forked from mrq/DL-Art-School
break out get_conditioning_latent from unified_voice
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@ -365,6 +365,17 @@ class UnifiedVoice(nn.Module):
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else:
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return first_logits
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def get_conditioning_latent(self, speech_conditioning_input):
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speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
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conds = []
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for j in range(speech_conditioning_input.shape[1]):
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conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
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conds = torch.stack(conds, dim=1)
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if self.average_conditioning_embeddings:
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conds = conds.mean(dim=1).unsqueeze(1)
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return conds
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def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
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return_latent=False, clip_inputs=True):
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"""
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@ -399,13 +410,7 @@ class UnifiedVoice(nn.Module):
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text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
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mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
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speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
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conds = []
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for j in range(speech_conditioning_input.shape[1]):
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conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
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conds = torch.stack(conds, dim=1)
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if self.average_conditioning_embeddings:
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conds = conds.mean(dim=1).unsqueeze(1)
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conds = self.get_conditioning_latent(speech_conditioning_input)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
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text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
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@ -327,7 +327,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_tortoise_reverse_classifier.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_tortoise_random_latent_gen.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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