现代TTS系统实战：从文本到自然语音的完整实现

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import torch
import torch.nn as nn
from dataclasses import dataclass
from typing import Optional, List, Tuple

@dataclass
class TTSConfig:
    sample_rate: int = 22050
    n_mels: int = 80
    n_fft: int = 1024
    hop_length: int = 256
    win_length: int = 1024
    mel_fmin: float = 0.0
    mel_fmax: float = 8000.0
    
    # Model config
    hidden_dim: int = 512
    encoder_layers: int = 6
    decoder_layers: int = 6
    attention_heads: int = 8
    
    # Training config
    batch_size: int = 32
    learning_rate: float = 1e-4
    warmup_steps: int = 4000

class ModernTTSSystem:
    def __init__(self, config: TTSConfig):
        self.config = config
        self.text_processor = TextProcessor()
        self.acoustic_model = AcousticModel(config)
        self.vocoder = NeuralVocoder(config)
        self.prosody_controller = ProsodyController()
        self.speaker_encoder = SpeakerEncoder()
        
    def synthesize(self, text: str, speaker_id: Optional[str] = None,
                  emotion: Optional[str] = None) -> np.ndarray:
        """完整的TTS合成流程"""
        # 1. 文本处理
        phonemes, durations = self.text_processor.process(text)
        
        # 2. 说话人编码
        speaker_embedding = None
        if speaker_id:
            speaker_embedding = self.speaker_encoder.encode(speaker_id)
        
        # 3. 韵律预测
        prosody_features = self.prosody_controller.predict(
            phonemes, emotion
        )
        
        # 4. 声学模型预测
        mel_spectrogram = self.acoustic_model.predict(
            phonemes, 
            durations,
            speaker_embedding,
            prosody_features
        )
        
        # 5. 声码器合成
        audio = self.vocoder.generate(mel_spectrogram)
        
        return audio

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import re
from typing import List, Tuple
import phonemizer
from g2p_en import G2p

class TextProcessor:
    def __init__(self):
        self.g2p = G2p()
        self.phonemizer = phonemizer.backend.EspeakBackend(
            language='en-us',
            preserve_punctuation=True
        )
        self.abbreviations = self.load_abbreviations()
        self.number_normalizer = NumberNormalizer()
        
    def process(self, text: str) -> Tuple[List[str], List[int]]:
        """处理文本到音素序列"""
        # 1. 文本规范化
        normalized = self.normalize_text(text)
        
        # 2. 分词和词性标注
        tokens = self.tokenize(normalized)
        pos_tags = self.pos_tagging(tokens)
        
        # 3. 音素转换
        phonemes = self.text_to_phonemes(tokens, pos_tags)
        
        # 4. 持续时间预测
        durations = self.predict_durations(phonemes, pos_tags)
        
        return phonemes, durations
    
    def normalize_text(self, text: str) -> str:
        """文本规范化"""
        # 展开缩写
        for abbr, full in self.abbreviations.items():
            text = re.sub(r'\b' + abbr + r'\b', full, text, flags=re.IGNORECASE)
        
        # 数字规范化
        text = self.number_normalizer.normalize(text)
        
        # 处理特殊符号
        text = self.handle_special_chars(text)
        
        return text
    
    def text_to_phonemes(self, tokens: List[str], pos_tags: List[str]) -> List[str]:
        """文本转音素"""
        phonemes = []
        
        for token, pos in zip(tokens, pos_tags):
            if self.is_oov(token):
                # 处理未登录词
                phone_seq = self.handle_oov(token)
            else:
                # 标准G2P转换
                phone_seq = self.g2p(token)
            
            # 添加词边界标记
            phonemes.extend(phone_seq)
            phonemes.append('|')  # 词边界
        
        return phonemes
    
    def predict_durations(self, phonemes: List[str], pos_tags: List[str]) -> List[int]:
        """预测音素持续时间"""
        durations = []
        
        for i, phoneme in enumerate(phonemes):
            # 基础持续时间
            base_duration = self.get_base_duration(phoneme)
            
