From 0ba41e02ea0b54ddef42d0b7a7b3a0b9b396bd57 Mon Sep 17 00:00:00 2001
From: Vuong Hoang <vh@phasefinal.com>
Date: Tue, 28 Apr 2026 01:52:57 -0700
Subject: [PATCH] =?UTF-8?q?fish-cpp:=20delete=20the=20stack=20=E2=80=94=20?=
 =?UTF-8?q?s2.cpp=20is=20too=20alpha=20to=20use=20today?=
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Three deploy iterations + four backend attempts (subprocess CUDA,
resident-server CUDA, Vulkan rebuild) all failed to deliver speedup
over fish-s2:

* CUDA path: ggml_cuda_init succeeded, weights loaded onto GPU per
  s2's logs, but nvidia-smi showed 0% utilization during synthesis.
  Wall time 20s/long phrase vs fish-s2's 7.5s. The "CUDA get_rows
  unsupported for type q6_K" warning hints at incomplete op coverage
  in s2.cpp's alpha CUDA backend for fish-speech architecture.

* Vulkan path: vk::IncompatibleDriverError on container init. NVIDIA
  Vulkan ICD not accessible inside the container despite
  NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics. Would need
  host-side nvidia-utils-vulkan installation or manual ICD bind
  mount. Didn't pursue.

Both are fixable — CUDA needs op coverage upstream (author actively
working on it; "selective embedding dequant" commit landed 16 days
ago), Vulkan needs host-side ICD setup. Neither is a config-flip,
both are real work for marginal-or-zero return. Better to delete the
stack and revisit when s2.cpp matures or when we tackle FP8
quantization on ana-ml2's RTX 6000 Ada (sm_89, native FP8 hardware).

Local image rmi'd, /opt/docker/compose/fish-cpp removed on irv-ml1.
/worktank/fish-cpp left for user-side sudo cleanup.

Future Fish acceleration paths (in order of decreasing certainty):
1. Wait for s2.cpp CUDA op coverage to mature (track upstream commits).
2. Quantize Fish BF16 → FP8 via TransformerEngine, deploy on
   ana-ml2's RTX 6000 Ada (Ada has native FP8 tensor cores, A6000
   doesn't). ~2x speedup if it works.
3. vLLM port of Fish (no upstream support today).
---
 playbooks/deploy-fish-cpp.yaml | 142 ---------------------------------
 stacks/fish-cpp/.env.example   |  42 ----------
 stacks/fish-cpp/Dockerfile     | 100 -----------------------
 stacks/fish-cpp/README.md      | 108 -------------------------
 stacks/fish-cpp/compose.yaml   |  64 ---------------
 stacks/fish-cpp/entrypoint.sh  |  63 ---------------
 stacks/fish-cpp/server.py      | 105 ------------------------
 7 files changed, 624 deletions(-)
 delete mode 100644 playbooks/deploy-fish-cpp.yaml
 delete mode 100644 stacks/fish-cpp/.env.example
 delete mode 100644 stacks/fish-cpp/Dockerfile
 delete mode 100644 stacks/fish-cpp/README.md
 delete mode 100644 stacks/fish-cpp/compose.yaml
 delete mode 100644 stacks/fish-cpp/entrypoint.sh
 delete mode 100644 stacks/fish-cpp/server.py

diff --git a/playbooks/deploy-fish-cpp.yaml b/playbooks/deploy-fish-cpp.yaml
deleted file mode 100644
index cf9613a..0000000
--- a/playbooks/deploy-fish-cpp.yaml
+++ /dev/null
@@ -1,142 +0,0 @@
-# Deploy fish-cpp (Fish s2-pro via s2.cpp + GGML CUDA inference) to irv-ml1.
-#
-# Builds the image locally — multi-stage CUDA devel base (CMake + s2.cpp
-# compile, ~10 min cold) → CUDA runtime base + binary + python shim.
-# Pre-pulls rodrigomt/s2-pro-gguf weights (q6_k default, ~5 GB) into
-# the bind-mounted weights dir.
-#
-# Usage:
-#   scripts/elway irv-ml1 --playbook playbooks/deploy-fish-cpp.yaml
-#
-# Idempotent — every step is creates-/when-gated; rerun is safe.
