mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-03 00:33:57 +03:00
334 lines
13 KiB
Python
Executable File
334 lines
13 KiB
Python
Executable File
import os
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import json
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import argparse
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import itertools
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import math
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from psutil import cpu_count
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import torch
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from torch import nn, optim
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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import torch.multiprocessing as mp
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.nn.parallel import DataParallel as DP
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from torch.cuda.amp import autocast, GradScaler
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import datetime
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import pytz
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import time
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from tqdm import tqdm
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#import warnings
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import commons
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import utils
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from data_utils import (
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TextAudioSpeakerLoader,
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TextAudioSpeakerCollate,
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DistributedBucketSampler
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)
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from models import (
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SynthesizerTrn,
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MultiPeriodDiscriminator,
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)
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from losses import (
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generator_loss,
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discriminator_loss,
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feature_loss,
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kl_loss
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)
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from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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from text.symbols import symbols
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#stftの警告対策
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#warnings.resetwarnings()
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#warnings.simplefilter('ignore', UserWarning)
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#warnings.simplefilter('ignore', DeprecationWarning)
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torch.backends.cudnn.benchmark = True
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global_step = 0
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def main():
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"""Assume Single Node Multi GPUs Training Only"""
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assert torch.cuda.is_available(), "CPU training is not allowed."
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n_gpus = torch.cuda.device_count()
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os.environ['MASTER_ADDR'] = 'localhost'
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os.environ['MASTER_PORT'] = '8000'
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hps = utils.get_hparams()
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mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
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def run(rank, n_gpus, hps):
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global global_step
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if hps.others.os_type == "windows":
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backend_type = "gloo"
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parallel = DP
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else: # Colab
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backend_type = "nccl"
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parallel = DDP
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if rank == 0:
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logger = utils.get_logger(hps.model_dir)
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logger.info(hps)
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utils.check_git_hash(hps.model_dir)
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writer = SummaryWriter(log_dir=hps.model_dir)
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writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
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cpu_count = os.cpu_count()
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if cpu_count > 8:
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cpu_count = 8
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dist.init_process_group(backend=backend_type, init_method='env://', world_size=n_gpus, rank=rank)
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torch.manual_seed(hps.train.seed)
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torch.cuda.set_device(rank)
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train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data, augmentation=hps.augmentation.enable, augmentation_params=hps.augmentation)
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train_sampler = DistributedBucketSampler(
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train_dataset,
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hps.train.batch_size,
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[96,375,750,1125,1500,1875,2250,2625,3000],
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num_replicas=n_gpus,
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rank=rank,
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shuffle=True)
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collate_fn = TextAudioSpeakerCollate()
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train_loader = DataLoader(train_dataset, num_workers=cpu_count, shuffle=False, pin_memory=True,
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collate_fn=collate_fn, batch_sampler=train_sampler)
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if rank == 0:
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eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, augmentation=False)
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eval_sampler = DistributedBucketSampler(
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eval_dataset,
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hps.train.batch_size,
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[96,375,750,1125,1500,1875,2250,2625,3000],
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num_replicas=n_gpus,
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rank=rank,
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shuffle=True)
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eval_loader = DataLoader(eval_dataset, num_workers=cpu_count, shuffle=False, pin_memory=True,
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collate_fn=collate_fn, batch_sampler=eval_sampler)
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net_g = SynthesizerTrn(
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len(symbols),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model).cuda(rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
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optim_g = torch.optim.AdamW(
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net_g.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps)
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optim_d = torch.optim.AdamW(
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net_d.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps)
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net_g = parallel(net_g, device_ids=[rank])
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net_d = parallel(net_d, device_ids=[rank])
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logger.info('FineTuning : '+str(hps.fine_flag))
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if hps.fine_flag:
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logger.info('Load model : '+str(hps.fine_model_g))
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logger.info('Load model : '+str(hps.fine_model_d))
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_, _, _, epoch_str = utils.load_checkpoint(hps.fine_model_g, net_g, optim_g)
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_, _, _, epoch_str = utils.load_checkpoint(hps.fine_model_d, net_d, optim_d)
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epoch_str = 1
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global_step = 0
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else:
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try:
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
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global_step = (epoch_str - 1) * len(train_loader)
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except:
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epoch_str = 1
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global_step = 0
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
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scaler = GradScaler(enabled=hps.train.fp16_run)
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for epoch in range(epoch_str, hps.train.epochs + 1):
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if rank==0:
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train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
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else:
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train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
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scheduler_g.step()
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scheduler_d.step()
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def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
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net_g, net_d = nets
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optim_g, optim_d = optims
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scheduler_g, scheduler_d = schedulers
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train_loader, eval_loader = loaders
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if writers is not None:
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writer, writer_eval = writers
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train_loader.batch_sampler.set_epoch(epoch)
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global global_step
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net_g.train()
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net_d.train()
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(tqdm(train_loader, desc="Epoch {}".format(epoch))):
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x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
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spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
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y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
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speakers = speakers.cuda(rank, non_blocking=True)
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with autocast(enabled=hps.train.fp16_run):
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y_hat, attn, ids_slice, x_mask, z_mask,\
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(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
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mel = spec_to_mel_torch(
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spec,
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.mel_fmin,
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hps.data.mel_fmax)
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y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
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y_hat = y_hat.float()
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y_hat_mel = mel_spectrogram_torch(
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y_hat.squeeze(1),
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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hps.data.mel_fmin,
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hps.data.mel_fmax
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)
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y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
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# Discriminator
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y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
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with autocast(enabled=False):
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
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loss_disc_all = loss_disc
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optim_d.zero_grad()
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scaler.scale(loss_disc_all).backward()
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scaler.unscale_(optim_d)
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grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
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scaler.step(optim_d)
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with autocast(enabled=hps.train.fp16_run):
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# Generator
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y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
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with autocast(enabled=False):
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
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loss_fm = feature_loss(fmap_r, fmap_g)
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loss_gen, losses_gen = generator_loss(y_d_hat_g)
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
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optim_g.zero_grad()
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scaler.scale(loss_gen_all).backward()
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scaler.unscale_(optim_g)
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grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
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scaler.step(optim_g)
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scaler.update()
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if rank==0:
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if global_step % hps.train.log_interval == 0:
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lr = optim_g.param_groups[0]['lr']
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losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
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logger.info('Train Epoch: {} [{:.0f}%]'.format(
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epoch,
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100. * batch_idx / len(train_loader)))
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logger.info(datetime.datetime.now(pytz.timezone('Asia/Tokyo')))
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logger.info([x.item() for x in losses] + [global_step, lr])
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scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
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scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl})
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scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
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scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
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scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
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image_dict = {
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"slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
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"slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
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"all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
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"all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
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}
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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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utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_latest_99999999.pth"))
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utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_latest_99999999.pth"))
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if global_step % hps.train.eval_interval == 0 and global_step != 0:
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evaluate(hps, net_g, eval_loader, writer_eval, logger)
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utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
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utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
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utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_latest_99999999.pth"))
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utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_latest_99999999.pth"))
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global_step += 1
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def evaluate(hps, generator, eval_loader, writer_eval, logger):
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scalar_dict = {}
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scalar_dict.update({"loss/g/mel": 0.0, "loss/g/kl": 0.0})
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with torch.no_grad():
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#evalのデータセットを一周する
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(tqdm(eval_loader, desc="Epoch {}".format("eval"))):
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x, x_lengths = x.cuda(0), x_lengths.cuda(0)
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spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
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y, y_lengths = y.cuda(0), y_lengths.cuda(0)
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speakers = speakers.cuda(0)
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#autocastはfp16のおまじない
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with autocast(enabled=hps.train.fp16_run):
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#Generator
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y_hat, attn, ids_slice, x_mask, z_mask,\
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(z, z_p, m_p, logs_p, m_q, logs_q) = generator(x, x_lengths, spec, spec_lengths, speakers)
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mel = spec_to_mel_torch(
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spec,
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.mel_fmin,
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hps.data.mel_fmax)
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y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
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y_hat = y_hat.float()
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y_hat_mel = mel_spectrogram_torch(
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y_hat.squeeze(1),
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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hps.data.mel_fmin,
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hps.data.mel_fmax
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)
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batch_num = batch_idx
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y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
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with autocast(enabled=hps.train.fp16_run):
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with autocast(enabled=False):
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
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scalar_dict["loss/g/mel"] = scalar_dict["loss/g/mel"] + loss_mel
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scalar_dict["loss/g/kl"] = scalar_dict["loss/g/kl"] + loss_kl
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#lossをepoch1周の結果をiter単位の平均値に
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scalar_dict["loss/g/mel"] = scalar_dict["loss/g/mel"] / (batch_num+1)
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scalar_dict["loss/g/kl"] = scalar_dict["loss/g/kl"] / (batch_num+1)
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logger.info("loss/g/mel : {} lloss/g/kl : {}".format(str(scalar_dict["loss/g/mel"]), str(scalar_dict["loss/g/kl"])))
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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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scalars=scalar_dict,
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)
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if __name__ == "__main__":
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main()
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