            # 根据词性调整
            if i < len(pos_tags):
                pos_factor = self.get_pos_factor(pos_tags[i])
                base_duration *= pos_factor
            
            # 根据上下文调整
            context_factor = self.get_context_factor(phonemes, i)
            base_duration *= context_factor
            
            durations.append(int(base_duration))
        
        return durations

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class AcousticModel(nn.Module):
    def __init__(self, config: TTSConfig):
        super().__init__()
        self.config = config
        
        # 编码器
        self.phoneme_embedding = nn.Embedding(100, config.hidden_dim)
        self.position_encoding = PositionalEncoding(config.hidden_dim)
        
        self.encoder = TransformerEncoder(
            TransformerEncoderLayer(
                d_model=config.hidden_dim,
                nhead=config.attention_heads,
                dim_feedforward=config.hidden_dim * 4
            ),
            num_layers=config.encoder_layers
        )
        
        # 变分自编码器（用于韵律建模）
        self.prosody_encoder = VariationalProsodyEncoder(config)
        
        # 解码器
        self.decoder = TransformerDecoder(
            TransformerDecoderLayer(
                d_model=config.hidden_dim,
                nhead=config.attention_heads,
                dim_feedforward=config.hidden_dim * 4
            ),
            num_layers=config.decoder_layers
        )
        
        # 输出层
        self.mel_linear = nn.Linear(config.hidden_dim, config.n_mels)
        self.postnet = PostNet(config.n_mels)
        
    def forward(self, phonemes: torch.Tensor, 
                durations: torch.Tensor,
                speaker_embedding: Optional[torch.Tensor] = None,
                prosody_target: Optional[torch.Tensor] = None):
        """前向传播"""
        # 音素编码
        x = self.phoneme_embedding(phonemes)
        x = self.position_encoding(x)
        
        # 添加说话人信息
        if speaker_embedding is not None:
            x = x + speaker_embedding.unsqueeze(1)
        
        # 编码器
        encoder_output = self.encoder(x)
        
        # 韵律编码
        if prosody_target is not None:
            prosody_latent, kl_loss = self.prosody_encoder(
                prosody_target, encoder_output
            )
        else:
            prosody_latent = self.prosody_encoder.sample_prior(x.size(0))
            kl_loss = 0
        
        # 长度调节
        expanded = self.length_regulator(encoder_output, durations)
        
        # 解码器
        decoder_output = self.decoder(
            expanded,
            memory=encoder_output,
            prosody=prosody_latent
        )
        
        # 生成梅尔频谱
        mel_output = self.mel_linear(decoder_output)
        mel_postnet = self.postnet(mel_output)
        mel_output = mel_output + mel_postnet
        
        return mel_output, kl_loss
    
    def length_regulator(self, x: torch.Tensor, durations: torch.Tensor):
        """长度调节器"""
        output = []
        for i, d in enumerate(durations):
            output.append(x[i:i+1].repeat(d.item(), 1, 1))
        return torch.cat(output, dim=1)

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class VariationalProsodyEncoder(nn.Module):
    def __init__(self, config: TTSConfig):
        super().__init__()
        self.config = config
        
        # 参考编码器
        self.reference_encoder = ReferenceEncoder(config)
        
        # VAE组件
        self.fc_mu = nn.Linear(config.hidden_dim, config.hidden_dim)
        self.fc_var = nn.Linear(config.hidden_dim, config.hidden_dim)
        
        # 风格token注意力
        self.style_tokens = nn.Parameter(
            torch.randn(10, config.hidden_dim)
        )
        self.style_attention = MultiHeadAttention(
            config.hidden_dim,
            config.attention_heads
        )
        
    def forward(self, mel_target: torch.Tensor, text_encoding: torch.Tensor):
        """编码韵律信息"""
        # 从目标梅尔频谱提取韵律
        ref_embedding = self.reference_encoder(mel_target)
        
