init (not checked bat script yet)

webui_train.py

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+import json
+import os
+import shutil
+import subprocess
+import yaml
+
+import gradio as gr
+import sys
+
+python = sys.executable
+
+
+def subprocess_wrapper(cmd):
+    return subprocess.run(
+        cmd,
+        stdout=sys.stdout,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+
+
+def get_path(model_name):
+    assert model_name != "", "モデル名は空にできません"
+    dataset_path = os.path.join("Data", model_name)
+    lbl_path = os.path.join(dataset_path, "esd.list")
+    train_path = os.path.join(dataset_path, "train.list")
+    val_path = os.path.join(dataset_path, "val.list")
+    config_path = os.path.join(dataset_path, "config.json")
+    return dataset_path, lbl_path, train_path, val_path, config_path
+
+
+def initialize(model_name, batch_size, epochs):
+    dataset_path, _, train_path, val_path, config_path = get_path(model_name)
+    if os.path.isfile(config_path):
+        config = json.load(open(config_path, "r", encoding="utf-8"))
+    else:
+        # Use default config
+        config = json.load(open("configs/config.json", "r", encoding="utf-8"))
+    config["model_name"] = model_name
+    config["data"]["training_files"] = train_path
+    config["data"]["validation_files"] = val_path
+    config["train"]["batch_size"] = batch_size
+    config["train"]["epochs"] = epochs
+
+    model_path = os.path.join(dataset_path, "models")
+    try:
+        shutil.copytree(src="pretrained", dst=model_path)
+    except FileExistsError:
+        return f"Error: モデルフォルダ {model_path} が既に存在します。問題なければ削除してください。"
+    except FileNotFoundError:
+        return "Error: pretrainedフォルダが見つかりません。"
+
+    with open(config_path, "w", encoding="utf-8") as f:
+        json.dump(config, f, indent=2)
+    if not os.path.exists("config.yml"):
+        shutil.copy(src="default_config.yml", dst="config.yml")
+    # yml_data = safe_load(open("config.yml", "r", encoding="utf-8"))
+    with open("config.yml", "r", encoding="utf-8") as f:
+        yml_data = yaml.safe_load(f)
+    yml_data["model_name"] = model_name
+    yml_data["dataset_path"] = dataset_path
+    with open("config.yml", "w", encoding="utf-8") as f:
+        yaml.dump(yml_data, f, allow_unicode=True)
+    return "Step 1: 初期設定が完了しました"
+
+
+def resample(model_name):
+    dataset_path, _, _, _, _ = get_path(model_name)
+    in_dir = os.path.join(dataset_path, "raw")
+    out_dir = os.path.join(dataset_path, "wavs")
+    result = subprocess_wrapper(
+        [
+            python,
+            "resample.py",
+            "--in_dir",
+            in_dir,
+            "--out_dir",
+            out_dir,
+            "--sr",
+            "44100",
+        ]
+    )
+    if result.stderr:
+        return f"{result.stderr}"
+    return "Step 2: 音声ファイルの前処理が完了しました"
+
+
+def preprocess_text(model_name):
+    dataset_path, lbl_path, train_path, val_path, config_path = get_path(model_name)
+    lines = open(lbl_path, "r", encoding="utf-8").readlines()
+    with open(lbl_path, "w", encoding="utf-8") as f:
+        for line in lines:
+            path, spk, language, text = line.strip().split("|")
+            path = os.path.join(dataset_path, "wavs", os.path.basename(path)).replace(
+                "\\", "/"
+            )
+            f.writelines(f"{path}|{spk}|{language}|{text}\n")
+    result = subprocess_wrapper(
+        [
+            python,
+            "preprocess_text.py",
+            "--config-path",
+            config_path,
+            "--transcription-path",
+            lbl_path,
+            "--train-path",
+            train_path,
+            "--val-path",
+            val_path,
+        ]
+    )
+
+    if result.stderr:
+        return f"{result.stderr}"
+    return "Step 3: 書き起こしファイルの前処理が完了しました"
+
+
+def bert_gen(model_name):
+    _, _, _, _, config_path = get_path(model_name)
+    result = subprocess_wrapper([python, "bert_gen.py", "--config", config_path])
+    if result.stderr:
+        return f"{result.stderr}"
+    return "Step 4: BERT特徴ファイルの生成が完了しました"
+
+
+def style_gen(model_name):
+    dataset_path, _, _, _, config_path = get_path(model_name)
+    result = subprocess_wrapper(
+        [python, "style_gen.py", "--config", config_path, "--model", dataset_path]
+    )
+    if result.stderr:
+        return f"{result.stderr}"
+    return "Step 5: スタイル特徴ファイルの生成が完了しました"
+
+
+def train(model_name):
+    dataset_path, _, _, _, config_path = get_path(model_name)
+    result = subprocess_wrapper(
+        [python, "train_ms.py", "--config", config_path, "--model", dataset_path]
+    )
+    if result.stderr:
+        return f"{result.stderr}"
+    return "Final Step: 学習が完了しました!"
