"""辅助服务输出格式测试，确保不同 ModelScope 版本都能被统一解析。""" from __future__ import annotations import unittest import numpy as np from types import SimpleNamespace from unittest.mock import patch from scripts.auxiliary_server import ( AuxiliaryRuntime, _coerce_finite_float, _normalize_diarization_segments, _parse_form_float, ) class AuxiliaryServerTests(unittest.TestCase): """验证 CAM++ 常见的秒、毫秒和对象返回格式。""" def test_multipart_numeric_values_are_narrowed_before_model_calls(self): """表单值只接受有限数字，FileField 或非法文本回退/报错而不传入 float。""" self.assertEqual(_coerce_finite_float(" 12.5 "), 12.5) self.assertEqual(_coerce_finite_float(b"12.5"), 12.5) self.assertIsNone(_coerce_finite_float(float("nan"))) self.assertEqual(_parse_form_float(None, "start_time_ms", default=0.0), 0.0) with self.assertRaises(ValueError): _parse_form_float("not-a-number", "start_time_ms") def test_rejects_invalid_embedding_vectors(self): """模型成功返回也不代表向量有效，异常特征不能污染聚类池。""" for value in ([0, 0], [], [float("nan"), 1], [float("inf"), 0], [[1, 0], [0, 1]]): with self.assertRaises(RuntimeError): AuxiliaryRuntime._normalize_embedding(value) def test_optional_diarization_failure_does_not_block_core_startup(self): """完整 CAM++ diarization 不是实时核心加载路径，启动失败应只记录可选状态。""" runtime = AuxiliaryRuntime() runtime.assets = { "vad": {"kind": "vad"}, "diarization": {"kind": "diarization"}, "aligner": {"kind": "forced_aligner"}, } loaded_kinds = [] with patch("scripts.auxiliary_server._asset_ready", return_value=True), \ patch("scripts.auxiliary_server.model_directory", return_value=runtime.manifest and SimpleNamespace()), \ patch.object(runtime, "_load_asset", side_effect=lambda model_id, config, path: loaded_kinds.append(config["kind"]) or object()): runtime.preload() self.assertEqual(loaded_kinds, ["vad"]) self.assertEqual(runtime.status["diarization"]["state"], "optional_not_preloaded") self.assertEqual(runtime.status["aligner"]["state"], "optional_not_preloaded") def test_campplus_speaker_is_a_required_core_model(self): """实时在线聚类必须使用 CAM++ speaker_verification，不能只启动 VAD。""" runtime = AuxiliaryRuntime() runtime.assets = { "vad": {"kind": "vad"}, "campplus": {"kind": "speaker_verification"}, } with patch("scripts.auxiliary_server._asset_ready", side_effect=[True, False]), \ patch("scripts.auxiliary_server.model_directory", return_value=SimpleNamespace()): with self.assertRaisesRegex(RuntimeError, "campplus"): runtime.preload() def test_campplus_is_preferred_over_other_embedding_models(self): """即使同时存在 ERes2Net，实时聚类仍优先使用 CAM++。""" runtime = AuxiliaryRuntime() runtime.assets = { "campplus": {"kind": "speaker_verification"}, "eres2net": {"kind": "realtime_speaker_verification"}, } runtime.models = {"campplus": object(), "eres2net": object()} self.assertEqual(runtime._speaker_embedding_model_id(), "campplus") def test_missing_vad_reports_download_path(self): """VAD 是核心依赖，缺失时错误必须给出可执行的修复方向。""" runtime = AuxiliaryRuntime() runtime.assets = {"vad": {"kind": "vad"}} with patch("scripts.auxiliary_server._asset_ready", return_value=False), \ patch("scripts.auxiliary_server.model_directory", return_value=SimpleNamespace(__str__=lambda self: "/models/vad")): with self.assertRaisesRegex(RuntimeError, "download_models.py --auxiliary-only"): runtime.preload() def test_uses_public_pipeline_embedding_output(self) -> None: """声纹推理必须走 pipeline 的预处理和 output_emb 接口。""" class FakePipeline: def __init__(self) -> None: self.calls: list[tuple[list[str], bool]] = [] def __call__(self, audio_paths: list[str], output_emb: bool = False) -> dict[str, object]: self.calls.append((audio_paths, output_emb)) return {"outputs": {"text": "No similarity score output"}, "embs": [[1.0, 2.0, 3.0]]} pipeline = FakePipeline() result = AuxiliaryRuntime._run_embedding_pipeline(pipeline, "turn.wav") self.assertEqual(result, [1.0, 2.0, 3.0]) self.assertEqual(pipeline.calls, [(["turn.wav"], True)]) def test_supports_legacy_pipeline_without_output_emb_argument(self) -> None: """旧版 pipeline 不支持 output_emb 时，仍应使用其 preprocess/forward。""" class LegacyPipeline: def __init__(self) -> None: self.prepared: list[str] = [] def __call__(self, *_args: object, **_kwargs: