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gpt_sovits_v3

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gpt_sovits_v3
This commit is contained in:
RVC-Boss
2025-02-11 21:07:03 +08:00
committed by GitHub
parent ed207c4b87
commit fa42d26d0e
4 changed files with 585 additions and 56 deletions
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14
GPT_SoVITS/s2_train.py
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@@ -75,7 +75,7 @@ def run(rank, n_gpus, hps):
dist.init_process_group(
backend = "gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
init_method="env://",
init_method="env://?use_libuv=False",
world_size=n_gpus,
rank=rank,
)
@@ -193,7 +193,7 @@ def run(rank, n_gpus, hps):
try: # 如果能加载自动resume
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path("%s/logs_s2" % hps.data.exp_dir, "D_*.pth"),
utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_*.pth"),
net_d,
optim_d,
) # D多半加载没事
@@ -201,7 +201,7 @@ def run(rank, n_gpus, hps):
logger.info("loaded D")
# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path("%s/logs_s2" % hps.data.exp_dir, "G_*.pth"),
utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "G_*.pth"),
net_g,
optim_g,
)
@@ -455,7 +455,7 @@ def train_and_evaluate(
hps.train.learning_rate,
epoch,
os.path.join(
"%s/logs_s2" % hps.data.exp_dir, "G_{}.pth".format(global_step)
"%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "G_{}.pth".format(global_step)
),
)
utils.save_checkpoint(
@@ -464,7 +464,7 @@ def train_and_evaluate(
hps.train.learning_rate,
epoch,
os.path.join(
"%s/logs_s2" % hps.data.exp_dir, "D_{}.pth".format(global_step)
"%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_{}.pth".format(global_step)
),
)
else:
@@ -474,7 +474,7 @@ def train_and_evaluate(
hps.train.learning_rate,
epoch,
os.path.join(
"%s/logs_s2" % hps.data.exp_dir, "G_{}.pth".format(233333333333)
"%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "G_{}.pth".format(233333333333)
),
)
utils.save_checkpoint(
@@ -483,7 +483,7 @@ def train_and_evaluate(
hps.train.learning_rate,
epoch,
os.path.join(
"%s/logs_s2" % hps.data.exp_dir, "D_{}.pth".format(233333333333)
"%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_{}.pth".format(233333333333)
),
)
if rank == 0 and hps.train.if_save_every_weights == True: