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diff --git a/mcp_central/ace_music_gen/.python-version b/mcp_central/ace_music_gen/.python-version
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+3.11
diff --git a/mcp_central/ace_music_gen/README.md b/mcp_central/ace_music_gen/README.md
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+# ACE Music Generator
+
+基于ACE-Step的音乐生成工具包
+
+## 安装
+
+首先安装ACE-Step库
+
+```bash
+git clone https://github.com/ACE-Studio/ACE-Step.git
+cd ACE-Step
+pip install -r requirements.txt
+pip install -e .
+```
+
+然后安装本项目
+
+```bash
+cd ace_music_gen
+uv sync
+```
+
+## 使用
+
+运行程序
+
+```bash
+uv run python main.py
+```
+
+或者在代码中使用
+
+```python
+from src.ace_music_gen import SimpleACEMusicGen
+
+generator = SimpleACEMusicGen()
+generator.setup_api("your_api_key")
+result = generator.generate_and_create_music("生成一首轻快的歌")
+```
+
+## 项目结构
+
+```
+ace_music_gen/
+├── src/ace_music_gen/
+│   ├── generator.py        # 音乐生成
+│   ├── evaluator.py        # 音频评估
+│   └── llm_client.py       # LLM客户端
+└── main.py                 # 运行入口
+```
+
+## 配置
+
+可以设置阿里云API密钥来生成更好的歌词
+
+```python
+generator.setup_api("your_dashscope_api_key")
+```
+
+## 依赖
+
+- librosa
+- numpy
+- requests
+- pesq
+
+生成的音频文件保存在outputs目录下
\ No newline at end of file
diff --git a/mcp_central/ace_music_gen/gen_muslic.py b/mcp_central/ace_music_gen/gen_muslic.py
new file mode 100644
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+++ b/mcp_central/ace_music_gen/gen_muslic.py
@@ -0,0 +1,616 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import json
+import requests
+import librosa
+import numpy as np
+from typing import Dict
+from acestep.pipeline_ace_step import ACEStepPipeline
+try:
+    from pesq import pesq
+    HAS_PESQ = True
+except ImportError:
+    HAS_PESQ = False
+    print("PESQ library not available, will use simplified audio analysis")
+
+
+class AudioEvaluator:
+    """音频质量评估器"""
+    
+    def __init__(self):
+        self.has_pesq = HAS_PESQ
+    
+    def evaluate_audio(self, audio_path: str) -> Dict:
+        """
+        评估音频文件质量
+        
+        Args:
+            audio_path: 音频文件路径
+            
+        Returns:
+            评估结果字典
+        """
+        try:
+            # 使用librosa加载音频
+            y, sr = librosa.load(audio_path, sr=None)
+            
+            # 基础音频特征分析
+            audio_features = self._analyze_audio_features(y, sr)
+            
+            # 质量评估
+            quality_scores = self._calculate_quality_scores(y, sr)
+            
+            # 生成综合评估
+            overall_score = self._calculate_overall_score(audio_features, quality_scores)
+            
+            # 生成建议
+            recommendations = self._generate_recommendations(audio_features, quality_scores)
+            
+            return {
+                "overall_score": overall_score,
+                "audio_features": audio_features,
+                "quality_scores": quality_scores,
+                "recommendations": recommendations,
+                "analysis_info": {
+                    "duration": len(y) / sr,
+                    "sample_rate": sr,
+                    "channels": 1 if y.ndim == 1 else y.shape[0]
+                }
+            }
+            
+        except Exception as e:
+            return {
+                "error": f"音频分析失败: {str(e)}",
+                "overall_score": 0.0
+            }
+    
+    def _analyze_audio_features(self, y, sr):
+        """分析音频特征"""
+        features = {}
+        
+        # 频谱质心 (音色亮度)
+        spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
+        features['spectral_centroid_mean'] = np.mean(spectral_centroids)
+        features['spectral_centroid_std'] = np.std(spectral_centroids)
+        
+        # RMS能量 (响度)
+        rms = librosa.feature.rms(y=y)[0]
+        features['rms_mean'] = np.mean(rms)
+        features['rms_std'] = np.std(rms)
+        
+        # 零交叉率 (音频纯净度)
+        zcr = librosa.feature.zero_crossing_rate(y)[0]
+        features['zcr_mean'] = np.mean(zcr)
+        
+        # MFCC特征 (音色特征)
+        mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        features['mfcc_mean'] = np.mean(mfccs, axis=1).tolist()
+        
+        # 频谱对比度
+        spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
+        features['spectral_contrast_mean'] = np.mean(spectral_contrast)
+        
+        return features
+    
+    def _calculate_quality_scores(self, y, sr):
+        """计算音频质量分数"""
+        scores = {}
+        
+        # 动态范围
+        dynamic_range = np.max(y) - np.min(y)
+        scores['dynamic_range'] = min(dynamic_range * 10, 10.0)  # 归一化到0-10
+        
+        # 信噪比估算
+        rms = librosa.feature.rms(y=y)[0]
+        snr_estimate = 20 * np.log10(np.mean(rms) / (np.std(rms) + 1e-10))
+        scores['snr_estimate'] = min(max(snr_estimate / 10, 0), 10.0)  # 归一化到0-10
+        
+        # 频谱平衡度
+        freqs = np.abs(np.fft.fft(y))
+        freq_balance = 1.0 - np.std(freqs) / (np.mean(freqs) + 1e-10)
+        scores['frequency_balance'] = min(max(freq_balance * 10, 0), 10.0)
+        
+        # 如果有PESQ，计算感知质量
+        if self.has_pesq and sr == 16000:  # PESQ需要16kHz采样率
+            try:
+                # 对于单通道音频，与自身比较作为参考
+                pesq_score = pesq(sr, y, y, 'wb')  # 宽带模式
+                scores['pesq_score'] = pesq_score
+            except:
+                scores['pesq_score'] = None
+        else:
+            scores['pesq_score'] = None
+            
+        return scores
+    
+    def _calculate_overall_score(self, features, scores):
+        """计算综合评分"""
+        total_score = 0
+        count = 0
+        
+        # 动态范围权重: 30%
+        if 'dynamic_range' in scores:
+            total_score += scores['dynamic_range'] * 0.3
+            count += 0.3
+        
+        # 信噪比权重: 25% 
+        if 'snr_estimate' in scores:
+            total_score += scores['snr_estimate'] * 0.25
+            count += 0.25
+            
+        # 频谱平衡权重: 25%
+        if 'frequency_balance' in scores:
+            total_score += scores['frequency_balance'] * 0.25
