401 lines
18 KiB
Python
401 lines
18 KiB
Python
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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#from tensorboardX 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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from tqdm import tqdm
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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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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DurationDiscriminator,
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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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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
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torch.backends.cudnn.allow_tf32 = True
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torch.set_float32_matmul_precision('medium')
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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'] = '65280'
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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 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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dist.init_process_group(backend='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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train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
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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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[32, 300, 400, 500, 600, 700, 800, 900, 1000],
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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=24, shuffle=False, pin_memory=True,
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collate_fn=collate_fn, batch_sampler=train_sampler, persistent_workers=True,prefetch_factor=4)
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if rank == 0:
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eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
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eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
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batch_size=1, pin_memory=True,
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drop_last=False, collate_fn=collate_fn)
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if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
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print("Using noise scaled MAS for VITS2")
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use_noise_scaled_mas = True
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mas_noise_scale_initial = 0.01
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noise_scale_delta = 2e-6
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else:
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print("Using normal MAS for VITS1")
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use_noise_scaled_mas = False
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mas_noise_scale_initial = 0.0
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noise_scale_delta = 0.0
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if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
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print("Using duration discriminator for VITS2")
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use_duration_discriminator = True
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net_dur_disc = DurationDiscriminator(
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hps.model.hidden_channels,
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hps.model.hidden_channels,
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3,
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0.1,
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gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
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).cuda(rank)
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if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
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if hps.data.n_speakers == 0:
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raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
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use_spk_conditioned_encoder = True
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else:
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print("Using normal encoder for VITS1")
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use_spk_conditioned_encoder = False
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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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mas_noise_scale_initial = mas_noise_scale_initial,
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noise_scale_delta = noise_scale_delta,
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**hps.model).cuda(rank)
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freeze_enc = getattr(hps.model, "freeze_enc", False)
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if freeze_enc:
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print("freeze encoder !!!")
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for param in net_g.enc_p.parameters():
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param.requires_grad = False
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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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filter(lambda p: p.requires_grad, 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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if net_dur_disc is not None:
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optim_dur_disc = torch.optim.AdanW(
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net_dur_disc.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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else:
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optim_dur_disc = None
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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],find_unused_parameters=True)
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pretrain_dir = None
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if pretrain_dir is None:
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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=True)
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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=True)
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if net_dur_disc is not None:
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=True
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epoch_str = max(epoch_str, 1)
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global_step = (epoch_str - 1) * len(train_loader)
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except Exception as e:
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print(e)
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epoch_str = 1
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global_step = 0
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else:
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
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optim_g, True)
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
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optim_d, True)
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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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if net_dur_disc is not None:
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scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
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else:
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scheduler_dur_disc = None
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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, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], 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, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
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scheduler_g.step()
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scheduler_d.step()
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if net_dur_disc is not None:
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cheduler_dur_disc.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, net_dur_disc = nets
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optim_g, optim_d, optim_dur_disc = optims
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scheduler_g, scheduler_d, scheduler_dur_disc = 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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if net_dur_disc is not None:
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net_dur_disc.train()
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
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if net_g.module.use_noise_scaled_mas:
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current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
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net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
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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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tone = tone.cuda(rank, non_blocking=True)
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language = language.cuda(rank, non_blocking=True)
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bert = bert.cuda(rank, non_blocking=True)
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with autocast(enabled=hps.train.fp16_run):
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y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
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(z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
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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_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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if net_dur_disc is not None:
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y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
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with autocast(enabled=False):
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# TODO: I think need to mean using the mask, but for now, just mean all
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loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
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loss_dur_disc_all = loss_dur_disc
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optim_dur_disc.zero_grad()
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scaler.scale(loss_dur_disc_all).backward()
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scaler.unscale_(optim_dur_disc)
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grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
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scaler.step(optim_dur_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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if net_dur_disc is not None:
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y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
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with autocast(enabled=False):
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loss_dur = torch.sum(l_length.float())
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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_dur + loss_kl
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if net_dur_disc is not None:
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loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
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loss_gen_all += loss_dur_gen
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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_dur, 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([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,
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"grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
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scalar_dict.update(
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{"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
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if net_dur_disc is not None:
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scalar_dict.update({"loss/dur_disc/total": loss_dur_disc_all, "grad_norm_dur_disc": grad_norm_dur_disc})
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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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if net_dur_disc is not None:
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scalar_dict.update({"loss/dur_disc_r" : f"{losses_dur_disc_r}"})
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scalar_dict.update({"loss/dur_disc_g" : f"{losses_dur_disc_g}"})
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scalar_dict.update({"loss/dur_gen" : f"{loss_dur_gen}"})
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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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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', 3)
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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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logger.info('====> Epoch: {}'.format(epoch))
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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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print("Evaluating ...")
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with torch.no_grad():
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
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x, x_lengths = x.cuda(), x_lengths.cuda()
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spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
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y, y_lengths = y.cuda(), y_lengths.cuda()
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speakers = speakers.cuda()
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bert = bert.cuda()
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tone = tone.cuda()
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language = language.cuda()
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for use_sdp in [True, False]:
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y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
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y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
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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_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
|
|
)
|
|
image_dict.update({
|
|
f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
|
|
})
|
|
audio_dict.update({
|
|
f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
|
|
})
|
|
image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
|
|
audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
|
|
|
|
utils.summarize(
|
|
writer=writer_eval,
|
|
global_step=global_step,
|
|
images=image_dict,
|
|
audios=audio_dict,
|
|
audio_sampling_rate=hps.data.sampling_rate
|
|
)
|
|
generator.train()
|
|
|
|
if __name__ == "__main__":
|
|
main()
|