Two new audio-generation stacks alongside the TTS slate:
ace-step :8210 — Apache 2.0 music generation foundation model
(hybrid diffusion + LLM). Lyric-aware multi-minute songs. ~10-12 GB
VRAM during inference, A6000-pinned. Custom Dockerfile patches
upstream's torch/cu126 resolution bug (--extra-index-url cu126 was
falling back to pypi-default cu13 wheels, mismatching torchvision).
stable-audio-open :8211 — Stability AI 1.21B latent-diffusion SFX +
ambience. Up to 47s clips at 44.1 kHz. ~6 GB VRAM in fp16,
A6000-pinned. Custom FastAPI shim around diffusers' StableAudioPipeline
(no upstream HTTP server). Dockerfile pins torchsde explicitly —
diffusers doesn't pull it as a hard dep but
CosineDPMSolverMultistepScheduler needs it.
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).
CUDA backend confirmed broken for fish-speech ops on s2.cpp v0.x — alpha,
incomplete op coverage, GPU stays at 0% during generation despite
ggml_cuda_init succeeding. Vulkan was the original README example
(`-v 0`), so likely the more battle-tested path.
Build the image with BOTH backends so we can flip via env without
rebuilding:
* libvulkan-dev + glslc in the build stage (GGML's Vulkan backend
compiles its shaders with glslc at build time; without it the
cmake configure silently disables Vulkan).
* libvulkan1 + the libggml-vulkan.so copy in the runtime stage.
* compose env NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics —
default nvidia-container-toolkit only mounts compute libs; Vulkan
needs the graphics ICD (libGLX_nvidia + nvidia_icd.json) too.
* entrypoint reads FISH_CPP_BACKEND (cuda/vulkan/cpu) and selects
the appropriate -c/-v/no-flag invocation.
* Default backend = vulkan.
Subprocess-per-request architecture forced CUDA + model load on every
/v1/tts call (~10-20s init, then 5-15s generation). Even though CUDA
is now actually being used (`-c 0` fix landed), 32s for "Verify."
proved per-request init was the bottleneck.
s2.cpp ships a built-in HTTP server (`--server -H -P`) that keeps the
model resident on the GPU. Refactor:
* entrypoint.sh — backgrounds `s2 --server -P 3030 -c 0 -m ... -t ...`,
waits for it to bind 3030, then foregrounds uvicorn. tini supervises
via `wait -n` so either child dying takes down the container.
* server.py — drops subprocess.run; instead httpx-POSTs Fish-shaped
/v1/tts JSON to s2's localhost:3030/generate (multipart form: text
+ optional prompt_text/prompt_audio for cloning). Model load + CUDA
init now happen once at container start, not per-request.
* Dockerfile — added httpx (shim dep), curl (entrypoint readiness
probe), and the entrypoint.sh COPY+chmod. CMD now invokes
entrypoint.sh instead of uvicorn directly.
* deploy-fish-cpp.yaml — uploads entrypoint.sh alongside server.py.
s2.cpp's README example uses `-v 0` which is `--vulkan 0` (Vulkan
device 0), easy to misread as "voice 0". The shim copied that
verbatim, so even after fixing the libcuda.so build problem AND the
libgomp.so runtime dep, every synthesis ran on CPU because the wrong
backend was selected.
Direct verification: `[Model] NPU not compiled, falling back to CPU`
in stderr; nvidia-smi showed no s2 process; bench timed out at 60s
on phrases that fish-s2 (HF, GPU) does in 7s.
s2.cpp's CLI:
-v <id> = --vulkan <device>
-c <id> = --cuda <device>
-M = --metal (Apple Silicon)
Switched the shim to `-c 0`. The CUDA backend IS in the build (-DS2_CUDA=ON
worked, libggml-cuda.so links fine per ldd, libcuda.so.1 mounts at
runtime via NVIDIA container runtime) — just wasn't being told to use it.
Build succeeded after the libcuda.so symlink fix, but the first
/v1/tts request returned HTTP 500 with:
s2 binary failed (rc=127): /usr/local/bin/s2: error while loading
shared libraries: libgomp.so.1: cannot open shared object file
CMake auto-enabled OpenMP during the build (gcc's -fopenmp flag), so
the s2 binary dynamically links libgomp.so.1. The build-stage devel
image had it; the slim cuda:runtime base doesn't ship it by default.
Adding libgomp1 to the runtime image's apt install resolves it.
Second attempt's CMAKE_LIBRARY_PATH + LIBRARY_PATH didn't get picked
up by ggml's nested CMake — same linker errors as the first run.
Robust fix: symlink the stub at /usr/local/cuda/lib64/stubs/libcuda.so
into /usr/local/lib (which ld searches unconditionally) and provide
both libcuda.so AND libcuda.so.1 (the SONAME ggml-cuda's
libggml-cuda.so links against). ldconfig refreshes the cache.
The symlinks live only in the build stage. The runtime image inherits
the real driver-provided libcuda.so.1 via NVIDIA's container runtime
mount, so the stubs never get used at execution time.
Two issues from the first deploy attempt:
1) Build failure (real): linker errors on s2.cpp's CUDA build —
undefined references to cuMemSetAccess, cuDeviceGet, etc. These
are CUDA Driver API symbols (in libcuda.so), not Runtime API
(libcudart.so). The driver lib is provided by NVIDIA's container
runtime at RUN time, not BUILD time.
Fix: nvidia/cuda:devel images ship a stubs library at
/usr/local/cuda/lib64/stubs/libcuda.so that provides the symbols
for linking but is non-runnable. Adding that path via
LIBRARY_PATH + CMAKE_LIBRARY_PATH lets the linker resolve while
leaving runtime unchanged (real libcuda.so comes from the
driver mount).
2) Verify false positive: the /v1/tts verify step's last command was
`rm -f "$out"` — which always exits 0. This made the shell's
final exit code 0 regardless of whether curl/file/grep succeeded,
so verify reported OK even when nothing was running on host_port.
Fix: `set -e` at top + trap-based cleanup. Failures now propagate;
the rm still runs on either path via EXIT trap.