        # 计算分布参数
        mu = self.fc_mu(ref_embedding)
        log_var = self.fc_var(ref_embedding)
        
        # 重参数化技巧
        z = self.reparameterize(mu, log_var)
        
        # 风格token注意力
        style_embedding = self.style_attention(
            z.unsqueeze(1),
            self.style_tokens.unsqueeze(0).expand(z.size(0), -1, -1)
        )
        
        # KL散度损失
        kl_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
        
        return style_embedding, kl_loss
    
    def reparameterize(self, mu: torch.Tensor, log_var: torch.Tensor):
        """重参数化技巧"""
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        return mu + eps * std
    
    def sample_prior(self, batch_size: int):
        """从先验分布采样"""
        z = torch.randn(batch_size, self.config.hidden_dim)
        style_embedding = self.style_attention(
            z.unsqueeze(1),
            self.style_tokens.unsqueeze(0).expand(batch_size, -1, -1)
        )
        return style_embedding

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class WaveNetVocoder(nn.Module):
    def __init__(self, config: TTSConfig):
        super().__init__()
        self.config = config
        
        # 因果卷积层
        self.causal_conv = CausalConv1d(1, config.hidden_dim, kernel_size=2)
        
        # 残差块
        self.residual_blocks = nn.ModuleList([
            ResidualBlock(
                config.hidden_dim,
                config.hidden_dim,
                dilation=2**i,
                kernel_size=2
            )
            for _ in range(4)
            for i in range(10)
        ])
        
        # 输出层
        self.output_conv1 = nn.Conv1d(
            config.hidden_dim, config.hidden_dim, 1
        )
        self.output_conv2 = nn.Conv1d(
            config.hidden_dim, 256, 1  # μ-law quantization
        )
        
    def forward(self, mel_spectrogram: torch.Tensor):
        """生成音频波形"""
        # 上采样梅尔频谱
        mel_upsampled = self.upsample_mel(mel_spectrogram)
        
        # 初始化音频
        audio = torch.zeros(
            mel_spectrogram.size(0),
            1,
            mel_upsampled.size(-1)
        )
        
        # 自回归生成
        for t in range(audio.size(-1)):
            # 因果卷积
            x = self.causal_conv(audio[:, :, :t+1])
            
            # 残差网络
            skip_connections = []
            for block in self.residual_blocks:
                x, skip = block(x, mel_upsampled[:, :, t:t+1])
                skip_connections.append(skip)
            
            # 合并跳跃连接
            x = torch.stack(skip_connections).sum(dim=0)
            
            # 输出层
            x = torch.relu(self.output_conv1(x))
            logits = self.output_conv2(x)
            
            # 采样
            probs = torch.softmax(logits[:, :, -1], dim=1)
            sample = torch.multinomial(probs, 1)
            audio[:, :, t] = self.decode_mulaw(sample)
        
        return audio

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class HiFiGANVocoder(nn.Module):
    def __init__(self, config: TTSConfig):
        super().__init__()
        self.config = config
        
        # 生成器
        self.generator = HiFiGANGenerator(config)
        
        # 多尺度判别器
        self.msd = MultiScaleDiscriminator()
        
        # 多周期判别器
        self.mpd = MultiPeriodDiscriminator()
        
    def forward(self, mel_spectrogram: torch.Tensor):
        """生成高保真音频"""
        return self.generator(mel_spectrogram)
    
    def train_step(self, mel: torch.Tensor, audio: torch.Tensor):
        """训练步骤"""
        # 生成音频
        audio_fake = self.generator(mel)
        
        # 判别器损失
        d_loss = self.discriminator_loss(audio, audio_fake.detach())
        
        # 生成器损失
        g_loss = self.generator_loss(mel, audio, audio_fake)
        
        return g_loss, d_loss

class HiFiGANGenerator(nn.Module):
    def __init__(self, config: TTSConfig):
        super().__init__()
        