object) -> None: raise TypeError("output_emb is not supported") def preprocess(self, audio_paths: list[str]) -> list[str]: return [f"prepared:{audio_paths[0]}"] def forward(self, prepared: list[str]) -> list[list[float]]: self.prepared = prepared return [[0.1, 0.2, 0.3]] pipeline = LegacyPipeline() result = AuxiliaryRuntime._run_embedding_pipeline(pipeline, "turn.wav") self.assertEqual(result, [0.1, 0.2, 0.3]) self.assertEqual(pipeline.prepared, ["prepared:turn.wav"]) def test_normalizes_modelscope_text_seconds(self) -> None: result = _normalize_diarization_segments({"text": [[0.2, 1.4, 0], [1.4, 2.0, 1]]}) self.assertEqual( result, [ {"start_time": 200.0, "end_time": 1400.0, "speaker": "0"}, {"start_time": 1400.0, "end_time": 2000.0, "speaker": "1"}, ], ) def test_normalizes_named_millisecond_fields(self) -> None: result = _normalize_diarization_segments( {"segments": [{"start_time": 100, "end_time": 900, "speaker_id": "cluster-a"}]} ) self.assertEqual(result[0]["start_time"], 100.0) self.assertEqual(result[0]["end_time"], 900.0) def test_reads_object_text_attribute(self) -> None: result = _normalize_diarization_segments(SimpleNamespace(text=[[1, 2, "spk"]])) self.assertEqual(result[0]["speaker"], "spk") class OnlineSpeakerTests(unittest.IsolatedAsyncioTestCase): """使用独立的新鲜向量验证 A→B→A，无需显卡和模型权重。""" async def test_fresh_embeddings_preserve_a_b_a_and_reset(self): runtime = AuxiliaryRuntime() vectors = iter(([1, 0], [0, 1], [0.99, 0.01])) runtime._extract_embedding_sync = lambda _: np.array(next(vectors), dtype=np.float32) results = [await runtime.resolve_speaker("turn.wav", "test", i * 2000, i * 2000 + 1000) for i in range(3)] self.assertEqual([r["speaker_id"] for r in results], [0, 1, 0]) self.assertEqual([c["count"] for c in runtime.speaker_clusters["test"]], [2, 1]) runtime.reset_speaker_session("test") self.assertNotIn("test", runtime.speaker_clusters) async def test_missing_embedding_does_not_copy_previous_cluster(self): runtime = AuxiliaryRuntime() runtime._extract_embedding_sync = lambda _: np.array([1, 0], dtype=np.float32) await runtime.resolve_speaker("turn.wav", "test", 0, 1000) runtime._extract_embedding_sync = lambda _: None result = await runtime.resolve_speaker("short.wav", "test", 1000, 1200) self.assertEqual(result["speaker_id"], -1) self.assertEqual(runtime.speaker_clusters["test"][0]["count"], 1) async def test_unverified_turn_labels_without_touching_clusters(self): """换人边界段：允许匹配既有簇，但不更新质心，也不建立新簇。""" runtime = AuxiliaryRuntime() vectors = iter(([1, 0], [0.98, 0.02], [0.2, 0.98], [0.2, 0.98])) runtime._extract_embedding_sync = lambda _: np.array(next(vectors), dtype=np.float32) await runtime.resolve_speaker("a.wav", "test", 0, 1000) centroid_before = runtime.speaker_clusters["test"][0]["embedding"].copy() matched = await runtime.resolve_speaker("head.wav", "test", 1000, 2000, speaker_verified=False) self.assertEqual(matched["speaker_id"], 0) self.assertTrue(matched["speaker_evidence"] == "fresh") self.assertFalse(matched["speaker_centroid_updated"]) np.testing.assert_allclose(runtime.speaker_clusters["test"][0]["embedding"], centroid_before, atol=1e-6) suspect = await runtime.resolve_speaker("mixed.wav", "test", 2000, 3000, speaker_verified=False) self.assertLess(suspect["speaker_confidence"], 0.6) self.assertEqual(suspect["speaker_strategy"], "online_embedding_cluster_suspect_mixture") self.assertEqual(len(runtime.speaker_clusters["test"]), 1) # 该说话人已验证的后续片段仍应能正常定簇。 verified = await runtime.resolve_speaker("b.wav", "test", 3000, 4000) self.assertEqual(verified["speaker_id"], 1) self.assertTrue(verified["speaker_centroid_updated"]) async def test_window_embedding_never_touches_cluster_state(self): """/v1/speaker/embedding 只出向量：单位化且零聚类副作用。""" runtime = AuxiliaryRuntime() runtime._extract_embedding_sync = lambda _: np.array([3, 4], dtype=np.float32) embedding = await runtime.speaker_embedding("window.wav") self.assertEqual(len(embedding), 2) self.assertAlmostEqual(float(np.linalg.norm(embedding)), 1.0, places=6) self.assertEqual(runtime.speaker_clusters, {}) if __name__ == "__main__": unittest.main()