chore: atualização geral

code/tests/test_voice_features_tool.py

@@ -0,0 +1,147 @@
+"""Tests for the analyze_voice_features MCP tool.
+
+librosa and Whisper are monkeypatched so these run without the optional
+extras, matching the pattern used by TestDetectBeatsHandler.
+"""
+
+import json
+import struct
+import wave
+
+import pytest
+
+
+def _write_silent_wav(path: str, seconds: float = 2.0) -> None:
+    n = int(44100 * seconds)
+    with wave.open(path, "w") as f:
+        f.setnchannels(1)
+        f.setsampwidth(2)
+        f.setframerate(44100)
+        f.writeframes(struct.pack("<%dh" % n, *([0] * n)))
+
+
+_FAKE_TRANSCRIPT = {
+    "language": "pt",
+    "duration": 3.0,
+    "text": "isso e seguranca",
+    "segments": [{"text": "isso e seguranca", "start": 0.0, "end": 3.0}],
+    "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},
+    ],
+}
+
+
+@pytest.fixture
+def wav(tmp_path):
+    path = tmp_path / "clip.wav"
+    _write_silent_wav(str(path))
+    return path
+
+
+@pytest.fixture
+def patched_analysis(monkeypatch):
+    """Make the tool's transcription + librosa extractors deterministic."""
+    import server_tools._shared as _shared_mod
+    import server_tools.voice as server_mod
+
+    monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: _FAKE_TRANSCRIPT)
+    monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
+    # "seguranca" (2.0-2.9s) is the loud, high-pitched, emphatic word
+    monkeypatch.setattr(
+        server_mod,
+        "extract_pitch",
+        lambda *a, **k: [(0.2, 120.0), (0.6, 118.0), (2.4, 260.0)],
+    )
+    monkeypatch.setattr(
+        server_mod,
+        "extract_energy",
+        lambda *a, **k: [(0.2, 0.10), (0.6, 0.12), (2.4, 0.95)],
+    )
+
+
+class TestAnalyzeVoiceFeaturesHandler:
+    async def test_reports_when_librosa_unavailable(self, wav, monkeypatch):
+        import server_tools.voice as server_mod
+        from server import handle_analyze_voice_features
+
+        monkeypatch.setattr(
+            server_mod, "features_capability", lambda: (False, "componente librosa ausente.")
+        )
+        result = await handle_analyze_voice_features({"media_path": str(wav)})
+        assert "librosa" in result[0].text.lower()
+
+    async def test_rejects_disallowed_extension(self, tmp_path):
+        from server import handle_analyze_voice_features
+
+        bad = tmp_path / "clip.txt"
+        bad.write_text("not audio")
+        with pytest.raises(ValueError):
+            await handle_analyze_voice_features({"media_path": str(bad)})
+
+    async def test_writes_features_json_with_emphasis_per_word(self, wav, patched_analysis):
+        from server import handle_analyze_voice_features
+
+        result = await handle_analyze_voice_features({"media_path": str(wav)})
+        text = result[0].text
+
+        json_path = wav.parent / "clip_voice_features.json"
+        assert str(json_path) in text
+        data = json.loads(json_path.read_text())
+        assert len(data["words"]) == 3
+        assert all("emphasis" in w for w in data["words"])
+        assert all(0.0 <= w["emphasis"] <= 1.0 for w in data["words"])
+
+    async def test_loudest_word_scores_highest_emphasis(self, wav, patched_analysis):
+        from server import handle_analyze_voice_features
+
+        await handle_analyze_voice_features({"media_path": str(wav)})
+        data = json.loads((wav.parent / "clip_voice_features.json").read_text())
+        by_word = {w["word"]: w["emphasis"] for w in data["words"]}
+        assert by_word["seguranca"] > by_word["isso"]
+        assert by_word["seguranca"] > by_word["e"]
+
+    async def test_persisted_config_is_embedded_in_output(self, wav, patched_analysis):
+        from server import handle_analyze_voice_features
+
+        await handle_analyze_voice_features({"media_path": str(wav)})
+        data = json.loads((wav.parent / "clip_voice_features.json").read_text())
+        assert "energy_threshold" in data["config"]
+        assert "emphasis_weights" in data["config"]
+
+    async def test_empty_transcript_reports_instead_of_crashing(self, wav, monkeypatch):
+        import server_tools._shared as _shared_mod
+        import server_tools.voice as server_mod
+        from server import handle_analyze_voice_features
+
+        monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
+        monkeypatch.setattr(
+            _shared_mod, "transcribe", lambda *a, **k: {**_FAKE_TRANSCRIPT, "words": []}
+        )
+        result = await handle_analyze_voice_features({"media_path": str(wav)})
+        assert "no words" in result[0].text.lower()
+
+    async def test_untranscribable_media_reports_install_hint(self, wav, monkeypatch):
+        import server_tools._shared as _shared_mod
+        import server_tools.voice as server_mod
+        from server import handle_analyze_voice_features
+
+        monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
+        monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: None)
+        result = await handle_analyze_voice_features({"media_path": str(wav)})
+        assert "faster-whisper" in result[0].text
+
+    async def test_missing_pitch_track_degrades_without_crashing(self, wav, monkeypatch):
+        import server_tools._shared as _shared_mod
+        import server_tools.voice as server_mod
+        from server import handle_analyze_voice_features
+
+        monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: _FAKE_TRANSCRIPT)
+        monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
+        monkeypatch.setattr(server_mod, "extract_pitch", lambda *a, **k: None)
+        monkeypatch.setattr(server_mod, "extract_energy", lambda *a, **k: None)
+        result = await handle_analyze_voice_features({"media_path": str(wav)})
+        assert "Voice Feature Analysis" in result[0].text
+        data = json.loads((wav.parent / "clip_voice_features.json").read_text())
+        assert all(w["emphasis"] >= 0.0 for w in data["words"])