        # 输入卷积
        self.conv_pre = nn.Conv1d(
            config.n_mels,
            config.hidden_dim,
            kernel_size=7,
            padding=3
        )
        
        # 上采样块
        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip([8, 8, 2, 2], [16, 16, 4, 4])):
            self.ups.append(
                nn.ConvTranspose1d(
                    config.hidden_dim // (2**i),
                    config.hidden_dim // (2**(i+1)),
                    kernel_size=k,
                    stride=u,
                    padding=(k-u)//2
                )
            )
        
        # 多感受野融合块
        self.mrfs = nn.ModuleList([
            MultiReceptiveFieldFusion(
                config.hidden_dim // (2**(i+1)),
                [3, 7, 11],
                [1, 3, 5]
            )
            for i in range(4)
        ])
        
        # 输出卷积
        self.conv_post = nn.Conv1d(
            config.hidden_dim // 16,
            1,
            kernel_size=7,
            padding=3
        )
        
    def forward(self, mel: torch.Tensor):
        """生成音频"""
        x = self.conv_pre(mel)
        
        for up, mrf in zip(self.ups, self.mrfs):
            x = torch.relu(up(x))
            x = mrf(x)
        
        audio = torch.tanh(self.conv_post(x))
        
        return audio

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class SpeakerEncoder(nn.Module):
    def __init__(self, n_speakers: int = 100, embedding_dim: int = 256):
        super().__init__()
        
        # 说话人嵌入表
        self.speaker_embedding = nn.Embedding(n_speakers, embedding_dim)
        
        # 说话人验证网络（用于zero-shot）
        self.verification_network = SpeakerVerificationNetwork()
        
        # 自适应层
        self.adaptation_layers = nn.ModuleList([
            AdaptationLayer(embedding_dim) for _ in range(4)
        ])
        
    def encode_from_id(self, speaker_id: int):
        """从ID编码说话人"""
        return self.speaker_embedding(speaker_id)
    
    def encode_from_audio(self, reference_audio: torch.Tensor):
        """从参考音频编码说话人（zero-shot）"""
        return self.verification_network(reference_audio)
    
    def adapt(self, base_embedding: torch.Tensor, 
              adaptation_samples: List[torch.Tensor]):
        """说话人自适应"""
        # 提取自适应特征
        adaptation_features = []
        for sample in adaptation_samples:
            features = self.verification_network(sample)
            adaptation_features.append(features)
        
        # 融合特征
        adaptation_embedding = torch.stack(adaptation_features).mean(dim=0)
        
        # 自适应
        adapted = base_embedding
        for layer in self.adaptation_layers:
            adapted = layer(adapted, adaptation_embedding)
        
        return adapted

class SpeakerVerificationNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        
        # 帧级特征提取
        self.frame_encoder = nn.LSTM(
            input_size=80,  # mel features
            hidden_size=256,
            num_layers=3,
            batch_first=True
        )
        
        # 注意力池化
        self.attention = nn.Sequential(
            nn.Linear(256, 128),
            nn.Tanh(),
            nn.Linear(128, 1)
        )
        
        # 说话人嵌入
        self.embedding_layer = nn.Linear(256, 256)
        
    def forward(self, mel: torch.Tensor):
        """提取说话人嵌入"""
        # LSTM编码
        frames, _ = self.frame_encoder(mel)
        
        # 注意力权重
        attention_weights = torch.softmax(
            self.attention(frames).squeeze(-1), dim=1
        )
        
        # 加权平均
        weighted_mean = torch.sum(
            frames * attention_weights.unsqueeze(-1), dim=1
        )
        
        # 说话人嵌入
        embedding = self.embedding_layer(weighted_mean)
        
        # L2正则化
        embedding = torch.nn.functional.normalize(embedding, p=2, dim=1)
        
        return embedding

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class AdaptiveTTS:
    def __init__(self, base_model: ModernTTSSystem):
        self.base_model = base_model
        self.adaptation_module = SpeakerAdaptationModule()
        self.fine_tuning_optimizer = None
        
    def adapt_to_speaker(self, reference_audios: List[np.ndarray],
                         reference_texts: List[str],
                         adaptation_steps: int = 100):
        """适应到新说话人"""
        # 提取说话人特征
        speaker_features = self.extract_speaker_features(reference_audios)
        
