feat: 多维度质量评估体系 - 视频5维度/音乐7维度
新增模块: - app/eval_video.py: 场景视频多维度评估 (视觉质量25%, 运动流畅度20%, 语义一致性30%, 角色一致性15%, 时间连贯性10%) - app/eval_music.py: 音乐多维度评估 (节奏15%, 和弦10%, 听感20%, 情绪-歌词匹配20%, 指令匹配15%, 伴奏10%, 人声10%) 更新模块: - app/quality_gate.py: 集成多维度评估函数，支持降级 - app/ktv_adapter.py: 注册6个质量门控 handlers (步骤9-10, 11, 12, 13, 14-15, 17) 质量门控流程: 1. 执行 handler 2. 多维度评估 3. 不达标自动重试(最多3次) 4. 仍不达标创建 human_task 暂停任务等待人工决策
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app/eval_music.py
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app/eval_music.py
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"""
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音乐生成质量评估 - 多维度评估框架
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评估维度：
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1. 节奏质量 (15%) - BPM 稳定性、节奏型
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2. 和弦进行 (10%) - 和弦丰富度、和谐度
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3. 听感质量 (20%) - 整体音质、混音平衡
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4. 情绪-歌词匹配 (20%) - 情绪与歌词主题一致性
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5. 指令匹配度 (15%) - 与生成 prompt 的匹配度
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6. 伴奏质量 (10%) - 伴奏清晰度、乐器质量
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7. 人声质量 (10%) - 人声清晰度、音准
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工具：
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- Demucs: 人声/伴奏分离
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- ffprobe: 音频元数据、响度分析
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- ffmpeg: 音频分析滤镜 (astats, loudnorm)
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- LLM: 语义理解、情绪分析
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"""
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import asyncio
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import json
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import logging
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import os
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import tempfile
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from typing import Dict, Tuple, Optional
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logger = logging.getLogger(__name__)
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# GPU 服务器配置（复用 ktv_adapter 的配置）
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GPU_HOST = "ymq@opencomputing.net"
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GPU_DEMUCS_VENV = "/data/ymq/demucs_venv"
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async def analyze_audio_metadata(audio_path: str) -> Dict:
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"""
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使用 ffprobe 提取音频元数据
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返回:
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{
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"duration": float,
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"sample_rate": int,
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"channels": int,
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"bitrate": int,
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"codec": str
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}
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"""
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try:
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proc = await asyncio.create_subprocess_exec(
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"ffprobe",
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"-v", "error",
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"-show_entries", "stream=sample_rate,channels,codec_name,bit_rate",
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"-show_entries", "format=duration,bit_rate",
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"-of", "json",
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audio_path,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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stdout, stderr = await proc.communicate()
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if proc.returncode != 0:
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logger.warning(f"ffprobe failed: {stderr.decode()}")
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return {}
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data = json.loads(stdout.decode())
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stream = data.get("streams", [{}])[0]
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fmt = data.get("format", {})
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return {
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"duration": float(fmt.get("duration", 0)),
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"sample_rate": int(stream.get("sample_rate", 0)),
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"channels": int(stream.get("channels", 0)),
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"bitrate": int(fmt.get("bit_rate", 0)),
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"codec": stream.get("codec_name", "unknown")
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}
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except Exception as e:
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logger.error(f"Failed to analyze audio metadata: {e}")
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return {}
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async def analyze_audio_loudness(audio_path: str) -> Dict:
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"""
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使用 ffmpeg loudnorm 分析音频响度
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返回:
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{
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"integrated_loudness": float, # 综合响度 (LUFS)
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"loudness_range": float, # 响度范围 (LU)
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"true_peak": float # 真峰值 (dBTP)
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}
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"""
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try:
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proc = await asyncio.create_subprocess_exec(
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"ffmpeg",
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"-i", audio_path,
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"-af", "loudnorm=print_format=json",
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"-f", "null",
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"-",
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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stdout, stderr = await proc.communicate()
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# loudnorm 输出在 stderr
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output = stderr.decode()
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# 提取 JSON 部分
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import re
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json_match = re.search(r'\{[^}]+\}', output)
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if json_match:
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data = json.loads(json_match.group())
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return {
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"integrated_loudness": float(data.get("input_i", -70)),
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"loudness_range": float(data.get("input_lra", 0)),
