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serverAppConfigs/serverAppConfigs-docs/Research/Local TTS and Voice Cloning.md
T
Frank 2dc31ff09c docs(frank-tts): retire the 5.5 s cold-voice claim everywhere it still lived 
PR #15 corrected the engine's page, the research brief's law bullet and the
warmer's header, but the retired ladder had six more homes: both quadlet files
("6.2-8.0 s… 0.5 ms", "~6-8 s each", "paying the 5.5 s cold decode"),
`scripts/frank-tts-engine.env` (the whole law paragraph and the WARM_TEXT note),
`scripts/frank-tts.env`, the [[Frank TTS]] troubleshooting row that told an
operator to expect ~5.5 s, the research brief's consequences paragraph, and one
test comment.

All re-keyed to what was measured on the deployed container 2026-10-18: the
first synthesis of a freshly registered voice costs 1,883 ms against 1,226-1,283
ms afterwards, so the warm pass buys about 0.6 s — and the reason it exists at
all, which the pages never led with, is that the registry is memory-only: without
the hook every voice name is a 400 until somebody synthesizes. The trial's dated
worklog entry stays as written; the correction points at it.

No behavior change: comments, unit lore and docs only, so no cfhash move and no
image rebuild.
2026-10-18 18:52:50 -07:00

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type, title, description, tags, timestamp
type title description tags timestamp
research Local TTS and Voice Cloning How to create a new local TTS voice from sample files in oh-my-pi, the 2026 open-weight voice-cloning landscape, the measured CPU engine A/B behind frank-tts and the arm A decision, the full local AI audio stack, and a recommended homelab deployment.
research
tts
voice-cloning
audio
stt
vllm-omni
kokoro
2026-10-18T16:05:00Z

Local TTS and Voice Cloning

Research brief (2026-09-22) on creating a new local TTS voice from sample files, the open-weight voice-cloning landscape, the full local AI audio stack, and a recommended deployment for the fleet. Every claim is cited in Citations. The fleet has no audio input today (that capability left with the retired NPU endpoint, returned with the Gemma4 audio projector on 2026-09-17, and left again with that retirement on 2026-10-01 — audio is still greenfield: every fleet endpoint declares input: [text, image] (~/.omp/agent/models.yml:63,149,214).

Summary

  • Kokoro-82M (OMP's built-in local TTS) cannot zero-shot clone a voice. Voices are a fixed pack. A community fine-tune recipe exists (semidark/kikiri-tts): a ~51 h multi-speaker base stage + a few hundred single-speaker samples, then voice-pack extraction. Feasible on one GPU, but the output is Kokoro-styled, and OMP's local backend only accepts its built-in voice list (a custom pack needs a patch).
  • The 2026 open-weight cloning landscape is strong. Permissive-licensed leaders for a single-GPU homelab: Chatterbox (MIT), Qwen3-TTS (Apache-2.0), CosyVoice 3 (Apache-2.0), GLM-TTS (MIT weights), VoxCPM2 (Apache-2.0), F5-TTS (MIT), GPT-SoVITS (MIT).
  • OMP integration (re-verified 2026-10-18): the speech tool is gated by speechgen.enabled and resolves models through the speech role and its fallback chain (modelRoles.speech; this workstation runs local/kokoro). Three APIs: local Kokoro ONNX (always WAV/PCM16), xai-tts, and openai-speech — a generic OpenAI-compatible /v1/audio/speech client (MP3 or WAV, voice_id forwarded only when explicitly set, 15 000 characters max; DeepInfra is now just a model on that API). The 2026-09-22 finding that OMP has no configurable OpenAI TTS endpoint is retired: a local server needs a speech-role entry, not a patch.
  • Fleet plan (2026-10-18): CPU-only on Dankclaw, not the 3090. Gitea issue #2 rules out every GPU option (the 3090 has carried frank-zealot since 2026-10-17; both R9700s are frank-archon's). The shape is a TTS engine on 8880 behind frank-tts, the fleet's speech endpoint on 7772 (scripts/frank-tts.env, dankclaw/voiceservice/): one OpenAI /v1/audio/speech façade that resolves a voice by name and never shows a path. Nothing is deployed — no frank-tts unit and no container exist on the host and nothing listens on 7772 or 8880 (checked 2026-10-18). STT (Parakeet TDT 1.1B, CC-BY-4.0) stays on CPU. The engine is chosen: arm A qwentts.cpp resident tts-server, 1.7 B talker — measured in Measured CPU engine A/B below and picked on the live-output criterion in Decision: arm A for live output.

