Merge pull request #150 from alibaba-damo-academy/dev_lhn

fix uniasr decoding bug

funasr/bin/asr_inference_uniasr.py

@@ -398,6 +398,19 @@ def inference_modelscope(
     else:
         device = "cpu"
     
+    if param_dict is not None and "decoding_model" in param_dict:
+        if param_dict["decoding_model"] == "fast":
+            decoding_ind = 0
+            decoding_mode = "model1"
+        elif param_dict["decoding_model"] == "normal":
+            decoding_ind = 0
+            decoding_mode = "model2"
+        elif param_dict["decoding_model"] == "offline":
+            decoding_ind = 1
+            decoding_mode = "model2"
+        else:
+            raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
+
     # 1. Set random-seed
     set_all_random_seed(seed)
 
@@ -440,18 +453,6 @@ def inference_modelscope(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        if param_dict is not None and "decoding_model" in param_dict:
-            if param_dict["decoding_model"] == "fast":
-                speech2text.decoding_ind = 0
-                speech2text.decoding_mode = "model1"
-            elif param_dict["decoding_model"] == "normal":
-                speech2text.decoding_ind = 0
-                speech2text.decoding_mode = "model2"
-            elif param_dict["decoding_model"] == "offline":
-                speech2text.decoding_ind = 1
-                speech2text.decoding_mode = "model2"
-            else:
-                raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
         loader = ASRTask.build_streaming_iterator(
             data_path_and_name_and_type,
             dtype=dtype,

funasr/bin/asr_inference_uniasr_vad.py

@@ -398,6 +398,19 @@ def inference_modelscope(
     else:
         device = "cpu"
 
+    if param_dict is not None and "decoding_model" in param_dict:
+        if param_dict["decoding_model"] == "fast":
+            decoding_ind = 0
+            decoding_mode = "model1"
+        elif param_dict["decoding_model"] == "normal":
+            decoding_ind = 0
+            decoding_mode = "model2"
+        elif param_dict["decoding_model"] == "offline":
+            decoding_ind = 1
+            decoding_mode = "model2"
+        else:
+            raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
+
     # 1. Set random-seed
     set_all_random_seed(seed)
 
@@ -440,18 +453,6 @@ def inference_modelscope(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        if param_dict is not None and "decoding_model" in param_dict:
-            if param_dict["decoding_model"] == "fast":
-                speech2text.decoding_ind = 0
-                speech2text.decoding_mode = "model1"
-            elif param_dict["decoding_model"] == "normal":
-                speech2text.decoding_ind = 0
-                speech2text.decoding_mode = "model2"
-            elif param_dict["decoding_model"] == "offline":
-                speech2text.decoding_ind = 1
-                speech2text.decoding_mode = "model2"
-            else:
-                raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
         loader = ASRTask.build_streaming_iterator(
             data_path_and_name_and_type,
             dtype=dtype,