mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-08-21 02:29:47 +08:00
Update training scripts for utils
This commit is contained in:
Jarod Mica
parent
b4c6e3e72a
commit
079f1e61e0
@ -3,7 +3,7 @@ import warnings
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warnings.filterwarnings("ignore")
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import os
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import utils
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import GPT_SoVITS.utils as utils
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hps = utils.get_hparams(stage=2)
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
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@ -71,7 +71,7 @@ def main():
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def run(rank, n_gpus, hps):
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global global_step
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if rank == 0:
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logger = GPT_SoVITS.utils.get_logger(hps.data.exp_dir)
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logger = utils.get_logger(hps.data.exp_dir)
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logger.info(hps)
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# utils.check_git_hash(hps.s2_ckpt_dir)
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writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
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@ -204,7 +204,7 @@ def run(rank, n_gpus, hps):
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net_d = net_d.to(device)
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try: # 如果能加载自动resume
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_, _, _, epoch_str = GPT_SoVITS.utils.load_checkpoint(
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_, _, _, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_*.pth"),
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net_d,
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optim_d,
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@ -212,7 +212,7 @@ def run(rank, n_gpus, hps):
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if rank == 0:
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logger.info("loaded D")
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# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
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_, _, _, epoch_str = GPT_SoVITS.utils.load_checkpoint(
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_, _, _, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"),
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net_g,
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optim_g,
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@ -479,30 +479,30 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
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image_dict = None
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try: ###Some people installed the wrong version of matplotlib.
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image_dict = {
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"slice/mel_org": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
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"slice/mel_org": utils.plot_spectrogram_to_numpy(
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y_mel[0].data.cpu().numpy(),
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),
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"slice/mel_gen": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
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"slice/mel_gen": utils.plot_spectrogram_to_numpy(
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y_hat_mel[0].data.cpu().numpy(),
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),
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"all/mel": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
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"all/mel": utils.plot_spectrogram_to_numpy(
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mel[0].data.cpu().numpy(),
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),
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"all/stats_ssl": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
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"all/stats_ssl": utils.plot_spectrogram_to_numpy(
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stats_ssl[0].data.cpu().numpy(),
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),
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}
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except:
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pass
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if image_dict:
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GPT_SoVITS.utils.summarize(
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utils.summarize(
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writer=writer,
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global_step=global_step,
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images=image_dict,
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scalars=scalar_dict,
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)
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else:
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GPT_SoVITS.utils.summarize(
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utils.summarize(
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writer=writer,
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global_step=global_step,
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scalars=scalar_dict,
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@ -510,7 +510,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
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global_step += 1
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if epoch % hps.train.save_every_epoch == 0 and rank == 0:
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if hps.train.if_save_latest == 0:
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GPT_SoVITS.utils.save_checkpoint(
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utils.save_checkpoint(
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net_g,
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optim_g,
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hps.train.learning_rate,
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@ -520,7 +520,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
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"G_{}.pth".format(global_step),
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),
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)
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GPT_SoVITS.utils.save_checkpoint(
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utils.save_checkpoint(
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net_d,
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optim_d,
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hps.train.learning_rate,
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@ -531,7 +531,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
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),
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)
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else:
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GPT_SoVITS.utils.save_checkpoint(
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utils.save_checkpoint(
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net_g,
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optim_g,
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hps.train.learning_rate,
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@ -541,7 +541,7 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
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"G_{}.pth".format(233333333333),
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),
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)
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GPT_SoVITS.utils.save_checkpoint(
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utils.save_checkpoint(
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net_d,
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optim_d,
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hps.train.learning_rate,
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@ -644,7 +644,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
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)
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image_dict.update(
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{
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f"gen/mel_{batch_idx}_{test}": GPT_SoVITS.utils.plot_spectrogram_to_numpy(
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f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy(
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y_hat_mel[0].cpu().numpy(),
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),
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}
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@ -656,7 +656,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
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)
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image_dict.update(
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{
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f"gt/mel_{batch_idx}": GPT_SoVITS.utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()),
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f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()),
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},
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)
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audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
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@ -666,7 +666,7 @@ def evaluate(hps, generator, eval_loader, writer_eval):
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# f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :]
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# })
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GPT_SoVITS.utils.summarize(
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utils.summarize(
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writer=writer_eval,
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global_step=global_step,
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images=image_dict,
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@ -3,7 +3,7 @@ import warnings
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warnings.filterwarnings("ignore")
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import os
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import utils
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import GPT_SoVITS.utils as utils
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hps = utils.get_hparams(stage=2)
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
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@ -23,20 +23,20 @@ logging.getLogger("h5py").setLevel(logging.INFO)
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logging.getLogger("numba").setLevel(logging.INFO)
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from random import randint
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from module import commons
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from module.data_utils import (
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from GPT_SoVITS.module import commons
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from GPT_SoVITS.module.data_utils import (
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DistributedBucketSampler,
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)
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from module.data_utils import (
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from GPT_SoVITS.module.data_utils import (
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TextAudioSpeakerCollateV3 as TextAudioSpeakerCollate,
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)
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from module.data_utils import (
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from GPT_SoVITS.module.data_utils import (
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TextAudioSpeakerLoaderV3 as TextAudioSpeakerLoader,
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)
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from module.models import (
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from GPT_SoVITS.module.models import (
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SynthesizerTrnV3 as SynthesizerTrn,
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)
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from process_ckpt import savee
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from GPT_SoVITS.process_ckpt import savee
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = False
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@ -3,7 +3,7 @@ import warnings
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warnings.filterwarnings("ignore")
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import os
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import utils
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import GPT_SoVITS.utils as utils
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hps = utils.get_hparams(stage=2)
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
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@ -24,8 +24,8 @@ logging.getLogger("numba").setLevel(logging.INFO)
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from collections import OrderedDict as od
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from random import randint
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from module import commons
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from module.data_utils import (
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from GPT_SoVITS.module import commons
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from GPT_SoVITS.module.data_utils import (
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DistributedBucketSampler,
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TextAudioSpeakerCollateV3,
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TextAudioSpeakerLoaderV3,
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@ -33,7 +33,7 @@ from module.data_utils import (
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TextAudioSpeakerLoaderV4,
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)
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from module.models import (
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from GPT_SoVITS.module.models import (
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SynthesizerTrnV3 as SynthesizerTrn,
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)
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from peft import LoraConfig, get_peft_model
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