-
-vars:
-  compose_dir: /opt/docker/compose/fish-cpp
-  references_dir: /worktank/fish-cpp/references
-  weights_dir: /worktank/fish-cpp/weights
-  host_port: "8199"
-  weights_repo: rodrigomt/s2-pro-gguf
-  default_quant: s2-pro-q6_k.gguf
-
-steps:
-  # ── host-side dirs ──────────────────────────────────────────────────
-
-  - name: Ensure /worktank/fish-cpp root exists (one-time, sudo)
-    shell: mkdir -p /worktank/fish-cpp
-    sudo: true
-    creates: /worktank/fish-cpp
-
-  - name: Chown /worktank/fish-cpp to lkraven
-    shell: chown -R lkraven:lkraven /worktank/fish-cpp
-    sudo: true
-    when: "[ \"$(stat -c %U /worktank/fish-cpp)\" != \"lkraven\" ]"
-
-  - name: Ensure references dir exists
-    shell: mkdir -p {{ references_dir }}
-    creates: "{{ references_dir }}"
-
-  - name: Ensure weights dir exists
-    shell: mkdir -p {{ weights_dir }}
-    creates: "{{ weights_dir }}"
-
-  - name: Ensure compose dir exists
-    shell: mkdir -p {{ compose_dir }}
-    creates: "{{ compose_dir }}"
-
-  # ── deploy build context ────────────────────────────────────────────
-  # s2.cpp is built INSIDE the docker image, but the Dockerfile + shim
-  # need to be present in the compose dir so `docker compose build`
-  # can find them.
-
-  - name: Upload compose.yaml
-    upload:
-      src: stacks/fish-cpp/compose.yaml
-      dest: "{{ compose_dir }}/compose.yaml"
-      mode: "0644"
-
-  - name: Upload Dockerfile
-    upload:
-      src: stacks/fish-cpp/Dockerfile
-      dest: "{{ compose_dir }}/Dockerfile"
-      mode: "0644"
-
-  - name: Upload server.py (FastAPI shim)
-    upload:
-      src: stacks/fish-cpp/server.py
-      dest: "{{ compose_dir }}/server.py"
-      mode: "0644"
-
-  - name: Upload entrypoint.sh (starts s2 server + uvicorn shim)
-    upload:
-      src: stacks/fish-cpp/entrypoint.sh
-      dest: "{{ compose_dir }}/entrypoint.sh"
-      mode: "0755"
-
-  - name: Seed .env from template (only if absent)
-    upload:
-      src: stacks/fish-cpp/.env.example
-      dest: "{{ compose_dir }}/.env"
-      mode: "0644"
-    when: "[ ! -f {{ compose_dir }}/.env ]"
-
-  # ── pre-pull weights ────────────────────────────────────────────────
-  # q6_k + tokenizer.json (~5 GB total). Same one-shot
-  # python:3.12-slim + huggingface_hub.snapshot_download + hf_transfer
-  # pattern we've used for fish-s2, voxtral, etc. Idempotent on rerun
-  # via `creates:` on the model file.
-
-  - name: Pre-pull rodrigomt/s2-pro-gguf weights (q6_k + tokenizer, ~5 GB)
-    shell: |
-      docker run --rm --user 1000:1000 \
-        -e HOME=/tmp/h -e HF_HUB_ENABLE_HF_TRANSFER=1 \
-        -v {{ weights_dir }}:/dest \
-        python:3.12-slim sh -c 'set -e; mkdir -p /tmp/h /tmp/pip /tmp/site; PIP_CACHE_DIR=/tmp/pip pip install --quiet --target /tmp/site huggingface_hub hf_transfer; PYTHONPATH=/tmp/site python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id=\"{{ weights_repo }}\", local_dir=\"/dest\", allow_patterns=[\"{{ default_quant }}\",\"tokenizer.json\"])"'
-    creates: "{{ weights_dir }}/{{ default_quant }}"
-
-  # ── build + bring up ────────────────────────────────────────────────
-
-  - name: docker compose build (~10 min first time; CUDA toolchain + s2.cpp compile)
-    shell: |
-      set -o pipefail
-      cd {{ compose_dir }} && docker compose build 2>&1 \
-        | grep -vE '^#[0-9]+ |^ => |^=> |Collecting|Downloading|Requirement|Using cached|Installing collected|Successfully (installed|built)|━'
-
-  - name: docker compose up -d
-    shell: cd {{ compose_dir }} && docker compose up -d
-
-  - name: Wait for /v1/health to respond
-    shell: |
-      for i in $(seq 1 60); do
-        curl -sf -o /dev/null --max-time 3 http://localhost:{{ host_port }}/v1/health && exit 0
-        sleep 5
-      done
-      exit 1
-    changed_when: "false"
-
-verify:
-  - name: /v1/health returns 200 + reports model loaded
-    shell: |
-      curl -sf http://localhost:{{ host_port }}/v1/health \
-        | python3 -c "import json,sys; d=json.load(sys.stdin); assert d.get('status')=='ok' and d.get('model')"
-    changed_when: "false"
-
-  - name: /v1/tts returns a real WAV (POST with text body)
-    # `set -e` so curl/file/grep failures actually propagate. The
-    # previous version put `rm -f` as the last command, which always
-    # exits 0 — masking real failures (verify reported OK even when
-    # nothing was running on host_port). Trap-based cleanup runs the
-    # rm even on failure.
