Upload seed-vc/modules/bigvgan/bigvgan.py with huggingface_hub

seed-vc/modules/bigvgan/bigvgan.py

@@ -415,36 +415,45 @@ class BigVGAN(
             cls,
             *,
             model_id: str,
-            revision: str,
-            cache_dir: str,
-            force_download: bool,
-            proxies: Optional[Dict],
-            resume_download: bool,
-            local_files_only: bool,
-            token: Union[str, bool, None],
+            revision: str = None,
+            cache_dir: str = None,
+            force_download: bool = False,
+            proxies: Optional[Dict] = None,
+            resume_download: Optional[bool] = None,
+            local_files_only: bool = False,
+            token: Union[str, bool, None] = None,
             map_location: str = "cpu",  # Additional argument
             strict: bool = False,  # Additional argument
             use_cuda_kernel: bool = False,
             **model_kwargs,
     ):
         """Load Pytorch pretrained weights and return the loaded model."""
+        import inspect
 
-        # Download and load hyperparameters (h) used by BigVGAN
-        if os.path.isdir(model_id):
-            print("Loading config.json from local directory")
-            config_file = os.path.join(model_id, "config.json")
-        else:
-            config_file = hf_hub_download(
+        # huggingface_hub >=1.0 dropped proxies/resume_download from mixin + download API
+        def _hub_download(filename: str) -> str:
+            kwargs = dict(
                 repo_id=model_id,
-                filename="config.json",
+                filename=filename,
                 revision=revision,
                 cache_dir=cache_dir,
                 force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
                 token=token,
                 local_files_only=local_files_only,
             )
+            params = inspect.signature(hf_hub_download).parameters
+            if "proxies" in params:
+                kwargs["proxies"] = proxies
+            if "resume_download" in params and resume_download is not None:
+                kwargs["resume_download"] = resume_download
+            return hf_hub_download(**kwargs)
+
+        # Download and load hyperparameters (h) used by BigVGAN
+        if os.path.isdir(model_id):
+            print("Loading config.json from local directory")
+            config_file = os.path.join(model_id, "config.json")
+        else:
+            config_file = _hub_download("config.json")
         h = load_hparams_from_json(config_file)
 
         # instantiate BigVGAN using h
@@ -466,17 +475,7 @@ class BigVGAN(
             model_file = os.path.join(model_id, "bigvgan_generator.pt")
         else:
             print(f"Loading weights from {model_id}")
-            model_file = hf_hub_download(
-                repo_id=model_id,
-                filename="bigvgan_generator.pt",
-                revision=revision,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
-                token=token,
-                local_files_only=local_files_only,
-            )
+            model_file = _hub_download("bigvgan_generator.pt")
 
         checkpoint_dict = torch.load(model_file, map_location=map_location)