b17961e9e3
Adds a dedicated MeloTTS Korean voice (speed 1.5) as the primary TTS engine, served by a long-lived in-container worker so each Discord turn pays only inference cost, not model-load cost. - bridge/melo_worker.py: tiny HTTP service in its own /opt/melo py3.11 venv, keeps the KR model warm, returns PCM16 WAV on POST /synth. - bridge/server.py: synthesize() routes to the melo worker first; Piper stays as an opt-in fallback (MELO_FALLBACK_PIPER, default off so Korean is never mangled through the English voice). /health reports tts_engine. - docker/setup-melo.sh: builds the isolated venv (pinned torch 2.12.0 / torchaudio 2.11.0 CPU, MeloTTS pinned to a commit for reproducible rebuilds), pre-fetches mecab-ko, and warms a dedicated HF cache (/opt/melo-cache) with a real KR synth so all BERT + KR checkpoint assets are baked into the image. - docker/supervisord.conf: runs melo-worker before the bridge with HF_HOME=/opt/melo-cache (the whisper_cache volume shadows the default HF cache) plus HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE so it reads the baked cache and never retries the network on load. - Dockerfile/.env.example: wire the melo build layer and config knobs. Verified: offline synth passes with --network none and the prod volume mounted; prod container recreated, all supervisord services up, bot logged in, and an end-to-end /tts call returns a 44.1kHz mono PCM16 WAV. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
78 lines
3.8 KiB
Bash
Executable File
78 lines
3.8 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 pinned to the CPU build: TTS runs on CPU so the GPU stays reserved
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# for Ollama + Whisper, and we avoid pulling multi-GB CUDA wheels.
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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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# CPU-only torch first, so MeloTTS's unpinned `torch` dep is already satisfied
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# and pip does not pull the CUDA build. Pinned for reproducible rebuilds (these
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# are the versions the CPU index resolved when this layer was verified).
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/opt/melo/bin/pip install --no-cache-dir torch==2.12.0 torchaudio==2.11.0 \
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--index-url https://download.pytorch.org/whl/cpu
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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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