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_translate.py

@@ -84,12 +84,14 @@ def run_translation(in_queue, out_queue, args):
             
             if args.only_translate_llm:
                 text_to_translate = paragraph_text
-                if args.en and paragraph_text:
-                    payload["en"] = paragraph_text
             else:
                 text_to_translate = paragraph_text or corrected_text or raw_text
-                if args.en:
-                    payload["en"] = text_to_translate
+
+            english_output = text_to_translate or ""
+            if english_output:
+                payload["english_output"] = english_output
+            if args.en and english_output:
+                payload["en"] = english_output
 
             if text_to_translate and translation_engines:
                 for lang_key, (model, tokenizer) in translation_engines.items():