Update inference_gradio.py

inference_gradio.py

@@ -35,12 +35,11 @@ device = (
 
 REPO_ROOT = Path(__file__).resolve().parent
 # Local pretrained root (used when running from a repo / Space that bundles weights)
-PRETRAINED_ROOT = str(REPO_ROOT / "pretrained_models")
-CKPTS_ROOT = os.path.join(PRETRAINED_ROOT, "ckpts")
+# PRETRAINED_ROOT = str(REPO_ROOT / "pretrained_models")
 
 # HF location for pretrained assets (used as a fallback when local files are missing)
-# HF_PRETRAINED_ROOT = "hf://LEMAS-Project/LEMAS-TTS/blob/main/pretrained_models"
-
+PRETRAINED_ROOT = "hf://LEMAS-Project/LEMAS-TTS/pretrained_models"
+CKPTS_ROOT = os.path.join(PRETRAINED_ROOT, "ckpts")
 
 # 1) 指向你仓库里的 libespeak-ng.so
 ESPEAK_LIB = os.path.join(PRETRAINED_ROOT, "espeak-ng-lib", "libespeak-ng.so")
@@ -91,8 +90,8 @@ class UVR5:
         return output_audio.squeeze().T.numpy(), 44100
 
 denoise_model = UVR5(
-    model_dir=os.path.join(PRETRAINED_ROOT, "uvr5"),
-    code_dir=str(REPO_ROOT / "uvr5"),
+    model_dir=cached_path(os.path.join(PRETRAINED_ROOT, "uvr5")),
+    code_dir=cached_path(str(REPO_ROOT / "uvr5")),
 )
 
 def load_wav(audio_info, sr=16000, channel=1):
@@ -181,7 +180,7 @@ def get_checkpoints_project(project_name=None, is_gradio=True):
 def get_available_projects():
     """Get available project names from data directory"""
     data_paths = [
-        str(PRETRAINED_ROOT / "data"),
+        cached_path(str(PRETRAINED_ROOT / "data")),
     ]
     
     project_list = []
@@ -238,13 +237,13 @@ def infer(
         use_prosody_encoder = True if "prosody" in str(ckpt_resolved) else False
 
         # Resolve vocab file (local or HF)
-        local_vocab = Path(PRETRAINED_ROOT) / "data" / project / "vocab.txt"
+        local_vocab = cached_path(str(PRETRAINED_ROOT / "data" / project / "vocab.txt"))
         if local_vocab.is_file():
             vocab_file = str(local_vocab)
         else:
             remote_vocab_map = {
-                "multilingual_grl": f"{PRETRAINED_ROOT}/data/multilingual_grl/vocab.txt",
-                "multilingual_prosody": f"{PRETRAINED_ROOT}/data/multilingual_prosody/vocab.txt",
+                "multilingual_grl": cached_path(f"{PRETRAINED_ROOT}/data/multilingual_grl/vocab.txt"),
+                "multilingual_prosody": cached_path(f"{PRETRAINED_ROOT}/data/multilingual_prosody/vocab.txt"),
             }
             remote_vocab = remote_vocab_map.get(project)
             if remote_vocab is None:
@@ -482,13 +481,13 @@ with gr.Blocks(title="LEMAS-TTS Inference") as app:
 
     # Examples
     def _resolve_example(name: str) -> str:
-        local = os.path.join(PRETRAINED_ROOT, "data", "test_examples", name)
+        local = cached_path(os.path.join(PRETRAINED_ROOT, "data", "test_examples", name))
         if os.path.isfile(local):
             return local
         remote_map = {
-            "en.wav": os.path.join(PRETRAINED_ROOT, "data", "test_examples", "en.wav"),
-            "es.wav": os.path.join(PRETRAINED_ROOT, "data", "test_examples", "es.wav"),
-            "pt.wav": os.path.join(PRETRAINED_ROOT, "data", "test_examples", "pt.wav"),
+            "en.wav": cached_path(os.path.join(PRETRAINED_ROOT, "data", "test_examples", "en.wav")),
+            "es.wav": cached_path(os.path.join(PRETRAINED_ROOT, "data", "test_examples", "es.wav")),
+            "pt.wav": cached_path(os.path.join(PRETRAINED_ROOT, "data", "test_examples", "pt.wav")),
         }
         url = remote_map.get(name)
         return str(cached_path(url)) if url is not None else ""
@@ -599,7 +598,7 @@ def main(port, host, share, api):
         server_port=port,
         share=share,
         show_api=api,
-        allowed_paths=[str(os.path.join(PRETRAINED_ROOT, "data"))],
+        allowed_paths=[str(cached_path(os.path.join(PRETRAINED_ROOT, "data")))],
     )