Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.
2026-07-09 22:38:58 +08:00
commit 6116b1f3c6
3683 changed files with 990984 additions and 0 deletions
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+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()