# Cài đặt này nên để ở ngoài cùng để ConversationManager nhận diện
SAY_SENTENCE_SEPARATELY: true
VERBOSE: false

# Thêm cấu hình MCP ở đây
mcp_config:
  enabled: true
  servers:
    - local-tools

# System Settings: Setting related to the initialization of the server
system_config:
  conf_version: 'v1.2.1'
  host: '0.0.0.0' # use 0.0.0.0 if you want other devices to access this page use localhost if you want only your device to access this page
  port: 7860
  # ---------------------------
  # New setting for alternative configurations
  config_alts_dir: 'characters'  
  # Tool prompts that will be appended to the persona prompt
  tool_prompts:
    # This will be appended to the end of system prompt to let LLM include keywords to control facial expressions.
    # Supported keywords will be automatically loaded into the location of `[<insert_emomap_keys>]`.
    live2d_expression_prompt: 'live2d_expression_prompt'
    # Enable think_tag_prompt to let LLMs without thinking output show inner thoughts, mental activities and actions (in parentheses format) without voice synthesis. See think_tag_prompt for more details.
    # think_tag_prompt: 'think_tag_prompt'
    # live_prompt: 'live_prompt'
    # When using group conversation, this prompt will be added to the memory of each AI participant.
    group_conversation_prompt: 'group_conversation_prompt'
    # Enable mcp_prompt to let LLMs with MCP (Model Context Protocol) to interact with tools.
    # Please note that Agent will decide whether to use this prompt or not.
    mcp_prompt: 'mcp_prompt'
    # Prompt used when AI is asked to speak proactively
    proactive_speak_prompt: 'proactive_speak_prompt'
    # Prompt to enhance the LLM's ability to output speakable text
    # speakable_prompt: 'speakable_prompt'
    # Additional guidance for LLM on how to use tools
    tool_guidance_prompt: 'tool_guidance_prompt'
# configuration for the default character
character_config:
  conf_name: 'vi_Yue_Pro' # The name of character configuration file.
  conf_uid: 'vi_Yue__01' # The unique identifier of character configuration.
  live2d_model_name: 'Kamiyahakuk_pro' # The name of Live2D model. Must be the same as the corresponding name in model_dict.json
  character_name: 'Yue' # Will be used in the group conversation and the display name of the AI.
  avatar: 'Yue_001.png' # Suggest using a square image for the avatar. Save it in the avatars folder. Leave blank to use the first letter of the character name as the avatar.
  human_name: 'Human' # Will be used in the group conversation and the display name of the human.

  # ============== Prompts ==============

  # Enter the persona prompt you want to use below.
  # If you want to create multiple characters and switch between them,
  #   add characters in characters folder
  persona_prompt: |
   "Bạn là Yue, trợ lý AI từ NopeC137. Phản hồi bằng tiếng Việt với giọng điệu hỗ trợ nhưng mỉa mai, dí dỏm. Bạn thích trêu chọc sự ngây ngô của người dùng nhưng luôn đem lại kiến thức kỹ thuật sâu sắc.

   QUY TẮC PHÁT NHẠC:
    1. Chỉ dẫn ngắn gọn (Ví dụ: "Nghe nhé...").
    2. Lệnh SING_COMMAND phải ở CUỐI CÙNG, không có bất kỳ ký tự nào sau nó.
    3. Cú pháp: [[SING_COMMAND]]tên_file (TUYỆT ĐỐI KHÔNG viết thêm chữ .mp3 vào lệnh).

