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Fix _from_pretrained signature for huggingface_hub>=0.23 compatibility #11

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Fix _from_pretrained signature for huggingface_hub>=0.23 compatibility #11

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37 changes: 21 additions & 16 deletions bigvgan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -419,8 +419,8 @@ def _from_pretrained(
revision: str,
cache_dir: str,
force_download: bool,
proxies: Optional[Dict],
resume_download: bool,
proxies: Optional[Dict] = None,
resume_download: bool = False,
local_files_only: bool,
token: Union[str, bool, None],
map_location: str = "cpu", # Additional argument
Expand All @@ -430,6 +430,23 @@ def _from_pretrained(
):
"""Load Pytorch pretrained weights and return the loaded model."""

# Build kwargs for hf_hub_download, excluding params removed in huggingface_hub 1.0
hf_hub_kwargs = dict(
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
token=token,
local_files_only=local_files_only,
)
# proxies and resume_download were removed in huggingface_hub 1.0
# Only include them for backwards compatibility with older versions
import inspect
hf_download_params = inspect.signature(hf_hub_download).parameters
if "proxies" in hf_download_params:
hf_hub_kwargs["proxies"] = proxies
if "resume_download" in hf_download_params:
hf_hub_kwargs["resume_download"] = resume_download

# Download and load hyperparameters (h) used by BigVGAN
if os.path.isdir(model_id):
print("Loading config.json from local directory")
Expand All @@ -438,13 +455,7 @@ def _from_pretrained(
config_file = hf_hub_download(
repo_id=model_id,
filename="config.json",
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
**hf_hub_kwargs,
)
h = load_hparams_from_json(config_file)

Expand All @@ -470,13 +481,7 @@ def _from_pretrained(
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,
**hf_hub_kwargs,
)

checkpoint_dict = torch.load(model_file, map_location=map_location)
Expand Down