        # 初始化自适应参数
        self.adaptation_module.initialize(speaker_features)
        
        # 设置优化器
        self.fine_tuning_optimizer = torch.optim.Adam(
            self.adaptation_module.parameters(),
            lr=1e-4
        )
        
        # 微调循环
        for step in range(adaptation_steps):
            loss = self.adaptation_step(
                reference_audios,
                reference_texts
            )
            
            if step % 10 == 0:
                print(f"Adaptation step {step}, loss: {loss:.4f}")
        
        return self.adaptation_module
    
    def adaptation_step(self, audios: List[np.ndarray], 
                       texts: List[str]) -> float:
        """单步自适应"""
        total_loss = 0
        
        for audio, text in zip(audios, texts):
            # 文本处理
            phonemes, durations = self.base_model.text_processor.process(text)
            
            # 提取目标梅尔频谱
            target_mel = self.audio_to_mel(audio)
            
            # 前向传播（带自适应）
            adapted_params = self.adaptation_module(phonemes)
            predicted_mel = self.base_model.acoustic_model(
                phonemes,
                durations,
                adapted_params
            )
            
            # 计算损失
            loss = torch.nn.functional.mse_loss(predicted_mel, target_mel)
            
            # 反向传播
            self.fine_tuning_optimizer.zero_grad()
            loss.backward()
            self.fine_tuning_optimizer.step()
            
            total_loss += loss.item()
        
        return total_loss / len(audios)

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class StreamingTTS:
    def __init__(self, model: ModernTTSSystem):
        self.model = model
        self.chunk_size = 1024  # 音频块大小
        self.lookahead = 5      # 前瞻字符数
        
    async def stream_synthesize(self, text: str):
        """流式合成音频"""
        # 分句
        sentences = self.split_sentences(text)
        
        for sentence in sentences:
            # 分块处理
            chunks = self.split_into_chunks(sentence)
            
            for i, chunk in enumerate(chunks):
                # 添加前瞻上下文
                if i < len(chunks) - 1:
                    context_chunk = chunk + chunks[i+1][:self.lookahead]
                else:
                    context_chunk = chunk
                
                # 合成音频块
                audio_chunk = await self.synthesize_chunk(context_chunk)
                
                # 平滑边界
                if i > 0:
                    audio_chunk = self.smooth_boundary(
                        previous_chunk,
                        audio_chunk
                    )
                
                # 输出音频块
                yield audio_chunk
                
                previous_chunk = audio_chunk
    
    async def synthesize_chunk(self, text_chunk: str) -> np.ndarray:
        """合成单个文本块"""
        # 异步处理
        loop = asyncio.get_event_loop()
        
        # 在线程池中运行模型推理
        audio = await loop.run_in_executor(
            None,
            self.model.synthesize,
            text_chunk
        )
        
        return audio
    
    def smooth_boundary(self, prev_audio: np.ndarray, 
                       curr_audio: np.ndarray,
                       overlap: int = 256) -> np.ndarray:
        """平滑音频边界"""
        # 交叉淡入淡出
        fade_in = np.linspace(0, 1, overlap)
        fade_out = np.linspace(1, 0, overlap)
        
        # 混合重叠部分
        prev_overlap = prev_audio[-overlap:] * fade_out
        curr_overlap = curr_audio[:overlap] * fade_in
        mixed_overlap = prev_overlap + curr_overlap
        
