329 lines
9.4 KiB
Python
329 lines
9.4 KiB
Python
"""
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CosyVoice 3.0 声音克隆服务
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端口: 8010
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GPU: 0
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启动方式:
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conda activate cosyvoice
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python cosyvoice_server.py
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PM2 启动:
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pm2 start run_cosyvoice.sh --name vigent2-cosyvoice
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"""
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import os
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import sys
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import tempfile
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import time
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import asyncio
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from pathlib import Path
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# 设置 GPU
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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# CosyVoice 需要 Matcha-TTS 子模块
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SCRIPT_DIR = Path(__file__).parent
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sys.path.append(str(SCRIPT_DIR / "third_party" / "Matcha-TTS"))
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from fastapi import FastAPI, HTTPException, UploadFile, File, Form
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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import uvicorn
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app = FastAPI(title="CosyVoice 3.0 Voice Clone Service", version="1.0")
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MODEL_DIR = SCRIPT_DIR / "pretrained_models" / "Fun-CosyVoice3-0.5B"
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# 全局模型实例
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_model = None
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_model_loaded = False
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_poisoned = False
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# GPU 推理锁
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_inference_lock = asyncio.Lock()
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def _schedule_force_exit(reason: str, delay_sec: float = 1.5):
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"""超时后强制退出进程，让 PM2 立即拉起新进程。"""
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import threading
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def _killer():
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time.sleep(delay_sec)
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print(f"💥 Force exiting process: {reason}")
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os._exit(1)
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threading.Thread(target=_killer, daemon=True).start()
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def load_model():
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"""加载模型（启动时调用）"""
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global _model, _model_loaded
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if _model_loaded:
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return
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print(f"🔄 Loading CosyVoice 3.0 model from {MODEL_DIR}...")
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start = time.time()
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from cosyvoice.cli.cosyvoice import AutoModel
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_model = AutoModel(model_dir=str(MODEL_DIR), fp16=True)
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_model_loaded = True
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print(f"✅ CosyVoice 3.0 model loaded in {time.time() - start:.1f}s")
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class HealthResponse(BaseModel):
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service: str
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model: str
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ready: bool
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gpu_id: int
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def _startup_selftest():
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"""启动自检：用短文本做一次推理，验证 GPU 推理链路可用。"""
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import torch
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print("🔍 Running startup self-test inference...")
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start = time.time()
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test_text = "你好"
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# 使用一段静音作为参考音频（0.5秒 24kHz）
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ref_audio_path = None
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try:
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
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ref_audio_path = tmp.name
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import torchaudio
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silence = torch.zeros(1, 12000) # 0.5s @ 24kHz
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torchaudio.save(ref_audio_path, silence, 24000)
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prompt_text = f"You are a helpful assistant.<|endofprompt|>你好"
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results = list(_model.inference_zero_shot(
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test_text,
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prompt_text,
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ref_audio_path,
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stream=False,
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text_frontend=True,
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))
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if not results:
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raise RuntimeError("Self-test returned empty results")
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segments = [r["tts_speech"] for r in results if isinstance(r, dict) and "tts_speech" in r]
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if not segments:
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raise RuntimeError("Self-test returned no tts_speech segments")
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torch.cuda.empty_cache()
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print(f"✅ Self-test passed in {time.time() - start:.1f}s "
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f"(output shape: {segments[0].shape})")
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return True
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except Exception as e:
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print(f"❌ Self-test FAILED: {e}")
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import traceback
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traceback.print_exc()
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try:
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torch.cuda.empty_cache()
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except:
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pass
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return False
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finally:
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if ref_audio_path:
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try:
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os.unlink(ref_audio_path)
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except:
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pass
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@app.on_event("startup")
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async def startup():
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"""服务启动时预加载模型并自检推理"""
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try:
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load_model()
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except Exception as e:
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print(f"❌ Model loading failed: {e}")
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import traceback
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traceback.print_exc()
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return
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# 自检推理 — 失败则标记为不可用
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global _model_loaded
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if not _startup_selftest():
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_model_loaded = False
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print("⚠️ Self-test failed, marking service as NOT ready")
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@app.get("/health", response_model=HealthResponse)
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async def health():
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"""健康检查"""
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gpu_ok = False
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try:
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import torch
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gpu_ok = torch.cuda.is_available()
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except:
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pass
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return HealthResponse(
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service="CosyVoice 3.0 Voice Clone",
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model="Fun-CosyVoice3-0.5B",
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ready=_model_loaded and gpu_ok and not _poisoned,
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gpu_id=0
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)
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@app.post("/generate")
