下载地址：下载地址：https://www.pan38.com/share.php?code=pvvmX 提取码：8888

一、系统架构设计

本系统采用Tacotron2+WaveRNN的端到端语音合成架构，包含以下模块：
声学特征提取模块（Mel频谱）
语音编码器（Speaker Encoder）
序列到序列合成模块
神经声码器
二、核心代码实现

import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
import librosa
import soundfile as sf

class SpeakerEncoder(nn.Module):
def init(self, input_dim=80, hidden_dim=256, num_layers=3):
super(SpeakerEncoder, self).init()
self.lstm = nn.LSTM(input_size=input_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True)
self.proj = nn.Linear(hidden_dim, hidden_dim)

def forward(self, x):
    _, (hidden, _) = self.lstm(x)
    # 取最后一层隐藏状态
    embedding = self.proj(hidden[-1])
    return embedding

class Tacotron2(nn.Module):
def init(self, vocab_size, embedding_dim=512):
super(Tacotron2, self).init()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.encoder = Encoder(embedding_dim)
self.decoder = Decoder()
self.postnet = PostNet()

def forward(self, text, mel_spec):
    # 文本编码
    embedded = self.embedding(text)
    encoder_outputs = self.encoder(embedded)

    # 结合说话人特征
    speaker_embed = speaker_encoder(mel_spec)
    encoder_outputs = encoder_outputs + speaker_embed.unsqueeze(1)

    # 频谱预测
    mel_outputs, alignments = self.decoder(encoder_outputs, mel_spec)
    mel_outputs_postnet = self.postnet(mel_outputs)

    return mel_outputs, mel_outputs_postnet, alignments

完整的训练流程（简化版）

def train(model, dataloader, optimizer, criterion, epochs=100):
for epoch in range(epochs):
for i, (text, mel, speaker_id) in enumerate(dataloader):
optimizer.zero_grad()

        # 前向传播
        mel_pred, mel_postnet, _ = model(text, mel)

        # 计算损失
        loss = criterion(mel_pred, mel) + criterion(mel_postnet, mel)

        # 反向传播
        loss.backward()
        optimizer.step()

        if i % 100 == 0:
            print(f'Epoch: {epoch}, Batch: {i}, Loss: {loss.item()}')

声音克隆推理代码

def clone_voice(reference_audio, target_text):

# 提取参考音频特征
mel = extract_mel(reference_audio)

# 获取说话人嵌入
with torch.no_grad():
    speaker_embed = speaker_encoder(mel.unsqueeze(0))

# 文本编码
text_seq = text_to_sequence(target_text)

# 生成目标语音
mel_outputs, _, _ = tacotron2(text_seq, speaker_embed)
audio = waveglow.infer(mel_outputs)

return audio

三、关键技术实现细节

梅尔频谱提取：

def extract_mel(wav_path, sr=22050, n_fft=1024, hop_length=256, n_mels=80):
y, sr = librosa.load(wav_path, sr=sr)
S = librosa.feature.melspectrogram(
y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels)
return torch.FloatTensor(S)

语音合成增强：

class PostNet(nn.Module):
def init(self):
super(PostNet, self).init()
self.convolutions = nn.ModuleList([
nn.Sequential(
nn.Conv1d(80, 512, kernelsize=5, stride=1, padding=2),
nn.BatchNorm1d(512),
nn.Tanh(),
nn.Dropout(0.5)
) for in range(5)
])

def forward(self, x):
    for conv in self.convolutions:
        x = conv(x)
    return x