asr:
  url: "http://localhost:8100"

llm:
  base_url: "http://localhost:8080/v1"     # mlx_lm.server (or Ollama, OpenRouter, OpenAI, etc.)
  api_key: "none"                          # set if using a hosted API
  model: "mlx-community/gemma-3-4b-it-qat-4bit"
  max_messages: 20
  max_tokens: 300
  system_prompt: "You are a helpful voice assistant. Keep responses concise (under 2 sentences)."

tts:
  backend: "qwen3"                         # "qwen3" (local) or "elevenlabs" (cloud)
  qwen3:
    url: "http://localhost:8880"            # mlx-audio server (start with: python -m mlx_audio.server --port 8880)
    model: "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-4bit"
    ref_audio: ""                           # path to 3-10s reference clip for voice cloning
    ref_text: ""                           # optional transcript of reference audio
  elevenlabs:
    api_key: ""                            # your ElevenLabs API key
    default_voice: "Rachel"
    voices:
      Rachel: "21m00Tcm4TlvDq8ikWAM"      # add your voice IDs here

vad:
  threshold: 0.5          # speech onset probability
  neg_threshold: 0.35     # speech offset probability (hysteresis)
  silence_time: 1.5
  pre_buffer_s: 1.0

network:
  udp_port: 3000
  tcp_port: 3001
  multicast_group: "239.0.0.1"
  multicast_port: 12345

audio:
  sample_rate: 16000
  chunk_size: 512       # 32ms at 16kHz (Silero VAD requirement)
  opus_frame_size: 320  # 20ms at 16kHz

device:
  default_volume: 14
  default_mic_timeout: 60
  led_fade: 6
  led_power: 35
  led_update_period: 0.2
  persist_file: "data/devices.json"
  greeting_wav: "data/hello_imhere.wav"

logging:
  level: "INFO"