+            count += 0.25
+            
+        # PESQ分数权重: 20%
+        if scores.get('pesq_score') is not None:
+            pesq_normalized = min(scores['pesq_score'] / 4.5 * 10, 10.0)
+            total_score += pesq_normalized * 0.2
+            count += 0.2
+            
+        return total_score / count if count > 0 else 5.0
+    
+    def _generate_recommendations(self, features, scores):
+        """生成改进建议"""
+        recommendations = []
+        
+        # 动态范围建议
+        if scores.get('dynamic_range', 5) < 3:
+            recommendations.append("动态范围较小，建议增强音频的响度变化")
+        elif scores.get('dynamic_range', 5) > 8:
+            recommendations.append("动态范围很好，音频层次丰富")
+            
+        # 信噪比建议
+        if scores.get('snr_estimate', 5) < 3:
+            recommendations.append("音频可能存在噪声问题，建议降噪处理")
+        elif scores.get('snr_estimate', 5) > 7:
+            recommendations.append("音频信噪比良好，声音清晰")
+            
+        # 频谱平衡建议
+        if scores.get('frequency_balance', 5) < 4:
+            recommendations.append("频谱分布不够均衡，建议调整EQ")
+        elif scores.get('frequency_balance', 5) > 7:
+            recommendations.append("频谱分布均衡，音色自然")
+            
+        # PESQ建议
+        if scores.get('pesq_score'):
+            if scores['pesq_score'] < 2.0:
+                recommendations.append("感知音质较低，建议检查音频编码质量")
+            elif scores['pesq_score'] > 3.5:
+                recommendations.append("感知音质优秀")
+        
+        if not recommendations:
+            recommendations.append("音频质量整体良好")
+            
+        return recommendations
+
+
+class SimpleACEMusicGen:
+    def __init__(self):
+        self.api_key = None
+        self.base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"
+        self.model = "qwen-turbo-latest"
+        # 自动从网络下载模型，Mac设置bf16=False
+        self.pipeline = ACEStepPipeline(bf16=False)
+        # 初始化音频评估器
+        self.audio_evaluator = AudioEvaluator()
+        
+    def setup_api(self, api_key: str):
+        self.api_key = api_key
+        
+    def generate_and_create_music(self, user_idea: str):
+        print("第一步：生成音乐参数...")
+        
+        # 获取音乐参数
+        music_params = self._get_music_params(user_idea)
+        
+        print(f"生成的 Prompt: {music_params['prompt']}")
+        print(f"生成的 Lyrics: {music_params['lyrics'][:100]}...")
+        
+        print("\n第二步：开始生成音乐...")
+        
+        # 调用ACE-Step生成音乐，下面是调参后适合说唱的参数
+        result = self.pipeline(
+            prompt=music_params['prompt'],
+            lyrics=music_params['lyrics'],
+            lora_name_or_path="ACE-Step/ACE-Step-v1-chinese-rap-LoRA",
+            lora_weight=1.0,
+            audio_duration=120,
+            infer_step=60,
+            guidance_scale=15,  
+            scheduler_type="euler",
+            cfg_type="apg", 
+            omega_scale=10,
+            guidance_interval=0.3,
+            guidance_interval_decay=0,
+            min_guidance_scale=3,  
+            use_erg_tag=True,
+            use_erg_lyric=False,
+            use_erg_diffusion=True,
+            guidance_scale_text=0,  
+            guidance_scale_lyric=0  
+        )
+        
+        # 第三步：音频质量评估
+        print("\n第三步：分析音频质量...")
+        audio_evaluation = self._evaluate_generated_audio(result)
+        
+        # 将评估结果和原始结果一起返回
+        return {
+            "ace_step_result": result,
+            "audio_evaluation": audio_evaluation
+        }
+    
+    def _evaluate_generated_audio(self, result):
+        """评估生成的音频文件"""
+        try:
+            # 尝试从结果中获取音频文件路径
+            audio_path = None
+            
+            if isinstance(result, list) and len(result) > 0:
+                if isinstance(result[0], dict) and 'audio_path' in result[0]:
+                    audio_path = result[0]['audio_path']
+            elif isinstance(result, dict) and 'audio_path' in result:
+                audio_path = result['audio_path']
+            
+            # 如果没有找到路径，尝试查找outputs目录中最新的文件
+            if not audio_path:
+                import os
+                import glob
+                output_dir = "./outputs"
+                if os.path.exists(output_dir):
+                    wav_files = glob.glob(os.path.join(output_dir, "*.wav"))
+                    if wav_files:
+                        # 获取最新的wav文件
+                        audio_path = max(wav_files, key=os.path.getctime)
+            
+            if audio_path and os.path.exists(audio_path):
+                print(f"正在分析音频文件: {audio_path}")
+                evaluation = self.audio_evaluator.evaluate_audio(audio_path)
+                self._display_evaluation_results(evaluation)
+                return evaluation
+            else:
+                print("未找到生成的音频文件，跳过质量评估")
+                return {"error": "音频文件未找到"}
+                
+        except Exception as e:
+            print(f"音频评估过程出错: {str(e)}")
+            return {"error": f"评估失败: {str(e)}"}
+    
+    def _display_evaluation_results(self, evaluation):
+        """显示评估结果"""
+        if 'error' in evaluation:
+            print(f"错误: {evaluation['error']}")
+            return
+            
+        # 显示技术评分
+        print(f"\n技术评分结果:")
+        print(f"综合评分: {evaluation['overall_score']:.1f}/10.0")
+        
+        if 'analysis_info' in evaluation:
+            info = evaluation['analysis_info']
+            print(f"音频时长: {info['duration']:.1f}秒")
+            print(f"采样率: {info['sample_rate']}Hz")
+        
+        if 'quality_scores' in evaluation:
+            scores = evaluation['quality_scores']
+            print(f"\n详细评分:")
+            if 'dynamic_range' in scores:
+                print(f"   动态范围: {scores['dynamic_range']:.1f}/10.0")
+            if 'snr_estimate' in scores:
+                print(f"   信噪比: {scores['snr_estimate']:.1f}/10.0")
+            if 'frequency_balance' in scores:
+                print(f"   频谱平衡: {scores['frequency_balance']:.1f}/10.0")
+            if scores.get('pesq_score'):
+                print(f"   感知质量: {scores['pesq_score']:.1f}/4.5")
+        
+        # 获取LLM专业评价
+        print(f"\n正在生成AI专业评价...")