Can Kokoro learn a new voice?

Kokoro (hexgrad/Kokoro, Apache-2.0, 82 M params, non-autoregressive) ships fixed voice packs — there is no reference-audio path, so zero-shot cloning is not an option. OMP's local backend runs onnx-community/Kokoro-82M-v1.0-ONNX offline, choosing a voice from a built-in list (tts.localVoice).

The best-documented fine-tune path is semidark/kikiri-tts (Apache-2.0):

Stage Data Purpose
Multi-speaker base ~51 h audio, 51 speakers language adaptation / base fine-tune
Single-speaker 455–627 samples (~1–2 h of one voice) voice specialization
Extraction — voice-pack (.pt) extraction for inference

Verdict: fine-tuning Kokoro to a personal voice is possible on a 3090, but you must collect ~1–2 h of clean single-speaker audio, run the two-stage recipe, and patch OMP to load the custom pack. The effort/quality trade is worse than running a modern zero-shot cloner on the same GPU.

2026 voice-cloning landscape (permissive-licensed, single-GPU)

Ranked (2026-09-22) for a 3090 homelab, English-primary, personal voice; the 2026-10-18 fleet decision below moved this work off the GPU entirely:

  1. Chatterbox (MIT) — best license/quality/compute ratio; ~10 s reference (the README's own example is your_10s_ref_clip.wav); 110 M (Nano) to 0.5 B (Turbo/Multilingual); ~1–2 GB VRAM (unverified 2026-10-18); Nano is documented for CPU — "3x faster than realtime on 8 CPU cores". Top pick for a personal English voice. No server in the README (library use only; wrap it yourself); every generated file carries Resemble's PerTh watermark.
  2. Qwen3-TTS-1.7B-Base (Apache-2.0) — 3 s cloning, 10 languages, streaming, vLLM-Omni serving with a POST /v1/audio/voices upload (unverified 2026-10-18 — the reachable docs do not list it), the closest thing to "named voice" semantics, official fine-tuning code. Top pick for a OpenAI-compatible deployment.
  3. CosyVoice 3 (Apache-2.0) — 0.5 B, 9 languages + 18 zh dialects, 150 ms bi-streaming, FastAPI and gRPC servers.
  4. GLM-TTS (MIT model weights, Apache-2.0 code) — CER 0.89 after multi-reward GRPO (down from 1.03), RL emotion control, zh/en; the prompt audio shipped in the repo is marked research-only.
  5. VoxCPM2 (Apache-2.0) — 30 languages, 48 kHz, LoRA fine-tune from 5–10 min of audio, ~8 GB VRAM. Best "fine-tune my voice" option.
  6. F5-TTS (MIT) — 0.3–0.5 B, en/zh; Docker + Triton.
  7. GPT-SoVITS (MIT) — best few-shot fine-tune UX (1 min of data), WebUI, Docker images.

Notable non-commercial / restricted: Higgs Audio v3, Breeze-TTS-2, Spark-TTS (non-commercial); IndexTTS (bilibili custom license), Fish Speech S2 (research license), XTTS v2 (CPML, non-commercial, vendor defunct); MegaTTS3 (WaveVAE encoder not released); Voxtral TTS (no official cloning — codec encoder not released). Excluded from cloning: Parler-TTS (style-text only), VibeVoice-TTS (code withdrawn 2025-09).

OMP integration (definitive)

The tool ships hidden and is enabled by speechgen.enabled (~/.omp/agent/config.yml:222-223); the harness docs list it as "Generate speech". Models come from the speech role and its fallback chain, not from a hardcoded backend list (modelRoles.speech, ~/.omp/agent/config.yml:16 = local/kokoro today):

API Endpoint Notes (from the installed bundle, 2026-10-18)
local-inference on-device ONNX, onnx-community/Kokoro-82M-v1.0-ONNX (Apache-2.0) no network, always WAV/PCM16, voice from the built-in list (tts.localVoice)
xai-tts POST https://api.x.ai/v1/tts ara/eve/leo/rex/sal + custom voice IDs; MP3 or WAV
openai-speech POST <provider baseUrl>/audio/speech the generic OpenAI-compatible client — MP3 or WAV, voice_id forwarded only when explicitly set, 15 000-character cap on input; DeepInfra is one model entry on it (https://api.deepinfra.com/v1/openai)

The 2026-09-22 finding — "no generic OpenAI-compatible TTS endpoint … deepinfra is a hardcoded URL, not a configurable base URL" — is retired: openai-speech is that configurable client, selected by putting a model on the speech role. Still true from the original audit: the local Kokoro backend accepts only its built-in voice list, so a custom pack needs a patch.