New stack scaffolding for the Fish quantized-realtime experiment. Not
deployed yet — this commit lands the canonical files; deploy follows.
Architecture decisions made in Phase 1:
* CUDA backend, NOT Vulkan. s2.cpp's CMakeLists exposes both
-DS2_VULKAN and -DS2_CUDA; the most recent upstream commit
(2026-04-12) was specifically about CUDA improvements, and CUDA
on the A6000 will be substantially faster than Vulkan for ML
matmul. -DS2_CUDA=ON in the Dockerfile build args.
* Pinned to s2.cpp commit e48ce8e02d8335bd9a0ba94679f605724b31d12
(2026-04-12 HEAD of main). Repo is alpha software per README;
pin tightly so future churn doesn't break our build. Bump
deliberately when wanting upstream improvements.
* Multi-stage Dockerfile: nvidia/cuda:12.6.0-devel for build (needs
CMake + ninja + git + the CUDA toolchain) → nvidia/cuda:12.6.0-runtime
for serve (slimmer; just the s2 binary + GGML libs + a small Python
shim). Cuts image size by ~50% vs single-stage devel.
* FastAPI shim (server.py) wraps s2.cpp CLI in Fish's `/v1/tts`
contract so the same bench harness + clients work against fish-cpp
with no changes. Per-request flow: decode optional reference WAV
from base64 → write to temp → subprocess.run the s2 binary → stream
resulting WAV back. Adds ~50-100ms per-request fork+exec overhead;
negligible vs the multi-second generation cost.
* `streaming: true` accepted in request body but IGNORED — s2.cpp
writes a complete WAV before returning, so chunked output isn't
available. Unlike fish-s2 (HF wrapper) where streaming drops TTFB
to 26ms, fish-cpp's TTFB ≈ total wall time. Speed depends entirely
on raw generation throughput.
* q6_k as default quant — sweet spot per typical GGUF guidance:
near-bf16 quality at ~5GB. Other variants (q4_k_m, q5_k_m, q8_0,
f16) selectable via FISH_CPP_MODEL env.
* Pinned to GPU 1 (A6000) by default to share with fish-s2 for
direct A/B benching. q6_k weights ~5GB + runtime ~3GB ≈ 8GB —
comfortable on either GPU.
* Port 8199 (next free in the irv-ml1 TTS slate).
Phase 2 (next) is the actual deploy + first build. Reserved 30-45 min
for cold-cache build + weights pull.
Voxtral final fix (8th iteration):
* The bundled voxtral_tts.yaml hardcodes gpu_memory_utilization: 0.8
on the language_model stage — overrides the CLI flag. Mounted a
patched copy (0.4) at /etc/voxtral/voxtral_tts.yaml and pointed
--stage-configs-path there.
* With Kyutai stopped to free 5 GB on the 3090, both stages fit
(target 9.4 + 2.4 GB ≈ 11.8 GB; 17 GB free post-kyutai-stop).
* Voxtral now healthy on GPU 0 — bench: 1.9-2.7 s TTFB, real WAV.
Fish s2-pro optimization (per-request sweep, no model swap):
* `streaming: true` in request body drops TTFB from 7.7 s → 0.026 s
(300×). Total time goes up ~1 s (chunked HTTP overhead) but
perceived latency = TTFB. Use stream:true for any interactive use.
* `latency: "balanced"` actually slower than default — bad name; skip.
* `use_memory_cache: "on"` no measurable benefit.
* `chunk_length: 100` (default 200) no TTFB benefit non-streaming.
* Server-side `--half` (fp16 inference) added via compose `command`
override — passes through start_server.sh's $@ unchanged into
api_server.py. Should reduce total time too. Validation pending
the post-restart bench.
Kyutai stopped to free GPU 0 budget — the bench numbers earlier
(3.4 s avg) were unimpressive vs Voxtral's 2.3 s in the same
multilingual slot. Kept the stack files for future re-deploy if
needed; just the running container is gone.
Fourth attempt finally found the right invocation. Voxtral is a
two-stage TTS pipeline (language_model → acoustic_transformer →
audio output), not a flat MistralForCausalLM. Standard `vllm serve`
errored with "no module named 'acoustic_transformer'" because it
loads the model as a vanilla Mistral causal LM.
Pattern from /workspace/vllm-omni/examples/online_serving/
qwen3_tts/run_server.sh (closest in-image analog):
vllm-omni serve <MODEL> \
--stage-configs-path vllm_omni/model_executor/stage_configs/voxtral_tts.yaml \
--host 0.0.0.0 --port 8000 \
--gpu-memory-utilization 0.45 \
--trust-remote-code --omni
Key differences from previous attempt:
* `vllm-omni` binary, not `vllm`
* `--omni` flag activates multi-stage pipeline
* `--stage-configs-path` points at the bundled YAML that maps
stages to GPU + scheduler + worker classes
* Dropped --load-format/--tokenizer-mode/--config-format=mistral
flags — the stage config handles tokenizer_mode internally
* --trust-remote-code is required for the acoustic_transformer
custom code path
Default .env.example now: GPU 0 (3090) with util 0.45 (~10.6 GB
target on 24 GB GPU). The A6000 is fully booked by Fish s2-pro.
Third voxtral attempt: image pulled clean (3 min, v0.18.0), entrypoint
parsed correctly, vLLM started, but engine init failed two ways:
1. HF rate-limited the irv-ml1 IP (38.120.94.3) during the metadata
fetch — 429 Too Many Requests from too many large unauthenticated
pulls today (heretic, 27b, fish-s2, fish-s1-mini, voxtral). Added
HF_TOKEN env passthrough; user generates a token at
https://huggingface.co/settings/tokens and sets VOXTRAL_HF_TOKEN
in .env.
2. Voxtral uses Mistral's native model format (params.json +
tekken.json tokenizer + consolidated.safetensors single file),
NOT HF transformers format (config.json + tokenizer.json + sharded
.safetensors). vLLM errored with "ensure presence of params.json
for Mistral models." Fix: pass --load-format=mistral
--tokenizer-mode=mistral --config-format=mistral to vllm serve.
