330 lines
14 KiB
Python
330 lines
14 KiB
Python
import logging
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import multiprocessing
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import time
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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logging.getLogger('numba').setLevel(logging.WARNING)
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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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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.cuda.amp import autocast, GradScaler
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import modules.commons as commons
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import utils
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from data_utils import TextAudioSpeakerLoader, TextAudioCollate
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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 modules.losses import (
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kl_loss,
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generator_loss, discriminator_loss, feature_loss
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)
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from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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torch.backends.cudnn.benchmark = True
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global_step = 0
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start_time = time.time()
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# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
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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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hps = utils.get_hparams()
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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'] = hps.train.port
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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 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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# for pytorch on win, backend use gloo
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dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', 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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collate_fn = TextAudioCollate()
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all_in_mem = hps.train.all_in_mem # If you have enough memory, turn on this option to avoid disk IO and speed up training.
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train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem)
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num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count()
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if all_in_mem:
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num_workers = 0
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train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True,
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batch_size=hps.train.batch_size, collate_fn=collate_fn)
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if rank == 0:
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eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem)
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eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
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batch_size=1, pin_memory=False,
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drop_last=False, collate_fn=collate_fn)
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net_g = SynthesizerTrn(
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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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**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 = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True)
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net_d = DDP(net_d, device_ids=[rank])
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skip_optimizer = False
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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,
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optim_g, skip_optimizer)
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
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optim_d, skip_optimizer)
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epoch_str = max(epoch_str, 1)
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name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth")
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global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1
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#global_step = (epoch_str - 1) * len(train_loader)
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except:
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print("load old checkpoint failed...")
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epoch_str = 1
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global_step = 0
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if skip_optimizer:
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epoch_str = 1
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global_step = 0
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warmup_epoch = hps.train.warmup_epochs
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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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# set up warm-up learning rate
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if epoch <= warmup_epoch:
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for param_group in optim_g.param_groups:
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param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
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for param_group in optim_d.param_groups:
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param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
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# training
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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,
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[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,
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[train_loader, None], None, None)
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# update learning rate
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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, items in enumerate(train_loader):
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c, f0, spec, y, spk, lengths, uv = items
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g = spk.cuda(rank, non_blocking=True)
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spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
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c = c.cuda(rank, non_blocking=True)
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f0 = f0.cuda(rank, non_blocking=True)
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uv = uv.cuda(rank, non_blocking=True)
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lengths = lengths.cuda(rank, non_blocking=True)
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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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with autocast(enabled=hps.train.fp16_run):
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y_hat, ids_slice, z_mask, \
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(z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
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spec_lengths=lengths)
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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_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_lf0 = F.mse_loss(pred_lf0, lf0)
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
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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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reference_loss=0
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for i in losses:
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reference_loss += i
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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(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}")
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scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
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"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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"loss/g/lf0": loss_lf0})
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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/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
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pred_lf0[0, 0, :].detach().cpu().numpy()),
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"all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
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norm_lf0[0, 0, :].detach().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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)
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if global_step % hps.train.eval_interval == 0:
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evaluate(hps, net_g, eval_loader, writer_eval)
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utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
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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,
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os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
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keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
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if keep_ckpts > 0:
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utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
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global_step += 1
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if rank == 0:
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global start_time
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now = time.time()
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durtaion = format(now - start_time, '.2f')
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logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
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start_time = now
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def evaluate(hps, generator, eval_loader, writer_eval):
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generator.eval()
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image_dict = {}
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audio_dict = {}
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with torch.no_grad():
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for batch_idx, items in enumerate(eval_loader):
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c, f0, spec, y, spk, _, uv = items
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g = spk[:1].cuda(0)
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spec, y = spec[:1].cuda(0), y[:1].cuda(0)
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c = c[:1].cuda(0)
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f0 = f0[:1].cuda(0)
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uv= uv[:1].cuda(0)
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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_hat,_ = generator.module.infer(c, f0, uv, g=g)
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y_hat_mel = mel_spectrogram_torch(
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y_hat.squeeze(1).float(),
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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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audio_dict.update({
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f"gen/audio_{batch_idx}": y_hat[0],
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f"gt/audio_{batch_idx}": y[0]
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})
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image_dict.update({
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f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
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"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
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})
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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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audios=audio_dict,
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audio_sampling_rate=hps.data.sampling_rate
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)
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generator.train()
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if __name__ == "__main__":
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main()
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