Files

T

vh b805075bdf stacks: parakeet healthcheck (curl→wget); qwen3-tts variant labels

- parakeet/compose.yaml: healthcheck was using curl which isn't in the
  image (only wget is, via apt). 2,190 failing checks — switched to
  `wget -q -O /dev/null`, container went healthy on recreate.
- qwen3-tts/.env.example: variant annotation was reversed. The upstream
  wrapper's runtime error is unambiguous: voice cloning requires the
  -Base variant, not -CustomVoice. Corrected the comment block and
  flipped the default to Qwen/Qwen3-TTS-12Hz-1.7B-Base.
- qwen3-tts/README.md: 0.6B switch snippet now suffixes -Base too,
  since plain `Qwen/Qwen3-TTS-12Hz-0.6B` isn't published on HF.

2026-04-24 21:56:57 -07:00

3.7 KiB

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Qwen3-TTS

Alibaba's open-weight TTS (Apache 2.0, released Jan 2026), deployed via the groxaxo/Qwen3-TTS-Openai-Fastapi OpenAI-compatible wrapper.

Server: irv-ml1 (Irvine, WireGuard-only) Port: 8191 (container 8880) GPUs: both exposed (NVIDIA_VISIBLE_DEVICES=all); 1.7B model fits on either the RTX 3090 (24 GB) or A6000 (48 GB) with headroom Image: local/qwen3-tts:v1 — built locally from a pinned git SHA of the wrapper repo via docker buildx's git URL context

Why this stack alongside cosyvoice

CosyVoice 3 (the other stack on this host) emits Chinese-flavored phonemes when given English content. Confirmed against upstream issue FunAudioLLM/CosyVoice#1790 — closed without a fix as of 2026-04-14.

Qwen3-TTS is from the same Alibaba team but built English-first into the checkpoint: 10 languages, 97 ms streaming TTFB, instruction-driven emotional expression, voice cloning. It's the better choice for any English-narration use; keep CosyVoice 3 around for Chinese / dialect work where it shines.

API endpoints

Method + path	Purpose
`POST /v1/audio/speech`	OpenAI-compatible TTS (drop-in for OpenAI clients)
`GET /v1/voices`	List cloned voice profiles
`GET /v1/models`	List available model checkpoints
`GET /health`	Healthcheck
`GET /`	Web UI
`GET /voice-studio`	Gradio voice-cloning UI (when `ENABLE_VOICE_STUDIO=true`)

Voice cloning — different shape from cosyvoice

Profile-based, not voice-id-based:

Clone a voice via the /voice-studio web UI (uploads reference audio + transcript, names the profile, e.g. glados).
The wrapper writes /root/qwen3-tts/voice_library/profiles/<name>/{meta.json,reference.wav} inside the container (bind-mounted to /worktank/qwen3-tts/voices/profiles/<name>/ on the host).
Reference the profile in synthesis requests as voice="clone:<name>".

# OpenAI-shape, English with emotion via instruction
curl -X POST http://10.100.79.3:8191/v1/audio/speech \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "Qwen/Qwen3-TTS-12Hz-1.7B",
    "voice": "clone:glados",
    "input": "You should leave now. Visitors are not welcome.",
    "instructions": "speak with cold contempt",
    "response_format": "wav"
  }' \
  -o glados_en_angry.wav

(Note instructions field name; OpenAI standard. Whether English instructions actually drive emotion correctly here is the open question this stack exists to test — see the deploy playbook's verify section.)

Path layout

Host path	Container path	Purpose	Restic?
`/worktank/qwen3-tts/cache/`	`/root/.cache/huggingface`	Model cache (~5 GB after first run)	excluded
`/worktank/qwen3-tts/voices/`	`/root/qwen3-tts/voice_library`	Cloned profiles	included (precious)

Deploy

Via elway — see playbooks/deploy-qwen3-tts.yaml in the eshpfi-management root. The playbook builds the image, ensures the host dirs, installs compose + .env, brings up. First boot downloads the 1.7B model from HF (~6 GB), which is the slowest step.

scripts/elway irv-ml1 --playbook playbooks/deploy-qwen3-tts.yaml

To pin a different upstream wrapper SHA, set --var sha=<new-sha> or edit .env on the server and rebuild.

Switching to the smaller model

If 1.7B is too heavy or you need to share GPUs more aggressively:

ssh irv-ml1 '
  cd /opt/docker/compose/qwen3-tts && \
  sed -i "s|^QWEN3_TTS_MODEL=.*|QWEN3_TTS_MODEL=Qwen/Qwen3-TTS-12Hz-0.6B-Base|" .env && \
  docker compose up -d
'

The new model auto-downloads on next start (~2–3 GB).

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