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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).
Kokoro
hexgrad/Kokoro-82M served via remsky/Kokoro-FastAPI.
Why this stack exists
Lowest-latency English TTS in the fleet by a wide margin — ~300 ms
time-to-first-audio on GPU, 35-100x realtime, ~1 GB VRAM at fp16.
Native streaming (Kokoro's KPipeline.__call__ is a per-phrase
generator) and the wrapper exposes it via OpenAI-compat stream=true
over chunked HTTP — drop-in for any OpenAI SDK client.
Complementary to the rest of the TTS slate:
| use case | |
|---|---|
| Kokoro | low-latency English, fixed voice library |
| 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 podcast / multi-speaker dialogue |
API
OpenAI-compat at http://10.100.79.3:8193:
# List built-in voices (~60 of them, named like af_bella, am_adam, jf_*, zf_*).
curl http://10.100.79.3:8193/v1/audio/voices
# Single-shot synthesis.
curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \
-H 'Content-Type: application/json' \
-d '{"model":"kokoro","input":"Hello there.","voice":"af_bella","response_format":"wav"}' \
> out.wav
# Streaming — pipe straight into a player.
curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \
-H 'Content-Type: application/json' \
-d '{"model":"kokoro","input":"long passage here…","voice":"af_bella","stream":true}' \
| mpv --no-cache -
# Voice mixing — sum voicepacks with weights.
curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \
-H 'Content-Type: application/json' \
-d '{"model":"kokoro","input":"hello","voice":"af_bella(2)+af_heart(1)","response_format":"wav"}' \
> mix.wav
Web UI at /web (browse voices + try in-place); OpenAPI at /docs.
Supported response_format: mp3 | wav | opus | flac | pcm.
Voices
- Built-in: 60+ in 8 languages (en-US, en-GB, ja, zh, es, fr, hi, it).
Discoverable via
GET /v1/audio/voices— naming convention is<lang_code><gender_letter>_<name>(e.g.af_bella,am_adam,jf_alpha,zf_xiaobei). - Custom: drop
.ptvoicepacks into/worktank/kokoro/user_voices/on the host. Wrapper auto-discovers them on next request (no restart). Training Kokoro voices is non-trivial — consult the hexgrad community for how-to.
Deploy
scripts/elway irv-ml1 --playbook playbooks/deploy-kokoro.yaml
First deploy: ~6.5 GB image pull from GHCR (~2-5 min on a fast link). No model download on first run — Kokoro-82M weights are baked in. Subsequent starts: a few seconds.
License
Apache-2.0 for both the wrapper code (remsky/Kokoro-FastAPI) and the Kokoro-82M weights (hexgrad).