Refactor: Use PathConfig and pathlib instead of paths.yml loading

gradio_tabs/style_vectors.py

@@ -1,27 +1,23 @@
 import json
-import os
 import shutil
 from pathlib import Path
 
 import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
-import yaml
 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 config import get_path_config
 from style_bert_vits2.constants import DEFAULT_STYLE, GRADIO_THEME
 from style_bert_vits2.logging import logger
 
 
-# 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(path_config["dataset_root"])
-    # assets_root = path_config["assets_root"]
+path_config = get_path_config()
+dataset_root = path_config.dataset_root
+assets_root = path_config.assets_root
 
 MAX_CLUSTER_NUM = 10
 MAX_AUDIO_NUM = 10
@@ -39,11 +35,7 @@ centroids = []
 
 def load(model_name: str, reduction_method: str):
     global wav_files, x, x_reduced, mean
-    # wavs_dir = os.path.join(dataset_root, model_name, "wavs")
     wavs_dir = dataset_root / model_name / "wavs"
-    # style_vector_files = [
-    #     os.path.join(wavs_dir, f) for f in os.listdir(wavs_dir) if f.endswith(".npy")
-    # ]
     style_vector_files = [f for f in wavs_dir.rglob("*.npy") if f.is_file()]
     # foo.wav.npy -> foo.wav
     wav_files = [f.with_suffix("") for f in style_vector_files]
@@ -196,21 +188,21 @@ def do_clustering_gradio(n_clusters=4, method="KMeans"):
     ] * MAX_AUDIO_NUM
 
 
-def save_style_vectors_from_clustering(model_name, style_names_str: str):
+def save_style_vectors_from_clustering(model_name: str, style_names_str: str):
     """centerとcentroidsを保存する"""
-    result_dir = os.path.join(config.assets_root, model_name)
-    os.makedirs(result_dir, exist_ok=True)
+    result_dir = assets_root / model_name
+    result_dir.mkdir(parents=True, exist_ok=True)
     style_vectors = np.stack([mean] + centroids)
-    style_vector_path = os.path.join(result_dir, "style_vectors.npy")
-    if os.path.exists(style_vector_path):
+    style_vector_path = result_dir / "style_vectors.npy"
+    if style_vector_path.exists():
         logger.info(f"Backup {style_vector_path} to {style_vector_path}.bak")
         shutil.copy(style_vector_path, f"{style_vector_path}.bak")
     np.save(style_vector_path, style_vectors)
     logger.success(f"Saved style vectors to {style_vector_path}")
 
     # config.jsonの更新
-    config_path = os.path.join(result_dir, "config.json")
-    if not os.path.exists(config_path):
+    config_path = result_dir / "config.json"
+    if not config_path.exists():
         return f"{config_path}が存在しません。"
     style_names = [name.strip() for name in style_names_str.split(",")]
     style_name_list = [DEFAULT_STYLE] + style_names
@@ -233,7 +225,7 @@ def save_style_vectors_from_clustering(model_name, style_names_str: str):
 
 
 def save_style_vectors_from_files(
-    model_name, audio_files_str: str, style_names_str: str
+    model_name: str, audio_files_str: str, style_names_str: str
 ):
     """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
@@ -241,8 +233,8 @@ def save_style_vectors_from_files(
         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.assets_root, model_name)
-    os.makedirs(result_dir, exist_ok=True)
+    result_dir = assets_root / model_name
+    result_dir.mkdir(parents=True, exist_ok=True)
     audio_files = [name.strip() for name in audio_files_str.split(",")]
     style_names = [name.strip() for name in style_names_str.split(",")]
     if len(audio_files) != len(style_names):
@@ -252,23 +244,23 @@ def save_style_vectors_from_files(
         return "スタイル名が重複しています。"
     style_vectors = [mean]
 
-    wavs_dir = os.path.join(dataset_root, model_name, "wavs")
+    wavs_dir = dataset_root / model_name / "wavs"
     for audio_file in audio_files:
-        path = os.path.join(wavs_dir, audio_file)
-        if not os.path.exists(path):
+        path = wavs_dir / audio_file
+        if not path.exists():
             return f"{path}が存在しません。"
         style_vectors.append(np.load(f"{path}.npy"))
     style_vectors = np.stack(style_vectors)
     assert len(style_name_list) == len(style_vectors)
-    style_vector_path = os.path.join(result_dir, "style_vectors.npy")
-    if os.path.exists(style_vector_path):
+    style_vector_path = result_dir / "style_vectors.npy"
+    if style_vector_path.exists():
         logger.info(f"Backup {style_vector_path} to {style_vector_path}.bak")
         shutil.copy(style_vector_path, f"{style_vector_path}.bak")
     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):
+    config_path = result_dir / "config.json"
+    if not config_path.exists():
         return f"{config_path}が存在しません。"
     logger.info(f"Backup {config_path} to {config_path}.bak")
     shutil.copy(config_path, f"{config_path}.bak")