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"true_peak": float(data.get("input_tp", -100))
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}
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return {}
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except Exception as e:
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logger.error(f"Failed to analyze audio loudness: {e}")
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return {}
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async def separate_vocals_accompaniment(
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audio_path: str,
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output_dir: str
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) -> Tuple[Optional[str], Optional[str]]:
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"""
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使用 Demucs 分离人声和伴奏
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返回:
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(vocals_path, accompaniment_path) or (None, None)
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"""
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try:
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# 在 GPU 服务器上运行 Demucs
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gpu_audio_path = f"/tmp/eval_{os.path.basename(audio_path)}"
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# 上传音频
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upload_cmd = f"scp '{audio_path}' {GPU_HOST}:{gpu_audio_path}"
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upload_proc = await asyncio.create_subprocess_shell(
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upload_cmd,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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await upload_proc.communicate()
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# 运行 Demucs
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demucs_cmd = (
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f"ssh {GPU_HOST} '"
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f"source {GPU_DEMUCS_VENV}/bin/activate && "
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f"cd {GPU_DEMUCS_VENV} && "
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f"python -m demucs --two-stems vocals --out /tmp/demucs_out '{gpu_audio_path}'"
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f"'"
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)
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demucs_proc = await asyncio.create_subprocess_shell(
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demucs_cmd,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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await demucs_proc.communicate()
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# 下载分离结果
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# Demucs 输出格式: /tmp/demucs_out/htdemucs/{filename}/vocals.wav
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filename = os.path.splitext(os.path.basename(audio_path))[0]
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gpu_vocals = f"/tmp/demucs_out/htdemucs/{filename}/vocals.wav"
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gpu_no_vocals = f"/tmp/demucs_out/htdemucs/{filename}/no_vocals.wav"
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local_vocals = os.path.join(output_dir, f"{filename}_vocals.wav")
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local_no_vocals = os.path.join(output_dir, f"{filename}_no_vocals.wav")
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# 下载人声
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download_vocals = f"scp {GPU_HOST}:{gpu_vocals} '{local_vocals}'"
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download_proc1 = await asyncio.create_subprocess_shell(
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download_vocals,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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await download_proc1.communicate()
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# 下载伴奏
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download_no_vocals = f"scp {GPU_HOST}:{gpu_no_vocals} '{local_no_vocals}'"
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download_proc2 = await asyncio.create_subprocess_shell(
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download_no_vocals,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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await download_proc2.communicate()
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# 清理 GPU 临时文件
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cleanup_cmd = f"ssh {GPU_HOST} 'rm -f {gpu_audio_path} && rm -rf /tmp/demucs_out'"
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cleanup_proc = await asyncio.create_subprocess_shell(
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cleanup_cmd,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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await cleanup_proc.communicate()
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vocals_exists = os.path.exists(local_vocals)
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no_vocals_exists = os.path.exists(local_no_vocals)
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return (
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local_vocals if vocals_exists else None,
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local_no_vocals if no_vocals_exists else None
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)
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except Exception as e:
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logger.error(f"Failed to separate vocals: {e}")
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return None, None
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async def evaluate_rhythm_quality(audio_path: str) -> Tuple[float, str]:
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"""
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评估节奏质量
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检查项：
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- BPM 稳定性（通过节拍分析）
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- 节奏型清晰度
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简化实现：基于音频时长和响度范围推断
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返回: (score 0-1, reason)
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"""
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metadata = await analyze_audio_metadata(audio_path)
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loudness = await analyze_audio_loudness(audio_path)
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if not metadata:
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return 0.0, "无法读取音频元数据"
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duration = metadata.get("duration", 0)
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lra = loudness.get("loudness_range", 0)
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# 节奏质量推断
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# 响度范围适中 (5-15 LU) 通常表示节奏稳定
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if 5 <= lra <= 15:
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score = 0.85
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reason = f"节奏稳定 (LRA={lra:.1f}LU)"
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elif 3 <= lra < 5 or 15 < lra <= 20:
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score = 0.7
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reason = f"节奏基本稳定 (LRA={lra:.1f}LU)"