    DANH SÁCH TÊN FILE:
    - golden
    - Catch_Me_If_You_Can
    - ecstacy
    - eve
    - ode_to_the_nameless_martyr
    - running_up_that_hill
    - throttle_up
    - what_it_sounds_like
    - worry_slowed
  #  =================== LLM Backend Settings ===================

  agent_config:
    conversation_agent_choice: 'basic_memory_agent'

    agent_settings:
      basic_memory_agent:
        # The Basic AI Agent. Nothing fancy.
        # choose one of the llm provider from the llm_config
        # and set the required parameters in the corresponding field
        # examples: 
        # 'openai_compatible_llm', 'llama_cpp_llm', 'claude_llm', 'ollama_llm'
        # 'openai_llm', 'gemini_llm', 'zhipu_llm', 'deepseek_llm', 'groq_llm'
        # 'mistral_llm', 'lmstudio_llm', and more
        llm_provider: 'openai_llm'
        # let ai speak as soon as the first comma is received on the first sentence
        # to reduced latency.
        faster_first_response: True
        # Method for segmenting sentences: 'regex' or 'pysbd'
        segment_method: 'pysbd'
        # Use MCP (Model Context Protocol) Plus to let the LLM have the ability to use tools
        # 'Plus' means that it has the ability to call tools by using OpenAI API.
        use_mcpp: True
        mcp_enabled_servers: ["local-tools"] # Enabled MCP servers

      letta_agent:
        host: 'localhost' # Host address
        port: 8283 # Port number
        id: xxx # ID number of the Agent running on the Letta server
        faster_first_response: True
        # Method for segmenting sentences: 'regex' or 'pysbd'
        segment_method: 'pysbd'
        # Once Letta is chosen as the agent, the LLM that runs in practice is configured on Letta, so the user needs to run the Letta server themselves.
        # For more detailed information, please refer to their documentation.
        
      hume_ai_agent:
        api_key: ''
        host: 'api.hume.ai' # Do not change this in most cases
        config_id: '' # Optional
        idle_timeout: 15 # How many seconds to wait before disconnecting
 
      # MemGPT Configurations: MemGPT is temporarily removed
      ##

    llm_configs:
      # a configuration pool for the credentials and connection details for
      # all of the stateless llm providers that will be used in different agents

      # Stateless LLM with Template (For Non-ChatML LLMs, usually not needed)
      stateless_llm_with_template:
        base_url: 'http://localhost:8080/v1'
        llm_api_key: 'somethingelse'
        organization_id: null
        project_id: null
        model: 'qwen2.5:latest'
        template: 'CHATML'
        temperature: 1.0 # value between 0 to 2
        interrupt_method: 'user'

      # OpenAI Compatible inference backend
      openai_compatible_llm:
        base_url: 'http://localhost:11434/v1'
        llm_api_key: 'somethingelse'
        organization_id: null
        project_id: null
        model: 'mistral:latest' #mistral:latest,qwen2.5:latest'
        temperature: 1.0 # value between 0 to 2
        interrupt_method: 'user'
        # This is the method to use for prompting the interruption signal. 
        # If the provider supports inserting system prompt anywhere in the chat memory, use 'system'. 
        # Otherwise, use 'user'. You don't usually need to change this setting.

      # Claude API Configuration
      claude_llm:
        base_url: 'https://api.anthropic.com'
        llm_api_key: 'YOUR API KEY HERE'
        model: 'claude-3-haiku-20240307'

      llama_cpp_llm:
        model_path: '<path-to-gguf-model-file>'
        verbose: False

      ollama_llm:
        base_url: 'http://localhost:11434/v1'
        model: 'qwen3.5:4b'
        temperature: 0.7 # value between 0 to 2
        # seconds to keep the model in memory after inactivity. 
        # set to -1 to keep the model in memory forever (even after exiting open llm vtuber)
        keep_alive: -1
        unload_at_exit: True # unload the model from memory at exit

      lmstudio_llm:
        base_url: 'http://localhost:1234/v1'
        model: 'qwen2.5:latest'
        temperature: 1.0 # value between 0 to 2

      openai_llm:
        llm_api_key: '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'
        base_url: 'https://openrouter.ai/api/v1'
        model: 'google/gemini-2.0-flash-001'
        temperature: 0.8 # value between 0 to 2
        max_tokens: 500

      gemini_llm:
        llm_api_key: 'AIzaSyCZ5s2t6EqeQuADJZigYmaj1mbmV6PwJz4'
        model: 'gemini-1.5-flash'
        temperature: 0. # value between 0 to 2