+
+
+prepare_md = """
+次のようにデータを置いてください。
+```
+├── Data
+│   ├── {モデルの名前}
+│   │   ├── esd.list
+│   │   ├── raw
+│   │   │   ├── ****.wav
+│   │   │   ├── ****.wav
+│   │   │   ├── ...
+```
+
+wavファイル名やモデルの名前は空白を含まない半角で、wavファイルの拡張子は小文字`.wav`である必要があります。
+`raw` フォルダにはすべてのwavファイルを入れ、`esd.list` ファイルには、以下のフォーマットで各wavファイルの情報を記述してください。
+```
+****.wav|{話者名}|{言語ID、ZHかJPかEN}|{書き起こしテキスト}
+```
+
+例：
+```
+wav_number1.wav|hanako|JP|こんにちは、聞こえて、いますか？
+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。
+"""
+
+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。")
+        with gr.Accordion(label="データの前準備", open=False):
+            gr.Markdown(prepare_md)
+        model_name = gr.Textbox(
+            label="モデル名",
+        )
+        info = gr.Textbox(label="状況")
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown(value="### Step 1: 設定ファイルの生成")
+                with gr.Row():
+                    batch_size = gr.Slider(
+                        label="バッチサイズ",
+                        info="VRAM 12GBで4くらい",
+                        value=4,
+                        minimum=1,
+                        maximum=64,
+                        step=1,
+                    )
+                    epochs = gr.Slider(
+                        label="エポック数",
+                        info="100もあれば十分そう",
+                        value=100,
+                        minimum=1,
+                        maximum=1000,
+                        step=1,
+                    )
+                generate_config_btn = gr.Button(value="実行", variant="primary")
+            with gr.Column():
+                gr.Markdown(value="### Step 2: 音声ファイルの前処理")
+                resample_btn = gr.Button(value="実行", variant="primary")
+            with gr.Column():
+                gr.Markdown(value="### Step 3: ラベルファイルの前処理")
+                preprocess_text_btn = gr.Button(value="実行", variant="primary")
+            with gr.Column():
+                gr.Markdown(value="### Step 4: BERT特徴ファイルの生成")
+                bert_gen_btn = gr.Button(value="実行", variant="primary")
+            with gr.Column():
+                gr.Markdown(value="### Step 5: スタイル特徴ファイルの生成")
+                style_gen_btn = gr.Button(value="実行", variant="primary")
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown(value="### Final Step: 学習")
+                train_btn = gr.Button(value="学習", variant="primary")
+
+        generate_config_btn.click(
+            initialize, inputs=[model_name, batch_size, epochs], outputs=[info]
+        )
+        resample_btn.click(resample, inputs=[model_name], outputs=[info])
+        preprocess_text_btn.click(preprocess_text, inputs=[model_name], outputs=[info])
+        bert_gen_btn.click(bert_gen, inputs=[model_name], outputs=[info])
+        style_gen_btn.click(style_gen, inputs=[model_name], outputs=[info])
+        train_btn.click(train, inputs=[model_name], outputs=[info])
+
+    app.launch(share=False, server_port=7860, inbrowser=True)