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async def generate(
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ref_audio: UploadFile = File(...),
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text: str = Form(...),
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ref_text: str = Form(...),
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language: str = Form("Chinese"),
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speed: float = Form(1.0),
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):
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"""
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声音克隆生成
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Args:
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ref_audio: 参考音频文件 (WAV)
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text: 要合成的文本
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ref_text: 参考音频的转写文字
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language: 语言（兼容参数，CosyVoice 自动检测语言）
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Returns:
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生成的音频文件 (WAV)
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"""
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global _poisoned
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if not _model_loaded:
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raise HTTPException(status_code=503, detail="Model not loaded")
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if _poisoned:
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raise HTTPException(status_code=503, detail="Service poisoned after timeout, waiting for restart")
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if _inference_lock.locked():
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raise HTTPException(status_code=429, detail="GPU busy, please retry later")
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import torch
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import torchaudio
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# 保存上传的参考音频到临时文件
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_ref:
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content = await ref_audio.read()
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tmp_ref.write(content)
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ref_audio_path = tmp_ref.name
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# 参考音频过长时自动截取前 10 秒（CosyVoice 建议 3-10 秒）
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MAX_REF_SEC = 10
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try:
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info = torchaudio.info(ref_audio_path)
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ref_dur = info.num_frames / info.sample_rate
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if ref_dur > MAX_REF_SEC:
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print(f"✂️ Ref audio too long ({ref_dur:.1f}s), trimming to {MAX_REF_SEC}s")
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wav, sr = torchaudio.load(ref_audio_path, num_frames=int(info.sample_rate * MAX_REF_SEC))
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torchaudio.save(ref_audio_path, wav, sr)
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except Exception as e:
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print(f"⚠️ Could not check ref audio duration: {e}")
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output_path = tempfile.mktemp(suffix=".wav")
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try:
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async with _inference_lock:
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print(f"🎤 Generating: {text[:50]}... ({len(text)} chars)")
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print(f"📝 Ref text: {ref_text[:50]}...")
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print(f"🌐 Language: {language}")
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print(f"⚡ Speed: {speed}")
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start = time.time()
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# 超时保护：基础60秒 + 每字符2秒，上限300秒
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timeout_sec = min(60 + len(text) * 2, 300)
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# CosyVoice3 的 prompt_text 格式
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prompt_text = f"You are a helpful assistant.<|endofprompt|>{ref_text}"
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def _do_inference():
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"""在线程池中执行推理"""
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results = list(_model.inference_zero_shot(
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text,
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prompt_text,
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ref_audio_path,
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stream=False,
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speed=speed,
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text_frontend=True,
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))
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if not results:
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raise RuntimeError("CosyVoice returned empty results")
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segments = [r["tts_speech"] for r in results if isinstance(r, dict) and "tts_speech" in r]
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if not segments:
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raise RuntimeError("CosyVoice returned no tts_speech segments")
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if len(segments) == 1:
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merged = segments[0]
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else:
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gap = torch.zeros((segments[0].shape[0], int(_model.sample_rate * 0.05)), dtype=segments[0].dtype)
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parts = [segments[0]]
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for seg in segments[1:]:
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parts.append(gap)
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parts.append(seg)
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merged = torch.cat(parts, dim=-1)
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return merged, _model.sample_rate
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try:
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speech, sr = await asyncio.wait_for(
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asyncio.to_thread(_do_inference),
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timeout=timeout_sec,
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)
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except asyncio.TimeoutError:
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_poisoned = True
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print(f"⏰ Generation timed out after {timeout_sec}s for {len(text)} chars — service POISONED")
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torch.cuda.empty_cache()
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_schedule_force_exit("generation timeout")
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raise HTTPException(status_code=500, detail=f"生成超时({timeout_sec}s)，请缩短文本后重试")
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torch.cuda.empty_cache()
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torchaudio.save(output_path, speech, sr)
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duration = speech.shape[-1] / sr
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print(f"✅ Generated in {time.time() - start:.1f}s, duration: {duration:.1f}s")
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return FileResponse(
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output_path,
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media_type="audio/wav",
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filename="output.wav",
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background=None
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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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print(f"❌ Generation failed: {e}")
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try:
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torch.cuda.empty_cache()
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except:
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pass
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raise HTTPException(status_code=500, detail=str(e))
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finally:
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try:
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os.unlink(ref_audio_path)
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except:
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pass
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@app.on_event("shutdown")
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async def shutdown():
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"""清理临时文件"""
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import glob
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for f in glob.glob("/tmp/tmp*.wav"):
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try:
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os.unlink(f)
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except:
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pass
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if __name__ == "__main__":
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uvicorn.run(
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app,
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host="0.0.0.0",
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port=8010,
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log_level="info"
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)
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