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