        # 拼接
        smoothed = np.concatenate([
            curr_audio[:0],  # 前面部分（如果有）
            mixed_overlap,
            curr_audio[overlap:]
        ])
        
        return smoothed

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class OptimizedTTS:
    def __init__(self, model: ModernTTSSystem):
        self.model = model
        self.quantized_model = None
        self.onnx_session = None
        
    def quantize_model(self):
        """模型量化"""
        import torch.quantization as quantization
        
        # 准备量化
        self.model.eval()
        
        # 动态量化
        self.quantized_model = quantization.quantize_dynamic(
            self.model,
            {nn.Linear, nn.LSTM, nn.GRU},
            dtype=torch.qint8
        )
        
        print(f"Model size reduced: {self.get_model_size(self.model):.2f}MB -> "
              f"{self.get_model_size(self.quantized_model):.2f}MB")
        
        return self.quantized_model
    
    def export_onnx(self, dummy_input: torch.Tensor):
        """导出ONNX模型"""
        import torch.onnx
        
        torch.onnx.export(
            self.model,
            dummy_input,
            "tts_model.onnx",
            export_params=True,
            opset_version=11,
            do_constant_folding=True,
            input_names=['text'],
            output_names=['audio'],
            dynamic_axes={
                'text': {0: 'batch_size', 1: 'sequence'},
                'audio': {0: 'batch_size', 1: 'time'}
            }
        )
        
        # 加载ONNX运行时
        import onnxruntime as ort
        self.onnx_session = ort.InferenceSession("tts_model.onnx")
        
    def optimize_with_tensorrt(self):
        """TensorRT优化"""
        import tensorrt as trt
        
        # 创建builder
        builder = trt.Builder(trt.Logger(trt.Logger.WARNING))
        network = builder.create_network()
        parser = trt.OnnxParser(network, trt.Logger(trt.Logger.WARNING))
        
        # 解析ONNX
        with open("tts_model.onnx", 'rb') as model:
            parser.parse(model.read())
        
        # 配置优化
        config = builder.create_builder_config()
        config.max_workspace_size = 1 << 30  # 1GB
        config.set_flag(trt.BuilderFlag.FP16)  # 使用FP16
        
        # 构建引擎
        engine = builder.build_engine(network, config)
        
        return engine

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class EmotionalTTS:
    def __init__(self, base_model: ModernTTSSystem):
        self.base_model = base_model
        self.emotion_encoder = EmotionEncoder()
        self.emotion_classifier = EmotionClassifier()
        
    def synthesize_with_emotion(self, text: str, 
                               emotion: str,
                               intensity: float = 0.5) -> np.ndarray:
        """带情感的语音合成"""
        # 编码情感
        emotion_embedding = self.emotion_encoder.encode(emotion, intensity)
        
        # 文本情感分析
        text_emotion = self.emotion_classifier.classify(text)
        
        # 融合文本和指定情感
        combined_emotion = self.blend_emotions(
            text_emotion,
            emotion_embedding,
            blend_ratio=0.7
        )
        
        # 修改韵律参数
        prosody_params = self.emotion_to_prosody(combined_emotion)
        
        # 合成
        audio = self.base_model.synthesize(
            text,
            prosody_override=prosody_params
        )
        
        return audio
    
    def emotion_to_prosody(self, emotion_embedding: torch.Tensor) -> Dict:
        """情感到韵律参数的映射"""
        # 解码到韵律空间
        prosody = {
            'pitch_mean': 0.0,
            'pitch_std': 1.0,
            'energy_mean': 1.0,
            'energy_std': 0.1,
            'duration_scale': 1.0
        }
        
