chore: atualização geral

code/tests/test_voice_timeline_tool.py

@@ -0,0 +1,107 @@
+"""Tests for the build_voice_timeline MCP tool."""
+
+import json
+
+import pytest
+
+from tests.test_voice_features_tool import _write_silent_wav
+
+_TRANSCRIPT = {
+    "language": "pt",
+    "duration": 4.0,
+    "text": "isso e seguranca total",
+    "segments": [
+        {"text": "isso e", "start": 0.0, "end": 1.0},
+        {"text": "seguranca total", "start": 2.0, "end": 4.0},
+    ],
+    "words": [
+        {"word": "isso", "start": 0.0, "end": 0.4, "confidence": 0.9},
+        {"word": "e", "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},
+    ],
+}
+
+
+@pytest.fixture
+def wav(tmp_path):
+    path = tmp_path / "clip.wav"
+    _write_silent_wav(str(path))
+    return path
+
+
+@pytest.fixture
+def patched(monkeypatch):
+    """Deterministic transcription + acoustics, no optional extras needed."""
+    import fcpxml.voice_timeline as vt
+    import server_tools._shared as _shared_mod
+
+    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 TestBuildVoiceTimelineHandler:
+    async def test_writes_timeline_json(self, wav, patched):
+        from server import handle_build_voice_timeline
+
+        result = await handle_build_voice_timeline({"media_path": str(wav)})
+        text = result[0].text
+
+        json_path = wav.parent / "clip_voice_timeline.json"
+        assert str(json_path) in text
+        data = json.loads(json_path.read_text(encoding="utf-8"))
+        assert data["summary"]["word_count"] == 4
+        assert len(data["segments"]) == 2
+
+    async def test_reports_which_layers_ran(self, wav, patched):
+        from server import handle_build_voice_timeline
+
+        result = await handle_build_voice_timeline({"media_path": str(wav)})
+        text = result[0].text
+        assert "Analysis Layers" in text
+        assert "Transcript" in text
+
+    async def test_rejects_disallowed_extension(self, tmp_path):
+        from server import handle_build_voice_timeline
+
+        bad = tmp_path / "clip.txt"
+        bad.write_text("not audio")
+        with pytest.raises(ValueError):
+            await handle_build_voice_timeline({"media_path": str(bad)})
+
+    async def test_untranscribable_media_reports_hint(self, wav, monkeypatch):
+        import server_tools._shared as _shared_mod
+        from server import handle_build_voice_timeline
+
+        monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: None)
+        result = await handle_build_voice_timeline({"media_path": str(wav)})
+        assert "faster-whisper" in result[0].text
+
+    async def test_uses_persisted_peak_settings(self, wav, patched, monkeypatch):
+        """A wider percentile must surface more peak moments."""
+        import server_tools.voice as server_mod
+        from server import handle_build_voice_timeline
+
+        def config(percentile):
+            return {
+                "energy_threshold": 0.5,
+                "peak_percentile": percentile,
+                "emphasis_floor": 0.0,
+                "emphasis_weights": {
+                    "energy": 0.30, "pitch_variation": 0.25, "rate_variation": 0.20,
+                    "pause_before": 0.15, "duration": 0.10,
+                },
+                "emotion_enabled": False,
+                "emotion_sensitivity": 0.5,
+            }
+
+        monkeypatch.setattr(server_mod, "load_voice_analysis_config", lambda: config(0.01))
+        await handle_build_voice_timeline({"media_path": str(wav)})
+        strict = json.loads((wav.parent / "clip_voice_timeline.json").read_text())
+
+        monkeypatch.setattr(server_mod, "load_voice_analysis_config", lambda: config(1.0))
+        await handle_build_voice_timeline({"media_path": str(wav)})
+        loose = json.loads((wav.parent / "clip_voice_timeline.json").read_text())
+
+        assert loose["summary"]["peak_count"] > strict["summary"]["peak_count"]