diff --git a/tools/damo_asr/cmd-asr.py b/tools/damo_asr/cmd-asr.py
index 27755622..982f9d53 100644
--- a/tools/damo_asr/cmd-asr.py
+++ b/tools/damo_asr/cmd-asr.py
@@ -2,33 +2,60 @@
 
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
+from modelscope.models import Model
+import multiprocessing 
 import sys,os,traceback
+from threading import Lock
+lock = Lock()
+
+# 进程数
+processes = 2
+
 dir=sys.argv[1]
 # opt_name=dir.split("\\")[-1].split("/")[-1]
 opt_name=os.path.basename(dir)
+# FunAsr三语转写model
+lang2model = {
+            'zh': 'tools/damo_asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+            'ja': "tools/damo_asr/models/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline",
+            "en": "tools/damo_asr/models/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline",
+}
+
+model = Model.from_pretrained(lang2model["zh"])
 
-path_asr='tools/damo_asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
-path_vad='tools/damo_asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch'
-path_punc='tools/damo_asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
-path_asr=path_asr if os.path.exists(path_asr)else "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-path_vad=path_vad if os.path.exists(path_vad)else "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-path_punc=path_punc if os.path.exists(path_punc)else "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
 inference_pipeline = pipeline(
     task=Tasks.auto_speech_recognition,
-    model=path_asr,
-    vad_model=path_vad,
-    punc_model=path_punc,
+    model=model,
+    vad_model='tools/damo_asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    punc_model='tools/damo_asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
 )
 
-opt=[]
-for name in os.listdir(dir):
+
+def process_audio_file(dir,filename,name,opt_name):
+
     try:
-        text = inference_pipeline(audio_in="%s/%s"%(dir,name))["text"]
-        opt.append("%s/%s|%s|ZH|%s"%(dir,name,opt_name,text))
+        text = inference_pipeline(audio_in="%s/%s" % (dir, name))["text"]
+
+        with lock:
+            with open(filename,"a",encoding="utf-8")as f:f.write("%s/%s|%s|ZH|%s\n" % (dir, name, opt_name, text.strip()))
+
     except:
         print(traceback.format_exc())
 
-opt_dir="output/asr_opt"
-os.makedirs(opt_dir,exist_ok=True)
-with open("%s/%s.list"%(opt_dir,opt_name),"w",encoding="utf-8")as f:f.write("\n".join(opt))
+
+def run__process():  # 主进程
+
+    opt_dir="output/asr_opt"
+    os.makedirs(opt_dir,exist_ok=True)
+    filename = "%s/%s.list"%(opt_dir,opt_name)
+    if os.path.exists(filename):
+        os.remove(filename)
+
+    with multiprocessing.Pool(processes=processes) as pool:
+        pool.starmap(process_audio_file, [(dir,filename,name ,opt_name) for name in os.listdir(dir)])
+    
+
+if __name__ == '__main__':
+    
+    run__process()