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Commit History
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Phase 1: Sahel-Voice-Lab — The Memory Loop
jefffffff9 +1: Claude Sonnet 4.6New project pivot: self-learning voice assistant for Bambara/Fula using 100% non-Meta tech stack. New files: - app_lab.py — Gradio UI: push-to-talk + last 5 words panel - src/memory/memory_manager.py — persists vocabulary.jsonl to HF Hub - src/llm/gemma_client.py — Gemma via HF Serverless Inference API - data/vocabulary.jsonl — empty initial vocabulary store Flow: audio → Whisper STT → Gemma (with vocabulary context) → teaching intent → MemoryManager.add_word_pair() → Hub push question intent → answer from vocabulary conversation → natural reply README.md updated: app_file changed to app_lab.py, stack documented. Stack: openai/whisper-large-v3-turbo (STT), google/gemma-3-4b-it (LLM), Waxal TTS (Phase 2), HF Dataset for memory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>096b19d
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Add phrase library + fuzzy matcher for general Bambara/Fula conversation
jefffffff9 +1: Claude Sonnet 4.6Core problem: the app responded to 'I ni ce' with a sensor default message because it had no conversational knowledge. Fixed with a phrase-library approach that requires zero ML training. New files: - data/phrases/bambara.json — 37 curated Bambara phrases with Whisper variants - data/phrases/fula.json — 38 curated Fula/Pular phrases with Whisper variants - src/conversation/phrase_matcher.py — fuzzy matcher using rapidfuzz (falls back to substring match if rapidfuzz unavailable) app.py changes: - Pipeline tries phrase library FIRST; sensor pipeline is fallback - Low-confidence unknown intents return 'I didn't understand' in native lang - New 'Knowledge Base' tab: paste phrase pairs (native|english) — immediate - YouTube import tab: download audio + save transcription to training dataset - User additions persisted to HF Hub (survive Space restarts) - Phrase additions loaded from Hub in background at startup requirements.txt: added rapidfuzz==3.13.0, yt-dlp==2025.3.31 Co-Author…6f4d8d0