        # 根据情感调整
        emotion_name = self.decode_emotion(emotion_embedding)
        
        if emotion_name == 'happy':
            prosody['pitch_mean'] = 0.2
            prosody['pitch_std'] = 1.3
            prosody['energy_mean'] = 1.2
            prosody['duration_scale'] = 0.95
        elif emotion_name == 'sad':
            prosody['pitch_mean'] = -0.1
            prosody['pitch_std'] = 0.8
            prosody['energy_mean'] = 0.8
            prosody['duration_scale'] = 1.1
        elif emotion_name == 'angry':
            prosody['pitch_mean'] = 0.1
            prosody['pitch_std'] = 1.5
            prosody['energy_mean'] = 1.4
            prosody['duration_scale'] = 0.9
        elif emotion_name == 'surprised':
            prosody['pitch_mean'] = 0.3
            prosody['pitch_std'] = 1.6
            prosody['energy_mean'] = 1.3
            prosody['duration_scale'] = 0.85
        
        return prosody

class EmotionEncoder(nn.Module):
    def __init__(self, num_emotions: int = 7, embedding_dim: int = 128):
        super().__init__()
        
        # 基础情感嵌入
        self.emotion_embeddings = nn.Embedding(num_emotions, embedding_dim)
        
        # 强度调节
        self.intensity_modulation = nn.Sequential(
            nn.Linear(1, embedding_dim),
            nn.Tanh()
        )
        
        # 混合网络
        self.mixture_network = nn.Sequential(
            nn.Linear(embedding_dim * 2, embedding_dim),
            nn.ReLU(),
            nn.Linear(embedding_dim, embedding_dim)
        )
        
    def encode(self, emotion: str, intensity: float) -> torch.Tensor:
        """编码情感"""
        # 获取基础嵌入
        emotion_id = self.emotion_to_id(emotion)
        base_embedding = self.emotion_embeddings(
            torch.tensor([emotion_id])
        )
        
        # 强度调节
        intensity_tensor = torch.tensor([[intensity]])
        intensity_mod = self.intensity_modulation(intensity_tensor)
        
        # 混合
        combined = torch.cat([base_embedding, intensity_mod], dim=-1)
        emotion_encoding = self.mixture_network(combined)
        
        return emotion_encoding

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class VoiceCloning:
    def __init__(self):
        self.encoder = SpeakerEncoder()
        self.synthesizer = AdaptiveSynthesizer()
        self.vocoder = UniversalVocoder()
        
    def clone_voice(self, reference_audios: List[np.ndarray],
                    target_text: str,
                    num_adaptation_steps: int = 10) -> np.ndarray:
        """克隆语音"""
        # 1. 提取说话人特征
        speaker_embedding = self.extract_speaker_embedding(reference_audios)
        
        # 2. 快速自适应
        adapted_model = self.quick_adaptation(
            speaker_embedding,
            reference_audios,
            num_adaptation_steps
        )
        
        # 3. 合成目标文本
        cloned_audio = adapted_model.synthesize(
            target_text,
            speaker_embedding
        )
        
        return cloned_audio
    
    def extract_speaker_embedding(self, audios: List[np.ndarray]) -> torch.Tensor:
        """提取说话人嵌入"""
        embeddings = []
        
        for audio in audios:
            # 预处理音频
            processed = self.preprocess_audio(audio)
            
            # 提取特征
            mel = self.audio_to_mel(processed)
            
            # 编码
            embedding = self.encoder(mel)
            embeddings.append(embedding)
        
        # 平均池化
        speaker_embedding = torch.stack(embeddings).mean(dim=0)
        
        return speaker_embedding
    
    def quick_adaptation(self, speaker_embedding: torch.Tensor,
                        reference_audios: List[np.ndarray],
                        num_steps: int) -> AdaptiveSynthesizer:
        """快速自适应"""
        # 复制基础模型
        adapted_model = copy.deepcopy(self.synthesizer)
        
        # 设置MAML优化器
        optimizer = torch.optim.SGD(
            adapted_model.parameters(),
            lr=0.01
        )
        
        for step in range(num_steps):
            # 随机选择参考音频
            audio = random.choice(reference_audios)
            
            # 自监督任务
            loss = self.self_supervised_loss(
                adapted_model,
                audio,
                speaker_embedding
            )
            