-    shell: |
-      set -e
-      out=$(mktemp --suffix=.wav)
-      trap 'rm -f "$out"' EXIT
-      curl -sf -X POST http://localhost:{{ host_port }}/v1/tts \
-        -H 'Content-Type: application/json' \
-        -d '{"text":"Verify."}' \
-        -o "$out" --max-time 60
-      file -b "$out" | grep -q '^RIFF.*WAVE'
-    changed_when: "false"
-
-  - name: Container is running
-    shell: docker inspect fish-cpp --format '{{.State.Status}}' | grep -q running
-    changed_when: "false"
diff --git a/stacks/fish-cpp/.env.example b/stacks/fish-cpp/.env.example
deleted file mode 100644
index ba9ba64..0000000
--- a/stacks/fish-cpp/.env.example
+++ /dev/null
@@ -1,42 +0,0 @@
-# fish-cpp stack tunables. Copy to `.env` on irv-ml1 before deploying.
-
-# ── build pin ────────────────────────────────────────────────────────
-# SHA of rodrigomatta/s2.cpp to build from. The repo is alpha — pin a
-# specific SHA so future upstream churn doesn't break our build. Update
-# deliberately when you want upstream improvements.
-FISH_CPP_S2_SHA=e48ce8e02d8335bd9a0ba94679f605724b31d123
-
-# Local image tag — bump when you change build context.
-FISH_CPP_TAG=v1
-
-# ── network ──────────────────────────────────────────────────────────
-# Host port. Container listens on 8000 internally.
-# Reservations on irv-ml1: 8188 ComfyUI, 8190 CosyVoice, 8191 Qwen3-TTS,
-# 8192 IndexTTS-2, 8193 Kokoro, 8194 VibeVoice, 8195 fish-s2 (HF),
-# 8196 Chatterbox, 8197 Voxtral, 8765 Parakeet ASR. 8199 picked here.
-FISH_CPP_PORT=8199
-FISH_CPP_BIND=0.0.0.0
-
-# ── runtime / GPU ────────────────────────────────────────────────────
-# GPU pinning. "0" = RTX 3090 (24 GB), "1" = RTX A6000 (48 GB).
-# Pinned to GPU 1 to share with fish-s2 (HF) for direct A/B benching;
-# q6_k weights need ~5 GB + 3 GB runtime ≈ 8 GB. A6000 has plenty.
-FISH_CPP_GPU_DEVICES=1
-
-# Quantization variant to load. Available files in
-# rodrigomt/s2-pro-gguf:
-#   s2-pro-q4_k_m.gguf  — smaller / faster, quality drops noticeably
-#   s2-pro-q5_k_m.gguf  — middle ground
-#   s2-pro-q6_k.gguf    — sweet spot (default), near-bf16 quality
-#   s2-pro-q8_0.gguf    — closest to bf16, larger / slower
-#   s2-pro-f16.gguf     — full precision, no quantization win
-FISH_CPP_MODEL=s2-pro-q6_k.gguf
-
-# ── persistent storage on the host ───────────────────────────────────
-# Weights — pre-pulled by the deploy playbook (~5 GB for q6_k +
-# ~12 MB tokenizer.json).
-FISH_CPP_WEIGHTS_DIR=/worktank/fish-cpp/weights
-
-# Reference audio for voice cloning. Drop ~5-15 s WAV/MP3/FLAC clips
-# here; reference them by basename in the API request body.
-FISH_CPP_REFERENCE_DIR=/worktank/fish-cpp/references
diff --git a/stacks/fish-cpp/Dockerfile b/stacks/fish-cpp/Dockerfile
deleted file mode 100644
index b1afff3..0000000
--- a/stacks/fish-cpp/Dockerfile
+++ /dev/null
@@ -1,100 +0,0 @@
-# fish-cpp — s2.cpp (pure C++/GGML inference for Fish s2-pro GGUFs) +
-# tiny FastAPI shim exposing Fish's /v1/tts contract.
