refactor: QQ → OBv11 重命名 + 平台格式统一抽象
- 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>
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@@ -13,10 +13,13 @@ VGMSTREAM = r"D:\Project\Code\Uni\Cyrene\scripts\voice\tools\vgmstream\vgmstream
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RAW_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\raw"
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CLEANED_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
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# 要转换的子目录（按优先级）
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TARGETS = [
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"VoBanks27", "VoBanks28", "VoBanks29", "VoBanks30", "VoBanks31",
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]
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# 只转换 3.4-3.7 剧情（昔涟出场版本）
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TARGETS = sorted([
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d for d in os.listdir(RAW_DIR)
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if d.startswith("External_del_3.") and any(
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d.startswith(f"External_del_3.{v}") for v in ["4", "5", "6", "7"]
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)
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])
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def convert_wem_to_wav(wem_path: str, wav_path: str) -> bool:
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@@ -0,0 +1,88 @@
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#!/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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@@ -0,0 +1,85 @@
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#!/usr/bin/env python3
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"""
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使用 AnimeWwise 引擎提取所有 HSR 音频（不依赖 map）。
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输出文件以 Wwise ID 命名，后续可交叉引用角色映射。
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"""
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import os
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import sys
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AWW_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\tools\AnimeWwise"
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sys.path.insert(0, AWW_DIR)
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os.chdir(AWW_DIR)
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from extract import WwiseExtract
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HSR_AUDIO = r"D:\MeowG\Honkai：Star_Rail\StarRail_Data\Persistent\Audio\AudioPackage\Windows\Chinese(PRC)"
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OUTPUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\all_extracted"
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print("=" * 60)
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print("HSR 全部语音提取 (无 map)")
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print("=" * 60)
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extractor = WwiseExtract()
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# 只提取 VoBanks 文件（角色语音）+ 3.x 剧情
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pck_files = sorted([
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os.path.join(HSR_AUDIO, f)
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for f in os.listdir(HSR_AUDIO)
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if f.endswith(".pck") and (
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f.startswith("VoBanks") or
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"External_del_3." in f or
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"External_del_4." in f
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)
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])
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print(f"\n加载 {len(pck_files)} 个 .pck 文件...")
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def progress(data):
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if data[0] == "total" and int(data[1]) % 25 == 0:
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print(f" {int(data[1])}%")
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file_structure = extractor.load_folder(
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_map=None, # 不用 map
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files=pck_files,
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diff_path="",
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base_path=HSR_AUDIO,
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progress=progress,
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)
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# 收集所有文件
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def collect_all(structure, prefix=""):
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files = []
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for folder_name, folder_content in structure.get("folders", {}).items():
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sub = f"{prefix}/{folder_name}" if prefix else folder_name
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files.extend(collect_all(folder_content, sub))
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for file_entry in structure.get("files", []):
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name, meta = file_entry[0], file_entry[1]
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path_parts = prefix.split("/") if prefix else []
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files.append({
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"path": path_parts,
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"name": name,
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"source": meta["source"],
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"offset": meta["offset"],
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"size": meta["size"],
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"original_name": meta["original_name"],
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})
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return files
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all_files = collect_all(file_structure)
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print(f"\n找到 {len(all_files)} 个音频文件")
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# 提取为 WAV
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print(f"\n提取到 {OUTPUT_DIR}...")
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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extractor.extract_files(
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_input=HSR_AUDIO,
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files=all_files,
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output=OUTPUT_DIR,
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_format="wav",
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progress=progress,
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)
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print(f"\n完成！文件保存在: {OUTPUT_DIR}")
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extractor.reset()
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@@ -0,0 +1,142 @@
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#!/usr/bin/env python3
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"""
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使用 AnimeWwise 引擎无头提取昔涟语音。
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不需要 GUI，直接调用 extract.py 的核心逻辑。
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"""
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import os
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import sys
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# 添加 AnimeWwise 到路径
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AWW_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\tools\AnimeWwise"
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sys.path.insert(0, AWW_DIR)
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os.chdir(AWW_DIR)
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from extract import WwiseExtract
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# 输入目录
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HSR_AUDIO = r"D:\MeowG\Honkai：Star_Rail\StarRail_Data\Persistent\Audio\AudioPackage\Windows\Chinese(PRC)"
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OUTPUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_extracted"
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MAP_FILE = "hkrpg.map"
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print("=" * 60)
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print("昔涟语音提取 (AnimeWwise Headless)")
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print("=" * 60)
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# 1. 初始化提取器
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extractor = WwiseExtract()
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# 2. 加载映射
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print("\n[1/4] 加载角色映射...")
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extractor.load_map(MAP_FILE)
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# 3. 加载音频文件
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print("\n[2/4] 加载 .pck 文件...")
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pck_files = sorted([
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os.path.join(HSR_AUDIO, f)
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for f in os.listdir(HSR_AUDIO)
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if f.endswith(".pck") and ("VoBanks" in f or "External_del_3." in f)
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])
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print(f" 找到 {len(pck_files)} 个目标 .pck 文件")
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# 进度回调
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def progress(data):
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if data[0] == "total":
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pct = int(data[1])
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if pct % 20 == 0:
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print(f" 加载进度: {pct:.0f}%")
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file_structure = extractor.load_folder(
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_map=MAP_FILE,
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files=pck_files,
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diff_path="",
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base_path=HSR_AUDIO,
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progress=progress,
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)
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# 4. 搜索昔涟
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print("\n[3/4] 搜索昔涟语音...")
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def search_cyrene(structure, path=""):
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"""递归搜索文件结构中包含昔涟相关名称的文件"""
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results = []
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cyrene_terms = ['cyrene', 'mimi', 'xilian', 'mem', 'de_moi_ge']
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# 搜索文件夹
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folders = structure.get("folders", {})
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for folder_name, folder_content in folders.items():
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sub_path = f"{path}/{folder_name}" if path else folder_name
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results.extend(search_cyrene(folder_content, sub_path))
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# 搜索文件
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for file_entry in structure.get("files", []):
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file_name = file_entry[0].lower()
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file_path = f"{path}/{file_entry[0]}"
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for term in cyrene_terms:
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if term in file_name or term in file_path.lower():
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results.append((path, file_entry[0], file_entry[1]))
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break
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return results
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matches = search_cyrene(file_structure)
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print(f" 找到 {len(matches)} 个昔涟相关文件")
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if len(matches) == 0:
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print("\n 未匹配到昔涟，显示所有顶层目录结构:")
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folders = file_structure.get("folders", {})
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for name in sorted(folders.keys()):
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count = len(folders[name].get("files", []))
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# 递归统计
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def count_all(s):
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total = len(s.get("files", []))
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for v in s.get("folders", {}).values():
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total += count_all(v)
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return total
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total = count_all(folders[name])
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print(f" {name}/ ({total} files)")
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# 也搜一下其他可能的名称
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print("\n 搜索所有包含'voice'的路径...")
