AskaEth
41f653b672
- 所有对外称呼从 QQ 改为 OBv11（注释/提示词/日志/配置项） - 新增 PlatformFormat 结构体，统一管理平台消息标记格式 - defaultPlatformFormats() 注册表替代硬编码 qqTargetRe - extractProactiveMessage 改为 Thinker 方法，遍历格式注册表匹配 - 配置项重命名: QQ_BOT_PORT → OBV11_BOT_PORT, QQBotPort → OBv11BotPort - 标记格式: 【QQ群聊】→【OBv11群聊】、【QQ私聊】→【OBv11私聊】 Co-Authored-By: Claude <noreply@anthropic.com>
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""K-means 聚类自动分组声纹，每组抽 1 个样本供试听确认"""
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import os, sys, json, warnings, shutil
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import numpy as np
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import librosa
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from sklearn.cluster import KMeans
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warnings.filterwarnings('ignore')
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SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
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SAMPLE_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\voice_samples"
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N_CLUSTERS = 8 # 预期 3-5 个女声 + 若干男声/杂音组
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def extract_features(wav_path):
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try:
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y, sr = librosa.load(wav_path, sr=22050, mono=True)
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if len(y) < sr * 0.3: return None
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f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
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f0 = f0[~np.isnan(f0)]
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if len(f0) < 10: return None
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mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
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mfcc_d = librosa.feature.delta(mfcc)
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feat = np.concatenate([
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[np.mean(f0), np.std(f0),
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np.percentile(f0, 25), np.percentile(f0, 50), np.percentile(f0, 75)],
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np.mean(mfcc, axis=1), np.std(mfcc, axis=1),
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np.mean(mfcc_d, axis=1), np.std(mfcc_d, axis=1),
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])
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return feat.astype(np.float32)
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except:
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return None
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print("提取声纹特征...")
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wav_files = []
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features = []
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for root, dirs, files in os.walk(SEARCH_DIR):
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for f in files:
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if f.endswith('.wav') and 'VoBanks' in root:
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path = os.path.join(root, f)
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feat = extract_features(path)
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if feat is not None:
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wav_files.append(path)
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features.append(feat)
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features = np.array(features)
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print(f" 有效文件: {len(features)}")
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# K-means 聚类
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print(f"\nK-means 聚类 (k={N_CLUSTERS})...")
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kmeans = KMeans(n_clusters=N_CLUSTERS, random_state=42, n_init=10)
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labels = kmeans.fit_predict(features)
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# 统计每组
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clusters = {}
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for i, (label, path) in enumerate(zip(labels, wav_files)):
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if label not in clusters:
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clusters[label] = []
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clusters[label].append((path, features[i]))
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# 每组选最接近中心的样本
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print(f"\n=== 聚类结果 ===\n")
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for label in sorted(clusters.keys()):
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group = clusters[label]
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center = kmeans.cluster_centers_[label]
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# 找离中心最近的
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best_idx = min(range(len(group)), key=lambda i: np.linalg.norm(group[i][1] - center))
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best_path = group[best_idx][0]
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# 统计音高
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pitches = [g[1][0] for g in group] # mean pitch
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avg_pitch = np.mean(pitches)
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voice_type = "男" if avg_pitch < 170 else "女"
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print(f" Group {label+1}: {len(group):4d} files, pitch={avg_pitch:.0f}Hz ({voice_type}), "
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f"sample: {os.path.basename(best_path)}")
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# 复制每组样本到样本目录
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os.makedirs(SAMPLE_DIR, exist_ok=True)
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for label in sorted(clusters.keys()):
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group = clusters[label]
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center = kmeans.cluster_centers_[label]
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best_idx = min(range(len(group)), key=lambda i: np.linalg.norm(group[i][1] - center))
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src = group[best_idx][0]
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dst = os.path.join(SAMPLE_DIR, f"group_{label+1:02d}_{os.path.basename(src)}")
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shutil.copy2(src, dst)
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print(f"\n每组样本已复制到: {SAMPLE_DIR}")
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print("试听每个 group_*.wav，找到昔涟的组，告诉我编号。")
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