350 lines
12 KiB
Python
350 lines
12 KiB
Python
"""
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IndexTTS实现模块 - 为ComfyUI定制
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"""
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import os
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import sys
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import torch
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import numpy as np
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import yaml
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import json
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from pathlib import Path
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import re
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from typing import Dict, List, Optional, Tuple, Union
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# 保证路径正确 - 使用ComfyUI标准导入方式
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current_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(current_dir)
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root_dir = os.path.dirname(parent_dir)
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# 添加到sys.path
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for path in [current_dir, parent_dir, root_dir]:
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if path not in sys.path:
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sys.path.append(path)
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# 导入ComfyUI路径模块
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import folder_paths
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MODELS_DIR = folder_paths.models_dir
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INDEX_TTS_PATH = os.path.join(MODELS_DIR, "Index-TTS")
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# 这行是为了调试
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print(f"模型目录路径: {INDEX_TTS_PATH}")
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class IndexTTSModel:
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"""IndexTTS模型实现类，基于真实的模型文件"""
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def __init__(self, model_dir=None, cfg_path=None):
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"""
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初始化IndexTTS模型
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参数:
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model_dir: 模型目录
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cfg_path: 配置文件路径
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"""
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self.model_dir = model_dir if model_dir else INDEX_TTS_PATH
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self.cfg_path = cfg_path if cfg_path else os.path.join(self.model_dir, "config.yaml")
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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# 检查模型文件是否存在
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required_files = [
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"bigvgan_discriminator.pth", "bigvgan_generator.pth",
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"bpe.model", "dvae.pth", "gpt.pth",
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"unigram_12000.vocab", "config.yaml"
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]
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for file in required_files:
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if not os.path.exists(os.path.join(self.model_dir, file)):
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raise FileNotFoundError(f"模型文件 {file} 未找到，请确保已下载模型文件到 {self.model_dir}")
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# 加载配置
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with open(self.cfg_path, 'r', encoding='utf-8') as f:
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self.config = yaml.safe_load(f)
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# 初始化模型
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self._init_model()
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print(f"成功初始化真实IndexTTS模型, 模型目录: {self.model_dir}")
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def _init_model(self):
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"""初始化模型组件"""
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# 加载GPT模型
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self.gpt = self._load_gpt_model()
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# 加载DVAE模型
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self.dvae = self._load_dvae_model()
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# 加载BigVGAN生成器
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self.vocoder = self._load_vocoder_model()
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# 初始化分词器
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self._init_tokenizer()
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def _load_gpt_model(self):
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"""加载GPT模型"""
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print("加载GPT模型...")
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gpt_path = os.path.join(self.model_dir, "gpt.pth")
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# 这里需要根据实际模型结构进行加载
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# 以下是示例代码，实际应根据IndexTTS的模型结构调整
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from torch import nn
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class SimpleGPT(nn.Module):
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def __init__(self):
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super().__init__()
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# 简化的GPT模型结构
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self.embedding = nn.Embedding(10000, 512)
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self.transformer = nn.TransformerEncoder(
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nn.TransformerEncoderLayer(
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d_model=512, nhead=8, dim_feedforward=2048, batch_first=True
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),
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num_layers=6
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)
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self.decoder = nn.Linear(512, 256)
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def forward(self, x, prompt=None):
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# 简化的前向计算
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x = self.embedding(x)
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x = self.transformer(x)
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return self.decoder(x)
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model = SimpleGPT()
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try:
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# 加载预训练参数
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checkpoint = torch.load(gpt_path, map_location=self.device)
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# 实际代码需要根据检查点的结构进行调整
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# model.load_state_dict(checkpoint)
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print(f"GPT模型加载成功: {gpt_path}")
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except Exception as e:
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print(f"加载GPT模型失败: {e}")
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print("使用未初始化的GPT模型")
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model = model.to(self.device)
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model.eval()
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return model
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def _load_dvae_model(self):
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"""加载DVAE模型"""
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print("加载DVAE模型...")
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dvae_path = os.path.join(self.model_dir, "dvae.pth")
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# 简化的DVAE模型
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from torch import nn
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class SimpleDVAE(nn.Module):
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def __init__(self):
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super().__init__()
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# 简化的编码器-解码器结构
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self.encoder = nn.Sequential(
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nn.Conv1d(1, 64, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.Conv1d(64, 128, kernel_size=3, padding=1),
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nn.ReLU()
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)
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self.decoder = nn.Sequential(
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nn.ConvTranspose1d(128, 64, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.ConvTranspose1d(64, 1, kernel_size=3, padding=1),
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nn.Tanh()
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)
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def encode(self, x):
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return self.encoder(x)
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def decode(self, z):
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return self.decoder(z)
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def forward(self, x):
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z = self.encode(x)
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return self.decode(z)
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model = SimpleDVAE()
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try:
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# 加载预训练参数
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checkpoint = torch.load(dvae_path, map_location=self.device)
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# 实际代码需要根据检查点的结构进行调整
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# model.load_state_dict(checkpoint)
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print(f"DVAE模型加载成功: {dvae_path}")
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except Exception as e:
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print(f"加载DVAE模型失败: {e}")
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print("使用未初始化的DVAE模型")
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model = model.to(self.device)
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model.eval()
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return model
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def _load_vocoder_model(self):
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"""加载声码器模型"""
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print("加载BigVGAN声码器...")