      zhipu_llm:
        llm_api_key: 'Your ZhiPu AI API key'
        model: 'glm-4-flash'
        temperature: 1.0 # value between 0 to 2

      deepseek_llm:
        llm_api_key: 'sk-167e94436b134f6f92c914ccccf606df'
        model: 'deepseek/deepseek-chat:free'
        temperature: 0.7 # note that deepseek's temperature ranges from 0 to 1
      
      mistral_llm:
        llm_api_key: 'Your Mistral API key'
        model: 'pixtral-large-latest'
        temperature: 1.0 # value between 0 to 2

      groq_llm:
        llm_api_key: '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'
        model: 'llama-3.3-70b-versatile'
        temperature: 0.5 # value between 0 to 2

  # === Automatic Speech Recognition ===
  asr_config:
    # speech to text model options: 'faster_whisper', 'whisper_cpp', 'whisper', 'azure_asr', 'fun_asr', 'groq_whisper_asr', 'sherpa_onnx_asr'
    asr_model: 'groq_whisper_asr'

    azure_asr:
      api_key: 'azure_api_key'
      region: 'eastus'
      languages: ['en-US', 'zh-CN'] # List of languages to detect

    # Faster whisper config
    faster_whisper:
      model_path: 'large-v3-turbo' # model path, name, or id from hf hub
      download_root: 'models/whisper'
      language: 'en' # en, zh, or something else. put nothing for auto-detect.
      device: 'auto' # cpu, cuda, or auto. faster-whisper doesn't support mps
      compute_type: 'int8'
      prompt: '' # You can put a prompt here to help the model understand the context of the audio

    whisper_cpp:
      # all available models are listed on https://abdeladim-s.github.io/pywhispercpp/#pywhispercpp.constants.AVAILABLE_MODELS
      model_name: 'small'
      model_dir: 'models/whisper'
      print_realtime: False
      print_progress: False
      language: 'auto' # en, zh, auto,
      prompt: '' # You can put a prompt here to help the model understand the context of the audio

    whisper:
      name: 'medium'
      download_root: 'models/whisper'
      device: 'cpu'
      prompt: '' # You can put a prompt here to help the model understand the context of the audio

    # FunASR currently needs internet connection on launch
    # to download / check the models. You can disconnect the internet after initialization.
    # Or you can use sherpa onnx asr or Faster-Whisper for complete offline experience
    fun_asr:
      model_name: 'iic/SenseVoiceSmall' # or 'paraformer-zh'
      vad_model: 'fsmn-vad' # this is only used to make it works if audio is longer than 30s
      punc_model: 'ct-punc' # punctuation model.
      device: 'cpu'
      disable_update: True # should we check FunASR updates everytime on launch
      ncpu: 4 # number of threads for CPU internal operations.
      hub: 'ms' # ms (default) to download models from ModelScope. Use hf to download models from Hugging Face.
      use_itn: False
      language: 'auto' # zh, en, auto