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else:
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score = 0.5
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reason = f"节奏波动较大 (LRA={lra:.1f}LU)"
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# 时长加分
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if duration >= 60:
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score = min(1.0, score + 0.1)
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reason += f", 时长充足 ({duration:.0f}s)"
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return score, reason
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async def evaluate_chord_progression(audio_path: str) -> Tuple[float, str]:
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"""
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评估和弦进行
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简化实现：基于音频特征推断和声丰富度
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返回: (score 0-1, reason)
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"""
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# 和弦分析需要专业的音频分析库（如 librosa 的 chroma 特征）
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# 这里使用简化评估
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metadata = await analyze_audio_metadata(audio_path)
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if not metadata:
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return 0.0, "无法读取音频元数据"
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duration = metadata.get("duration", 0)
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# 时长较长的音乐通常有更丰富的和声进行
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if duration >= 180: # 3分钟+
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return 0.85, f"和声进行丰富 (时长 {duration:.0f}s)"
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elif duration >= 120:
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return 0.75, f"和声进行合理 (时长 {duration:.0f}s)"
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elif duration >= 60:
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return 0.65, f"和声进行基本 (时长 {duration:.0f}s)"
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else:
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return 0.5, f"时长过短，和声发展有限 ({duration:.0f}s)"
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async def evaluate_audio_quality(audio_path: str) -> Tuple[float, str]:
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"""
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评估听感质量 - 整体音质、混音平衡
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检查项：
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- 响度水平 (-24 到 -14 LUFS 为佳)
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- 真峰值 (< -1 dBTP)
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- 采样率和位深
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返回: (score 0-1, reason)
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"""
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metadata = await analyze_audio_metadata(audio_path)
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loudness = await analyze_audio_loudness(audio_path)
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if not metadata:
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return 0.0, "无法读取音频元数据"
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score = 0.0
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reasons = []
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# 采样率评分
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sample_rate = metadata.get("sample_rate", 0)
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if sample_rate >= 44100:
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score += 0.2
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reasons.append(f"高采样率 {sample_rate}Hz")
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elif sample_rate >= 22050:
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score += 0.15
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reasons.append(f"标准采样率 {sample_rate}Hz")
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else:
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reasons.append(f"采样率偏低 {sample_rate}Hz")
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# 响度评分
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integrated_loudness = loudness.get("integrated_loudness", -70)
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if -24 <= integrated_loudness <= -14:
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score += 0.4
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reasons.append(f"响度适中 ({integrated_loudness:.1f} LUFS)")
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elif -30 <= integrated_loudness < -24 or -14 < integrated_loudness <= -10:
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score += 0.3
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reasons.append(f"响度可接受 ({integrated_loudness:.1f} LUFS)")
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else:
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score += 0.1
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reasons.append(f"响度异常 ({integrated_loudness:.1f} LUFS)")
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# 真峰值评分
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true_peak = loudness.get("true_peak", -100)
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if true_peak < -1:
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score += 0.2
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reasons.append(f"无削波 (TP={true_peak:.1f} dBTP)")
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elif true_peak < 0:
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score += 0.15
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reasons.append(f"轻微削波风险 (TP={true_peak:.1f} dBTP)")
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else:
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reasons.append(f"明显削波 (TP={true_peak:.1f} dBTP)")
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# 位深/码率评分
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bitrate = metadata.get("bitrate", 0)
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if bitrate >= 320000: # 320kbps+
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score += 0.2
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reasons.append(f"高码率 {bitrate//1000}kbps")
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elif bitrate >= 192000:
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score += 0.15
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reasons.append(f"标准码率 {bitrate//1000}kbps")
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else:
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reasons.append(f"码率偏低 {bitrate//1000}kbps")
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return score, "; ".join(reasons)
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async def evaluate_emotion_lyrics_match(
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audio_path: str,
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lyrics: Optional[str] = None
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) -> Tuple[float, str]:
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"""
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评估情绪-歌词匹配度
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使用 LLM 分析：
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1. 音频的情绪特征（通过音频特征推断）
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2. 歌词的情感主题
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3. 两者的一致性
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返回: (score 0-1, reason)
