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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='stable_vae', quantization_first=True):
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.")
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