+from collections.abc import Generator

+from pathlib import Path

+import typing

+

+import huggingface_hub

+

+from faster_whisper_server.logger import logger

+

+LIBRARY_NAME = "ctranslate2"

+TASK_NAME = "automatic-speech-recognition"

+

+

+def does_local_model_exist(model_id: str) -> bool:

+    return any(model_id == model.repo_id for model, _ in list_local_models())

+

+

+def list_local_models() -> Generator[tuple[huggingface_hub.CachedRepoInfo, huggingface_hub.ModelCardData], None, None]:

+    hf_cache = huggingface_hub.scan_cache_dir()

+    hf_models = [repo for repo in list(hf_cache.repos) if repo.repo_type == "model"]

+    for model in hf_models:

+        revision = next(iter(model.revisions))

+        cached_readme_file = next((f for f in revision.files if f.file_name == "README.md"), None)

+        if cached_readme_file:

+            readme_file_path = Path(cached_readme_file.file_path)

+        else:

+            # NOTE: the README.md doesn't get downloaded when `WhisperModel` is called

+            logger.debug(f"Model {model.repo_id} does not have a README.md file. Downloading it.")

+            readme_file_path = Path(huggingface_hub.hf_hub_download(model.repo_id, "README.md"))

+

+        model_card = huggingface_hub.ModelCard.load(readme_file_path)

+        model_card_data = typing.cast(huggingface_hub.ModelCardData, model_card.data)

+        if (

+            model_card_data.library_name == LIBRARY_NAME

+            and model_card_data.tags is not None

+            and TASK_NAME in model_card_data.tags

+        ):

+            yield model, model_card_data