作るスタイルの数（平均スタイルを除く）",
-        info="上の図を見ながらスタイルの数を試行錯誤してください。",
-    )
-    c_method = gr.Radio(
-        ["Agglomerative after t-SNE", "KMeans after t-SNE", "Agglomerative", "KMeans"],
-        label="アルゴリズム",
-        info="分類する（クラスタリング）アルゴリズムを選択します。いろいろ試してみてください。",
-        value="Agglomerative after t-SNE",
-    )
-    c_button = gr.Button("スタイル分けを実行")
-    audio_list = []
-    md_list = []
-    gr.Markdown("スタイル分けの結果と、各スタイルの特徴的な代表音声（図の黒い x 印）")
-    gr.Markdown("注意: もともと256次元なものをを2次元に落としているので、正確なベクトルの位置関係ではありません。")
-    with gr.Row():
-        gr_plot = gr.Plot()
+    with gr.Tab("方法1: 平均スタイルのみを保存"):
+        gr.Markdown("平均（Neutral）スタイルのみを保存する場合は、以下のボタンを押してください。")
         with gr.Row():
-            for i in range(MAX_CLUSTER_NUM):
-                with gr.Column():
-                    md_list.append(gr.Markdown(visible=False))
-                    audio_list.append(
-                        gr.Audio(
-                            visible=False,
-                            scale=1,
-                            show_label=True,
-                            label=f"スタイル{i+1}",
+            save_button1 = gr.Button("スタイルベクトルを保存", variant="primary")
+            info1 = gr.Textbox(label="保存結果")
+        save_button1.click(save_only_mean, inputs=[model_name], outputs=[info1])
+    with gr.Tab("方法2: スタイル分けを自動で行う"):
+        n_clusters = gr.Slider(
+            minimum=2,
+            maximum=10,
+            step=1,
+            value=4,
+            label="作るスタイルの数（平均スタイルを除く）",
+            info="上の図を見ながらスタイルの数を試行錯誤してください。",
+        )
+        c_method = gr.Radio(
+            choices=[
+                "Agglomerative after t-SNE",
+                "KMeans after t-SNE",
+                "Agglomerative",
+                "KMeans",
+            ],
+            label="アルゴリズム",
+            info="分類する（クラスタリング）アルゴリズムを選択します。いろいろ試してみてください。",
+            value="Agglomerative after t-SNE",
+        )
+        c_button = gr.Button("スタイル分けを実行")
+        audio_list = []
+        md_list = []
+        gr.Markdown("スタイル分けの結果と、各スタイルの特徴的な代表音声（図の黒い x 印）")
+        gr.Markdown("注意: もともと256次元なものをを2次元に落としているので、正確なベクトルの位置関係ではありません。")
+        with gr.Row():
+            gr_plot = gr.Plot()
+            with gr.Row():
+                for i in range(MAX_CLUSTER_NUM):
+                    with gr.Column():
+                        md_list.append(gr.Markdown(visible=False))
+                        audio_list.append(
+                            gr.Audio(
+                                visible=False,
+                                scale=1,
+                                show_label=True,
+                                label=f"スタイル{i+1}",
+                            )
                         )
-                    )
-    c_button.click(
-        do_clustering_gradio,
-        inputs=[n_clusters, c_method],
-        outputs=[gr_plot] + audio_list + md_list,
-    )
-    gr.Markdown("結果が良さそうなら、これを保存します。")
-    with gr.Row():
+        c_button.click(
+            do_clustering_gradio,
+            inputs=[n_clusters, c_method],
+            outputs=[gr_plot] + audio_list + md_list,
+        )
+        gr.Markdown("結果が良さそうなら、これを保存します。")
         style_names = gr.Textbox(
             "Angry, Sad, Happy",
             label="スタイルの名前",
             info="スタイルの名前を`,`で区切って入力してください（日本語可）。例: `Angry, Sad, Happy`や`怒り, 悲しみ, 喜び`など。平均音声はNeutralとして自動的に保存されます。",
         )
-        save_button = gr.Button("スタイルベクトルを保存", variant="primary")
-        info = gr.Textbox(label="保存結果")
+        with gr.Row():
+            save_button = gr.Button("スタイルベクトルを保存", variant="primary")
+            info2 = gr.Textbox(label="保存結果")
+
+        save_button.click(
+            save_style_vectors, inputs=[model_name, style_names], outputs=[info2]
+        )
+    with gr.Tab("方法3: 手動でスタイルを選ぶ"):
+        gr.Markdown("下のテキスト欄に、各スタイルの代表音声のファイル名を`,`区切りで、その横に対応するスタイル名を`,`区切りで入力してください。")
+        gr.Markdown("例: `angry.wav, sad.wav, happy.wav`と`Angry, Sad, Happy`")
+        gr.Markdown("注意: Neutralスタイルは自動的に保存されます、手動ではNeutralという名前のスタイルは指定しないでください。")
+        with gr.Row():
+            audio_files_text = gr.Textbox(
+                label="音声ファイル名", placeholder="angry.wav, sad.wav, happy.wav"
+            )
+            style_names_text = gr.Textbox(
+                label="スタイル名", placeholder="Angry, Sad, Happy"
+            )
+        with gr.Row():
+            save_button3 = gr.Button("スタイルベクトルを保存", variant="primary")
+            info3 = gr.Textbox(label="保存結果")
+            save_button3.click(
+                save_style_vectors_from_files,
+                inputs=[model_name, audio_files_text, style_names_text],
+                outputs=[info3],
+            )
+    with gr.Tab("方法4: がんばる"):
+        gr.Markdown(
+            "`clustering.ipynb`にjvnvコーパスの場合の作り方とかクラスタ分けのいろいろを書いています。これを参考に自分で頑張って作ってください。"
+        )
 
