stacks: add Kokoro, VibeVoice 1.5B, Chatterbox Turbo (TTS slate fill-in)
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).
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# Chatterbox Turbo
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Resemble AI's 350M-param low-latency English TTS with zero-shot voice
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cloning, served via [devnen/Chatterbox-TTS-Server](https://github.com/devnen/Chatterbox-TTS-Server)
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— the most actively-maintained OpenAI-compat wrapper supporting Turbo.
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Model: [ResembleAI/chatterbox-turbo](https://huggingface.co/ResembleAI/chatterbox-turbo)
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— released April 2026, ~6× realtime, ~75 ms latency, MIT-licensed.
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## Why this stack exists
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Fills the **low-latency English voice-cloning** slot none of the other
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TTS own cleanly: Kokoro is fast but fixed-voice; IndexTTS-2 clones
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beautifully but is slow; Qwen3-TTS-Base clones at a higher quality bar
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but isn't tuned for sub-second latency. Chatterbox Turbo trades some
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fidelity for **6× realtime + 5-second-reference cloning**, ideal for
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real-time voice-agent use cases.
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| | use case |
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|---|---|
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| **Chatterbox Turbo** | low-latency English w/ voice cloning + paralinguistic tags |
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| Kokoro | low-latency English, fixed voice library |
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| IndexTTS-2 | English voice cloning + emotion vector / text control |
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| Qwen3-TTS-1.7B-Base | high-quality English voice cloning |
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| CosyVoice 3 | multilingual (Chinese-leaning) |
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| VibeVoice 1.5B | long-form / multi-speaker dialogue |
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## Headline features
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- **Zero-shot voice cloning from ~5 s reference** (base Chatterbox
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needs ~10 s; Turbo cuts that in half).
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- **Native paralinguistic tags inline in text** — drop these into your
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prompt and the model honors them:
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```
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[laugh] [cough] [sigh] [gasp] [whisper] [breath]
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```
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Different shape from IndexTTS-2's 8-vector emotion control: cleaner
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for "say it like this" markup directly in the prompt.
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- **Mandatory PerTh watermark** on outputs (Resemble policy, cannot
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be disabled). Non-issue for internal use; mention it if you ever
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ship Chatterbox-generated audio externally.
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## API
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OpenAI-compat at `http://10.100.79.3:8196`:
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```bash
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# Built-in voices.
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curl http://10.100.79.3:8196/v1/audio/voices
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# Single-shot synthesis with a built-in voice.
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curl -fsS -X POST http://10.100.79.3:8196/v1/audio/speech \
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-H 'Content-Type: application/json' \
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-d '{"model":"chatterbox-turbo","input":"Hello there. [laugh] What a day.","voice":"alloy","response_format":"wav"}' \
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> out.wav
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# Voice cloning — drop a 5 s reference WAV into
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# /worktank/chatterbox/reference_audio/glados.wav, then:
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curl -fsS -X POST http://10.100.79.3:8196/v1/audio/speech \
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-H 'Content-Type: application/json' \
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-d '{"model":"chatterbox-turbo","input":"I have all the time in the world.","voice":"glados"}' \
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> glados.wav
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# Streaming (where supported by the wrapper).
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curl -fsS -X POST http://10.100.79.3:8196/v1/audio/speech \
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-H 'Content-Type: application/json' \
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-d '{"model":"chatterbox-turbo","input":"long passage…","voice":"alloy","stream":true}' \
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| mpv --no-cache -
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```
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Native `/tts` endpoint (devnen wrapper extension) and OpenAPI at `/docs`.
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Healthcheck at `/health`.
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## Voice library
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Drop reference WAV / MP3 / FLAC into
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`/worktank/chatterbox/reference_audio/` on the host. The wrapper
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discovers new files on next request — no restart needed. Use clean
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~5 s clips, single speaker.
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Built-in OpenAI-style voice aliases (alloy, echo, fable, onyx, nova,
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shimmer) map to bundled presets — useful for OpenAI SDK clients that
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hardcode those names.
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## Deploy
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```bash
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scripts/elway irv-ml1 --playbook playbooks/deploy-chatterbox.yaml
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```
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Cold deploy budget:
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- Image build: ~5-8 GB (CUDA + torch + Chatterbox deps)
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- Model download: ~6 GB (Chatterbox Turbo weights, first run)
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- **Total: ~12 GB on /worktank/chatterbox/**
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First build: ~8-10 min. First synthesis: ~10-30 s warmup.
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## Switching the model
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```bash
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ssh irv-ml1 '
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cd /opt/docker/compose/chatterbox
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sed -i "s|^CHATTERBOX_MODEL_REPO=.*|CHATTERBOX_MODEL_REPO=ResembleAI/chatterbox|" .env
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docker compose up -d
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'
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```
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Options for `CHATTERBOX_MODEL_REPO`:
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- `ResembleAI/chatterbox-turbo` — flagship Turbo (default, fastest)
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- `ResembleAI/chatterbox` — base Chatterbox, 500M, slower but with
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exaggeration / CFG-weight knobs Turbo doesn't expose
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- `ResembleAI/chatterbox-multilingual` — 23 languages (slower than
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Turbo, useful if you need non-English on this stack vs CosyVoice 3)
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## Gotchas
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- **Python 3.10 only** (devnen wrapper). Image bakes that in; not
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something you'd hit unless you fork the Dockerfile.
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- **PerTh watermark** is unconditional. Can't disable.
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- **Turbo loses some knobs vs base Chatterbox** — no `exaggeration`
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or CFG-weight tuning. If you need expressive amplitude control,
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flip to base Chatterbox via the config swap above.
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- **Repo is fresh** (~weekly commits). Pin to a SHA in `.env`
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(`CHATTERBOX_SHA=...`) and rebuild monthly to ride upstream
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bug-fix progress.
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- **License**: wrapper MIT; weights MIT (Resemble) — including the
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watermark requirement.
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