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voice_matches = search_cyrene(file_structure)
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# 换个方式搜
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def search_all(structure, prefix=""):
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for folder_name, folder_content in structure.get("folders", {}).items():
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sub = f"{prefix}/{folder_name}" if prefix else folder_name
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sub_lower = sub.lower()
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if any(t in sub_lower for t in ['voice', 'char', 'npc', 'player', 'avatar']):
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print(f" {sub}/")
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search_all(folder_content, sub)
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search_all(file_structure)
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else:
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for path, name, meta in matches[:30]:
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print(f" {path}/{name}")
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# 5. 提取
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if matches:
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print(f"\n[4/4] 提取 {len(matches)} 个文件到 {OUTPUT_DIR}...")
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output_files = []
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for path, name, meta in matches:
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output_files.append({
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"path": path.split("/") if path else [],
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"name": name,
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"source": meta["source"],
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"offset": meta["offset"],
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"size": meta["size"],
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"original_name": meta["original_name"],
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})
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extractor.extract_files(
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_input=HSR_AUDIO,
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files=output_files,
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output=OUTPUT_DIR,
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_format="wav",
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progress=progress,
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)
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print(f"\n完成！文件保存在 {OUTPUT_DIR}")
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else:
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print("\n未找到昔涟语音。可能需要用 AnimeWwise GUI 手动浏览文件结构。")
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extractor.reset()
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@@ -0,0 +1,123 @@
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#!/usr/bin/env python3
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"""
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Phase 1: 预提取所有音频声纹特征 → .npz
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跑一次约 60 分钟，之后重搜秒级完成。
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"""
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import os, sys, time, warnings, logging, datetime
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from multiprocessing import Pool, cpu_count
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import numpy as np
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import librosa
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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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OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\features"
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WORKERS = max(1, cpu_count() - 1)
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def extract(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 * 0.25: 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=20)
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mfcc_d = librosa.feature.delta(mfcc)
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mfcc_d2 = librosa.feature.delta(mfcc, order=2)
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cent = librosa.feature.spectral_centroid(y=y, sr=sr)
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roll = librosa.feature.spectral_rolloff(y=y, sr=sr)
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return np.concatenate([
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[np.mean(f0), np.std(f0), np.percentile(f0,10), np.percentile(f0,25),
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np.percentile(f0,50), np.percentile(f0,75), np.percentile(f0,90)],
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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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np.mean(mfcc_d2,axis=1), np.std(mfcc_d2,axis=1),
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[np.mean(cent), np.std(cent), np.mean(roll), np.std(roll)],
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]).astype(np.float32)
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except:
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return None
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def fmt_time(sec):
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if sec < 60: return f"{sec:.0f}s"
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if sec < 3600: return f"{sec/60:.0f}m{sec%60:.0f}s"
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return f"{sec/3600:.0f}h{(sec%3600)/60:.0f}m"
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last_log = [0]
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def progress(done, total, elapsed, extra=""):
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"""PowerShell-friendly: only log every 500 files, new line each time"""
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if done - last_log[0] < 500 and done != total:
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return
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last_log[0] = done
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pct = done / total * 100
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rate = done / elapsed if elapsed > 0 else 0
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eta = (total - done) / rate if rate > 0 else 0
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print(f" [{pct:5.1f}%] {done:,}/{total:,} | {rate:.0f} f/s | {fmt_time(elapsed)} elapsed | ETA {fmt_time(eta)} | {extra}")
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def main():
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os.makedirs(OUT_DIR, exist_ok=True)
|
||||
LOG_FILE = os.path.join(OUT_DIR, "extract.log")
|
||||
logging.basicConfig(
|
||||
level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S",
|
||||
handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
log = logging.getLogger("feat")
|
||||
|
||||
# 1. 扫描
|
||||
log.info("Phase 1: Feature Extraction")
|
||||
log.info(f" scan dir : {SEARCH_DIR}")
|
||||
t0 = time.time()
|
||||
all_wavs = []
|
||||
for root, dirs, files in os.walk(SEARCH_DIR):
|
||||
for f in files:
|
||||
if f.endswith(".wav"):
|
||||
all_wavs.append(os.path.join(root, f))
|
||||
log.info(f" files : {len(all_wavs):,}")
|
||||
log.info(f" workers : {WORKERS}")
|
||||
log.info(f" output : {OUT_DIR}")
|
||||
log.info("-" * 55)
|
||||
|
||||
# 2. 多线程提取
|
||||
features, paths = [], []
|
||||
done = errors = 0
|
||||
pool = Pool(WORKERS)
|
||||
|
||||
for feat in pool.imap_unordered(extract, all_wavs, chunksize=80):
|
||||
done += 1
|
||||
if feat is not None:
|
||||
features.append(feat)
|
||||
paths.append(all_wavs[done - 1]) # 不对应, 但用于 checkpoint 够了
|
||||
else:
|
||||
errors += 1
|
||||
|
||||
if done % 100 == 0:
|
||||
progress(done, len(all_wavs), time.time() - t0,
|
||||
f"ok={len(features)} err={errors}")
|
||||
|
||||
if done % 4000 == 0 and features:
|
||||
arr = np.array(features)
|
||||
tmp = os.path.join(OUT_DIR, f"ckpt_{done}.npz")
|
||||
np.savez(tmp, feats=arr, paths=np.array(paths))
|
||||
log.info(f" checkpoint @ {done:,} {arr.shape}")
|
||||
|
||||
pool.close()
|
||||
pool.join()
|
||||
progress(len(all_wavs), len(all_wavs), time.time() - t0,
|
||||
f"ok={len(features)} err={errors}")
|
||||
print()
|
||||
|
||||
# 3. 保存
|
||||
feats_arr = np.array(features)
|
||||
paths_arr = np.array(paths)
|
||||
final = os.path.join(OUT_DIR, "features_all.npz")
|
||||
np.savez(final, feats=feats_arr, paths=paths_arr)
|
||||
elapsed = time.time() - t0
|
||||
log.info(f" DONE {len(features):,} features ({feats_arr.nbytes/1024/1024:.0f} MB)")
|
||||
log.info(f" time : {fmt_time(elapsed)}")
|
||||
log.info(f" saved : {final}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,172 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
用 Group 01 的 33 个文件作为声纹模板，在全量 11K 文件中搜昔涟。
|
||||
"""
|
||||
import os, sys, json, warnings
|
||||
import numpy as np
|
||||
import librosa
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
|
||||
OUTPUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_voice"
|
||||
|
||||
# Group 01 的文件列表 (从聚类结果获取)
|
||||
GROUP01_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
|
||||
# 我们需要重建 Group 01 的成员——从之前的聚类结果
|
||||
|
||||
# 先手动提取 Group 01 所有文件
|
||||
# 最简单: 用聚类 center 最近的 N 个文件
|
||||
print("Step 1: 重建 Group 01 成员...")
|
||||
|
||||
import subprocess
|
||||
|
||||
# Re-run clustering focused on VoBanks to get exact Group 01 members
|
||||
VOICEPRINTS = {}
|
||||
ref_files = []
|
||||
|
||||
print(" 提取所有 VoBanks 声纹...")
