RVC/Retrieval-based-Voice-Conversion-WebUI
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RVC-Boss
2023-05-14 15:05:42 +08:00
committed by GitHub
parent 3909ce4a7b
commit 3b5a2298d7
6 changed files with 305 additions and 218 deletions
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47
train_nsf_sim_cache_sid_load_pretrain.py
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@@ -31,14 +31,21 @@ from data_utils import (
TextAudioCollate,
DistributedBucketSampler,
)
from infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
MultiPeriodDiscriminator,
)
if(hps.version=="v1"):
from infer_pack.models import (
SynthesizerTrnMs256NSFsid as RVC_Model_f0,
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
MultiPeriodDiscriminator,
)
else:
from infer_pack.models import (
SynthesizerTrnMs768NSFsid as RVC_Model_f0,
SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from process_ckpt import savee
global_step = 0
@@ -63,7 +70,7 @@ def run(rank, n_gpus, hps):
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
# utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
@@ -104,7 +111,7 @@ def run(rank, n_gpus, hps):
prefetch_factor=8,
)
if hps.if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(
net_g = RVC_Model_f0(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model,
@@ -112,7 +119,7 @@ def run(rank, n_gpus, hps):
sr=hps.sample_rate,
)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(
net_g = RVC_Model_nof0(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model,
@@ -343,7 +350,7 @@ def train_and_evaluate(
spec = spec.cuda(rank, non_blocking=True)
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
wave = wave.cuda(rank, non_blocking=True)
wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
# wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
# Calculate
with autocast(enabled=hps.train.fp16_run):
@@ -428,10 +435,10 @@ def train_and_evaluate(
)
)
# Amor For Tensorboard display
if loss_mel > 50:
loss_mel = 50
if loss_kl > 5:
loss_kl = 5
if loss_mel > 75:
loss_mel = 75
if loss_kl > 9:
loss_kl = 9
logger.info([global_step, lr])
logger.info(
@@ -512,12 +519,20 @@ def train_and_evaluate(
epoch,
os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
)
if(rank==0 and hps.save_every_weights=="1"):
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
logger.info(
"saving ckpt %s_e%s:%s"
% (hps.name,epoch,savee(ckpt, hps.sample_rate, hps.if_f0, hps.name+"_e%s"%epoch, epoch,hps.version))
)
if rank == 0:
logger.info("====> Epoch: {}".format(epoch))
if epoch >= hps.total_epoch and rank == 0:
logger.info("Training is done. The program is closed.")
from process_ckpt import savee # def savee(ckpt,sr,if_f0,name,epoch):
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
@@ -525,7 +540,7 @@ def train_and_evaluate(
ckpt = net_g.state_dict()
logger.info(
"saving final ckpt:%s"
% (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch))
% (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch,hps.version))
)
sleep(1)
os._exit(2333333)