365 lines
14 KiB
Python
365 lines
14 KiB
Python
"""
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唇形同步服务
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通过 subprocess 调用 LatentSync conda 环境进行推理
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配置为使用 GPU1 (CUDA:1)
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"""
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import os
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import shutil
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import subprocess
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import tempfile
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import httpx
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from pathlib import Path
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from loguru import logger
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from typing import Optional
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from app.core.config import settings
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class LipSyncService:
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"""唇形同步服务 - LatentSync 1.6 集成 (Subprocess 方式)"""
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def __init__(self):
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self.use_local = settings.LATENTSYNC_LOCAL
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self.api_url = settings.LATENTSYNC_API_URL
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self.latentsync_dir = settings.LATENTSYNC_DIR
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self.gpu_id = settings.LATENTSYNC_GPU_ID
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# Conda 环境 Python 路径
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# 根据服务器实际情况调整
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self.conda_python = Path.home() / "ProgramFiles" / "miniconda3" / "envs" / "latentsync" / "bin" / "python"
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# 运行时检测
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self._weights_available: Optional[bool] = None
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def _check_weights(self) -> bool:
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"""检查模型权重是否存在"""
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if self._weights_available is not None:
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return self._weights_available
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required_files = [
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self.latentsync_dir / "checkpoints" / "latentsync_unet.pt",
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self.latentsync_dir / "checkpoints" / "whisper" / "tiny.pt",
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]
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required_dirs = [
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self.latentsync_dir / "configs" / "unet",
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self.latentsync_dir / "latentsync",
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self.latentsync_dir / "scripts",
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]
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files_ok = all(f.exists() for f in required_files)
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dirs_ok = all(d.exists() for d in required_dirs)
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self._weights_available = files_ok and dirs_ok
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if self._weights_available:
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logger.info("✅ LatentSync 权重文件已就绪")
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else:
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missing_files = [str(f) for f in required_files if not f.exists()]
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missing_dirs = [str(d) for d in required_dirs if not d.exists()]
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logger.warning(f"⚠️ 缺少文件: {missing_files}")
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logger.warning(f"⚠️ 缺少目录: {missing_dirs}")
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return self._weights_available
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def _check_conda_env(self) -> bool:
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"""检查 conda 环境是否可用"""
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if not self.conda_python.exists():
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logger.warning(f"⚠️ Conda Python 不存在: {self.conda_python}")
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return False
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return True
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def _preprocess_video(self, video_path: str, output_path: str, target_height: int = 720) -> str:
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"""
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视频预处理：压缩视频以加速后续处理
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- 限制最大高度为 target_height (默认720p)
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- 保持宽高比
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- 使用快速编码预设
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Returns: 预处理后的视频路径
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"""
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import subprocess
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import json
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# 获取视频信息 (使用 JSON 格式更可靠)
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probe_cmd = [
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"ffprobe", "-v", "error",
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"-select_streams", "v:0",
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"-show_entries", "stream=height,width",
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"-of", "json",
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video_path
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]
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try:
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result = subprocess.run(probe_cmd, capture_output=True, text=True, timeout=10)
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if result.returncode != 0:
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logger.warning(f"⚠️ ffprobe 失败: {result.stderr[:100]}")
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return video_path
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probe_data = json.loads(result.stdout)
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streams = probe_data.get("streams", [])
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if not streams:
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logger.warning("⚠️ 无法获取视频流信息，跳过预处理")
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return video_path
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current_height = streams[0].get("height", 0)
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current_width = streams[0].get("width", 0)
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if current_height == 0:
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logger.warning("⚠️ 视频高度为 0，跳过预处理")
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return video_path
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logger.info(f"📹 原始视频分辨率: {current_width}×{current_height}")
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except json.JSONDecodeError as e:
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logger.warning(f"⚠️ ffprobe 输出解析失败: {e}")
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return video_path
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except subprocess.TimeoutExpired:
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logger.warning("⚠️ ffprobe 超时，跳过预处理")
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return video_path
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except Exception as e:
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logger.warning(f"⚠️ 获取视频信息失败: {e}")
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return video_path