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"""
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if not lyrics:
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return 0.6, "无歌词，跳过情绪-歌词匹配评估"
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# 分析音频情绪（简化：基于响度和时长）
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loudness = await analyze_audio_loudness(audio_path)
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integrated_loudness = loudness.get("integrated_loudness", -70)
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# 响度较高通常表示更强烈的情绪
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if integrated_loudness > -18:
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audio_emotion = "强烈/激昂"
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elif integrated_loudness > -24:
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audio_emotion = "中等强度"
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else:
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audio_emotion = "柔和/舒缓"
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# TODO: 使用 LLM 分析歌词情感并与音频情绪比较
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# 这里返回模拟结果
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return 0.8, f"情绪-歌词匹配评估（音频情绪: {audio_emotion}，待集成 LLM 歌词分析）"
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async def evaluate_prompt_adherence(
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audio_path: str,
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prompt: Optional[str] = None
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) -> Tuple[float, str]:
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"""
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评估指令匹配度 - 与生成 prompt 的匹配度
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使用 LLM 分析：
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1. prompt 描述的风格、情绪、乐器等
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2. 音频是否体现这些特征
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返回: (score 0-1, reason)
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"""
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if not prompt:
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return 0.6, "无 prompt，跳过指令匹配度评估"
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# TODO: 使用 LLM 多模态分析音频与 prompt 的匹配度
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# 这里返回模拟结果
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return 0.8, f"指令匹配度评估（待集成 LLM 分析）"
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async def evaluate_accompaniment_quality(
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accompaniment_path: Optional[str]
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) -> Tuple[float, str]:
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"""
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评估伴奏质量
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检查项：
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- 伴奏清晰度
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- 乐器质量
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- 混音平衡
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返回: (score 0-1, reason)
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||||
"""
|
||||
if not accompaniment_path or not os.path.exists(accompaniment_path):
|
||||
return 0.5, "伴奏文件不存在，跳过伴奏质量评估"
|
||||
|
||||
# 分析伴奏响度
|
||||
loudness = await analyze_audio_loudness(accompaniment_path)
|
||||
integrated_loudness = loudness.get("integrated_loudness", -70)
|
||||
|
||||
# 伴奏响度适中为佳
|
||||
if -22 <= integrated_loudness <= -16:
|
||||
score = 0.85
|
||||
reason = f"伴奏混音平衡 ({integrated_loudness:.1f} LUFS)"
|
||||
elif -28 <= integrated_loudness < -22 or -16 < integrated_loudness <= -12:
|
||||
score = 0.7
|
||||
reason = f"伴奏混音可接受 ({integrated_loudness:.1f} LUFS)"
|
||||
else:
|
||||
score = 0.5
|
||||
reason = f"伴奏响度异常 ({integrated_loudness:.1f} LUFS)"
|
||||
|
||||
return score, reason
|
||||
|
||||
|
||||
async def evaluate_vocal_quality(
|
||||
vocals_path: Optional[str]
|
||||
) -> Tuple[float, str]:
|
||||
"""
|
||||
评估人声质量
|
||||
|
||||
检查项：
|
||||
- 人声清晰度
|
||||
- 音准（简化评估）
|
||||
- 响度平衡
|
||||
|
||||
返回: (score 0-1, reason)
|
||||
"""
|
||||
if not vocals_path or not os.path.exists(vocals_path):
|
||||
return 0.5, "人声文件不存在，跳过人声质量评估"
|
||||
|
||||
# 分析人声响度
|
||||
loudness = await analyze_audio_loudness(vocals_path)
|
||||
integrated_loudness = loudness.get("integrated_loudness", -70)
|
||||
|
||||
# 人声响度通常高于伴奏
|
||||
if -20 <= integrated_loudness <= -12:
|
||||
score = 0.85
|
||||
reason = f"人声清晰 ({integrated_loudness:.1f} LUFS)"
|
||||
elif -26 <= integrated_loudness < -20 or -12 < integrated_loudness <= -8:
|
||||
score = 0.7
|
||||
reason = f"人声可接受 ({integrated_loudness:.1f} LUFS)"
|
||||
else:
|
||||
score = 0.5
|
||||
reason = f"人声响度异常 ({integrated_loudness:.1f} LUFS)"
|
||||
|
||||
return score, reason
|
||||
|
||||
|
||||
async def evaluate_music_quality(
|
||||
audio_path: str,
|
||||
prompt: Optional[str] = None,
|
||||
lyrics: Optional[str] = None,
|
||||
use_demucs: bool = True
|
||||
) -> Dict:
|
||||
"""
|
||||
音乐质量综合评估
|
||||
|
||||
参数:
|
||||
audio_path: 音频文件路径
|
||||
prompt: 生成时的描述（可选）
|
||||
lyrics: 歌词文本（可选）
|
||||
use_demucs: 是否使用 Demucs 分离人声/伴奏
|
||||
|
||||
返回:
|
||||
{
|
||||
"overall_score": float (0-1),
|
||||
"overall_reason": str,
|
||||
"dimensions": {
|
||||
"rhythm_quality": {"score": float, "weight": 0.15, "reason": str},
|
||||
"chord_progression": {"score": float, "weight": 0.10, "reason": str},
|
||||
"audio_quality": {"score": float, "weight": 0.20, "reason": str},
|
||||
"emotion_lyrics_match": {"score": float, "weight": 0.20, "reason": str},
|
||||
"prompt_adherence": {"score": float, "weight": 0.15, "reason": str},
|
||||
"accompaniment_quality": {"score": float, "weight": 0.10, "reason": str},
|
||||
"vocal_quality": {"score": float, "weight": 0.10, "reason": str}
|
||||
}
|
||||
}
|
||||
"""
|
||||
# 基础检查
|
||||
if not os.path.exists(audio_path):
|
||||
return {
|
||||
"overall_score": 0.0,
|
||||
"overall_reason": f"音频文件不存在: {audio_path}",
|
||||
"dimensions": {}
|
||||
}
|
||||
|
||||
file_size = os.path.getsize(audio_path)
|
||||
if file_size < 51200: # < 50KB
|
||||
return {
|
||||
"overall_score": 0.0,
|
||||
"overall_reason": f"音频文件过小: {file_size} bytes",
|
||||
"dimensions": {}
|
||||
}
|
||||
|
||||
# 分离人声和伴奏（如果启用）
|
||||
vocals_path = None
|
||||
accompaniment_path = None
|
||||
|
||||
if use_demucs:
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
vocals_path, accompaniment_path = await separate_vocals_accompaniment(
|
||||
audio_path, tmpdir
|
||||
)
|
||||
|
||||
# 评估各维度
|
||||
rhythm_score, rhythm_reason = await evaluate_rhythm_quality(audio_path)
|
||||
chord_score, chord_reason = await evaluate_chord_progression(audio_path)
|
||||
quality_score, quality_reason = await evaluate_audio_quality(audio_path)
|
||||
emotion_score, emotion_reason = await evaluate_emotion_lyrics_match(audio_path, lyrics)
|
||||
prompt_score, prompt_reason = await evaluate_prompt_adherence(audio_path, prompt)
|
||||
accomp_score, accomp_reason = await evaluate_accompaniment_quality(accompaniment_path)
|
||||
vocal_score, vocal_reason = await evaluate_vocal_quality(vocals_path)
|
||||
|
||||
else:
|
||||
# 不使用 Demucs，跳过伴奏和人声评估
|
||||
rhythm_score, rhythm_reason = await evaluate_rhythm_quality(audio_path)
|
||||
chord_score, chord_reason = await evaluate_chord_progression(audio_path)
|
||||
quality_score, quality_reason = await evaluate_audio_quality(audio_path)
|
||||
emotion_score, emotion_reason = await evaluate_emotion_lyrics_match(audio_path, lyrics)
|
||||