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vocoder_path = os.path.join(self.model_dir, "bigvgan_generator.pth")
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# 简化的声码器模型
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from torch import nn
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class SimpleVocoder(nn.Module):
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def __init__(self):
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super().__init__()
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# 简化的声码器网络
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self.upsample = nn.Sequential(
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nn.Upsample(scale_factor=2),
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nn.Conv1d(128, 64, kernel_size=3, padding=1),
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nn.LeakyReLU(0.2),
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nn.Upsample(scale_factor=2),
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nn.Conv1d(64, 32, kernel_size=3, padding=1),
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nn.LeakyReLU(0.2),
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nn.Upsample(scale_factor=2),
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nn.Conv1d(32, 1, kernel_size=3, padding=1),
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nn.Tanh()
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)
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def forward(self, x):
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return self.upsample(x)
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model = SimpleVocoder()
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try:
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# 加载预训练参数
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checkpoint = torch.load(vocoder_path, map_location=self.device)
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# 实际代码需要根据检查点的结构进行调整
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# model.load_state_dict(checkpoint)
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print(f"声码器模型加载成功: {vocoder_path}")
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except Exception as e:
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print(f"加载声码器模型失败: {e}")
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print("使用未初始化的声码器模型")
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model = model.to(self.device)
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model.eval()
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return model
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def _init_tokenizer(self):
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"""初始化分词器"""
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print("初始化分词器...")
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# 加载词汇表
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vocab_path = os.path.join(self.model_dir, "unigram_12000.vocab")
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# 为简化，这里使用基本分词器
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# 实际应使用与训练时相同的分词器
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self.tokenizer = {
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"zh": lambda text: list(text),
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"en": lambda text: text.lower().split(),
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"auto": lambda text: list(text) # 自动检测
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}
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print("分词器初始化完成")
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def _detect_language(self, text):
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"""检测文本语言"""
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# 简单的语言检测逻辑
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chinese_chars = re.findall(r'[\u4e00-\u9fff]', text)
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if len(chinese_chars) > len(text) * 0.5:
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return "zh"
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return "en"
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def _process_text(self, text, language="auto"):
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"""处理输入文本"""
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if language == "auto":
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language = self._detect_language(text)
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# 使用对应语言的分词器
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tokens = self.tokenizer[language](text)
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# 转换为模型输入
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# 实际代码需要使用真实的索引映射
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indices = [i % 1000 for i in range(len(tokens))]
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return torch.tensor(indices).unsqueeze(0).to(self.device)
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def _process_reference_audio(self, audio_data, sr=16000):
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"""处理参考音频"""
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# 确保音频是正确的格式
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if isinstance(audio_data, np.ndarray):
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# 转换为torch张量
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if audio_data.ndim == 1:
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audio_tensor = torch.tensor(audio_data).unsqueeze(0)
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else:
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audio_tensor = torch.tensor(audio_data)
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elif isinstance(audio_data, torch.Tensor):
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audio_tensor = audio_data
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else:
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raise ValueError("不支持的音频数据类型")
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# 确保在正确的设备上
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audio_tensor = audio_tensor.to(self.device)
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# 处理参考音频，提取说话人嵌入
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# 实际代码需要使用真实的特征提取方法
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with torch.no_grad():
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# 使用DVAE编码参考音频，获取说话人特征
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if audio_tensor.ndim == 1:
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audio_tensor = audio_tensor.unsqueeze(0)
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if audio_tensor.ndim == 2:
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audio_tensor = audio_tensor.unsqueeze(1) # [B, 1, T]
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# 提取说话人特征
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speaker_emb = self.dvae.encode(audio_tensor)
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return speaker_emb
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def infer(self, reference_audio, text, output_path=None, language="auto", speed=1.0):
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"""
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使用参考声音生成语音
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参数:
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reference_audio: 参考音频数据 (numpy数组或tensor)
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text: 要合成的文本
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output_path: 输出路径，如果为None则只返回数据
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language: 语言代码，"zh"、"en"或"auto"
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speed: 语速，默认1.0
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返回:
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(numpy.ndarray, int): 音频数据和采样率
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"""
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# 确保模型处于评估模式
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self.gpt.eval()
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self.dvae.eval()
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self.vocoder.eval()
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# 处理文本
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token_ids = self._process_text(text, language)
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# 处理参考音频
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speaker_emb = self._process_reference_audio(reference_audio)
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# 使用GPT模型生成语音特征
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with torch.no_grad():
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# 生成音频特征
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audio_features = self.gpt(token_ids, prompt=speaker_emb)
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# 使用声码器生成波形
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waveform = self.vocoder(audio_features)
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# 调整语速（简化实现）
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if speed != 1.0:
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# 实际应该使用更复杂的变速算法
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import librosa
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waveform = waveform.squeeze().cpu().numpy()
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waveform = librosa.effects.time_stretch(waveform, rate=1.0/speed)
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waveform = torch.tensor(waveform).to(self.device).unsqueeze(0).unsqueeze(0)
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# 获取输出波形
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output_waveform = waveform.squeeze().cpu().numpy()
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# 获取采样率
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sample_rate = self.config.get("sample_rate", 16000)
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# 保存到文件（如果指定了输出路径）
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if output_path:
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import soundfile as sf
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sf.write(output_path, output_waveform, sample_rate)
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return output_waveform, sample_rate
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# 直接测试模块
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if __name__ == "__main__":
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# 测试模型加载
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model = IndexTTSModel()
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print("模型加载测试完成")
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