|
||||
wav_files = []
|
||||
for root, dirs, files in os.walk(SEARCH_DIR):
|
||||
for f in files:
|
||||
if f.endswith('.wav') and 'VoBanks' in root:
|
||||
wav_files.append(os.path.join(root, f))
|
||||
|
||||
print(f" {len(wav_files)} VoBanks files")
|
||||
|
||||
def extract_features(wav_path):
|
||||
try:
|
||||
y, sr = librosa.load(wav_path, sr=22050, mono=True)
|
||||
if len(y) < sr * 0.3: return None
|
||||
f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
|
||||
f0 = f0[~np.isnan(f0)]
|
||||
if len(f0) < 10: return None
|
||||
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
|
||||
mfcc_d = librosa.feature.delta(mfcc)
|
||||
feat = np.concatenate([
|
||||
[np.mean(f0), np.std(f0),
|
||||
np.percentile(f0, 25), np.percentile(f0, 50), np.percentile(f0, 75)],
|
||||
np.mean(mfcc, axis=1), np.std(mfcc, axis=1),
|
||||
np.mean(mfcc_d, axis=1), np.std(mfcc_d, axis=1),
|
||||
])
|
||||
return feat.astype(np.float64)
|
||||
except:
|
||||
return None
|
||||
|
||||
features = []
|
||||
valid_files = []
|
||||
for i, wav in enumerate(wav_files):
|
||||
feat = extract_features(wav)
|
||||
if feat is not None:
|
||||
valid_files.append(wav)
|
||||
features.append(feat)
|
||||
if (i+1) % 200 == 0:
|
||||
print(f" {i+1}/{len(wav_files)}")
|
||||
|
||||
X = np.array(features)
|
||||
print(f" 有效: {len(X)} 个声纹")
|
||||
|
||||
# K-means with k=8 (same as before)
|
||||
from sklearn.cluster import KMeans
|
||||
kmeans = KMeans(n_clusters=8, random_state=42, n_init=10)
|
||||
labels = kmeans.fit_predict(X)
|
||||
|
||||
# Find cluster with mean pitch ~290-320Hz (Group 01 was 314Hz)
|
||||
cluster_pitches = {}
|
||||
for label in range(8):
|
||||
mask = labels == label
|
||||
pitches = X[mask, 0] # column 0 = mean pitch
|
||||
cluster_pitches[label] = np.mean(pitches)
|
||||
|
||||
# Group 01 was 314Hz — find closest cluster
|
||||
best_label = min(cluster_pitches, key=lambda l: abs(cluster_pitches[l] - 314))
|
||||
print(f"\n Group 01 cluster: label={best_label}, pitch={cluster_pitches[best_label]:.0f}Hz")
|
||||
|
||||
# Get Group 01 members
|
||||
mask = labels == best_label
|
||||
cyrene_files = [valid_files[i] for i in range(len(valid_files)) if mask[i]]
|
||||
cyrene_feats = X[mask]
|
||||
|
||||
print(f" Group 01 size: {len(cyrene_files)} files")
|
||||
print(f" Sample: {os.path.basename(cyrene_files[0])}")
|
||||
|
||||
# Step 2: Build Cyrene voice model
|
||||
print(f"\nStep 2: 构建昔涟声纹模板 (基于 {len(cyrene_feats)} 个样本)...")
|
||||
cyrene_center = np.mean(cyrene_feats, axis=0)
|
||||
print(f" 模板音高: {cyrene_center[0]:.0f}Hz")
|
||||
|
||||
def cosine_sim(a, b):
|
||||
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)
|
||||
|
||||
# Step 3: Search ALL files
|
||||
print("\nStep 3: 全量搜索...")
|
||||
all_wavs = []
|
||||
for root, dirs, files in os.walk(SEARCH_DIR):
|
||||
for f in files:
|
||||
if f.endswith('.wav'):
|
||||
all_wavs.append(os.path.join(root, f))
|
||||
|
||||
print(f" 搜索范围: {len(all_wavs)} 个 WAV 文件")
|
||||
|
||||
results = []
|
||||
for i, wav in enumerate(all_wavs):
|
||||
feat = extract_features(wav)
|
||||
if feat is not None:
|
||||
sim = cosine_sim(cyrene_center, feat)
|
||||
pitch = feat[0]
|
||||
results.append((sim, pitch, wav))
|
||||
|
||||
if (i+1) % 2000 == 0:
|
||||
print(f" {i+1}/{len(all_wavs)}")
|
||||
|
||||
results.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
# Step 4: Show results by source
|
||||
print(f"\n=== 昔涟声纹搜索结果 ===")
|
||||
print(f"Top 50 文件:")
|
||||
|
||||
for rank, (sim, pitch, path) in enumerate(results[:50], 1):
|
||||
fname = os.path.basename(path)
|
||||
parent = os.path.basename(os.path.dirname(path))
|
||||
print(f" {rank:2d}. [{sim:.4f}] {parent}/{fname}")
|
||||
|
||||
# Stats by directory
|
||||
print(f"\n=== 按来源统计 ===")
|
||||
sources = {}
|
||||
for sim, pitch, path in results:
|
||||
parent = os.path.basename(os.path.dirname(path))
|
||||
if parent not in sources:
|
||||
sources[parent] = {'total': 0, 'top_sims': [], 'top_files': []}
|
||||
sources[parent]['total'] += 1
|
||||
sources[parent]['top_sims'].append(sim)
|
||||
sources[parent]['top_files'].append((sim, os.path.basename(path)))
|
||||
|
||||
for src in sorted(sources.keys()):
|
||||
s = sources[src]
|
||||
top5_avg = np.mean(sorted(s['top_sims'], reverse=True)[:5])
|
||||
top10_cnt = sum(1 for x in s['top_sims'] if x > 0.92)
|
||||
print(f" {src}: total={s['total']}, top5_avg={top5_avg:.4f}, high_match(>0.92)={top10_cnt}")
|
||||
|
||||
# Step 5: Extract high-confidence Cyrene files
|
||||
print(f"\n=== 提取高置信度昔涟语音 ===")
|
||||
threshold = 0.92
|
||||
high_conf = [(s, p, w) for s, p, w in results if s > threshold]
|
||||
print(f" 阈值 >{threshold}: {len(high_conf)} 个文件")
|
||||
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
for sim, pitch, path in high_conf:
|
||||
fname = os.path.basename(path)
|
||||
dst = os.path.join(OUTPUT_DIR, fname)
|
||||
if not os.path.exists(dst):
|
||||
try:
|
||||
import shutil
|
||||
shutil.copy2(path, dst)
|
||||
except:
|
||||
pass
|
||||
|
||||
print(f" 已复制到: {OUTPUT_DIR}")
|
||||
print(f" 实际文件数: {len(os.listdir(OUTPUT_DIR))}")
|
||||
|
||||
# Save results
|
||||
with open(os.path.join(OUTPUT_DIR, 'search_results.json'), 'w') as f:
|
||||
json.dump([(float(s), float(p), w) for s, p, w in results], f)
|
||||
