feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
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Warren
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scripts/asrx_self/speaker_encoder.py
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scripts/asrx_self/speaker_encoder.py
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#!/opt/homebrew/bin/python3.11
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"""
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Speaker Encoder - 聲紋特徵提取
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使用 ECAPA-TDNN 模型提取聲紋嵌入向量
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技術來源:
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- ECAPA-TDNN: Desplanques et al. (2020), Interspeech
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- 論文：https://arxiv.org/abs/2005.07143
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- 模型：SpeechBrain spkrec-ecapa-voxceleb
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- 準確度：EER 0.80% (VoxCeleb1)
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"""
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import torch
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import numpy as np
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from speechbrain.inference.speaker import EncoderClassifier
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def load_speaker_encoder(model_name="speechbrain/spkrec-ecapa-voxceleb"):
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"""
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載入聲紋編碼器模型
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Args:
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model_name: 模型名稱（HuggingFace）
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Returns:
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classifier: 聲紋編碼器
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"""
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print(f"[SpeakerEncoder] Loading model: {model_name}")
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classifier = EncoderClassifier.from_hparams(
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source=model_name,
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run_opts={"device": "cpu"}, # 使用 CPU
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)
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# 獲取模型資訊
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print(f"[SpeakerEncoder] Model loaded successfully")
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print(f"[SpeakerEncoder] Embedding dimension: 192")
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return classifier
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def extract_speaker_embedding(classifier, audio_waveform, sample_rate=16000):
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"""
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從音頻波形提取聲紋嵌入
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Args:
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classifier: 聲紋編碼器
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audio_waveform: 音頻波形 (numpy array)
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sample_rate: 採樣率
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Returns:
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embedding: 聲紋嵌入向量 (192 維)
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"""
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# 轉換為 torch tensor
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if isinstance(audio_waveform, np.ndarray):
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audio_tensor = torch.from_numpy(audio_waveform).float()
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else:
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audio_tensor = audio_waveform
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# 確保是 2D [batch, time]
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if audio_tensor.dim() == 1:
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audio_tensor = audio_tensor.unsqueeze(0)
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# 提取嵌入
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with torch.no_grad():
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embedding = classifier.encode_batch(audio_tensor)
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# 轉換為 numpy
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embedding = embedding.squeeze().cpu().numpy()
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return embedding
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def extract_speaker_embeddings_batch(classifier, audio_segments, sample_rate=16000):
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"""
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批量提取多個語音片段的聲紋嵌入
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Args:
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classifier: 聲紋編碼器
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audio_segments: 音頻片段列表 [numpy array, ...]
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sample_rate: 採樣率
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Returns:
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embeddings: 嵌入矩陣 [n_segments, 192]
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"""
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embeddings = []
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for i, audio in enumerate(audio_segments):
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emb = extract_speaker_embedding(classifier, audio, sample_rate)
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embeddings.append(emb)
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if (i + 1) % 50 == 0:
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print(f"[SpeakerEncoder] Processed {i + 1} segments")
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embeddings = np.vstack(embeddings)
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print(f"[SpeakerEncoder] Extracted {embeddings.shape[0]} embeddings")
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return embeddings
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def compute_similarity_matrix(embeddings, method="cosine"):
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"""
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計算聲紋相似度矩陣
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Args:
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embeddings: 嵌入矩陣 [n_segments, 192]
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method: 相似度計算方法 ('cosine', 'euclidean')
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Returns:
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similarity_matrix: 相似度矩陣 [n_segments, n_segments]
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"""
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from sklearn.metrics.pairwise import cosine_similarity
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# 清洗數據：移除 NaN 和 Inf
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embeddings = np.nan_to_num(embeddings, nan=0.0, posinf=0.0, neginf=0.0)
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# 正規化
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embeddings = normalize_embeddings(embeddings)
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# 再次清洗
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embeddings = np.nan_to_num(embeddings, nan=0.0, posinf=0.0, neginf=0.0)
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if method == "cosine":
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similarity = cosine_similarity(embeddings)
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elif method == "euclidean":
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from sklearn.metrics.pairwise import euclidean_distances
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# 將距離轉換為相似度
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distances = euclidean_distances(embeddings)
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similarity = 1 / (1 + distances)
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else:
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raise ValueError(f"Unknown method: {method}")
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# 確保沒有 NaN
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similarity = np.nan_to_num(similarity, nan=0.5)
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return similarity
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def normalize_embeddings(embeddings):
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"""
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正規化嵌入向量（單位長度）
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Args:
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embeddings: 嵌入矩陣 [n_segments, 192]
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Returns:
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normalized: 正規化後的嵌入矩陣
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"""
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from sklearn.preprocessing import normalize
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return normalize(embeddings, norm="l2")
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if __name__ == "__main__":
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# 測試聲紋編碼器
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import sys
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import torchaudio
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if len(sys.argv) < 2:
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print("Usage: python3 speaker_encoder.py <audio_path>")
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sys.exit(1)
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audio_path = sys.argv[1]
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print("[Test] Loading speaker encoder...")
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classifier = load_speaker_encoder()
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print(f"\n[Test] Loading audio: {audio_path}")
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wav, sr = torchaudio.load(audio_path)
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# 重採樣到 16kHz
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if sr != 16000:
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transform = torchaudio.transforms.Resample(sr, 16000)
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wav = transform(wav)
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print(f"[Test] Audio shape: {wav.shape}")
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print(f"[Test] Duration: {wav.shape[1] / 16000:.2f}s")
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# 提取嵌入
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print("\n[Test] Extracting speaker embedding...")
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embedding = extract_speaker_embedding(classifier, wav.numpy())
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print(f"[Test] Embedding shape: {embedding.shape}")
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print(f"[Test] Embedding norm: {np.linalg.norm(embedding):.4f}")
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print(f"[Test] Embedding mean: {embedding.mean():.4f}")
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print(f"[Test] Embedding std: {embedding.std():.4f}")
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# 顯示部分嵌入值
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print(f"\n[Test] First 10 embedding values:")
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print(f" {embedding[:10]}")
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