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Format & polish training and webui

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This commit is contained in:
Lengyue
2023-09-12 02:21:48 -04:00
parent c99131fcd6
commit f63acb913f
18 changed files with 91 additions and 115 deletions
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37
train_ms.py
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@@ -1,14 +1,10 @@
# flake8: noqa: E402
import os
import json
import argparse
import itertools
import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
@@ -42,7 +38,7 @@ torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not avaliable if torch version is lower than 2.0
) # Not available if torch version is lower than 2.0
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
@@ -97,23 +93,20 @@ def run():
)
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas == True
and hps.model.use_noise_scaled_mas is True
):
print("Using noise scaled MAS for VITS2")
use_noise_scaled_mas = True
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
use_noise_scaled_mas = False
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator == True
and hps.model.use_duration_discriminator is True
):
print("Using duration discriminator for VITS2")
use_duration_discriminator = True
net_dur_disc = DurationDiscriminator(
hps.model.hidden_channels,
hps.model.hidden_channels,
@@ -123,16 +116,14 @@ def run():
).cuda(rank)
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder == True
and hps.model.use_spk_conditioned_encoder is True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
use_spk_conditioned_encoder = True
else:
print("Using normal encoder for VITS1")
use_spk_conditioned_encoder = False
net_g = SynthesizerTrn(
len(symbols),
@@ -176,19 +167,25 @@ def run():
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
skip_optimizer=True,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
net_g,
optim_g,
skip_optimizer=True,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
net_d,
optim_d,
skip_optimizer=True,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_g.param_groups[0].get("initial_lr"):
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
@@ -371,9 +368,7 @@ def train_and_evaluate(
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
grad_norm_dur_disc = commons.clip_grad_value_(
net_dur_disc.parameters(), None
)
commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()