Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -127,7 +127,7 @@ def get_state_grad_loss(k1=True,
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discriminator=True):
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return {'k1':k1,'mel':mel,'duration':duration,'generator':generator,'discriminator':discriminator}
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-
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def clip_grad_value_(parameters, clip_value, norm_type=2):
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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@@ -145,7 +145,7 @@ def clip_grad_value_(parameters, clip_value, norm_type=2):
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total_norm = total_norm ** (1. / norm_type)
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return total_norm
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-
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def get_embed_speaker(self,speaker_id):
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if self.config.num_speakers > 1 and speaker_id is not None:
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if isinstance(speaker_id, int):
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@@ -160,6 +160,7 @@ def get_embed_speaker(self,speaker_id):
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return self.embed_speaker(speaker_id).unsqueeze(-1)
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else:
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return None
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def get_data_loader(train_dataset_dirs,eval_dataset_dir,full_generation_dir,device):
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ctrain_datasets=[]
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for dataset_dir ,id_sp in train_dataset_dirs:
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discriminator=True):
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return {'k1':k1,'mel':mel,'duration':duration,'generator':generator,'discriminator':discriminator}
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+
@spaces.GPU
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def clip_grad_value_(parameters, clip_value, norm_type=2):
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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total_norm = total_norm ** (1. / norm_type)
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return total_norm
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+
@spaces.GPU
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def get_embed_speaker(self,speaker_id):
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if self.config.num_speakers > 1 and speaker_id is not None:
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if isinstance(speaker_id, int):
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return self.embed_speaker(speaker_id).unsqueeze(-1)
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else:
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return None
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+
@spaces.GPU
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def get_data_loader(train_dataset_dirs,eval_dataset_dir,full_generation_dir,device):
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ctrain_datasets=[]
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for dataset_dir ,id_sp in train_dataset_dirs:
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