927d59f805
CPU MeloTTS serialised under concurrent load (whisper STT + bot) and blew voice-reply TTS to 7-8s. Install the Blackwell-verified cu128 torch in the melo venv, select the GPU via MELO_DEVICE=cuda, and do a throwaway synth at worker startup so the one-off CUDA kernel-init (~5s) doesn't land on the user's first reply. Measured: ~0.3s/sentence on GPU vs ~1.2-2.6s on CPU. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
81 lines
4.0 KiB
Bash
Executable File
81 lines
4.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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# ============================================================================
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# Install a dedicated MeloTTS (Korean voice) venv at /opt/melo.
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#
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# Why a SEPARATE venv (not the brain-bridge /opt/venv):
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# - MeloTTS pins old deps (transformers 4.27.4 / tokenizers 0.13.3 / fugashi)
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# whose binary wheels exist only for cp311, so we use python3.11 here even
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# though the image's default interpreter is 3.12.
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# - It isolates the heavy torch/transformers stack from the slim bridge env,
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# which pins numpy<2 for faster-whisper.
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#
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# torch is the CUDA (cu128) build so MeloTTS runs on the GPU alongside Ollama +
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# Whisper. CPU synth serialised under concurrent load (whisper STT + bot) and
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# blew TTS up to 7-8s per reply; on the GPU a sentence synthesises in ~0.3s.
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# cu128 is the Blackwell (sm_120) wheel verified on this host's RTX 5050.
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# The worker selects the device via MELO_DEVICE=cuda (compose).
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# ============================================================================
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set -euxo pipefail
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export DEBIAN_FRONTEND=noninteractive
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apt-get update
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# Build deps for fugashi / mecab-python3 + a system MeCab dict, plus python3.11.
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apt-get install -y --no-install-recommends \
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software-properties-common build-essential pkg-config swig \
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libmecab-dev mecab mecab-ipadic-utf8
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add-apt-repository -y ppa:deadsnakes/ppa
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apt-get update
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apt-get install -y --no-install-recommends python3.11 python3.11-venv python3.11-dev
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rm -rf /var/lib/apt/lists/*
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python3.11 -m venv /opt/melo
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/opt/melo/bin/pip install --no-cache-dir --upgrade pip wheel setuptools
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# CUDA (cu128) torch first, so MeloTTS's unpinned `torch` dep is already
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# satisfied with the GPU build. Pinned to the Blackwell-verified versions
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# (2.11.0+cu128) for reproducible rebuilds.
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/opt/melo/bin/pip install --no-cache-dir torch==2.11.0+cu128 torchaudio==2.11.0+cu128 \
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--index-url https://download.pytorch.org/whl/cu128
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# MeloTTS from GitHub. The PyPI sdist is broken (its setup.py reads a
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# requirements.txt that is not shipped in the sdist), so install from the repo.
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# Pinned to a commit (not refs/heads/main) so rebuilds are reproducible.
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/opt/melo/bin/pip install --no-cache-dir \
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"https://github.com/myshell-ai/MeloTTS/archive/209145371cff8fc3bd60d7be902ea69cbdb7965a.tar.gz"
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# Korean g2p backend. MeloTTS otherwise tries to pip-install this on the first
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# Korean request, which fails in a network-isolated container at runtime.
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/opt/melo/bin/pip install --no-cache-dir python-mecab-ko python-mecab-ko-dic
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# Remove the full `unidic` package (its dictionary is never downloaded, only a
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# stub) so mecab-python3 falls back to the bundled `unidic_lite` dict. Without
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# this, importing melo's Japanese module fails with a missing-mecabrc error.
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/opt/melo/bin/pip uninstall -y unidic || true
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# Pre-cache every model asset MeloTTS pulls at runtime, so the worker starts
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# offline and the first Discord turn pays no download cost. Importing melo.api
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# fetches the Japanese (tohoku-nlp/bert-base-japanese-v3) and Korean
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# (kykim/bert-kor-base) BERT tokenizers plus nltk g2p data; loading the KR voice
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# downloads the OpenVoice KR config+checkpoint, and a real synth pulls the
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# Korean BERT weights. All of these go through huggingface_hub.
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#
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# CRITICAL: at runtime docker-compose mounts the `whisper_cache` named volume
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# over /root/.cache/huggingface (for faster-whisper). That volume would SHADOW
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# anything baked into the default HF cache, so we pin the melo worker to a
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# DEDICATED, non-volume cache dir (/opt/melo-cache) here AND in supervisord, and
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# warm it once. nltk_data (/root/nltk_data) is not volume-mounted so it stays.
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export HF_HOME=/opt/melo-cache
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mkdir -p "$HF_HOME"
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MELO_LANGUAGE=KR HF_HOME=/opt/melo-cache /opt/melo/bin/python - <<'PY'
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import tempfile
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from melo.api import TTS
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model = TTS(language="KR", device="cpu")
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out = tempfile.mktemp(suffix=".wav")
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model.tts_to_file("초기화 워밍업입니다.", model.hps.data.spk2id["KR"], out, speed=1.5)
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print("[setup-melo] warm-up KR synth OK ->", out)
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PY
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echo "[setup-melo] MeloTTS venv ready at /opt/melo"
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