train_first.py

import os
import os.path as osp
import re
import sys
import yaml
import shutil
import numpy as np
import torch
import click
import warnings
warnings.simplefilter('ignore')

# load packages
import random
import yaml
from munch import Munch
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
import librosa

from models import *
from meldataset import build_dataloader
from utils import *
from optimizers import build_optimizer
import time

from accelerate import Accelerator
from accelerate.utils import LoggerType
from accelerate import DistributedDataParallelKwargs

from torch.utils.tensorboard import SummaryWriter

# # simple fix for dataparallel that allows access to class attributes
# class MyDataParallel(torch.nn.DataParallel):
#     def __getattr__(self, name):
#         try:
#             return super().__getattr__(name)
#         except AttributeError:
#             return getattr(self.module, name)

import logging
from logging import StreamHandler
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
handler = StreamHandler()
handler.setLevel(logging.DEBUG)
logger.addHandler(handler)

@click.command()
@click.option('-p', '--config_path', default='Configs/config.yml', type=str)
def main(config_path):

    config = yaml.safe_load(open(config_path))

    log_dir = config['log_dir']
    if not osp.exists(log_dir): os.makedirs(log_dir, exist_ok=True)
    shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))

    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
    accelerator = Accelerator(project_dir=log_dir, split_batches=True, kwargs_handlers=[ddp_kwargs])
    if accelerator.is_main_process:
        writer = SummaryWriter(log_dir + "/tensorboard")

    # write logs
    file_handler = logging.FileHandler(osp.join(log_dir, 'train.log'))
    file_handler.setLevel(logging.DEBUG)
    file_handler.setFormatter(logging.Formatter('%(levelname)s:%(asctime)s: %(message)s'))
    logger.addHandler(file_handler)

    batch_size = config.get('batch_size', 10)
    device = accelerator.device
    epochs = config.get('epochs_1st', 200)
    save_freq = config.get('save_freq', 2)
    train_path = config.get('train_data', None)
    val_path = config.get('val_data', None)
    multigpu = config.get('multigpu', False)
    log_interval = config.get('log_interval', 10)
    saving_epoch = config.get('save_freq', 2)

    # load data
    train_list, val_list = get_data_path_list(train_path, val_path)

    train_dataloader = build_dataloader(train_list,
                                        batch_size=batch_size,
                                        num_workers=8,
                                        dataset_config={},
                                        device=device)

    val_dataloader = build_dataloader(val_list,
                                      batch_size=batch_size,
                                      validation=True,
                                      num_workers=2,
                                      device=device,
                                      dataset_config={})

    with accelerator.main_process_first():                                      
      # load pretrained ASR model
      ASR_config = config.get('ASR_config', False)
      ASR_path = config.get('ASR_path', False)
      text_aligner = load_ASR_models(ASR_path, ASR_config)

      # load pretrained F0 model
      F0_path = config.get('F0_path', False)
      pitch_extractor = load_F0_models(F0_path)

      scheduler_params = {
          "max_lr": float(config['optimizer_params'].get('lr', 1e-4)),
          "pct_start": float(config['optimizer_params'].get('pct_start', 0.0)),
          "epochs": epochs,
          "steps_per_epoch": len(train_dataloader),
      }

    model_params = recursive_munch(config['model_params'])
    multispeaker = model_params.multispeaker
    model = build_model(model_params, text_aligner, pitch_extractor)
    max_len = config.get('max_len', 200)

    for k in model:
        model[k] = accelerator.prepare(model[k])

    train_dataloader, val_dataloader = accelerator.prepare(
        train_dataloader, val_dataloader
    )
    _ = [model[key].to(device) for key in model]

    optimizer = build_optimizer({key: model[key].parameters() for key in model},
                                      scheduler_params_dict= {key: scheduler_params.copy() for key in model})
    
    for k, v in optimizer.optimizers.items():
        optimizer.optimizers[k] = accelerator.prepare(optimizer.optimizers[k])
        optimizer.schedulers[k] = accelerator.prepare(optimizer.schedulers[k])