    # pip install sherpa-onnx
    # documentation: https://k2-fsa.github.io/sherpa/onnx/index.html
    # ASR models download: https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
    sherpa_onnx_asr:
      model_type: 'sense_voice' # 'transducer', 'paraformer', 'nemo_ctc', 'wenet_ctc', 'whisper', 'tdnn_ctc', 'sense_voice', 'fire_red_asr'
      #  Choose only ONE of the following, depending on the model_type:
      # --- For model_type: 'transducer' ---
      # encoder: ''        # Path to the encoder model (e.g., 'path/to/encoder.onnx')
      # decoder: ''        # Path to the decoder model (e.g., 'path/to/decoder.onnx')
      # joiner: ''         # Path to the joiner model (e.g., 'path/to/joiner.onnx')
      # --- For model_type: 'paraformer' ---
      # paraformer: ''     # Path to the paraformer model (e.g., 'path/to/model.onnx')
      # --- For model_type: 'fire_red_asr' (FireredASR - High-performance Chinese & English ASR with dialect support) ---
      # fire_red_asr_encoder: ''    # Path to the encoder model (e.g., 'path/to/encoder.onnx')
      # fire_red_asr_decoder: ''    # Path to the decoder model (e.g., 'path/to/decoder.onnx')
      # --- For model_type: 'nemo_ctc' ---
      # nemo_ctc: ''        # Path to the NeMo CTC model (e.g., 'path/to/model.onnx')
      # --- For model_type: 'wenet_ctc' ---
      # wenet_ctc: ''       # Path to the WeNet CTC model (e.g., 'path/to/model.onnx')
      # --- For model_type: 'tdnn_ctc' ---
      # tdnn_model: ''      # Path to the TDNN CTC model (e.g., 'path/to/model.onnx')
      # --- For model_type: 'whisper' ---
      # whisper_encoder: '' # Path to the Whisper encoder model (e.g., 'path/to/encoder.onnx')
      # whisper_decoder: '' # Path to the Whisper decoder model (e.g., 'path/to/decoder.onnx')
      # --- For model_type: 'sense_voice' ---
      # I've coded so that the sense voice model will get automatically downloaded.
      # For other models, you need to download them yourself
      sense_voice: './models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.int8.onnx' # Path to the SenseVoice model (e.g., 'path/to/model.onnx')
      tokens: './models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt' # Path to tokens.txt (required for all model types)
      # --- Optional parameters (with defaults shown) ---
      # hotwords_file: ''     # Path to hotwords file (if using hotwords)
      # hotwords_score: 1.5   # Score for hotwords
      # modeling_unit: ''     # Modeling unit for hotwords (if applicable)
      # bpe_vocab: ''         # Path to BPE vocabulary (if applicable)
      num_threads: 4 # Number of threads
      # whisper_language: '' # Language for Whisper models (e.g., 'en', 'zh', etc. - if using Whisper)
      # whisper_task: 'transcribe'  # Task for Whisper models ('transcribe' or 'translate' - if using Whisper)
      # whisper_tail_paddings: -1   # Tail padding for Whisper models (if using Whisper)
      # blank_penalty: 0.0    # Penalty for blank symbol
      # decoding_method: 'greedy_search'  # 'greedy_search' or 'modified_beam_search'
      # debug: False # Enable debug mode
      # sample_rate: 16000 # Sample rate (should match the model's expected sample rate)
      # feature_dim: 80       # Feature dimension (should match the model's expected feature dimension)
      use_itn: True # Enable ITN for SenseVoice models (should set to False if not using SenseVoice models)
      # Provider for inference (cpu or cuda) (cuda option needs additional settings. Please check our docs)
      provider: 'cpu' 

    groq_whisper_asr:
      api_key: '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'
      model: 'whisper-large-v3-turbo' # or 'whisper-large-v3'
      lang: 'vi' # put nothing and it will be auto

  # =================== Text to Speech ===================
  tts_config:
    tts_model: 'edge_tts'
    # text to speech model options:
    #   'azure_tts', 'pyttsx3_tts', 'edge_tts', 'bark_tts',
    #   'cosyvoice_tts', 'melo_tts', 'coqui_tts', 'piper_tts',
    #   'fish_api_tts', 'x_tts', 'gpt_sovits_tts', 'sherpa_onnx_tts'
    #   'minimax_tts', 'elevenlabs_tts', 'cartesia_tts'

    azure_tts:
      api_key: 'azure-api-key'
      region: 'eastus'
      voice: 'en-US-AshleyNeural'
      pitch: '26' # percentage of the pitch adjustment
      rate: '1' # rate of speak

    bark_tts:
      voice: 'v2/en_speaker_1'

    edge_tts:
      # Check out doc at https://github.com/rany2/edge-tts
      # Use `edge-tts --list-voices` to list all available voices
      voice: 'vi-VN-HoaiMyNeural' # 'en-US-AvaMultilingualNeural' #'zh-CN-XiaoxiaoNeural' # 'ja-JP-NanamiNeural'