-#
-# Two-stage build:
-#   1. builder — compiles s2.cpp with CUDA backend
-#   2. runtime — slim image with the s2 binary + Python shim
-#
-# The s2.cpp binary is the actual inference engine; the Python shim is
-# just an HTTP-to-CLI bridge so this stack drops into the same fleet
-# pattern as the other TTS (POST /v1/tts, Fish-shaped request body).
-
-# ── Stage 1: build s2.cpp with CUDA ────────────────────────────────────
-FROM nvidia/cuda:12.6.0-devel-ubuntu24.04 AS builder
-
-ARG S2_CPP_SHA=e48ce8e02d8335bd9a0ba94679f605724b31d123
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-      git ca-certificates cmake ninja-build build-essential pkg-config \
-      libvulkan-dev glslc \
-    && rm -rf /var/lib/apt/lists/*
-# libvulkan-dev = Vulkan headers + loader for build-time linking.
-# glslc = GLSL→SPIR-V compiler; GGML's Vulkan backend compiles its
-# shaders with this at build time. Without it, the cmake configure
-# step disables Vulkan silently.
-
-WORKDIR /src
-RUN git clone --recurse-submodules https://github.com/rodrigomatta/s2.cpp.git \
-    && cd s2.cpp \
-    && git checkout ${S2_CPP_SHA} \
-    && git submodule update --init --recursive
-
-WORKDIR /src/s2.cpp
-# CUDA Driver API symbols (cuMemSetAccess, cuDeviceGet, etc.) live in
-# libcuda.so which the NVIDIA driver provides at RUNTIME. Build-time
-# uses the stubs library at /usr/local/cuda/lib64/stubs/. CMake's
-# find_library doesn't reliably pick it up via CMAKE_LIBRARY_PATH for
-# nested ggml-cuda builds — the most robust fix is symlinking the stub
-# into /usr/local/lib (which ld checks unconditionally) AND providing
-# libcuda.so.1 (the SONAME ggml-cuda links against). The symlinks live
-# only in this build stage; the runtime image gets the real driver-
-# provided libcuda.so.1 via NVIDIA's container runtime.
-RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/lib/libcuda.so \
-    && ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/lib/libcuda.so.1 \
-    && ldconfig
-# Build with BOTH CUDA and Vulkan backends — runtime selects via the
-# entrypoint (-c <id> for CUDA, -v <id> for Vulkan). We've established
-# that s2.cpp's CUDA path silently falls back to CPU on this model
-# (alpha software, incomplete op coverage); Vulkan is the README's
-# canonical example so it likely has more complete op coverage on
-# fish-speech. Keep both built so we can flip without rebuilding.
-RUN cmake -G Ninja -B build -DCMAKE_BUILD_TYPE=Release \
-      -DS2_CUDA=ON -DS2_VULKAN=ON \
-    && cmake --build build --parallel $(nproc) --target s2
-
-# ── Stage 2: runtime — slim image with the binary + python shim ────────
-FROM nvidia/cuda:12.6.0-runtime-ubuntu24.04
-
-ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-      python3 python3-pip python3-venv tini \
-      libgomp1 curl \
-      libvulkan1 \
-    && rm -rf /var/lib/apt/lists/*
-# libgomp1   = GNU OpenMP runtime — required by the s2 binary.
-# curl       = entrypoint uses it to wait for s2 server to bind :3030.
-# libvulkan1 = Vulkan loader. The NVIDIA Vulkan ICD itself comes from
-#              the host driver via NVIDIA container runtime when
-#              NVIDIA_DRIVER_CAPABILITIES includes "graphics".
-
-# Pull the shim deps into an isolated venv so we don't fight system pip.
-RUN python3 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:${PATH}"
-RUN pip install --no-cache-dir 'fastapi>=0.115' 'uvicorn[standard]>=0.30' 'pydantic>=2' 'httpx>=0.27'
-
-# Copy the s2 binary + GGML runtime libs from the builder stage.