Paths for a Frank-cloned voice (2026-10-18):

  1. Local server + speech role, no patch. Register frank-tts (port 7772) as a provider model on the openai-speech API and point modelRoles.speech at it; the façade turns a voice name into that pack's reference clip and forwards it as base64 reference_audio (scripts/frank-tts.env, TTS_BACKEND=omni-http). Not deployed as of 2026-10-18.
  2. xAI console custom voice — works today, zero code. Record 2–3 min clean speech (≤120 s, 24 kHz 16-bit mono WAV) → console → voice_id → speech tool. Free (≤30 voices), cloud, US-only.
  3. No-patch workaround. The bash tool can curl the local TTS server ad hoc, and the harness tts device writes a speech file to a path you give it.
  4. Kokoro fine-tune → OMP local backend. Highest effort, Kokoro-styled.

Full local AI audio stack

STT / ASR: whisper.cpp (MIT, tiny→large-v3 2.9 GiB, all backends, built-in VAD); faster-whisper (MIT, CTranslate2 int8, ~4× faster); whisperX (BSD-2, word timestamps + diarization); NVIDIA Parakeet TDT 1.1B (CC-BY-4.0, LibriSpeech-clean WER 1.39% — best English open ASR); Parakeet 0.6B v3 / Canary 1B v2 (multilingual); Moonshine (MIT, on-device); VibeVoice-ASR-7B (MIT, 60-min, 50+ langs).

VAD: Silero VAD (MIT, ~2 MB, <1 ms per 30 ms chunk; embedded in faster-whisper/whisperX).

TTS serving: vLLM-Omni (vllm-project/vllm-omni, Apache-2.0) publishes the official images vllm/vllm-omni (CUDA) and vllm/vllm-omni-rocm (ROCm), both tagged v0.28.0 (Docker Hub tag lists, 2026-10-18), and serves Qwen3-TTS (docs/models/supported_models.md). It serves the OpenAI speech path POST /v1/audio/speech; the POST /v1/audio/voices upload route is (unverified 2026-10-18) — the docs reachable today do not document it. Its gfx942/gfx950 validation note is a footnote scoped to MiniMax H3 diffusion, not to TTS, and the fleet runs no vLLM anywhere: the R9700s carry radiance as frank-archon (scripts/serve-frank-archon.sh:2). It is also no longer "the one server" with the OpenAI shape — llama.cpp's own llama-tts-server has it too (next section). Others: Kokoro-FastAPI (Docker, :8880, Apache-2.0), CosyVoice FastAPI and gRPC (runtime/python/fastapi, runtime/python/grpc; the README documents no WebSocket server), F5-TTS Gradio/Triton, GPT-SoVITS WebUI/api_v2, Fish S2 tools/api_server.py (own format, not OpenAI-shaped).

Post-processing: SoX/ffmpeg (resample/normalize; 24 kHz mono 16-bit is the reference-clip standard); Demucs (htdemucs) for vocal separation before cloning; the Perth watermark is built into Chatterbox.

llama.cpp-native TTS serving (verified 2026-10-18)

The CPU shape does not need a Python server at all:

  • llama-tts-server (tc-mb/llama.cpp-omni, MIT, "Omni inference in C/C++") is a standalone OpenAI-compatible TTS server that runs without an LLM model (build target llama-tts-server). POST /v1/audio/speech takes model ("voxcpm", also accepts "voxcpm2"), input, voice — documented "reserved", so do not build named-voice semantics on it — response_format (wav/pcm), reference_audio (base64 WAV for cloning), seed 42, cfg_value 2.0, inference_timesteps 10, max_steps 200, temperature 1.0. POST /v1/audio/speech/stream returns chunked WAV; POST /v1/voxcpm2/init hot-loads a runtime from base_lm + acoustic GGUFs with n_gpu_layers (-1 = all); GET /v1/audio/speech/models lists what is loaded (tools/omni/voxcpm2/README.md:232, 321-354).
  • Mainline llama-tts (ggml-org/llama.cpp) has --tts-speaker-file for a speaker reference with Qwen3-TTS and with Kyutai Pocket-TTS — the latter requires it ("the model produces almost no audio without it"), and Pocket-TTS converts one languages/<name> directory, never the repo root (tools/tts/README.md:20-58; kyutai/pocket-tts is CC-BY-4.0).
  • VoxCPM2 GGUF provenance: openbmb/VoxCPM2 publishes PyTorch weights only (model.safetensors, no .gguf sibling — HF API 2026-10-18). The GGUFs the fork loads come from DennisHuang648/VoxCPM2-GGUF (VoxCPM2-BaseLM-F16.gguf, VoxCPM2-BaseLM-Q8_0.gguf, VoxCPM2-Acoustic-F16.gguf, Apache-2.0 card) or from the fork's own tools/omni/voxcpm2/convert_voxcpm2_to_gguf.py.

Arm B (llama-tts) and arm A (qwentts.cpp) have both been measured on this box — RTF, first-audio latency and RSS are in the next section. The other servers in the table above (vLLM-Omni, Kokoro-FastAPI, CosyVoice, F5-TTS, GPT-SoVITS) remain unmeasured here; that absence is still the finding.

Measured CPU engine A/B (2026-10-18)

Both candidate upstreams for frank-tts were measured on Dankclaw's CPU — 8 threads, no GPU touched, three runs per arm, seed 42, identical text, against the fleet's own audition voice packs.

  • Arm A — ServeurpersoCom/qwentts.cpp at commit 5916bf7c79dbb4e66fc059cdc507a79a23a28bb4 (2026-10-16), weights from Serveurperso/Qwen3-TTS-GGUF: qwen-talker-1.7b-base-Q4_K_M.gguf (1,219,245,248 B), qwen-talker-0.6b-base-Q4_K_M.gguf (628,905,056 B), qwen-tokenizer-12hz-Q8_0.gguf (291,150,624 B) — byte counts verified against the Hub API. Builds with zero compiler warnings; GGML_BLAS=OFF is forced on this host (only libopenblas.so.0 exists, no dev headers, so the vendor buildcpu.sh cannot configure).
  • Arm B — mainline llama-tts with --tts-speaker-file, i.e. the llama-tts-cli backend scripts/frank-tts.env already implements.

RTF is seconds of compute per second of audio (lower is better; below 1 is faster than real time).

Arm RTF Run spread Peak RSS
A · 0.6 B talker, CLI 0.44 ±0.002 3.4 GiB
A · 1.7 B, CLI, uncloned 0.57–0.58 ±0.006 4.7 GiB
A · 1.7 B, CLI, embedding-only clone 0.63 ±0.02 n/m
A · 1.7 B, CLI, ICL clone 0.92 ±0.01 n/m
A · 1.7 B, resident tts-server, warm 0.55–0.57 ±0.01 5.7 GiB
A · same, ICL voice warm / cold (first request) 0.67 / 1.65 one-shot 5.7 GiB
B · mainline llama-tts 0.93–0.99 ±0.25 n/m

Read RTF only within an arm. Conditioning changes clip length — the same seed yields 18.00 s of audio embedding-only and 15.12 s under ICL — so the denominator is the arm's own audio duration.

Arm A's resident server is ~1.7× faster than arm B and about 40× tighter in run-to-run spread, and it already serves the HTTP shape frank-tts's façade speaks: /health, /v1/models, /v1/audio/voices (GET/POST/DELETE), /v1/audio/speech (wav/pcm), chunked streaming with the first audio byte at 86–451 ms and a server-side time-to-first-audio of 94–112 ms. Arm B has no HTTP path at all — that is what the façade's CLI backend exists for.

Four laws came out of the trial, each worth a line of configuration:

  • Boot-warm every voice. The trial measured the first request for a registered voice at 6.2–8.0 s (first audio byte 5,530 ms) and later requests restoring the snapshot in 0.5 ms. That ladder did not survive deployment: measured 2026-10-18 on the built container against an empty registry — register, then time — the first synthesis of a freshly registered voice cost 1,883 ms against 1,226–1,283 ms for the requests after it, and a ~110-word request cost 9.4 s then 9.8 s, so request time tracks the audio generated rather than snapshot state. What still holds is the reason to warm at all: the registry is memory-only and re-registering does not warm, so an engine unit must run one throwaway synthesis per voice at start. It buys registration and about 0.6 s, not five seconds.
  • Speaker artifacts are reusable and exact. qwen-codec turns a reference WAV into .spk (8,192 B x-vector) + .rvq (5,456–6,864 B, 16 codebooks × T, 11-bit packed) in 1.8–2.1 s at 1.3–1.5 GiB peak; replaying them yields a byte-identical WAV and saves 1.4 s per request. That pair is the engine-artifact term the voice-pack law in scripts/frank-tts.env reserves a slot for — arm A pins it as .spk + .rvq beside reference.wav.
  • Streaming is not bit-identical to buffered, whatever the upstream README claims: same text, seed and voice, 92.2 % of samples differ (peak delta 1139/32768, −41.6 dB RMS — inaudible). A fixed seed does not pin output across response_format, so never mix the two paths in a cache or a golden fixture.
  • A voice cannot be registered from an embedding alone: POST /v1/audio/voices accepts wav_b64 or both spk_b64 and rvq_b64 (src/tts-server.h:480), so an embedding-only voice has to be sent as the WAV, or the lone-spk path has to be upstreamed.

Likeness is not measurable at this resolution. x-vector cosine against each speaker's own reference: ICL 0.9886 / 0.9901, embedding-only 0.9844 / 0.9812, uncloned 0.9228 — but two different speakers score 0.9497 on the same metric. The whole clone effect lives in a 0.04-wide band and the ICL-over-embedding gap is inside noise, so the four audition clips (/home/blank/voice-audition/qwentts-*.mp3, each against its armB-*.mp3 mainline partner, guide in LISTEN-engine.md) are the instrument. The operator heard them on 2026-10-18 and passed every clone, which removed the last axis measurement could not reach; the engine was then picked on live-output grounds (Decision). The cosine extractor is still worth keeping: it is engine-neutral (it scores mainline output through the same encoder) and it separates an uncloned voice cleanly.

Two operational details. The engine takes 8 threads and exposes no -t flag — src/backend.h uses hardware_concurrency()/2 and prints the choice at load, so pinning a cpuset on the quadlet changes that number implicitly. And ICL needs a reference transcript that did not exist: the fleet has no ASR endpoint and no audio input, so the crops were transcribed with vosk-small-en-us inside the work dir, and two of three transcripts came back suspect ("demon souls" for Dark Souls) — a reason to curate ref_text into the pack manifest instead of transcribing on the host.

Decision: arm A for live output (2026-10-18)

Every audition pair passed, so likeness stopped being the tiebreak. The question became which engine is better for live output — the harness speaking a reply, an alert read aloud, a click-to-speak — and on that criterion RTF only has to stay under 1.0 with margin: what separates the arms is time to first audio and the spread, because an arm whose RTF can cross 1.0 stalls a live stream mid-sentence.

arm A · qwentts.cpp tts-server arm B · mainline llama-tts
first audio byte 86–451 ms model load, per request
warm RTF 0.55–0.67 0.93–0.99
run spread ±0.01 ±0.25
HTTP serves /v1/audio/speech none — façade CLI only
RAM 5.7 GiB resident per-request, freed after

Chosen: arm A, 1.7 B talker, resident tts-server in a frank-tts-engine container, with TTS_BACKEND=llama-tts-cli kept in the repo as the rollback. Three consequences are configuration, not opinion: the unit boot-warms every voice — the registry is memory-only, so an unwarm'd name is a 400 and the first synthesis otherwise costs about 0.6 s more than the ones after it, as corrected above —, it does not pin a cpuset casually (threads are hardware_concurrency()/2, no flag), and every pack registers with the WAV because the engine rejects a lone embedding.

Two follow-ups stay open on purpose, and neither blocks the deploy:

  • Talker size. The 0.6 B arm measured 0.44 RTF at 3.4 GiB and has never been auditioned — every clip above is 1.7 B. It is the lever for a CPU co-tenancy problem, not a preemptive saving.
  • ICL vs embedding-only. Both passed the ear test. Embedding-only is the shipped default: 0.57–0.63 vs 0.67 warm, and it needs no ref_text, which this fleet cannot produce honestly (the ASR crops came back suspect). Packs still carry .spk + .rvq, so ICL remains a per-voice opt-in for batch work like the podcast render, where total time matters more than first byte.