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# 如果视频已经足够小，跳过压缩
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if current_height <= target_height:
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logger.info(f"📹 视频高度 {current_height}p <= {target_height}p，无需压缩")
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return video_path
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logger.info(f"📹 预处理视频: {current_height}p → {target_height}p")
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# 使用 FFmpeg 压缩
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compress_cmd = [
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"ffmpeg", "-y",
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"-i", video_path,
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"-vf", f"scale=-2:{target_height}", # 保持宽高比，高度设为 target_height
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"-c:v", "libx264",
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"-preset", "ultrafast", # 最快编码速度
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"-crf", "23", # 质量因子
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"-c:a", "copy", # 音频直接复制
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output_path
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]
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try:
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result = subprocess.run(
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compress_cmd,
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capture_output=True,
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text=True,
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timeout=120 # 增加超时时间到2分钟
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)
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if result.returncode == 0 and Path(output_path).exists():
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original_size = Path(video_path).stat().st_size / 1024 / 1024
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new_size = Path(output_path).stat().st_size / 1024 / 1024
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logger.info(f"✅ 视频压缩完成: {original_size:.1f}MB → {new_size:.1f}MB")
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return output_path
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else:
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logger.warning(f"⚠️ 视频压缩失败: {result.stderr[:200]}")
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return video_path
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except subprocess.TimeoutExpired:
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logger.warning("⚠️ 视频压缩超时，使用原始视频")
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return video_path
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except Exception as e:
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logger.warning(f"⚠️ 视频压缩异常: {e}")
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return video_path
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async def generate(
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self,
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video_path: str,
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audio_path: str,
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output_path: str,
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fps: int = 25
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) -> str:
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"""生成唇形同步视频"""
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logger.info(f"🎬 唇形同步任务: {Path(video_path).name} + {Path(audio_path).name}")
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Path(output_path).parent.mkdir(parents=True, exist_ok=True)
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if self.use_local:
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return await self._local_generate(video_path, audio_path, output_path, fps)
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else:
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return await self._remote_generate(video_path, audio_path, output_path, fps)
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async def _local_generate(
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self,
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video_path: str,
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audio_path: str,
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output_path: str,
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fps: int
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) -> str:
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"""使用 subprocess 调用 LatentSync conda 环境"""
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# 检查前置条件
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if not self._check_conda_env():
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logger.warning("⚠️ Conda 环境不可用，使用 Fallback")
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shutil.copy(video_path, output_path)
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return output_path
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if not self._check_weights():
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logger.warning("⚠️ 模型权重不存在，使用 Fallback")
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shutil.copy(video_path, output_path)
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return output_path
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logger.info("🔄 调用 LatentSync 推理 (subprocess)...")
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# 使用临时目录存放输出
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with tempfile.TemporaryDirectory() as tmpdir:
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tmpdir = Path(tmpdir)
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temp_output = tmpdir / "output.mp4"
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# 视频预处理：压缩高分辨率视频以加速处理
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preprocessed_video = tmpdir / "preprocessed_input.mp4"
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actual_video_path = self._preprocess_video(
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video_path,
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str(preprocessed_video),
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target_height=720
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)
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# 构建命令
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cmd = [
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str(self.conda_python),
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"-m", "scripts.inference",
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"--unet_config_path", "configs/unet/stage2_512.yaml",
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"--inference_ckpt_path", "checkpoints/latentsync_unet.pt",
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"--inference_steps", str(settings.LATENTSYNC_INFERENCE_STEPS),
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"--guidance_scale", str(settings.LATENTSYNC_GUIDANCE_SCALE),
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"--video_path", str(actual_video_path), # 使用预处理后的视频
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"--audio_path", str(audio_path),
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"--video_out_path", str(temp_output),
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"--seed", str(settings.LATENTSYNC_SEED),
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"--temp_dir", str(tmpdir / "cache"),
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]
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if settings.LATENTSYNC_ENABLE_DEEPCACHE:
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cmd.append("--enable_deepcache")
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# 设置环境变量
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env = os.environ.copy()
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env["CUDA_VISIBLE_DEVICES"] = str(self.gpu_id)
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logger.info(f"🖥️ 执行命令: {' '.join(cmd[:8])}...")
