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Update training scripts for utils

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Jarod Mica 2025-06-08 20:21:08 -07:00
parent b4c6e3e72a
commit 079f1e61e0
3 changed files with 28 additions and 28 deletions
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34
GPT_SoVITS/s2_train.py
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@ -3,7 +3,7 @@ import warnings
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
import os import os
import utils import GPT_SoVITS.utils as utils
hps = utils.get_hparams(stage=2) hps = utils.get_hparams(stage=2)
os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",") os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
@ -71,7 +71,7 @@ def main():
def run(rank, n_gpus, hps): def run(rank, n_gpus, hps):
global global_step global global_step
if rank == 0: if rank == 0:
logger = GPT_SoVITS.utils.get_logger(hps.data.exp_dir) logger = utils.get_logger(hps.data.exp_dir)
logger.info(hps) logger.info(hps)
# utils.check_git_hash(hps.s2_ckpt_dir) # utils.check_git_hash(hps.s2_ckpt_dir)
writer = SummaryWriter(log_dir=hps.s2_ckpt_dir) writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
@ -204,7 +204,7 @@ def run(rank, n_gpus, hps):
net_d = net_d.to(device) net_d = net_d.to(device)
try: # 如果能加载自动resume try: # 如果能加载自动resume
_, _, _, epoch_str = GPT_SoVITS.utils.load_checkpoint( _, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_*.pth"), utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_*.pth"),
net_d, net_d,
optim_d, optim_d,
@ -212,7 +212,7 @@ def run(rank, n_gpus, hps):
if rank == 0: if rank == 0:
logger.info("loaded D") 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(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
_, _, _, epoch_str = GPT_SoVITS.utils.load_checkpoint( _, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"), utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"),
net_g, net_g,
optim_g, optim_g,
@ -479,30 +479,30 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
image_dict = None image_dict = None
try: ###Some people installed the wrong version of matplotlib. try: ###Some people installed the wrong version of matplotlib.
image_dict = { image_dict = {
"slice/mel_org": GPT_SoVITS.utils.plot_spectrogram_to_numpy( "slice/mel_org": utils.plot_spectrogram_to_numpy(
y_mel[0].data.cpu().numpy(), y_mel[0].data.cpu().numpy(),
), ),
"slice/mel_gen": GPT_SoVITS.utils.plot_spectrogram_to_numpy( "slice/mel_gen": utils.plot_spectrogram_to_numpy(
y_hat_mel[0].data.cpu().numpy(), y_hat_mel[0].data.cpu().numpy(),
), ),
"all/mel": GPT_SoVITS.utils.plot_spectrogram_to_numpy( "all/mel": utils.plot_spectrogram_to_numpy(
mel[0].data.cpu().numpy(), mel[0].data.cpu().numpy(),
), ),
"all/stats_ssl": GPT_SoVITS.utils.plot_spectrogram_to_numpy( "all/stats_ssl": utils.plot_spectrogram_to_numpy(
stats_ssl[0].data.cpu().numpy(), stats_ssl[0].data.cpu().numpy(),
), ),
} }
except: except:
pass pass
if image_dict: if image_dict:
GPT_SoVITS.utils.summarize( utils.summarize(
writer=writer, writer=writer,
global_step=global_step, global_step=global_step,
images=image_dict, images=image_dict,
scalars=scalar_dict, scalars=scalar_dict,
) )
else: else:
GPT_SoVITS.utils.summarize( utils.summarize(
writer=writer, writer=writer,
global_step=global_step, global_step=global_step,
scalars=scalar_dict, scalars=scalar_dict,
@ -510,7 +510,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
global_step += 1 global_step += 1
if epoch % hps.train.save_every_epoch == 0 and rank == 0: if epoch % hps.train.save_every_epoch == 0 and rank == 0:
if hps.train.if_save_latest == 0: if hps.train.if_save_latest == 0:
GPT_SoVITS.utils.save_checkpoint( utils.save_checkpoint(
net_g, net_g,
optim_g, optim_g,
hps.train.learning_rate, hps.train.learning_rate,
@ -520,7 +520,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
"G_{}.pth".format(global_step), "G_{}.pth".format(global_step),
), ),
) )
GPT_SoVITS.utils.save_checkpoint( utils.save_checkpoint(
net_d, net_d,
optim_d, optim_d,
hps.train.learning_rate, hps.train.learning_rate,
@ -531,7 +531,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
), ),
) )
else: else:
GPT_SoVITS.utils.save_checkpoint( utils.save_checkpoint(
net_g, net_g,
optim_g, optim_g,
hps.train.learning_rate, hps.train.learning_rate,
@ -541,7 +541,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
"G_{}.pth".format(233333333333), "G_{}.pth".format(233333333333),
), ),
) )
GPT_SoVITS.utils.save_checkpoint( utils.save_checkpoint(
net_d, net_d,
optim_d, optim_d,
hps.train.learning_rate, hps.train.learning_rate,
@ -644,7 +644,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
) )
image_dict.update( image_dict.update(
{ {
f"gen/mel_{batch_idx}_{test}": GPT_SoVITS.utils.plot_spectrogram_to_numpy( f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy(
y_hat_mel[0].cpu().numpy(), y_hat_mel[0].cpu().numpy(),
), ),
} }
@ -656,7 +656,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
) )
image_dict.update( image_dict.update(
{ {
f"gt/mel_{batch_idx}": GPT_SoVITS.utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()), f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()),
}, },
) )
audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]}) audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
@ -666,7 +666,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
# f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :] # f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
# }) # })
GPT_SoVITS.utils.summarize( utils.summarize(
writer=writer_eval, writer=writer_eval,
global_step=global_step, global_step=global_step,
images=image_dict, images=image_dict,

