feat: implement accumulative LLM paragraph engine and harden 4-process pipeline

history.md

@@ -214,3 +214,27 @@ The project now features a high-performance, Apple Silicon-optimized pipeline th
     python3 transcribe.py --silence 100 -q -s -c -es -en -fr -ar --lang en --filter-lang --mt-max-chars 450 --mt-max-new-tokens 220 --smart-correct --post-correct --post-correct-llm --post-correct-model qwen2.5:3b-instruct --session-log-file debug_session_1.log
     ```
 - **Outcome:** The project now has a repeatable "known good" runtime profile plus persistent session diagnostics for reviewing caption issues after a run.
+
+## Phase 26: Multi-Process Decoupled Architecture
+- **Goal:** Improve system stability, reduce latency, and fully decouple audio capture from heavy LLM/Translation tasks.
+- **Approach:**
+    - **4-Process Pipeline:** Refactored the monolithic script into four independent services coordinated via `multiprocessing.Queue`:
+        1. **`engine_transcribe.py` (Whisper):** Dedicated to high-priority audio capture and ASR.
+        2. **`engine_llm.py` (Ollama):** Handles asynchronous post-correction and paragraph structuring without blocking the transcription loop.
+        3. **`engine_translate.py` (MarianMT):** Manages multi-language translation for both raw and refined text.
+        4. **`engine_distribute.py` (API/CLI):** Handles data delivery and terminal display.
+    - **Centralized Configuration:** Introduced `config.json` for managing all defaults and parameters in one place, with `main_v2.py` as the entry point.
+    - **Dual Payload Strategy:** Implemented a robust data flow where the translation and distribution engines receive both raw and LLM-corrected versions of the text, allowing for fallback and comparison.
+    - **Hardware Isolation:** Transcription and Translation processes independently leverage Apple Silicon (MPS), while the LLM process utilizes Ollama's external server, preventing resource contention.
+- **Outcome:** Significantly increased resilience. If the LLM or Translation engine stalls, the Transcription engine continues to capture and buffer audio safely, preventing data loss.
+
+## Phase 27: Intelligent LLM Refinement & Pipeline Hardening
+- **Goal:** Transform raw ASR segments into professional paragraphs and stabilize advanced features.
+- **Approach:**
+    - **Accumulative LLM Engine:** Developed a stateful refinement logic in `engine_llm.py` that maintains a "working paragraph," allowing the LLM to continuously integrate and polish new segments in real-time.
+    - **OpenAI Integration:** Added direct support for OpenAI's GPT API (via `requests` to avoid subprocess dependency issues), enabling higher-quality refinement than lightweight local models.
+    - **Rolling Context Window:** Implemented a smart context management system that "finalizes" paragraphs once they reach a natural break (detected via `\n\n`), clearing the prompt history to save tokens and maintain focus.
+    - **Speaker Diarization Hardening:** Refactored `engine_transcribe.py` to support `pyannote.audio` 4.0.4, specifically handling the new `DiarizeOutput` object structure and ensuring speaker labels (`SPEAKER_XX`) propagate through the entire multi-process pipeline.
+    - **English Bridge Optimization:** Forced a two-pass transcription strategy (ASR first, then optional translation) to ensure word-level timestamps are captured for speaker mapping even when translating to English.
+    - **Stability Fixes:** Added hallucination filtering for Whisper's repetitive loops, enforced line-buffering for terminal logging, and implemented a global `lzma` mock to ensure compatibility across restricted Python environments.
+- **Outcome:** A robust, production-ready pipeline that produces high-quality, speaker-attributed, and professionally formatted live transcripts.