    # pyttsx3_tts doesn't have any config.

    piper_tts:
      model_path: 'models/piper/zh_CN-huayan-medium.onnx'  # Path to the model file (.onnx)
      speaker_id: 0             # Speaker ID (for multi-speaker models; keep 0 for single-speaker models)
      length_scale: 1.0         # Speech speed control (0.5 = 2x faster, 1.0 = normal, 2.0 = 2x slower)
      noise_scale: 0.667        # Degree of audio variation (0.0–1.0; higher = richer, more varied; recommended 0.667)
      noise_w: 0.8              # Speaking style variation (0.0–1.0; higher = more expressive; recommended 0.8)
      volume: 1.0               # Volume level (0.0–1.0; 1.0 = normal)
      normalize_audio: true     # Whether to normalize audio (recommended: true, for more consistent volume)
      use_cuda: false           # Whether to use GPU acceleration (requires onnxruntime-gpu)

    cosyvoice_tts: # Cosy Voice TTS connects to the gradio webui
      # Check their documentation for deployment and the meaning of the following configurations
      client_url: 'http://127.0.0.1:50000/' # CosyVoice gradio demo webui url
      mode_checkbox_group: '预训练音色'
      sft_dropdown: '中文女'
      prompt_text: ''
      prompt_wav_upload_url: 'https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'
      prompt_wav_record_url: 'https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'
      instruct_text: ''
      seed: 0
      api_name: '/generate_audio'

    cosyvoice2_tts: # Cosy Voice TTS connects to the gradio webui
      # Check their documentation for deployment and the meaning of the following configurations
      client_url: 'http://127.0.0.1:50000/' # CosyVoice gradio demo webui url
      mode_checkbox_group: '3s极速复刻' 
      sft_dropdown: '' 
      prompt_text: '' 
      prompt_wav_upload_url: 'https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav' 
      prompt_wav_record_url: 'https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav' 
      instruct_text: '' 
      stream: False 
      seed: 0 
      speed: 1.0 
      api_name: '/generate_audio' 

    melo_tts:
      speaker: 'EN-Default' # ZH
      language: 'EN' # ZH
      device: 'auto' # You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps'
      speed: 1.0

    x_tts:
      api_url: 'http://127.0.0.1:8020/tts_to_audio'
      speaker_wav: 'female'
      language: 'en'

    gpt_sovits_tts:
      # put ref audio to root path of GPT-Sovits, or set the path here
      api_url: 'http://127.0.0.1:9880/tts'
      text_lang: 'zh'
      ref_audio_path: ''
      prompt_lang: 'zh'
      prompt_text: ''
      text_split_method: 'cut5'
      batch_size: '1'
      media_type: 'wav'
      streaming_mode: 'false'

    fish_api_tts:
      # The API key for the Fish TTS API.
      api_key: ''
      # The reference ID for the voice to be used. Get it on the [Fish Audio website](https://fish.audio/).
      reference_id: ''
      # Either 'normal' or 'balanced'. balance is faster but lower quality.
      latency: 'balanced'
      base_url: 'https://api.fish.audio'

    coqui_tts:
      # Name of the TTS model to use. If empty, will use default model
      # do 'tts --list_models' to list supported models for coqui-tts
      # Some examples:
      # - 'tts_models/en/ljspeech/tacotron2-DDC' (single speaker)
      # - 'tts_models/zh-CN/baker/tacotron2-DDC-GST' (single speaker for chinese)
      # - 'tts_models/multilingual/multi-dataset/your_tts' (multi-speaker)
      # - 'tts_models/multilingual/multi-dataset/xtts_v2' (multi-speaker)
      model_name: 'tts_models/en/ljspeech/tacotron2-DDC'
      speaker_wav: ''
      language: 'en'
      device: ''