+        llm_evaluation = self._get_llm_evaluation(evaluation)
+        
+        print(f"\nAI音乐评价:")
+        print(f"{llm_evaluation}")
+    
+    def _get_llm_evaluation(self, evaluation):
+        """使用LLM生成专业音乐评价"""
+        if not self.api_key:
+            return "未配置API密钥，无法生成AI评价"
+        
+        # 构建评估数据摘要
+        eval_summary = self._build_evaluation_summary(evaluation)
+        
+        # 构建专业音乐评价提示词
+        system_prompt = """你是一位资深的音乐制作人和音频工程师，拥有丰富的音乐制作和音频评估经验。
+
+你的任务是根据提供的技术评分数据，生成一份专业的音乐评价报告。
+
+评价要求：
+1. 基于技术数据进行专业分析
+2. 使用通俗易懂但专业的语言
+3. 指出音乐的优点和不足
+4. 提供具体的改进建议
+5. 评价要客观、建设性
+6. 长度控制在100-150字
+
+请从音乐制作人的角度，给出专业而友好的评价。"""
+
+        user_prompt = f"""请基于以下技术评分数据，对这首AI生成的音乐进行专业评价：
+
+{eval_summary}
+
+请生成一份专业的音乐评价，包含对音质、制作水平的分析和改进建议。"""
+
+        return self._call_llm_for_evaluation(system_prompt, user_prompt)
+    
+    def _build_evaluation_summary(self, evaluation):
+        """构建评估数据摘要"""
+        summary = f"综合评分: {evaluation['overall_score']:.1f}/10.0\n"
+        
+        if 'analysis_info' in evaluation:
+            info = evaluation['analysis_info']
+            summary += f"音频时长: {info['duration']:.1f}秒\n"
+            summary += f"采样率: {info['sample_rate']}Hz\n"
+        
+        if 'quality_scores' in evaluation:
+            scores = evaluation['quality_scores']
+            summary += "技术指标:\n"
+            if 'dynamic_range' in scores:
+                summary += f"- 动态范围: {scores['dynamic_range']:.1f}/10.0\n"
+            if 'snr_estimate' in scores:
+                summary += f"- 信噪比: {scores['snr_estimate']:.1f}/10.0\n"
+            if 'frequency_balance' in scores:
+                summary += f"- 频谱平衡: {scores['frequency_balance']:.1f}/10.0\n"
+            if scores.get('pesq_score'):
+                summary += f"- 感知质量(PESQ): {scores['pesq_score']:.1f}/4.5\n"
+        
+        return summary
+    
+    def _call_llm_for_evaluation(self, system_prompt: str, user_prompt: str) -> str:
+        """调用LLM生成评价"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        data = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            "temperature": 0.7,
+            "max_tokens": 300
+        }
+        
+        try:
+            response = requests.post(self.base_url, headers=headers, json=data, timeout=30)
+            response.raise_for_status()
+            
+            result = response.json()
+            content = result["choices"][0]["message"]["content"]
+            return content.strip()
+            
+        except Exception as e:
+            return f"AI评价生成失败: {str(e)}"
+    
+    def _get_music_params(self, user_idea: str) -> Dict[str, str]:   
+        # 构建系统提示
+        system_prompt = """你是一个专业的中文音乐创作AI助手，专门为ACE-Step音乐生成模型创作内容。
+
+用户会告诉你他们想要什么样的音乐，你需要生成两个参数：
+
+1. **prompt** (英文): 音乐风格技术描述，格式参考：
+   - 伤感: "Rap, adult, male, emotional, melancholic, slow tempo, introspective"
+   - 激昂: "Rap, energetic, powerful, fast tempo, aggressive, clear"  
+   - 温柔: "Rap, soft, gentle, melodic, warm, acoustic elements"
+   - 愤怒: "Rap, intense, angry, heavy bass, distorted, raw vocals"
+
+2. **lyrics** (中文): 必须是纯中文说唱歌词，绝对不能包含任何英文，必须包含标准结构：
+   - [Intro] - 中文开场
+   - [Verse] - 中文主歌部分（1-2段）
+   - [Chorus] - 中文副歌（重复性强，朗朗上口）
+   - [Bridge] - 中文桥段（可选）
+   - [Outro] - 中文结尾
+
+歌词要求：
+- 必须完全使用中文，不能有任何英文单词
+- 符合用户描述的情感和风格
+- 有深度和感染力，避免空洞
+- 保持中文说唱的韵律感和押韵
+- 每句歌词的最后一个字必须押韵，特别是副歌部分
+- 注意韵脚的统一性，如：疼/行/声，或者强/光/王等
+- 长度适中，不要过长
+
+请严格按JSON格式返回：
+{
+    "prompt": "英文风格描述",
+    "lyrics": "完整纯中文歌词"
+}"""
+
+        # 构建用户提示  
+        user_prompt = f"用户想法: {user_idea}\n\n请为这个想法生成合适的音乐内容。"
+        
+        return self._call_api(system_prompt, user_prompt)
+    
+    def _call_api(self, system_prompt: str, user_prompt: str) -> Dict[str, str]:
+        """调用阿里云API"""
+        if not self.api_key:
+            return self._get_fallback_content(user_prompt)
+            
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        data = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            "temperature": 0.8,
+            "max_tokens": 1500
+        }
+        
+        try:
+            response = requests.post(self.base_url, headers=headers, json=data, timeout=30)
+            response.raise_for_status()
+            
+            result = response.json()
+            content = result["choices"][0]["message"]["content"]
+            
+            # 尝试解析JSON
+            try:
+                music_content = json.loads(content.strip())
+                if "prompt" in music_content and "lyrics" in music_content:
+                    return music_content
+            except json.JSONDecodeError:
+                pass
+            
+            # 备用解析
+            return self._parse_response(content)
+                
+        except Exception as e:
+            print(f"API调用失败: {e}")
+            return self._get_fallback_content(user_prompt)
+    
+    def _parse_response(self, content: str) -> Dict[str, str]:
+        # 简单的文本解析逻辑
+        lines = content.split('\n')
+        prompt = ""
+        lyrics = ""
+        
+        in_lyrics = False
+        
+        for line in lines:
+            line = line.strip()
+            if 'prompt' in line.lower() and ':' in line:
+                prompt = line.split(':', 1)[1].strip().strip('"{}')
+            elif 'lyrics' in line.lower() and ':' in line:
+                lyrics_start = line.split(':', 1)[1].strip().strip('"{}')
+                lyrics = lyrics_start
+                in_lyrics = True
+            elif in_lyrics and line and not line.startswith('{') and not line.startswith('}'):
+                lyrics += '\n' + line
+        
+        return {
+            "prompt": prompt or "Rap, adult, male, spoken word, clear",
+            "lyrics": lyrics or self._get_default_lyrics()
+        }
+    
