RingFormer/train.py

# import warnings
# warnings.simplefilter(action='ignore', category=FutureWarning)
# import itertools
# import os
# import time
# import argparse
# import json
# import torch
# import torch.nn.functional as F
# from torch.utils.tensorboard import SummaryWriter
# from torch.utils.data import DistributedSampler, DataLoader
# import torch.multiprocessing as mp
# from torch.distributed import init_process_group
# from torch.nn.parallel import DistributedDataParallel
# from env import AttrDict, build_env
# from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
# from models import Generator, MultiPeriodDiscriminator, feature_loss, generator_loss,\
#     discriminator_loss, discriminator_TPRLS_loss, generator_TPRLS_loss, MultiScaleSubbandCQTDiscriminator
# from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
# from stft import TorchSTFT
# from Utils.JDC.model import JDCNet

# torch.backends.cudnn.benchmark = True


# def train(rank, a, h):
#     if h.num_gpus > 1:
#         init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'],
#                            world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank)

#     torch.cuda.manual_seed(h.seed)
#     device = torch.device('cuda:{:d}'.format(rank))

#     F0_model = JDCNet(num_class=1, seq_len=192)
#     params = torch.load(h.F0_path)['net']
#     F0_model.load_state_dict(params)
    
#     generator = Generator(h, F0_model, device=device).to(device)
#     mpd = MultiPeriodDiscriminator().to(device)
#     msd = MultiScaleSubbandCQTDiscriminator().to(device)
#     stft = TorchSTFT(filter_length=h.gen_istft_n_fft, hop_length=h.gen_istft_hop_size, win_length=h.gen_istft_n_fft).to(device)

#     if rank == 0:
#         print(generator)
#         os.makedirs(a.checkpoint_path, exist_ok=True)
#         print("checkpoints directory : ", a.checkpoint_path)

#     if os.path.isdir(a.checkpoint_path):
#         cp_g = scan_checkpoint(a.checkpoint_path, 'g_')
#         cp_do = scan_checkpoint(a.checkpoint_path, 'do_')

#     steps = 0
#     if cp_g is None or cp_do is None:
#         state_dict_do = None
#         last_epoch = -1
#     else:
#         state_dict_g = load_checkpoint(cp_g, device)
#         state_dict_do = load_checkpoint(cp_do, device)
#         generator.load_state_dict(state_dict_g['generator'])
#         mpd.load_state_dict(state_dict_do['mpd'])
#         msd.load_state_dict(state_dict_do['msd'])
#         steps = state_dict_do['steps'] + 1
#         last_epoch = state_dict_do['epoch']

#     if h.num_gpus > 1:
#         generator = DistributedDataParallel(generator, device_ids=[rank], find_unused_parameters=True).to(device)
#         mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
#         msd = DistributedDataParallel(msd, device_ids=[rank]).to(device)

#     optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2])
#     optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()),
#                                 h.learning_rate, betas=[h.adam_b1, h.adam_b2])

#     if state_dict_do is not None:
#         optim_g.load_state_dict(state_dict_do['optim_g'])
#         optim_d.load_state_dict(state_dict_do['optim_d'])

#     scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch)
#     scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch)

#     training_filelist, validation_filelist = get_dataset_filelist(a)

#     trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels,
#                           h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0,
#                           shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device,
#                           fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir)

#     train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None

#     train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False,
#                               sampler=train_sampler,
#                               batch_size=h.batch_size,
#                               pin_memory=True,
#                               drop_last=True)
    
    
    

#     if rank == 0:
#         validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels,
#                               h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0,
#                               fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning,
#                               base_mels_path=a.input_mels_dir)
#         validation_loader = DataLoader(validset, num_workers=1, shuffle=False,
#                                        sampler=None,
#                                        batch_size=1,
#                                        pin_memory=True,
#                                        drop_last=True)

#         sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'))

#     generator.train()
#     mpd.train()
#     msd.train()
#     for epoch in range(max(0, last_epoch), a.training_epochs):
#         if rank == 0:
#             start = time.time()
#             print("Epoch: {}".format(epoch+1))