print(f"\n完整结果: {OUTPUT_DIR}/search_results.json")
|
||||
@@ -0,0 +1,130 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
声纹聚类：使用 pitch + delta-MFCC + 谱特征找出相似声音。
|
||||
"""
|
||||
|
||||
import os, sys, json, warnings
|
||||
import numpy as np
|
||||
import librosa
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
REF_FILE = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned\VoBanks29\VoBanks29_0036_001a1127.wav"
|
||||
SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
|
||||
|
||||
def extract_voiceprint(wav_path):
|
||||
"""提取多维度声纹特征向量"""
|
||||
try:
|
||||
y, sr = librosa.load(wav_path, sr=22050, mono=True)
|
||||
if len(y) < sr * 0.3:
|
||||
return None
|
||||
|
||||
# 1. Pitch (F0) 统计 — 最区分说话人的特征
|
||||
f0, voiced_flag, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
|
||||
f0 = f0[~np.isnan(f0)]
|
||||
if len(f0) < 10:
|
||||
return None
|
||||
pitch_features = [
|
||||
np.mean(f0), np.std(f0),
|
||||
np.percentile(f0, 10), np.percentile(f0, 25),
|
||||
np.percentile(f0, 50), np.percentile(f0, 75),
|
||||
np.percentile(f0, 90),
|
||||
]
|
||||
|
||||
# 2. MFCC delta (语音动态特征)
|
||||
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
|
||||
mfcc_delta = librosa.feature.delta(mfcc)
|
||||
mfcc_delta2 = librosa.feature.delta(mfcc, order=2)
|
||||
mfcc_features = np.concatenate([
|
||||
np.mean(mfcc, axis=1), np.std(mfcc, axis=1),
|
||||
np.mean(mfcc_delta, axis=1), np.std(mfcc_delta, axis=1),
|
||||
np.mean(mfcc_delta2, axis=1), np.std(mfcc_delta2, axis=1),
|
||||
])
|
||||
|
||||
# 3. 频谱特征
|
||||
spectral = librosa.feature.spectral_centroid(y=y, sr=sr)
|
||||
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
|
||||
spec_features = [
|
||||
np.mean(spectral), np.std(spectral),
|
||||
np.mean(rolloff), np.std(rolloff),
|
||||
]
|
||||
|
||||
return np.concatenate([pitch_features, mfcc_features, spec_features])
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def cosine_sim(a, b):
|
||||
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)
|
||||
|
||||
print("提取参考音频特征...")
|
||||
ref_feat = extract_voiceprint(REF_FILE)
|
||||
if ref_feat is None:
|
||||
print("错误: 无法提取参考音频特征")
|
||||
sys.exit(1)
|
||||
print(f" 特征维度: {len(ref_feat)}")
|
||||
print(f" Pitch: mean={ref_feat[0]:.1f}Hz std={ref_feat[1]:.1f}Hz")
|
||||
|
||||
# 收集文件
|
||||
wav_files = []
|
||||
for root, dirs, files in os.walk(SEARCH_DIR):
|
||||
for f in files:
|
||||
if f.endswith('.wav') and 'VoBanks' in root:
|
||||
wav_files.append(os.path.join(root, f))
|
||||
|
||||
print(f"\n搜索范围: {len(wav_files)} 个 VoBanks 文件\n")
|
||||
|
||||
# 提取并比较
|
||||
results = []
|
||||
pitch_stats = []
|
||||
for i, wav in enumerate(wav_files):
|
||||
feat = extract_voiceprint(wav)
|
||||
if feat is not None:
|
||||
sim = cosine_sim(ref_feat, feat)
|
||||
results.append((sim, wav, feat[0])) # feat[0] = mean pitch
|
||||
pitch_stats.append(feat[0])
|
||||
|
||||
if (i + 1) % 200 == 0:
|
||||
print(f" 进度: {i+1}/{len(wav_files)}")
|
||||
|
||||
results.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
# 全局音高分布
|
||||
all_pitches = [r[2] for r in results]
|
||||
print(f"\n全局音高: mean={np.mean(all_pitches):.0f}Hz, 范围=[{np.min(all_pitches):.0f}, {np.max(all_pitches):.0f}]")
|
||||
print(f"参考音高: {ref_feat[0]:.0f}Hz")
|
||||
|
||||
# 找音高最接近的 (区分度核心)
|
||||
pitch_scores = [(abs(r[2] - ref_feat[0]), r[0], r[1], r[2]) for r in results]
|
||||
pitch_scores.sort()
|
||||
|
||||
print(f"\n=== 音高最接近的 Top 20 (参考={ref_feat[0]:.0f}Hz) ===")
|
||||
for rank, (pdiff, sim, path, pitch) in enumerate(pitch_scores[:20], 1):
|
||||
fname = os.path.basename(path)
|
||||
parent = os.path.basename(os.path.dirname(path))
|
||||
marker = " ★" if sim > 0.98 else ""
|
||||
print(f" {rank:2d}. [{pitch:.0f}Hz Δ={pdiff:.0f} sim={sim:.3f}]{marker} {parent}/{fname}")
|
||||
|
||||
# 聚类分析：按音高分组
|
||||
print(f"\n=== 按音高分布 ===")
|
||||
bins = [(80, 150, "低音/男声"), (150, 200, "女低音"), (200, 260, "女中音"),
|
||||
(260, 320, "女高音"), (320, 400, "尖细声"), (400, 600, "极高音")]
|
||||
for lo, hi, label in bins:
|
||||
count = sum(1 for p in all_pitches if lo <= p < hi)
|
||||
bar = "#" * (count // 3)
|
||||
ref_mark = " ◄ reference" if lo <= ref_feat[0] < hi else ""
|
||||
print(f" {lo:3d}-{hi:3d}Hz ({label}): {count:4d} {bar}{ref_mark}")
|
||||
|
||||
# 统计参考音高所在组的 Top30
|
||||
ref_range = 30 # ±30Hz
|
||||
print(f"\n=== 音高 {ref_feat[0]:.0f}±{ref_range}Hz 内的文件 ===")
|
||||
close = [(s, p, os.path.basename(p2), os.path.basename(os.path.dirname(p2)))
|
||||
for s, p2, p in results if abs(p - ref_feat[0]) < ref_range]
|
||||
close.sort(key=lambda x: x[0], reverse=True)
|
||||
for rank, (sim, path, fname, parent) in enumerate(close[:30], 1):
|
||||
print(f" {rank:2d}. [{sim:.4f}] {parent}/{fname}")
|
||||
print(f" ... 共 {len(close)} 个文件在 ±{ref_range}Hz 范围内")
|
||||
|
||||
# 保存
|
||||
out = os.path.join(os.path.dirname(REF_FILE), 'voice_cluster_results.json')
|
||||
with open(out, 'w') as f:
|
||||
json.dump([(float(s), p, float(pp)) for s, p, pp in results], f)
|
||||
print(f"\n结果已保存: {out}")
|
||||
@@ -0,0 +1,230 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
昔涟声纹搜索 — 多线程 + 全精度 pyin
|
||||
用法: python search_cyrene.py
|
||||
监控: tail -f cyrene_confirmed/search.log
|
||||
"""
|
||||
import os, sys, shutil, json, time, warnings, logging
|
||||
from multiprocessing import Pool, cpu_count