Confirmed by inspecting the Voxtral-4B-TTS-2603 HF tree:
25 files, ships params.json + tekken.json + consolidated.safetensors.
Both fixes baked into compose. User needs to drop their HF_TOKEN into
.env once and recreate.
Side note discovered while debugging: fish-s2 s1-mini variant uses
the tiktoken tokenizer format; the wrapper can't load it (errors with
"NoneType has no attribute encode" on warmup). So s1-mini isn't a
drop-in optimization for s2-pro — different code path needed. Fish
back on s2-pro for now.
Second voxtral attempt got past the image pull (v0.18.0 published,
~3 min download) but container init failed:
unable to start container process: error during container init:
exec: "--model=mistralai/Voxtral-4B-TTS-2603": stat ...: no such file
vllm/vllm-omni:v0.18.0 has Entrypoint=null AND Cmd=null — there's no
default executable. The compose's `command:` array becomes the full
exec invocation, with --model=... interpreted as the binary name.
Standard vLLM serving CLI is `vllm serve <model> [flags]`. The
binary's at /usr/local/bin/vllm. Set entrypoint: ["vllm", "serve"]
and pass the model as a positional arg.
While we're here: HF cache was empty too (Voxtral 4B BF16 ~8 GB
download on first start) — vLLM auto-downloads from HF on model
load, so no separate pre-pull step needed.
Three fixes from the second-wave deploy attempts:
* voxtral: vllm/vllm-omni doesn't publish a `latest` tag — pull
failed with "manifest unknown". Pinned VOXTRAL_VLLM_TAG to v0.18.0
(released 2026-03-29, the day after the Voxtral 4B TTS release —
first cut with Voxtral support).
* kyutai-tts: NillPointer wrapper exposes ONLY /health (root) and
POST /v1/audio/speech. No /v1/models, no /v1/audio/voices —
those return 404. Verified by /openapi.json against the live
container. Compose healthcheck + playbook wait + verify steps
all repointed at the actual paths. POST /v1/audio/speech is now
smoke-tested with a RIFF WAV assertion (same pattern as fish-s2).
* fish-s2: added FISH_S2_MODEL env var so the model variant is
swappable via .env without rebuilding. Both s2-pro (default) and
s1-mini are pre-pulled into the bind-mount; LLAMA_CHECKPOINT_PATH
+ DECODER_CHECKPOINT_PATH now use ${FISH_S2_MODEL:-s2-pro}.
s1-mini was originally gated on fishaudio's HF org (401), but
niobures/OpenAudio-S1 mirrors the same files openly — pulled
from there via a one-shot snapshot_download.
After getting fish-s2 finally healthy on attempt #5, the playbook's
verify still failed because /v1/audio/voices doesn't exist. Discovery:
the Fish wrapper has a custom API surface, not OpenAI-compatible.
Real endpoints:
POST /v1/tts — synthesis (text body, optional `references`
field for voice cloning, returns audio/wav)
GET /v1/health — liveness (used by Docker healthcheck)
GET /heartbeat — alternate liveness signal
GET / — Swagger Editor UI for the OpenAPI spec
No /v1/audio/speech, /v1/audio/voices, /v1/models — those return 404.
Updated:
* Playbook verify — replaced the JSON-shape /v1/audio/voices check
with a POST /v1/tts smoke that asserts a real RIFF WAV comes back.
* README API section — replaced the OpenAI-compat examples with
Fish's actual {"text":"...","references":[...]} body shape.
* README disk footprint — corrected ~9 GB → ~11 GB (codec.pth was
larger than I estimated; 1.9 GB + 9 GB safetensors).
* README Lessons learned section — recorded the 5-iteration deploy
story so the next time we touch a Fish-style upstream we don't
re-walk the dockerfile / target / pre-pull / API-shape traps.
Fourth fish-s2 attempt got past build + checkpoints, then container
crashlooped silently again. Diagnosis: the upstream docker/Dockerfile
is multi-stage with `webui` and `server` targets; without specifying
a target, docker builds the LAST stage (webui — gradio-only, no
start_server.sh, no API server). start_server.sh is the entrypoint
script that lives only in the `server` stage.
Confirmed by `cat /app/start_server.sh` inside the built image:
"No such file or directory."
Upstream's compose.yml uses target: server on its server service —
doing the same here.
Wrapped .masthead-brand in <a href="index.html"> in both digest.html.j2
and archive.html.j2 so the hero is a clickable shortcut to the latest
edition. Useful when reading an archived edition and you want to jump
back to the freshest one without going through the archive list.
CSS: color: inherit + text-decoration: none keeps the visual
identical; hover drops opacity to 0.85 for affordance; focus-visible
gets an accent outline so keyboard nav is discoverable.
Second deploy attempt failed at build time:
failed to fetch anonymous token: ... ghcr.io/fishaudio/fish-speech ... 403 Forbidden
Root cause: dockerfile.dev is a thin two-line wrapper around
`FROM ghcr.io/fishaudio/fish-speech:${VERSION}`, which is a private
GHCR base image. Anonymous pulls 403, and we'd need GHCR auth to use
that path. The dev variant is meant for upstream's CI / fish-speech
contributors, not external consumers.
The REAL production path (from upstream's compose.base.yml) is to
build from `docker/Dockerfile` with build args BACKEND=cuda,
CUDA_VER=12.9.0, UV_EXTRA=cu129, UV_VERSION=0.8.15. That builds
everything from source — slower (15-20 min cold), but fully self-
contained.
irv-ml1's driver (595.58.03, CUDA 13.2 capable) is forward-compatible
with the 12.9 PyTorch wheels.
Took three iterations to find the right Dockerfile because:
1. First try: dockerfile (lowercase) — doesn't exist
2. Second try: dockerfile.dev — exists but pulls a private base
3. Third try: docker/Dockerfile — actual production path
First fish-s2 deploy attempt failed in step 9/11:
failed to read dockerfile: open dockerfile: no such file or directory
Upstream fishaudio/fish-speech ships:
* dockerfile.dev (lowercase, dev/test image)
* compose.yml + compose.base.yml (intended deploy path:
`docker compose --profile server up`)
There is no standalone production Dockerfile. The dockerfile.dev
image is what their own compose.yml builds from anyway, so building
against it directly is functionally equivalent to using their compose
profile — we just keep our own restart-policy / labels / bind-mount
conventions on the outer compose.
