Refactor

webui/style_vectors.py

@@ -6,12 +6,12 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 import yaml
+from config import config
 from scipy.spatial.distance import pdist, squareform
 from sklearn.cluster import DBSCAN, AgglomerativeClustering, KMeans
 from sklearn.manifold import TSNE
 from umap import UMAP
 
-from config import config
 from style_bert_vits2.constants import DEFAULT_STYLE, GRADIO_THEME
 from style_bert_vits2.logging import logger
 

webui/train.py

@@ -24,32 +24,31 @@ tensorboard_executed = False
 # Get path settings
 with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f:
     path_config: dict[str, str] = yaml.safe_load(f.read())
-    dataset_root = path_config["dataset_root"]
-    # assets_root = path_config["assets_root"]
+    dataset_root = Path(path_config["dataset_root"])
 
 
-def get_path(model_name):
+def get_path(model_name: str) -> tuple[Path, Path, Path, Path, Path]:
     assert model_name != "", "モデル名は空にできません"
-    dataset_path = os.path.join(dataset_root, 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")
+    dataset_path = dataset_root / model_name
+    lbl_path = dataset_path / "esd.list"
+    train_path = dataset_path / "train.list"
+    val_path = dataset_path / "val.list"
+    config_path = dataset_path / "config.json"
     return dataset_path, lbl_path, train_path, val_path, config_path
 
 
 def initialize(
-    model_name,
-    batch_size,
-    epochs,
-    save_every_steps,
-    freeze_EN_bert,
-    freeze_JP_bert,
-    freeze_ZH_bert,
-    freeze_style,
-    freeze_decoder,
-    use_jp_extra,
-    log_interval,
+    model_name: str,
+    batch_size: int,
+    epochs: int,
+    save_every_steps: int,
+    freeze_EN_bert: bool,
+    freeze_JP_bert: bool,
+    freeze_ZH_bert: bool,
+    freeze_style: bool,
+    freeze_decoder: bool,
+    use_jp_extra: bool,
+    log_interval: int,
 ):
     global logger_handler
     dataset_path, _, train_path, val_path, config_path = get_path(model_name)
@@ -72,8 +71,8 @@ def initialize(
     with open(default_config_path, "r", encoding="utf-8") as f:
         config = json.load(f)
     config["model_name"] = model_name
-    config["data"]["training_files"] = train_path
-    config["data"]["validation_files"] = val_path
+    config["data"]["training_files"] = str(train_path)
+    config["data"]["validation_files"] = str(val_path)
     config["train"]["batch_size"] = batch_size
     config["train"]["epochs"] = epochs
     config["train"]["eval_interval"] = save_every_steps
@@ -90,18 +89,18 @@ def initialize(
     # 今はデフォルトであるが、以前は非JP-Extra版になくバグの原因になるので念のため
     config["data"]["use_jp_extra"] = use_jp_extra
 
-    model_path = os.path.join(dataset_path, "models")
-    if os.path.exists(model_path):
+    model_path = dataset_path / "models"
+    if model_path.exists():
         logger.warning(
             f"Step 1: {model_path} already exists, so copy it to backup to {model_path}_backup"
         )
         shutil.copytree(
             src=model_path,
-            dst=os.path.join(dataset_path, "models_backup"),
+            dst=dataset_path / "models_backup",
             dirs_exist_ok=True,
         )
         shutil.rmtree(model_path)
-    pretrained_dir = "pretrained" if not use_jp_extra else "pretrained_jp_extra"
+    pretrained_dir = Path("pretrained" if not use_jp_extra else "pretrained_jp_extra")
     try:
         shutil.copytree(
             src=pretrained_dir,
@@ -113,30 +112,29 @@ def initialize(
 
     with open(config_path, "w", encoding="utf-8") as f:
         json.dump(config, f, indent=2, ensure_ascii=False)
-    if not os.path.exists("config.yml"):
+    if not Path("config.yml").exists():
         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
+    yml_data["dataset_path"] = str(dataset_path)
     with open("config.yml", "w", encoding="utf-8") as f:
         yaml.dump(yml_data, f, allow_unicode=True)
     logger.success("Step 1: initialization finished.")
     return True, "Step 1, Success: 初期設定が完了しました"
 
 
-def resample(model_name, normalize, trim, num_processes):
+def resample(model_name: str, normalize: bool, trim: bool, num_processes: int):
     logger.info("Step 2: start resampling...")
     dataset_path, _, _, _, _ = get_path(model_name)
-    input_dir = os.path.join(dataset_path, "raw")
-    output_dir = os.path.join(dataset_path, "wavs")
+    input_dir = dataset_path / "raw"
+    output_dir = dataset_path / "wavs"
     cmd = [
         "resample.py",
         "-i",
-        input_dir,
+        str(input_dir),
         "-o",
-        output_dir,
+        str(output_dir),
         "--num_processes",
         str(num_processes),
         "--sr",
@@ -157,42 +155,30 @@ def resample(model_name, normalize, trim, num_processes):
     return True, "Step 2, Success: 音声ファイルの前処理が完了しました"
 
