chore: atualização geral

code/tests/test_refine_voice_timeline_tool.py

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+"""Tests for the refine_voice_timeline MCP tool.
+
+The tool exists because emphasis is *relative*: cut the loudest moment of a
+recording and every surviving score is still measured against something the
+viewer never sees. These tests pin the re-normalization actually happening,
+and the times staying in original source seconds so the result can be fed
+straight back to apply_voice_actions.
+"""
+
+import json
+
+import pytest
+
+from tests.test_voice_features_tool import _write_silent_wav
+from tests.test_voice_timeline_tool import _TRANSCRIPT, patched, wav  # noqa: F401
+
+_ = _write_silent_wav, _TRANSCRIPT  # re-exported fixtures need the imports
+
+
+async def _build(wav_path):
+    from server import handle_build_voice_timeline
+
+    await handle_build_voice_timeline({"media_path": str(wav_path)})
+
+
+@pytest.fixture
+def two_candidates(monkeypatch):
+    """A transcript where BOTH lines carry a content word.
+
+    The shared fixture's first line is "isso e" — two function words, which
+    ``suggest_zoom_windows`` skips by design, so it can never produce more
+    than one candidate to cap.
+    """
+    import fcpxml.voice_timeline as vt
+    import server_tools._shared as _shared_mod
+
+    transcript = {
+        "language": "pt",
+        "duration": 4.0,
+        "text": "cirurgia rapida seguranca total",
+        "segments": [
+            {"text": "cirurgia rapida", "start": 0.0, "end": 1.0},
+            {"text": "seguranca total", "start": 2.0, "end": 4.0},
+        ],
+        "words": [
+            {"word": "cirurgia", "start": 0.0, "end": 0.4, "confidence": 0.9},
+            {"word": "rapida", "start": 0.5, "end": 0.7, "confidence": 0.9},
+            {"word": "seguranca", "start": 2.0, "end": 2.9, "confidence": 0.9},
+            {"word": "total", "start": 3.0, "end": 3.5, "confidence": 0.9},
+        ],
+    }
+    monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: transcript)
+    monkeypatch.setattr(vt, "extract_pitch", lambda *a, **k: [(2.4, 260.0), (0.2, 120.0)])
+    monkeypatch.setattr(vt, "extract_energy", lambda *a, **k: [(2.4, 0.95), (0.2, 0.10)])
+
+
+class TestRefineVoiceTimelineHandler:
+    async def test_requires_an_existing_timeline(self, wav):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        result = await handle_refine_voice_timeline({"media_path": str(wav), "cuts": []})
+        assert "build_voice_timeline" in result[0].text
+
+    async def test_rejects_disallowed_extension(self, tmp_path):
+        from server import handle_refine_voice_timeline
+
+        bad = tmp_path / "clip.txt"
+        bad.write_text("not audio")
+        with pytest.raises(ValueError):
+            await handle_refine_voice_timeline({"media_path": str(bad), "cuts": []})
+
+    async def test_compares_raw_against_survivors(self, wav, patched):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+        result = await handle_refine_voice_timeline(
+            {"media_path": str(wav), "cuts": [{"start": 0.0, "end": 1.0}]}
+        )
+        text = result[0].text
+        assert "Survivors only" in text
+        assert "Average emphasis" in text
+
+    async def test_cut_words_are_excluded(self, wav, patched):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+        result = await handle_refine_voice_timeline(
+            {"media_path": str(wav), "cuts": [{"start": 0.0, "end": 1.0}], "save": True}
+        )
+        assert "_voice_timeline_refined.json" in result[0].text
+        data = json.loads(
+            (wav.parent / "clip_voice_timeline_refined.json").read_text(encoding="utf-8")
+        )
+        words = [w["text"] for s in data["segments"] for w in s["words"]]
+        assert "isso" not in words
+        assert "seguranca" in words
+
+    async def test_times_stay_in_original_source_seconds(self, wav, patched):  # noqa: F811
+        """A cut at the head must NOT slide the survivors back to zero."""
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+        await handle_refine_voice_timeline(
+            {"media_path": str(wav), "cuts": [{"start": 0.0, "end": 1.0}], "save": True}
+        )
+        data = json.loads(
+            (wav.parent / "clip_voice_timeline_refined.json").read_text(encoding="utf-8")
+        )
+        first = data["segments"][0]["words"][0]
+        assert first["start"] == pytest.approx(2.0)
+
+    async def test_proposes_zoom_candidates(self, wav, patched):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+        result = await handle_refine_voice_timeline({"media_path": str(wav), "cuts": []})
+        assert "Zoom Candidates" in result[0].text
+
+    async def test_max_zooms_caps_the_list(self, wav, two_candidates):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+
+        async def zoom_rows(**extra):
+            result = await handle_refine_voice_timeline(
+                {"media_path": str(wav), "cuts": [], "min_gap": 0.0, **extra}
+            )
+            section = result[0].text.split("## Zoom Candidates", 1)[1]
+            return [
+                ln for ln in section.splitlines()
+                if ln.startswith("| ") and ln.rstrip().endswith("|") and "Start" not in ln
+            ]
+
+        assert len(await zoom_rows()) == 2
+        assert len(await zoom_rows(max_zooms=1)) == 1
+
+    async def test_malformed_cut_is_reported_not_raised(self, wav, patched):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+        result = await handle_refine_voice_timeline(
+            {"media_path": str(wav), "cuts": [{"start": 3.0, "end": 1.0}]}
+        )
+        text = result[0].text
+        assert "Rejected cuts" in text
+        assert "must be after start" in text
+
+    async def test_cutting_everything_says_so(self, wav, patched):  # noqa: F811
+        from server import handle_refine_voice_timeline
+
+        await _build(wav)
+        result = await handle_refine_voice_timeline(
+            {"media_path": str(wav), "cuts": [{"start": 0.0, "end": 60.0}]}
+        )
+        assert "removed every word" in result[0].text
+
+
+class TestRefineVoiceTimelineRegistration:
+    async def test_tool_is_listed(self):
+        from server import list_tools
+
+        assert "refine_voice_timeline" in {t.name for t in await list_tools()}
+
+    async def test_tool_is_dispatched(self):
+        from server import TOOL_HANDLERS, handle_refine_voice_timeline
+
+        assert TOOL_HANDLERS["refine_voice_timeline"] is handle_refine_voice_timeline