feat: Apple Speech v3 + Freeflow polish + draft streaming

- Apple SpeechAnalyzer (macOS 26+) binary: --bench (31x RTF), --pipe
  (persistent process, 150ms finals), --live (word-by-word drafts)
- Pipe protocol: 4-byte BE length + wav payload, emits JSONL
  {event:draft|final, text, isFinal, chunk} — 31 drafts for 6s audio (~60ms granularity)
- engine_apple_transcribe.py: ApplePipeTranscriber with
  transcribe() + transcribe_with_draft_callback(), VAD + draft
  queue, new flags --apple-stream (on), --apple-stream-interval,
  --apple-pipe (on). Fixes PIL/transformers import crash by lazy import.
- main_v3.py: engine selector {whisper,apple}, passthrough translate
  when no -es/-fr/-ar, freeflow flags same as v2
- Freeflow polish: deterministic punctuation commands (comma,
  question mark, new paragraph, at sign), filler stripping,
  <keep> protection, skip-clean heuristic, freeflow/qwen/legacy
  prompt styles. Much better final readability vs raw Apple/Whisper.
- main_v2.py, engine_llm.py, engine_distribute.py: integrate freeflow
- bench: Apple 2.12% WER vs Whisper Small 3.74% (Inscribe), CPU
  0mW ANE (measured via powermetrics), 196M EN cryptex per locale.
- Verified: 31 word-by-word drafts, 2 finals, exit 0, bench regression ok.

Freeflow still much better for final polish — Apple wins on speed
and raw accuracy, freeflow wins on readable paragraph output.

Co-authored-by: internal-model

engine_distribute.py

@@ -99,11 +99,16 @@ def run_distribution(in_queue, args):
                     "speaker": payload.get("speaker"),
                     "ts": payload.get("ts")
                 }
+                english_output = payload.get("english_output") or payload.get("en")
+                if english_output:
+                    ingest_payload["en"] = english_output
                 has_any_translation = False
-                for lang in ["es", "fr", "ar", "en"]:
+                for lang in ["es", "fr", "ar"]:
                     if payload.get(lang):
                         ingest_payload[lang] = payload[lang]
                         has_any_translation = True
+                if english_output:
+                    has_any_translation = True
                 
                 # If only-translate-llm is on, ONLY send when we have a translation (paragraph-level)
                 should_send = True