            # 更新
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        
        return adapted_model

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class TTSEvaluator:
    def __init__(self):
        self.mos_predictor = MOSPredictor()
        self.similarity_scorer = SimilarityScorer()
        
    def evaluate(self, synthesized: np.ndarray, 
                reference: np.ndarray) -> Dict:
        """全面评估TTS质量"""
        metrics = {}
        
        # 1. MOS预测
        metrics['predicted_mos'] = self.mos_predictor.predict(synthesized)
        
        # 2. 梅尔倒谱失真
        metrics['mcd'] = self.calculate_mcd(synthesized, reference)
        
        # 3. F0相关性
        metrics['f0_corr'] = self.calculate_f0_correlation(
            synthesized, reference
        )
        
        # 4. 说话人相似度
        metrics['speaker_similarity'] = self.similarity_scorer.score(
            synthesized, reference
        )
        
        # 5. 韵律评估
        metrics['prosody_score'] = self.evaluate_prosody(
            synthesized, reference
        )
        
        # 6. 可懂度
        metrics['intelligibility'] = self.evaluate_intelligibility(
            synthesized
        )
        
        return metrics
    
    def calculate_mcd(self, synth: np.ndarray, ref: np.ndarray) -> float:
        """计算梅尔倒谱失真"""
        import librosa
        
        # 提取MFCC
        mfcc_synth = librosa.feature.mfcc(y=synth, sr=22050, n_mfcc=13)
        mfcc_ref = librosa.feature.mfcc(y=ref, sr=22050, n_mfcc=13)
        
        # 动态时间规整
        from scipy.spatial.distance import euclidean
        from fastdtw import fastdtw
        
        distance, path = fastdtw(
            mfcc_synth.T,
            mfcc_ref.T,
            dist=euclidean
        )
        
        # 计算MCD
        mcd = (10 / np.log(10)) * np.sqrt(2 * distance / len(path))
        
        return mcd

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from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import StreamingResponse
import io

app = FastAPI()

# 初始化TTS系统
tts_system = ModernTTSSystem(TTSConfig())
streaming_tts = StreamingTTS(tts_system)

@app.post("/synthesize")
async def synthesize(
    text: str = Form(...),
    speaker_id: Optional[str] = Form(None),
    emotion: Optional[str] = Form(None),
    speed: float = Form(1.0),
    pitch: float = Form(0.0)
):
    """合成语音API"""
    try:
        # 合成音频
        audio = tts_system.synthesize(
            text,
            speaker_id=speaker_id,
            emotion=emotion,
            speed=speed,
            pitch=pitch
        )
        
        # 转换为字节流
        audio_bytes = audio_to_bytes(audio)
        
        return StreamingResponse(
            io.BytesIO(audio_bytes),
            media_type="audio/wav"
        )
    except Exception as e:
        return {"error": str(e)}

@app.websocket("/stream")
async def stream_synthesis(websocket: WebSocket):
    """WebSocket流式合成"""
    await websocket.accept()
    
    try:
        while True:
            # 接收文本
            data = await websocket.receive_json()
            text = data.get("text", "")
            
            # 流式合成
            async for audio_chunk in streaming_tts.stream_synthesize(text):
                # 发送音频块
                await websocket.send_bytes(audio_chunk.tobytes())
    
    except WebSocketDisconnect:
        pass

@app.post("/clone")
async def clone_voice(
    reference_audio: UploadFile = File(...),
    target_text: str = Form(...)
):
    """语音克隆API"""
    # 读取参考音频
    audio_data = await reference_audio.read()
    reference = load_audio(audio_data)
    
    # 克隆
    cloner = VoiceCloning()
    cloned_audio = cloner.clone_voice(
        [reference],
        target_text
    )
    
    return StreamingResponse(
        io.BytesIO(audio_to_bytes(cloned_audio)),
        media_type="audio/wav"
    )