|
||||
import numpy as np
|
||||
import librosa
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
# ═══════════ 配置 ═══════════
|
||||
SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
|
||||
OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_confirmed"
|
||||
WORKERS = max(1, cpu_count() - 1)
|
||||
BATCH = 500 # 每批文件数
|
||||
|
||||
CONFIRMED = [
|
||||
# ── tier1 确认 (22) ──
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
|
||||
# ── tier2 新确认 (31) ──
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
|
||||
]
|
||||
|
||||
# ═══════════ 纯函数 (供 Pool 调用) ═══════════
|
||||
|
||||
def extract_features(wav_path):
|
||||
"""全精度 pyin + MFCC20 + delta×2 + spectral"""
|
||||
try:
|
||||
y, sr = librosa.load(wav_path, sr=22050, mono=True)
|
||||
if len(y) < sr * 0.25:
|
||||
return None
|
||||
f0, _, _ = librosa.pyin(y, fmin=80, fmax=600, sr=sr)
|
||||
f0 = f0[~np.isnan(f0)]
|
||||
if len(f0) < 10:
|
||||
return None
|
||||
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
|
||||
mfcc_d = librosa.feature.delta(mfcc)
|
||||
mfcc_d2 = librosa.feature.delta(mfcc, order=2)
|
||||
cent = librosa.feature.spectral_centroid(y=y, sr=sr)
|
||||
roll = librosa.feature.spectral_rolloff(y=y, sr=sr)
|
||||
return np.concatenate([
|
||||
[np.mean(f0), np.std(f0), np.percentile(f0,10), np.percentile(f0,25),
|
||||
np.percentile(f0,50), np.percentile(f0,75), np.percentile(f0,90)],
|
||||
np.mean(mfcc,axis=1), np.std(mfcc,axis=1),
|
||||
np.mean(mfcc_d,axis=1), np.std(mfcc_d,axis=1),
|
||||
np.mean(mfcc_d2,axis=1), np.std(mfcc_d2,axis=1),
|
||||
[np.mean(cent), np.std(cent), np.mean(roll), np.std(roll)],
|
||||
]).astype(np.float64)
|
||||
except:
|
||||
return None
|
||||
|
||||
# ═══════════ 主流程 ═══════════
|
||||
|
||||
def main():
|
||||
os.makedirs(OUT_DIR, exist_ok=True)
|
||||
LOG_FILE = os.path.join(OUT_DIR, "search.log")
|
||||
|
||||
# 日志: 文件 + 终端
|
||||
log = logging.getLogger("cyrene")
|
||||
log.setLevel(logging.INFO)
|
||||
for h in [logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)]:
|
||||
h.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(message)s", "%H:%M:%S"))
|
||||
log.addHandler(h)
|
||||
|
||||
def progress(current, total, suffix=""):
|
||||
pct = current / total if total else 0
|
||||
bar = "=" * int(30*pct) + ">" + " " * max(0, 29-int(30*pct))
|
||||
sys.stdout.write(f"\r [{bar}] {pct*100:5.1f}% {current}/{total} {suffix}")
|
||||
sys.stdout.flush()
|
||||
|
||||
log.info("=" * 55)
|
||||
log.info("昔涟声纹搜索 — 多线程全精度模式")
|
||||
log.info(f" 线程: {WORKERS} | 批量: {BATCH}")
|
||||
log.info("=" * 55)
|
||||
|
||||
t_start = time.time()
|
||||
|
||||
# ── Step 1: 参考模板 ──
|
||||
log.info("\n[1/3] 提取参考模板...")
|
||||
ref_feats = []
|
||||
for fname in CONFIRMED:
|
||||
path = os.path.join(SEARCH_DIR, fname)
|
||||
f = extract_features(path)
|
||||
if f is not None:
|
||||
ref_feats.append(f)
|
||||
log.info(f" OK {os.path.basename(fname)}")
|
||||
template = np.mean(ref_feats, axis=0)
|
||||
log.info(f" 模板: {len(ref_feats)} 文件 | pitch={template[0]:.0f}Hz | dim={len(template)}")
|
||||
|
||||
# ── Step 2: 文件列表 ──
|
||||
log.info(f"\n[2/3] 收集文件...")
|
||||
all_wavs = []
|
||||
for root, dirs, files in os.walk(SEARCH_DIR):
|
||||
for f in files:
|
||||
if f.endswith(".wav"):
|
||||
all_wavs.append(os.path.join(root, f))
|
||||
log.info(f" {len(all_wavs):,} 个 WAV 文件")
|
||||
|
||||
# ── Step 3: 多线程搜索 ──
|
||||
log.info(f"\n[3/3] 多线程搜索 ({len(all_wavs):,} 文件, {WORKERS} 线程)...")
|
||||
log.info("-" * 55)
|
||||
t2 = time.time()
|
||||
results = []
|
||||
errors = 0
|
||||
|
||||
pool = Pool(WORKERS)
|
||||
done = 0
|
||||
|
||||
for feats in pool.imap(extract_features, all_wavs, chunksize=50):
|
||||
wav = all_wavs[done]
|
||||
done += 1
|
||||
|
||||
if feats is not None:
|
||||
sim = np.dot(feats, template) / (np.linalg.norm(feats) * np.linalg.norm(template) + 1e-8)
|
||||
p = feats[0]
|
||||
penalty = 1.0 / (1.0 + abs(p - template[0]) / 100)
|
||||
results.append((sim * 0.6 + penalty * 0.4, sim, p, wav))
|
||||
else:
|
||||
errors += 1
|
||||
|
||||
# 进度更新
|
||||
if done % 100 == 0 or done == len(all_wavs):
|
||||
elapsed = time.time() - t2
|
||||
rate = done / elapsed if elapsed else 0
|
||||
eta = (len(all_wavs) - done) / rate if rate else 0
|
||||
progress(done, len(all_wavs), f"{rate:.0f}f/s ETA{eta:.0f}s ok={len(results):,}")
|
||||
|
||||
# checkpoint 每 2000
|
||||
if done % 2000 == 0 and results:
|
||||
top = sorted(results, key=lambda x: x[0], reverse=True)
|
||||
with open(os.path.join(OUT_DIR, f"ckpt_{done}.json"), 'w') as jf:
|
||||
json.dump([(float(x[0]), float(x[2]), x[3]) for x in top[:500]], jf)
|
||||
|
||||
pool.close()
|
||||
pool.join()
|
||||
|
||||
print()
|
||||
log.info(f" 完成: {len(results):,} ok | {errors} skip | {time.time()-t2:.0f}s ({len(results)/(time.time()-t2):.0f} f/s)")
|
||||
log.info(f" 总耗时: {time.time()-t_start:.0f}s")
|
||||
|
||||
# ── 排序 ──
|
||||
results.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
log.info(f"\n{'='*55}")
|
||||
log.info(f"Top 50 候选 (★ = combo > 0.85)")
|
||||
log.info("=" * 55)
|
||||
for i, (combo, sim, pitch, path) in enumerate(results[:50], 1):
|
||||
star = " ★" if combo > 0.85 else ""
|
||||
d = os.path.basename(os.path.dirname(path))
|
||||
f = os.path.basename(path)
|
||||
log.info(f" {i:2d}. [{combo:.4f}]{star} {d}/{f}")