-COPY --from=builder /src/s2.cpp/build/s2 /usr/local/bin/s2
-COPY --from=builder /src/s2.cpp/build/ggml/src/libggml*.so /usr/local/lib/
-COPY --from=builder /src/s2.cpp/build/ggml/src/ggml-cuda/libggml-cuda.so /usr/local/lib/
-COPY --from=builder /src/s2.cpp/build/ggml/src/ggml-vulkan/libggml-vulkan.so /usr/local/lib/
-RUN ldconfig
-
-WORKDIR /app
-COPY server.py /app/server.py
-COPY entrypoint.sh /app/entrypoint.sh
-RUN chmod +x /app/entrypoint.sh
-
-# Bind-mounted at runtime: weights at /weights, references at /references.
-VOLUME /weights
-VOLUME /references
-
-EXPOSE 8000
-# tini supervises the entrypoint script which manages both s2 server
-# (bound to localhost:3030, model resident) and the uvicorn shim
-# (bound to 0.0.0.0:8000, proxies /v1/tts → s2's /generate).
-ENTRYPOINT ["/usr/bin/tini", "--"]
-CMD ["/app/entrypoint.sh"]
diff --git a/stacks/fish-cpp/README.md b/stacks/fish-cpp/README.md
deleted file mode 100644
index 69b90ad..0000000
--- a/stacks/fish-cpp/README.md
+++ /dev/null
@@ -1,108 +0,0 @@
-# fish-cpp
-
-Fish s2-pro served via [s2.cpp](https://github.com/rodrigomatta/s2.cpp)
-— a pure C++/GGML inference engine for Fish s2-pro, with weights from
-[rodrigomt/s2-pro-gguf](https://huggingface.co/rodrigomt/s2-pro-gguf).
-Wrapped by a tiny FastAPI shim exposing Fish's `/v1/tts` HTTP contract
-so it slots into the same bench harness + client patterns as `fish-s2`.
-
-## Why this stack alongside `fish-s2`
-
-`fish-s2` (HF transformers wrapper) measured at **0.78× realtime** on
-the A6000 — phenomenal quality but sub-realtime, meaning streaming
-clients hit buffer underruns on phrases longer than ~3-4 seconds of
-audio. `fish-cpp` targets the same s2-pro architecture but runs it
-through s2.cpp's C++/GGML/CUDA inference path with q6_k quantization
-— typically 2-5× faster than HF transformers for equivalent precision
-(GGML is what makes llama.cpp fast).
-
-**Goal**: hit ≥ 1× realtime on the A6000 so streaming actually flows
-without stutters, while keeping Fish's quality near-equivalent to BF16.
-
-## Status: alpha
-
-s2.cpp is alpha software per its upstream README. Pin the SHA in
-`.env`, don't track main blindly — community alpha projects break
-weekly.
-
-## What works (and what doesn't) vs `fish-s2`
-
-| feature | fish-s2 (HF) | fish-cpp (this) |
-|---|---|---|
-| `/v1/tts` POST endpoint | ✓ | ✓ |
-| `text` body field | ✓ | ✓ |
-| `references` body field (cloning) | ✓ | ✓ (single ref only) |
-| `streaming: true` | ✓ (TTFB → 26 ms) | ✗ accepted but ignored |
-| Paralinguistic tags | ✓ | should work (same weights) |
-| Quantization | bf16 | q4_k_m / q5_k_m / **q6_k** (default) / q8_0 |
-| Realtime factor | 0.78× | targeting 1-1.5× |
-
-The streaming gap matters: fish-s2 with `streaming: true` returns the
-first audio chunk in 26 ms (perceived latency feels instant). fish-cpp
-returns nothing until generation completes. So fish-cpp's appeal is
-RAW THROUGHPUT, not perceived latency. Combined with realtime+
-generation, total wall time stays low enough that polling clients
-don't notice.
-
-## API
-
-OpenAPI shape mirrors `fish-s2`:
-
-```bash
-# Basic — text only, default voice
-curl -fsS -X POST http://10.100.79.3:8199/v1/tts \
-  -H 'Content-Type: application/json' \
-  -d '{"text":"Hello there."}' \
-  > out.wav
-
-# With voice cloning — base64-encoded reference audio inline
-B64=$(base64 -w 0 /worktank/fish-cpp/references/glados.wav)  # on irv-ml1
-echo "{\"text\":\"Welcome.\",\"references\":[{\"audio\":\"$B64\",\"text\":\"transcript\"}]}" \
-  | curl -fsS -X POST http://10.100.79.3:8199/v1/tts \
-      -H 'Content-Type: application/json' --data-binary @- \
-      > out.wav
-```
-
-Health probe at `GET /v1/health`.
