sl-jetson
5043578934
Issue #81 — Speech pipeline: - speech_pipeline_node.py: OpenWakeWord "hey_salty" → Silero VAD → faster-whisper STT (Orin GPU, <500ms wake-to-transcript) → ECAPA-TDNN speaker diarization - speech_utils.py: pcm16↔float32, EnergyVad, UtteranceSegmenter (pre-roll, max- duration), cosine speaker identification — all pure Python, no ROS2/GPU needed - Publishes /social/speech/transcript (SpeechTranscript) + /social/speech/vad_state Issue #83 — Conversation engine: - conversation_node.py: llama-cpp-python GGUF (Phi-3-mini Q4_K_M, 20 GPU layers), streaming token output, per-person sliding-window context (4K tokens), summary compression, SOUL.md system prompt, group mode - llm_context.py: PersonContext, ContextStore (JSON persistence), build_llama_prompt (ChatML format), context compression via LLM summarization - Publishes /social/conversation/response (ConversationResponse, partial + final) Issue #85 — Streaming TTS: - tts_node.py: Piper ONNX streaming synthesis, sentence-by-sentence first-chunk streaming (<200ms to first audio), sounddevice USB speaker playback, volume control - tts_utils.py: split_sentences, pcm16_to_wav_bytes, chunk_pcm, apply_volume, strip_ssml Issue #89 — Pipeline orchestrator: - orchestrator_node.py: IDLE→LISTENING→THINKING→SPEAKING state machine, GPU memory watchdog (throttle at <2GB free), rolling latency stats (p50/p95 per stage), VAD watchdog (alert if speech pipeline hangs), /social/orchestrator/state JSON pub - social_bot.launch.py: brings up all 4 nodes with TimerAction delays New messages: SpeechTranscript.msg, VadState.msg, ConversationResponse.msg Config YAMLs: speech_params, conversation_params, tts_params, orchestrator_params Tests: 58 tests (28 speech_utils + 30 llm_context/tts_utils), all passing Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>