deepgeek/CosyVoice
2
0
Fork 0
mirror of https://github.com/FunAudioLLM/CosyVoice.git synced 2026-02-05 01:49:25 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix cosyvoice3 training

Browse Source
This commit is contained in:
lyuxiang.lx
2025-12-29 10:02:59 +00:00
parent 8524c81acd
commit 4d7295a9a7
5 changed files with 25 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
cosyvoice/utils/train_utils.py
View File

@@ -53,7 +53,7 @@ def init_distributed(args):
def init_dataset_and_dataloader(args, configs, gan, dpo):
data_pipeline = configs['data_pipeline_gan'] if gan is True else configs['data_pipeline']
train_dataset = Dataset(args.train_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=True, partition=True)
cv_dataset = Dataset(args.cv_data, data_pipeline=data_pipeline, mode='train', gan=gan, dpo=dpo, shuffle=False, partition=False)
cv_dataset = Dataset(args.cv_data, data_pipeline=data_pipeline, mode='dev', gan=gan, dpo=dpo, shuffle=False, partition=False)
# do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts
train_data_loader = DataLoader(train_dataset,
@@ -164,18 +164,18 @@ def init_optimizer_and_scheduler(args, configs, model, gan):
raise ValueError("unknown scheduler: " + configs['train_conf'])
if configs['train_conf']['optim_d'] == 'adam':
optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
optimizer_d = optim.Adam(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf_d'])
elif configs['train_conf']['optim_d'] == 'adamw':
optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf'])
optimizer_d = optim.AdamW(model.module.discriminator.parameters(), **configs['train_conf']['optim_conf_d'])
else:
raise ValueError("unknown optimizer: " + configs['train_conf'])
if configs['train_conf']['scheduler_d'] == 'warmuplr':
scheduler_type = WarmupLR
scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_conf'])
scheduler_d = WarmupLR(optimizer_d, **configs['train_conf']['scheduler_d'])
elif configs['train_conf']['scheduler_d'] == 'NoamHoldAnnealing':
scheduler_type = NoamHoldAnnealing
scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_conf'])
scheduler_d = NoamHoldAnnealing(optimizer_d, **configs['train_conf']['scheduler_d'])
elif configs['train_conf']['scheduler'] == 'constantlr':
scheduler_type = ConstantLR
scheduler_d = ConstantLR(optimizer_d)