fix(auto): honor merge_length_s passed to generate()

docs/python_api.md

@@ -42,7 +42,7 @@ The wrapper accepts `**kwargs`, not a universally validated parameter schema. A
 | `batch_size_s` | `300` | VAD-path batching budget in seconds, based on longest padded segment times segment count, not a hard file-duration limit. CPU VAD processing uses single-segment batches. |
 | `batch_size_threshold_s` | `60` | VAD batching threshold for segment duration, not a VAD splitting setting. |
 | `merge_vad` | `False` | Enables merging detected VAD regions for this call. |
-| `merge_length_s` | `15` | VAD merge target read before call overrides are merged into ASR config. Set this at construction in this checkout. |
+| `merge_length_s` | `15` | Target length in seconds for merged VAD regions when `merge_vad=True`. A per-call value overrides the construction value. |
 | `cache` | No wrapper-managed session | Pass a caller-owned dictionary on every streaming call. It is not a hub/model download cache. |
 | `sentence_timestamp` | `False` | Requests sentence records in the VAD pipeline; depends on available timestamps and punctuation, with limited VAD fallback. |
 | `return_spk_res` | `True` | Includes clustered speaker results when VAD and a speaker model are configured. `False` does not avoid construction or segment embedding computation. |

docs/python_api_zh.md

@@ -42,7 +42,7 @@
 | `batch_size_s` | `300` | VAD 路径的秒级批处理预算，按最长补齐片段长度乘以片段数量估算，不是文件时长硬上限。CPU 的 VAD 路径按单片段处理。 |
 | `batch_size_threshold_s` | `60` | VAD 片段参与组批的时长阈值，不是 VAD 切分参数。 |
 | `merge_vad` | `False` | 为本次调用启用 VAD 区域合并。 |
-| `merge_length_s` | `15` | VAD 合并目标，在本次参数合并进 ASR 配置之前读取。当前代码版本应在构建时设置它。 |
+| `merge_length_s` | `15` | `merge_vad=True` 时合并后 VAD 片段的目标长度，单位为秒。本次调用传入的值会覆盖构建时设置的值。 |
 | `cache` | 包装层不维护会话 | 每次流式调用都传入调用方持有的字典。它不是模型下载缓存。 |
 | `sentence_timestamp` | `False` | 请求 VAD 流程输出句子记录，依赖实际时间戳与标点，部分情形可回退到 VAD 区域。 |
 | `return_spk_res` | `True` | 配置 VAD 与说话人模型时输出聚类结果。设为 `False` 并不能省去模型构建或片段说话人特征计算。 |

funasr/auto/auto_model.py

@@ -1043,10 +1043,10 @@ def inference_with_vad(self, input, input_len=None, **cfg):
 
         #  FIX(gcf): concat the vad clips for sense vocie model for better aed
         if cfg.get("merge_vad", False):
+            # Call options are merged into kwargs only after this point.
+            merge_length_s = cfg.get("merge_length_s", kwargs.get("merge_length_s", 15))
             for i in range(len(res)):
-                res[i]["value"] = merge_vad(
-                    res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
-                )
+                res[i]["value"] = merge_vad(res[i]["value"], merge_length_s * 1000)
 
         # step.2 compute asr model
         model = self.model

tests/test_merge_vad_length.py

@@ -0,0 +1,81 @@
+"""Regression tests for the merge_length_s option of AutoModel.generate()."""
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+
+
+VAD_SEGMENTS = [[0, 4000], [5000, 9000], [10000, 14000]]
+
+
+class TestMergeVadLength(unittest.TestCase):
+    def _make_auto_model(self, **init_kwargs):
+        from funasr.auto.auto_model import AutoModel
+
+        am = AutoModel.__new__(AutoModel)
+        am.model = MagicMock()
+        am.vad_model = MagicMock()
+        am.punc_model = None
+        am.punc_kwargs = {}
+        am.spk_model = None
+        am.spk_kwargs = {}
+        am.cb_model = None
+        am.spk_mode = None
+        am.vad_kwargs = {}
+        am.kwargs = {
+            "batch_size_s": 300,
+            "batch_size_threshold_s": 60,
+            "device": "cpu",
+            "disable_pbar": True,
+            "frontend": MagicMock(fs=16000),
+            "fs": 16000,
+            **init_kwargs,
+        }
+        am._reset_runtime_configs = MagicMock()
+
+        def mock_inference(data, input_len=None, model=None, kwargs=None, **cfg):
+            if model is am.vad_model:
+                return [{"key": "utt", "value": [list(s) for s in VAD_SEGMENTS]}]
+            return [{"text": "x"} for _ in data]
+
+        am.inference = MagicMock(side_effect=mock_inference)
+        return am
+
+    @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 _asr_segments(self, am, mock_prep, mock_load, mock_slice, **cfg):
+        mock_prep.return_value = (["utt"], [np.zeros(16000 * 14, dtype=np.float32)])
+        mock_load.return_value = np.zeros(16000 * 14, dtype=np.float32)
+        mock_slice.side_effect = lambda speech, speech_lengths, segments: (
+            [np.zeros(16000, dtype=np.float32) for _ in segments],
+            [16000 for _ in segments],
+        )
+
+        am.inference_with_vad("dummy_input", merge_vad=True, **cfg)
+
+        segments = []
+        for call in mock_slice.call_args_list:
+            segments.extend(seg for seg, _ in call.args[2])
+        return sorted(segments)
+
+    def test_default_merge_length_is_15_seconds(self):
+        am = self._make_auto_model()
+        self.assertEqual(self._asr_segments(am), [[0, 14000]])
+
+    def test_generate_merge_length_s_is_applied(self):
+        am = self._make_auto_model()
+        self.assertEqual(self._asr_segments(am, merge_length_s=10), [[0, 9000], [9000, 14000]])
+
+    def test_construction_merge_length_s_is_applied(self):
+        am = self._make_auto_model(merge_length_s=10)
+        self.assertEqual(self._asr_segments(am), [[0, 9000], [9000, 14000]])
+
+    def test_generate_merge_length_s_overrides_construction_value(self):
+        am = self._make_auto_model(merge_length_s=10)
+        self.assertEqual(self._asr_segments(am, merge_length_s=15), [[0, 14000]])
+
+
+if __name__ == "__main__":
+    unittest.main()