deepgeek/CosyVoice
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remove unnecessary f0 loss in discrimnator

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lyuxiang.lx
2024-10-18 15:58:19 +08:00
parent a4db3db8ed
commit 5bd5dfecab
3 changed files with 27 additions and 27 deletions
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2
cosyvoice/bin/train.py
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@@ -95,6 +95,8 @@ def main():
override_dict.pop('hift')
with open(args.config, 'r') as f:
configs = load_hyperpyyaml(f, overrides=override_dict)
if gan is True:
configs['train_conf'] = configs['train_conf_gan']
configs['train_conf'].update(vars(args))
# Init env for ddp

5
cosyvoice/hifigan/hifigan.py
View File

@@ -64,6 +64,5 @@ class HiFiGan(nn.Module):
loss_tpr = tpr_loss(y_d_rs, y_d_gs, self.tpr_loss_tau)
else:
loss_tpr = torch.zeros(1).to(device)
loss_f0 = F.l1_loss(generated_f0, pitch_feat)
loss = loss_disc + self.tpr_loss_weight * loss_tpr + loss_f0
return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}
loss = loss_disc + self.tpr_loss_weight * loss_tpr
return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr}

47
cosyvoice/utils/train_utils.py
View File

@@ -110,30 +110,29 @@ def wrap_cuda_model(args, model):
def init_optimizer_and_scheduler(args, configs, model, gan):
key = 'train_conf_gan' if gan is True else 'train_conf'
if configs[key]['optim'] == 'adam':
optimizer = optim.Adam(model.parameters(), **configs[key]['optim_conf'])
elif configs[key]['optim'] == 'adamw':
optimizer = optim.AdamW(model.parameters(), **configs[key]['optim_conf'])
if configs['train_conf']['optim'] == 'adam':
optimizer = optim.Adam(model.parameters(), **configs['train_conf']['optim_conf'])
elif configs['train_conf']['optim'] == 'adamw':
optimizer = optim.AdamW(model.parameters(), **configs['train_conf']['optim_conf'])
else:
raise ValueError("unknown optimizer: " + configs[key])
raise ValueError("unknown optimizer: " + configs['train_conf'])
if configs[key]['scheduler'] == 'warmuplr':
if configs['train_conf']['scheduler'] == 'warmuplr':
scheduler_type = WarmupLR
scheduler = WarmupLR(optimizer, **configs[key]['scheduler_conf'])
elif configs[key]['scheduler'] == 'NoamHoldAnnealing':
scheduler = WarmupLR(optimizer, **configs['train_conf']['scheduler_conf'])
elif configs['train_conf']['scheduler'] == 'NoamHoldAnnealing':
scheduler_type = NoamHoldAnnealing
scheduler = NoamHoldAnnealing(optimizer, **configs[key]['scheduler_conf'])
elif configs[key]['scheduler'] == 'constantlr':
scheduler = NoamHoldAnnealing(optimizer, **configs['train_conf']['scheduler_conf'])
elif configs['train_conf']['scheduler'] == 'constantlr':
scheduler_type = ConstantLR
scheduler = ConstantLR(optimizer)
else:
raise ValueError("unknown scheduler: " + configs[key])
raise ValueError("unknown scheduler: " + configs['train_conf'])
# use deepspeed optimizer for speedup
if args.train_engine == "deepspeed":
def scheduler(opt):
return scheduler_type(opt, **configs[key]['scheduler_conf'])
return scheduler_type(opt, **configs['train_conf']['scheduler_conf'])
model, optimizer, _, scheduler = deepspeed.initialize(
args=args,
model=model,
@@ -143,24 +142,24 @@ def init_optimizer_and_scheduler(args, configs, model, gan):
# currently we wrap generator and discriminator in one model, so we cannot use deepspeed
if gan is True:
if configs[key]['optim_d'] == 'adam':
optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs[key]['optim_conf'])
elif configs[key]['optim_d'] == 'adamw':
optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs[key]['optim_conf'])
if configs['train_conf']['optim_d'] == 'adam':
optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
elif configs['train_conf']['optim_d'] == 'adamw':
optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
else:
raise ValueError("unknown optimizer: " + configs[key])
raise ValueError("unknown optimizer: " + configs['train_conf'])
if configs[key]['scheduler_d'] == 'warmuplr':
if configs['train_conf']['scheduler_d'] == 'warmuplr':
scheduler_type = WarmupLR
scheduler_d = WarmupLR(optimizer_d, **configs[key]['scheduler_conf'])
elif configs[key]['scheduler_d'] == 'NoamHoldAnnealing':
scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_conf'])
elif configs['train_conf']['scheduler_d'] == 'NoamHoldAnnealing':
scheduler_type = NoamHoldAnnealing
scheduler_d = NoamHoldAnnealing(optimizer_d, **configs[key]['scheduler_conf'])
elif configs[key]['scheduler'] == 'constantlr':
scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_conf'])
elif configs['train_conf']['scheduler'] == 'constantlr':
scheduler_type = ConstantLR
scheduler_d = ConstantLR(optimizer_d)
else:
raise ValueError("unknown scheduler: " + configs[key])
raise ValueError("unknown scheduler: " + configs['train_conf'])
else:
optimizer_d, scheduler_d = None, None
return model, optimizer, scheduler, optimizer_d, scheduler_d