-
-## Deploy
-
-```bash
-scripts/elway irv-ml1 --playbook playbooks/deploy-fish-cpp.yaml
-```
-
-Cold deploy ~30-45 min: ~10 min image build (CUDA dev toolchain +
-CMake + s2.cpp compile), ~3 min weights pull (~5 GB for q6_k +
-12 MB tokenizer), ~1 min container boot.
-
-## Hardware footprint
-
-- **VRAM**: ~8 GB practical for q6_k (5 GB weights + 3 GB runtime).
-  Pinned to GPU 1 (A6000) by default to share with `fish-s2` for
-  direct A/B comparison. Could also run on GPU 0 (3090) with room
-  to spare.
-- **Disk**: ~5 GB for q6_k checkpoint + tokenizer.
-
-## Bench plan
-
-Same 3-phrase suite as the other TTS:
-
-```
-P1 = "Hello, this is a test of the voice synthesis system. The quick brown fox jumps over the lazy dog."
-P2 = "Oh my god, I cannot believe what just happened. That was absolutely incredible!"
-P3 = "What the hell is going on. This is some bullshit and I am not putting up with it."
-```
-
-Compare:
-- TTFB / total wall-clock per phrase
-- audio-seconds / wall-seconds (realtime factor)
-- Quality (ear test) vs `fish-s2` BF16 baseline
-
-If fish-cpp lands ≥ 1× realtime AND the q6_k quality holds up under
-ear test, this stack becomes the default Fish path. fish-s2 stays
-deployed for paralinguistic tag fidelity reference + streaming
-(if that turns out to matter for any specific use case).
-
-## Lessons learned
-
-(Populate after deploy iteration.)
diff --git a/stacks/fish-cpp/compose.yaml b/stacks/fish-cpp/compose.yaml
deleted file mode 100644
index 94ea93b..0000000
--- a/stacks/fish-cpp/compose.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# fish-cpp — Fish s2-pro served via s2.cpp (pure C++/GGML inference,
-# CUDA backend) with a tiny FastAPI shim exposing Fish's /v1/tts
-# contract. Built locally from rodrigomatta/s2.cpp + a pinned SHA.
-#
-# Why this stack alongside fish-s2:
-#   * fish-s2 (HF transformers wrapper): ~7-8 s TTFB, 0.78× realtime.
-#     Phenomenal quality but sub-realtime, buffer-underruns on long
-#     phrases even with streaming.
-#   * fish-cpp (this): targets ~2-5× speedup from the GGML inference
-#     path + q6_k quantization. Goal: hit realtime for streaming use.
-#
-# CAVEATS:
-#   * s2.cpp is alpha software (per upstream README). Pin the SHA;
-#     don't track main blindly.
-#   * Per-request subprocess spawn — each /v1/tts call forks the s2
-#     binary. Adds ~50-100 ms over a long-running daemon. Negligible
-#     vs the multi-second generation cost.
-#   * No streaming — s2.cpp writes a complete WAV before returning,
-#     so the shim's `streaming: true` field is accepted but ignored.
-#     TTFB ≈ total wall time (fast generation is the only path to
-#     low-latency here, not chunked output).
-
-services:
-  fish-cpp:
-    image: local/fish-cpp:${FISH_CPP_TAG}
-    build:
-      context: .
-      dockerfile: Dockerfile
-      args:
-        S2_CPP_SHA: ${FISH_CPP_S2_SHA:-e48ce8e02d8335bd9a0ba94679f605724b31d123}
-    container_name: fish-cpp
-    restart: unless-stopped
-    runtime: nvidia
-    ports:
-      - "${FISH_CPP_BIND:-0.0.0.0}:${FISH_CPP_PORT}:8000"
-    environment:
-      - NVIDIA_VISIBLE_DEVICES=${FISH_CPP_GPU_DEVICES:-1}
-      # Default nvidia-container-toolkit only mounts compute libs.
-      # Vulkan needs the graphics ICD too (libGLX_nvidia, vulkan ICD
-      # JSON). Without this, Vulkan init in the container fails with
-      # "no Vulkan ICD" even though the GPU is present.
