export_onnx.py

import torch
import torch.nn as nn
from openvoice.api import ToneColorConverter
from openvoice.models import SynthesizerTrn
import os

os.chdir(os.path.dirname(os.path.abspath(__file__)))

class ToneColorExtractWrapper(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model
        
    def forward(self, audio):
        # audio: [1, source_audio_len, 513]
        # 将mel谱图转置为模型需要的格式 [1, 513, source_audio_len]
        audio = audio.contiguous()
        # 提取声纹
        g = self.model.ref_enc(audio)
        # 扩展最后一维
        # g = g.unsqueeze(-1)  # [1, 256, 1]
        return g

class ToneCloneWrapper(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model
        
    def forward(self, audio, audio_lengths, src_tone, dest_tone, tau):
        # 确保张量连续
        audio = audio.contiguous()
        src_tone = src_tone.contiguous()
        dest_tone = dest_tone.contiguous()
        
        # 语音转换
        o_hat, _, _ = self.model.voice_conversion(
            audio, 
            audio_lengths,
            sid_src=src_tone,
            sid_tgt=dest_tone,
            tau=tau[0]
        )
        return o_hat

def export_models(ckpt_path, output_dir, target_audio_lens, source_audio_lens):
    """
    导出音色提取和克隆模型为ONNX格式
    
    Args:
        ckpt_path: 模型检查点路径
        output_dir: 输出目录
        target_audio_lens: 目标音频长度列表
        source_audio_lens: 源音频长度列表
    """
    
    # 加载模型
    device = "cpu"
    converter = ToneColorConverter(f'{ckpt_path}/config.json', device=device)
    converter.load_ckpt(f'{ckpt_path}/checkpoint.pth')
    
    # 创建输出目录
    os.makedirs(output_dir, exist_ok=True)
    
    # 导出音色提取模型
    extract_wrapper = ToneColorExtractWrapper(converter.model)
    extract_wrapper.eval()
    
    for source_len in source_audio_lens:
        dummy_input = torch.randn(1, source_len, 513).contiguous()
        output_path = f"{output_dir}/tone_color_extract_model.onnx"
        
        torch.onnx.export(
            extract_wrapper,
            dummy_input,
            output_path,
            input_names=['input'],
            output_names=['tone_embedding'],
            dynamic_axes={
                'input': {1: 'source_audio_len'},
            },
            opset_version=11,
            do_constant_folding=True,
            verbose=True
        )
        print(f"Exported tone extract model to {output_path}")
    
    # 导出音色克隆模型  
    clone_wrapper = ToneCloneWrapper(converter.model)
    clone_wrapper.eval()
    
    for target_len in target_audio_lens:
        dummy_inputs = (
            torch.randn(1, 513, target_len).contiguous(),  # audio
            torch.LongTensor([target_len]),   # audio_lengths  
            torch.randn(1, 256, 1).contiguous(),          # src_tone
            torch.randn(1, 256, 1).contiguous(),          # dest_tone
            torch.FloatTensor([0.3])         # tau
        )
        
        output_path = f"{output_dir}/tone_clone_model.onnx"
        
        torch.onnx.export(
            clone_wrapper,
            dummy_inputs,
            output_path,
            input_names=['audio', 'audio_length', 'src_tone', 'dest_tone', 'tau'],
            output_names=['converted_audio'],
            dynamic_axes={
                'audio': {2: 'target_audio_len'},
            },
            opset_version=17,
            do_constant_folding=True,
            verbose=True
        )
        print(f"Exported tone clone model to {output_path}")

if __name__ == "__main__":
    # 示例用法
    TARGET_AUDIO_LENS = [1024]  # 根据需要设置目标长度
    SOURCE_AUDIO_LENS = [1024]  # 根据需要设置源长度
    
    export_models(
        ckpt_path="checkpoints_v2/converter",
        output_dir="onnx_models",
        target_audio_lens=TARGET_AUDIO_LENS,
        source_audio_lens=SOURCE_AUDIO_LENS
    )