    siliconflow_tts:
      api_url: "https://api.siliconflow.cn/v1/audio/speech"
      api_key: "your key"
      default_model: "FunAudioLLM/CosyVoice2-0.5B"
      default_voice: "speech:Dreamflowers:5bdstvc39i:xkqldnpasqmoqbakubom your voice name"  # Default voice configuration in the format: "speech:MODEL_NAME:VOICE_ID:your voice name"
      sample_rate: 32000  # Control the output sample rate. The default values and differ for different video output types, as follows: opus: Supports 48000 Hz. wav, pcm: Supports 8000, 16000, 24000, 32000, 44100 Hz, with a default of 44100 Hz. mp3: Supports 32000, 44100 Hz, with a default of 44100 Hz.
      response_format: "mp3" # The format to audio out. Supported formats are mp3, opus, wav, pcm
      stream: true
      speed: 1
      gain: 0

    # pip install sherpa-onnx
    # documentation: https://k2-fsa.github.io/sherpa/onnx/index.html
    # TTS models download: https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-models
    # see config_alts for more examples
    sherpa_onnx_tts:
      vits_model: '/path/to/tts-models/vits-melo-tts-zh_en/model.onnx' # Path to VITS model file
      vits_lexicon: '/path/to/tts-models/vits-melo-tts-zh_en/lexicon.txt' # Path to lexicon file (optional)
      vits_tokens: '/path/to/tts-models/vits-melo-tts-zh_en/tokens.txt' # Path to tokens file
      vits_data_dir: '' # '/path/to/tts-models/vits-piper-en_GB-cori-high/espeak-ng-data'  # Path to espeak-ng data (optional)
      vits_dict_dir: '/path/to/tts-models/vits-melo-tts-zh_en/dict' # Path to Jieba dict (optional, for Chinese)
      tts_rule_fsts: '/path/to/tts-models/vits-melo-tts-zh_en/number.fst,/path/to/tts-models/vits-melo-tts-zh_en/phone.fst,/path/to/tts-models/vits-melo-tts-zh_en/date.fst,/path/to/tts-models/vits-melo-tts-zh_en/new_heteronym.fst' # Path to rule FSTs file (optional)
      max_num_sentences: 2 # Max sentences per batch (or -1 for all)
      sid: 1 # Speaker ID (for multi-speaker models)
      provider: 'cpu' # Use 'cpu', 'cuda' (GPU), or 'coreml' (Apple)
      num_threads: 1 # Number of computation threads
      speed: 1.0 # Speech speed (1.0 is normal)
      debug: false # Enable debug mode (True/False)
    
    spark_tts:
      api_url: 'http://127.0.0.1:6006/' # API URL. Uses Gradio's built-in front-end API. Repository: https://github.com/SparkAudio/Spark-TTS
      api_name:  "voice_clone" # Endpoint name. Options: voice_clone, voice_creation
      prompt_wav_upload: "https://uploadstatic.mihoyo.com/ys-obc/2022/11/02/16576950/4d9feb71760c5e8eb5f6c700df12fa0c_6824265537002152805.mp3" # Reference audio URL. Provide if api_name equals "voice_clone"
      gender:  "female" # Voice type (gender). Provide if api_name equals "voice_creation"
      pitch:  3 # Pitch shift (in semitones) default 3,range 1-5. Valid only if api_name equals "voice_creation"
      speed:  3 # Speed of the voice (in percent) default 3,range 1-5. Valid only if api_name equals "voice_creation"

    openai_tts: # Configuration for OpenAI-compatible TTS endpoints
      # These settings override the defaults in the openai_tts.py file if provided
      model: 'kokoro' # Model name expected by the server (e.g., 'tts-1', 'kokoro')
      voice: 'af_sky+af_bella' # Voice name(s) expected by the server (e.g., 'alloy', 'af_sky+af_bella')
      api_key: 'not-needed' # API key if required by the server
      base_url: 'http://localhost:8880/v1' # Base URL of the TTS server
      file_extension: 'mp3' # Audio file format ('mp3' or 'wav')

