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).
59 lines
3.0 KiB
Bash
59 lines
3.0 KiB
Bash
# Qwen3-TTS stack tunables. Copy to `.env` on irv-ml1 before deploying.
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# ── build pin ────────────────────────────────────────────────────────
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# SHA of groxaxo/Qwen3-TTS-Openai-Fastapi to build from. Bump + rebuild
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# when you want upstream wrapper updates.
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QWEN3_TTS_SHA=10323ce778c48a75dbda93d0a4891983fb371f58
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# Local image tag — bump when you change build context to force a
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# fresh layer build. v2 = first GPU build (target=production); v1
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# was the accidental CPU-only image (last stage of upstream's
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# multi-stage Dockerfile).
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QWEN3_TTS_TAG=v2
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# ── network ──────────────────────────────────────────────────────────
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# Host port (container listens on 8880 internally).
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QWEN3_TTS_PORT=8191
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# Bind address. 0.0.0.0 exposes on all interfaces (incl. WG tunnel
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# interface 10.100.79.3); 127.0.0.1 restricts to local-only.
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QWEN3_TTS_BIND=0.0.0.0
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# ── runtime ──────────────────────────────────────────────────────────
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# Inference backend. `official` = default upstream; `optimized` =
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# faster but slightly less robust; `vllm_omni` = vLLM-backed (needs
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# more VRAM); `pytorch` = bare pytorch path.
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QWEN3_TTS_BACKEND=official
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# Model variant. Upstream publishes four checkpoints on HF:
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# Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice — flagship, voice cloning
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# Qwen/Qwen3-TTS-12Hz-1.7B-Base — flagship, no cloning
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# Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice — lightweight, voice cloning
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# Qwen/Qwen3-TTS-12Hz-0.6B-Base — lightweight, no cloning
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# 1.7B = ~6–8 GB VRAM bfloat16, best quality. 0.6B = ~2–3 GB.
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# Use -CustomVoice for `voice="clone:<name>"` to work.
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QWEN3_TTS_MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
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# Warm the model on container start so the first synthesis request
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# doesn't pay the load latency. Adds ~30 s to startup. Recommended.
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QWEN3_TTS_WARMUP=true
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# Concurrency cap on synthesis requests. Single GPU + 1.7B model →
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# leave at 1 unless you're load-testing.
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QWEN3_TTS_MAX_CONCURRENT=1
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# Mount the gradio voice-studio UI at /voice-studio for browser-side
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# voice cloning. Set "false" to disable for headless deployments.
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QWEN3_TTS_VOICE_STUDIO=true
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# ── persistent storage on the host ───────────────────────────────────
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# HuggingFace cache (model weights, ~5 GB after first run). Bind-mounted
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# so model state survives container recreate. Excluded from restic
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# (regenerable from HF Hub).
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QWEN3_TTS_CACHE_DIR=/worktank/qwen3-tts/cache
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# Cloned voice profiles (meta.json + reference.wav per voice). Precious
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# — cloned voices need the original reference audio to recreate.
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# Included in restic.
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QWEN3_TTS_VOICES_DIR=/worktank/qwen3-tts/voices
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