|
||||
|
||||
# ── 来源统计 ──
|
||||
log.info(f"\n来源分布 (combo > 0.85):")
|
||||
srcs = {}
|
||||
for combo, sim, pitch, path in results:
|
||||
if combo > 0.85:
|
||||
d = os.path.basename(os.path.dirname(path))
|
||||
srcs[d] = srcs.get(d, 0) + 1
|
||||
for d in sorted(srcs):
|
||||
log.info(f" {d}: {srcs[d]} 文件")
|
||||
|
||||
# ── 导出 ──
|
||||
copied = 0
|
||||
for combo, sim, pitch, path in results:
|
||||
if combo > 0.85:
|
||||
dst = os.path.join(OUT_DIR, os.path.basename(path))
|
||||
if not os.path.exists(dst):
|
||||
shutil.copy2(path, dst)
|
||||
copied += 1
|
||||
log.info(f"\n导出: {copied} 文件 → {OUT_DIR}")
|
||||
|
||||
rp = os.path.join(OUT_DIR, "search_results.json")
|
||||
with open(rp, 'w') as f:
|
||||
json.dump([(float(s), float(p), w) for s, p, w in results], f, ensure_ascii=False)
|
||||
log.info(f"结果: {rp}")
|
||||
log.info(f"日志: {LOG_FILE}")
|
||||
log.info(f"\n{'='*55}")
|
||||
log.info("DONE")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,174 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Phase 2: 加载预提取特征 → 秒级搜索昔涟。
|
||||
前提: 先跑完 extract_features.py
|
||||
用法: python search_cyrene_v2.py
|
||||
"""
|
||||
import os, sys, shutil, json, time, logging
|
||||
import numpy as np
|
||||
|
||||
FEAT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\features"
|
||||
OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_round2"
|
||||
FEAT_FILE = os.path.join(FEAT_DIR, "features_all.npz")
|
||||
|
||||
# ── 用户确认的昔涟样本 (53 个) ──
|
||||
CONFIRMED = [
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
|
||||
]
|
||||
|
||||
|
||||
def main():
|
||||
os.makedirs(OUT_DIR, exist_ok=True)
|
||||
LOG_FILE = os.path.join(OUT_DIR, "search.log")
|
||||
logging.basicConfig(
|
||||
level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S",
|
||||
handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
log = logging.getLogger("search")
|
||||
|
||||
log.info("Phase 2: Cyrene Voice Search (Round 2)")
|
||||
log.info(f" refs : {len(CONFIRMED)} confirmed samples")
|
||||
log.info(f" input: {FEAT_FILE}")
|
||||
|
||||
# 加载特征
|
||||
t0 = time.time()
|
||||
data = np.load(FEAT_FILE, allow_pickle=True)
|
||||
feats = data["feats"]
|
||||
paths = data["paths"]
|
||||
log.info(f" loaded: {len(feats):,} features ({feats.nbytes/1024/1024:.0f} MB) in {time.time()-t0:.1f}s")
|
||||
|
||||
# 构建参考模板
|
||||
ref_indices = []
|
||||
for i, p in enumerate(paths):
|
||||
for cf in CONFIRMED:
|
||||
if p.endswith(cf.replace("/", os.sep)):
|
||||
ref_indices.append(i)
|
||||
break
|
||||
|
||||
log.info(f" matched refs in dataset: {len(ref_indices)}/{len(CONFIRMED)}")
|
||||
if len(ref_indices) < 5:
|
||||
log.error(" too few refs matched, check paths!")
|
||||
return
|
||||
|
||||
template = np.mean(feats[ref_indices], axis=0)
|
||||
log.info(f" template pitch: {template[0]:.0f}Hz dim: {len(template)}")
|
||||
|
||||
# 全量比对 (向量化, 秒级)
|
||||
t1 = time.time()
|
||||
norm_feats = feats / (np.linalg.norm(feats, axis=1, keepdims=True) + 1e-8)
|
||||
norm_template = template / (np.linalg.norm(template) + 1e-8)
|
||||
sims = np.dot(norm_feats, norm_template)
|
||||
|
||||
pitches = feats[:, 0]
|
||||
penalty = 1.0 / (1.0 + np.abs(pitches - template[0]) / 100)
|
||||
combos = sims * 0.6 + penalty * 0.4
|
||||
log.info(f" compared {len(combos):,} vectors in {time.time()-t1:.1f}s")
|
||||
|
||||
# 排序
|
||||
order = np.argsort(-combos)
|
||||
results = [(combos[i], sims[i], pitches[i], paths[i]) for i in order]
|
||||
log.info(f" sorted in {time.time()-t1:.1f}s")
|
||||
|
||||
# ── 输出 ──
|
||||
log.info(f"\n{'='*55}")
|
||||
log.info(f"Top 50 Candidates (round 2)")
|
||||
log.info("=" * 55)
|
||||
for rank, (combo, sim, pitch, path) in enumerate(results[:50], 1):
|
||||
star = " *" if combo > 0.90 else ""
|
||||
d = os.path.basename(os.path.dirname(path))
|
||||
f = os.path.basename(path)
|
||||
log.info(f" {rank:2d}. [{combo:.4f}]{star} {d}/{f}")
|
||||
|
||||
# 来源分布
|
||||
log.info(f"\nSource distribution (combo > 0.85):")
|
||||
srcs = {}
|
||||
for combo, sim, pitch, path in results:
|
||||
if combo > 0.85:
|
||||
d = os.path.basename(os.path.dirname(path))
|
||||
srcs[d] = srcs.get(d, 0) + 1
|
||||
for d in sorted(srcs):
|
||||
log.info(f" {d}: {srcs[d]}")
|
||||
|
||||
# 分级导出
|
||||
tiers = [
|
||||
("tier1_095_100", 0.95),
|
||||
("tier2_092_095", 0.92),
|
||||
("tier3_090_092", 0.90),
|
||||
("tier4_085_090", 0.85),
|
||||
]
|
||||
total_copied = 0
|
||||
for tier_name, threshold in tiers:
|
||||
tier_dir = os.path.join(OUT_DIR, tier_name)
|
||||
os.makedirs(tier_dir, exist_ok=True)
|
||||
n = 0
|
||||
for combo, sim, pitch, path in results:
|
||||
if combo >= threshold:
|
||||
dst = os.path.join(tier_dir, os.path.basename(path))
|
||||
if os.path.exists(path) and not os.path.exists(dst):
|
||||
shutil.copy2(path, dst)
|
||||
n += 1
|
||||
else:
|
||||
break # results are sorted, stop when below threshold
|
||||
log.info(f" {tier_name}: {n} files")
|
||||
total_copied += n
|
||||
|
||||
# 保存结果
|
||||
rp = os.path.join(OUT_DIR, "results.json")
|
||||
with open(rp, 'w') as f:
|
||||
json.dump([(float(s), float(p), w) for s, _, p, w in results], f, ensure_ascii=False)
|
||||
|
||||
log.info(f"\n total exported: {total_copied}")
|
||||
log.info(f" results JSON : {rp}")
|
||||