Recommended deployment (Dankclaw)

As decided in Gitea issue #2, this stays CPU-only on Dankclaw: the RTX 3090 has carried frank-zealot since 2026-10-17 and both R9700s are frank-archon's, so no card may be asked to co-host TTS. The shape is one engine plus one façade, and nothing here is deployed as of 2026-10-18: frank-tts has neither a unit nor a container on the host, and nothing listens on 7772 or 8880.

Service Image / install Port CPU Notes
TTS engine arm A qwentts.cpp tts-server on the Qwen3-TTS GGUFs (decided 2026-10-18, above); arm B mainline llama-tts stays in the repo as the llama-tts-cli rollback; vllm/vllm-omni + Qwen3-TTS only if a GPU is ever deliberately freed 8880 (container-internal) open, evidence above arm A serves OpenAI /v1/audio/speech itself and boot-warms each voice; arm B has no HTTP path and runs through the façade's CLI backend
frank-tts dankclaw/voiceservice/ (stdlib container) + scripts/frank-tts.env + its quadlet 7772 yes voice name → /voices/<name>/reference.wav, voice/voice_id on the wire, mp3 re-encode; backends omni-http (forwards base64 reference_audio) and llama-tts-cli (--tts-speaker-file, nice 19)
STT (English) Parakeet TDT 1.1B behind a FastAPI wrapper, or whisper.cpp server 8882 yes CC-BY-4.0; LibriSpeech-clean WER 1.39 (model card)
STT (multilingual, opt.) faster-whisper large-v3 int8 8883 yes MIT
VAD Silero (embedded) — yes MIT; model file 2 327 524 B

7772 is frank-tts's host port — the next slot in the fleet's model-endpoint family (7770 frank-dragoon, 7771 frank-zealot). The engine behind it keeps 8880, but not as a host-loopback publish: netavark DNATs published ports only, and a bridge container cannot reach a 127.0.0.1 bind on the host — that is the lesson that 502'd hermes (AGENTS.md). host.containers.internal (169.254.1.2) is the gateway, so it reaches a host service only when that service binds a routable address; scripts/frank-tts.env names it as the engine's address, which holds for a 0.0.0.0 bind and not for a loopback one. The deployed pair therefore shares one quadlet network and the façade addresses the engine by container name.

Voice-creation workflow (the "from sample files" answer)

  1. Record 2–3 min clean speech (24 kHz 16-bit mono WAV; ffmpeg-normalize).
  2. Zero-shot: keep the clip as ref_audio — Chatterbox/Qwen3-TTS/CosyVoice/ GLM-TTS clone from it per request (no training).
  3. Dedicated voice: upload once to Qwen3-TTS via /v1/audio/voices (vLLM-Omni) and reference by ID; or LoRA-fine-tune VoxCPM2 (5–10 min audio); or the GPT-SoVITS 1-min fine-tune for the classic WebUI workflow.
  4. Kokoro fine-tune only if the voice must live inside OMP's offline local backend — highest effort, Kokoro-styled output.

Best practices

  • Prefer zero-shot cloning over fine-tuning for a personal voice: a modern cloner (Chatterbox/Qwen3-TTS) on a 10 s–3 min reference beats a Kokoro fine-tune on quality and effort.
  • Normalize reference clips to 24 kHz 16-bit mono WAV before use; extract clean voice with Demucs if the source is music/mixed.
  • Serve through vLLM-Omni when you want a single OpenAI-compatible endpoint for many TTS models — it is the only server with the standard /v1/audio/speech + /v1/audio/voices shape across the 2026 lineup.
  • Check the license before serving publicly: several strong cloners are non-commercial (Higgs v3, Breeze-TTS-2, Spark-TTS, Voxtral) or carry custom licenses (IndexTTS, Fish S2, XTTS v2). For a personal homelab voice, MIT / Apache-2.0 (Chatterbox, Qwen3-TTS, GLM-TTS, VoxCPM2) is the safe set.
  • Keep TTS off the GPUs. The 2026-09-22 advice to "co-locate on the 3090 at --gpu-memory-utilization ~0.35" is retired: that card belongs to frank-zealot (2026-10-17) and the R9700s to frank-archon, and Gitea issue #2 measures both as full. Run TTS on CPU; revisit only if the operator frees a card on purpose.
  • Guard the endpoint like any other service: if vhosted, it goes behind the Anubis → Authentik chain; non-browser clients (OMP's curl) must target the internal address, never the public hostname.

Citations

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