#         if h.num_gpus > 1:
#             train_sampler.set_epoch(epoch)

#         for i, batch in enumerate(train_loader):
#             if rank == 0:
#                 start_b = time.time()
#             x, y, _, y_mel = batch
#             x = torch.autograd.Variable(x.to(device, non_blocking=True))
#             y = torch.autograd.Variable(y.to(device, non_blocking=True))
#             y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
#             y = y.unsqueeze(1)
#             # y_g_hat = generator(x)
#             spec, phase = generator(x)

#             y_g_hat = stft.inverse(spec, phase)

#             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,
#                                           h.fmin, h.fmax_for_loss)

#             optim_d.zero_grad()

#             # MPD
#             y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
#             loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
#             loss_disc_f += discriminator_TPRLS_loss(y_df_hat_r, y_df_hat_g)
            
#             # MSD
#             y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach())
#             loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
#             loss_disc_s += discriminator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)
            
#             loss_disc_all = loss_disc_s + loss_disc_f

#             loss_disc_all.backward()
#             optim_d.step()

#             # Generator
#             optim_g.zero_grad()

#             # L1 Mel-Spectrogram Loss
#             loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45

#             y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
#             y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
#             loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
#             loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
#             loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
#             loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
            
#             loss_gen_f += generator_TPRLS_loss(y_df_hat_r, y_df_hat_g)
#             loss_gen_s += generator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)

#             loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel

#             loss_gen_all.backward()
#             optim_g.step()

#             if rank == 0:
#                 # STDOUT logging
#                 if steps % a.stdout_interval == 0:
#                     with torch.no_grad():
#                         mel_error = F.l1_loss(y_mel, y_g_hat_mel).item()

#                     print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.
#                           format(steps, loss_gen_all, mel_error, time.time() - start_b))

#                 # checkpointing
#                 if steps % a.checkpoint_interval == 0 and steps != 0:
#                     checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps)
#                     save_checkpoint(checkpoint_path,
#                                     {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()})
#                     checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps)
#                     save_checkpoint(checkpoint_path, 
#                                     {'mpd': (mpd.module if h.num_gpus > 1
#                                                          else mpd).state_dict(),
#                                      'msd': (msd.module if h.num_gpus > 1
#                                                          else msd).state_dict(),
#                                      'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps,
#                                      'epoch': epoch})

#                 # Tensorboard summary logging
#                 if steps % a.summary_interval == 0:
#                     sw.add_scalar("training/gen_loss_total", loss_gen_all, steps)
#                     sw.add_scalar("training/mel_spec_error", mel_error, steps)

#                 # Validation
#                 if steps % a.validation_interval == 0:  # and steps != 0:
#                     generator.eval()
#                     torch.cuda.empty_cache()
#                     val_err_tot = 0
#                     with torch.no_grad():
#                         for j, batch in enumerate(validation_loader):
#                             x, y, _, y_mel = batch
#                             # y_g_hat = generator(x.to(device))
#                             spec, phase = generator(x.to(device))

#                             y_g_hat = stft.inverse(spec, phase)

#                             y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
#                             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,
#                                                           h.fmin, h.fmax_for_loss)
#                             val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()

#                             if j <= 4:
#                                 if steps == 0:
#                                     sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate)
#                                     sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps)

#                                 sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate)
#                                 y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels,
#                                                              h.sampling_rate, h.hop_size, h.win_size,
#                                                              h.fmin, h.fmax)
#                                 sw.add_figure('generated/y_hat_spec_{}'.format(j),
#                                               plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps)

#                         val_err = val_err_tot / (j+1)
#                         sw.add_scalar("validation/mel_spec_error", val_err, steps)

#                     generator.train()

#             steps += 1

#         scheduler_g.step()
#         scheduler_d.step()
        
#         if rank == 0:
#             print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start)))


# def main():
#     print('Initializing Training Process..')

#     parser = argparse.ArgumentParser()

#     parser.add_argument('--group_name', default=None)
#     parser.add_argument('--input_wavs_dir', default='')
#     parser.add_argument('--input_mels_dir', default='ft_dataset')
#     # parser.add_argument('--input_training_file', default='/home/ubuntu/RINGFORMER/LJSpeech-1.1/training.txt')
#     parser.add_argument('--input_training_file', default='/home/ubuntu/RINGFORMER/LJSpeech-1.1/validation.txt')
#     parser.add_argument('--input_validation_file', default='/home/ubuntu/RINGFORMER/LJSpeech-1.1/validation.txt')
#     parser.add_argument('--checkpoint_path', default='cp_ringformer_24')
#     parser.add_argument('--config', default='config_v1.json')
#     parser.add_argument('--training_epochs', default=3100, type=int)
#     parser.add_argument('--stdout_interval', default=5, type=int)
#     parser.add_argument('--checkpoint_interval', default=5000, type=int)
#     parser.add_argument('--summary_interval', default=100, type=int)
#     parser.add_argument('--validation_interval', default=1000, type=int)
#     parser.add_argument('--fine_tuning', default=False, type=bool)