-      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics
-      - FISH_CPP_MODEL=${FISH_CPP_MODEL:-s2-pro-q6_k.gguf}
-      - FISH_CPP_TOKENIZER=tokenizer.json
-      - FISH_CPP_DEVICE=0
-      # Backend selection for the s2 server in entrypoint.sh:
-      #   cuda    — -c <id>; alpha CUDA path, GPU 0% util on fish-speech (broken)
-      #   vulkan  — -v <id>; README's canonical example, more battle-tested
-      - FISH_CPP_BACKEND=${FISH_CPP_BACKEND:-vulkan}
-    volumes:
-      - ${FISH_CPP_WEIGHTS_DIR}:/weights:ro
-      - ${FISH_CPP_REFERENCE_DIR}:/references:ro
-    healthcheck:
-      test: ["CMD-SHELL", "python3 -c \"import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://127.0.0.1:8000/v1/health', timeout=5).status==200 else 1)\""]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 60s
-    labels:
-      - homepage.group=AI Systems
-      - homepage.name=Fish (s2.cpp)
-      - homepage.icon=mdi-fish
-      - homepage.description=Fish s2-pro via s2.cpp/GGML — quantized for realtime (irv-ml1)
-      - homepage.href=http://10.100.79.3:${FISH_CPP_PORT}
diff --git a/stacks/fish-cpp/entrypoint.sh b/stacks/fish-cpp/entrypoint.sh
deleted file mode 100644
index 2a1cb3d..0000000
--- a/stacks/fish-cpp/entrypoint.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/bin/bash
-# fish-cpp entrypoint — start s2 server + uvicorn shim.
-#
-# s2 server holds the model resident on the GPU; uvicorn proxies
-# Fish-shaped /v1/tts JSON requests to s2's multipart /generate.
-# Both processes share the container; tini supervises both via
-# `wait` after backgrounding s2.
-
-set -euo pipefail
-
-MODEL_PATH="${WEIGHTS_DIR:-/weights}/${FISH_CPP_MODEL:-s2-pro-q6_k.gguf}"
-TOKENIZER_PATH="${WEIGHTS_DIR:-/weights}/${FISH_CPP_TOKENIZER:-tokenizer.json}"
-DEVICE="${FISH_CPP_DEVICE:-0}"
-BACKEND="${FISH_CPP_BACKEND:-vulkan}"
-
-case "$BACKEND" in
-  cuda)   BACKEND_FLAG=(-c "$DEVICE") ;;
-  vulkan) BACKEND_FLAG=(-v "$DEVICE") ;;
-  cpu)    BACKEND_FLAG=() ;;  # no flag → s2 stays on CPU
-  *)      echo "[entrypoint] unknown FISH_CPP_BACKEND=$BACKEND (use cuda/vulkan/cpu)" >&2; exit 2 ;;
-esac
-
-if [ ! -f "$MODEL_PATH" ];     then echo "missing model: $MODEL_PATH" >&2;     exit 1; fi
-if [ ! -f "$TOKENIZER_PATH" ]; then echo "missing tokenizer: $TOKENIZER_PATH" >&2; exit 1; fi
-
-# Background s2 server. Loads model on GPU once (per backend), then
-# accepts multipart POSTs on localhost:3030/generate.
-echo "[entrypoint] starting s2 server on :3030 with $BACKEND backend, device $DEVICE"
-/usr/local/bin/s2 \
-  --server -H 127.0.0.1 -P 3030 \
-  -m "$MODEL_PATH" \
-  -t "$TOKENIZER_PATH" \
-  "${BACKEND_FLAG[@]}" &
-S2_PID=$!
-
-# Wait for s2 to bind 3030 before starting the shim. Avoids the
-# obvious /v1/health 502 race on first boot.
-echo "[entrypoint] waiting for s2 server to bind :3030"
-for i in $(seq 1 60); do
-  if curl -sf -o /dev/null --max-time 1 http://127.0.0.1:3030/ 2>/dev/null; then
-    echo "[entrypoint] s2 server up after ${i}s"
-    break
-  fi
-  if ! kill -0 "$S2_PID" 2>/dev/null; then
-    echo "[entrypoint] s2 server died during startup" >&2
-    exit 1
-  fi
-  sleep 1
-done
-
-# Foreground uvicorn. tini (PID 1) gets uvicorn signals; on SIGTERM
-# uvicorn exits, then s2 gets reaped via `wait` below.
-echo "[entrypoint] starting uvicorn shim on :8000"
-uvicorn server:app --host 0.0.0.0 --port 8000 --no-access-log &
-UV_PID=$!
-
-# Block until either child exits; propagate exit code.
-wait -n "$S2_PID" "$UV_PID"
-EXIT=$?