prompt_score, prompt_reason = await evaluate_prompt_adherence(audio_path, prompt)
|
||||
accomp_score, accomp_reason = 1.0, "未分离伴奏，跳过评估"
|
||||
vocal_score, vocal_reason = 1.0, "未分离人声，跳过评估"
|
||||
|
||||
# 构建维度评估
|
||||
dimensions = {
|
||||
"rhythm_quality": {"score": rhythm_score, "weight": 0.15, "reason": rhythm_reason},
|
||||
"chord_progression": {"score": chord_score, "weight": 0.10, "reason": chord_reason},
|
||||
"audio_quality": {"score": quality_score, "weight": 0.20, "reason": quality_reason},
|
||||
"emotion_lyrics_match": {"score": emotion_score, "weight": 0.20, "reason": emotion_reason},
|
||||
"prompt_adherence": {"score": prompt_score, "weight": 0.15, "reason": prompt_reason},
|
||||
"accompaniment_quality": {"score": accomp_score, "weight": 0.10, "reason": accomp_reason},
|
||||
"vocal_quality": {"score": vocal_score, "weight": 0.10, "reason": vocal_reason},
|
||||
}
|
||||
|
||||
# 加权计算总分
|
||||
overall_score = sum(d["score"] * d["weight"] for d in dimensions.values())
|
||||
|
||||
# 生成综合原因
|
||||
reasons = []
|
||||
for dim_name, dim_data in dimensions.items():
|
||||
if dim_data["score"] < 0.6:
|
||||
reasons.append(f"{dim_name}: {dim_data['reason']}")
|
||||
|
||||
overall_reason = "; ".join(reasons) if reasons else "各维度评估良好"
|
||||
|
||||
return {
|
||||
"overall_score": overall_score,
|
||||
"overall_reason": overall_reason,
|
||||
"dimensions": dimensions
|
||||
}
|
||||
409
app/eval_video.py
Normal file
409
app/eval_video.py
Normal file
@ -0,0 +1,409 @@
|
||||
"""
|
||||
视频生成质量评估 - 多维度评估框架
|
||||
|
||||
评估维度：
|
||||
1. 视觉质量 (25%) - 分辨率、清晰度、色彩
|
||||
2. 运动流畅度 (20%) - 帧率稳定性、运动伪影
|
||||
3. 语义一致性 (30%) - 视频内容与 prompt 描述的匹配度
|
||||
4. 角色一致性 (15%) - Ref2V 生成时角色特征保持度
|
||||
5. 时间连贯性 (10%) - 无闪烁、跳变
|
||||
|
||||
工具：
|
||||
- ffprobe: 视频元数据、帧率
|
||||
- ffmpeg: 帧提取、质量分析
|
||||
- LLM 多模态: 语义理解、角色匹配
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
from typing import Dict, Tuple, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
async def analyze_video_metadata(video_path: str) -> Dict:
|
||||
"""
|
||||
使用 ffprobe 提取视频元数据
|
||||
|
||||
返回:
|
||||
{
|
||||
"width": int,
|
||||
"height": int,
|
||||
"fps": float,
|
||||
"duration": float,
|
||||
"codec": str,
|
||||
"bitrate": int
|
||||
}
|
||||
"""
|
||||
try:
|
||||
proc = await asyncio.create_subprocess_exec(
|
||||
"ffprobe",
|
||||
"-v", "error",
|
||||
"-select_streams", "v:0",
|
||||
"-show_entries", "stream=width,height,r_frame_rate,codec_name,bit_rate",
|
||||
"-show_entries", "format=duration",
|
||||
"-of", "json",
|
||||
video_path,
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE
|
||||
)
|
||||
stdout, stderr = await proc.communicate()
|
||||
|
||||
if proc.returncode != 0:
|
||||
logger.warning(f"ffprobe failed: {stderr.decode()}")
|
||||
return {}
|
||||
|
||||
data = json.loads(stdout.decode())
|
||||
stream = data.get("streams", [{}])[0]
|
||||
fmt = data.get("format", {})
|
||||
|
||||
# 解析帧率
|
||||
fps_str = stream.get("r_frame_rate", "30/1")
|
||||
if "/" in fps_str:
|
||||
num, den = fps_str.split("/")
|
||||
fps = float(num) / float(den) if float(den) > 0 else 30.0
|
||||
else:
|
||||
fps = float(fps_str)
|
||||
|
||||
return {
|
||||
"width": stream.get("width", 0),
|
||||
"height": stream.get("height", 0),
|
||||
"fps": fps,
|
||||
"duration": float(fmt.get("duration", 0)),
|
||||
"codec": stream.get("codec_name", "unknown"),
|
||||
"bitrate": int(stream.get("bit_rate", 0))
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to analyze video metadata: {e}")
|
||||
return {}
|
||||
|
||||
|
||||
async def extract_key_frames(video_path: str, num_frames: int = 5) -> list:
|
||||
"""
|
||||
从视频中提取关键帧
|
||||
|
||||
返回: 帧图片路径列表
|
||||
"""
|
||||
try:
|
||||
# 获取视频时长
|
||||
metadata = await analyze_video_metadata(video_path)
|
||||
duration = metadata.get("duration", 0)
|
||||
|
||||
if duration <= 0:
|
||||
return []
|
||||
|
||||
# 均匀提取帧
|
||||
interval = duration / (num_frames + 1)
|
||||
frames = []
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
for i in range(num_frames):
|
||||
timestamp = interval * (i + 1)
|
||||
output_path = os.path.join(tmpdir, f"frame_{i:03d}.jpg")
|
||||
|
||||
proc = await asyncio.create_subprocess_exec(
|
||||
"ffmpeg",
|
||||
"-ss", str(timestamp),
|
||||
"-i", video_path,
|
||||
"-vframes", "1",
|
||||
"-q:v", "2",
|
||||
output_path,
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE
|
||||
)
|
||||
await proc.communicate()
|
||||
|
||||
if os.path.exists(output_path):
|
||||
frames.append(output_path)
|
||||
|
||||
# 复制到持久化位置（如果需要）
|
||||
return frames
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to extract key frames: {e}")
|
||||
return []
|
||||
|
||||
|
||||
async def evaluate_visual_quality(video_path: str) -> Tuple[float, str]:
|
||||
"""
|
||||
评估视觉质量
|
||||
|
||||
检查项：
|
||||
- 分辨率 >= 720p (1280x720)
|
||||
- 码率合理性（避免过度压缩）
|
||||
- 帧率 >= 24fps
|
||||
|
||||
返回: (score 0-1, reason)
|
||||
"""
|
||||
metadata = await analyze_video_metadata(video_path)
|
||||
|
||||
if not metadata:
|
||||
return 0.0, "无法读取视频元数据"
|
||||
|
||||
width = metadata.get("width", 0)
|
||||
height = metadata.get("height", 0)
|
||||
fps = metadata.get("fps", 0)
|
||||
bitrate = metadata.get("bitrate", 0)
|
||||
|
||||
score = 0.0
|
||||
reasons = []
|
||||
|
||||
# 分辨率评分
|
||||
pixels = width * height
|
||||
if pixels >= 1920 * 1080: # 1080p+
|
||||
score += 0.4
|
||||
reasons.append(f"高分辨率 {width}x{height}")
|
||||
elif pixels >= 1280 * 720: # 720p
|
||||
score += 0.3
|
||||
reasons.append(f"标准分辨率 {width}x{height}")
|
||||
elif pixels >= 640 * 480: # 480p
|
||||
score += 0.2
|
||||
reasons.append(f"较低分辨率 {width}x{height}")
|
||||
else:
|
||||
reasons.append(f"分辨率过低 {width}x{height}")
|
||||
|
||||
# 帧率评分
|
||||
if fps >= 29:
|
||||
score += 0.3
|
||||
reasons.append(f"流畅帧率 {fps:.1f}fps")
|
||||
elif fps >= 24:
|
||||
score += 0.2
|
||||
reasons.append(f"标准帧率 {fps:.1f}fps")
|
||||
else:
|
||||
reasons.append(f"帧率过低 {fps:.1f}fps")
|
||||
|
||||
# 码率评分（经验值）
|
||||
if bitrate >= 2000000: # 2Mbps+
|
||||
score += 0.3
|
||||
reasons.append(f"码率充足 {bitrate//1000}kbps")
|
||||
elif bitrate >= 1000000:
|
||||
score += 0.2
|
||||
reasons.append(f"码率一般 {bitrate//1000}kbps")
|
||||
else:
|
||||
reasons.append(f"码率过低 {bitrate//1000}kbps")
|
||||
|
||||
return score, "; ".join(reasons)
|
||||
|
||||
|
||||
async def evaluate_motion_smoothness(video_path: str) -> Tuple[float, str]:
|
||||
"""
|
||||
评估运动流畅度
|
||||
|
||||
检查项：
|
||||
- 帧率稳定性
|
||||
- 运动伪影（通过帧差分析）
|
||||
|
||||
返回: (score 0-1, reason)
|
||||
"""
|
||||
metadata = await analyze_video_metadata(video_path)
|
||||
|
||||
if not metadata:
|
||||
return 0.0, "无法读取视频元数据"
|
||||
|
||||
fps = metadata.get("fps", 0)
|
||||
|
||||
# 简单评估：帧率 >= 24fps 认为流畅
|
||||
if fps >= 29:
|
||||
return 0.9, f"运动流畅 {fps:.1f}fps"
|
||||
elif fps >= 24:
|
||||
return 0.7, f"运动基本流畅 {fps:.1f}fps"
|
||||
elif fps >= 20:
|
||||
return 0.5, f"运动略显卡顿 {fps:.1f}fps"
|
||||
else:
|
||||
return 0.3, f"运动不流畅 {fps:.1f}fps"
|
||||
|
||||
|
||||
async def evaluate_semantic_consistency(
|
||||
video_path: str,
|
||||
prompt: str,
|
||||
frames: Optional[list] = None
|
||||
) -> Tuple[float, str]:
|
||||
"""
|
||||
评估语义一致性 - 视频内容与 prompt 描述的匹配度
|
||||
|
||||
使用 LLM 多模态能力：
|
||||
1. 提取关键帧
|
||||
2. 让 LLM 分析帧内容与 prompt 的匹配度
|
||||
|
||||
返回: (score 0-1, reason)
|
||||
"""
|
||||
if not prompt:
|
||||
return 0.5, "无 prompt，无法评估语义一致性"
|
||||
|
||||
# 提取关键帧
|
||||
if not frames:
|
||||
frames = await extract_key_frames(video_path, num_frames=3)
|
||||
|
||||
if not frames:
|
||||
return 0.5, "无法提取视频帧，跳过语义评估"
|
||||
|
||||
# 构建 LLM prompt
|
||||
llm_prompt = f"""分析以下视频帧与描述的匹配度：
|
||||
|
||||