Comment in the build block now documents this so future-Claude doesn't
re-walk the path.
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.
Investigation of the slow (8-12s) qwen3-tts TTFB found the upstream
wrapper has 5 backend options. The advertised path to fast TTFB is
TTS_BACKEND=optimized (torch.compile + CUDA graphs + real-time
streaming). It loads cleanly but crashes the container during its
hardcoded warmup phase — silent exit (ExitCode 0, no traceback,
no OOM kill), repeats every ~22s under restart policy.
TTS_WARMUP_ON_START=false suppresses the factory-level warmup but
the optimized backend has its own internal warmup that fires
regardless and triggers the crash.
Updated the .env.example block to enumerate all 5 backend options
with their actual current behavior so future-Claude doesn't re-walk
this path. official is staying as the default.
The wrapper's `optimized` backend (torch.compile + CUDA graphs +
real-time streaming) reads its model registry from a YAML config:
default path is ~/qwen3-tts/config.yaml inside the container, which
doesn't exist. Without TTS_CONFIG set, the backend boots with an
empty registry and every synthesis request fails with
"Unknown model key: '<name>'. Available: []".
The repo ships /app/config.yaml with all 4 model variants defined.
Pointing TTS_CONFIG at it lets the optimized backend load cleanly.
This is a prerequisite for benching the optimized backend properly
— it's the path to the upstream's claimed 97 ms streaming TTFB. The
default `official` backend uses naive HF transformers autoregressive
generation that pegged GPU at only 27% utilization and gave us 8-12 s
TTFB on bench (no recompile theory needed — same phrase repeated 4x
plateaued at 8.5 s, ruling out shape-specific recompilation).
qwen3-tts: deploy was using the -Base checkpoint, which sounds like
the right one ("supports voice cloning") but the upstream wrapper's
only synthesis path goes through generate_custom_voice. The -Base
variant doesn't expose that, so every request — including ones with
the wrapper's listed built-in voices like Ryan/Vivian — errored with
"does not support generate_custom_voice". The -CustomVoice variant
exposes both the cloning machinery and the preset voices, and is
what the wrapper actually needs.
The .env.example comments had the variant labels backward; fixed in
this commit. Live host already updated to -CustomVoice via direct
.env edit (model downloaded on container restart).
chatterbox README listed [whisper] and [breath] as supported tags —
those are in the base Chatterbox tag set but NOT in the Turbo set
that's actually loaded. Replaced with the canonical 9-tag list
verified against /api/model-info: laugh, chuckle, sigh, gasp, cough,
clear throat, sniff, groan, shush.
Both reported (unhealthy) in docker ps. Two distinct root causes:
* news-digest-web: switched from nginx:alpine to python:3.12-alpine
(uvicorn) but kept the wget healthcheck against `localhost`. Alpine's
/etc/hosts maps localhost to BOTH ::1 and 127.0.0.1; busybox wget
tries IPv6 first, hits "connection refused" because uvicorn binds
IPv4-only, and doesn't fall back. Pinned to 127.0.0.1.
* chatterbox: devnen's image is built from a python:3.10 base and
doesn't ship curl, so `curl -fsS http://localhost:8004/api/model-info`
failed with `/bin/sh: 1: curl: not found`. Replaced with a python
urllib one-liner that fetches + asserts `b'"loaded":true' in body`,
also pinned to 127.0.0.1 to dodge the same IPv4/IPv6 race.
Both YAML extractions tested directly inside the running containers
(via `sh < script`) — chatterbox python check returns 0 when the model
is loaded.
Symptom: granite-4-small and qwen3.6-27b were evicting each other
when called in alternation. granite is the news-digest curator (fires
twice daily on cron) — being evicted means a cold reload (~5s) on
every digest tick, plus visible churn whenever the user uses 27b
concurrently.
Added granite-4-small to the `pinned` group as a persistent member.
~5-6 GB at Q4_K_M + 120K KV ≈ comfortable inside the existing pin
budget (qwen3.5-9b ~6 GB → ~12 GB total persistent). Single RTX 6000
Ada is 48 GB, leaves ~36 GB headroom for whichever non-pinned model
the user invokes (qwen3.6-27b at ~30 GB fits cleanly).
Updated the pinned group's docstring to capture the current member set
+ VRAM math + the historical context (qwen3.6-35-a3b was here, was
too heavy, got removed yesterday). Marked the granite ttl: 0 with the
matching "pinned — never unloads" comment as the other group members.
Symptom: qwen3.6-35-a3b refused to deload when other models needed
the VRAM, even with the model itself at ttl: 0. The pinning came from
the `pinned` group's `persistent: true` flag, which exempts members
from eviction by the scheduler regardless of memory pressure. The
model's ttl: 0 only governs idle-timeout, NOT scheduler eviction —
those are separate concerns.
Removed qwen3.6-35-a3b from the group's members. Kept ttl: 0 on the
model itself: still no idle-unload, but the scheduler CAN now evict
it when another non-coexistent model is requested. qwen3.5-9b stays
pinned (~6 GB at Q4 — cheap to hold).
Updated the inline comment + the group-header docstring to reflect
the new semantics so future-Claude doesn't undo this.
The base qwen3.6-35-a3b is already ttl: 0 via the `pinned` group.
The three other Qwen 3.6 variants (abliterated, heretic, 27b) had
ttl: 600 → llama-swap auto-unloaded them after 10 min idle, costing
the next request a full reload (~5-15s). Removed so they stay loaded
once warm. Still get evicted by the normal swap when another
non-pinned model is requested — these aren't joining the pinned group,
just losing their idle-unload timer.
devnen/Chatterbox-TTS-Server doesn't expose /health — neither in code
nor OpenAPI. The deploy hung on the playbook's `Wait for /health to
respond` loop indefinitely (each curl -> 404, retry forever) even
though the container was up and the model loaded clean to CUDA at
22:52:21 (~42s after start).
