fix: respect explicit VAD silence threshold

Author: LauraGPT

docs/vllm_guide.md

@@ -45,6 +45,14 @@ cd /path/to/FunASR && pip install -e .
 
 **Hardware**: GPU ≥ 8 GB VRAM, CUDA ≥ 11.8. 16 GB+ recommended.
 
+Install a PyTorch/torchaudio/vLLM combination that matches your NVIDIA driver and
+CUDA runtime. Do not blindly keep the newest wheel if it was built for a newer
+CUDA runtime than your driver supports; PyTorch can fail during CUDA
+initialization with `The NVIDIA driver on your system is too old` before FunASR
+starts. If that happens, reinstall compatible PyTorch, torchaudio, and vLLM
+wheels for the CUDA version reported by `nvidia-smi`, or update the NVIDIA
+driver first.
+
 ---
 
 ## 2. vLLM Engine Architecture

docs/vllm_guide_zh.md

@@ -44,6 +44,13 @@ cd /path/to/FunASR && pip install -e .
 
 **硬件**：GPU ≥ 8GB VRAM，CUDA ≥ 11.8。推荐 16GB+。
 
+请根据 NVIDIA 驱动和 `nvidia-smi` 显示的 CUDA 版本选择匹配的
+PyTorch/torchaudio/vLLM 组合，不要无条件保留 pip 拉到的最新 wheel。若
+vLLM 或 PyTorch wheel 依赖的 CUDA runtime 高于当前驱动支持范围，可能在
+FunASR 启动前就报 `The NVIDIA driver on your system is too old`。遇到该错误时，
+优先重装与当前驱动/CUDA 匹配的 PyTorch、torchaudio、vLLM wheel，或先升级
+NVIDIA 驱动。
+
 ---
 
 ## 2. vLLM 推理引擎架构

docs/vllm_guide_zh_v2.md

@@ -45,6 +45,13 @@ cd /path/to/FunASR && pip install -e .
 
 **硬件**：GPU ≥ 8GB VRAM，CUDA ≥ 11.8。推荐 16GB+。
 
+请根据 NVIDIA 驱动和 `nvidia-smi` 显示的 CUDA 版本选择匹配的
+PyTorch/torchaudio/vLLM 组合，不要无条件保留 pip 拉到的最新 wheel。若
+vLLM 或 PyTorch wheel 依赖的 CUDA runtime 高于当前驱动支持范围，可能在
+FunASR 启动前就报 `The NVIDIA driver on your system is too old`。遇到该错误时，
+优先重装与当前驱动/CUDA 匹配的 PyTorch、torchaudio、vLLM wheel，或先升级
+NVIDIA 驱动。
+
 ---
 
 ## 2. vLLM 推理引擎架构

funasr/models/fsmn_vad_streaming/model.py

@@ -938,8 +938,10 @@ def inference(
         n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
         m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
         segments = []
-        # Dynamic silence threshold
-        dynamic_silence = kwargs.get("dynamic_silence", True)
+        # Keep explicit fixed-threshold requests from being overwritten by the dynamic schedule.
+        dynamic_silence = kwargs.get(
+            "dynamic_silence", kwargs.get("max_end_silence_time") is None
+        )
         silence_schedule = kwargs.get("silence_schedule", DEFAULT_SILENCE_SCHEDULE)
         speech_to_sil_ms = self.vad_opts.speech_to_sil_time_thres
         accumulated_ms = cache.get("_dynamic_accumulated_ms", 0)

tests/test_fsmn_vad_dynamic_silence.py

@@ -0,0 +1,68 @@
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import torch
+
+from funasr.models.fsmn_vad_streaming import model as vad_model
+
+
+class TestFsmnVadDynamicSilence(unittest.TestCase):
+    def _run_inference(self, **kwargs):
+        vad = vad_model.FsmnVADStreaming.__new__(vad_model.FsmnVADStreaming)
+        vad.vad_opts = SimpleNamespace(speech_to_sil_time_thres=100)
+        vad.forward = lambda **batch: []
+
+        cache = {
+            "frontend": {},
+            "prev_samples": torch.empty(0),
+            "encoder": {},
+            "stats": SimpleNamespace(
+                vad_state_machine=vad_model.VadStateMachine.kVadInStateInSpeechSegment,
+                max_end_sil_frame_cnt_thresh=200,
+                speech_noise_thres=0.6,
+            ),
+        }
+        frontend = SimpleNamespace(fs=16000, frame_shift=10, lfr_n=1)
+
+        def fake_extract_fbank(*args, **kwargs):
+            cache["frontend"]["waveforms"] = torch.zeros(1, 16000)
+            return torch.zeros(1, 1, 80), torch.tensor([100])
+
+        with (
+            patch.object(
+                vad_model,
+                "load_audio_text_image_video",
+                return_value=[torch.zeros(16000)],
+            ),
+            patch.object(vad_model, "extract_fbank", side_effect=fake_extract_fbank),
+        ):
+            vad_model.FsmnVADStreaming.inference(
+                vad,
+                torch.zeros(16000),
+                frontend=frontend,
+                cache=cache,
+                key=["utt"],
+                chunk_size=1000,
+                is_final=False,
+                device="cpu",
+                **kwargs,
+            )
+
+        return cache
+
+    def test_explicit_max_end_silence_time_keeps_fixed_threshold_by_default(self):
+        cache = self._run_inference(max_end_silence_time=300)
+
+        self.assertEqual(cache["stats"].max_end_sil_frame_cnt_thresh, 200)
+        self.assertNotIn("_dynamic_accumulated_ms", cache)
+
+    def test_explicit_dynamic_silence_still_enables_schedule(self):
+        cache = self._run_inference(max_end_silence_time=300, dynamic_silence=True)
+
+        self.assertEqual(cache["stats"].max_end_sil_frame_cnt_thresh, 1900)
+        self.assertEqual(cache["_dynamic_accumulated_ms"], 1000)
+
+
+if __name__ == "__main__":
+    unittest.main()