wasmdashai commited on
Commit
8cfab1d
·
verified ·
1 Parent(s): 25942bd

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +53 -53
app.py CHANGED
@@ -409,7 +409,7 @@ class TrinerModelVITS:
409
  self.init_wandb()
410
  # self.training_args=load_training_args(self.path_training_args)
411
  # training_args= self.training_args
412
- # scaler = GradScaler(enabled=True)
413
  # for disc in self.model.discriminator.discriminators:
414
  # disc.apply_weight_norm()
415
  # self.model.decoder.apply_weight_norm()
@@ -423,32 +423,32 @@ class TrinerModelVITS:
423
  self.model.discriminator = None
424
  self.models=(self.model,discriminator)
425
 
426
- # optimizer = torch.optim.AdamW(
427
- # self.model.parameters(),
428
- # 2e-4,
429
- # betas=[0.8, 0.99],
430
- # # eps=training_args.adam_epsilon,
431
- # )
432
 
433
- # # Hack to be able to train on multiple device
434
- # disc_optimizer = torch.optim.AdamW(
435
- # discriminator.parameters(),
436
- # 2e-4,
437
- # betas=[0.8, 0.99],
438
- # # eps=training_args.adam_epsilon,
439
- # )
440
- # lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
441
- # optimizer,gamma=0.999875, last_epoch=-1
442
- # )
443
- # disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
444
- # disc_optimizer, gamma=0.999875,last_epoch=-1
445
- # )
446
- # self.models=(self.model,discriminator)
447
- # self.optimizers=(optimizer,disc_optimizer,scaler)
448
- # self.lr_schedulers=(lr_scheduler,disc_lr_scheduler)
449
- # self.tools=load_tools()
450
- # self.stute_mode=True
451
- # print(self.lr_schedulers)
452
 
453
 
454
 
@@ -504,7 +504,7 @@ class TrinerModelVITS:
504
  training_args.eval_steps=1000
505
 
506
  set_seed(training_args.seed)
507
- scaler = GradScaler(enabled=training_args.fp16)
508
 
509
 
510
  # # Initialize optimizer, lr_scheduler
@@ -519,34 +519,34 @@ class TrinerModelVITS:
519
 
520
  # discriminator = self.model.discriminator
521
  # self.model.discriminator = None
522
- model,discriminator=self.models
523
 
524
- optimizer = torch.optim.AdamW(
525
- model.parameters(),
526
- training_args.learning_rate,
527
- betas=[training_args.adam_beta1, training_args.adam_beta2],
528
- eps=training_args.adam_epsilon,
529
- )
530
 
531
- # Hack to be able to train on multiple device
532
- disc_optimizer = torch.optim.AdamW(
533
- discriminator.parameters(),
534
- training_args.d_learning_rate,
535
- betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
536
- eps=training_args.adam_epsilon,
537
- )
538
- lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
539
- optimizer, gamma=training_args.lr_decay, last_epoch=-1
540
- )
541
- disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
542
- disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1
543
- )
544
- # self.models=(self.model,discriminator)
545
- self.optimizers=(optimizer,disc_optimizer,scaler)
546
- self.lr_schedulers=(lr_scheduler,disc_lr_scheduler)
547
- self.tools=load_tools()
548
- self.stute_mode=True
549
- print(self.lr_schedulers)
550
 
551
 
552
 
 
409
  self.init_wandb()
410
  # self.training_args=load_training_args(self.path_training_args)
411
  # training_args= self.training_args
412
+ scaler = GradScaler(enabled=True)
413
  # for disc in self.model.discriminator.discriminators:
414
  # disc.apply_weight_norm()
415
  # self.model.decoder.apply_weight_norm()
 
423
  self.model.discriminator = None
424
  self.models=(self.model,discriminator)
425
 
426
+ optimizer = torch.optim.AdamW(
427
+ self.model.parameters(),
428
+ 2e-4,
429
+ betas=[0.8, 0.99],
430
+ # eps=training_args.adam_epsilon,
431
+ )
432
 
433
+ # Hack to be able to train on multiple device
434
+ disc_optimizer = torch.optim.AdamW(
435
+ discriminator.parameters(),
436
+ 2e-4,
437
+ betas=[0.8, 0.99],
438
+ # eps=training_args.adam_epsilon,
439
+ )
440
+ lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
441
+ optimizer,gamma=0.999875, last_epoch=-1
442
+ )
443
+ disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
444
+ disc_optimizer, gamma=0.999875,last_epoch=-1
445
+ )
446
+ # self.models=(self.model,discriminator)
447
+ self.optimizers=(optimizer,disc_optimizer,scaler)
448
+ self.lr_schedulers=(lr_scheduler,disc_lr_scheduler)
449
+ self.tools=load_tools()
450
+ self.stute_mode=True
451
+ print(self.lr_schedulers)
452
 
453
 
454
 
 
504
  training_args.eval_steps=1000
505
 
506
  set_seed(training_args.seed)
507
+ # scaler = GradScaler(enabled=training_args.fp16)
508
 
509
 
510
  # # Initialize optimizer, lr_scheduler
 
519
 
520
  # discriminator = self.model.discriminator
521
  # self.model.discriminator = None
522
+ # model,discriminator=self.models
523
 
524
+ # optimizer = torch.optim.AdamW(
525
+ # model.parameters(),
526
+ # training_args.learning_rate,
527
+ # betas=[training_args.adam_beta1, training_args.adam_beta2],
528
+ # eps=training_args.adam_epsilon,
529
+ # )
530
 
531
+ # # Hack to be able to train on multiple device
532
+ # disc_optimizer = torch.optim.AdamW(
533
+ # discriminator.parameters(),
534
+ # training_args.d_learning_rate,
535
+ # betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
536
+ # eps=training_args.adam_epsilon,
537
+ # )
538
+ # lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
539
+ # optimizer, gamma=training_args.lr_decay, last_epoch=-1
540
+ # )
541
+ # disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
542
+ # disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1
543
+ # )
544
+ # # self.models=(self.model,discriminator)
545
+ # self.optimizers=(optimizer,disc_optimizer,scaler)
546
+ # self.lr_schedulers=(lr_scheduler,disc_lr_scheduler)
547
+ # self.tools=load_tools()
548
+ # self.stute_mode=True
549
+ # print(self.lr_schedulers)
550
 
551
 
552