| | from typing import Literal, Optional
|
| |
|
| | import torch
|
| | import torch.nn as nn
|
| |
|
| | from ..autoencoder.vae import VAE, get_my_vae
|
| | from ..bigvgan import BigVGAN
|
| | from ..bigvgan_v2.bigvgan import BigVGAN as BigVGANv2
|
| | from ...model.utils.distributions import DiagonalGaussianDistribution
|
| |
|
| |
|
| | class AutoEncoderModule(nn.Module):
|
| |
|
| | def __init__(self,
|
| | *,
|
| | vae_ckpt_path,
|
| | vocoder_ckpt_path: Optional[str] = None,
|
| | mode: Literal['16k', '44k'],
|
| | need_vae_encoder: bool = True):
|
| | super().__init__()
|
| | self.vae: VAE = get_my_vae(mode).eval()
|
| | vae_state_dict = torch.load(vae_ckpt_path, weights_only=True, map_location='cpu')
|
| | self.vae.load_state_dict(vae_state_dict)
|
| | self.vae.remove_weight_norm()
|
| |
|
| | if mode == '16k':
|
| | assert vocoder_ckpt_path is not None
|
| | self.vocoder = BigVGAN(vocoder_ckpt_path).eval()
|
| | elif mode == '44k':
|
| | self.vocoder = BigVGANv2.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x',
|
| | use_cuda_kernel=False)
|
| | self.vocoder.remove_weight_norm()
|
| | else:
|
| | raise ValueError(f'Unknown mode: {mode}')
|
| |
|
| | for param in self.parameters():
|
| | param.requires_grad = False
|
| |
|
| | if not need_vae_encoder:
|
| | del self.vae.encoder
|
| |
|
| | @torch.inference_mode()
|
| | def encode(self, x: torch.Tensor) -> DiagonalGaussianDistribution:
|
| | return self.vae.encode(x)
|
| |
|
| | @torch.inference_mode()
|
| | def decode(self, z: torch.Tensor) -> torch.Tensor:
|
| | return self.vae.decode(z)
|
| |
|
| | @torch.inference_mode()
|
| | def vocode(self, spec: torch.Tensor) -> torch.Tensor:
|
| | return self.vocoder(spec)
|
| |
|