#     a = parser.parse_args()

#     with open(a.config) as f:
#         data = f.read()

#     json_config = json.loads(data)
#     h = AttrDict(json_config)
#     build_env(a.config, 'config.json', a.checkpoint_path)

#     torch.manual_seed(h.seed)
#     if torch.cuda.is_available():
#         torch.cuda.manual_seed(h.seed)
#         h.num_gpus = torch.cuda.device_count()
#         h.batch_size = int(h.batch_size / h.num_gpus)
#         print('Batch size per GPU :', h.batch_size)
#     else:
#         pass

#     if h.num_gpus > 1:
#         mp.spawn(train, nprocs=h.num_gpus, args=(a, h,))
#     else:
#         train(0, a, h)


# if __name__ == '__main__':
#     main()


import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import itertools
import os
import time
import argparse
import json
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DistributedSampler, DataLoader
import torch.multiprocessing as mp
from torch.distributed import init_process_group
from torch.nn.parallel import DistributedDataParallel
from env import AttrDict, build_env
from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
from models import Generator, MultiPeriodDiscriminator, feature_loss, generator_loss,\
    discriminator_loss, discriminator_TPRLS_loss, generator_TPRLS_loss, MultiScaleSubbandCQTDiscriminator
from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
from stft import TorchSTFT
from Utils.JDC.model import JDCNet
from accelerate import Accelerator
from accelerate.utils import LoggerType
from accelerate import DistributedDataParallelKwargs


torch.backends.cudnn.benchmark = True


def train(accelerator, a, h):
    # if h.num_gpus > 1:
    #     init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'],
    #                        world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank)
        


    torch.cuda.manual_seed(h.seed)
    device = accelerator.device

    F0_model = JDCNet(num_class=1, seq_len=192)
    params = torch.load(h.F0_path)['model']
    F0_model.load_state_dict(params)
    
    generator = Generator(h, F0_model).to(device)
    mpd = MultiPeriodDiscriminator().to(device)
    msd = MultiScaleSubbandCQTDiscriminator().to(device)
    stft = TorchSTFT(filter_length=h.gen_istft_n_fft, hop_length=h.gen_istft_hop_size, win_length=h.gen_istft_n_fft).to(device)

    with accelerator.main_process_first():
        accelerator.print(generator)
        os.makedirs(a.checkpoint_path, exist_ok=True)
        accelerator.print("checkpoints directory : ", a.checkpoint_path)

    if os.path.isdir(a.checkpoint_path):
        cp_g = scan_checkpoint(a.checkpoint_path, 'g_')
        cp_do = scan_checkpoint(a.checkpoint_path, 'do_')

    steps = 0
    if cp_g is None or cp_do is None:
        state_dict_do = None
        last_epoch = -1
    else:
        state_dict_g = load_checkpoint(cp_g, device)
        state_dict_do = load_checkpoint(cp_do, device)
        generator.load_state_dict(state_dict_g['generator'])
        mpd.load_state_dict(state_dict_do['mpd'])
        msd.load_state_dict(state_dict_do['msd'])
        steps = state_dict_do['steps'] + 1
        last_epoch = state_dict_do['epoch']

    # if h.num_gpus > 1:
    generator = accelerator.prepare(generator).to(device)
    mpd = accelerator.prepare(mpd).to(device)
    msd = accelerator.prepare(msd).to(device)

    optim_g = accelerator.prepare(torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]))
    optim_d = accelerator.prepare(torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()),
                                h.learning_rate, betas=[h.adam_b1, h.adam_b2]))
    
    

    if state_dict_do is not None:
        optim_g.load_state_dict(state_dict_do['optim_g'])
        optim_d.load_state_dict(state_dict_do['optim_d'])

    scheduler_g = accelerator.prepare(torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch))
    scheduler_d = accelerator.prepare(torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch))

    training_filelist, validation_filelist = get_dataset_filelist(a)

    trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels,
                          h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0,
                          shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device,
                          fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir)

