VibeRave/.env.example at main · weijt606/VibeRave · GitHub

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# VibeRave — environment configuration
# Copy to .env and fill in. .env is gitignored; never commit secrets.
#
# Most users won't need to touch this — the in-app "API Settings" panel
# lets you configure provider, key, base URL, and model from the browser
# (settings live in localStorage, never persisted on the server).
# The values below are only the *default fallback* used when the
# frontend doesn't send overrides.

# ─── LLM provider (any OpenAI-compatible endpoint, plus Ollama) ─────────────
# Common providers (drop in baseURL + model + your key):
#   OpenAI       https://api.openai.com/v1                  gpt-4o-mini
#   Groq         https://api.groq.com/openai/v1             llama-3.3-70b-versatile
#   OpenRouter   https://openrouter.ai/api/v1               anthropic/claude-3.5-sonnet
#   Qwen         https://dashscope.aliyuncs.com/compatible-mode/v1   qwen-plus
#   LM Studio    http://localhost:1234/v1                   (any local model)
# Most users won't touch this — the in-app API Settings panel lets you
# pick a provider, paste a key, and go. Settings live in the browser only.
LLM_PROVIDER=api
LLM_API_KEY=
LLM_BASE_URL=https://api.openai.com/v1
LLM_MODEL=gpt-4o-mini
LLM_TEMPERATURE=0.85

# Set LLM_PROVIDER=ollama for fully-offline mode (no API key needed)
# OLLAMA_BASE_URL=http://localhost:11434/v1
# OLLAMA_MODEL=qwen2.5:14b

# Optional LLM cleanup pass over the STT output (~500-2000ms, off by default)
LLM_CORRECT_TRANSCRIPT=false

# ─── STT provider ───────────────────────────────────────────────────────────
# whisper → local smart-whisper        (no network, ~700-900ms warm)
# vosk    → local closed-grammar VOSK  (~10ms, only canonical phrases —
#                                        download model first, see
#                                        services/api/README.md)
# api     → any OpenAI-compatible /audio/transcriptions endpoint
#           (OpenAI Whisper, Groq Whisper, etc.)
STT_PROVIDER=whisper

# Whisper STT (smart-whisper auto-downloads the model on first use into
# services/api/models/whisper/)
# Default `base` is multilingual — works for English AND Chinese once the
# user enables "Chinese-English mixed input" in the API Settings panel.
# For English-only setups, base.en / small.en give a small accuracy bump.
WHISPER_MODEL=base             # base | base.en | small | small.en | medium | large-v3-turbo
# WHISPER_LANGUAGE=en          # pin to a single language; default 'auto' detects per utterance
WHISPER_GPU=true               # Metal on macOS; CUDA on Linux

# OpenAI-compatible STT API (only used when STT_PROVIDER=api)
STT_API_KEY=
STT_BASE_URL=https://api.openai.com/v1
STT_MODEL=whisper-1

# VOSK model directory (only used when STT_PROVIDER=vosk).
# Defaults to services/api/models/vosk-model-small-en-us-0.15
# Download from https://alphacephei.com/vosk/models
# VOSK_MODEL_PATH=

# ─── API server ─────────────────────────────────────────────────────────────
API_PORT=4322
API_HOST=0.0.0.0
# Per-call audio + transcript dumps under services/api/data/stage-dumps/
# (PII! disabled by default — set to 1 only for local debugging)
API_DUMP_STAGES=

# ─── Optional Supabase backend (frontend, vite/astro `PUBLIC_*` prefix) ─────
# Set both to enable short-link sharing (`?abcd1234`) and the
# public/featured pattern lists in the website. When unset, the app
# falls back to long-hash links (`#<base64>`) and the community lists
# stay empty. Bring your own Supabase project — never reuse someone
# else's anon key.
# PUBLIC_SUPABASE_URL=
# PUBLIC_SUPABASE_ANON_KEY=

# ─── Advanced (most users won't touch these) ────────────────────────────────
# Override the upload size limit, sessions / metrics / dump file paths.
# API_MAX_BYTES=52428800
# API_SESSIONS_DIR=
# API_METRICS_FILE=
# API_DUMP_DIR=

# Whisper lazy-load idle timeout (seconds). Default ~1 day = effectively
# resident; lower to free RAM after a session.
# WHISPER_OFFLOAD_SECS=86400

# Override the DJ-vocab biasing prompt fed to whisper as initial_prompt.
# Empty → use the built-in DJ/Strudel vocab from whisper-transcriber.mjs.
# WHISPER_INITIAL_PROMPT=