# dots-tts stack tunables. Copy to `.env` on irv-ml1 before deploying.

# ── image ────────────────────────────────────────────────────────────
# v2 (2026-08-10): curly->ASCII sanitize (fixes "Donut's"->"donut ess" on
# typographic apostrophes) + server-side sentence-chunking (long turns no longer
# truncate at dots' ~40s single-generate cap).
DOTS_TAG=v2

# ── network ──────────────────────────────────────────────────────────
DOTS_BIND=0.0.0.0
DOTS_PORT=8198

# ── GPU ──────────────────────────────────────────────────────────────
# 0 = 3090 in Docker (PCI order), co-resident with chatterbox-fast. soar needs
# ~6GB; the 3090 has headroom with Zonos parked down.
DOTS_GPU_DEVICES=0

# ── model / inference ────────────────────────────────────────────────
DOTS_MODEL=dots-studio/dots.tts-soar
DOTS_DEFAULT_VOICE=donut
DOTS_NUM_STEPS=10          # 10 = full quality @ RTF ~0.22; lower = faster/rougher
DOTS_GUIDANCE_SCALE=1.2
DOTS_CHUNK_MAX_CHARS=280   # max chars per generate() chunk (dots caps ~40s/~500 patches)

# ── host mounts ──────────────────────────────────────────────────────
# HF cache holding the downloaded soar snapshot (~5GB). Reuse the burn-in cache.
DOTS_HFCACHE_DIR=/home/lkraven/dots-tts/hf_cache
# dots-derived voice references (derive.py dots -> derived/dots/<name>.{wav,txt}).
# Burn-in points at the corpus output directly; for a durable deploy, copy the
# derived set to /opt/docker/conf/dots-tts/voices and point here.
DOTS_VOICES_HOST_DIR=/home/lkraven/voice-corpus/derived/dots