| 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.") |
|
|