/api/model-info returns `{"loaded":true,...}` only after the model
finishes loading, so it doubles as liveness + readiness. Updated:
* compose.yaml healthcheck — grep for `"loaded":true` from
/api/model-info.
* playbook wait step — same probe instead of /health.
* verify /health → verify /api/model-info reports loaded.
* verify /v1/audio/voices — switched from greping for `voice|alloy|echo`
literals to parsing JSON and asserting the actual response shape:
`{"status":"ok","voices":[...]}` (devnen's shape — note this is NOT
the OpenAI list-format vibevoice uses).
Both deploys failed against irv-ml1 today with upstream-changed-on-us
errors:
* vibevoice: VIBEVOICE_SHA=7614c469a145 (12-char short) made docker
buildx report "repository does not contain ref 7614c469a145" — same
commit IS still HEAD of main, but buildx's git source resolver
doesn't accept short hashes even when unambiguous. Now full 40-char.
* chatterbox: dockerfile: docker/Dockerfile.gpu — devnen restructured
the repo to put Dockerfiles at root, renamed by CUDA version
(Dockerfile.cu128, .cpu, .rocm). Switched to Dockerfile.cu128 (GPU
build for CUDA 12.8 toolkit; works on irv-ml1's 595.58.03 driver).
Also pinned CHATTERBOX_SHA to a full 40-char SHA instead of `main`
so future upstream churn doesn't break the deploy without warning.
Live host .env files patched directly (the playbook only seeds .env
when absent, so canonical edits don't propagate to existing installs).
irv-ml1's driver upgrade to 595.58.03 (kernel 6.1.0-37, CUDA 13.2) is
working — both GPUs detected, modules loaded. The gpu variant of the
Kokoro-FastAPI image (which requires CUDA >= 12.9) is now the right
default for new deploys. Flipping KOKORO_VARIANT=gpu, KOKORO_USE_GPU=true,
KOKORO_GPU_DEVICES=0 (pins to the RTX 3090 — Kokoro is ~1 GB VRAM and
doesn't need the A6000).
Decision recorded in CLAUDE.md ("Stack tree convention") and memory
(convention_stacks_vs_mirror.md):
stacks/<stack>/ canonical / intent. git-tracked.
deploy-stack.sh reads from here.
stacks-mirror/<host>/<stack>/ snapshot / reality. gitignored.
sync-stacks.sh writes here. Used
for drift inspection only — never
a deploy source.
Bug this fixes: deploy-stack.sh was reading from the mirror, so edits
to stacks/llama-swap/config.yaml never reached ana-ml2. Today's
two new model entries (qwen3.6-35-a3b-heretic + qwen3.6-27b) lived
in the canonical for hours but the deploy reported "in sync" because
the script only diffed mirror vs server.
Changes:
* deploy-stack.sh: source switched from MIRROR_DIR/$HOST/$STACK to
STACKS_DIR/$STACK. Header comment + error message updated.
* sync-stacks.sh: header explicitly identifies its role as drift
detection; documents the diff command for comparing canonical vs
mirror.
* stacks/llama-swap/{config.yaml → conf/config.yaml}: matches the
deploy mapping (conf/ in canonical → /opt/docker/conf/ on host).
* CLAUDE.md: "Stack mirror (pull / push)" section rewritten as
"Stack tree convention (canonical vs mirror)" with the role table
+ workflow rules + diff recipe. Layout diagram updated.
Both models pre-pulled into /tank/aimodels/huggingface (HF_HOME=/hfcache
inside the container) via huggingface_hub.snapshot_download with
hf_transfer for parallel chunked download — heretic's 29 GB landed in
~4 min, unsloth's 26.5 GB in ~3:46 (~118 MB/s each).
heretic: llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF:Q6_K
27b: unsloth/Qwen3.6-27B-GGUF:UD-Q6_K_XL
Both repos include mmproj-BF16.gguf alongside the main GGUF, and
llama-server's -hf flag auto-loads the mmproj when present in the same
repo (-hf docs: "mmproj is also downloaded automatically if available").
So both entries get vision (image-text-to-text) without needing an
explicit --mmproj path. ttl: 600 (10-min idle unload), matching the
existing abliterated entry's style.
The server-rendered .source-count / .desk-count badges were correct
at render time but went stale the moment the user hid anything —
"r/HOMELAB (4)" stayed at 4 even after all 4 items were hidden.
Worse, the entire source header still rendered with a (0) badge
once every item underneath was gone.
app.js gains a refreshCounts() pass that walks every .source and
.desk, recomputes the visible (non-.is-hidden) child count, updates
the badge text, and toggles an .is-empty class. CSS rule for
.source.is-empty and .desk.is-empty sets display:none so empty
groups collapse out entirely. Hooked into hideItem, restoreItem,
and the initial-paint hidden-set application.
Adds a small × on each item that hides it from the page. State is
server-side at /output/hidden.json so the same hidden set follows
the user across devices (home, ipad, laptop, work). A "Hidden (N)"
tray at the bottom shows what's hidden on the current page with a
restore button per row; older hidden ids that aren't on this page
sit silently and continue to filter future editions that include
the same article.
Architecture change: news-digest-web swaps from nginx:alpine to a
FastAPI app on uvicorn, built from the same Dockerfile as the
worker. Same image, different command (`uvicorn web:app` overrides
the worker's cron entrypoint via compose). Drops one image dependency,
adds /api/{hidden,hide,restore}.
Item ids are stable 12-char sha1 prefixes (`reddit:<post_id>` /
`miniflux:<entry_id>`) computed in digest.py at render time and
emitted as `data-id` on each .item. The frontend reads /api/hidden
once on load, applies `is-hidden` to matching items, and POSTs
hide/restore on user interaction (optimistic, with rollback on
network error).
Storage: single JSON array at /output/hidden.json, atomic writes
via tempfile + rename, threading.Lock around the read-modify-write
inside the single uvicorn worker. No auth — the digest itself is
unauthenticated on LAN; same trust boundary applies.
Playbook also drops the DOCKER_BUILDKIT=0 fallback now that
ana-docker is on docker-ce 29, and adds three verify steps
(/api/hidden returns a JSON array, app.js is reachable, full
hide/restore round-trip with a synthetic id).
