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- # from funasr import AutoModel
- # import time
- # def vocal_text(input_video_path):
- # model = AutoModel(model="./Voice_translation", model_revision="v2.0.4",
- # vad_model="./Endpoint_detection", vad_model_revision="v2.0.4",
- # punc_model="./Ct_punc", punc_model_revision="v2.0.4",
- # use_cuda=True,use_fast = True,
- # )
- # res = model.generate(input_video_path,
- # batch_size_s=30,
- # hotword='test')
- # texts = [item['text'] for item in res]
- # result = ' '.join(texts)
- # return result
- # if __name__ == "__main__":
- # start_time = time.time()
- # model = AutoModel(model="./Voice_translation", model_revision="v2.0.4",
- # vad_model="./Endpoint_detection", vad_model_revision="v2.0.4",
- # punc_model="./Ct_punc", punc_model_revision="v2.0.4",
- # )
- # res = model.generate(input="./data/audio/5bf77846-0193-4f35-92f7-09ce51ee3793.mp3",
- # batch_size_s=30,
- # hotword='test')
- # print(res)
- # texts = [item['text'] for item in res]
- # print(texts)
- # result = ' '.join(texts)
- # print(result)
- # # def save(input,savepath):
- # # outputs = open(savepath, 'w', encoding='utf-8')
- # # outputs.write(input+'\n')
- # # outputs.close()
- # # save(input=result,savepath=r"F:\work\voice_translation\datasets\1.txt")
- # end_time = time.time()
- # # 计算时间差
- # elapsed_time = end_time - start_time
- # print(f"耗时: {elapsed_time} 秒")
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