-    save_button.click(
-        save_style_vectors, inputs=[model_name, style_names], outputs=[info]
-    )
 
 app.launch(inbrowser=True)
diff --git a/webui_train.py b/webui_train.py
index ee3a931..28c5c38 100644
--- a/webui_train.py
+++ b/webui_train.py
@@ -2,10 +2,10 @@ import json
 import os
 import shutil
 import subprocess
-import yaml
+import sys
 
 import gradio as gr
-import sys
+import yaml
 
 python = sys.executable
 
@@ -143,8 +143,23 @@ def train(model_name):
     return "Final Step: 学習が完了しました!"
 
 
+initial_md = """
+# Style-Bert-VITS2 学習用WebUI
+
+## 使い方
+
+- データを準備して、各ステップを順に実行してください。進捗状況等はターミナルに表示されます。
+
+- 途中から学習を再開する場合は、モデル名を入力してFinal Stepだけ実行すればよいです。
+
+注意: 音声合成で使うには、スタイルベクトルファイル`style_vectors.npy`を作る必要があります。これは、`Style.bat`を実行してそこで作成してください。
+動作は軽いはずなので、学習中でも実行でき、何度でも繰り返して試せます。
+"""
+
 prepare_md = """
-次のようにデータを置いてください。
+まず音声データ（wavファイルで1ファイルが2-15秒程度の、長すぎず短すぎない発話のものをいくつか）と、書き起こしテキストを用意してください。
+
+それを次のように配置します。
 ```
 ├── Data
 │   ├── {モデルの名前}
@@ -168,13 +183,12 @@ wav_next.wav|taro|JP|はい、聞こえています……。
 english_teacher.wav|Mary|EN|How are you? I'm fine, thank you, and you?
 ...
 ```
-もちろん日本語話者の単一話者データセットでも構いません。
+日本語話者の単一話者データセットでも構いません。
 """
 
 if __name__ == "__main__":
     with gr.Blocks(theme="NoCrypt/miku") as app:
-        gr.Markdown("# Style Bert-VITS2 データ前処理")
-        gr.Markdown("途中から学習を再開する場合は、モデル名を入力してFinal Stepだけ実行すればよいです。")
+        gr.Markdown(initial_md)
         with gr.Accordion(label="データの前準備", open=False):
             gr.Markdown(prepare_md)
         model_name = gr.Textbox(