acdf098c41
Mirrors the ASR backend pattern exactly: a BACKENDS dict in
tts/router.py maps a backend name to its worker URL, so adding the
next backend is just a new worker file + venv + supervisord entry +
one dict line. XTTS-v2 runs in its own venv (--system-site-packages,
inherits base image torch/CUDA) as a new supervisord program on
port 8005.
XTTS is zero-shot voice cloning, so a reference-voice library was
added (/tts/voices CRUD, stored under /srv/tts/voices) — synthesis
requires picking a previously uploaded voice. Results and model
cache live under /srv/tts/{results,models-cache}, new quadlet
volumes, owned by the same 983:983 user as the existing /srv/asr
dirs.
Fixed two environment issues uncovered while getting XTTS to
actually run inside the container (non-root user, root-built venvs):
- coqui-tts only pins transformers>=4.57 with no ceiling, so pip
installed an incompatible 5.x; pinned to the last 4.x release.
- HOME defaults to /app (owned by root) for the container's runtime
user, so numba/matplotlib/torch cache writes failed; HOME is now
forced to /tmp in all three workers (faster_whisper, qwen3, xtts)
before any of those libraries get imported.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
118 lines
3.6 KiB
Python
118 lines
3.6 KiB
Python
from __future__ import annotations
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import argparse
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import os
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import tempfile
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from pathlib import Path
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from typing import Any, Dict
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import sys
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sys.path.insert(0, "/app")
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from tts.config import TTS_MODEL_CACHE, ensure_runtime_dirs
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# TTS.api를 import하기 전에 설정해야 적용된다.
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os.environ.setdefault("TTS_HOME", str(TTS_MODEL_CACHE))
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os.environ.setdefault("COQUI_TOS_AGREED", "1") # XTTS(CPML 라이선스) 동의 프롬프트를 비대화식으로 통과
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# 컨테이너는 983 유저로 실행되는데 HOME(/app)이 root 소유라 numba/matplotlib/torch가
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# 각자 ~/.cache, ~/.config 아래에 쓰려다 실패한다. HOME을 쓰기 가능한 곳으로 돌린다.
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os.environ["HOME"] = "/tmp" # 컨테이너 기본 HOME=/app은 983 유저가 쓰기 불가 (setdefault로는 덮어쓰기 안 됨)
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os.environ.setdefault("NUMBA_CACHE_DIR", "/tmp/numba_cache")
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import soundfile as sf
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import uvicorn
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from fastapi import FastAPI, Form, HTTPException
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from fastapi.responses import Response
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app = FastAPI(title="TTS XTTS Worker")
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MODEL_NAME = "tts_models/multilingual/multi-dataset/xtts_v2"
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_MODEL_CACHE: Dict[str, Any] = {}
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def _log(msg: str) -> None:
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print(f"[xtts] {msg}", flush=True)
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def _device() -> str:
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try:
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import torch
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return "cuda" if torch.cuda.is_available() else "cpu"
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except Exception:
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return "cpu"
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def _load_model() -> Any:
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if MODEL_NAME in _MODEL_CACHE:
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return _MODEL_CACHE[MODEL_NAME]
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from TTS.api import TTS
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_log(f"loading model {MODEL_NAME}")
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tts = TTS(MODEL_NAME).to(_device())
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_MODEL_CACHE[MODEL_NAME] = tts
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_log("model loaded")
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return tts
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@app.on_event("startup")
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def startup() -> None:
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ensure_runtime_dirs()
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@app.get("/health")
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def health() -> Dict[str, Any]:
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return {"status": "ok", "device": _device(), "loaded": MODEL_NAME in _MODEL_CACHE}
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@app.post("/synthesize")
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def synthesize(
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text: str = Form(...),
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language: str = Form("ko"),
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speaker_wav: str = Form(...),
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) -> Response:
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speaker_path = Path(speaker_wav)
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if not speaker_path.exists():
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raise HTTPException(status_code=400, detail=f"speaker_wav not found: {speaker_wav}")
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try:
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tts = _load_model()
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except Exception as e:
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_log(f"model load error: {type(e).__name__}: {e}")
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raise HTTPException(status_code=500, detail=f"모델 로드 실패: {type(e).__name__}: {e}")
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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out_path = Path(tmp.name)
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try:
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_log(f"synthesizing language={language} chars={len(text)}")
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tts.tts_to_file(
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text=text,
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speaker_wav=str(speaker_path),
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language=language,
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file_path=str(out_path),
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)
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audio_bytes = out_path.read_bytes()
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duration = sf.info(str(out_path)).duration
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return Response(
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content=audio_bytes,
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media_type="audio/wav",
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headers={"x-audio-duration": str(round(duration, 3))},
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)
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except HTTPException:
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raise
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except Exception as e:
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_log(f"synthesize error: {type(e).__name__}: {e}")
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raise HTTPException(status_code=500, detail=f"{type(e).__name__}: {e}")
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finally:
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out_path.unlink(missing_ok=True)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--host", default="0.0.0.0")
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parser.add_argument("--port", type=int, default=8005)
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args = parser.parse_args()
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ensure_runtime_dirs()
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_log(f"starting host={args.host} port={args.port} device={_device()}")
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uvicorn.run(app, host=args.host, port=args.port)
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