+    def _get_fallback_content(self, user_input: str) -> Dict[str, str]:
+        # 根据用户输入关键词判断风格
+        user_lower = user_input.lower()
+        
+        if any(word in user_lower for word in ['伤感', '悲伤', '难过', '失落']):
+            return {
+                "prompt": "Rap, adult, male, emotional, melancholic, slow tempo, introspective, clear vocals",
+                "lyrics": """[Intro]
+夜深了，思绪又开始泛滥
+这些年的得失，在心里翻转
+
+[Verse]
+走过这么多路，回头看那些伤
+有些痛不会忘，像刻在心上的疤
+曾经以为时间会带走所有难过
+现在才发现，有些记忆越久越深刻
+
+[Chorus]
+伤感不是软弱，是成长的代价
+每一次跌倒，都让我更懂得珍惜
+虽然心会痛，但我还在这里
+用音乐诉说，那些说不出的话
+
+[Outro]
+伤感也是一种美
+让我学会了更深的体会"""
+            }
+        elif any(word in user_lower for word in ['激昂', '热血', '激情', '燃']):
+            return {
+                "prompt": "Rap, energetic, powerful, fast tempo, aggressive, clear, motivational",
+                "lyrics": """[Intro]
+点燃心中的火焰
+这一刻，全世界都听见我的声音
+
+[Verse]
+不服输的心永远年轻
+每一次挑战都让我更加坚定
+汗水是我最好的证明
+成功的路上从不缺少拼搏的身影
+
+[Chorus]
+热血在沸腾，梦想在召唤
+没有什么能阻挡我前进的步伐
+燃烧吧青春，释放吧力量
+这是属于我们的时代
+
+[Outro]
+永不熄灭的火
+照亮前行的路"""
+            }
+        else:
+            return {
+                "prompt": "Rap, adult, male, spoken word, moderate tempo, clear, versatile",
+                "lyrics": """[Intro]
+这是我的声音，这是我的故事
+
+[Verse]
+生活就像一场说唱
+有高有低，有快有慢
+重要的是保持自己的节拍
+在这个世界上留下属于自己的印记
+
+[Chorus]
+用音乐表达内心的想法
+让每一个音符都有灵魂
+这就是说唱的魅力
+真实而有力量
+
+[Outro]
+音乐永不停息
+我们的故事还在继续"""
+            }
+    
+    def _get_default_lyrics(self) -> str:
+        """默认歌词"""
+        return """[Intro]
+这是我的声音
+
+[Verse]
+用说唱诉说心声
+每一个字都是真实的表达
+
+[Chorus]
+音乐就是我的语言
+说出心里话
+
+[Outro]
+永不停息的节拍"""
+
+
+def main():
+    generator = SimpleACEMusicGen()
+    generator.setup_api("your key")
+    
+    print("告诉我你想要什么样的音乐，我会生成参数并直接创建音乐！")
+    
+    user_input = input("请描述你想要的音乐: ").strip()
+    
+    if user_input:
+        try:
+            print("\n" + "="*50)
+            result = generator.generate_and_create_music(user_input)
+            print(f"\n 音乐生成完成！")
+            print(f"音频文件已保存到: ./outputs/ 目录")
+            print("="*50 + "\n")
+        except Exception as e:
+            print(f"生成失败: {e}")
+            print("="*50 + "\n")
+    else:
+        print("未输入音乐描述，程序退出")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/mcp_central/ace_music_gen/main.py b/mcp_central/ace_music_gen/main.py
new file mode 100644
index 0000000..e21f75a
--- /dev/null
+++ b/mcp_central/ace_music_gen/main.py
@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+
+from src.ace_music_gen import SimpleACEMusicGen
+
+def main():
+    # 创建生成器实例
+    generator = SimpleACEMusicGen()
+    
+    # 设置API密钥
+    generator.setup_api("your-key")
+    
+    
+    # 示例：生成一首轻快的音乐
+    user_idea = "生成一首轻快的流行音乐"
+    
+    try:
+        print(f"正在生成音乐：{user_idea}")
+        result = generator.generate_and_create_music(user_idea)
+        
+        print("\n生成完成！")
+        print("结果已保存到 outputs/ 目录")
+        
+        if 'audio_evaluation' in result:
+            evaluation = result['audio_evaluation']
+            if 'overall_score' in evaluation:
+                print(f"音频质量评分: {evaluation['overall_score']:.1f}/10.0")
+                
+    except Exception as e:
+        print(f"生成失败: {str(e)}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/mcp_central/ace_music_gen/pyproject.toml b/mcp_central/ace_music_gen/pyproject.toml
new file mode 100644
index 0000000..c0c9b02
--- /dev/null
+++ b/mcp_central/ace_music_gen/pyproject.toml
@@ -0,0 +1,33 @@
+[project]
+name = "ace-music-gen"
+version = "0.1.0"
+description = "基于ACE-Step的音乐生成工具包"
+readme = "README.md"
+requires-python = ">=3.8"
+dependencies = [
+    "librosa>=0.9.0",
+    "numpy>=1.21.0",
+    "requests>=2.25.0",
+    "pesq>=0.0.4",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=6.0",
+    "black>=21.0",
+    "isort>=5.0",
+]
+
+[project.scripts]
+ace-music-gen = "ace_music_gen.main:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.black]
+line-length = 88
+target-version = ['py38']
+
+[tool.isort]
+profile = "black"
diff --git a/mcp_central/ace_music_gen/src/ace_music_gen/__init__.py b/mcp_central/ace_music_gen/src/ace_music_gen/__init__.py
new file mode 100644
index 0000000..da35c67
--- /dev/null
+++ b/mcp_central/ace_music_gen/src/ace_music_gen/__init__.py
@@ -0,0 +1,8 @@
+"""ACE Music Generator - 基于ACE-Step的音乐生成工具包"""
+
+from .generator import SimpleACEMusicGen, main
+from .evaluator import AudioEvaluator
+from .llm_client import LLMClient
+
+__version__ = "0.1.0"
+__all__ = ["SimpleACEMusicGen", "AudioEvaluator", "LLMClient", "main"]
\ No newline at end of file
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diff --git a/mcp_central/ace_music_gen/src/ace_music_gen/evaluator.py b/mcp_central/ace_music_gen/src/ace_music_gen/evaluator.py
new file mode 100644
index 0000000..cd0ee62
--- /dev/null
+++ b/mcp_central/ace_music_gen/src/ace_music_gen/evaluator.py
@@ -0,0 +1,184 @@
+"""音频质量评估器模块"""
+
+import librosa
+import numpy as np
+from typing import Dict
+
+try:
+    from pesq import pesq
+    HAS_PESQ = True
+except ImportError:
+    HAS_PESQ = False
+    print("PESQ library not available, will use simplified audio analysis")
+
+
+class AudioEvaluator:
+    """音频质量评估器"""
+    
+    def __init__(self):
+        self.has_pesq = HAS_PESQ
+    
+    def evaluate_audio(self, audio_path: str) -> Dict:
+        """
+        评估音频文件质量
+        
+        Args:
+            audio_path: 音频文件路径
+            
+        Returns:
+            评估结果字典
+        """
+        try:
+            # 使用librosa加载音频
+            y, sr = librosa.load(audio_path, sr=None)
+            
+            # 基础音频特征分析
+            audio_features = self._analyze_audio_features(y, sr)
+            
+            # 质量评估
+            quality_scores = self._calculate_quality_scores(y, sr)
+            
+            # 生成综合评估
