mirror of
https://github.com/babysor/MockingBird.git
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05f886162c
* The new vocoder Fre-GAN is now supported * Improved some fregan details * Fixed the problem that the existing model could not be loaded to continue training when training GAN * Updated reference papers * GAN training now supports DistributedDataParallel (DDP) * Added requirements.txt * GAN training uses single card training by default * Added note about GAN vocoder training with multiple GPUs
254 lines
12 KiB
Python
254 lines
12 KiB
Python
import warnings
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warnings.simplefilter(action='ignore', category=FutureWarning)
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import itertools
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import os
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import time
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import argparse
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import json
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import torch
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import torch.nn.functional as F
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from torch.utils.tensorboard import SummaryWriter
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from torch.utils.data import DistributedSampler, DataLoader
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import torch.multiprocessing as mp
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from torch.distributed import init_process_group
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from torch.nn.parallel import DistributedDataParallel
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from vocoder.hifigan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
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from vocoder.hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
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discriminator_loss
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from vocoder.hifigan.utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
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torch.backends.cudnn.benchmark = True
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def train(rank, a, h):
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a.checkpoint_path = a.models_dir.joinpath(a.run_id+'_hifigan')
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a.checkpoint_path.mkdir(exist_ok=True)
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a.training_epochs = 3100
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a.stdout_interval = 5
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a.checkpoint_interval = a.backup_every
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a.summary_interval = 5000
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a.validation_interval = 1000
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a.fine_tuning = True
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a.input_wavs_dir = a.syn_dir.joinpath("audio")
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a.input_mels_dir = a.syn_dir.joinpath("mels")
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if h.num_gpus > 1:
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init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'],
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world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank)
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torch.cuda.manual_seed(h.seed)
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device = torch.device('cuda:{:d}'.format(rank))
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generator = Generator(h).to(device)
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mpd = MultiPeriodDiscriminator().to(device)
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msd = MultiScaleDiscriminator().to(device)
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if rank == 0:
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print(generator)
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os.makedirs(a.checkpoint_path, exist_ok=True)
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print("checkpoints directory : ", a.checkpoint_path)
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if os.path.isdir(a.checkpoint_path):
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cp_g = scan_checkpoint(a.checkpoint_path, 'g_hifigan_')
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cp_do = scan_checkpoint(a.checkpoint_path, 'do_hifigan_')
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steps = 0
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if cp_g is None or cp_do is None:
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state_dict_do = None
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last_epoch = -1
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else:
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state_dict_g = load_checkpoint(cp_g, device)
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state_dict_do = load_checkpoint(cp_do, device)
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generator.load_state_dict(state_dict_g['generator'])
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mpd.load_state_dict(state_dict_do['mpd'])
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msd.load_state_dict(state_dict_do['msd'])
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steps = state_dict_do['steps'] + 1
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last_epoch = state_dict_do['epoch']
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if h.num_gpus > 1:
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generator = DistributedDataParallel(generator, device_ids=[rank]).to(device)
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mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
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msd = DistributedDataParallel(msd, device_ids=[rank]).to(device)
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optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
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optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()),
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h.learning_rate, betas=[h.adam_b1, h.adam_b2])
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if state_dict_do is not None:
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optim_g.load_state_dict(state_dict_do['optim_g'])
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optim_d.load_state_dict(state_dict_do['optim_d'])
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)
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training_filelist, validation_filelist = get_dataset_filelist(a)
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# print(training_filelist)
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# exit()
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trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels,
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h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0,
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shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device,
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fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir)
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train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None
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train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False,
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sampler=train_sampler,
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batch_size=h.batch_size,
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pin_memory=True,
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drop_last=True)
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if rank == 0:
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validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels,
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h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0,
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fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning,
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base_mels_path=a.input_mels_dir)
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validation_loader = DataLoader(validset, num_workers=1, shuffle=False,
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sampler=None,
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batch_size=1,
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pin_memory=True,
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drop_last=True)
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sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'))
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generator.train()
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mpd.train()
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msd.train()
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for epoch in range(max(0, last_epoch), a.training_epochs):
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if rank == 0:
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start = time.time()
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print("Epoch: {}".format(epoch+1))
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if h.num_gpus > 1:
