+ TV-jj^RIHt^RIHtHt^RIHt^RIHtRt RRRR/t R RR llt R R R llt R#) )Path)OptionalUnionN)base_load_modeli>z ecapa-tdnn ecapa_tdnnc~V^8dQhR\\\3,R\R\R\P /# model_pathlazystrictreturnrstrrboolnnModule)formats"d/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/mlx_audio/lid/utils.py __annotate__rs9c4i (,>BYYc 4\RRVRRR\RVRV/VB#)a} Load and initialize a LID model from a given path. Args: model_path: The path or HuggingFace repo to load the model from. lazy: If False, evaluate model parameters immediately. strict: If True, raise an error if any weights are missing. **kwargs: Additional keyword arguments (revision, force_download). Returns: nn.Module: The loaded and initialized model. r categorylidmodel_remappingr r )rMODEL_REMAPPINGr r r kwargss&&&,r load_modelr sI    (         rc~V^8dQhR\\\3,R\R\R\P /#r r)rs"rrr)s?FFc4i F(,F>BFYYFrc "\V3RVRV/VB#)a Load a language identification model from a local path or HuggingFace repository. This is the main entry point for loading LID models. It automatically detects the model type and initializes the appropriate model class. Args: model_path: The local path or HuggingFace repo ID to load from. lazy: If False, evaluate model parameters immediately. strict: If True, raise an error if any weights are missing. **kwargs: Additional keyword arguments: - revision (str): HuggingFace revision/branch to use - force_download (bool): Force re-download of model files Returns: nn.Module: The loaded and initialized model. Example: >>> from mlx_audio.lid import load >>> model = load("facebook/mms-lid-256") >>> results = model.predict(audio) r r )r rs&&&,rloadr#)s2 j Et EF Ef EEr)FF) pathlibrtypingrrmlx.nnrmlx_audio.utilsr SAMPLE_RATErr r#rrrr)s:"+ ,, 2FFr