From 09ff7d4516128bfe1db8a81ca6de0d89ea55d88c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=BB=81=E8=BF=B7?= <haoneng.lhn@alibaba-inc.com>
Date: Thu, 23 Feb 2023 16:28:05 +0800
Subject: [PATCH] fix uniasr decoding bug

---
 funasr/bin/asr_inference_uniasr.py     | 25 +++++++++++++------------
 funasr/bin/asr_inference_uniasr_vad.py | 25 +++++++++++++------------
 2 files changed, 26 insertions(+), 24 deletions(-)

diff --git a/funasr/bin/asr_inference_uniasr.py b/funasr/bin/asr_inference_uniasr.py
index c50bf17f6..8b31fad13 100644
--- a/funasr/bin/asr_inference_uniasr.py
+++ b/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,
diff --git a/funasr/bin/asr_inference_uniasr_vad.py b/funasr/bin/asr_inference_uniasr_vad.py
index ac3b4b6a8..e5815df11 100644
--- a/funasr/bin/asr_inference_uniasr_vad.py
+++ b/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,