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logger.info(f"🖥️ GPU: CUDA_VISIBLE_DEVICES={self.gpu_id}")
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try:
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import asyncio
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# 使用 asyncio subprocess 实现真正的异步执行
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# 这样事件循环可以继续处理其他请求（如进度查询）
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process = await asyncio.create_subprocess_exec(
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*cmd,
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cwd=str(self.latentsync_dir),
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env=env,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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)
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# 等待进程完成，带超时
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try:
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stdout, stderr = await asyncio.wait_for(
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process.communicate(),
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timeout=900 # 15分钟超时
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)
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except asyncio.TimeoutError:
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process.kill()
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await process.wait()
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logger.error("⏰ LatentSync 推理超时 (15分钟)")
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shutil.copy(video_path, output_path)
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return output_path
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stdout_text = stdout.decode() if stdout else ""
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stderr_text = stderr.decode() if stderr else ""
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if process.returncode != 0:
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logger.error(f"LatentSync 推理失败:\n{stderr_text}")
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logger.error(f"stdout:\n{stdout_text[-1000:] if stdout_text else 'N/A'}")
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# Fallback
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shutil.copy(video_path, output_path)
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return output_path
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logger.info(f"LatentSync 输出:\n{stdout_text[-500:] if stdout_text else 'N/A'}")
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# 检查输出文件
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if temp_output.exists():
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shutil.copy(temp_output, output_path)
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logger.info(f"✅ 唇形同步完成: {output_path}")
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return output_path
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else:
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logger.warning("⚠️ 未找到输出文件，使用 Fallback")
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shutil.copy(video_path, output_path)
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return output_path
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except Exception as e:
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logger.error(f"❌ 推理异常: {e}")
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shutil.copy(video_path, output_path)
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return output_path
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async def _remote_generate(
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self,
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video_path: str,
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audio_path: str,
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output_path: str,
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fps: int
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) -> str:
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"""调用远程 LatentSync API 服务"""
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logger.info(f"📡 调用远程 API: {self.api_url}")
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try:
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async with httpx.AsyncClient(timeout=600.0) as client:
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with open(video_path, "rb") as vf, open(audio_path, "rb") as af:
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files = {
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"video": (Path(video_path).name, vf, "video/mp4"),
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"audio": (Path(audio_path).name, af, "audio/mpeg"),
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}
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data = {"fps": fps}
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response = await client.post(
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f"{self.api_url}/lipsync",
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files=files,
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data=data
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)
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if response.status_code == 200:
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with open(output_path, "wb") as f:
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f.write(response.content)
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logger.info(f"✅ 远程推理完成: {output_path}")
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return output_path
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else:
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raise RuntimeError(f"API 错误: {response.status_code}")
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except Exception as e:
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logger.error(f"远程 API 调用失败: {e}")
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shutil.copy(video_path, output_path)
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return output_path
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async def check_health(self) -> dict:
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"""健康检查"""
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conda_ok = self._check_conda_env()
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weights_ok = self._check_weights()
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# 检查 GPU
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gpu_ok = False
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gpu_name = "Unknown"
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if conda_ok:
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try:
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result = subprocess.run(
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[str(self.conda_python), "-c",
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"import torch; print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A')"],
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capture_output=True,
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text=True,
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env={**os.environ, "CUDA_VISIBLE_DEVICES": str(self.gpu_id)},
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timeout=30
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)
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gpu_name = result.stdout.strip()
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gpu_ok = gpu_name != "N/A" and result.returncode == 0
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except:
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pass
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return {
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"model": "LatentSync 1.6",
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"conda_env": conda_ok,
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"weights": weights_ok,
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"gpu": gpu_ok,
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"gpu_name": gpu_name,
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"gpu_id": self.gpu_id,
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"inference_steps": settings.LATENTSYNC_INFERENCE_STEPS,
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"guidance_scale": settings.LATENTSYNC_GUIDANCE_SCALE,
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"ready": conda_ok and weights_ok and gpu_ok
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}
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