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import os
from glob import glob
from logging import getLogger
from typing import Literal, Tuple
from pathlib import Path
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from huggingface_hub import HfApi, upload_folder
from accelerate import Accelerator
from datasets import Dataset
from .constants import *
from .synthesizer import commons
from .synthesizer.models import (
SynthesizerTrnMs768NSFsid,
MultiPeriodDiscriminator,
)
from .utils.losses import (
discriminator_loss,
feature_loss,
generator_loss,
kl_loss,
)
from .utils.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from .utils.data_utils import TextAudioCollateMultiNSFsid
logger = getLogger(__name__)
class TrainingCheckpoint:
def __init__(
self,
epoch: int,
G: SynthesizerTrnMs768NSFsid,
D: MultiPeriodDiscriminator,
optimizer_G: torch.optim.AdamW,
optimizer_D: torch.optim.AdamW,
scheduler_G: torch.optim.lr_scheduler.ExponentialLR,
scheduler_D: torch.optim.lr_scheduler.ExponentialLR,
loss_gen: float,
loss_fm: float,
loss_mel: float,
loss_kl: float,
loss_gen_all: float,
loss_disc: float,
):
self.epoch = epoch
self.G = G
self.D = D
self.optimizer_G = optimizer_G
self.optimizer_D = optimizer_D
self.scheduler_G = scheduler_G
self.scheduler_D = scheduler_D
self.loss_gen = loss_gen
self.loss_fm = loss_fm
self.loss_mel = loss_mel
self.loss_kl = loss_kl
self.loss_gen_all = loss_gen_all
self.loss_disc = loss_disc
def save(
self,
checkpoint_dir="./",
g_checkpoint: str | None = None,
d_checkpoint: str | None = None,
):
g_path = g_checkpoint if g_checkpoint is not None else f"G_{self.epoch}.pth"
d_path = d_checkpoint if d_checkpoint is not None else f"D_{self.epoch}.pth"
torch.save(
{
"epoch": self.epoch,
"model": self.G.state_dict(),
"optimizer": self.optimizer_G.state_dict(),
"scheduler": self.scheduler_G.state_dict(),
"loss_gen": self.loss_gen,
"loss_fm": self.loss_fm,
"loss_mel": self.loss_mel,
"loss_kl": self.loss_kl,
"loss_gen_all": self.loss_gen_all,
"loss_disc": self.loss_disc,
},
os.path.join(checkpoint_dir, g_path),
)
torch.save(
{
"epoch": self.epoch,
"model": self.D.state_dict(),
"optimizer": self.optimizer_D.state_dict(),
"scheduler": self.scheduler_D.state_dict(),
},
os.path.join(checkpoint_dir, d_path),
)
class RVCTrainer:
def __init__(self, checkpoint_dir: str = None, sr: int = SR_48K):
self.checkpoint_dir = checkpoint_dir
self.sr = sr
def latest_checkpoint(self):
files_g = glob(os.path.join(self.checkpoint_dir, "G_*.pth"))
if not files_g:
return None
latest_g = max(files_g, key=os.path.getctime)
files_d = glob(os.path.join(self.checkpoint_dir, "D_*.pth"))
if not files_d:
return None
latest_d = max(files_d, key=os.path.getctime)
return latest_g, latest_d
def setup_models(
self,
resume_from: Tuple[str, str] | None = None,
accelerator: Accelerator | None = None,
lr=1e-4,
lr_decay=0.999875,
betas: Tuple[float, float] = (0.8, 0.99),
eps=1e-9,
use_spectral_norm=False,
segment_size=17280,
filter_length=N_FFT,
hop_length=HOP_LENGTH,
inter_channels=192,
hidden_channels=192,
filter_channels=768,
n_heads=2,
n_layers=6,
kernel_size=3,
p_dropout=0.0,
resblock: Literal["1", "2"] = "1",
resblock_kernel_sizes: list[int] = [3, 7, 11],
resblock_dilation_sizes: list[list[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
upsample_initial_channel=512,
upsample_rates: list[int] = [12, 10, 2, 2],
upsample_kernel_sizes: list[int] = [24, 20, 4, 4],
spk_embed_dim=109,
gin_channels=256,
):
if accelerator is None:
accelerator = Accelerator()
G = SynthesizerTrnMs768NSFsid(
spec_channels=filter_length // 2 + 1,
segment_size=segment_size // hop_length,
inter_channels=inter_channels,
hidden_channels=hidden_channels,
filter_channels=filter_channels,
n_heads=n_heads,
n_layers=n_layers,
kernel_size=kernel_size,