+            overall_score = self._calculate_overall_score(audio_features, quality_scores)
+            
+            # 生成建议
+            recommendations = self._generate_recommendations(audio_features, quality_scores)
+            
+            return {
+                "overall_score": overall_score,
+                "audio_features": audio_features,
+                "quality_scores": quality_scores,
+                "recommendations": recommendations,
+                "analysis_info": {
+                    "duration": len(y) / sr,
+                    "sample_rate": sr,
+                    "channels": 1 if y.ndim == 1 else y.shape[0]
+                }
+            }
+            
+        except Exception as e:
+            return {
+                "error": f"音频分析失败: {str(e)}",
+                "overall_score": 0.0
+            }
+    
+    def _analyze_audio_features(self, y, sr):
+        """分析音频特征"""
+        features = {}
+        
+        # 频谱质心 (音色亮度)
+        spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
+        features['spectral_centroid_mean'] = np.mean(spectral_centroids)
+        features['spectral_centroid_std'] = np.std(spectral_centroids)
+        
+        # RMS能量 (响度)
+        rms = librosa.feature.rms(y=y)[0]
+        features['rms_mean'] = np.mean(rms)
+        features['rms_std'] = np.std(rms)
+        
+        # 零交叉率 (音频纯净度)
+        zcr = librosa.feature.zero_crossing_rate(y)[0]
+        features['zcr_mean'] = np.mean(zcr)
+        
+        # MFCC特征 (音色特征)
+        mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        features['mfcc_mean'] = np.mean(mfccs, axis=1).tolist()
+        
+        # 频谱对比度
+        spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
+        features['spectral_contrast_mean'] = np.mean(spectral_contrast)
+        
+        return features
+    
+    def _calculate_quality_scores(self, y, sr):
+        """计算音频质量分数"""
+        scores = {}
+        
+        # 动态范围
+        dynamic_range = np.max(y) - np.min(y)
+        scores['dynamic_range'] = min(dynamic_range * 10, 10.0)  # 归一化到0-10
+        
+        # 信噪比估算
+        rms = librosa.feature.rms(y=y)[0]
+        snr_estimate = 20 * np.log10(np.mean(rms) / (np.std(rms) + 1e-10))
+        scores['snr_estimate'] = min(max(snr_estimate / 10, 0), 10.0)  # 归一化到0-10
+        
+        # 频谱平衡度
+        freqs = np.abs(np.fft.fft(y))
+        freq_balance = 1.0 - np.std(freqs) / (np.mean(freqs) + 1e-10)
+        scores['frequency_balance'] = min(max(freq_balance * 10, 0), 10.0)
+        
+        # 如果有PESQ，计算感知质量
+        if self.has_pesq and sr == 16000:  # PESQ需要16kHz采样率
+            try:
+                # 对于单通道音频，与自身比较作为参考
+                pesq_score = pesq(sr, y, y, 'wb')  # 宽带模式
+                scores['pesq_score'] = pesq_score
+            except:
+                scores['pesq_score'] = None
+        else:
+            scores['pesq_score'] = None
+            
+        return scores
+    
+    def _calculate_overall_score(self, features, scores):
+        """计算综合评分"""
+        total_score = 0
+        count = 0
+        
+        # 动态范围权重: 30%
+        if 'dynamic_range' in scores:
+            total_score += scores['dynamic_range'] * 0.3
+            count += 0.3
+        
+        # 信噪比权重: 25% 
+        if 'snr_estimate' in scores:
+            total_score += scores['snr_estimate'] * 0.25
+            count += 0.25
+            
+        # 频谱平衡权重: 25%
+        if 'frequency_balance' in scores:
+            total_score += scores['frequency_balance'] * 0.25
+            count += 0.25
+            
+        # PESQ分数权重: 20%
+        if scores.get('pesq_score') is not None:
+            pesq_normalized = min(scores['pesq_score'] / 4.5 * 10, 10.0)
+            total_score += pesq_normalized * 0.2
+            count += 0.2
+            
+        return total_score / count if count > 0 else 5.0
+    
+    def _generate_recommendations(self, features, scores):
+        """生成改进建议"""
+        recommendations = []
+        
+        # 动态范围建议
+        if scores.get('dynamic_range', 5) < 3:
+            recommendations.append("动态范围较小，建议增强音频的响度变化")
+        elif scores.get('dynamic_range', 5) > 8:
+            recommendations.append("动态范围很好，音频层次丰富")
+            
+        # 信噪比建议
+        if scores.get('snr_estimate', 5) < 3:
+            recommendations.append("音频可能存在噪声问题，建议降噪处理")
+        elif scores.get('snr_estimate', 5) > 7:
+            recommendations.append("音频信噪比良好，声音清晰")
+            
+        # 频谱平衡建议
+        if scores.get('frequency_balance', 5) < 4:
+            recommendations.append("频谱分布不够均衡，建议调整EQ")
+        elif scores.get('frequency_balance', 5) > 7:
+            recommendations.append("频谱分布均衡，音色自然")
+            
+        # PESQ建议
+        if scores.get('pesq_score'):
+            if scores['pesq_score'] < 2.0:
+                recommendations.append("感知音质较低，建议检查音频编码质量")
+            elif scores['pesq_score'] > 3.5:
+                recommendations.append("感知音质优秀")
+        
+        if not recommendations:
+            recommendations.append("音频质量整体良好")
+            
+        return recommendations
\ No newline at end of file
diff --git a/mcp_central/ace_music_gen/src/ace_music_gen/generator.py b/mcp_central/ace_music_gen/src/ace_music_gen/generator.py
new file mode 100644
index 0000000..5efdb50
--- /dev/null
+++ b/mcp_central/ace_music_gen/src/ace_music_gen/generator.py
@@ -0,0 +1,189 @@
+"""主要的音乐生成器模块"""
+
+import os
+import glob
+import sys
+from typing import Dict
+
+# ACE-Step库的导入
+try:
+    # 尝试从系统安装的ace-step包导入
+    from acestep.pipeline_ace_step import ACEStepPipeline
+except ImportError:
+    try:
+        # 尝试从父目录导入（当ace-step在同级目录时）
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        ace_step_path = os.path.join(current_dir, "..", "..", "..", "..")
