From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交

---
 funasr/auto/auto_model.py |   68 ++++++++++++++--------------------
 1 files changed, 28 insertions(+), 40 deletions(-)

diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index f5cbe01..a864dad 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -182,7 +182,10 @@
         set_all_random_seed(kwargs.get("seed", 0))
 
         device = kwargs.get("device", "cuda")
-        if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
+        if ((device =="cuda" and not torch.cuda.is_available())
+            or (device == "xpu" and not torch.xpu.is_available())
+            or (device == "mps" and not torch.backends.mps.is_available())
+            or kwargs.get("ngpu", 1) == 0):
             device = "cpu"
             kwargs["batch_size"] = 1
         kwargs["device"] = device
@@ -298,14 +301,27 @@
         res = self.model(*args, kwargs)
         return res
 
-    def generate(self, input, input_len=None, **cfg):
+    def generate(self, input, input_len=None, progress_callback=None, **cfg):
         if self.vad_model is None:
-            return self.inference(input, input_len=input_len, **cfg)
+            return self.inference(
+                input, input_len=input_len, progress_callback=progress_callback, **cfg
+            )
 
         else:
-            return self.inference_with_vad(input, input_len=input_len, **cfg)
+            return self.inference_with_vad(
+                input, input_len=input_len, progress_callback=progress_callback, **cfg
+            )
 
-    def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
+    def inference(
+        self,
+        input,
+        input_len=None,
+        model=None,
+        kwargs=None,
+        key=None,
+        progress_callback=None,
+        **cfg,
+    ):
         kwargs = self.kwargs if kwargs is None else kwargs
         if "cache" in kwargs:
             kwargs.pop("cache")
@@ -362,6 +378,11 @@
             if pbar:
                 pbar.update(end_idx - beg_idx)
                 pbar.set_description(description)
+            if progress_callback:
+                try:
+                    progress_callback(end_idx, num_samples)
+                except Exception as e:
+                    logging.error(f"progress_callback error: {e}")
             time_speech_total += batch_data_time
             time_escape_total += time_escape
 
@@ -549,41 +570,8 @@
 
             # speaker embedding cluster after resorted
             if self.spk_model is not None and kwargs.get("return_spk_res", True):
-                # 1. 鍏堟鏌ユ椂闂存埑
-                has_timestamp = (
-                    hasattr(self.model, "internal_punc") or
-                    self.punc_model is not None or
-                    "timestamp" in result
-                )
-                
-                if not has_timestamp:
-                    logging.error("Need timestamp support...")
-                    return results_ret_list
-
-                # 2. 鍒濆鍖� punc_res
-                punc_res = None
-                
-                # 3. 鏍规嵁涓嶅悓鎯呭喌璁剧疆 punc_res
-                if hasattr(self.model, "internal_punc"):
-                    punc_res = [{
-                        "text": result["text"],
-                        "punc_array": result.get("punc_array", []),
-                        "timestamp": result.get("timestamp", [])
-                    }]
-                elif self.punc_model is not None:
-                    punc_res = self.inference(
-                        result["text"], 
-                        model=self.punc_model, 
-                        kwargs=self.punc_kwargs, 
-                        **cfg
-                    )
-                else:
-                    # 濡傛灉鍙湁鏃堕棿鎴筹紝鍒涘缓涓�涓熀鏈殑 punc_res
-                    punc_res = [{
-                        "text": result["text"],
-                        "punc_array": [],  # 绌虹殑鏍囩偣鏁扮粍
-                        "timestamp": result["timestamp"]
-                    }]
+                if raw_text is None:
+                    logging.error("Missing punc_model, which is required by spk_model.")
                 all_segments = sorted(all_segments, key=lambda x: x[0])
                 spk_embedding = result["spk_embedding"]
                 labels = self.cb_model(

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