From 45d7aa9004763684fb748ee17942ecba81042201 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 19 六月 2024 10:26:40 +0800
Subject: [PATCH] decoding

---
 funasr/auto/auto_model.py |   16 +++++-----------
 1 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 603c0a0..a6cd3a6 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -213,7 +213,6 @@
         deep_update(model_conf, kwargs.get("model_conf", {}))
         deep_update(model_conf, kwargs)
         model = model_class(**model_conf, vocab_size=vocab_size)
-        model.to(device)
 
         # init_param
         init_param = kwargs.get("init_param", None)
@@ -236,6 +235,7 @@
             model.to(torch.float16)
         elif kwargs.get("bf16", False):
             model.to(torch.bfloat16)
+        model.to(device)
         return model, kwargs
 
     def __call__(self, *args, **cfg):
@@ -324,7 +324,7 @@
             input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg
         )
         end_vad = time.time()
-            
+
         #  FIX(gcf): concat the vad clips for sense vocie model for better aed
         if kwargs.get("merge_vad", False):
             for i in range(len(res)):
@@ -466,7 +466,7 @@
                             result[k] = restored_data[j][k]
                         else:
                             result[k] += restored_data[j][k]
-                            
+
             if not len(result["text"].strip()):
                 continue
             return_raw_text = kwargs.get("return_raw_text", False)
@@ -481,7 +481,7 @@
                 if return_raw_text:
                     result["raw_text"] = raw_text
                 result["text"] = punc_res[0]["text"]
-                
+
             # speaker embedding cluster after resorted
             if self.spk_model is not None and kwargs.get("return_spk_res", True):
                 if raw_text is None:
@@ -602,12 +602,6 @@
         )
 
         with torch.no_grad():
-
-            if type == "onnx":
-                export_dir = export_utils.export_onnx(model=model, data_in=data_list, **kwargs)
-            else:
-                export_dir = export_utils.export_torchscripts(
-                    model=model, data_in=data_list, **kwargs
-                )
+            export_dir = export_utils.export(model=model, data_in=data_list, **kwargs)
 
         return export_dir

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