少样本声音克隆技术：几秒音频复制你的声音

"""少样本声音克隆原理演示：说话人编码 + 声纹匹配 + 零样本迁移""" import numpy as npfrom collections import Counter  class VoiceEncoder:    """模拟说话人编码器：将任意长度音频压缩成固定维度声纹向量"""     def __init__(self, dim=32, seed=42):        self.dim = dim        rng = np.random.RandomState(seed)        # 随机投影矩阵仅用于教学演示，真实场景由训练学得        self.projection = rng.randn(dim, dim * 4)     def encode(self, audio_features: np.ndarray) -> np.ndarray:        """输入声学特征矩阵，输出声纹嵌入向量（L2归一化）"""        if audio_features.ndim != 2 or audio_features.shape[0] == 0:            raise ValueError("需要二维特征矩阵")        mean_feat = audio_features.mean(axis=0)        if mean_feat.shape[0] != self.projection.shape[1]:            raise ValueError("特征维度不匹配")        embedding = self.projection.dot(mean_feat)        norm = np.linalg.norm(embedding)        return embedding / (norm + 1e-9)  def cosine_similarity(vec_a: np.ndarray, vec_b: np.ndarray) -> float:    """计算两个声纹向量的余弦相似度"""    return float(np.dot(vec_a, vec_b))  def synthesize_mock_audio(freq_base: float, duration: float, sr: int = 16000) -> np.ndarray:    """生成模拟说话人的声学特征（教学演示用，非真实语音）"""    t = np.linspace(0, duration, int(sr * duration), endpoint=False)    tone = np.sin(2 * np.pi * freq_base * t)    tone += 0.3 * np.sin(2 * np.pi * freq_base * 2 * t)    noise = 0.05 * np.random.RandomState(int(freq_base)).randn(len(t))    return tone + noise  def extract_features(waveform: np.ndarray, frame_size: int = 400) -> np.ndarray:    """将波形切成帧，每帧取均值作为简化声学特征"""    features = []    for start in range(0, len(waveform) - frame_size, frame_size):        frame = waveform[start:start + frame_size]        features.append([np.abs(frame).mean(), np.fft.rfft(frame).real.mean()])    return np.array(features)  def train_speaker_encoder(speaker_pool: dict) -> VoiceEncoder:    """训练阶段：让编码器对不同说话人的输出尽量分离（示意）"""    encoder = VoiceEncoder(dim=32, seed=int(len(speaker_pool) * 7))    centroids: dict = {}    for speaker, waveform in speaker_pool.items():        features = extract_features(waveform)        embedding = encoder.encode(features)        centroids[speaker] = embedding    # 示意：展示各说话人质心距离    names = list(centroids.keys())    distance_matrix = [        [round(cosine_similarity(centroids[names[i]], centroids[names[j]]), 3)         for j in range(len(names))]        for i in range(len(names))    ]    print("说话人质心余弦相似度矩阵:")    for i, name in enumerate(names):        print(f"  {name}: {distance_matrix[i]}")    return encoder  def few_shot_clone(encoder: VoiceEncoder,                   speaker_db: dict,                   reference_audio: tuple,                   target_text: str) -> str:    """零样本克隆：参考音频 -> 声纹向量 -> 匹配说话人 -> 合成语音"""    ref_freq, ref_duration = reference_audio    ref_waveform = synthesize_mock_audio(ref_freq, ref_duration)    ref_embedding = encoder.encode(extract_features(ref_waveform))     print("候选说话人声纹相似度:")    best_speaker, best_score = None, -1.0    for speaker, waveform in speaker_db.items():        embedding = encoder.encode(extract_features(waveform))        score = cosine_similarity(ref_embedding, embedding)        print(f"  {speaker}: {score:.4f}")        if score > best_score:            best_speaker, best_score = speaker, score     print(f"\n匹配结果: 最接近的说话人是 [{best_speaker}]")    print(f"目标文本: {target_text}")    print("生成结果: (合成语音输出)")    return f"[{best_speaker}的声音] {target_text}"  def main() -> None:    # 1. 准备训练数据：3个说话人，每人3秒语音    training_pool = {        "angela": synthesize_mock_audio(180, 3.0),        "bob": synthesize_mock_audio(120, 3.0),        "cathy": synthesize_mock_audio(240, 3.0),    }     # 2. 训练/获得说话人编码器    encoder = train_speaker_encoder(training_pool)     # 3. 构建说话人库（预存声纹）    speaker_db = {        "angela": training_pool["angela"],        "bob": training_pool["bob"],        "cathy": training_pool["cathy"],    }     # 4. 零样本克隆：新参考音频（2秒）-> 找出匹配说话人    unseen_reference = (124.0, 2.0)  # 频率接近bob的新声音    result = few_shot_clone(encoder, speaker_db, unseen_reference, "你好，我是人工智能")    print(f"\n输出: {result}")  if __name__ == "__main__":    main()