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Add confidence loop, curiosity engine, and lightweight TTS
jefffffff9 +1: Claude Sonnet 4.6Task 1 — TTS refactor (src/tts/waxal_tts.py): Switch Bambara TTS from Qwen2-based MALIBA-AI to ynnov/ekodi-bambara-tts-female (VitsModel + AutoTokenizer) — much lighter on CPU Basic; no trust_remote_code needed. Fula is an explicit generate_pular_tts() placeholder returning None until the model is trained. Task 2 — Active learning / confidence loop (src/engine/stt_processor.py): transcribe_with_confidence() wraps Whisper generate() with output_scores=True and computes avg_logprob via compute_transition_scores(). If avg_logprob < -1.0, app_lab.py replaces the transcript with CONFUSION_PROMPT so the LLM asks the user in English to repeat and explain the word. Task 3 — Proactive gaps (src/engine/curiosity.py): CuriosityEngine.maybe_ask() fires every 5 interactions — sends the last 10 vocabulary entries to Qwen and appends a 🌱 question in the chat asking the user to teach a missing agricultural term. Task 4 — Zero-cost persistence: MemoryManager._push_to_h…3657607