vh
4549d241a7
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).
81 lines
2.8 KiB
Markdown
81 lines
2.8 KiB
Markdown
# Kokoro
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[hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) served
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via [remsky/Kokoro-FastAPI](https://github.com/remsky/Kokoro-FastAPI).
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## Why this stack exists
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Lowest-latency English TTS in the fleet by a wide margin — ~300 ms
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time-to-first-audio on GPU, 35-100x realtime, ~1 GB VRAM at fp16.
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Native streaming (Kokoro's `KPipeline.__call__` is a per-phrase
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generator) and the wrapper exposes it via OpenAI-compat `stream=true`
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over chunked HTTP — drop-in for any OpenAI SDK client.
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Complementary to the rest of the TTS slate:
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| **Kokoro** | low-latency English, fixed voice library |
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| **Chatterbox Turbo** | low-latency English w/ voice cloning + paralinguistic tags |
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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 podcast / multi-speaker dialogue |
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## API
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OpenAI-compat at `http://10.100.79.3:8193`:
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```bash
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# List built-in voices (~60 of them, named like af_bella, am_adam, jf_*, zf_*).
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curl http://10.100.79.3:8193/v1/audio/voices
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# Single-shot synthesis.
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curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \
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-H 'Content-Type: application/json' \
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-d '{"model":"kokoro","input":"Hello there.","voice":"af_bella","response_format":"wav"}' \
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> out.wav
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# Streaming — pipe straight into a player.
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curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \
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-H 'Content-Type: application/json' \
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-d '{"model":"kokoro","input":"long passage here…","voice":"af_bella","stream":true}' \
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| mpv --no-cache -
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# Voice mixing — sum voicepacks with weights.
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curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \
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-H 'Content-Type: application/json' \
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-d '{"model":"kokoro","input":"hello","voice":"af_bella(2)+af_heart(1)","response_format":"wav"}' \
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> mix.wav
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```
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Web UI at `/web` (browse voices + try in-place); OpenAPI at `/docs`.
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Supported `response_format`: `mp3 | wav | opus | flac | pcm`.
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## Voices
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- **Built-in**: 60+ in 8 languages (en-US, en-GB, ja, zh, es, fr, hi, it).
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Discoverable via `GET /v1/audio/voices` — naming convention is
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`<lang_code><gender_letter>_<name>` (e.g. `af_bella`, `am_adam`,
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`jf_alpha`, `zf_xiaobei`).
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- **Custom**: drop `.pt` voicepacks into `/worktank/kokoro/user_voices/`
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on the host. Wrapper auto-discovers them on next request (no
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restart). Training Kokoro voices is non-trivial — consult the
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hexgrad community for how-to.
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## Deploy
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```bash
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scripts/elway irv-ml1 --playbook playbooks/deploy-kokoro.yaml
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```
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First deploy: ~6.5 GB image pull from GHCR (~2-5 min on a fast link).
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No model download on first run — Kokoro-82M weights are baked in.
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Subsequent starts: a few seconds.
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## License
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Apache-2.0 for both the wrapper code (remsky/Kokoro-FastAPI) and the
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Kokoro-82M weights (hexgrad).
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