    # train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None
    
    accelerator.print("bs", h.batch_size)

    train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False,
                            #   sampler=train_sampler,
                              batch_size=h.batch_size,
                              pin_memory=True,
                              drop_last=True)
    
    
    train_loader = accelerator.prepare(train_loader)
    
    

    validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels,
                            h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0,
                            fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning,
                            base_mels_path=a.input_mels_dir)
    
    
    
    validation_loader = DataLoader(validset, num_workers=1, shuffle=False,
                                    sampler=None,
                                    batch_size=1,
                                    pin_memory=True,
                                    drop_last=True)

    sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'))
    
    # validation_loader = accelerator.prepare(validation_loader)

    generator.train()
    mpd.train()
    msd.train()
    for epoch in range(max(0, last_epoch), a.training_epochs):
        with accelerator.main_process_first():
            start = time.time()
            accelerator.print("Epoch: {}".format(epoch+1))

        # if h.num_gpus > 1:
        #     train_sampler.set_epoch(epoch)

        for i, batch in enumerate(train_loader):
            # with accelerator.main_process_first():
            start_b = time.time()
            x, y, _, y_mel = batch
            x = torch.autograd.Variable(x.to(device, non_blocking=True))
            y = torch.autograd.Variable(y.to(device, non_blocking=True))
            y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
            y = y.unsqueeze(1)
            # y_g_hat = generator(x)
            spec, phase = generator(x)

            y_g_hat = stft.inverse(spec, phase)

            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,
                                          h.fmin, h.fmax_for_loss)

            optim_d.zero_grad()

            # MPD
            y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach())
            loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)
            loss_disc_f += discriminator_TPRLS_loss(y_df_hat_r, y_df_hat_g)
            
            # MSD
            y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach())
            loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g)
            loss_disc_s += discriminator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)
            
            loss_disc_all = loss_disc_s + loss_disc_f
            
            accelerator.backward(loss_disc_all)
            # accelerator.clip_grad_norm_(mpd.parameters(), max_norm=10.0)
            # accelerator.clip_grad_norm_(msd.parameters(), max_norm=10.0)
            
            # loss_disc_all.backward()
            optim_d.step()

            # Generator
            optim_g.zero_grad()

            # L1 Mel-Spectrogram Loss
            loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45

            y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
            y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
            loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
            loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
            loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
            loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
            
            loss_gen_f += generator_TPRLS_loss(y_df_hat_r, y_df_hat_g)
            loss_gen_s += generator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)

            loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
            
            
            accelerator.backward(loss_gen_all)
            
            # accelerator.clip_grad_norm_(generator.parameters(), max_norm=10.0)

            # loss_gen_all.backward()
            optim_g.step()
            
            # accelerator.print('done +',i)


            # STDOUT logging
            if steps % a.stdout_interval == 0:
                
                with accelerator.main_process_first():
                    
                    with torch.no_grad():
                        mel_error = F.l1_loss(y_mel, y_g_hat_mel).item()

                    accelerator.print('Steps : {:d}, Gen Loss Total : {:4.3f},  Disc Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.
                            format(steps, loss_gen_all, loss_disc_all, mel_error, time.time() - start_b))

                # checkpointing
                # In the main code:
                    # if steps % a.checkpoint_interval == 0 and steps != 0:
                    # # with accelerator.main_process_first():
                    #     # Unwrap the generator
                    #     unwrapped_generator = accelerator.unwrap_model(generator)
                    #     checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps)
                    #     save_checkpoint(checkpoint_path,
                    #                 {'generator': unwrapped_generator.state_dict()})
                        
                    #     # Unwrap discriminators
                    #     unwrapped_mpd = accelerator.unwrap_model(mpd)
                    #     unwrapped_msd = accelerator.unwrap_model(msd)
                        
                    #     checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps)
                    #     save_checkpoint(checkpoint_path,
                    #                 {'mpd': unwrapped_mpd.state_dict(),
                    #                     'msd': unwrapped_msd.state_dict(),
                    #                     'optim_g': optim_g.state_dict(), 
                    #                     'optim_d': optim_d.state_dict(), 
                    #                     'steps': steps,
                    #                     'epoch': epoch})
                    
