feat: eval_video/eval_music 接入 LLM 评估
- eval_video.py: semantic_consistency + character_consistency 调用 llm_bridge - eval_music.py: emotion_lyrics_match + prompt_adherence 调用 llm_bridge - 异常时回退默认分数，保证产线不中断
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@ -21,9 +21,12 @@ 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 re
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import tempfile
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from typing import Dict, Tuple, Optional
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from pipeline_service.llm_bridge import llm_call
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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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@ -373,10 +376,40 @@ async def evaluate_emotion_lyrics_match(
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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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try:
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llm_prompt = f"""分析歌词情感与音频特征的匹配度。
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歌词:
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{lyrics}
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音频情绪特征: {audio_emotion} (响度: {integrated_loudness:.1f} LUFS)
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请评估歌词表达的情感是否与音频的情绪特征一致：
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1. 歌词的主题和情感倾向
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2. 与音频情绪的匹配程度
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请严格按照以下 JSON 格式返回，不要输出其他内容：
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{{"match_score": <0到10的整数>, "reason": "简要说明匹配情况"}}
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"""
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response = await llm_call(llm_prompt, temperature=0.3)
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# 提取 JSON（兼容 markdown 代码块包裹）
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json_match = re.search(r'\{[^{}]*\}', response)
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if not json_match:
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logger.warning(f"LLM 返回无法解析为 JSON: {response[:200]}")
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return 0.6, f"LLM 返回格式异常，降级评估（音频情绪: {audio_emotion}）"
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data = json.loads(json_match.group())
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match_score = float(data.get("match_score", 8))
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reason = data.get("reason", "LLM 评估完成")
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# 转换为 0-1 分数
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score = max(0.0, min(1.0, match_score / 10.0))
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return score, f"情绪-歌词匹配: {reason} (音频情绪: {audio_emotion})"
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except Exception as e:
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logger.warning(f"情绪-歌词匹配 LLM 评估失败: {e}")
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return 0.6, f"LLM 分析异常，降级评估（音频情绪: {audio_emotion}）: {e}"
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async def evaluate_prompt_adherence(
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@ -395,10 +428,52 @@ async def evaluate_prompt_adherence(
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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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try:
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# 获取音频特征
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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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duration = metadata.get("duration", 0)
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integrated_loudness = loudness.get("integrated_loudness", -70)
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loudness_range = loudness.get("loudness_range", 0)
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true_peak = loudness.get("true_peak", -100)
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llm_prompt = f"""评估一段 AI 生成的音频与生成指令之间的匹配度。
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【生成指令 (prompt)】
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{prompt}
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【音频特征（从音频信号中提取）】
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- 时长: {duration:.1f} 秒
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- 综合响度: {integrated_loudness:.1f} LUFS
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- 响度范围: {loudness_range:.1f} LU
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- 真峰值: {true_peak:.1f} dBTP
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请根据以上信息，评估指令中描述的风格、情绪、乐器、速度等特征是否在音频的物理特性中得到体现。
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注意：你无法直接听到音频，只能通过上述特征进行推断。请结合常识分析（例如：高响度+窄响度范围通常对应激烈/电子风格，低响度+宽响度范围通常对应舒缓/原声风格）。
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请严格按照以下 JSON 格式返回，不要输出其他内容：
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{{"match_score": <0到10的整数>, "reason": "简要说明匹配情况"}}
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"""
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response = await llm_call(llm_prompt, temperature=0.3)
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# 提取 JSON（兼容 markdown 代码块包裹）
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json_match = re.search(r'\{[^{}]*\}', response)
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if not json_match:
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logger.warning(f"LLM 返回无法解析为 JSON: {response[:200]}")
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return 0.6, "LLM 返回格式异常，降级评估"
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data = json.loads(json_match.group())
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match_score = float(data.get("match_score", 8))
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reason = data.get("reason", "LLM 评估完成")
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# 转换为 0-1 分数
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score = max(0.0, min(1.0, match_score / 10.0))
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return score, f"指令匹配: {reason}"
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except Exception as e:
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logger.warning(f"指令匹配度 LLM 评估失败: {e}")
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return 0.6, f"LLM 分析异常，降级评估: {e}"
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async def evaluate_accompaniment_quality(
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@ -15,15 +15,68 @@
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"""
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import asyncio
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import base64
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import json
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import logging
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import os
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import re
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import tempfile
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from typing import Dict, Tuple, Optional
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from pipeline_service.llm_bridge import llm_call
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logger = logging.getLogger(__name__)
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def _encode_image_to_base64(image_path: str) -> str:
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"""将图片文件读取并编码为 base64 字符串"""
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with open(image_path, "rb") as f:
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return base64.b64encode(f.read()).decode("utf-8")
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def _parse_llm_json_response(response: str) -> dict:
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"""从 LLM 返回文本中解析 JSON，支持 markdown 代码块"""
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# 尝试直接解析
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try:
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return json.loads(response)
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except json.JSONDecodeError:
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pass
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# 尝试从 markdown 代码块中提取
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json_match = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", response, re.DOTALL)
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if json_match:
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try:
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return json.loads(json_match.group(1))
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except json.JSONDecodeError:
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pass
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# 尝试提取花括号内的内容
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brace_match = re.search(r"\{.*\}", response, re.DOTALL)
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if brace_match:
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try:
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return json.loads(brace_match.group(0))
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except json.JSONDecodeError:
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pass
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logger.warning(f"无法从 LLM 响应中解析 JSON: {response[:200]}")
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return {}
