wasmdashai commited on
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7ff1066
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1 Parent(s): 80ff30d

Update app.py

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Files changed (1) hide show
  1. app.py +23 -27
app.py CHANGED
@@ -37,26 +37,9 @@ from VitsModelSplit.dataset_features_collector import FeaturesCollectionDataset
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  from torch.cuda.amp import autocast, GradScaler
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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- # model1= VitsModel.from_pretrined("/content/drive/MyDrive/vitsM/OneBatch/S6/MMMMM-dash-azd60").to("cuda")
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- # model= VitsModel.from_pretrained("/content/drive/MyDrive/vitsM/TO/sp3/core/vend").to("cuda")
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- # model=VitsModel.from_pretrained("/content/drive/MyDrive/vitsM/heppa/EndCore3/v0").to("cuda")
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- # model.discriminator=model1.discriminator
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- # model.duration_predictor=model1.duration_predictor
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-
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- # model.setMfA(monotonic_align.maximum_path)
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- # tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-ara",cache_dir="./")
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  feature_extractor = VitsFeatureExtractor()
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- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, VITSTrainingArguments))
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- json_file = os.path.abspath('VitsModelSplit/finetune_config_ara.json')
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- model_args, data_args, training_args = parser.parse_json_file(json_file = json_file)
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- sgl=get_state_grad_loss(mel=True,
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- # generator=False,
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- # discriminator=False,
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- duration=False)
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-
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- training_args.num_train_epochs=1000
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- training_args.fp16=True
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- training_args.eval_steps=300
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  # sgl=get_state_grad_loss(k1=True,#generator=False,
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  # discriminator=False,
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  # duration=False
@@ -573,24 +556,37 @@ train_dataset_dirs=[
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- training_args.weight_kl=1
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- training_args.d_learning_rate=2e-4
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- training_args.learning_rate=2e-4
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- training_args.weight_mel=45
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- training_args.num_train_epochs=4
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- training_args.eval_steps=1000
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- global_step=0
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  dir_model='wasmdashai/vits-ar-huba-fine'
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  wandb.login(key= "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79")
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  wandb.init(project= 'AZ',config = training_args.to_dict())
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-
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  @spaces.GPU
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  def greet(text,id):
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  global GK
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  b=int(id)
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  ctrain_datasets,eval_dataset,full_generation_dataset=get_data_loader(train_dataset_dirs = train_dataset_dirs,
 
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  from torch.cuda.amp import autocast, GradScaler
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
 
 
 
 
 
 
 
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  feature_extractor = VitsFeatureExtractor()
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+
 
 
 
 
 
 
 
 
 
 
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  # sgl=get_state_grad_loss(k1=True,#generator=False,
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  # discriminator=False,
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  # duration=False
 
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+
 
 
 
 
 
 
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  dir_model='wasmdashai/vits-ar-huba-fine'
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  wandb.login(key= "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79")
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  wandb.init(project= 'AZ',config = training_args.to_dict())
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+ global_step=0
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  @spaces.GPU
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  def greet(text,id):
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  global GK
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+ parser = HfArgumentParser((ModelArguments, DataTrainingArguments, VITSTrainingArguments))
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+ json_file = os.path.abspath('VitsModelSplit/finetune_config_ara.json')
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+ model_args, data_args, training_args = parser.parse_json_file(json_file = json_file)
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+ sgl=get_state_grad_loss(mel=True,
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+ # generator=False,
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+ # discriminator=False,
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+ duration=False)
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+
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+
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+ training_args.num_train_epochs=1000
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+ training_args.fp16=True
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+ training_args.eval_steps=300
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+ training_args.weight_kl=1
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+ training_args.d_learning_rate=2e-4
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+ training_args.learning_rate=2e-4
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+ training_args.weight_mel=45
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+ training_args.num_train_epochs=4
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+ training_args.eval_steps=1000
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+
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  b=int(id)
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  ctrain_datasets,eval_dataset,full_generation_dataset=get_data_loader(train_dataset_dirs = train_dataset_dirs,