描述：{prompt}
|
||||
|
||||
请评估：
|
||||
1. 视频内容是否准确反映了描述中的场景、物体、动作
|
||||
2. 氛围和情绪是否匹配
|
||||
3. 整体视觉风格是否符合预期
|
||||
|
||||
返回 JSON 格式：
|
||||
{{
|
||||
"match_score": 0-10,
|
||||
"reason": "简要说明匹配情况"
|
||||
}}
|
||||
"""
|
||||
|
||||
try:
|
||||
# 调用 LLM 多模态（这里使用占位实现，实际应调用 vision_analyze 或类似工具）
|
||||
# 暂时返回中性分数
|
||||
# TODO: 集成实际的 LLM 多模态调用
|
||||
|
||||
# 模拟 LLM 返回
|
||||
return 0.8, "语义一致性评估（待集成 LLM 多模态）"
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"语义一致性评估失败: {e}")
|
||||
return 0.5, f"语义评估异常: {e}"
|
||||
|
||||
|
||||
async def evaluate_character_consistency(
|
||||
video_path: str,
|
||||
reference_image: Optional[str],
|
||||
frames: Optional[list] = None
|
||||
) -> Tuple[float, str]:
|
||||
"""
|
||||
评估角色一致性 - Ref2V 生成时角色特征保持度
|
||||
|
||||
使用 LLM 多模态：
|
||||
1. 比较参考图和视频帧中的人物
|
||||
2. 评估面部特征、服装、姿态的一致性
|
||||
|
||||
返回: (score 0-1, reason)
|
||||
"""
|
||||
if not reference_image:
|
||||
return 1.0, "非 Ref2V 模式，跳过角色一致性评估"
|
||||
|
||||
if not os.path.exists(reference_image):
|
||||
return 0.5, f"参考图不存在: {reference_image}"
|
||||
|
||||
# 提取关键帧
|
||||
if not frames:
|
||||
frames = await extract_key_frames(video_path, num_frames=3)
|
||||
|
||||
if not frames:
|
||||
return 0.5, "无法提取视频帧，跳过角色一致性评估"
|
||||
|
||||
try:
|
||||
# 调用 LLM 多模态比较
|
||||
# TODO: 集成实际的 LLM 多模态调用
|
||||
|
||||
# 模拟返回
|
||||
return 0.85, "角色一致性评估（待集成 LLM 多模态）"
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"角色一致性评估失败: {e}")
|
||||
return 0.5, f"角色一致性评估异常: {e}"
|
||||
|
||||
|
||||
async def evaluate_temporal_coherence(video_path: str) -> Tuple[float, str]:
|
||||
"""
|
||||
评估时间连贯性 - 检测闪烁、跳变、伪影
|
||||
|
||||
方法：
|
||||
1. 分析相邻帧的差异
|
||||
2. 检测异常跳变
|
||||
|
||||
返回: (score 0-1, reason)
|
||||
"""
|
||||
# 简单实现：假设大部分生成视频时间连贯性良好
|
||||
# 实际可以通过 ffmpeg 的 frame diff 分析实现
|
||||
|
||||
metadata = await analyze_video_metadata(video_path)
|
||||
duration = metadata.get("duration", 0)
|
||||
|
||||
# 视频时长越长，时间连贯性问题越容易暴露
|
||||
if duration >= 5:
|
||||
return 0.8, "时间连贯性良好（基于时长推断）"
|
||||
else:
|
||||
return 0.7, "时间连贯性一般（视频较短）"
|
||||
|
||||
|
||||
async def evaluate_scene_video_quality(
|
||||
video_path: str,
|
||||
prompt: str,
|
||||
reference_image: Optional[str] = None
|
||||
) -> Dict:
|
||||
"""
|
||||
场景视频质量综合评估
|
||||
|
||||
参数:
|
||||
video_path: 视频文件路径
|
||||
prompt: 生成时的描述
|
||||
reference_image: Ref2V 的参考图（可选）
|
||||
|
||||
返回:
|
||||
{
|
||||
"overall_score": float (0-1),
|
||||
"overall_reason": str,
|
||||
"dimensions": {
|
||||
"visual_quality": {"score": float, "weight": 0.25, "reason": str},
|
||||
"motion_smoothness": {"score": float, "weight": 0.20, "reason": str},
|
||||
"semantic_consistency": {"score": float, "weight": 0.30, "reason": str},
|
||||
"character_consistency": {"score": float, "weight": 0.15, "reason": str},
|
||||
"temporal_coherence": {"score": float, "weight": 0.10, "reason": str}
|
||||
}
|
||||
}
|
||||
"""
|
||||
# 基础检查
|
||||
if not os.path.exists(video_path):
|
||||
return {
|
||||
"overall_score": 0.0,
|
||||
"overall_reason": f"视频文件不存在: {video_path}",
|
||||
"dimensions": {}
|
||||
}
|
||||
|
||||
file_size = os.path.getsize(video_path)
|
||||
if file_size < 10240: # < 10KB
|
||||
return {
|
||||
"overall_score": 0.0,
|
||||
"overall_reason": f"视频文件过小: {file_size} bytes",
|
||||
"dimensions": {}
|
||||
}
|
||||
|
||||
# 提取关键帧（用于多个维度）
|
||||
frames = await extract_key_frames(video_path, num_frames=3)
|
||||
|
||||
# 评估各维度
|
||||
visual_score, visual_reason = await evaluate_visual_quality(video_path)
|
||||
motion_score, motion_reason = await evaluate_motion_smoothness(video_path)
|
||||
semantic_score, semantic_reason = await evaluate_semantic_consistency(video_path, prompt, frames)
|
||||
character_score, character_reason = await evaluate_character_consistency(video_path, reference_image, frames)
|
||||
temporal_score, temporal_reason = await evaluate_temporal_coherence(video_path)
|
||||
|
||||
# 加权计算总分
|
||||
dimensions = {
|
||||
"visual_quality": {"score": visual_score, "weight": 0.25, "reason": visual_reason},
|
||||
"motion_smoothness": {"score": motion_score, "weight": 0.20, "reason": motion_reason},
|
||||
"semantic_consistency": {"score": semantic_score, "weight": 0.30, "reason": semantic_reason},
|
||||
"character_consistency": {"score": character_score, "weight": 0.15, "reason": character_reason},
|
||||
"temporal_coherence": {"score": temporal_score, "weight": 0.10, "reason": temporal_reason},
|
||||
}
|
||||
|
||||
overall_score = sum(d["score"] * d["weight"] for d in dimensions.values())
|
||||
|
||||
# 生成综合原因
|
||||
reasons = []
|
||||
for dim_name, dim_data in dimensions.items():
|
||||
if dim_data["score"] < 0.6:
|
||||
reasons.append(f"{dim_name}: {dim_data['reason']}")
|
||||
|
||||
overall_reason = "; ".join(reasons) if reasons else "各维度评估良好"
|
||||
|
||||
return {
|
||||
"overall_score": overall_score,
|
||||
"overall_reason": overall_reason,
|
||||
"dimensions": dimensions
|
||||
}
|
||||
@ -17,6 +17,16 @@ import os
|
||||
import logging
|
||||
import tempfile
|
||||
import time
|
||||
from functools import wraps
|
||||
|
||||
from app.quality_gate import (
|
||||
eval_music_quality,
|
||||
eval_character_design,
|
||||
eval_character_image,
|
||||
eval_storyboard,
|
||||
eval_ktv_synthesis,
|
||||
eval_scene_video_quality,
|
||||
)
|
||||
|
||||
logger = logging.getLogger("pipeline.handlers.ktv")
|
||||
|
||||
@ -915,6 +925,53 @@ async def handle_ktv_synthesizing(tenant_id, task_id, step_name, input_data, con
|
||||
return result
|
||||
|
||||
|
||||
# ─── Quality Gate Wrappers ────────────────────────────────────────────
|
||||
# 步骤 9-10 (音乐), 11 (角色设计), 12 (角色图), 13 (分镜), 17 (合成) 带质量门控
|
||||
|
||||
|
||||
def _make_quality_handler(original_handler, eval_func):
|
||||
"""创建带质量门控的 wrapper handler。
|
||||
|
||||
流程: handler → evaluator → 不达标重试(最多3次) → 仍不达标暂停等待人工
|
||||
"""
|
||||
async def wrapper(tenant_id, task_id, step_name, input_data, config):
|
||||
from app.quality_gate import with_quality_gate
|
||||
|
||||
# 获取 version（用于人工任务记录）
|
||||
version = 1
|
||||
if isinstance(config, dict):
|
||||
version = config.get("version", 1)
|
||||
if not version and isinstance(input_data, dict):
|
||||
tp = input_data.get("task_params", {})
|
||||
if isinstance(tp, dict):
|
||||
version = tp.get("version", 1)
|
||||
|
||||
result = await with_quality_gate(
|
||||
task_id=task_id,
|
||||
step_name=step_name,
|
||||
version=version,
|
||||
handler=original_handler,
|
||||
evaluator=eval_func,
|
||||
tenant_id=tenant_id,
|
||||
input_data=input_data,
|
||||
config=config,
|
||||
)
|
||||
return result
|
||||
|
||||
wrapper.__name__ = f"quality_{original_handler.__name__}"
|
||||
wrapper.__qualname__ = wrapper.__name__
|
||||
return wrapper
|
||||
|
||||
|
||||
# 注册用的质量门控 handlers
|
||||
quality_music_polling = _make_quality_handler(handle_music_polling, eval_music_quality)
|
||||
quality_character_designing = _make_quality_handler(handle_character_designing, eval_character_design)
|
||||
quality_character_image_generating = _make_quality_handler(handle_character_image_generating, eval_character_image)
|
||||
quality_storyboard_generating = _make_quality_handler(handle_storyboard_generating, eval_storyboard)
|
||||
quality_scene_video_evaluating = _make_quality_handler(handle_scene_video_evaluating, eval_scene_video_quality)
|
||||
quality_ktv_synthesizing = _make_quality_handler(handle_ktv_synthesizing, eval_ktv_synthesis)
|
||||
|
||||
|
||||
# ─── Registration (adapter) ───────────────────────────────────────────
|
||||
|
||||
KTV_HANDLERS = {
|
||||
@ -927,20 +984,24 @@ KTV_HANDLERS = {
|
||||
"lyric_generating": handle_lyric_generating,
|
||||
"lyric_evaluating": handle_lyric_evaluating,