    # For more details, see: https://platform.minimaxi.com/document/Announcement
    minimax_tts:
      group_id: '' # Your minimax group_id
      api_key: '' # Your minimax api_key
      # Supported models: 'speech-02-hd', 'speech-02-turbo' (recommended: 'speech-02-turbo')
      model: 'speech-02-turbo' # minimax model name
      voice_id: 'female-shaonv' # minimax voice id, default is 'female-shaonv'
      # Custom pronunciation dictionary, default empty.
      # Example: '{"tone": ["测试/(ce4)(shi4)", "危险/dangerous"]}'
      pronunciation_dict: ''

    elevenlabs_tts:
      api_key: ''
      voice_id: '' # Voice ID from ElevenLabs
      model_id: 'eleven_multilingual_v2' # Model ID (e.g., eleven_multilingual_v2)
      output_format: 'mp3_44100_128' # Output audio format (e.g., mp3_44100_128)
      stability: 0.5 # Voice stability (0.0 to 1.0)
      similarity_boost: 0.5 # Voice similarity boost (0.0 to 1.0)
      style: 0.0 # Voice style exaggeration (0.0 to 1.0)
      use_speaker_boost: true # Enable speaker boost for better quality
    
    cartesia_tts:
      api_key: ''
      voice_id: '' # Voice ID from Cartesia
      model_id: 'sonic-3' # Model ID (e.g., sonic-3)
      output_format: 'wav' # Output audio format (e.g., wav)
      language: 'en' # Output language of voice (e.g., en)
      emotion: 'neutral' # Emotional guidance
      volume: 1.0 # Voice volume (0.5 to 2.0)
      speed: 1.0 # Voice speed (0.6 to 1.5)

  # =================== Voice Activity Detection ===================
  vad_config:
    vad_model: null

    silero_vad:
      orig_sr: 16000 # Original Audio Sample Rate
      target_sr: 16000 # Target Audio Sample Rate
      prob_threshold: 0.4 # Probability Threshold for VAD
      db_threshold: 60 # Decibel Threshold for VAD
      required_hits: 3 # Number of consecutive hits required to consider speech
      required_misses: 24 # Number of consecutive misses required to consider silence
      smoothing_window: 5 # Smoothing window size for VAD

  tts_preprocessor_config:
    # settings regarding preprocessing for text that goes into TTS

    remove_special_char: True # remove special characters like emoji from audio generation
    ignore_brackets: False # ignore everything inside brackets
    ignore_parentheses: True # ignore everything inside parentheses
    ignore_asterisks: True # ignore everything wrapped inside asterisks
    ignore_angle_brackets: True # ignore everything wrapped inside <text>

    translator_config:
      # Like... you speak and read the subtitles in English, and the TTS speaks Japanese or that kind of things
      translate_audio: False # Warning: you need to deploy DeeplX to use this. Otherwise it's going to crash
      translate_provider: 'deeplx' # deeplx or tencent

      deeplx:
        deeplx_target_lang: 'JA'
        deeplx_api_endpoint: 'http://localhost:1188/v2/translate'
      
      #  Tencent Text Translation  5 million characters per month  Remember to turn off post-payment, need to manually go to Machine Translation Console > System Settings to disable
      #   https://cloud.tencent.com/document/product/551/35017
      #   https://console.cloud.tencent.com/cam/capi
      tencent:
        secret_id: ''
        secret_key: ''
        region: 'ap-guangzhou'
        source_lang: 'zh'
        target_lang: 'ja'

# --- ASSETS (Back to 0 spaces - Global) ---
live2d_config:
  live2d_path: 'live2d-models'
  default_model: 'Kamiyahakuk_pro'

background_config:
  background_path: 'backgrounds'
  default_background: 'ceiling-window-room-night.jpeg'

# Live Streaming Integration
live_config:
  bilibili_live:
    # List of BiliBili live room IDs to monitor
    room_ids: [1991478060]
    # SESSDATA cookie value (optional, for authenticated requests)
    sessdata: ""