-echo "[entrypoint] one child exited (rc=$EXIT) — shutting down peer"
-kill "$S2_PID" "$UV_PID" 2>/dev/null || true
-wait || true
-exit "$EXIT"
diff --git a/stacks/fish-cpp/server.py b/stacks/fish-cpp/server.py
deleted file mode 100644
index bc2b7cd..0000000
--- a/stacks/fish-cpp/server.py
+++ /dev/null
@@ -1,105 +0,0 @@
-"""fish-cpp — HTTP shim wrapping s2.cpp's built-in server in Fish's
-/v1/tts API contract.
-
-Architecture: an `s2 --server` instance runs IN THE SAME CONTAINER on
-localhost:3030 with the model loaded once on the GPU; this FastAPI
-shim translates each Fish-shape JSON POST into a multipart form POST
-against localhost:3030/generate and streams the WAV back.
-
-The earlier subprocess-per-request design paid CUDA init + model load
-(~10-20s) on every call, which dominated wall time. Keeping s2 server
-resident moves the cost to startup (paid once) so per-request latency
-matches what the model can actually do.
-
-The shim still accepts (and ignores) `streaming: true` in the request
-body for contract parity with fish-s2 — s2.cpp's /generate is
-synchronous, so TTFB ≈ total wall time. If/when s2.cpp grows
-incremental output, wire it through here.
-"""
-
-from __future__ import annotations
-
-import base64
-import io
-import os
-import tempfile
-from pathlib import Path
-from typing import Optional
-
-import httpx
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import Response
-from pydantic import BaseModel
-
-S2_BASE_URL = os.environ.get("S2_BASE_URL", "http://127.0.0.1:3030")
-WEIGHTS_DIR = Path(os.environ.get("WEIGHTS_DIR", "/weights"))
-MODEL_FILE = os.environ.get("FISH_CPP_MODEL", "s2-pro-q6_k.gguf")
-TOKENIZER_FILE = os.environ.get("FISH_CPP_TOKENIZER", "tokenizer.json")
-
-MODEL_PATH = WEIGHTS_DIR / MODEL_FILE
-TOKENIZER_PATH = WEIGHTS_DIR / TOKENIZER_FILE
-
-app = FastAPI(title="fish-cpp")
-
-
-class ReferenceAudio(BaseModel):
-    audio: str  # base64-encoded WAV bytes
-    text: str   # transcript of the reference clip
-
-
-class TTSRequest(BaseModel):
-    text: str
-    references: list[ReferenceAudio] = []
-    format: str = "wav"
-    streaming: bool = False  # accepted for parity; ignored
-
-
-@app.get("/v1/health")
-def health() -> dict:
-    if not MODEL_PATH.exists():
-        raise HTTPException(503, f"model missing: {MODEL_PATH}")
-    if not TOKENIZER_PATH.exists():
-        raise HTTPException(503, f"tokenizer missing: {TOKENIZER_PATH}")
-    # Probe the s2 server backend — readiness, not just liveness.
-    try:
-        r = httpx.get(f"{S2_BASE_URL}/", timeout=2.0)
-    except httpx.HTTPError as exc:
-        raise HTTPException(503, f"s2 server unreachable: {exc}")
-    return {"status": "ok", "model": MODEL_FILE, "s2_status": r.status_code}
-
-
-@app.post("/v1/tts")
-def tts(req: TTSRequest) -> Response:
-    if req.format not in ("wav",):
-        raise HTTPException(400, f"only wav format supported, got {req.format}")
-
-    # Translate Fish-shaped JSON to s2.cpp's multipart form.
-    files: dict[str, tuple] = {
-        "text": (None, req.text),
-    }
-    if req.references:
-        # s2.cpp accepts a single reference at a time. If the client
-        # sent multiple, use the first.
-        ref = req.references[0]
-        files["prompt_text"] = (None, ref.text)
-        files["prompt_audio"] = ("ref.wav", base64.b64decode(ref.audio), "audio/wav")
-
-    try:
-        # Long timeout — s2 generation is sub-realtime on q6_k. The
-        # /v1/health probe upstream already vetted s2 server is live.
-        r = httpx.post(
-            f"{S2_BASE_URL}/generate", files=files, timeout=180.0,
-        )
-    except httpx.HTTPError as exc:
-        raise HTTPException(502, f"s2 server request failed: {exc}")
-
-    if r.status_code != 200:
-        # s2 returns JSON `{"error": "..."}` on failure
-        raise HTTPException(
-            500, f"s2 server returned {r.status_code}: {r.text[:500]}"
-        )
-
-    return Response(
-        content=r.content,
-        media_type=r.headers.get("content-type", "audio/wav"),
-    )