log.info(f" log file : {LOG_FILE}")
|
||||
log.info(f" DONE ({time.time()-t0:.1f}s)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,214 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
ECAPA-TDNN 声纹搜索 — 多线程版本。
|
||||
模型已缓存在 ~/.cache/huggingface，不再重复下载。
|
||||
"""
|
||||
import os, sys, shutil, json, time, logging
|
||||
from multiprocessing import Pool, cpu_count
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
SEARCH_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cleaned"
|
||||
OUT_DIR = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_ecapa"
|
||||
WORKERS = max(1, cpu_count() - 1)
|
||||
os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
|
||||
|
||||
CONFIRMED = [
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
|
||||
"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
|
||||
"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
|
||||
]
|
||||
|
||||
|
||||
def load_audio(path):
|
||||
wav, sr = torchaudio.load(path)
|
||||
if sr != 16000:
|
||||
wav = torchaudio.functional.resample(wav, sr, 16000)
|
||||
if wav.shape[0] > 1:
|
||||
wav = wav.mean(dim=0, keepdim=True)
|
||||
return wav # [1, samples]
|
||||
|
||||
|
||||
def get_ref_embeddings():
|
||||
"""主进程: 加载模型, 提取参考嵌入"""
|
||||
from speechbrain.inference.speaker import EncoderClassifier
|
||||
classifier = EncoderClassifier.from_hparams(
|
||||
source="speechbrain/spkrec-ecapa-voxceleb",
|
||||
run_opts={"device": "cpu"},
|
||||
)
|
||||
refs = []
|
||||
for fname in CONFIRMED:
|
||||
path = os.path.join(SEARCH_DIR, fname)
|
||||
wav = load_audio(path)
|
||||
wav = wav.squeeze(0).unsqueeze(0) # [1, time]
|
||||
if wav.shape[1] < 16000:
|
||||
wav = torch.nn.functional.pad(wav, (0, 16000 - wav.shape[1]))
|
||||
with torch.no_grad():
|
||||
emb = classifier.encode_batch(wav).squeeze()
|
||||
refs.append(emb.numpy())
|
||||
return np.mean(refs, axis=0).astype(np.float32)
|
||||
|
||||
|
||||
def scan_chunk(args):
|
||||
"""Worker: 加载模型, 扫描一批文件, 返回 [(sim, path), ...]"""
|
||||
paths, template_arr = args
|
||||
from speechbrain.inference.speaker import EncoderClassifier
|
||||
classifier = EncoderClassifier.from_hparams(
|
||||
source="speechbrain/spkrec-ecapa-voxceleb",
|
||||
run_opts={"device": "cpu"},
|
||||
)
|
||||
template = torch.from_numpy(template_arr)
|
||||
results = []
|
||||
for path in paths:
|
||||
try:
|
||||
wav = load_audio(path)
|
||||
wav = wav.squeeze(0).unsqueeze(0)
|
||||
if wav.shape[1] < 8000:
|
||||
continue
|
||||
with torch.no_grad():
|
||||
emb = classifier.encode_batch(wav).squeeze()
|
||||
sim = torch.nn.functional.cosine_similarity(emb, template, dim=0).item()
|
||||
results.append((sim, path))
|
||||
except:
|
||||
pass
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
os.makedirs(OUT_DIR, exist_ok=True)
|
||||
LOG_FILE = os.path.join(OUT_DIR, "search.log")
|
||||
logging.basicConfig(
|
||||
level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S",
|
||||
handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
log = logging.getLogger("ecapa")
|
||||
|
||||
log.info("ECAPA-TDNN Search (multiprocess)")
|
||||
log.info(f" refs: {len(CONFIRMED)} | workers: {WORKERS}")
|
||||
|
||||
# Step 1: 参考嵌入 (主进程)
|
||||
log.info("Extracting reference embeddings...")
|
||||
t0 = time.time()
|
||||
template = get_ref_embeddings()
|
||||
log.info(f" template: dim={len(template)}, pitch proxy={template[0]:.4f} ({time.time()-t0:.0f}s)")
|
||||
|
||||
# Step 2: 收集文件
|
||||
all_wavs = []
|
||||
for root, dirs, files in os.walk(SEARCH_DIR):
|
||||
for f in files:
|
||||
if f.endswith(".wav"):
|
||||
all_wavs.append(os.path.join(root, f))
|
||||
log.info(f" files: {len(all_wavs):,}")
|
||||
|
||||
# Step 3: 分块, 多线程扫描
|
||||
chunk_size = max(50, len(all_wavs) // (WORKERS * 4))
|
||||
chunks = [all_wavs[i:i+chunk_size] for i in range(0, len(all_wavs), chunk_size)]
|
||||
chunk_args = [(chunk, template) for chunk in chunks]
|
||||
log.info(f" chunks: {len(chunks)} x ~{chunk_size} | starting pool...")
|
||||
|
||||
t1 = time.time()
|
||||
results = []
|
||||
done = 0
|
||||
pool = Pool(WORKERS)
|
||||
for chunk_results in pool.imap_unordered(scan_chunk, chunk_args):
|
||||
results.extend(chunk_results)
|
||||
done += chunk_size
|
||||
elapsed = time.time() - t1
|
||||
rate = min(done, len(all_wavs)) / elapsed if elapsed else 0
|
||||
eta = (len(all_wavs) - min(done, len(all_wavs))) / rate if rate else 0
|
||||
pct = min(done, len(all_wavs)) * 100 / len(all_wavs)
|
||||
print(f" [{pct:5.1f}%] {min(done, len(all_wavs)):,}/{len(all_wavs):,} | "
|
||||
f"{rate:.0f} f/s | ETA {eta:.0f}s | {len(results):,} ok")
|
||||
pool.close()
|
||||
pool.join()
|
||||
print()
|
||||
log.info(f" scanned in {time.time()-t1:.0f}s | {len(results):,} results")
|
||||
|
||||
# Step 4: 排序 + 输出
|
||||
results.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
log.info(f"\n{'='*55}")
|
||||
log.info("Top 50 Candidates")
|
||||
log.info("=" * 55)
|
||||
for rank, (sim, path) in enumerate(results[:50], 1):
|
||||
d = os.path.basename(os.path.dirname(path))
|
||||
f = os.path.basename(path)
|
||||
log.info(f" {rank:2d}. [{sim:.4f}] {d}/{f}")
|
||||
|
||||
log.info(f"\nSource distribution (sim > 0.65):")
|
||||
srcs = {}
|
||||
for sim, path in results:
|
||||
if sim > 0.65:
|
||||
d = os.path.basename(os.path.dirname(path))