p_dropout=p_dropout,
resblock=resblock,
resblock_kernel_sizes=resblock_kernel_sizes,
resblock_dilation_sizes=resblock_dilation_sizes,
upsample_initial_channel=upsample_initial_channel,
upsample_rates=upsample_rates,
upsample_kernel_sizes=upsample_kernel_sizes,
spk_embed_dim=spk_embed_dim,
gin_channels=gin_channels,
sr=self.sr,
).to(accelerator.device)
D = MultiPeriodDiscriminator(use_spectral_norm=use_spectral_norm).to(
accelerator.device
)
optimizer_G = torch.optim.AdamW(
G.parameters(),
lr,
betas=betas,
eps=eps,
)
optimizer_D = torch.optim.AdamW(
D.parameters(),
lr,
betas=betas,
eps=eps,
)
if resume_from is not None:
g_checkpoint, d_checkpoint = resume_from
logger.info(f"Resuming from {g_checkpoint} and {d_checkpoint}")
G_checkpoint = torch.load(g_checkpoint, map_location=accelerator.device)
D_checkpoint = torch.load(d_checkpoint, map_location=accelerator.device)
if "epoch" in G_checkpoint:
finished_epoch = int(G_checkpoint["epoch"])
try:
finished_epoch = int(Path(g_checkpoint).stem.split("_")[1])
except:
finished_epoch = 0
scheduler_G = torch.optim.lr_scheduler.ExponentialLR(
optimizer_G, gamma=lr_decay, last_epoch=finished_epoch - 1
)
scheduler_D = torch.optim.lr_scheduler.ExponentialLR(
optimizer_D, gamma=lr_decay, last_epoch=finished_epoch - 1
)
G.load_state_dict(G_checkpoint["model"])
if "optimizer" in G_checkpoint:
optimizer_G.load_state_dict(G_checkpoint["optimizer"])
if "scheduler" in G_checkpoint:
scheduler_G.load_state_dict(G_checkpoint["scheduler"])
D.load_state_dict(D_checkpoint["model"])
if "optimizer" in D_checkpoint:
optimizer_D.load_state_dict(D_checkpoint["optimizer"])
if "scheduler" in D_checkpoint:
scheduler_D.load_state_dict(D_checkpoint["scheduler"])
else:
finished_epoch = 0
scheduler_G = torch.optim.lr_scheduler.ExponentialLR(
optimizer_G, gamma=lr_decay, last_epoch=-1
)
scheduler_D = torch.optim.lr_scheduler.ExponentialLR(
optimizer_D, gamma=lr_decay, last_epoch=-1
)
G, D, optimizer_G, optimizer_D = accelerator.prepare(
G, D, optimizer_G, optimizer_D
)
G: torch.Module = G
D: torch.Module = D
return G, D, optimizer_G, optimizer_D, scheduler_G, scheduler_D, finished_epoch
def setup_dataloader(
self,
dataset: Dataset,
batch_size=1,
accelerator: Accelerator | None = None,
):
if accelerator is None:
accelerator = Accelerator()
dataset = dataset.with_format("torch", device=accelerator.device)
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=TextAudioCollateMultiNSFsid(),
)
loader = accelerator.prepare(loader)
return loader
def run(
self,
G,
D,
optimizer_G,
optimizer_D,
scheduler_G,
scheduler_D,
finished_epoch,
loader,
accelerator: Accelerator | None = None,
epochs=100,
segment_size=17280,
filter_length=N_FFT,
hop_length=HOP_LENGTH,
n_mel_channels=N_MELS,
win_length=WIN_LENGTH,
mel_fmin=0.0,
mel_fmax: float | None = None,
c_mel=45,
c_kl=1.0,
):
if accelerator is None:
accelerator = Accelerator()
if accelerator.is_main_process:
logger.info("Start training")
prev_loss_gen = -1.0
prev_loss_fm = -1.0
prev_loss_mel = -1.0
prev_loss_kl = -1.0
prev_loss_disc = -1.0
prev_loss_gen_all = -1.0
with accelerator.autocast():
for epoch in range(1, epochs + 1):
if epoch <= finished_epoch:
continue
G.train()
D.train()
for (
phone,
phone_lengths,
pitch,
pitchf,
spec,
spec_lengths,
wave,
wave_lengths,
sid,
) in loader:
# Generator
optimizer_G.zero_grad()
(
y_hat,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
) = G(
phone,
phone_lengths,
pitch,
pitchf,
spec,
spec_lengths,
sid,
)
mel = spec_to_mel_torch(
spec,
filter_length,
n_mel_channels,
self.sr,
mel_fmin,
mel_fmax,
)
y_mel = commons.slice_segments(
mel, ids_slice, segment_size // hop_length
)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1),
filter_length,
n_mel_channels,
self.sr,
hop_length,
win_length,
mel_fmin,
mel_fmax,
)
wave = commons.slice_segments(
