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| # coding=utf-8 | |
| # CoT generate step-by-step | |
| from third_party.VideoLLaMA2.videollama2 import model_init, mm_infer | |
| import logging | |
| class Step0: | |
| def __init__(self, model_path, modal_type='v'): | |
| self.log = logging.getLogger(self.__class__.__name__) | |
| self.log.setLevel(logging.INFO) | |
| self.model, self.processor, self.tokenizer = model_init(model_path) | |
| self.modal_type=modal_type | |
| if modal_type == "a": | |
| self.model.model.vision_tower = None | |
| elif modal_type == "v": | |
| self.model.model.audio_tower = None | |
| elif modal_type == "av": | |
| pass | |
| else: | |
| raise NotImplementedError | |
| self.modal = 'audio' if modal_type == "a" else "video" | |
| self.question = f"Generate high-quality audio from video step-by-step." | |
| self.preprocess = self.processor[self.modal] | |
| def run(self, video_path): | |
| self.log.info("######################################################################################################") | |
| self.log.info("Generate high-quality audio from video step-by-step...") | |
| audio_video_tensor = self.preprocess(video_path, va=False) | |
| output = mm_infer( | |
| audio_video_tensor, | |
| self.question, | |
| model=self.model, | |
| tokenizer=self.tokenizer, | |
| modal=self.modal, | |
| do_sample=False, | |
| ) | |
| return output | |