Editorial-briefing favicon: 32×32 SVG, Australis palette. Cyan
masthead-rule across the top echoes the page's aurora-rule, four
descending text-line indicators below evoke a newspaper column.
Reads cleanly at 16×16 (the typical browser tab size). Static
markup only — no script, no animation — so all browsers honor
it for tab + bookmark icons.
Linked from both digest.html.j2 and archive.html.j2 with the
proper type="image/svg+xml" attribute. Served by nginx from
the bind-mounted /output dir alongside index.html and style.css.
Deploy playbook also updated to copy the favicon into /output at
deploy-time so a fresh deploy doesn't 404 on the icon before the
first cron fire.
Two requested polish items:
1. Reddit items now show TWO distinct action chips in the footer:
[↗ SOURCE huggingface.co] [⌥ REDDIT THREAD] 3h · u/foo
Previously the external link was a tiny dot-separated text link
that visually competed with the post metadata. Now: explicit
labeled buttons, distinct colors (cyan for source, blue for
thread), with hover states that match the Australis accent
palette. Non-Reddit items get just the SOURCE chip.
2. Archive page at /archive.html — lists every edition-*.html on
disk, newest-first, sorted PM-before-AM within a day. Each row
is the date in editorial serif + an AM/PM chip color-coded
(yellow morning / cyan evening) + a hover-affordance arrow.
Header link from the main edition reads "ARCHIVE →".
Generation: digest.py walks OUTPUT_DIR for the edition-*.html
filename pattern on every run, sorts, renders archive.html.j2,
writes atomically. Cheap (~1ms even with hundreds of editions).
No retention cap — twice-daily for a year is ~700 small files,
well under any reasonable concern.
CSS additions: .action chip styles (with per-action color variants),
.archive-link in masthead, full .archive-row + .archive-edition
treatment.
Three iterations to get end-to-end:
1. Dockerfile missed COPY run-digest.sh — cron's exec target wasn't
in the image, every fire failed. Added COPY + chmod.
2. Jinja template used {{ list|sum(attribute='items') }} which
sum()s lists with start=0 → TypeError int+list. Switched to
computing reddit_total / tech_total in Python and passing as
template args.
3. LLM defaulted to qwen3.5-35-a3b which (a) is broken in
llama-swap (model process exits on launch), (b) when working,
defaults to extended-thinking mode that eats the entire token
budget without producing any visible content. Same pattern with
qwen3.6-35-a3b. Switched default to granite-4-small — small (4B),
fast (~1s/call), no thinking-mode pathology, returns clean JSON.
Whole pipeline now runs in ~35s total across 8 sources.
Also hardened the LLM response parser to fall back to
reasoning_content when content is empty — catches the thinking-mode
case if anyone ever points the digest at one of those models. Plus
the deploy playbook gained DOCKER_BUILDKIT=0 because ana-docker is
on docker 20.10 which doesn't carry the buildx driver versions our
newer client expects ("client version 1.52 is too new"). Real fix is
upgrading docker on the fleet — separate workstream.
The Miniflux inbox got noisy after a few subreddits + HN + Lobste.rs.
This stack distills a single static page twice a day — at 0800 and
2000 local — that surfaces only what cleared score + ratio filters,
each item tldr'd by qwen3.5-35-a3b on llama-swap.
Pipeline (digest.py, ~330 lines):
1. Discover subreddits from Miniflux feeds (any reddit.com/r/<sub>/
URL — single source of truth, no duplicated config).
2. Reddit JSON top-of-day per sub. Filter: score >= 50,
upvote_ratio >= 0.85. Cap 8 items per sub.
3. Miniflux /v1/entries for the 'Tech aggregators' category
(HN, Lobste.rs) — last 12 hours.
4. Batched per-source summarization via llama-swap
/v1/chat/completions. Each post gets a one-sentence tldr +
one-word tag (news / tutorial / release / discussion /
question / showcase / drama / meme).
5. Render Jinja2 template. Atomic write to /output/index.html
(.tmp + rename) so partial pages never get served. Per-edition
archive at /output/edition-YYYY-MM-DD-{am,pm}.html.
Two containers:
news-digest-worker python:3.12-alpine + busybox crond
news-digest-web nginx:alpine, port 8181, homepage card via
docker labels (group=News, fits next to Miniflux)
Both bind-mount /opt/docker/data/news-digest as /output and
/usr/share/nginx/html respectively.
Aesthetic — operations-center chrome (Australis cool-mono palette,
JetBrains Mono UPPERCASE eyebrows, mdi-glyph anchor) wrapping
editorial-serif news content (Fraunces variable serif w/ optical
sizes). Two type families that wouldn't normally meet, intentionally
combined: chrome says 'filed at 0800 from the bridge'; headlines say
'this is news, read it like news.' Sticky aurora-glow rule under the
masthead is the only sanctioned Australis gradient.
Edition stamp (AM/PM in big mono Australis-yellow) is the signature
piece — establishes the twice-daily rhythm at a glance.
All filtering + LLM + scheduling knobs in .env. Subreddit list is
implicit (read from Miniflux), so adding a sub = subscribing in
Miniflux, no config edit on this stack.
Subscribed live via Miniflux API; mirroring back to the canonical
OPML so a fresh deploy elsewhere starts with the same feed set.
All three land in the existing 'Subreddits — selfhosting' category
(retitled to mention image-gen + LLM + homelab to reflect scope).
Initial deploy failed with 'Container cannot be connected to network
endpoints: miniflux-net, traefik-net' — the docker engine balks at
joining a brand-new internal network and an existing external
network in one create step.
Flattened both containers onto traefik-net only. The DB password
still protects miniflux-db, and traefik-net is internal-LAN-only,
so co-locating them is fine. Verify step updated to check for
traefik-net membership instead of the (now-gone) miniflux-net.
Adds Miniflux on ana-docker as the unified inbox for tech blogs,
Hacker News, lobste.rs, and selected subreddits. Reddit serves clean
RSS for any sub at https://reddit.com/r/<sub>/.rss, so subreddit
follows fold into the same inbox as everything else — no Reddit
account needed, no manual polling.
