o pi{$ @sddlZddlmZddlmZmZddlZddlmZddl m Z ddl m Z ddl mZddlmZdd lmZ  dd e eBeBd edBd eeBdBd e fddZe eBeBZ  dded edBd eeBdBd e fddZeeBZeeBeBZ  dded edBd eeBdBd edBfddZeeBeBZ  dded edBd eeBdBd edBfddZded efddZd dedBfddZ dS)!N)Path)DictMapping) Calibration)Model)Pipeline)PLDA)BaseWaveformTransform) from_dictpipelinetoken cache_dirreturncCst|tr|}n?t|trtj|||d}n1t|tr=d|vr4|d||d|tjd i|}ntj|||d}n tdt|d|durStd|d|S) Nr r checkpointr r Unsupported type (z1) for loading pipeline: expected `str` or `dict`.zCould not load pipeline: .) isinstancerstrfrom_pretraineddict setdefault TypeErrortype ValueError)r r r _pipelinerrY/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/utils/getter.py get_pipeline%s$     rmodelcCst|trn7t|trtj|||dd}|r|}n$t|tr3|d||d|tjdi|}n tdt|d||S) aLLoad pretrained model and set it into `eval` mode. Parameter --------- model : Model, str, or dict When `Model`, returns `model` as is. When `str`, assumes that this is either the path to a checkpoint or the name of a pretrained model on Huggingface.co and loads with `Model.from_pretrained(model)` When `dict`, loads with `Model.from_pretrained(**model)`. token : str or bool, optional Huggingface token to be used for downloading from Huggingface hub. cache_dir: Path or str, optional Path to the folder where files downloaded from Huggingface hub are stored. Returns ------- model : Model Model in `eval` mode. Examples -------- >>> model = get_model("hbredin/VoiceActivityDetection-PyanNet-DIHARD") >>> model = get_model("/path/to/checkpoint.ckpt") >>> model = get_model({"checkpoint": "hbredin/VoiceActivityDetection-PyanNet-DIHARD", ... "map_location": torch.device("cuda")}) See also -------- pyannote.audio.core.model.Model.from_pretrained F)r r strictr r rz.) for loading model: expected `str` or `dict`.Nr) rrrrrrrreval)rr r _modelrrr get_modelJs* %    r# calibrationcCzt|tr |}|St|trtj|||d}|St|tr3|d||d|tjdi|}|Stdt|d)aDLoad pretrained calibration Parameters ---------- calibration : Calibration, str, or dict When `Calibration`, returns `calibration` as is. When `str`, assumes that this is either the path to a checkpoint or the name of a pretrained calibration on Huggingface.co and loads with `Calibration.from_pretrained(calibration)`. When `dict`, loads with `Calibration.from_pretrained(**calibration)`. token : str or bool, optional Huggingface token to be used for downloading from Huggingface hub. cache_dir: Path or str, optional Path to the folder where files downloaded from Huggingface hub are stored. Returns ------- calibration : Calibration Calibration. See also -------- pyannote.audio.core.calibration.Calibration.from_pretrained rr r rz4) for loading calibration: expected `str` or `dict`.Nr)rrrrrrrr)r$r r loaded_calibrationrrrget_calibrations$    r'pldacCr%)aLoad pretrained calibration Parameters ---------- plda : PLDA, str, or dict When `PLDA`, returns `plda` as is. When `str`, assumes that this is either the path to a checkpoint or the name of a pretrained PLDA on Huggingface.co and loads with `PLDA.from_pretrained(PLDA)`. When `dict`, loads with `PLDA.from_pretrained(**plda)`. token : str or bool, optional Huggingface token to be used for downloading from Huggingface hub. cache_dir: Path or str, optional Path to the folder where files downloaded from Huggingface hub are stored. Returns ------- plda : PLDA PLDA. See also -------- pyannote.audio.core.plda.PLDA.from_pretrained rr r rz-) for loading PLDA: expected `str` or `dict`.Nr)rrrrrrrr)r(r r loaded_pldarrrget_pldas    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`.)rr raugmentation_from_dictrr)r+rrrget_augmentations  r-needscCsntj}|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)r3_r2rrrr52s)r1cuda device_countr2rangezip itertoolscycle)r.num_gpusdevicesrrr get_devicess  r?)NN)N)!r;pathlibrtypingrrr1pyannote.audio.core.calibrationrpyannote.audio.core.modelrpyannote.audio.core.pipelinerpyannote.audio.core.pldar/torch_audiomentations.core.transforms_interfacer "torch_audiomentations.utils.configr r,rrr PipelineModelr#PipelineAugmentationPipelineCalibrationr' PipelinePLDAr*r-intr?rrrrsx          "  A   5  1