Merge pull request #287 from alibaba-damo-academy/dev_gzf

Dev gzf

egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/infer.py

@@ -17,13 +17,10 @@ inference_pipeline = pipeline(
 )
 
 vads = inputs.split("|")
-
-cache_out = []
 rec_result_all="outputs:"
+param_dict = {"cache": []}
 for vad in vads:
-    rec_result = inference_pipeline(text_in=vad, cache=cache_out)
-    #print(rec_result)
-    cache_out = rec_result['cache']
+    rec_result = inference_pipeline(text_in=vad, param_dict=param_dict)
     rec_result_all += rec_result['text']
 
 print(rec_result_all)

funasr/bin/punctuation_infer_vadrealtime.py

@@ -226,7 +226,7 @@ def inference_modelscope(
     ):
         results = []
         split_size = 10
-
+        cache_in = param_dict["cache"]
         if raw_inputs != None:
             line = raw_inputs.strip()
             key = "demo"
@@ -234,34 +234,12 @@ def inference_modelscope(
                 item = {'key': key, 'value': ""}
                 results.append(item)
                 return results
-            result, _, cache = text2punc(line, cache)
-            item = {'key': key, 'value': result, 'cache': cache}
+            result, _, cache = text2punc(line, cache_in)
+            param_dict["cache"] = cache
+            item = {'key': key, 'value': result}
             results.append(item)
             return results
 
-        for inference_text, _, _ in data_path_and_name_and_type:
-            with open(inference_text, "r", encoding="utf-8") as fin:
-                for line in fin:
-                    line = line.strip()
-                    segs = line.split("\t")
-                    if len(segs) != 2:
-                        continue
-                    key = segs[0]
-                    if len(segs[1]) == 0:
-                        continue
-                    result, _ = text2punc(segs[1])
-                    item = {'key': key, 'value': result}
-                    results.append(item)
-        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
-        if output_path != None:
-            output_file_name = "infer.out"
-            Path(output_path).mkdir(parents=True, exist_ok=True)
-            output_file_path = (Path(output_path) / output_file_name).absolute()
-            with open(output_file_path, "w", encoding="utf-8") as fout:
-                for item_i in results:
-                    key_out = item_i["key"]
-                    value_out = item_i["value"]
-                    fout.write(f"{key_out}\t{value_out}\n")
         return results
 
     return _forward

funasr/runtime/python/websocket/ASR_server.py

@@ -53,7 +53,7 @@ speek = Queue()
 inference_pipeline_vad = pipeline(
     task=Tasks.voice_activity_detection,
     model=args.vad_model,
-    model_revision="v1.2.0",
+    model_revision=None,
     output_dir=None,
     batch_size=1,
     mode='online',
@@ -62,7 +62,7 @@ inference_pipeline_vad = pipeline(
 param_dict_vad = {'in_cache': dict(), "is_final": False}
   
 # asr
-param_dict_asr = dict()
+param_dict_asr = {}
 # param_dict["hotword"] = "小五 小五月"  # 设置热词，用空格隔开
 inference_pipeline_asr = pipeline(
     task=Tasks.auto_speech_recognition,
@@ -71,10 +71,11 @@ inference_pipeline_asr = pipeline(
     ngpu=args.ngpu,
 )
 
-inference_pipline_punc = pipeline(
+param_dict_punc = {'cache': list()}
+inference_pipeline_punc = pipeline(
     task=Tasks.punctuation,
     model=args.punc_model,
-    model_revision="v1.0.1",
+    model_revision=None,
     ngpu=args.ngpu,
 )
 
@@ -116,13 +117,16 @@ def vad(data):  # 推理
 
 def asr():  # 推理
     global inference_pipeline2
-    global speek
+    global speek, param_dict_punc
     while True:
         while not speek.empty():
             audio_in = speek.get()
             speek.task_done()
-            rec_result = inference_pipeline_asr(audio_in=audio_in)
-            print(rec_result)
+            if len(audio_in) > 0:
+                rec_result = inference_pipeline_asr(audio_in=audio_in)
+                if 'text' in rec_result:
+                    rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict=param_dict_punc)
+                print(rec_result["text"])
             time.sleep(0.1)
         time.sleep(0.1)