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>
215 lines
9.5 KiB
Python
215 lines
9.5 KiB
Python
#!/usr/bin/env python3
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"""
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ECAPA-TDNN 声纹搜索 — 多线程版本。
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模型已缓存在 ~/.cache/huggingface，不再重复下载。
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"""
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import os, sys, shutil, json, time, logging
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from multiprocessing import Pool, cpu_count
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import numpy as np
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import torch
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import torchaudio
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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\cyrene_ecapa"
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WORKERS = max(1, cpu_count() - 1)
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os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
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CONFIRMED = [
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0358_02ff22a9.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0369_0315d90a.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0370_0317b5bf.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0391_033cbeea.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0392_033d9e2e.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0398_0346fa30.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0417_0367cbc5.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0330_02cb864d.wav",
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"External_del_3.5_chapter_2/External_del_3.5_chapter_2_0357_02fd4ab1.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0106_009dfe25.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0107_009fba53.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0112_00a89eb2.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0124_00c08c68.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0128_00c6ba19.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0365_026cf0de.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0371_0278f79f.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0374_02803ab5.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0409_02bcd547.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0410_02bec9f4.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0437_02e57ff0.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0440_02e7d98f.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0467_031daa9e.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0102_0095ba46.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0109_00a458cc.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0126_00c44990.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0130_00c95298.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0359_02670f55.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0364_026b8023.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0366_026f3922.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0369_0274d62a.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0372_027b67ef.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0392_029e92a6.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0413_02c1a418.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0415_02c466f1.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0417_02c8ffc0.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0430_02dbad24.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0431_02dda165.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0434_02e216cf.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0443_02eb0bc4.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0455_03048430.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0462_0316bcd9.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0463_03181c56.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0464_03195142.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0468_031e8bd6.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0469_03208c8a.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0484_03339e50.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0488_033a7664.wav",
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"External_del_3.4_chapter_0/External_del_3.4_chapter_0_0489_033b5c44.wav",
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]
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def load_audio(path):
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wav, sr = torchaudio.load(path)
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if sr != 16000:
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wav = torchaudio.functional.resample(wav, sr, 16000)
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if wav.shape[0] > 1:
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wav = wav.mean(dim=0, keepdim=True)
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return wav # [1, samples]
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def get_ref_embeddings():
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"""主进程: 加载模型, 提取参考嵌入"""
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from speechbrain.inference.speaker import EncoderClassifier
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classifier = EncoderClassifier.from_hparams(
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source="speechbrain/spkrec-ecapa-voxceleb",
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run_opts={"device": "cpu"},
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)
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refs = []
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for fname in CONFIRMED:
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path = os.path.join(SEARCH_DIR, fname)
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wav = load_audio(path)
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wav = wav.squeeze(0).unsqueeze(0) # [1, time]
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if wav.shape[1] < 16000:
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wav = torch.nn.functional.pad(wav, (0, 16000 - wav.shape[1]))
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with torch.no_grad():
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emb = classifier.encode_batch(wav).squeeze()
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refs.append(emb.numpy())
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return np.mean(refs, axis=0).astype(np.float32)
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def scan_chunk(args):
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"""Worker: 加载模型, 扫描一批文件, 返回 [(sim, path), ...]"""
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paths, template_arr = args
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from speechbrain.inference.speaker import EncoderClassifier
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classifier = EncoderClassifier.from_hparams(
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source="speechbrain/spkrec-ecapa-voxceleb",
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run_opts={"device": "cpu"},
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)
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template = torch.from_numpy(template_arr)
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results = []
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for path in paths:
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try:
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wav = load_audio(path)
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wav = wav.squeeze(0).unsqueeze(0)
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if wav.shape[1] < 8000:
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continue
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with torch.no_grad():
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emb = classifier.encode_batch(wav).squeeze()
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sim = torch.nn.functional.cosine_similarity(emb, template, dim=0).item()
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results.append((sim, path))
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except:
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pass
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return results
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def main():
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os.makedirs(OUT_DIR, exist_ok=True)
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LOG_FILE = os.path.join(OUT_DIR, "search.log")
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s %(message)s", datefmt="%H:%M:%S",
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handlers=[logging.FileHandler(LOG_FILE, encoding='utf-8'), logging.StreamHandler(sys.stdout)],
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)
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log = logging.getLogger("ecapa")
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log.info("ECAPA-TDNN Search (multiprocess)")
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log.info(f" refs: {len(CONFIRMED)} | workers: {WORKERS}")
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# Step 1: 参考嵌入 (主进程)
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log.info("Extracting reference embeddings...")
