o 9wi@sddlZddlmZmZmZmZddlZddlmZddl m Z ddl m Z mZeeeefZ ddedeedfdefd d Zee eeefZd ede fd d ZeeefZdedefddZddeefddZdS)N)MappingOptionalTextUnion)BaseWaveformTransform) from_dict) InferenceModelmodeluse_auth_tokenreturncCspt|trn,t|trtj||dd}nt|tr(|d|tjdi|}n tdt|d||S)ayLoad pretrained model and set it into `eval` mode. Parameter --------- model : Model, str, or dict When `Model`, returns `model` as is. When `str`, assumes that this is either the path to a checkpoint or the name of a pretrained model on Huggingface.co and loads with `Model.from_pretrained(model)` When `dict`, loads with `Model.from_pretrained(**model)`. use_auth_token : str, optional When loading a private or gated huggingface.co pipeline, set `use_auth_token` to True or to a string containing your hugginface.co authentication token that can be obtained by visiting https://hf.co/settings/tokens Returns ------- model : Model Model in `eval` mode. Examples -------- >>> model = get_model("hbredin/VoiceActivityDetection-PyanNet-DIHARD") >>> model = get_model("/path/to/checkpoint.ckpt") >>> model = get_model({"checkpoint": "hbredin/VoiceActivityDetection-PyanNet-DIHARD", ... "map_location": torch.device("cuda")}) See also -------- pyannote.audio.core.model.Model.from_pretrained F)r strictr Unsupported type (z.) for loading model: expected `str` or `dict`.N) isinstancer rfrom_pretrainedr setdefault TypeErrortypeeval)r r rrb/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/pyannote/audio/pipelines/utils/getter.py get_model#s $   r inferencecCsPt|tr|St|ttfrt|St|trtdi|Stdt|d)aLoad inference Parameter --------- inference : Inference, Model, str, or dict When `Inference`, returns `inference` as is. When `Model`, wraps it in `Inference(model)`. When `str`, assumes that this is either the path to a checkpoint or the name of a pretrained model on Huggingface.co and loads with `Inference(checkpoint)`. When `dict`, loads with `Inference(**inference)`. Returns ------- inference : Inference Inference. Examples -------- >>> inference = get_inference("hbredin/VoiceActivityDetection-PyanNet-DIHARD") >>> inference = get_inference("/path/to/checkpoint.ckpt") >>> inference = get_inference({"model": "hbredin/VoiceActivityDetection-PyanNet-DIHARD", ... "window": "sliding"}) See also -------- pyannote.audio.core.inference.Inference rz;) for loading inference: expected `Model`, `str` or `dict`.Nr)rrr rrrr)rrrr get_inference`s  r augmentationcCs@|durdSt|tr |St|trt|Stdt|d)a[Load augmentation Parameter --------- augmentation : BaseWaveformTransform, or dict When `BaseWaveformTransform`, returns `augmentation` as is. When `dict`, loads with `torch_audiomentations`'s `from_config` utility function. Returns ------- augmentation : BaseWaveformTransform Augmentation. NrzH) for loading augmentation: expected `BaseWaveformTransform`, or `dict`.)rrraugmentation_from_dictrr)rrrrget_augmentations  rneedscCsntj}|dkrtdg}|dur|S||Sddt|D}|dur(|Sddtt|t|DS)a7Get devices that can be used by the pipeline Parameters ---------- needs : int, optional Number of devices needed by the pipeline Returns ------- devices : list of torch.device List of available devices. When `needs` is provided, returns that many devices. rcpuNcSsg|] }td|dqS)zcuda:d)torchdevice).0indexrrr szget_devices..cSsg|]\}}|qSrr)r"_r!rrrr$s)r cuda device_countr!rangezip itertoolscycle)rnum_gpusdevicesrrr get_devicess  r.)N)r*typingrrrrr /torch_audiomentations.core.transforms_interfacer"torch_audiomentations.utils.configrrpyannote.audiorr PipelineModelrPipelineInferencerPipelineAugmentationrintr.rrrrs(    : -