148 lines
5.9 KiB
Python
148 lines
5.9 KiB
Python
"""Tests for the analyze_voice_features MCP tool.
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librosa and Whisper are monkeypatched so these run without the optional
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extras, matching the pattern used by TestDetectBeatsHandler.
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"""
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import json
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import struct
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import wave
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import pytest
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def _write_silent_wav(path: str, seconds: float = 2.0) -> None:
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n = int(44100 * seconds)
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with wave.open(path, "w") as f:
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f.setnchannels(1)
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f.setsampwidth(2)
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f.setframerate(44100)
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f.writeframes(struct.pack("<%dh" % n, *([0] * n)))
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_FAKE_TRANSCRIPT = {
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"language": "pt",
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"duration": 3.0,
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"text": "isso e seguranca",
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"segments": [{"text": "isso e seguranca", "start": 0.0, "end": 3.0}],
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"words": [
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{"word": "isso", "start": 0.0, "end": 0.4, "confidence": 0.9},
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{"word": "e", "start": 0.5, "end": 0.7, "confidence": 0.9},
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{"word": "seguranca", "start": 2.0, "end": 2.9, "confidence": 0.9},
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],
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}
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@pytest.fixture
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def wav(tmp_path):
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path = tmp_path / "clip.wav"
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_write_silent_wav(str(path))
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return path
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@pytest.fixture
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def patched_analysis(monkeypatch):
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"""Make the tool's transcription + librosa extractors deterministic."""
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import server_tools._shared as _shared_mod
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import server_tools.voice as server_mod
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monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: _FAKE_TRANSCRIPT)
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monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
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# "seguranca" (2.0-2.9s) is the loud, high-pitched, emphatic word
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monkeypatch.setattr(
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server_mod,
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"extract_pitch",
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lambda *a, **k: [(0.2, 120.0), (0.6, 118.0), (2.4, 260.0)],
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)
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monkeypatch.setattr(
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server_mod,
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"extract_energy",
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lambda *a, **k: [(0.2, 0.10), (0.6, 0.12), (2.4, 0.95)],
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)
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class TestAnalyzeVoiceFeaturesHandler:
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async def test_reports_when_librosa_unavailable(self, wav, monkeypatch):
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import server_tools.voice as server_mod
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from server import handle_analyze_voice_features
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monkeypatch.setattr(
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server_mod, "features_capability", lambda: (False, "componente librosa ausente.")
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)
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result = await handle_analyze_voice_features({"media_path": str(wav)})
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assert "librosa" in result[0].text.lower()
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async def test_rejects_disallowed_extension(self, tmp_path):
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from server import handle_analyze_voice_features
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bad = tmp_path / "clip.txt"
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bad.write_text("not audio")
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with pytest.raises(ValueError):
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await handle_analyze_voice_features({"media_path": str(bad)})
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async def test_writes_features_json_with_emphasis_per_word(self, wav, patched_analysis):
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from server import handle_analyze_voice_features
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result = await handle_analyze_voice_features({"media_path": str(wav)})
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text = result[0].text
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json_path = wav.parent / "clip_voice_features.json"
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assert str(json_path) in text
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data = json.loads(json_path.read_text())
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assert len(data["words"]) == 3
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assert all("emphasis" in w for w in data["words"])
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assert all(0.0 <= w["emphasis"] <= 1.0 for w in data["words"])
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async def test_loudest_word_scores_highest_emphasis(self, wav, patched_analysis):
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from server import handle_analyze_voice_features
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await handle_analyze_voice_features({"media_path": str(wav)})
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data = json.loads((wav.parent / "clip_voice_features.json").read_text())
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by_word = {w["word"]: w["emphasis"] for w in data["words"]}
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assert by_word["seguranca"] > by_word["isso"]
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assert by_word["seguranca"] > by_word["e"]
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async def test_persisted_config_is_embedded_in_output(self, wav, patched_analysis):
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from server import handle_analyze_voice_features
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await handle_analyze_voice_features({"media_path": str(wav)})
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data = json.loads((wav.parent / "clip_voice_features.json").read_text())
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assert "energy_threshold" in data["config"]
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assert "emphasis_weights" in data["config"]
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async def test_empty_transcript_reports_instead_of_crashing(self, wav, monkeypatch):
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import server_tools._shared as _shared_mod
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import server_tools.voice as server_mod
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from server import handle_analyze_voice_features
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monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
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monkeypatch.setattr(
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_shared_mod, "transcribe", lambda *a, **k: {**_FAKE_TRANSCRIPT, "words": []}
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)
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result = await handle_analyze_voice_features({"media_path": str(wav)})
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assert "no words" in result[0].text.lower()
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async def test_untranscribable_media_reports_install_hint(self, wav, monkeypatch):
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import server_tools._shared as _shared_mod
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import server_tools.voice as server_mod
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from server import handle_analyze_voice_features
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monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
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monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: None)
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result = await handle_analyze_voice_features({"media_path": str(wav)})
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assert "faster-whisper" in result[0].text
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async def test_missing_pitch_track_degrades_without_crashing(self, wav, monkeypatch):
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import server_tools._shared as _shared_mod
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import server_tools.voice as server_mod
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from server import handle_analyze_voice_features
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monkeypatch.setattr(_shared_mod, "transcribe", lambda *a, **k: _FAKE_TRANSCRIPT)
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monkeypatch.setattr(server_mod, "features_capability", lambda: (True, "ok"))
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monkeypatch.setattr(server_mod, "extract_pitch", lambda *a, **k: None)
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monkeypatch.setattr(server_mod, "extract_energy", lambda *a, **k: None)
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result = await handle_analyze_voice_features({"media_path": str(wav)})
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assert "Voice Feature Analysis" in result[0].text
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data = json.loads((wav.parent / "clip_voice_features.json").read_text())
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assert all(w["emphasis"] >= 0.0 for w in data["words"])
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