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t0 = time.time()
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template = get_ref_embeddings()
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log.info(f" template: dim={len(template)}, pitch proxy={template[0]:.4f} ({time.time()-t0:.0f}s)")
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# Step 2: 收集文件
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all_wavs = []
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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"):
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all_wavs.append(os.path.join(root, f))
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log.info(f" files: {len(all_wavs):,}")
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# Step 3: 分块, 多线程扫描
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chunk_size = max(50, len(all_wavs) // (WORKERS * 4))
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chunks = [all_wavs[i:i+chunk_size] for i in range(0, len(all_wavs), chunk_size)]
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chunk_args = [(chunk, template) for chunk in chunks]
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log.info(f" chunks: {len(chunks)} x ~{chunk_size} | starting pool...")
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t1 = time.time()
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results = []
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done = 0
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pool = Pool(WORKERS)
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for chunk_results in pool.imap_unordered(scan_chunk, chunk_args):
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results.extend(chunk_results)
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done += chunk_size
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elapsed = time.time() - t1
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rate = min(done, len(all_wavs)) / elapsed if elapsed else 0
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eta = (len(all_wavs) - min(done, len(all_wavs))) / rate if rate else 0
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pct = min(done, len(all_wavs)) * 100 / len(all_wavs)
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print(f" [{pct:5.1f}%] {min(done, len(all_wavs)):,}/{len(all_wavs):,} | "
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f"{rate:.0f} f/s | ETA {eta:.0f}s | {len(results):,} ok")
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pool.close()
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pool.join()
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print()
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log.info(f" scanned in {time.time()-t1:.0f}s | {len(results):,} results")
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# Step 4: 排序 + 输出
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results.sort(key=lambda x: x[0], reverse=True)
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log.info(f"\n{'='*55}")
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log.info("Top 50 Candidates")
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log.info("=" * 55)
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for rank, (sim, path) in enumerate(results[:50], 1):
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d = os.path.basename(os.path.dirname(path))
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f = os.path.basename(path)
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log.info(f" {rank:2d}. [{sim:.4f}] {d}/{f}")
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log.info(f"\nSource distribution (sim > 0.65):")
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srcs = {}
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for sim, path in results:
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if sim > 0.65:
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d = os.path.basename(os.path.dirname(path))
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srcs[d] = srcs.get(d, 0) + 1
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for d in sorted(srcs):
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log.info(f" {d}: {srcs[d]}")
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# Export
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tiers = [("tier1_075", 0.75), ("tier2_070", 0.70), ("tier3_065", 0.65), ("tier4_060", 0.60)]
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for tier_name, thresh in tiers:
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tier_dir = os.path.join(OUT_DIR, tier_name)
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os.makedirs(tier_dir, exist_ok=True)
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n = 0
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for sim, path in results:
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if sim >= thresh:
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dst = os.path.join(tier_dir, os.path.basename(path))
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if os.path.exists(path) and not os.path.exists(dst):
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shutil.copy2(path, dst)
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n += 1
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log.info(f" {tier_name}: {n} files")
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rp = os.path.join(OUT_DIR, "results.json")
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with open(rp, 'w') as f:
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json.dump([(float(s), p) for s, p in results], f, ensure_ascii=False)
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log.info(f"\n DONE ({time.time()-t0:.0f}s) | {rp}")
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if __name__ == "__main__":
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main()
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