vh
e0d1c44137
The 2026-09-06 headscale cutover retired irv-ml1's wg0 tunnel IP 10.100.79.3 (now 10.6.110.50). Repointed all LIVE canonical refs to the DNS NAME so the next move can't re-break them: homepage.href/siteMonitor labels across 25 stack composes, load-bearing env defaults (asset-engine INFERENCE_HOST, open-webui AUDIO_TTS_OPENAI_API_BASE_URL, skaldsong SKALDSONG_TTS_BASE_URL, zonos-gateway ZONOS_URL, dia), homepage services.yaml manual cards (Voice Design Studio, IRV-ML1), and servers/irv-ml1/ssh-target. Updated the stale 'WG tunnel' comment to the mesh reality. Left as-is: README curl-examples and .env.example comments (docs), and historical mentions in CLAUDE.md/persistent-memory. NOTE: applying the label repoints to the RUNNING irv-ml1 containers needs a recreate per service (labels read at creation); deployed .env values are separate from these canonical defaults.
Kyutai TTS
kyutai/tts-1.6b-en_fr — Kyutai's flagship streaming TTS (1.6B params, EN/FR bilingual, trained on 2.5M hours), served via the NillPointer/Kyutai-TTS-Server community OpenAI-compatible wrapper.
Why this stack exists
Kyutai's claim is the lowest streaming latency in this size class: 220 ms in solo setup; up to 32 simultaneous streams under 350 ms on a single L40-class GPU. Worth bench-comparing against:
| claimed latency | use case | |
|---|---|---|
| Kyutai TTS | ~220 ms | streaming EN/FR, conversational dialogue heritage |
| Kokoro | ~26 ms TTFB measured | low-latency English, fixed voice library |
| Chatterbox Turbo | ~1.2 s TTFB measured | English w/ cloning + 9 paralinguistic tags |
| Fish Audio S2-Pro | ~150 ms claimed | richest paralinguistic English |
| Voxtral | ~70 ms model latency | multilingual EN/FR/DE/ES/IT/PT/NL/HI |
Deployment notes
Kyutai's official deployment path is Rust + websockets only (no
HTTP, no OpenAI-compat). That doesn't fit the OpenAI-/v1/audio/speech
contract the rest of our TTS fleet uses. The
NillPointer/Kyutai-TTS-Server community wrapper bridges Kyutai's
native streaming to the OpenAI HTTP shape, which lets us slot it
into the same bench harness as the others.
Tradeoff: the wrapper adds Python overhead on the request path, so measured latency on this stack will be higher than Kyutai's 220 ms claim (which is for the bare Rust server). If we measure ~400-500 ms TTFB end-to-end, the wrapper is the floor — Kyutai itself is hitting its target.
Architecture heritage
Kyutai's TTS shares the Mimi neural codec + Moshi dialogue modeling framework. Both target full-duplex conversational AI (Moshi is their flagship speech-text foundation model). The TTS-only model is the "synthesis half" of the stack, distilled for low-latency streaming.
Trained on 2.5M hours — a different scaling regime from the others (CosyVoice 5k, Fish 10M, Voxtral undisclosed).
API
OpenAI-compat at http://10.100.79.3:8198:
# Single-shot synthesis.
curl -fsS -X POST http://10.100.79.3:8198/v1/audio/speech \
-H 'Content-Type: application/json' \
-d '{"model":"tts-1.6b-en_fr","input":"Hello there.","voice":"default","response_format":"wav"}' \
> out.wav
# Streaming.
curl -fsS -X POST http://10.100.79.3:8198/v1/audio/speech \
-H 'Content-Type: application/json' \
-d '{"model":"tts-1.6b-en_fr","input":"long passage…","voice":"default","stream":true}' \
| mpv --no-cache -
# Built-in voices.
curl http://10.100.79.3:8198/v1/audio/voices
Deploy
scripts/elway irv-ml1 --playbook playbooks/deploy-kyutai-tts.yaml
First boot pulls the kyutai/tts-1.6b-en_fr checkpoint (~3-6 GB) into the HF cache.
Hardware footprint
- VRAM: ~4-6 GB practical. Pinned to GPU 0 (RTX 3090) by default — the A6000 is hosting the heavier Fish S2-Pro / Voxtral.
- Disk: ~6 GB for the checkpoint + HF cache.