refactor: break up _record_utterance() into focused helpers

Extract _capture_audio_blocks() and _finalize_utterance() from the 73-line
_record_utterance() method, and promote _rms() to a module-level function.

Fixes #570

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/timmy/voice_loop.py

@@ -78,6 +78,11 @@ DEFAULT_MAX_UTTERANCE = 30.0  # safety cap — don't record forever
 DEFAULT_SESSION_ID = "voice"
 
 
+def _rms(block: np.ndarray) -> float:
+    """Compute root-mean-square energy of an audio block."""
+    return float(np.sqrt(np.mean(block.astype(np.float32) ** 2)))
+
+
 @dataclass
 class VoiceConfig:
     """Configuration for the voice loop."""
@@ -161,13 +166,6 @@ class VoiceLoop:
         min_blocks = int(self.config.min_utterance / 0.1)
         max_blocks = int(self.config.max_utterance / 0.1)
 
-        audio_chunks: list[np.ndarray] = []
-        silent_count = 0
-        recording = False
-
-        def _rms(block: np.ndarray) -> float:
-            return float(np.sqrt(np.mean(block.astype(np.float32) ** 2)))
-
         sys.stdout.write("\n  🎤 Listening... (speak now)\n")
         sys.stdout.flush()
 
@@ -177,6 +175,26 @@ class VoiceLoop:
             dtype="float32",
             blocksize=block_size,
         ) as stream:
+            chunks = self._capture_audio_blocks(stream, block_size, silence_blocks, max_blocks)
+
+        return self._finalize_utterance(chunks, min_blocks, sr)
+
+    def _capture_audio_blocks(
+        self,
+        stream,
+        block_size: int,
+        silence_blocks: int,
+        max_blocks: int,
+    ) -> list[np.ndarray]:
+        """Read audio blocks from *stream* until silence or safety cap.
+
+        Returns the list of captured audio blocks (may be empty if no
+        speech was detected).
+        """
+        audio_chunks: list[np.ndarray] = []
+        silent_count = 0
+        recording = False
+
         while self._running:
             block, overflowed = stream.read(block_size)
             if overflowed:
@@ -199,20 +217,28 @@ class VoiceLoop:
                 else:
                     silent_count = 0
 
-                    # End of utterance
                 if silent_count >= silence_blocks:
                     break
 
-                    # Safety cap
                 if len(audio_chunks) >= max_blocks:
                     logger.info("Max utterance length reached, stopping.")
                     break
 
-        if not audio_chunks or len(audio_chunks) < min_blocks:
+        return audio_chunks
+
+    @staticmethod
+    def _finalize_utterance(
+        chunks: list[np.ndarray], min_blocks: int, sample_rate: int
+    ) -> np.ndarray | None:
+        """Concatenate captured chunks and report duration.
+
+        Returns None if the utterance is too short (below *min_blocks*).
+        """
+        if not chunks or len(chunks) < min_blocks:
             return None
 
-        audio = np.concatenate(audio_chunks, axis=0).flatten()
+        audio = np.concatenate(chunks, axis=0).flatten()
-        duration = len(audio) / sr
+        duration = len(audio) / sample_rate
         sys.stdout.write(f"  ✂️  Captured {duration:.1f}s of audio\n")
         sys.stdout.flush()
         return audio

tests/timmy/test_voice_loop.py

@@ -15,7 +15,7 @@ except ImportError:
     np = None
 
 try:
-    from timmy.voice_loop import VoiceConfig, VoiceLoop, _strip_markdown
+    from timmy.voice_loop import VoiceConfig, VoiceLoop, _rms, _strip_markdown
 except ImportError:
     pass  # pytestmark will skip all tests anyway
 
@@ -336,3 +336,28 @@ class TestSpeakSetsFlag:
 
         # After speak
         assert loop._speaking is False
+
+
+class TestRms:
+    def test_rms_of_silence(self):
+        block = np.zeros(1600, dtype=np.float32)
+        assert _rms(block) == 0.0
+
+    def test_rms_of_signal(self):
+        block = np.ones(1600, dtype=np.float32) * 0.5
+        assert abs(_rms(block) - 0.5) < 1e-5
+
+
+class TestFinalizeUtterance:
+    def test_returns_none_for_empty(self):
+        assert VoiceLoop._finalize_utterance([], min_blocks=5, sample_rate=16000) is None
+
+    def test_returns_none_below_min(self):
+        chunks = [np.zeros(1600, dtype=np.float32) for _ in range(3)]
+        assert VoiceLoop._finalize_utterance(chunks, min_blocks=5, sample_rate=16000) is None
+
+    def test_concatenates_chunks(self):
+        chunks = [np.ones(1600, dtype=np.float32) for _ in range(5)]
+        result = VoiceLoop._finalize_utterance(chunks, min_blocks=3, sample_rate=16000)
+        assert result is not None
+        assert len(result) == 8000