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/compose.yaml

@@ -0,0 +1,76 @@
+# Fish Audio S2-Pro — the most expressive open-source TTS as of
+# 2026-04. 4B params, dual-AR architecture (Slow AR for semantic
+# codebook + Fast AR for 9 residual codebooks), trained on 10M+ hours
+# across 80+ languages. ~150 ms streaming TTFB on warm GPU.
+#
+# Why this stack alongside the existing TTS:
+#   * Headline feature: 15,000+ paralinguistic / emotion tags via
+#     natural language, e.g. [laugh] [whispers] [super happy] [sigh].
+#     Chatterbox Turbo only has 9 fixed tags — Fish's vocabulary is
+#     dramatically richer for any emotive use case.
+#   * 91.61% paralinguistic win rate on EmergentTTS-Eval — currently
+#     the leader on that benchmark.
+#   * Voice cloning + native multi-speaker / multi-turn generation.
+#   * MIT-style license (weights, training code, inference engine all
+#     open).
+#
+# Image is built locally from upstream's repo via docker buildx
+# git-context. Upstream ships a compose with `--profile server` for
+# the API path; we adapt that to our `restart: unless-stopped`
+# convention + bind-mount layout.
+#
+# All tunables live in .env — edit that, not this file.
+
+services:
+  fish-s2:
+    image: local/fish-s2:${FISH_S2_TAG}
+    build:
+      context: https://github.com/fishaudio/fish-speech.git#${FISH_S2_SHA}
+      dockerfile: dockerfile
+      args:
+        # Upstream's Dockerfile reads BACKEND to choose CUDA vs CPU
+        # paths during pip install. We always want CUDA on irv-ml1.
+        BACKEND: cuda
+    container_name: fish-s2
+    restart: unless-stopped
+    runtime: nvidia
+    ports:
+      - "${FISH_S2_BIND:-0.0.0.0}:${FISH_S2_PORT}:8080"
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=${FISH_S2_GPU_DEVICES:-1}
+      - BACKEND=cuda
+      # COMPILE=1 enables torch.compile — upstream claims ~10× speedup
+      # on the autoregressive forward, at the cost of ~60 s warmup the
+      # first time each input shape is seen. Worth the speedup; turn
+      # off via .env if you hit a torch.compile bug on a future model
+      # checkpoint.
+      - COMPILE=${FISH_S2_COMPILE:-1}
+      - API_PORT=8080
+      # Hugging Face cache for model weights — first start pulls
+      # fishaudio/s2-pro (~9 GB BF16) into this dir.
+      - HF_HOME=/app/hf_cache
+    volumes:
+      # Model checkpoints (auto-downloaded on first run, then cached).
+      - ${FISH_S2_CHECKPOINT_DIR}:/app/checkpoints
+      # Reference audio for voice cloning — drop ~5–15 s WAV clips here.
+      - ${FISH_S2_REFERENCE_DIR}:/app/references
+      # Persistent HF cache so model re-pull only happens on first deploy.
+      - ${FISH_S2_CACHE_DIR}:/app/hf_cache
+    healthcheck:
+      # Fish ships /v1/health on the API server. python urllib instead
+      # of curl because the upstream image is python-based and may not
+      # carry curl. 127.0.0.1 explicit to dodge the IPv6-first
+      # localhost trap we hit on chatterbox + news-digest.
+      test: ["CMD-SHELL", "python3 -c \"import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://127.0.0.1:8080/v1/health', timeout=5).status==200 else 1)\""]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      # First boot: torch.compile warmup + first-pull HF download +
+      # checkpoint load. Generous deadline.
+      start_period: 900s
+    labels:
+      - homepage.group=AI Systems
+      - homepage.name=Fish Audio S2-Pro
+      - homepage.icon=mdi-fish
+      - homepage.description=Most expressive open-source TTS — 15k+ paralinguistic tags, voice cloning, 80+ languages (irv-ml1)
+      - homepage.href=http://10.100.79.3:${FISH_S2_PORT}