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