+        ace_step_path = os.path.abspath(ace_step_path)
+        
+        if os.path.exists(os.path.join(ace_step_path, "acestep")):
+            sys.path.insert(0, ace_step_path)
+            from acestep.pipeline_ace_step import ACEStepPipeline
+        else:
+            raise ImportError("未找到ACE-Step库")
+    except ImportError:
+        # 如果无法导入，提供一个占位符
+        class ACEStepPipeline:
+            def __init__(self, **kwargs):
+                print("ACEStepPipeline placeholder - 请确保ACE-Step库已正确安装")
+                print("请参考README.md了解如何安装ACE-Step库")
+            
+            def __call__(self, **kwargs):
+                return {"audio_path": None}
+
+from .evaluator import AudioEvaluator
+from .llm_client import LLMClient
+
+
+class SimpleACEMusicGen:
+    """简单的ACE音乐生成器"""
+    
+    def __init__(self):
+        self.llm_client = LLMClient()
+        # 自动从网络下载模型，Mac设置bf16=False
+        self.pipeline = ACEStepPipeline(bf16=False)
+        # 初始化音频评估器
+        self.audio_evaluator = AudioEvaluator()
+        
+    def setup_api(self, api_key: str):
+        """设置API密钥"""
+        self.llm_client.setup_api(api_key)
+        
+    def generate_and_create_music(self, user_idea: str):
+        """生成并创建音乐的完整流程"""
+        print("第一步：生成音乐参数...")
+        
+        # 获取音乐参数
+        music_params = self.llm_client.generate_music_params(user_idea)
+        
+        print(f"生成的 Prompt: {music_params['prompt']}")
+        print(f"生成的 Lyrics: {music_params['lyrics'][:100]}...")
+        
+        print("\n第二步：开始生成音乐...")
+        
+        # 调用ACE-Step生成音乐，下面是调参后适合说唱的参数
+        result = self.pipeline(
+            prompt=music_params['prompt'],
+            lyrics=music_params['lyrics'],
+            lora_name_or_path="ACE-Step/ACE-Step-v1-chinese-rap-LoRA",
+            lora_weight=1.0,
+            audio_duration=120,
+            infer_step=60,
+            guidance_scale=15,  
+            scheduler_type="euler",
+            cfg_type="apg", 
+            omega_scale=10,
+            guidance_interval=0.3,
+            guidance_interval_decay=0,
+            min_guidance_scale=3,  
+            use_erg_tag=True,
+            use_erg_lyric=False,
+            use_erg_diffusion=True,
+            guidance_scale_text=0,  
+            guidance_scale_lyric=0  
+        )
+        
+        # 第三步：音频质量评估
+        print("\n第三步：分析音频质量...")
+        audio_evaluation = self._evaluate_generated_audio(result)
+        
+        # 将评估结果和原始结果一起返回
+        return {
+            "ace_step_result": result,
+            "audio_evaluation": audio_evaluation
+        }
+    
+    def _evaluate_generated_audio(self, result):
+        """评估生成的音频文件"""
+        try:
+            # 尝试从结果中获取音频文件路径
+            audio_path = None
+            
+            if isinstance(result, list) and len(result) > 0:
+                if isinstance(result[0], dict) and 'audio_path' in result[0]:
+                    audio_path = result[0]['audio_path']
+            elif isinstance(result, dict) and 'audio_path' in result:
+                audio_path = result['audio_path']
+            
+            # 如果没有找到路径，尝试查找outputs目录中最新的文件
+            if not audio_path:
+                output_dir = "./outputs"
+                if os.path.exists(output_dir):
+                    wav_files = glob.glob(os.path.join(output_dir, "*.wav"))
+                    if wav_files:
+                        # 获取最新的wav文件
+                        audio_path = max(wav_files, key=os.path.getctime)
+            
+            if audio_path and os.path.exists(audio_path):
+                print(f"正在分析音频文件: {audio_path}")
+                evaluation = self.audio_evaluator.evaluate_audio(audio_path)
+                self._display_evaluation_results(evaluation)
+                return evaluation
+            else:
+                print("未找到生成的音频文件，跳过质量评估")
+                return {"error": "音频文件未找到"}
+                
+        except Exception as e:
+            print(f"音频评估过程出错: {str(e)}")
+            return {"error": f"评估失败: {str(e)}"}
+    
+    def _display_evaluation_results(self, evaluation):
+        """显示评估结果"""
+        if 'error' in evaluation:
+            print(f"错误: {evaluation['error']}")
+            return
+            
+        # 显示技术评分
+        print(f"\n技术评分结果:")
+        print(f"综合评分: {evaluation['overall_score']:.1f}/10.0")
+        
+        if 'analysis_info' in evaluation:
+            info = evaluation['analysis_info']
+            print(f"音频时长: {info['duration']:.1f}秒")
+            print(f"采样率: {info['sample_rate']}Hz")
+        
+        if 'quality_scores' in evaluation:
+            scores = evaluation['quality_scores']
+            print(f"\n详细评分:")
+            if 'dynamic_range' in scores:
+                print(f"   动态范围: {scores['dynamic_range']:.1f}/10.0")
+            if 'snr_estimate' in scores:
+                print(f"   信噪比: {scores['snr_estimate']:.1f}/10.0")
+            if 'frequency_balance' in scores:
+                print(f"   频谱平衡: {scores['frequency_balance']:.1f}/10.0")
+            if scores.get('pesq_score'):
+                print(f"   感知质量: {scores['pesq_score']:.1f}/4.5")
+        
+        # 获取LLM专业评价
+        print(f"\n正在生成AI专业评价...")