Stack:
stacks/miniflux/
compose.yaml — miniflux + bundled postgres:16
.env.example — placeholders for DB password + admin user
starter-feeds.opml — initial subscriptions (HN, Lobste.rs,
r/selfhosted, r/homelab, r/LocalLLaMA, r/nba)
README.md — deploy / OPML import / r/nba spoiler
block-list / backup / update flow
Postgres bundled with the stack (not pfi-postgres) — single-user RSS
DB is tiny and the bundle keeps the dependency graph flat.
Homepage gets a new 'News' group at the TOP of the Main tab (above
Monitoring) so the Miniflux card sits prominently. The card itself
auto-discovers via the homepage.* labels on the miniflux container.
Per-feed block-list rule for r/nba documented in README — Reddit's
RSS titles for game threads include scores ("Lakers 108 - Warriors
102 [Final]") which spoil the game; a regex catches the score
patterns and skips those entries while keeping discussion/highlights.
Deploy:
scripts/elway ana-docker --playbook playbooks/deploy-miniflux.yaml
Then edit /opt/docker/compose/miniflux/.env on the host to fill in
the two CHANGE_ME passwords and `docker compose up -d` again.
Two fixes from the failed first deploy on irv-ml1:
1. CPU/GPU variant. Kokoro's GPU image needs CUDA >= 12.9; irv-ml1's
driver 570.124.06 caps at 12.8 so the gpu variant fails with
"nvidia-container-cli: requirement error: unsatisfied condition:
cuda>=12.9". Make the variant a knob:
KOKORO_VARIANT=cpu (default — works anywhere)
KOKORO_VARIANT=gpu (after driver bump)
KOKORO_USE_GPU=false|true (matches the variant)
Kokoro is tiny (82M params) so CPU is workable: TTFA ~1s vs ~300ms
on GPU. Acceptable while the driver bump gets scheduled. compose.yaml
no longer hard-codes `runtime: nvidia` — relies on the daemon's
default-runtime + NVIDIA_VISIBLE_DEVICES gating, same as how the
wrapper's USE_GPU flag selects the inference path inside the
container. Toggling between variants is now a `.env` edit + restart.
2. Tighter pull-log filter. --quiet on `docker compose pull` only
suppresses the pull command's stdout; the docker daemon still
emits per-layer extraction events on stderr ("ffbfd7a09415
Extracting 64.06MB" repeated dozens of times per layer). Drop those
too via grep on the SHA-prefixed pattern. set -o pipefail keeps a
real pull failure visible.
For existing deployments: removing /opt/docker/compose/kokoro/.env
on the host and rerunning the playbook re-seeds with the new schema.
Three TTS additions to round out coverage on irv-ml1, each filling a
distinct niche the existing slate doesn't own.
Final coverage matrix (all on irv-ml1):
Kokoro — low-latency English, fixed voice library, ~300ms TTFA
Chatterbox Turbo — low-latency English w/ voice cloning + paralinguistic tags
IndexTTS-2 — English voice cloning + emotion vector / text control
Qwen3-TTS-1.7B-Base — high-quality English voice cloning
CosyVoice 3 — multilingual (Chinese-leaning)
VibeVoice 1.5B — long-form / multi-speaker dialogue
stacks/kokoro:
- port 8193, GPU device 0 (3090)
- pulls ghcr.io/remsky/kokoro-fastapi-gpu:v0.2.4-master (no Dockerfile,
no first-run model download — models baked in)
- 60+ built-in voices, OpenAI-compat with stream=true over chunked HTTP
- Apache-2.0 weights + code, ~1 GB VRAM
stacks/vibevoice:
- port 8194, GPU device 1 (A6000 — for 7B headroom)
- builds groxaxo/VibeVoice-FastAPI1 (more current fork of ncoder-ai)
pinned to 7614c469a145
- default model microsoft/VibeVoice-1.5B (~7 GB bf16 VRAM); env var
swap to rsxdalv/VibeVoice-Large (7B) or FabioSarracino/VibeVoice-Large-Q8
- multi-speaker dialogue via /v1/vibevoice/generate with Speaker N: format
- long-form niche only — not low-latency
stacks/chatterbox:
- port 8196, GPU device 0 (3090)
- builds devnen/Chatterbox-TTS-Server (most active Turbo-supporting wrapper)
- default model ResembleAI/chatterbox-turbo (~2.5 GB fp16, ~75ms latency)
- paralinguistic tags inline ([laugh] [whisper] etc) — different shape
from IndexTTS-2's emotion vector; fills the speed+cloning niche
Kokoro/IndexTTS don't cover together
- mandatory PerTh watermark on outputs (Resemble policy)
Three matching playbooks under playbooks/deploy-{kokoro,vibevoice,
chatterbox}.yaml. All idempotent, creates-/when-gated.
Cold-deploy disk on /worktank/: ~7 GB Kokoro + ~19 GB VibeVoice 1.5B
+ ~12 GB Chatterbox = ~38 GB total. VRAM concurrent: ~10-11 GB across
both GPUs.
Skipped from the original four-stack proposal: VibeVoice Realtime
(overlaps Kokoro's niche; Kokoro wins on latency, license, and not
needing a build).
IndexTTS-2's tts.infer(stream_return=True) is a generator that yields
audio chunks per text segment as they finish, plus inter-segment
silence. Expose this via the existing POST /v1/audio/speech with a new
"stream": true field on the request body.
Wire-up:
- 44-byte WAV header emitted up front with placeholder data length
(0xFFFFFFFF) so chunks can be written before total samples are
known. Players that read until EOF (mpv, ffplay, aplay, sox,
browsers via <audio>) handle this fine.
- Each yielded chunk goes through _chunk_to_pcm_bytes(), which
handles torch tensors / numpy arrays in either int16 or float
(-1..1) form.
- 22050 Hz mono int16 — IndexTTS-2's hardcoded output shape.
Time-to-first-audio drops from full-file latency to ~one-segment
latency. Single-sentence inputs barely benefit; long passages /
multi-paragraph reads benefit a lot. Strict metadata parsers may
balk at the placeholder size — request without stream for a
closed-length WAV in that case.
