- apple-llm-polish binary (120KB Swift, FoundationModels): --check
now reports available:true after Apple Intelligence enabled,
ping OK, line polish 258-388ms ANE vs Ollama 3-8s CPU
- engine_apple_llm.py: AppleLLM class with persistent pipe,
thread reader, polish_line() + polish_paragraph(), JSONL protocol
{id,mode,text,prev1,prev2,context} -> {id,text,ok,ms}
- engine_llm.py: try Apple ANE first (permissiveContentTransformations,
temp 0.1 line / 0.2 para), log provider=apple apple_ms, fallback
to Ollama on guardrail/timeout. Fixes missing LLM log — now
logs provider=apple with ms.
- main_v3.py: --apple-llm / --no-apple-llm flag (on by default)
- Verified: direct polish 258ms, last log provider=apple ms=498,
speech binary still 240KB with 31 word-by-word drafts
Co-authored-by: internal-model
- 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
Adolfo Reyna