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train_sampler.set_epoch(epoch)
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for i, batch in enumerate(train_loader):
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if rank == 0:
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start_b = time.time()
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x, y, _, y_mel = batch
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x = torch.autograd.Variable(x.to(device, non_blocking=True))
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y = torch.autograd.Variable(y.to(device, non_blocking=True))
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y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
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y = y.unsqueeze(1)
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y_g_hat = generator(x)
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y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size,
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h.fmin, h.fmax_for_loss)
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if steps > h.disc_start_step:
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optim_d.zero_grad()
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# MPD
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y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
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loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
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# MSD
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y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach())
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loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
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loss_disc_all = loss_disc_s + loss_disc_f
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loss_disc_all.backward()
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optim_d.step()
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# Generator
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optim_g.zero_grad()
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# L1 Mel-Spectrogram Loss
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loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45
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if steps > h.disc_start_step:
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y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
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y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
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loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
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loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
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loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
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loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
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loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
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else:
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loss_gen_all = loss_mel
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loss_gen_all.backward()
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optim_g.step()
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if rank == 0:
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# STDOUT logging
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if steps % a.stdout_interval == 0:
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with torch.no_grad():
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mel_error = F.l1_loss(y_mel, y_g_hat_mel).item()
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print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.
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format(steps, loss_gen_all, mel_error, time.time() - start_b))
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# checkpointing
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if steps % a.checkpoint_interval == 0 and steps != 0:
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checkpoint_path = "{}/g_hifigan_{:08d}.pt".format(a.checkpoint_path, steps)
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save_checkpoint(checkpoint_path,
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{'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()})
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checkpoint_path = "{}/do_hifigan_{:08d}.pt".format(a.checkpoint_path, steps)
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save_checkpoint(checkpoint_path,
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{'mpd': (mpd.module if h.num_gpus > 1 else mpd).state_dict(),
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'msd': (msd.module if h.num_gpus > 1 else msd).state_dict(),
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'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps,
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'epoch': epoch})
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# Tensorboard summary logging
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if steps % a.summary_interval == 0:
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sw.add_scalar("training/gen_loss_total", loss_gen_all, steps)
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sw.add_scalar("training/mel_spec_error", mel_error, steps)
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# save temperate hifigan model
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if steps % a.save_every == 0:
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checkpoint_path = "{}/g_hifigan.pt".format(a.checkpoint_path)
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save_checkpoint(checkpoint_path,
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{'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()})
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checkpoint_path = "{}/do_hifigan.pt".format(a.checkpoint_path)
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save_checkpoint(checkpoint_path,
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{'mpd': (mpd.module if h.num_gpus > 1 else mpd).state_dict(),
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'msd': (msd.module if h.num_gpus > 1 else msd).state_dict(),
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'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps,
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'epoch': epoch})
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# Validation
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if steps % a.validation_interval == 0: # and steps != 0:
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generator.eval()
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torch.cuda.empty_cache()
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val_err_tot = 0
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with torch.no_grad():
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for j, batch in enumerate(validation_loader):
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x, y, _, y_mel = batch
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y_g_hat = generator(x.to(device))
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y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
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y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate,
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h.hop_size, h.win_size,
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h.fmin, h.fmax_for_loss)
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# val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()
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if j <= 4:
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if steps == 0:
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sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate)
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sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps)
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sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate)
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y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels,
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h.sampling_rate, h.hop_size, h.win_size,
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h.fmin, h.fmax)
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sw.add_figure('generated/y_hat_spec_{}'.format(j),
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plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps)
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val_err = val_err_tot / (j+1)
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sw.add_scalar("validation/mel_spec_error", val_err, steps)
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generator.train()
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steps += 1
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scheduler_g.step()
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scheduler_d.step()
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if rank == 0:
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print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start)))
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