14
GPT_SoVITS/s2_train_v3.py
View File

@ -3,7 +3,7 @@ import warnings
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
import os import os
import utils import GPT_SoVITS.utils as utils
hps = utils.get_hparams(stage=2) hps = utils.get_hparams(stage=2)
os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",") os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
@ -23,20 +23,20 @@ logging.getLogger("h5py").setLevel(logging.INFO)
logging.getLogger("numba").setLevel(logging.INFO) logging.getLogger("numba").setLevel(logging.INFO)
from random import randint from random import randint
from module import commons from GPT_SoVITS.module import commons
from module.data_utils import ( from GPT_SoVITS.module.data_utils import (
DistributedBucketSampler, DistributedBucketSampler,
) )
from module.data_utils import ( from GPT_SoVITS.module.data_utils import (
TextAudioSpeakerCollateV3 as TextAudioSpeakerCollate, TextAudioSpeakerCollateV3 as TextAudioSpeakerCollate,
) )
from module.data_utils import ( from GPT_SoVITS.module.data_utils import (
TextAudioSpeakerLoaderV3 as TextAudioSpeakerLoader, TextAudioSpeakerLoaderV3 as TextAudioSpeakerLoader,
) )
from module.models import ( from GPT_SoVITS.module.models import (
SynthesizerTrnV3 as SynthesizerTrn, SynthesizerTrnV3 as SynthesizerTrn,
) )
from process_ckpt import savee from GPT_SoVITS.process_ckpt import savee
torch.backends.cudnn.benchmark = False torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = False torch.backends.cudnn.deterministic = False

8
GPT_SoVITS/s2_train_v3_lora.py
View File

@ -3,7 +3,7 @@ import warnings
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
import os import os
import utils import GPT_SoVITS.utils as utils
hps = utils.get_hparams(stage=2) hps = utils.get_hparams(stage=2)
os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",") os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
@ -24,8 +24,8 @@ logging.getLogger("numba").setLevel(logging.INFO)
from collections import OrderedDict as od from collections import OrderedDict as od
from random import randint from random import randint
from module import commons from GPT_SoVITS.module import commons
from module.data_utils import ( from GPT_SoVITS.module.data_utils import (
DistributedBucketSampler, DistributedBucketSampler,
TextAudioSpeakerCollateV3, TextAudioSpeakerCollateV3,
TextAudioSpeakerLoaderV3, TextAudioSpeakerLoaderV3,
@ -33,7 +33,7 @@ from module.data_utils import (
TextAudioSpeakerLoaderV4, TextAudioSpeakerLoaderV4,
) )
from module.models import ( from GPT_SoVITS.module.models import (
SynthesizerTrnV3 as SynthesizerTrn, SynthesizerTrnV3 as SynthesizerTrn,
) )
from peft import LoraConfig, get_peft_model from peft import LoraConfig, get_peft_model