vh
7c560a67fb
Three fixes from the first deploy attempt on irv-ml1: - build.target=production. Upstream Dockerfile is multistage; the last stage `cpu-base` was selected by default, producing a CPU-only image with no flash-attn and `torch ... whl/cpu`. - user: "0:0". Upstream image declares USER appuser but writes runtime state under /root (mode 0700). appuser cannot traverse /root, so /v1/voices 500s on PermissionError. Run as root to sidestep. - QWEN3_TTS_MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice. The bare `1.7B` id we had isn't a real HF identifier; upstream publishes -CustomVoice / -Base variants of each size. Use -CustomVoice so `voice="clone:<name>"` works. Tag bumped to v2 to keep the v1 cpu image distinguishable in the local registry. After: all 5 verify steps pass, GPU synthesis ~5s for 3-4s of audio, three contrasting English `instructions` produce three distinct hashes — emotion steering actually works (unlike CosyVoice's English path).
77 lines
3.3 KiB
YAML
77 lines
3.3 KiB
YAML
# Qwen3-TTS — Alibaba's open-weight TTS, deployed via the
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# groxaxo/Qwen3-TTS-Openai-Fastapi wrapper.
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#
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# Why this stack exists alongside cosyvoice: CosyVoice 3 emits
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# Chinese phonemes for non-Chinese inputs (upstream issue
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# FunAudioLLM/CosyVoice#1790, no fix). Qwen3-TTS is from the same
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# Alibaba team but with English first-class — 10 languages, 97 ms
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# streaming TTFB, voice cloning, instruction-driven emotional
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# expression. Released Jan 2026, Apache 2.0.
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#
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# Build: no prebuilt image; pinned to a SHA via docker buildx's git
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# context URL so subsequent rebuilds are reproducible. ~5–10 min on
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# first build (CUDA torch + transformers).
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#
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# Model: 1.7B flagship (~6–8 GB VRAM with bfloat16) by default; the
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# host has plenty of VRAM. Switch to the 0.6B in .env if you ever
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# need more headroom.
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#
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# Voice cloning shape DIFFERS from cosyvoice: profile-based, not
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# voice-id. Profiles live under voice_library/profiles/<name>/ with
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# meta.json + reference.wav, and are referenced as
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# `voice="clone:<name>"` in /v1/audio/speech requests.
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#
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# All tunables live in .env — edit that, not this file.
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services:
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qwen3-tts:
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image: local/qwen3-tts:${QWEN3_TTS_TAG}
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build:
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context: https://github.com/groxaxo/Qwen3-TTS-Openai-Fastapi.git#${QWEN3_TTS_SHA}
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dockerfile: Dockerfile
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# Upstream Dockerfile defines five stages — without an explicit
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# target, buildkit picks the LAST named stage (`cpu-base`) and
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# produces a CPU-only image with no GPU torch + onnxruntime
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# (not -gpu). Pin to `production` to get the CUDA stack with
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# flash-attn that the model actually needs.
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target: production
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container_name: qwen3-tts
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restart: unless-stopped
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runtime: nvidia
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# Upstream image declares `USER appuser` (uid 1000) but writes
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# state under /root (mode 0700) — appuser can't traverse it, so
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# /v1/voices and any FS-touching endpoint 500s. The model loads
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# only because warmup happens in an early root phase. Pin to root
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# so all paths are reachable. Files written into the host bind
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# mounts become root-owned; that's fine for restic + sudo reads.
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user: "0:0"
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ports:
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- "${QWEN3_TTS_BIND:-0.0.0.0}:${QWEN3_TTS_PORT}:8880"
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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- PORT=8880
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- TTS_BACKEND=${QWEN3_TTS_BACKEND:-official}
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- TTS_MODEL_NAME=${QWEN3_TTS_MODEL:-Qwen/Qwen3-TTS-12Hz-1.7B}
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- TTS_WARMUP_ON_START=${QWEN3_TTS_WARMUP:-true}
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- TTS_MAX_CONCURRENT=${QWEN3_TTS_MAX_CONCURRENT:-1}
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- ENABLE_VOICE_STUDIO=${QWEN3_TTS_VOICE_STUDIO:-true}
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- VOICE_LIBRARY_DIR=/root/qwen3-tts/voice_library
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- HF_HOME=/root/.cache/huggingface
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volumes:
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- ${QWEN3_TTS_CACHE_DIR}:/root/.cache/huggingface
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- ${QWEN3_TTS_VOICES_DIR}:/root/qwen3-tts/voice_library
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healthcheck:
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test: ["CMD-SHELL", "curl -fsS http://localhost:8880/health >/dev/null || exit 1"]
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interval: 30s
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timeout: 10s
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retries: 3
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# First boot pulls torch + Qwen3-TTS-12Hz-1.7B (~6 GB) and
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# optionally warms the model — give it a generous budget.
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start_period: 600s
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labels:
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- homepage.group=AI Systems
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- homepage.name=Qwen3-TTS
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- homepage.icon=mdi-account-voice
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- homepage.description=Multilingual TTS with English-first emotion (irv-ml1)
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- homepage.href=http://10.100.79.3:${QWEN3_TTS_PORT}
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