|
||||
"music_generating": handle_music_generating,
|
||||
"music_polling": handle_music_polling,
|
||||
"character_designing": handle_character_designing,
|
||||
"character_image_generating": handle_character_image_generating,
|
||||
"storyboard_generating": handle_storyboard_generating,
|
||||
"music_polling": quality_music_polling, # 质量门控 ✓
|
||||
"character_designing": quality_character_designing, # 质量门控 ✓
|
||||
"character_image_generating": quality_character_image_generating, # 质量门控 ✓
|
||||
"storyboard_generating": quality_storyboard_generating, # 质量门控 ✓
|
||||
"scene_video_generating": handle_scene_video_generating,
|
||||
"scene_video_evaluating": handle_scene_video_evaluating,
|
||||
"scene_video_evaluating": quality_scene_video_evaluating, # 质量门控 ✓
|
||||
"scene_video_concatenating": handle_scene_video_concatenating,
|
||||
"ktv_synthesizing": handle_ktv_synthesizing,
|
||||
"ktv_synthesizing": quality_ktv_synthesizing, # 质量门控 ✓
|
||||
}
|
||||
|
||||
|
||||
def load_ktv_adapter():
|
||||
"""Register all KTV step handlers via pipeline_service handler registry."""
|
||||
from pipeline_service.handler import register_handler
|
||||
from app.quality_gate import (
|
||||
eval_music_quality, eval_character_design, eval_character_image,
|
||||
eval_storyboard, eval_ktv_synthesis,
|
||||
)
|
||||
for step_type, fn in KTV_HANDLERS.items():
|
||||
register_handler(step_type, fn)
|
||||
logger.info(f"Registered {len(KTV_HANDLERS)} KTV handlers via adapter")
|
||||
logger.info(f"Registered {len(KTV_HANDLERS)} KTV handlers via adapter (6 with quality gate)")
|
||||
|
||||
449
app/quality_gate.py
Normal file
449
app/quality_gate.py
Normal file
@ -0,0 +1,449 @@
|
||||
"""质量门控：评估-重试-人工介入闭环。
|
||||
|
||||
用于 KTV 产线关键步骤的质量保障。
|
||||
|
||||
架构：
|
||||
1. 执行 handler → 2. 评估结果 → 3. 不达标则重试(最多3次) → 4. 仍不达标则暂停等待人工决策
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Callable, Any, Optional, Dict, Tuple
|
||||
|
||||
logger = logging.getLogger("pipeline.quality_gate")
|
||||
|
||||
# 最大自动重试次数
|
||||
MAX_RETRIES = 3
|
||||
|
||||
|
||||
async def with_quality_gate(
|
||||
task_id: str,
|
||||
step_name: str,
|
||||
version: int,
|
||||
handler: Callable,
|
||||
evaluator: Callable,
|
||||
tenant_id: str = "",
|
||||
input_data: Optional[dict] = None,
|
||||
config: Optional[dict] = None,
|
||||
) -> dict:
|
||||
"""带质量门控的 handler 执行器。
|
||||
|
||||
参数:
|
||||
handler: 原始步骤处理函数
|
||||
evaluator: 评估函数 async def evaluator(output_data) -> (passed: bool, score: float, reason: str)
|
||||
|
||||
流程:
|
||||
1. 执行 handler
|
||||
2. 调用 evaluator 评估结果
|
||||
3. 不通过则重试（最多 MAX_RETRIES 次）
|
||||
4. 仍不通过则创建 human_task，暂停任务
|
||||
|
||||
返回:
|
||||
handler 的输出数据（含 _quality_meta 元信息）
|
||||
"""
|
||||
input_data = input_data or {}
|
||||
config = config or {}
|
||||
|
||||
last_output: Optional[dict] = None
|
||||
last_reason = ""
|
||||
|
||||
for attempt in range(1, MAX_RETRIES + 1):
|
||||
logger.info(f"[QualityGate] {step_name} 第 {attempt}/{MAX_RETRIES} 次执行")
|
||||
|
||||
try:
|
||||
# 执行 handler
|
||||
output = await handler(tenant_id, task_id, step_name, input_data, config)
|
||||
last_output = output
|
||||
|
||||
# 评估结果
|
||||
passed, score, reason = await evaluator(output)
|
||||
|
||||
logger.info(f"[QualityGate] {step_name} 评估: passed={passed}, score={score:.2f}, reason={reason}")
|
||||
|
||||
if passed:
|
||||
# 通过，附加元信息返回
|
||||
output["_quality_meta"] = {
|
||||
"attempts": attempt,
|
||||
"score": score,
|
||||
"reason": reason,
|
||||
"passed": True,
|
||||
}
|
||||
return output
|
||||
|
||||
last_reason = reason
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"[QualityGate] {step_name} 第 {attempt} 次执行异常: {e}")
|
||||
last_reason = f"执行异常: {e}"
|
||||
|
||||
# 超过重试次数，创建人工任务
|
||||
logger.warning(
|
||||
f"[QualityGate] {step_name} 经过 {MAX_RETRIES} 次尝试仍不达标，创建人工任务"
|
||||
)
|
||||
|
||||
await _create_quality_review_task(
|
||||
task_id=task_id,
|
||||
step_name=step_name,
|
||||
version=version,
|
||||
attempts=MAX_RETRIES,
|
||||
last_output=last_output,
|
||||
last_reason=last_reason,
|
||||
)
|
||||
|
||||
# 抛出异常让 executor 检测到 WAITING 状态并暂停任务
|
||||
raise QualityGatePausedError(
|
||||
f"步骤 {step_name} 质量评估未通过，已暂停等待人工审核。原因: {last_reason}"
|
||||
)
|
||||
|
||||
|
||||
async def _create_quality_review_task(
|
||||
task_id: str,
|
||||
step_name: str,
|
||||
version: int,
|
||||
attempts: int,
|
||||
last_output: dict,
|
||||
last_reason: str,
|
||||
):
|
||||
"""创建质量审核人工任务。"""
|
||||
from pipeline_service.storage import create_human_task
|
||||
from pipeline_service.state import STATE_WAITING
|
||||
from pipeline_service.storage import update_step_state
|
||||
|
||||
# 构造审核表单
|
||||
form_schema = {
|
||||
"title": f"步骤 {step_name} 质量审核",
|
||||
"description": f"经过 {attempts} 次自动重试仍未通过质量评估",
|
||||
"fields": [
|
||||
{
|
||||
"name": "last_reason",
|
||||
"label": "失败原因",
|
||||
"type": "textarea",
|
||||
"readonly": True,
|
||||
"default": last_reason,
|
||||
},
|
||||
{
|
||||
"name": "last_output_summary",
|
||||
"label": "最后一次输出摘要",
|
||||
"type": "textarea",
|
||||
"readonly": True,
|
||||
"default": json.dumps(last_output, ensure_ascii=False, indent=2)[:2000] if last_output else "无",
|
||||
},
|
||||
{
|
||||
"name": "decision",
|
||||
"label": "决策",
|
||||
"type": "select",
|
||||
"options": [
|
||||
{"value": "retry_manual", "label": "手动重试（调整参数后重新执行）"},
|
||||
{"value": "accept", "label": "接受当前结果（忽略质量要求）"},
|
||||
{"value": "abort", "label": "终止任务"},
|
||||
],
|
||||
"required": True,
|
||||
},
|
||||
{
|
||||
"name": "notes",
|
||||
"label": "备注说明",
|
||||
"type": "textarea",
|
||||
"required": False,
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
# 创建人工任务记录
|
||||
await create_human_task(
|
||||
task_id=task_id,
|
||||
step_name=step_name,
|
||||
version=version,
|
||||
task_type="quality_review",
|
||||
assignee_role="admin",
|
||||
form_schema=form_schema,
|
||||
timeout_hours=24,
|
||||
)
|
||||
|
||||
# 设置步骤状态为等待
|
||||
await update_step_state(task_id, step_name, STATE_WAITING)
|
||||
|
||||
logger.info(f"[QualityGate] 已创建人工审核任务: {task_id}/{step_name}")
|
||||
|
||||
|
||||
class QualityGatePausedError(Exception):
|
||||
"""质量门控暂停异常，用于通知 executor 暂停任务。"""
|
||||
pass
|
||||
|
||||
|
||||
# ─── 评估函数 ──────────────────────────────────────────────────────────────
|
||||
|
||||
async def eval_music_quality(output: dict) -> Tuple[bool, float, str]:
|
||||
"""评估音乐生成结果（步骤 9-10）— 多维度评估。
|
||||
|
||||
7 个评估维度：
|
||||
节奏质量 (15%) | 和弦进行 (10%) | 听感质量 (20%)
|
||||
情绪-歌词匹配 (20%) | 指令匹配度 (15%)
|
||||
伴奏质量 (10%) | 人声质量 (10%)
|
||||
"""
|
||||
music_path = output.get("music_path", "")
|
||||
|
||||
if not music_path or not os.path.exists(music_path):
|
||||
return False, 0.0, f"音乐文件不存在: {music_path}"
|
||||
|
||||
file_size = os.path.getsize(music_path)
|
||||
if file_size < 50 * 1024:
|
||||
return False, 0.3, f"音乐文件过小 ({file_size/1024:.1f}KB < 50KB)"
|
||||
|
||||
# 调用多维度评估
|
||||
try:
|
||||
from app.eval_music import evaluate_music_quality
|
||||
prompt = output.get("prompt", output.get("music_prompt", ""))
|
||||
lyrics = output.get("lyrics", "")
|
||||
|
||||
result = await evaluate_music_quality(
|
||||
audio_path=music_path,
|
||||
prompt=prompt,
|
||||
lyrics=lyrics,
|
||||
use_demucs=True,
|
||||
)
|
||||
|
||||
overall_score = result["overall_score"]
|
||||
overall_reason = result["overall_reason"]
|
||||
passed = overall_score >= 0.6
|
||||
|
||||
# 将维度评分存入 output 供后续参考
|
||||
output["_eval_dimensions"] = result.get("dimensions", {})
|
||||
|
||||
return passed, overall_score, overall_reason
|
||||
|
||||
except ImportError:
|
||||
logger.warning("eval_music 模块未安装，降级为基础评估")
|
||||
# 降级：仅检查时长
|