INDEX_TTS_TAG bumped to v2 to force a rebuild.
budget hazard + media-CDN workaround
Tried adding git-lfs install + git lfs pull to the build to get
real example WAVs into the image — failed with:
Error downloading object: examples/emo_hate.wav: Smudge error:
batch response: This repository exceeded its LFS budget. The
account responsible for the budget should increase it to
restore access.
The index-tts org's LFS bandwidth quota is exhausted upstream and
out of our control. Reverting the Dockerfile change. The examples
aren't needed for the wrapper to work; emotion_text and
emotion_vector are sufficient for end-to-end testing without any
WAV file at all.
For users who want the bundled example clips as starter audio,
README now documents the media-CDN URL trick — same LFS objects
served via a different code path that doesn't count against the
LFS API budget. INDEX_TTS_TAG stays at v1.
The IndexTTS-2 repo stores examples/emo_*.wav and examples/voice_*.wav
as Git LFS objects. v1 of our image cloned the repo without an LFS
pull, leaving those paths as ~130-byte pointer text files — unusable
for `docker cp` into /worktank/index-tts/{voices,emotions}/ as starter
references. (Caught when an emotion_voice="hate" call returned audio
that was actually the pointer text round-tripped through file IO.)
v2 adds git-lfs to the apt list, calls `git lfs install --system`
once, and `git lfs pull` after the checkout. Adds ~1-2 MB to the
image (the examples are small audio clips). INDEX_TTS_TAG bumped to
v2 to force a clean rebuild.
Adds a third TTS to the irv-ml1 fleet. IndexTTS-2 is Bilibili's
emotion-controllable zero-shot TTS (paper 2506.21619). Distinguishing
capability vs the existing two: timbre and emotion are disentangled —
clone a voice's timbre from one reference and the emotion from a
different reference, OR set emotion via 8-vector, OR derive it from a
text description. Neither CosyVoice 3 nor Qwen3-TTS-1.7B-Base does
this cleanly in English.
Wrapper is owned end-to-end (~150 lines in app.py) — the only existing
FastAPI fork (csllpr/index-tts-fastapi) targets v1 and is a dormant
single-commit repo. Upstream IndexTTS-2 ships only a Gradio webui.
Layout follows the qwen3-tts pattern:
stacks/index-tts/
Dockerfile — CUDA 12.8 base, IndexTTS pinned to a SHA
app.py — FastAPI: POST /v1/audio/speech + /v1/voices
entrypoint.sh — one-time HF snapshot_download of the weights
compose.yaml — env-driven, GPU pinning support, bind mounts
.env.example — port 8192, fp16, paths
README.md — API examples + comparison vs the other TTS
playbooks/deploy-index-tts.yaml — elway playbook for irv-ml1
Voice and emotion libraries are flat host dirs of WAVs, bind-mounted.
Drop a new <name>.wav and /v1/voices picks it up immediately.
License caveat: IndexTTS-2 weights ship under a custom Bilibili
license (free at our scale, not OSI-open). README documents it.
- parakeet/compose.yaml: healthcheck was using curl which isn't in the
image (only wget is, via apt). 2,190 failing checks — switched to
`wget -q -O /dev/null`, container went healthy on recreate.
- qwen3-tts/.env.example: variant annotation was reversed. The upstream
wrapper's runtime error is unambiguous: voice cloning requires the
-Base variant, not -CustomVoice. Corrected the comment block and
flipped the default to Qwen/Qwen3-TTS-12Hz-1.7B-Base.
- qwen3-tts/README.md: 0.6B switch snippet now suffixes -Base too,
since plain `Qwen/Qwen3-TTS-12Hz-0.6B` isn't published on HF.
Three fixes from the first deploy attempt on irv-ml1:
- build.target=production. Upstream Dockerfile is multistage; the last
stage `cpu-base` was selected by default, producing a CPU-only image
with no flash-attn and `torch ... whl/cpu`.
- user: "0:0". Upstream image declares USER appuser but writes runtime
state under /root (mode 0700). appuser cannot traverse /root, so
/v1/voices 500s on PermissionError. Run as root to sidestep.
- QWEN3_TTS_MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice. The bare
`1.7B` id we had isn't a real HF identifier; upstream publishes
-CustomVoice / -Base variants of each size. Use -CustomVoice so
`voice="clone:<name>"` works.
Tag bumped to v2 to keep the v1 cpu image distinguishable in the local
registry.
After: all 5 verify steps pass, GPU synthesis ~5s for 3-4s of audio,
three contrasting English `instructions` produce three distinct
hashes — emotion steering actually works (unlike CosyVoice's English
path).
Alibaba's open-weight TTS (Apache 2.0, Jan 2026), deployed via
groxaxo/Qwen3-TTS-Openai-Fastapi wrapper. Built locally from a
pinned git SHA via docker buildx's git context — no source
vendored. 1.7B flagship model by default; 0.6B available via
QWEN3_TTS_MODEL env override.
Why we need a second TTS stack: cosyvoice 3 emits Chinese phonemes
for English content per upstream FunAudioLLM/CosyVoice#1790
(unfixed). Qwen3-TTS is from the same Alibaba team but with
English first-class in the checkpoint — 10 languages, 97 ms
streaming TTFB, instruction-driven emotion. Coexists with cosyvoice
on irv-ml1 (port 8191; cosyvoice keeps 8190).
Voice cloning shape DIFFERS from cosyvoice: profile-based, not
voice-id. Profiles live under voice_library/profiles/<name>/ and
are referenced as voice="clone:<name>".
Path layout: /worktank/qwen3-tts/{cache,voices}/, with cache excluded
from restic (regenerable from HF Hub) and voices included (cloned
profiles need original reference audio to recreate).
playbooks/deploy-qwen3-tts.yaml: 10 steps + 5 verify, idempotent;
the wait step polls /health for up to ~10 min to absorb first-run
model download.
Stack only — restic profile update for /worktank/qwen3-tts/voices/
to follow when this is empirically validated against the GLaDOS
voice (the "did Qwen inherit the Chinese-bias bug?" question).
vh