Replaces gpt-4o-mini-transcribe (/usr/bin/bash.003/min) with gpt-realtime-whisper
(/usr/bin/bash.017/min). Expected to reduce transcription latency from ~2.6s
to ~1s due to the model's realtime optimization.
Replaced synchronous TTS (waiting for full audio at 5.9s) with
streaming TTS that sends audio chunks as they arrive. Backend now
accumulates chunks in audioBufferRef and plays the complete stream
on speaking_end. Reduces TTS latency from ~6s to ~1s first byte.
The memory context was being rebuilt on every conversation turn via
build_system_prompt(), which calls Honcho's dialectic reasoning API
twice (get_user_context + get_kira_context). Each call takes 5-15s.
Now the memory suffix is computed ONCE during identify and cached in
a memory_suffix variable for the session duration. Per-turn latency
drops from ~37s to ~3s.
Also removed duplicated _pcm16_to_wav and cleaned up orphaned code.
PCM16 capture via AudioContext was streaming raw audio continuously,
causing massive accumulated buffers that took ~20s to transcribe.
Replaced with MediaRecorder which records compressed Opus/webm and
sends a single blob on release — much smaller, faster to transcribe.
Also removed all unused PCM16/WAV helper functions from both frontend
and backend.
Frontend captures PCM16 mono 24kHz audio. The transcription API
expects a proper audio container format (wav, webm, etc.), not raw
PCM16 data. Added _pcm16_to_wav() to wrap the raw bytes in a WAV
header before sending to gpt-4o-mini-transcribe.
Hybrid approach gives streaming STT at ~/usr/bin/bash.017/min + cheap brain
at ~/usr/bin/bash.001/min + TTS at ~/usr/bin/bash.015/min = ~/usr/bin/bash.033/min total.
- gpt-realtime-whisper handles streaming transcription with VAD
- gpt-5.4-nano handles response generation (chat completions)
- OpenAI TTS (nova) for voice output
- Server VAD detects utterance boundaries
- Honcho memory context injected into system prompt
- Removed old full Realtime relay service
Replaced the 3-step sequential pipeline (Whisper STT → DeepSeek LLM
→ OpenAI TTS) with a single OpenAI Realtime API WebSocket using
gpt-4o-mini-realtime-preview.
- ~300-800ms latency vs 1-3s
- Server VAD for automatic turn detection
- Streaming audio chunks during playback
- Interruptions: user can speak over Kira mid-response
- Honcho memory still injected into session instructions
- Frontend captures PCM16 mono 24kHz via AudioContext
- Backend relays client ↔ OpenAI Realtime API
- Supports both voice (PCM16) and text input
- WelcomeScreen: first-time name entry with cute onboarding
- identify WS message: sets user_id, loads saved prefs from Honcho
- set_preference WS message: saves scene/outfit/accessory to Honcho metadata
- Preferences auto-load on return visits via localStorage + Honcho peer meta
- Kira uses the user's name in greeting and prompts
- Backend: get/set preference methods in KiraMemory service
- Frontend: optimistic preference updates, synced to backend on change
hobokenchicken