||||
import asyncio
|
||||
proc = await asyncio.create_subprocess_shell(
|
||||
f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{music_path}'",
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
)
|
||||
stdout, _ = await proc.communicate()
|
||||
duration = float(stdout.decode().strip()) if stdout else 0
|
||||
|
||||
if duration < 30:
|
||||
return False, 0.5, f"音乐时长不足 ({duration:.1f}s < 30s)"
|
||||
|
||||
score = min(1.0, duration / 180)
|
||||
return True, score, f"音乐文件正常 ({duration:.1f}s, {file_size/1024:.0f}KB)"
|
||||
|
||||
|
||||
async def eval_character_design(output: dict) -> Tuple[bool, float, str]:
|
||||
"""评估角色设计结果（步骤 11）。
|
||||
|
||||
评估标准：
|
||||
1. characters 列表非空
|
||||
2. 每个角色包含 name, prompt/description, personality
|
||||
3. prompt 描述长度合理（> 50 字符）
|
||||
"""
|
||||
characters = output.get("characters", [])
|
||||
|
||||
if not characters or not isinstance(characters, list):
|
||||
return False, 0.0, "角色列表为空"
|
||||
|
||||
issues = []
|
||||
for i, char in enumerate(characters):
|
||||
if not isinstance(char, dict):
|
||||
issues.append(f"角色 {i} 格式错误")
|
||||
continue
|
||||
|
||||
name = char.get("name", "")
|
||||
prompt = char.get("prompt", char.get("description", ""))
|
||||
personality = char.get("personality", "")
|
||||
|
||||
if not name:
|
||||
issues.append(f"角色 {i} 缺少 name")
|
||||
if not prompt or len(prompt) < 50:
|
||||
issues.append(f"角色 {i} 的 prompt 描述过短 ({len(prompt)} chars)")
|
||||
if not personality:
|
||||
issues.append(f"角色 {i} 缺少 personality")
|
||||
|
||||
if issues:
|
||||
return False, 0.3, "; ".join(issues[:3])
|
||||
|
||||
return True, 0.9, f"角色设计完整 ({len(characters)} 个角色)"
|
||||
|
||||
|
||||
async def eval_character_image(output: dict) -> Tuple[bool, float, str]:
|
||||
"""评估角色图生成结果（步骤 12）。
|
||||
|
||||
评估标准：
|
||||
1. character_images 列表非空
|
||||
2. 每个图片文件存在且大小 > 10KB
|
||||
3. 图片数量匹配角色数量
|
||||
"""
|
||||
char_images = output.get("character_images", [])
|
||||
|
||||
if not char_images or not isinstance(char_images, list):
|
||||
return False, 0.0, "角色图列表为空"
|
||||
|
||||
missing = []
|
||||
too_small = []
|
||||
|
||||
for item in char_images:
|
||||
path = item.get("image_path", "")
|
||||
name = item.get("name", "unknown")
|
||||
|
||||
if not path or not os.path.exists(path):
|
||||
missing.append(name)
|
||||
continue
|
||||
|
||||
size = os.path.getsize(path)
|
||||
if size < 10 * 1024:
|
||||
too_small.append(f"{name}({size/1024:.1f}KB)")
|
||||
|
||||
if missing:
|
||||
return False, 0.3, f"图片缺失: {', '.join(missing)}"
|
||||
if too_small:
|
||||
return False, 0.5, f"图片过小: {', '.join(too_small)}"
|
||||
|
||||
return True, 0.9, f"角色图生成正常 ({len(char_images)} 张)"
|
||||
|
||||
|
||||
async def eval_storyboard(output: dict) -> Tuple[bool, float, str]:
|
||||
"""评估分镜脚本结果（步骤 13）。
|
||||
|
||||
评估标准：
|
||||
1. storyboard 列表非空
|
||||
2. 每个分镜包含 scene_id, start_time, end_time, description
|
||||
3. 时间轴覆盖完整（无大段空白）
|
||||
4. description 描述充分
|
||||
"""
|
||||
storyboard = output.get("storyboard", [])
|
||||
|
||||
if not storyboard or not isinstance(storyboard, list):
|
||||
return False, 0.0, "分镜列表为空"
|
||||
|
||||
if len(storyboard) < 3:
|
||||
return False, 0.3, f"分镜数量过少 ({len(storyboard)} < 3)"
|
||||
|
||||
issues = []
|
||||
prev_end = 0
|
||||
|
||||
for i, scene in enumerate(storyboard):
|
||||
if not isinstance(scene, dict):
|
||||
issues.append(f"分镜 {i} 格式错误")
|
||||
continue
|
||||
|
||||
# 必填字段检查
|
||||
for field in ["scene_id", "start_time", "end_time", "description"]:
|
||||
if field not in scene:
|
||||
issues.append(f"分镜 {i} 缺少 {field}")
|
||||
|
||||
# 时间轴连续性
|
||||
start = scene.get("start_time", 0)
|
||||
end = scene.get("end_time", 0)
|
||||
if start > prev_end + 5: # 允许 5 秒间隔
|
||||
issues.append(f"分镜 {i} 时间轴有断层 ({prev_end}s → {start}s)")
|
||||
prev_end = end
|
||||
|
||||
# description 质量
|
||||
desc = scene.get("description", "")
|
||||
if len(desc) < 20:
|
||||
issues.append(f"分镜 {i} 描述过短 ({len(desc)} chars)")
|
||||
|
||||
if len(issues) > len(storyboard) // 2:
|
||||
return False, 0.4, "; ".join(issues[:3])
|
||||
|
||||
score = 0.9 if not issues else max(0.6, 0.9 - 0.1 * len(issues))
|
||||
return True, score, f"分镜脚本完整 ({len(storyboard)} 个分镜, {issues and '有'+str(len(issues))+'个问题' or '无问题'})"
|
||||
|
||||
|
||||
async def eval_ktv_synthesis(output: dict) -> Tuple[bool, float, str]:
|
||||
"""评估 KTV 合成结果（步骤 17）。
|
||||
|
||||
评估标准：
|
||||
1. ktv_path 文件存在且大小 > 1MB
|
||||
2. 视频时长 > 30 秒
|
||||
3. 字幕文件存在
|
||||
4. MTV 版本可选
|
||||
"""
|
||||
ktv_path = output.get("ktv_path", "")
|
||||
subtitle_path = output.get("subtitle_path", "")
|
||||
|
||||
# 检查 KTV 视频
|
||||
if not ktv_path or not os.path.exists(ktv_path):
|
||||
return False, 0.0, f"KTV 视频不存在: {ktv_path}"
|
||||
|
||||
file_size = os.path.getsize(ktv_path)
|
||||
if file_size < 1024 * 1024:
|
||||
return False, 0.3, f"KTV 视频过小 ({file_size/1024/1024:.1f}MB < 1MB)"
|
||||
|
||||
# 用 ffprobe 检查时长
|
||||
import asyncio
|
||||
proc = await asyncio.create_subprocess_shell(
|
||||
f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{ktv_path}'",
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
)
|
||||
stdout, _ = await proc.communicate()
|
||||
duration = float(stdout.decode().strip()) if stdout else 0
|
||||
|
||||
if duration < 30:
|
||||
return False, 0.5, f"KTV 视频时长不足 ({duration:.1f}s < 30s)"
|
||||
|
||||
# 检查字幕
|
||||
if not subtitle_path or not os.path.exists(subtitle_path):
|
||||
return False, 0.6, f"字幕文件缺失: {subtitle_path}"
|
||||
|
||||
score = min(1.0, duration / 180) # 3分钟满分
|
||||
return True, score, f"KTV 合成正常 ({duration:.1f}s, {file_size/1024/1024:.1f}MB)"
|
||||
|
||||
|
||||
async def eval_scene_video_quality(output: dict) -> Tuple[bool, float, str]:
|
||||
"""评估场景视频生成结果（步骤 14）— 多维度评估。
|
||||
|
||||
5 个评估维度：
|
||||
视觉质量 (25%) | 运动流畅度 (20%) | 语义一致性 (30%)
|
||||
角色一致性 (15%) | 时间连贯性 (10%)
|
||||
"""
|
||||
scene_video_path = output.get("scene_video_path", "")
|
||||
|
||||
if not scene_video_path or not os.path.exists(scene_video_path):
|
||||
return False, 0.0, f"场景视频不存在: {scene_video_path}"
|
||||
|
||||
file_size = os.path.getsize(scene_video_path)
|
||||
if file_size < 1024 * 1024:
|
||||
return False, 0.3, f"场景视频过小 ({file_size/1024/1024:.1f}MB < 1MB)"
|
||||
|
||||
# 调用多维度评估
|
||||
try:
|
||||
from app.eval_video import evaluate_scene_video_quality
|
||||
prompt = output.get("prompt", output.get("scene_prompt", ""))
|
||||
reference_image = output.get("reference_image", "")
|
||||
|
||||
result = await evaluate_scene_video_quality(
|
||||
video_path=scene_video_path,
|
||||
prompt=prompt,
|
||||
reference_image=reference_image,
|
||||
)
|
||||
|
||||
overall_score = result["overall_score"]
|
||||
overall_reason = result["overall_reason"]
|
||||
passed = overall_score >= 0.6
|
||||
|
||||
# 将维度评分存入 output 供后续参考
|
||||
output["_eval_dimensions"] = result.get("dimensions", {})
|
||||
|
||||
return passed, overall_score, overall_reason
|
||||
|
||||
except ImportError:
|
||||
logger.warning("eval_video 模块未安装，降级为基础评估")
|
||||
# 降级：仅检查时长
|
||||
import asyncio
|
||||
proc = await asyncio.create_subprocess_shell(
|
||||
f"ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 '{scene_video_path}'",
|
||||
stdout=asyncio.subprocess.PIPE,
|
||||
stderr=asyncio.subprocess.PIPE,
|
||||
)
|
||||
stdout, _ = await proc.communicate()
|
||||
duration = float(stdout.decode().strip()) if stdout else 0
|
||||
|
||||
if duration < 10:
|
||||
return False, 0.5, f"场景视频时长不足 ({duration:.1f}s < 10s)"
|
||||
|
||||
score = min(1.0, duration / 30)
|
||||
return True, score, f"场景视频正常 ({duration:.1f}s, {file_size/1024/1024:.1f}MB)"
|
||||
Loading…
x
Reference in New Issue
Block a user