stacks/{fish-s2,voxtral,kyutai-tts}: three new TTS deploys for irv-ml1 quality A/B

Adds the three premier 2026 TTS releases we missed during the original
fleet build-out (early April), all licensed for self-host:

* Fish Audio S2-Pro (port 8195, GPU 1 / A6000) — released 2026-03-09.
  4B dual-AR (Slow + Fast) trained on 10M+ hours / 80+ languages.
  Headline: 15,000+ paralinguistic / emotion tags via natural language
  ([laugh] [whispers] [super happy] etc.) — a step-function over
  Chatterbox Turbo's 9 fixed tags. 91.61% paralinguistic win rate on
  EmergentTTS-Eval. ~150 ms streaming TTFB, voice cloning, MIT-style
  open. ~17 GB VRAM.

* Voxtral TTS (port 8197, GPU 1 / A6000) — Mistral, released 2026-03-28.
  4B open-weight, 70 ms model latency, 9.7× realtime. 68.4% blind A/B
  win rate vs ElevenLabs Flash v2.5 in cloning. 8 languages
  (EN/FR/DE/ES/IT/PT/NL/HI). Served via vLLM-Omni (Mistral's partner
  serving stack) — published Docker image, no local build. ~16 GB VRAM.
  CC BY-NC license — personal/research use only; flagged in README.

* Kyutai TTS (port 8198, GPU 0 / 3090) — kyutai/tts-1.6b-en_fr.
  Trained on 2.5M hours from the Moshi/Mimi team. Claimed 220 ms in
  solo setup, 32 simultaneous streams under 350 ms on L40. Kyutai's
  official deploy is Rust + websockets only; using NillPointer's
  community OpenAI-compat wrapper to bridge to /v1/audio/speech so
  it slots into the same bench harness. ~4-6 GB VRAM.

Each stack: compose.yaml (build context, env, volumes, healthcheck,
homepage label), .env.example (all tunables documented), README.md
(why it exists, headline numbers, API, deploy + hardware notes).
Playbooks at playbooks/deploy-{fish-s2,voxtral,kyutai-tts}.yaml are
idempotent in the same shape as the existing deploy-vibevoice /
deploy-chatterbox playbooks.

Port allocations on irv-ml1 after this lands: 8188 ComfyUI, 8190
CosyVoice, 8191 Qwen3-TTS, 8192 IndexTTS-2, 8193 Kokoro, 8194
VibeVoice, 8195 Fish, 8196 Chatterbox, 8197 Voxtral, 8198 Kyutai,
8765 Parakeet ASR.

stacks/fish-s2/.env.example

@@ -0,0 +1,50 @@
+# Fish Audio S2-Pro stack tunables. Copy to `.env` on irv-ml1 before
+# deploying.
+
+# ── build pin ────────────────────────────────────────────────────────
+# SHA of fishaudio/fish-speech to build from. Bump + rebuild when you
+# want upstream wrapper updates. Use the FULL 40-char SHA — docker
+# buildx's git source resolver doesn't accept short hashes.
+FISH_S2_SHA=main
+
+# Local image tag — bump when you change build context to force a
+# fresh layer build.
+FISH_S2_TAG=v1
+
+# ── network ──────────────────────────────────────────────────────────
+# Host port (container listens on 8080 internally; we map to 8195
+# externally to fit alongside the rest of the irv-ml1 TTS slate).
+# Port reservations on irv-ml1: 8188 ComfyUI, 8190 CosyVoice, 8191
+# Qwen3-TTS, 8192 IndexTTS-2, 8193 Kokoro, 8194 VibeVoice, 8196
+# Chatterbox, 8765 Parakeet ASR.
+FISH_S2_PORT=8195
+FISH_S2_BIND=0.0.0.0
+
+# ── runtime / GPU ────────────────────────────────────────────────────
+# GPU pinning. "0" = RTX 3090 (24 GB), "1" = RTX A6000 (48 GB), "all"
+# = both visible. Fish S2-Pro consumes ~17 GB during inference (4B
+# model + KV cache), so the A6000 is the right home with comfortable
+# headroom. The 3090's 24 GB CAN fit it but leaves ~5 GB for KV which
+# is tight for long contexts.
+FISH_S2_GPU_DEVICES=1
+
+# torch.compile on first inference of each shape adds ~60 s warmup but
+# unlocks ~10× speedup per upstream. Worth it for sustained use; flip
+# to 0 to disable if you hit a torch.compile bug on a future
+# checkpoint.
+FISH_S2_COMPILE=1
+
+# ── persistent storage on the host ───────────────────────────────────
+# Model checkpoints — Fish auto-downloads s2-pro on first run (~9 GB
+# at BF16) and caches under here. Persistent across container
+# recreates so we don't re-pull.
+FISH_S2_CHECKPOINT_DIR=/worktank/fish-s2/checkpoints
+
+# Reference audio for voice cloning. Drop clean ~5-15 s clips here
+# (WAV / FLAC / MP3); the wrapper scans on request. One clean clip
+# per voice; name them descriptively.
+FISH_S2_REFERENCE_DIR=/worktank/fish-s2/references
+
+# HF cache — persistent across container recreates to skip the model
+# re-pull. Worth ~9 GB on disk.
+FISH_S2_CACHE_DIR=/worktank/fish-s2/hf_cache