Simplify editor cleanup and keep live ASR metadata

Keep the daemon path on the full ASR result so word timings and detected language survive into the editor pipeline instead of falling back to a plain transcript string.

Add PipelineEngine.run_asr_result(), have aman call it when live ASR data is available, and cover the word-aware alignment behavior in the daemon tests.

Collapse the llama cleanup flow to a single JSON-shaped completion while leaving the legacy pass1/pass2 parameters in place as compatibility no-ops.

Validated with PYTHONPATH=src python3 -m unittest tests.test_aiprocess tests.test_aman.

tests/test_aiprocess.py

@@ -186,6 +186,29 @@ class LlamaWarmupTests(unittest.TestCase):
         with self.assertRaisesRegex(RuntimeError, "expected JSON"):
             processor.warmup(profile="default")
 
+    def test_process_with_metrics_uses_single_completion_timing_shape(self):
+        processor = object.__new__(LlamaProcessor)
+        client = _WarmupClient(
+            {"choices": [{"message": {"content": '{"cleaned_text":"friday"}'}}]}
+        )
+        processor.client = client
+
+        cleaned_text, timings = processor.process_with_metrics(
+            "thursday, I mean friday",
+            lang="en",
+            dictionary_context="",
+            profile="default",
+        )
+
+        self.assertEqual(cleaned_text, "friday")
+        self.assertEqual(len(client.calls), 1)
+        call = client.calls[0]
+        self.assertEqual(call["messages"][0]["content"], aiprocess.SYSTEM_PROMPT)
+        self.assertIn('{"cleaned_text":"..."}', call["messages"][1]["content"])
+        self.assertEqual(timings.pass1_ms, 0.0)
+        self.assertGreater(timings.pass2_ms, 0.0)
+        self.assertEqual(timings.pass2_ms, timings.total_ms)
+
 
 class ModelChecksumTests(unittest.TestCase):
     def test_accepts_expected_checksum_case_insensitive(self):