o rri @s.ddlmZ          d dd ZdS) )utilsNumxlF,torchc Cs|durtj|||||| d| d}|| r|| |dur$td| r+|| }t|||j}||} |j| |d} | S)a} Open Unmix functional interface Separates a torch.Tensor or the content of an audio file. If a separator is provided, use it for inference. If not, create one and use it afterwards. Args: audio: audio to process torch Tensor: shape (channels, length), and `rate` must also be provided. rate: int or None: only used if audio is a Tensor. Otherwise, inferred from the file. model_str_or_path: the pretrained model to use, defaults to UMX-L targets (str): select the targets for the source to be separated. a list including: ['vocals', 'drums', 'bass', 'other']. If you don't pick them all, you probably want to activate the `residual=True` option. Defaults to all available targets per model. niter (int): the number of post-processingiterations, defaults to 1 residual (bool): if True, a "garbage" target is created wiener_win_len (int): the number of frames to use when batching the post-processing step aggregate_dict (str): if provided, must be a string containing a ' 'valid expression for a dictionary, with keys as output ' 'target names, and values a list of targets that are used to ' 'build it. For instance: '{"vocals":["vocals"], ' '"accompaniment":["drums","bass","other"]}' separator: if provided, the model.Separator object that will be used to perform separation device (str): selects device to be used for inference filterbank (str): filterbank implementation method. Supported are `['torch', 'asteroid']`. `torch` is about 30% faster compared to `asteroid` on large FFT sizes such as 4096. However, asteroids stft can be exported to onnx, which makes is practical for deployment. NT)model_str_or_pathtargetsniterresidualwiener_win_lendevice pretrained filterbankzrate` must be provided.)aggregate_dict)rload_separatorfreezeto Exception preprocess sample_rateto_dict) audioraterrr r r r separatorr r estimatesrE/home/ubuntu/.local/lib/python3.10/site-packages/openunmix/predict.pyseparates,3   r) NrNrFrNNNr) openunmixrrrrrrs