mirror of
https://github.com/Nighthawk42/mOrpheus.git
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96 lines
3.4 KiB
YAML
96 lines
3.4 KiB
YAML
# settings.yaml
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# -------------------------------
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# General Application Settings
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# -------------------------------
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general:
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log_level: "INFO" # DEBUG, INFO, WARNING, ERROR, CRITICAL
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interaction_mode: "push_to_talk" # "push_to_talk", "hotword", "both"
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post_response_delay_sec: 0.5
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# -------------------------------
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# Audio Input/Output
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# -------------------------------
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audio:
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input_device: null
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output_device: null
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# --- Voice Activity Detection (VAD) ---
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vad:
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enabled: true # Set to true to enable VAD (EXPERIMENTAL)
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sample_rate: 16000 # Should match STT and Hotword expected rate
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frame_duration_ms: 30
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aggressiveness: 2
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silence_duration_ms: 1200
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min_record_duration_ms: 500
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# -------------------------------
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# Hotword Detection (OpenWakeWord)
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# -------------------------------
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hotword:
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enabled: false # Set to true to enable hotword detection (EXPERIMENTAL) - This is fully broken for now, keep it false.
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# List of wake word models to listen for.
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# Find available models in the openwakeword documentation or train your own.
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# Example pre-trained models: "alexa", "hey_mycroft", "hey_jarvis", "timer"
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models: ["hey_jarvis"]
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# Path for custom models (optional):
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# custom_model_paths: ["path/to/my_custom_model.onnx"]
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inference_framework: "onnx" # Usually 'onnx', could be 'tflite' if supported/used
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threshold: 0.7 # Confidence threshold for detection (0.0 to 1.0)
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trigger_level: 1 # How many positive frames needed to trigger (usually 1)
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chunk_size_ms: 1280 # How much audio (ms) to feed the model at once (check owm recommendations)
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# -------------------------------
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# STT (Speech-to-Text - Faster Whisper)
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# -------------------------------
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stt:
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model_size: "small.en" # Whisper model size (tiny.en, base.en, small.en, medium.en, large-v2, large-v3)
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# Or path to a converted CTranslate2 model directory
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device: "cuda" # "cuda" or "cpu"
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compute_type: "float16" # "float16", "int8_float16", "int8", "float32" (check hardware/model support)
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# --- Transcription Options ---
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language: null # Language code (e.g., "en", "es") or null for auto-detect
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beam_size: 5
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# vad_filter: false # Use faster-whisper's internal VAD? (Keeping our external VAD for now)
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# vad_parameters: # Settings if vad_filter=true (e.g., min_silence_duration_ms: 500)
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# min_silence_duration_ms: 1000
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# -------------------------------
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# LLM (Language Model - LM Studio)
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# -------------------------------
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llm:
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base_url: "http://127.0.0.1:1234/v1"
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request_timeout_sec: 60.0
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max_retries: 3
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# --- Chat Completion ---
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chat:
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endpoint: "/chat/completions"
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model: "gemma-3-12b-it"
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system_prompt: "You are a helpful and concise voice assistant named Morpheus."
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max_tokens: 300
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temperature: 0.7
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top_p: 0.9
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repetition_penalty: 1.1
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# -------------------------------
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# TTS (Text-to-Speech - LM Studio/SNAC)
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# -------------------------------
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tts:
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# --- API Settings ---
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endpoint: "/completions"
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model: "orpheus-3b-ft.gguf@q2_k"
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# --- Synthesis Parameters ---
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default_voice: "tara"
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max_tokens: 8192
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temperature: 0.6
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top_p: 0.9
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repetition_penalty: 1.0
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speed: 1.0
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# --- Audio Output ---
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sample_rate: 24000
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normalize_volume: false
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# --- Text Segmentation ---
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segmentation:
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max_words_per_segment: 50
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# --- Output Files ---
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output_dir: "outputs"
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clear_output_on_start: true |