wave, ids_slice * hop_length, segment_size
)
# Discriminator
optimizer_D.zero_grad()
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = D(wave, y_hat.detach())
# Update Discriminator
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
accelerator.backward(loss_disc)
optimizer_D.step()
# Re-compute discriminator output (since we just got a "better" discriminator)
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = D(wave, y_hat)
# Update Generator
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_mel = F.l1_loss(y_mel, y_hat_mel) * c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * c_kl
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
accelerator.backward(loss_gen_all)
optimizer_G.step()
prev_loss_gen = loss_gen.item()
prev_loss_fm = loss_fm.item()
prev_loss_mel = loss_mel.item()
prev_loss_kl = loss_kl.item()
prev_loss_disc = loss_disc.item()
prev_loss_gen_all = loss_gen_all.item()
res = TrainingCheckpoint(
epoch,
G,
D,
optimizer_G,
optimizer_D,
scheduler_G,
scheduler_D,
prev_loss_gen,
prev_loss_fm,
prev_loss_mel,
prev_loss_kl,
prev_loss_gen_all,
prev_loss_disc,
)
yield res
def train(
self,
dataset: Dataset,
resume_from: Tuple[str, str] | None = None,
accelerator: Accelerator | None = None,
batch_size=1,
epochs=100,
lr=1e-4,
lr_decay=0.999875,
betas: Tuple[float, float] = (0.8, 0.99),
eps=1e-9,
use_spectral_norm=False,
segment_size=17280,
filter_length=N_FFT,
hop_length=HOP_LENGTH,
inter_channels=192,
hidden_channels=192,
filter_channels=768,
n_heads=2,
n_layers=6,
kernel_size=3,
p_dropout=0.0,
resblock: Literal["1", "2"] = "1",
resblock_kernel_sizes: list[int] = [3, 7, 11],
resblock_dilation_sizes: list[list[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
upsample_initial_channel=512,
upsample_rates: list[int] = [12, 10, 2, 2],
upsample_kernel_sizes: list[int] = [24, 20, 4, 4],
spk_embed_dim=109,
gin_channels=256,
n_mel_channels=N_MELS,
win_length=WIN_LENGTH,
mel_fmin=0.0,
mel_fmax: float | None = None,
c_mel=45,
c_kl=1.0,
):
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
if accelerator is None:
accelerator = Accelerator()
(
G,
D,
optimizer_G,
optimizer_D,
scheduler_G,
scheduler_D,
finished_epoch,
) = self.setup_models(
resume_from=resume_from,
accelerator=accelerator,
lr=lr,
lr_decay=lr_decay,
betas=betas,
eps=eps,
use_spectral_norm=use_spectral_norm,
segment_size=segment_size,
filter_length=filter_length,
hop_length=hop_length,
inter_channels=inter_channels,
hidden_channels=hidden_channels,
filter_channels=filter_channels,
n_heads=n_heads,
n_layers=n_layers,
kernel_size=kernel_size,
p_dropout=p_dropout,
resblock=resblock,
resblock_kernel_sizes=resblock_kernel_sizes,
resblock_dilation_sizes=resblock_dilation_sizes,
upsample_initial_channel=upsample_initial_channel,
upsample_rates=upsample_rates,
upsample_kernel_sizes=upsample_kernel_sizes,
spk_embed_dim=spk_embed_dim,
gin_channels=gin_channels,
)
loader = self.setup_dataloader(
dataset,
batch_size=batch_size,
accelerator=accelerator,
)
return self.run(
G,
D,
optimizer_G,
optimizer_D,
scheduler_G,
scheduler_D,
finished_epoch,
loader,
accelerator,
epochs=epochs,
segment_size=segment_size,
filter_length=filter_length,
hop_length=hop_length,
n_mel_channels=n_mel_channels,
win_length=win_length,
mel_fmin=mel_fmin,
mel_fmax=mel_fmax,
c_mel=c_mel,
c_kl=c_kl,
)
def push_to_hub(self, repo: str, **kwargs):
if not os.path.exists(self.checkpoint_dir):
raise FileNotFoundError("Checkpoints not found")
api = HfApi(token=kwargs.get("token"))
repo_id = api.create_repo(
repo_id=repo, private=kwargs.get("private"), exist_ok=True
).repo_id
return upload_folder(
repo_id=repo_id,
folder_path=self.checkpoint_dir,
commit_message="Upload via ZeroRVC",
token=kwargs.get("token"),
)
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