 
-def preprocess_text(model_name, use_jp_extra, val_per_lang, yomi_error):
+def preprocess_text(
+    model_name: str, use_jp_extra: bool, val_per_lang: int, yomi_error: str
+):
     logger.info("Step 3: start preprocessing text...")
-    dataset_path, lbl_path, train_path, val_path, config_path = get_path(model_name)
-    try:
-        lines = open(lbl_path, "r", encoding="utf-8").readlines()
-    except FileNotFoundError:
+    _, lbl_path, train_path, val_path, config_path = get_path(model_name)
+    if not lbl_path.exists():
         logger.error(f"Step 3: {lbl_path} not found.")
         return False, f"Step 3, Error: 書き起こしファイル {lbl_path} が見つかりません。"
-    new_lines = []
-    for line in lines:
-        if len(line.strip().split("|")) != 4:
-            logger.error(f"Step 3: {lbl_path} has invalid format at line:\n{line}")
-            return (
-                False,
-                f"Step 3, Error: 書き起こしファイル次の行の形式が不正です:\n{line}",
-            )
-        path, spk, language, text = line.strip().split("|")
-        # pathをファイル名だけ取り出して正しいパスに変更
-        path = Path(dataset_path) / "wavs" / Path(path).name
-        new_lines.append(f"{path}|{spk}|{language}|{text}\n")
-    with open(lbl_path, "w", encoding="utf-8") as f:
-        f.writelines(new_lines)
+
     cmd = [
         "preprocess_text.py",
         "--config-path",
-        config_path,
+        str(config_path),
         "--transcription-path",
-        lbl_path,
+        str(lbl_path),
         "--train-path",
-        train_path,
+        str(train_path),
         "--val-path",
-        val_path,
+        str(val_path),
         "--val-per-lang",
         str(val_per_lang),
         "--yomi_error",
         yomi_error,
+        "--correct_path",  # 音声ファイルのパスを正しいパスに修正する
     ]
     if use_jp_extra:
         cmd.append("--use_jp_extra")
@@ -213,17 +199,11 @@ def preprocess_text(model_name, use_jp_extra, val_per_lang, yomi_error):
     return True, "Step 3, Success: 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):
+def bert_gen(model_name: str):
     logger.info("Step 4: start bert_gen...")
     _, _, _, _, config_path = get_path(model_name)
     success, message = run_script_with_log(
-        [
-            "bert_gen.py",
-            "--config",
-            config_path,
-            # "--num_processes",  # bert_genは重いのでプロセス数いじらない
-            #  str(num_processes),
-        ]
+        ["bert_gen.py", "--config", str(config_path)]
     )
     if not success:
         logger.error("Step 4: bert_gen failed.")
@@ -238,14 +218,14 @@ def bert_gen(model_name):
     return True, "Step 4, Success: BERT特徴ファイルの生成が完了しました"
 
 
-def style_gen(model_name, num_processes):
+def style_gen(model_name: str, num_processes: int):
     logger.info("Step 5: start style_gen...")
     _, _, _, _, config_path = get_path(model_name)
     success, message = run_script_with_log(
         [
             "style_gen.py",
             "--config",
-            config_path,
+            str(config_path),
             "--num_processes",
             str(num_processes),
         ]
@@ -267,22 +247,22 @@ def style_gen(model_name, num_processes):
 
 
 def preprocess_all(
-    model_name,
-    batch_size,
-    epochs,
-    save_every_steps,
-    num_processes,
-    normalize,
-    trim,
-    freeze_EN_bert,
-    freeze_JP_bert,
-    freeze_ZH_bert,
-    freeze_style,
-    freeze_decoder,
-    use_jp_extra,
-    val_per_lang,
-    log_interval,
-    yomi_error,
+    model_name: str,
+    batch_size: int,
+    epochs: int,
+    save_every_steps: int,
+    num_processes: int,
+    normalize: bool,
+    trim: bool,
+    freeze_EN_bert: bool,
+    freeze_JP_bert: bool,
+    freeze_ZH_bert: bool,
+    freeze_style: bool,
+    freeze_decoder: bool,
+    use_jp_extra: bool,
+    val_per_lang: int,
+    log_interval: int,
+    yomi_error: str,
 ):
     if model_name == "":
         return False, "Error: モデル名を入力してください"
@@ -333,18 +313,23 @@ def preprocess_all(
     )
 
 
-def train(model_name, skip_style=False, use_jp_extra=True, speedup=False):
+def train(
+    model_name: str,
+    skip_style: bool = False,
+    use_jp_extra: bool = True,
+    speedup: bool = False,
+):
     dataset_path, _, _, _, config_path = get_path(model_name)
-    # 学習再開の場合は念のためconfig.ymlの名前等を更新
+    # 学習再開の場合を考えて念のためconfig.ymlの名前等を更新
     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
+    yml_data["dataset_path"] = str(dataset_path)
     with open("config.yml", "w", encoding="utf-8") as f:
         yaml.dump(yml_data, f, allow_unicode=True)
 
     train_py = "train_ms.py" if not use_jp_extra else "train_ms_jp_extra.py"
-    cmd = [train_py, "--config", config_path, "--model", dataset_path]
+    cmd = [train_py, "--config", str(config_path), "--model", str(dataset_path)]
     if skip_style:
         cmd.append("--skip_default_style")
     if speedup:
@@ -360,7 +345,7 @@ def train(model_name, skip_style=False, use_jp_extra=True, speedup=False):
     return True, "Success: 学習が完了しました"
 
 
-def wait_for_tensorboard(port=6006, timeout=10):
+def wait_for_tensorboard(port: int = 6006, timeout: float = 10):
     start_time = time.time()
     while True:
         try:
@@ -375,7 +360,7 @@ def wait_for_tensorboard(port=6006, timeout=10):
         time.sleep(0.1)
 
 
-def run_tensorboard(model_name):
+def run_tensorboard(model_name: str):
     global tensorboard_executed
     if not tensorboard_executed:
         python = sys.executable