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          }|                                 |	r|                    |	           |t	          d          |	r|                     |	          } t          j        | ||j                  }  ||           }|                    ||          }|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_pathtargetsniterresidualwiener_win_lendevice
pretrained
filterbankzrate` must be provided.)aggregate_dict)r   load_separatorfreezeto	Exception
preprocesssample_rateto_dict)audiorater	   r
   r   r   r   r   	separatorr   r   	estimatess               W/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/openunmix/predict.pyseparater      s    f (/)!	
 	
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