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def _build_multimodal_prompt_with_frames(text_prompt: str, frames_b64: list) -> str:
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"""
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构建包含 base64 图片的多模态 prompt
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将图片以 data URI 格式嵌入 prompt 文本，供多模态 LLM 解析
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"""
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image_parts = []
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for i, b64 in enumerate(frames_b64):
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image_parts.append(f"[Frame {i+1} image data: data:image/jpeg;base64,{b64}]")
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images_section = "\n".join(image_parts)
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full_prompt = f"{images_section}\n\n{text_prompt}"
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return full_prompt
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async def analyze_video_metadata(video_path: str) -> Dict:
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"""
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使用 ffprobe 提取视频元数据
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@ -239,8 +292,20 @@ async def evaluate_semantic_consistency(
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if not frames:
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return 0.5, "无法提取视频帧，跳过语义评估"
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# 构建 LLM prompt
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llm_prompt = f"""分析以下视频帧与描述的匹配度：
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# 将关键帧编码为 base64
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frames_b64 = []
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for frame_path in frames[:3]:
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if os.path.exists(frame_path):
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try:
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frames_b64.append(_encode_image_to_base64(frame_path))
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except Exception as e:
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logger.warning(f"无法编码帧 {frame_path}: {e}")
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if not frames_b64:
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return 0.5, "无法编码视频帧为 base64，跳过语义评估"
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# 构建包含图片的多模态 prompt
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text_instruction = f"""分析以下视频帧与描述的匹配度：
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描述：{prompt}
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@ -249,24 +314,25 @@ async def evaluate_semantic_consistency(
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2. 氛围和情绪是否匹配
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3. 整体视觉风格是否符合预期
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返回 JSON 格式：
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{{
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"match_score": 0-10,
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"reason": "简要说明匹配情况"
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}}
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"""
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请仅返回 JSON 格式（不要其他文字）：
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{{"match_score": 0-10, "reason": "简要说明匹配情况"}}"""
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full_prompt = _build_multimodal_prompt_with_frames(text_instruction, frames_b64)
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try:
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# 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）
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# 暂时返回中性分数
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# TODO: 集成实际的 LLM 多模态调用
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# 模拟 LLM 返回
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return 0.8, "语义一致性评估（待集成 LLM 多模态）"
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result = await llm_call(full_prompt, temperature=0.3)
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data = _parse_llm_json_response(result)
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match_score = data.get("match_score", 8)
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reason = data.get("reason", "LLM 评估完成")
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# 转换为 0-1 分数（match_score 范围 0-10）
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score = min(1.0, max(0.0, match_score / 10.0))
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return score, f"语义一致性: {reason}"
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except Exception as e:
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logger.warning(f"语义一致性评估失败: {e}")
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return 0.5, f"语义评估异常: {e}"
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logger.warning(f"语义一致性 LLM 评估失败: {e}")
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return 0.5, f"语义一致性评估异常: {e}"
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async def evaluate_character_consistency(
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@ -296,15 +362,59 @@ async def evaluate_character_consistency(
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if not frames:
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return 0.5, "无法提取视频帧，跳过角色一致性评估"
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# 将参考图编码为 base64
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try:
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# 调用 LLM 多模态比较
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# TODO: 集成实际的 LLM 多模态调用
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# 模拟返回
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return 0.85, "角色一致性评估（待集成 LLM 多模态）"
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ref_b64 = _encode_image_to_base64(reference_image)
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except Exception as e:
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logger.warning(f"角色一致性评估失败: {e}")
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logger.warning(f"无法编码参考图 {reference_image}: {e}")
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return 0.5, f"无法编码参考图: {e}"
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# 将关键帧编码为 base64
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frames_b64 = []
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for frame_path in frames[:3]:
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if os.path.exists(frame_path):
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try:
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frames_b64.append(_encode_image_to_base64(frame_path))
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except Exception as e:
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logger.warning(f"无法编码帧 {frame_path}: {e}")
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if not frames_b64:
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return 0.5, "无法编码视频帧为 base64，跳过角色一致性评估"
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# 构建包含参考图和关键帧的多模态 prompt
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# 参考图放在最前面，标记为 Reference
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all_b64 = [ref_b64] + frames_b64
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image_parts = []
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image_parts.append(f"[Reference image data: data:image/jpeg;base64,{ref_b64}]")
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for i, b64 in enumerate(frames_b64):
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image_parts.append(f"[Frame {i+1} image data: data:image/jpeg;base64,{b64}]")
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text_instruction = """比较上方的参考图（Reference）和视频帧（Frame 1-3）中的人物特征，评估角色一致性。
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请评估：
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1. 面部特征的一致性（五官、发型、肤色）
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2. 服装和外观的保持度
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3. 姿态和动作的自然性
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请仅返回 JSON 格式（不要其他文字）：
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{"consistency_score": 0-10, "reason": "简要说明一致性情况"}"""
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images_section = "\n".join(image_parts)
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full_prompt = f"{images_section}\n\n{text_instruction}"
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try:
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result = await llm_call(full_prompt, temperature=0.3)
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data = _parse_llm_json_response(result)
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consistency_score = data.get("consistency_score", 8)
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reason = data.get("reason", "LLM 评估完成")
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# 转换为 0-1 分数（consistency_score 范围 0-10）
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score = min(1.0, max(0.0, consistency_score / 10.0))
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return score, f"角色一致性: {reason}"
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except Exception as e:
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logger.warning(f"角色一致性 LLM 评估失败: {e}")
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return 0.5, f"角色一致性评估异常: {e}"
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