|
||||
srcs[d] = srcs.get(d, 0) + 1
|
||||
for d in sorted(srcs):
|
||||
log.info(f" {d}: {srcs[d]}")
|
||||
|
||||
# Export
|
||||
tiers = [("tier1_075", 0.75), ("tier2_070", 0.70), ("tier3_065", 0.65), ("tier4_060", 0.60)]
|
||||
for tier_name, thresh in tiers:
|
||||
tier_dir = os.path.join(OUT_DIR, tier_name)
|
||||
os.makedirs(tier_dir, exist_ok=True)
|
||||
n = 0
|
||||
for sim, path in results:
|
||||
if sim >= thresh:
|
||||
dst = os.path.join(tier_dir, os.path.basename(path))
|
||||
if os.path.exists(path) and not os.path.exists(dst):
|
||||
shutil.copy2(path, dst)
|
||||
n += 1
|
||||
log.info(f" {tier_name}: {n} files")
|
||||
|
||||
rp = os.path.join(OUT_DIR, "results.json")
|
||||
with open(rp, 'w') as f:
|
||||
json.dump([(float(s), p) for s, p in results], f, ensure_ascii=False)
|
||||
log.info(f"\n DONE ({time.time()-t0:.0f}s) | {rp}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,21 @@
|
||||
"""
|
||||
Python 3.8 兼容性补丁: 修复 speechbrain 中的 list[int] 语法。
|
||||
在导入 speechbrain 之前 import 本模块即可。
|
||||
"""
|
||||
import speechbrain.dataio.sampler as _s
|
||||
# Force evaluation of the module to catch the error
|
||||
# The fix: monkey-patch before import completes
|
||||
import sys
|
||||
from speechbrain.dataio import sampler as _sampler_mod
|
||||
|
||||
# Patch the problematic line: replace Optional[list[int]] with Optional["List[int]"]
|
||||
# This is done at the source level by editing the file
|
||||
import os
|
||||
_sp = os.path.join(os.path.dirname(_sampler_mod.__file__), "sampler.py")
|
||||
if os.path.exists(_sp):
|
||||
with open(_sp) as f:
|
||||
src = f.read()
|
||||
if "from __future__ import annotations" not in src:
|
||||
with open(_sp, "w") as f:
|
||||
f.write("from __future__ import annotations\n" + src)
|
||||
print("[speechbrain_fix] patched sampler.py for Python 3.8")
|
||||
@@ -0,0 +1,103 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
RVC 模型推理测试。纯 CPU 模式。
|
||||
两个音频：参考音频 (提取昔涟音色) + 输入音频 (要转换的声音)
|
||||
"""
|
||||
import os, sys, time, warnings
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
# 添加 RVC 到路径
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'rvc'))
|
||||
|
||||
import torch
|
||||
import librosa
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
from infer.modules.vc.modules import VC
|
||||
|
||||
# === 配置 ===
|
||||
MODEL_PATH = r"D:\Users\Aska\Documents\G_2333333.pth"
|
||||
HUBERT_PATH = r"D:\Project\Code\Uni\Cyrene\scripts\voice\rvc\assets\hubert\hubert_base.pt"
|
||||
REF_AUDIO = r"D:\Project\Code\Uni\Cyrene-Voice-Model\data\cyrene_group01\VoBanks31_0078_004ab065.wav" # 昔涟参考
|
||||
INPUT_AUDIO = REF_AUDIO # 测试: 用同一文件验证模型能正确重建
|
||||
OUTPUT_AUDIO = r"D:\Users\Aska\Documents\rvc_test_output.wav"
|
||||
|
||||
print("=" * 50)
|
||||
print("RVC 推理测试 (CPU)")
|
||||
print("=" * 50)
|
||||
|
||||
# 检查文件
|
||||
for label, path in [("模型", MODEL_PATH), ("HuBERT", HUBERT_PATH), ("参考音频", REF_AUDIO)]:
|
||||
if not os.path.exists(path):
|
||||
print(f"[ERROR] {label} 不存在: {path}")
|
||||
sys.exit(1)
|
||||
print(f"[OK] {label}: {os.path.getsize(path)/1024/1024:.1f} MB")
|
||||
|
||||
# 加载参考音频
|
||||
print(f"\n[1/3] 加载参考音频...")
|
||||
ref_audio, ref_sr = librosa.load(REF_AUDIO, sr=40000, mono=True)
|
||||
print(f" 采样率: {ref_sr}Hz, 时长: {len(ref_audio)/ref_sr:.1f}s")
|
||||
|
||||
# 加载输入音频
|
||||
print(f"[2/3] 加载输入音频...")
|
||||
input_audio, input_sr = librosa.load(INPUT_AUDIO, sr=40000, mono=True)
|
||||
print(f" 采样率: {input_sr}Hz, 时长: {len(input_audio)/input_sr:.1f}s")
|
||||
|
||||
# 推理
|
||||
print(f"[3/3] RVC 推理中...")
|
||||
t0 = time.time()
|
||||
|
||||
# 路径中有中文，先复制到临时路径
|
||||
import tempfile, shutil
|
||||
tmp_dir = tempfile.mkdtemp()
|
||||
tmp_model = os.path.join(tmp_dir, "model.pth")
|
||||
tmp_ref = os.path.join(tmp_dir, "ref.wav")
|
||||
tmp_input = os.path.join(tmp_dir, "input.wav")
|
||||
shutil.copy(MODEL_PATH, tmp_model)
|
||||
sf.write(tmp_ref, ref_audio, 40000)
|
||||
sf.write(tmp_input, input_audio, 40000)
|
||||
|
||||
# 保存当前目录
|
||||
old_cwd = os.getcwd()
|
||||
rvc_dir = os.path.join(os.path.dirname(__file__), 'rvc')
|
||||
os.chdir(rvc_dir)
|
||||
# RVC 需要 assets/hubert/hubert_base.pt
|
||||
os.makedirs('assets/hubert', exist_ok=True)
|
||||
if not os.path.exists('assets/hubert/hubert_base.pt'):
|
||||
import shutil as _sh
|
||||
_sh.copy(HUBERT_PATH, 'assets/hubert/hubert_base.pt')
|
||||
|
||||
try:
|
||||
# 初始化 VC pipeline
|
||||
vc = VC()
|
||||
vc.get_vc(tmp_model, device="cpu", use_jit=False)
|
||||
|
||||
# 转换
|
||||
output, output_sr = vc.vc_single(
|
||||
sid=0,
|
||||
input_audio_path=tmp_input,
|
||||
f0_up_key=0, # 不改变音高
|
||||
f0_file=None,
|
||||
f0_method="rmvpe",
|
||||
file_index="", # 不使用 index
|
||||
file_index2="",
|
||||
index_rate=0,
|
||||
filter_radius=3,
|
||||
resample_sr=40000,
|
||||
rms_mix_rate=0.25,
|
||||
protect=0.33,
|
||||
)
|
||||
|
||||
elapsed = time.time() - t0
|
||||
print(f"\n 完成! 耗时: {elapsed:.1f}s")
|
||||
print(f" 输出采样率: {output_sr}Hz, 时长: {len(output)/output_sr:.1f}s")
|
||||
|
||||
# 保存
|
||||
sf.write(OUTPUT_AUDIO, output, output_sr)
|
||||
print(f" 已保存: {OUTPUT_AUDIO}")
|
||||
|
||||
finally:
|
||||
os.chdir(old_cwd)
|
||||
shutil.rmtree(tmp_dir, ignore_errors=True)
|
||||
|
||||
print(f"\n试听: {OUTPUT_AUDIO}")
|
||||
Reference in New Issue
Block a user