+        llm_evaluation = self.llm_client.generate_music_evaluation(evaluation)
+        
+        print(f"\nAI音乐评价:")
+        print(f"{llm_evaluation}")
+
+
+def main():
+    """主函数，用于命令行使用"""
+    generator = SimpleACEMusicGen()
+    generator.setup_api("your key")
+    
+    print("告诉我你想要什么样的音乐，我会生成参数并直接创建音乐！")
+    
+    user_input = input("请描述你想要的音乐: ").strip()
+    
+    if user_input:
+        try:
+            print("\n" + "="*50)
+            result = generator.generate_and_create_music(user_input)
+            print(f"\n 音乐生成完成！")
+            print(f"音频文件已保存到: ./outputs/ 目录")
+            print("="*50 + "\n")
+        except Exception as e:
+            print(f"生成失败: {e}")
+            print("="*50 + "\n")
+    else:
+        print("未输入音乐描述，程序退出")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/mcp_central/ace_music_gen/src/ace_music_gen/llm_client.py b/mcp_central/ace_music_gen/src/ace_music_gen/llm_client.py
new file mode 100644
index 0000000..e1a5a59
--- /dev/null
+++ b/mcp_central/ace_music_gen/src/ace_music_gen/llm_client.py
@@ -0,0 +1,291 @@
+"""LLM客户端模块，用于与大语言模型交互"""
+
+import json
+import requests
+from typing import Dict
+
+
+class LLMClient:
+    """LLM API客户端"""
+    
+    def __init__(self, api_key: str = None, base_url: str = None, model: str = None):
+        self.api_key = api_key
+        self.base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"
+        self.model = model or "qwen-turbo-latest"
+    
+    def setup_api(self, api_key: str):
+        """设置API密钥"""
+        self.api_key = api_key
+    
+    def generate_music_params(self, user_idea: str) -> Dict[str, str]:
+        """生成音乐参数"""
+        system_prompt = """你是一个专业的中文音乐创作AI助手，专门为ACE-Step音乐生成模型创作内容。
+
+用户会告诉你他们想要什么样的音乐，你需要生成两个参数：
+
+1. **prompt** (英文): 音乐风格技术描述，格式参考：
+   - 伤感: "Rap, adult, male, emotional, melancholic, slow tempo, introspective"
+   - 激昂: "Rap, energetic, powerful, fast tempo, aggressive, clear"  
+   - 温柔: "Rap, soft, gentle, melodic, warm, acoustic elements"
+   - 愤怒: "Rap, intense, angry, heavy bass, distorted, raw vocals"
+
+2. **lyrics** (中文): 必须是纯中文说唱歌词，绝对不能包含任何英文，必须包含标准结构：
+   - [Intro] - 中文开场
+   - [Verse] - 中文主歌部分（1-2段）
+   - [Chorus] - 中文副歌（重复性强，朗朗上口）
+   - [Bridge] - 中文桥段（可选）
+   - [Outro] - 中文结尾
+
+歌词要求：
+- 必须完全使用中文，不能有任何英文单词
+- 符合用户描述的情感和风格
+- 有深度和感染力，避免空洞
+- 保持中文说唱的韵律感和押韵
+- 每句歌词的最后一个字必须押韵，特别是副歌部分
+- 注意韵脚的统一性，如：疼/行/声，或者强/光/王等
+- 长度适中，不要过长
+
+请严格按JSON格式返回：
+{
+    "prompt": "英文风格描述",
+    "lyrics": "完整纯中文歌词"
+}"""
+
+        user_prompt = f"用户想法: {user_idea}\n\n请为这个想法生成合适的音乐内容。"
+        
+        return self._call_api(system_prompt, user_prompt)
+    
+    def generate_music_evaluation(self, evaluation_data: Dict) -> str:
+        """生成音乐专业评价"""
+        if not self.api_key:
+            return "未配置API密钥，无法生成AI评价"
+        
+        eval_summary = self._build_evaluation_summary(evaluation_data)
+        
+        system_prompt = """你是一位资深的音乐制作人和音频工程师，拥有丰富的音乐制作和音频评估经验。
+
+你的任务是根据提供的技术评分数据，生成一份专业的音乐评价报告。
+
+评价要求：
+1. 基于技术数据进行专业分析
+2. 使用通俗易懂但专业的语言
+3. 指出音乐的优点和不足
+4. 提供具体的改进建议
+5. 评价要客观、建设性
+6. 长度控制在100-150字
+
+请从音乐制作人的角度，给出专业而友好的评价。"""
+
+        user_prompt = f"""请基于以下技术评分数据，对这首AI生成的音乐进行专业评价：
+
+{eval_summary}
+
+请生成一份专业的音乐评价，包含对音质、制作水平的分析和改进建议。"""
+
+        return self._call_llm_for_evaluation(system_prompt, user_prompt)
+    
+    def _build_evaluation_summary(self, evaluation):
+        """构建评估数据摘要"""
+        summary = f"综合评分: {evaluation['overall_score']:.1f}/10.0\n"
+        
+        if 'analysis_info' in evaluation:
+            info = evaluation['analysis_info']
+            summary += f"音频时长: {info['duration']:.1f}秒\n"
+            summary += f"采样率: {info['sample_rate']}Hz\n"
+        
+        if 'quality_scores' in evaluation:
+            scores = evaluation['quality_scores']
+            summary += "技术指标:\n"
+            if 'dynamic_range' in scores:
+                summary += f"- 动态范围: {scores['dynamic_range']:.1f}/10.0\n"
+            if 'snr_estimate' in scores:
+                summary += f"- 信噪比: {scores['snr_estimate']:.1f}/10.0\n"
+            if 'frequency_balance' in scores:
+                summary += f"- 频谱平衡: {scores['frequency_balance']:.1f}/10.0\n"
+            if scores.get('pesq_score'):
+                summary += f"- 感知质量(PESQ): {scores['pesq_score']:.1f}/4.5\n"
+        
+        return summary
+    
+    def _call_api(self, system_prompt: str, user_prompt: str) -> Dict[str, str]:
+        """调用阿里云API"""
+        if not self.api_key:
+            return self._get_fallback_content(user_prompt)
+            
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        data = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            "temperature": 0.8,
+            "max_tokens": 1500
+        }
+        
+        try:
+            response = requests.post(self.base_url, headers=headers, json=data, timeout=30)
+            response.raise_for_status()
+            
+            result = response.json()
+            content = result["choices"][0]["message"]["content"]
+            
+            # 尝试解析JSON
+            try:
+                music_content = json.loads(content.strip())
+                if "prompt" in music_content and "lyrics" in music_content:
+                    return music_content
+            except json.JSONDecodeError:
+                pass
+            
+            # 备用解析
+            return self._parse_response(content)
+                
+        except Exception as e:
