fix(auto): keep a result for inputs whose VAD speech decodes to no text

docs/python_api.md

@@ -161,7 +161,7 @@ For `sentence_timestamp=True` without speakers, the current wrapper can return V
 
 ### Result Fields
 
-`generate()` returns a Python `list` of model-result dictionaries, not an OpenAI response object. Do not assume every model produces every field, or index `results[0]` without checking: some VAD empty-text paths can skip a result. No-speech VAD results commonly contain `{"key": ..., "text": "", "timestamp": []}`.
+`generate()` returns a Python `list` of model-result dictionaries, not an OpenAI response object. Do not assume every model produces every field. With a VAD model, each input keeps one result: when no speech is detected, or the detected speech decodes to no text, the result is `{"key": ..., "text": "", "timestamp": []}`.
 
 | Field | Meaning |
 |---|---|

docs/python_api_zh.md

@@ -160,7 +160,7 @@ for result in model.generate(input=str(paths["audio"]), return_spk_res=True):
 
 ### 返回字段
 
-`generate()` 返回 Python `list`，其中是模型结果字典，而不是 OpenAI 响应对象。不要假设所有模型都有全部字段，也不要未经检查直接使用 `results[0]`：VAD 路径中的部分空文本分支会跳过一条结果。没有检测到语音时，VAD 流程通常返回 `{"key": ..., "text": "", "timestamp": []}`。
+`generate()` 返回 Python `list`，其中是模型结果字典，而不是 OpenAI 响应对象。不要假设所有模型都有全部字段。使用 VAD 模型时，每个输入都保留一条结果：没有检测到语音，或检测到的语音解码后没有文本时，结果为 `{"key": ..., "text": "", "timestamp": []}`。
 
 | 字段 | 含义 |
 |---|---|

funasr/auto/auto_model.py

@@ -1212,6 +1212,8 @@ def inference_with_vad(self, input, input_len=None, **cfg):
                 ]
 
             if not len(result["text"].strip()):
+                results_ret_list.append({"key": key, "text": "", "timestamp": []})
+                logging.info("decoding, utt: {}, empty text".format(key))
                 continue
             return_raw_text = kwargs.get("return_raw_text", False)
             aligned_words = result.get("words")

tests/test_punc_model_none.py

@@ -620,6 +620,56 @@ def mock_inference(data, *args, **kwargs):
                 )
                 self.assertEqual(results[0]["sentence_info"][-1]["timestamp"][-1], [1100, 1600])
 
+    @patch("funasr.auto.auto_model.slice_padding_audio_samples")
+    @patch("funasr.auto.auto_model.load_audio_text_image_video")
+    @patch("funasr.auto.auto_model.prepare_data_iterator")
+    def test_empty_asr_text_keeps_a_keyed_result(self, mock_prep, mock_load, mock_slice):
+        """VAD speech that decodes to no text must not remove the input from the results."""
+        for segments, asr_results in (
+            ([[0, 1000]], [[{"text": ""}]]),
+            ([[0, 1000], [2000, 3500]], [[{"text": ""}], [{"text": ""}]]),
+        ):
+            with self.subTest(segments=len(segments)):
+                am = self._make_auto_model(punc_model=MagicMock())
+                results_seq = [[{"key": "noise", "value": segments}], *asr_results]
+                am.inference = MagicMock(side_effect=lambda *args, **kwargs: results_seq.pop(0))
+                mock_prep.return_value = (["noise"], [np.zeros(16000, dtype=np.float32)])
+                mock_load.return_value = np.zeros(16000, dtype=np.float32)
+                mock_slice.return_value = ([np.zeros(16000, dtype=np.float32)], [16000])
+
+                results = am.inference_with_vad("dummy_input", sentence_timestamp=True)
+
+                self.assertEqual(results, [{"key": "noise", "text": "", "timestamp": []}])
+
+    @patch("funasr.auto.auto_model.slice_padding_audio_samples")
+    @patch("funasr.auto.auto_model.load_audio_text_image_video")
+    @patch("funasr.auto.auto_model.prepare_data_iterator")
+    def test_empty_asr_text_keeps_input_order(self, mock_prep, mock_load, mock_slice):
+        """Results stay aligned with a multi-input batch when one input has no text."""
+        am = self._make_auto_model()
+        results_seq = [
+            [
+                {"key": "first", "value": [[0, 1000]]},
+                {"key": "noise", "value": [[0, 1000]]},
+                {"key": "last", "value": [[0, 1000]]},
+            ],
+            [{"text": "hello", "timestamp": [[0, 500]]}],
+            [{"text": ""}],
+            [{"text": "world", "timestamp": [[0, 500]]}],
+        ]
+        am.inference = MagicMock(side_effect=lambda *args, **kwargs: results_seq.pop(0))
+        mock_prep.return_value = (
+            ["first", "noise", "last"],
+            [np.zeros(16000, dtype=np.float32)] * 3,
+        )
+        mock_load.return_value = np.zeros(16000, dtype=np.float32)
+        mock_slice.return_value = ([np.zeros(16000, dtype=np.float32)], [16000])
+
+        results = am.inference_with_vad("dummy_input")
+
+        self.assertEqual([r["key"] for r in results], ["first", "noise", "last"])
+        self.assertEqual([r["text"] for r in results], ["hello", "", "world"])
+
     @patch("funasr.auto.auto_model.slice_padding_audio_samples")
     @patch("funasr.auto.auto_model.load_audio_text_image_video")
     @patch("funasr.auto.auto_model.prepare_data_iterator")