                    
                
                    
                if steps % a.checkpoint_interval == 0 and steps != 0:
                    
                    with accelerator.main_process_first():   
                        checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps)
                        save_checkpoint(checkpoint_path,
                                        {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()})
                        checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps)
                        save_checkpoint(checkpoint_path, 
                                        {'mpd': (mpd.module if h.num_gpus > 1
                                                            else mpd).state_dict(),
                                        'msd': (msd.module if h.num_gpus > 1
                                                            else msd).state_dict(),
                                        'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps,
                                        'epoch': epoch})

                    # Tensorboard summary logging
                    if steps % a.summary_interval == 0:
                        sw.add_scalar("training/gen_loss_total", loss_gen_all, steps)
                        sw.add_scalar("training/mel_spec_error", mel_error, steps)

                # Validation
                if steps % a.validation_interval == 0:  # and steps != 0:
                    
                    # with accelerator.main_process_first():
                    accelerator.print('evalution...')
                        
                    
                    
                    generator.eval()
                    torch.cuda.empty_cache()
                    val_err_tot = 0
                    with torch.no_grad():
                        for j, batch in enumerate(validation_loader):
                            x, y, _, y_mel = batch
                            # y_g_hat = generator(x.to(device))
                            spec, phase = generator(x.to('cuda'))

                            y_g_hat = stft.inverse(spec, phase)
                            
                            

                            y_mel = torch.autograd.Variable(y_mel.to('cuda', non_blocking=True))
                            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,
                                                        h.fmin, h.fmax_for_loss)
                            
                            accelerator.print('done: ',j)
                            
                            
                            val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()

                            if j <= 4:
                                if steps == 0:
                                    sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate)
                                    sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps)
                                    

                                sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate)
                                y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels,
                                                            h.sampling_rate, h.hop_size, h.win_size,
                                                            h.fmin, h.fmax)
                                
                                
                                sw.add_figure('generated/y_hat_spec_{}'.format(j),
                                            plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps)
                                
                                
                        
                        val_err = val_err_tot / (j+1)
                        sw.add_scalar("validation/mel_spec_error", val_err, steps)
                    

                generator.train()

            steps += 1

        scheduler_g.step()
        scheduler_d.step()
        
        # with accelerator.main_process_first():
        accelerator.print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start)))


def main():
    
    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)

    

    parser = argparse.ArgumentParser()

    parser.add_argument('--group_name', default=None)
    parser.add_argument('--input_wavs_dir', default='')
    parser.add_argument('--input_mels_dir', default='ft_dataset')
    # parser.add_argument('--input_training_file', default='/home/ubuntu/RINGFORMER/LJSpeech-1.1/training.txt')
    parser.add_argument('--input_training_file', default='/home/ubuntu/respair/audio_files_over_2sec_SUB.txt')
    parser.add_argument('--input_validation_file', default='/home/ubuntu/RINGFORMER/LJSpeech-1.1/validation.txt')
    parser.add_argument('--checkpoint_path', default='cp_ringformer_44.1khz_NUM2')
    parser.add_argument('--config', default='config_v1.json')
    parser.add_argument('--training_epochs', default=3100, type=int)
    parser.add_argument('--stdout_interval', default=10, type=int)
    parser.add_argument('--checkpoint_interval', default=1000, type=int)
    parser.add_argument('--summary_interval', default=100, type=int)
    parser.add_argument('--validation_interval', default=1000, type=int)
    parser.add_argument('--fine_tuning', default=False, type=bool)

    a = parser.parse_args()
    
    accelerator = Accelerator(project_dir=a.checkpoint_path, split_batches=False, 
                            kwargs_handlers=[ddp_kwargs])
  
    accelerator.print('Initializing Training Process..')

    with open(a.config) as f:
        data = f.read()

    json_config = json.loads(data)
    h = AttrDict(json_config)
    build_env(a.config, 'config.json', a.checkpoint_path)

    torch.manual_seed(h.seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(h.seed)
        h.num_gpus = torch.cuda.device_count()
        # h.batch_size = int(h.batch_size / h.num_gpus)
        # print('Batch size per GPU :', h.batch_size)
    else:
        pass
  # Pass accelerator to train function instead of rank
    train(accelerator, a, h)

if __name__ == '__main__':
    main()