154 lines
4.6 KiB
TOML
154 lines
4.6 KiB
TOML
# Evanescere configuration file.
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#
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# Copy this file to config.toml for local Compose runs:
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#
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# cp config.example.toml config.toml
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#
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# In containers, mount your config to:
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#
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# /etc/evanescere/config.toml
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#
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# Secrets such as DeepSeek and WebDAV credentials belong in your private config.toml.
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[app]
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# Free-form environment label included in logs.
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env = "dev"
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# Python logging level: DEBUG, INFO, WARNING, ERROR.
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log_level = "DEBUG"
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# Enable SQLAlchemy SQL logging. Very noisy; keep false unless debugging DB behavior.
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log_sql = false
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# Frontend origins allowed to call FastAPI. Include the Vite dev server while developing.
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cors_origins = ["http://localhost:3000", "http://localhost:5173"]
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[database]
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# PostgreSQL connection string used by SQLAlchemy.
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# For Compose local-db profile: postgresql+psycopg://evanescere:evanescere@postgres:5432/evanescere
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# For an existing DB: postgresql+psycopg://USER:PASSWORD@HOST:5432/DBNAME
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url = "postgresql+psycopg://evanescere:evanescere@postgres:5432/evanescere"
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[redis]
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# Redis URL used by Dramatiq workers.
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url = "redis://redis:6379/0"
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[storage]
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# Container path for working media. Mount a Framework SSD directory here in Compose.
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local_root = "/data/evanescere"
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[webdav]
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# IIS WebDAV directory containing recording files. Point at the collection, not one file.
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base_url = "https://windows-server.example.local/webdav/recordings"
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# Leave username/password empty if your WebDAV endpoint does not require basic auth.
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username = ""
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password = ""
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# Set false only for trusted internal/self-signed testing.
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verify_tls = true
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# Scheduler interval. A file becomes stable after two equal positive size samples.
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poll_interval_seconds = 60
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[funasr]
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# FunASR-compatible API base URL. Evanescere calls /audio/transcriptions.
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base_url = "http://funasr:10096/v1"
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# Optional bearer token for the FunASR service.
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api_key = ""
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# Model name sent to FunASR. Adjust this to match your deployed service.
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model = "paraformer-zh"
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[deepseek]
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# OpenAI-compatible DeepSeek API base URL.
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base_url = "https://api.deepseek.com"
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# Required when clip suggestion is enabled.
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api_key = ""
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# Initial MVP model.
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model = "deepseek-v4-pro"
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# Lower values improve consistency.
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temperature = 0.2
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[llm_prompt]
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# Customize these prompts freely. The backend appends the required JSON response schema.
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system = """
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You are an expert editor for Mandarin VTuber livestream clips.
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Identify moments that work as entertaining standalone clips for Bilibili viewers.
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Prioritize strong reactions, jokes, surprising turns, memorable conversations, and moments with a clear payoff.
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Avoid repetitive stretches, dead air, and segments that require too much missing context.
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Return only the requested JSON object.
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"""
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# Available placeholders:
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# {max_candidates}, {min_clip_seconds}, {max_clip_seconds},
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# {chunk_start_sec}, {chunk_end_sec}, {transcript}
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user = """
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Review the timestamped livestream transcript below.
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Choose up to {max_candidates} compelling clip candidates.
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Each clip must be between {min_clip_seconds} and {max_clip_seconds} seconds long.
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Use absolute stream timestamps and give each candidate a concise Chinese title and summary.
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Transcript range: {chunk_start_sec}-{chunk_end_sec}
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Transcript:
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{transcript}
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"""
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[defaults]
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# Initial automatic pipeline settings. These can later be changed through the API/UI.
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suggest_enabled = true
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render_enabled = true
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# For testing, consider false until uploads are wired and verified.
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upload_enabled = true
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preserve_final_artifacts = true
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bake_subtitles = true
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[clip]
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# LLM clip duration bounds accepted by the backend.
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min_seconds = 30
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max_seconds = 360
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# Transcript seconds per DeepSeek request. Larger chunks use more tokens.
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transcript_chunk_seconds = 14400
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# Maximum number of suggestions kept across the entire transcript.
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max_candidates_total = 20
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[thumbnail]
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# Generate thumbnails during clip render.
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enabled = true
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# frame_overlay: use extracted video frame as background.
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# command: run command below to call a local image generation workflow.
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provider = "frame_overlay"
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width = 1920
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height = 1080
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# Directory of transparent VTuber character PNGs. One is selected randomly per thumbnail.
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character_overlay_dir = "/data/evanescere/assets/"
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# Character height as a fraction of thumbnail height.
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character_scale = 0.42
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# Supported: bottom-right, bottom-left, center-right, center-left.
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character_position = "bottom-right"
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# Draw the clip title onto the final thumbnail.
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title_enabled = true
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# Used only when provider = "command". Receives JSON on stdin and must write output_path.
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command = ""
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[upload]
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# noop: do not upload, only record what would have happened.
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# command: run command below with JSON metadata on stdin.
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adapter = "noop"
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command = ""
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