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    8wi                     @   sl   d dl mZ d dlZd dlZd dlZd dlZd dlZd dlm	Z	 d dlm
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 d dlmZ d dlZdd ZdS )    )PathN)utils)predict)datac               
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6 D ] \}}t|t0|4|j5 }t	j>|t7|d3|j'd@ qqd S )ANzUMX InferenceT)descriptionadd_helpformatter_classinput+z List of paths to wav/flac files.)typenargshelpz--modelumxlzCpath to mode base directory of pretrained models, defaults to UMX-L)defaultr   r   z	--targetsz^provide targets to be processed.               If none, all available targets will be computed)r   r   r   z--outdirz6Results path where audio evaluation results are stored)r   r   z--extz.wavz,Output extension which sets the audio format)r   r   r   z--startg        zAudio chunk start in secondsz
--durationz@Audio chunk duration in seconds, negative values load full trackz	--no-cuda
store_trueFzdisables CUDA inference)actionr   r   z--audio-backendzSets audio backend. Default to torchaudio's default backend: See https://pytorch.org/audio/stable/backend.html(`sox_io`, `sox`, `soundfile` or `stempeg`)z--niter   z*number of iterations for refining results.z--wiener-win-leni,  z:Number of frames on which to apply filtering independentlyz
--residualzSif provided, build a source with given name for the mix minus all estimated targetsz--aggregatezif 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"]}'z--filterbanktorchzfilterbank implementation method. Supported: `['torch', 'asteroid']`. `torch` is ~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.z	--verbosezEnable log messagesstempegcudacpuzUsing )model_str_or_pathtargetsniterresidualwiener_win_lendevice
pretrained
filterbankr   z$Please install pip package `stempeg`)startdurationsample_ratedtype)r   dur)audiorateaggregate_dict	separatorr   _)exist_okparentstarget)multiprocessoutput_sample_rate)r!   writer)r!   )?argparseArgumentParserRawDescriptionHelpFormatteradd_argumentstrfloatint
parse_argsaudio_backend
torchaudioset_audio_backendno_cudar   r   is_availabler   verboseprint	aggregatejsonloadsr   load_separatormodelr   r   r   r   r   freezetor   ImportErrorRuntimeErrortqdmr	   
read_stemsr   r    r!   npfloat32tensorr   
load_audior   separateoutdirr   existsstemmkdirwith_suffixextitemssqueezedetachr   numpyTwrite_stemsFilesWritersave)parserargsuse_cudar   r&   r'   r   
input_filer$   r%   	estimates
model_pathrN   target_pathestimates_numpyr+   estimate re   J/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/openunmix/cli.pyrM      s(  






 

rM   )pathlibr   r   r8   r?   rW   rI   rG   	openunmixr   r   r   r/   rM   re   re   re   rf   <module>   s    