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35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
# -*- coding:utf-8 -*-
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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import sys,os,traceback
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dir=sys.argv[1]
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# opt_name=dir.split("\\")[-1].split("/")[-1]
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opt_name=os.path.basename(dir)
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path_asr='tools/damo_asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
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path_vad='tools/damo_asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch'
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path_punc='tools/damo_asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
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path_asr=path_asr if os.path.exists(path_asr)else "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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path_vad=path_vad if os.path.exists(path_vad)else "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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path_punc=path_punc if os.path.exists(path_punc)else "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model=path_asr,
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vad_model=path_vad,
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punc_model=path_punc,
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)
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opt=[]
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for name in os.listdir(dir):
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try:
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text = inference_pipeline(audio_in="%s/%s"%(dir,name))["text"]
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opt.append("%s/%s|%s|ZH|%s"%(dir,name,opt_name,text))
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except:
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print(traceback.format_exc())
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opt_dir="output/asr_opt"
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os.makedirs(opt_dir,exist_ok=True)
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with open("%s/%s.list"%(opt_dir,opt_name),"w",encoding="utf-8")as f:f.write("\n".join(opt))
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