+            print(f"API调用失败: {e}")
+            return self._get_fallback_content(user_prompt)
+    
+    def _call_llm_for_evaluation(self, system_prompt: str, user_prompt: str) -> str:
+        """调用LLM生成评价"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        data = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt}
+            ],
+            "temperature": 0.7,
+            "max_tokens": 300
+        }
+        
+        try:
+            response = requests.post(self.base_url, headers=headers, json=data, timeout=30)
+            response.raise_for_status()
+            
+            result = response.json()
+            content = result["choices"][0]["message"]["content"]
+            return content.strip()
+            
+        except Exception as e:
+            return f"AI评价生成失败: {str(e)}"
+    
+    def _parse_response(self, content: str) -> Dict[str, str]:
+        """简单的文本解析逻辑"""
+        lines = content.split('\n')
+        prompt = ""
+        lyrics = ""
+        
+        in_lyrics = False
+        
+        for line in lines:
+            line = line.strip()
+            if 'prompt' in line.lower() and ':' in line:
+                prompt = line.split(':', 1)[1].strip().strip('"{}')
+            elif 'lyrics' in line.lower() and ':' in line:
+                lyrics_start = line.split(':', 1)[1].strip().strip('"{}')
+                lyrics = lyrics_start
+                in_lyrics = True
+            elif in_lyrics and line and not line.startswith('{') and not line.startswith('}'):
+                lyrics += '\n' + line
+        
+        return {
+            "prompt": prompt or "Rap, adult, male, spoken word, clear",
+            "lyrics": lyrics or self._get_default_lyrics()
+        }
+    
+    def _get_fallback_content(self, user_input: str) -> Dict[str, str]:
+        """根据用户输入关键词判断风格的备用内容"""
+        user_lower = user_input.lower()
+        
+        if any(word in user_lower for word in ['伤感', '悲伤', '难过', '失落']):
+            return {
+                "prompt": "Rap, adult, male, emotional, melancholic, slow tempo, introspective, clear vocals",
+                "lyrics": """[Intro]
+夜深了，思绪又开始泛滥
+这些年的得失，在心里翻转
+
+[Verse]
+走过这么多路，回头看那些伤
+有些痛不会忘，像刻在心上的疤
+曾经以为时间会带走所有难过
+现在才发现，有些记忆越久越深刻
+
+[Chorus]
+伤感不是软弱，是成长的代价
+每一次跌倒，都让我更懂得珍惜
+虽然心会痛，但我还在这里
+用音乐诉说，那些说不出的话
+
+[Outro]
+伤感也是一种美
+让我学会了更深的体会"""
+            }
+        elif any(word in user_lower for word in ['激昂', '热血', '激情', '燃']):
+            return {
+                "prompt": "Rap, energetic, powerful, fast tempo, aggressive, clear, motivational",
+                "lyrics": """[Intro]
+点燃心中的火焰
+这一刻，全世界都听见我的声音
+
+[Verse]
+不服输的心永远年轻
+每一次挑战都让我更加坚定
+汗水是我最好的证明
+成功的路上从不缺少拼搏的身影
+
+[Chorus]
+热血在沸腾，梦想在召唤
+没有什么能阻挡我前进的步伐
+燃烧吧青春，释放吧力量
+这是属于我们的时代
+
+[Outro]
+永不熄灭的火
+照亮前行的路"""
+            }
+        else:
+            return {
+                "prompt": "Rap, adult, male, spoken word, moderate tempo, clear, versatile",
+                "lyrics": """[Intro]
+这是我的声音，这是我的故事
+
+[Verse]
+生活就像一场说唱
+有高有低，有快有慢
+重要的是保持自己的节拍
+在这个世界上留下属于自己的印记
+
+[Chorus]
+用音乐表达内心的想法
+让每一个音符都有灵魂
+这就是说唱的魅力
+真实而有力量
+
+[Outro]
+音乐永不停息
+我们的故事还在继续"""
+            }
+    
+    def _get_default_lyrics(self) -> str:
+        """默认歌词"""
+        return """[Intro]
+这是我的声音
+
+[Verse]
+用说唱诉说心声
+每一个字都是真实的表达
+
+[Chorus]
+音乐就是我的语言
+说出心里话
+
+[Outro]
+永不停息的节拍"""
\ No newline at end of file
diff --git a/mcp_central/ace_music_gen/uv.lock b/mcp_central/ace_music_gen/uv.lock
new file mode 100644
index 0000000..16ba20b
--- /dev/null
+++ b/mcp_central/ace_music_gen/uv.lock
@@ -0,0 +1,1772 @@
+version = 1
+revision = 2
+requires-python = ">=3.8"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version >= '3.11' and python_full_version < '3.13'",
+    "python_full_version == '3.10.*'",
+    "python_full_version == '3.9.*'",
+    "python_full_version < '3.9'",
+]
+
+[[package]]
+name = "ace-music-gen"
+version = "0.1.0"
+source = { editable = "." }
+dependencies = [
+    { name = "librosa" },
+    { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "pesq" },
+    { name = "requests" },
+]
+
+[package.optional-dependencies]
+dev = [
+    { name = "black", version = "24.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "black", version = "25.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "isort", version = "5.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "isort", version = "6.0.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+    { name = "pytest", version = "8.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "pytest", version = "8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9'" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "black", marker = "extra == 'dev'", specifier = ">=21.0" },
+    { name = "isort", marker = "extra == 'dev'", specifier = ">=5.0" },
+    { name = "librosa", specifier = ">=0.9.0" },
+    { name = "numpy", specifier = ">=1.21.0" },
+    { name = "pesq", specifier = ">=0.0.4" },
+    { name = "pytest", marker = "extra == 'dev'", specifier = ">=6.0" },
+    { name = "requests", specifier = ">=2.25.0" },
+]
+provides-extras = ["dev"]
+
+[[package]]
+name = "audioop-lts"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/38/53/946db57842a50b2da2e0c1e34bd37f36f5aadba1a929a3971c5d7841dbca/audioop_lts-0.2.2.tar.gz", hash = "sha256:64d0c62d88e67b98a1a5e71987b7aa7b5bcffc7dcee65b635823dbdd0a8dbbd0", size = 30686, upload-time = "2025-08-05T16:43:17.409Z" }
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