[app] project_version="0.7.5" # LLM API 超时配置(秒) llm_vision_timeout = 120 # 视觉模型基础超时时间 llm_text_timeout = 180 # 文本模型基础超时时间(解说文案生成等复杂任务需要更长时间) llm_max_retries = 3 # API 重试次数(LiteLLM 会自动处理重试) ########################################## # 🚀 LLM 配置 - 使用 LiteLLM 统一接口 ########################################## # LiteLLM 是统一的 LLM 接口库,支持 100+ providers # 优势: # ✅ 代码量减少 80%,统一的 API 接口 # ✅ 自动重试和智能错误处理 # ✅ 内置成本追踪和 token 统计 # ✅ 支持更多 providers:OpenAI, Anthropic, Gemini, Qwen, DeepSeek, # Cohere, Together AI, Replicate, Groq, Mistral 等 # # 文档:https://docs.litellm.ai/ # 支持的模型:https://docs.litellm.ai/docs/providers # ===== 视觉模型配置 ===== vision_llm_provider = "litellm" # 模型格式:provider/model_name # 常用视觉模型示例: # - Gemini: gemini/gemini-2.0-flash-lite (推荐,速度快成本低) # - Gemini: gemini/gemini-1.5-pro (高精度) # - OpenAI: gpt-4o, gpt-4o-mini # - Qwen: qwen/qwen2.5-vl-32b-instruct # - SiliconFlow: siliconflow/Qwen/Qwen2.5-VL-32B-Instruct vision_litellm_model_name = "gemini/gemini-2.0-flash-lite" vision_litellm_api_key = "" # 填入对应 provider 的 API key vision_litellm_base_url = "" # 可选:自定义 API base URL # ===== 文本模型配置 ===== text_llm_provider = "litellm" # 常用文本模型示例: # - DeepSeek: deepseek/deepseek-chat (推荐,性价比高) # - DeepSeek: deepseek/deepseek-reasoner (推理能力强) # - Gemini: gemini/gemini-2.0-flash (速度快) # - OpenAI: gpt-4o, gpt-4o-mini, gpt-4-turbo # - Qwen: qwen/qwen-plus, qwen/qwen-turbo # - SiliconFlow: siliconflow/deepseek-ai/DeepSeek-R1 # - Moonshot: moonshot/moonshot-v1-8k text_litellm_model_name = "deepseek/deepseek-chat" text_litellm_api_key = "" # 填入对应 provider 的 API key text_litellm_base_url = "" # 可选:自定义 API base URL # ===== API Keys 参考 ===== # 主流 LLM Providers API Key 获取地址: # # OpenAI: https://platform.openai.com/api-keys # Gemini: https://makersuite.google.com/app/apikey # DeepSeek: https://platform.deepseek.com/api_keys # Qwen (阿里): https://bailian.console.aliyun.com/?tab=model#/api-key # SiliconFlow: https://cloud.siliconflow.cn/account/ak (手机号注册) # Moonshot: https://platform.moonshot.cn/console/api-keys # Anthropic: https://console.anthropic.com/settings/keys # Cohere: https://dashboard.cohere.com/api-keys # Together AI: https://api.together.xyz/settings/api-keys ########################################## # 🔧 高级配置(可选) ########################################## # WebUI 界面是否显示配置项 hide_config = true ########################################## # 📚 传统配置示例(仅供参考,不推荐使用) ########################################## # 如果需要使用传统的单独 provider 实现,可以参考以下配置 # 但强烈推荐使用上面的 LiteLLM 配置 # # 传统视觉模型配置示例: # vision_llm_provider = "gemini" # 可选:gemini, qwenvl, siliconflow # vision_gemini_api_key = "" # vision_gemini_model_name = "gemini-2.0-flash-lite" # # 传统文本模型配置示例: # text_llm_provider = "openai" # 可选:openai, gemini, qwen, deepseek, siliconflow, moonshot # text_openai_api_key = "" # text_openai_model_name = "gpt-4o-mini" # text_openai_base_url = "https://api.openai.com/v1" ########################################## # TTS (文本转语音) 配置 ########################################## [azure] # Azure TTS 配置 # 获取密钥:https://portal.azure.com speech_key = "" speech_region = "" [tencent] # 腾讯云 TTS 配置 # 访问 https://console.cloud.tencent.com/cam/capi 获取密钥 secret_id = "" secret_key = "" region = "ap-beijing" # 地域配置 [soulvoice] # SoulVoice TTS API 配置 api_key = "" voice_uri = "speech:mcg3fdnx:clzkyf4vy00e5qr6hywum4u84:bzznlkuhcjzpbosexitr" api_url = "https://tts.scsmtech.cn/tts" model = "FunAudioLLM/CosyVoice2-0.5B" [tts_qwen] # 通义千问 Qwen3 TTS 配置 # 访问 https://bailian.console.aliyun.com/?tab=model#/api-key 获取你的 API 密钥 api_key = "" model_name = "qwen3-tts-flash" [ui] # TTS 引擎选择 # 可选:edge_tts, azure_speech, soulvoice, tencent_tts, tts_qwen tts_engine = "edge_tts" # Edge TTS 配置 edge_voice_name = "zh-CN-XiaoyiNeural-Female" edge_volume = 80 edge_rate = 1.0 edge_pitch = 0 # Azure Speech Services 配置 azure_voice_name = "zh-CN-XiaoyiNeural-Female" azure_volume = 80 azure_rate = 1.0 azure_pitch = 0 ########################################## # 代理和网络配置 ########################################## [proxy] # HTTP/HTTPS 代理配置(如需要) # clash 默认地址:http://127.0.0.1:7890 http = "" https = "" enabled = false ########################################## # 视频处理配置 ########################################## [frames] # 提取关键帧的间隔时间(秒) frame_interval_input = 3 # 大模型单次处理的关键帧数量 vision_batch_size = 10