| import torch
|
| import torch.nn as nn
|
| from .dac import DAC
|
| from .stable_vae import load_vae
|
|
|
|
|
| class Autoencoder(nn.Module):
|
| def __init__(self, ckpt_path, model_type='dac', quantization_first=False):
|
| super(Autoencoder, self).__init__()
|
| self.model_type = model_type
|
| if self.model_type == 'dac':
|
| model = DAC.load(ckpt_path)
|
| elif self.model_type == 'stable_vae':
|
| model = load_vae(ckpt_path)
|
| else:
|
| raise NotImplementedError(f"Model type not implemented: {self.model_type}")
|
| self.ae = model.eval()
|
| self.quantization_first = quantization_first
|
| print(f'Autoencoder quantization first mode: {quantization_first}')
|
|
|
| @torch.no_grad()
|
| def forward(self, audio=None, embedding=None):
|
| if self.model_type == 'dac':
|
| return self.process_dac(audio, embedding)
|
| elif self.model_type == 'encodec':
|
| return self.process_encodec(audio, embedding)
|
| elif self.model_type == 'stable_vae':
|
| return self.process_stable_vae(audio, embedding)
|
| else:
|
| raise NotImplementedError(f"Model type not implemented: {self.model_type}")
|
|
|
| def process_dac(self, audio=None, embedding=None):
|
| if audio is not None:
|
| z = self.ae.encoder(audio)
|
| if self.quantization_first:
|
| z, *_ = self.ae.quantizer(z, None)
|
| return z
|
| elif embedding is not None:
|
| z = embedding
|
| if self.quantization_first:
|
| audio = self.ae.decoder(z)
|
| else:
|
| z, *_ = self.ae.quantizer(z, None)
|
| audio = self.ae.decoder(z)
|
| return audio
|
| else:
|
| raise ValueError("Either audio or embedding must be provided.")
|
|
|
| def process_encodec(self, audio=None, embedding=None):
|
| if audio is not None:
|
| z = self.ae.encoder(audio)
|
| if self.quantization_first:
|
| code = self.ae.quantizer.encode(z)
|
| z = self.ae.quantizer.decode(code)
|
| return z
|
| elif embedding is not None:
|
| z = embedding
|
| if self.quantization_first:
|
| audio = self.ae.decoder(z)
|
| else:
|
| code = self.ae.quantizer.encode(z)
|
| z = self.ae.quantizer.decode(code)
|
| audio = self.ae.decoder(z)
|
| return audio
|
| else:
|
| raise ValueError("Either audio or embedding must be provided.")
|
|
|
| def process_stable_vae(self, audio=None, embedding=None):
|
| if audio is not None:
|
| z = self.ae.encoder(audio)
|
| if self.quantization_first:
|
| z = self.ae.bottleneck.encode(z)
|
| return z
|
| if embedding is not None:
|
| z = embedding
|
| if self.quantization_first:
|
| audio = self.ae.decoder(z)
|
| else:
|
| z = self.ae.bottleneck.encode(z)
|
| audio = self.ae.decoder(z)
|
| return audio
|
| else:
|
| raise ValueError("Either audio or embedding must be provided.")
|
|
|
