
    9i
                        S SK r S SKJrJrJrJrJrJr  S SKrS SK	r	S SK
Jr  S SKJr  S SKJr  S SKJr  S SKJr  S SKJrJr  S S	KJrJrJr  S S
KJr  \" 5       rS/r\R>                  " \R@                  \RB                  S9\R>                  " \RD                  \RB                  S9\R>                  " \RF                  \RB                  S9\R>                  " \RH                  \RB                  S9\R>                  " \RJ                  \RB                  S9\R>                  " \RL                  \RB                  S9\R>                  " \RN                  \RB                  S9\R>                  " \RP                  \RB                  S9\R>                  " \RR                  \RB                  S9 " S S\5      5       5       5       5       5       5       5       5       5       r*g)    N)AnyDictListSequenceTupleUnion)	Pipelines)Model)
OutputKeys)Pipeline)	PIPELINES)generate_scp_from_urlupdate_local_model)
Frameworks	ModelFileTasks)
get_loggerFunASRPipeline)module_namec                   Z   ^  \ rS rSrSrSS\\\4   4U 4S jjjrS\	\\
4   4S jrSrU =r$ )	r      a_  Voice Activity Detection Inference Pipeline
use `model` to create a Voice Activity Detection pipeline.

Args:
    model: A model instance, or a model local dir, or a model id in the model hub.
    kwargs (dict, `optional`):
        Extra kwargs passed into the preprocessor's constructor.

Example:
    >>> from modelscope.pipelines import pipeline
    >>> p = pipeline(
    >>>    task=Tasks.voice_activity_detection, model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch')
    >>> audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.pcm'
    >>> print(p(audio_in))

modelc                 *   > [         TU ]  " SSU0UD6  g)z=use `model` to create an vad pipeline for prediction
        r   N )super__init__)selfr   kwargs	__class__s      j/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/pipelines/audio/funasr_pipeline.pyr   FunASRPipeline.__init__;   s     	/u//    returnc                 *    U R                   " U0 UD6nU$ )zj
Decoding the input audios
Args:
    input('str' or 'bytes'):
Return:
    a list of dictionary of result.
)r   )r   argsr   outputs       r    __call__FunASRPipeline.__call__@   s     T,V,r"   r   )N)__name__
__module____qualname____firstlineno____doc__r   r
   strr   r   r   r'   __static_attributes____classcell__)r   s   @r    r   r      s;    &"0eE3J/ 0 0
4S>  r"   )+ostypingr   r   r   r   r   r   jsonyamlmodelscope.metainfor	   modelscope.modelsr
   modelscope.outputsr   modelscope.pipelines.baser   modelscope.pipelines.builderr   "modelscope.utils.audio.audio_utilsr   r   modelscope.utils.constantr   r   r   modelscope.utils.loggerr   logger__all__register_moduleauto_speech_recognitionfunasr_pipelinevoice_activity_detectionlanguage_score_predictionpunctuationspeaker_diarizationspeaker_verificationspeech_separationspeech_timestampemotion_recognitionr   r   r"   r    <module>rJ      s   	 : :   ) # ) . 2D B B .	
 	!!y/H/HJ
	""	0I0IK
	##1J1JL
	9#<#<>
	9+D+DF
	I,E,EG
	)B)BD
		(A(AC
	9+D+DF"X "FCDGF>LKJ$"r"   