    # # multi-GPU support
    # if multigpu:
    #     for key in model:
    #         model[key] = MyDataParallel(model[key])

    with accelerator.main_process_first():
        if config.get('pretrained_model', '') != '':
            model, optimizer, start_epoch, iters = load_checkpoint(model,  optimizer, config['pretrained_model'],
                                        load_only_params=config.get('load_only_params', True))
        else:
            start_epoch = 0
            iters = 0


    try:
      n_down = model.text_aligner.module.n_down

    except:
      n_down = model.text_aligner.n_down

    best_loss = float('inf')  # best test loss
    loss_train_record = list([])
    loss_test_record = list([])

    loss_params = Munch(config['loss_params'])
    TMA_epoch = loss_params.TMA_epoch

    for epoch in range(start_epoch, epochs):
        running_loss = 0
        start_time = time.time()
        criterion = nn.L1Loss() 

        _ = [model[key].train() for key in model]

        for i, batch in enumerate(train_dataloader):

            waves = batch[0]
            batch = [b.to(device) for b in batch[1:]]
            texts, input_lengths, mels, mel_input_length, _ = batch
            
            mask = length_to_mask(mel_input_length // (2 ** n_down)).to('cuda')
            m = length_to_mask(input_lengths)

            text_mask = length_to_mask(input_lengths).to(texts.device)
            ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)

            s2s_attn = s2s_attn.transpose(-1, -2)
            s2s_attn = s2s_attn[..., 1:]
            s2s_attn = s2s_attn.transpose(-1, -2)
            
            with torch.no_grad():
                text_mask = length_to_mask(input_lengths).to(texts.device)
                attn_mask = (~mask).unsqueeze(-1).expand(mask.shape[0], mask.shape[1], text_mask.shape[-1]).float().transpose(-1, -2)
                attn_mask = attn_mask.float() * (~text_mask).unsqueeze(-1).expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1]).float()
                attn_mask = (attn_mask < 1)
                
            s2s_attn.masked_fill_(attn_mask, -float("inf"))


            # get monotonic version 
            with torch.no_grad():
                mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** model.text_aligner.n_down))
                s2s_attn_mono = maximum_path(s2s_attn, mask_ST)

            s2s_attn = torch.nan_to_num(s2s_attn)

            # encode
            t_en = model.text_encoder(texts, input_lengths, m)
            print(f"t_en shape: {t_en.shape}")

            # 50% of chance of using monotonic version
            if bool(random.getrandbits(1)):
                asr = (t_en @ s2s_attn)
            else:
                asr = (t_en @ s2s_attn_mono)

            # get clips
            mel_input_length_all = accelerator.gather(mel_input_length) # for balanced load
            mel_len = min([int(mel_input_length_all.min().item() / 2 - 1), max_len // 2])
            mel_len_st = int(mel_input_length.min().item() / 2 - 1)

            en = []
            gt = []
            st = [] # STTS2

            for bib in range(len(mel_input_length)):
                mel_length = int(mel_input_length[bib].item() / 2)

                random_start = np.random.randint(0, mel_length - mel_len)
                en.append(asr[bib, :, random_start:random_start + mel_len])
                gt.append(mels[bib, :, (random_start * 2):((random_start + mel_len) * 2)])

                # style reference (better to be different from the GT)
                random_start = np.random.randint(0, mel_length - mel_len_st)
                st.append(mels[bib, :, (random_start * 2):((random_start + mel_len_st) * 2)])

            en = torch.stack(en)
            gt = torch.stack(gt).detach()
            st = torch.stack(st).detach()

            # clip too short to be used by the style encoder
            if gt.shape[-1] < 80:
                continue

            real_norm = log_norm(gt.unsqueeze(1)).squeeze(1).detach()
            F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))

            s = model.style_encoder(gt.unsqueeze(1))

            # reconstruction
            mel_rec = model.decoder(en, F0_real, real_norm, s)

            # discriminator loss
            optimizer.zero_grad()
            gt.requires_grad_()
            out, _ = model.discriminator(gt.unsqueeze(1))
            loss_real = adv_loss(out, 1)
            loss_reg = r1_reg(out, gt)
            out, _ = model.discriminator(mel_rec.detach().unsqueeze(1))
            loss_fake = adv_loss(out, 0)
            d_loss = loss_real + loss_fake + loss_reg * loss_params.lambda_reg
            accelerator.backward(d_loss)
            optimizer.step('discriminator')

            # generator loss
            optimizer.zero_grad()
            loss_mel = criterion(mel_rec, gt)

            if epoch > TMA_epoch: # start TMA training
                loss_s2s = 0
                for _s2s_pred, _text_input, _text_length in zip(s2s_pred, texts, input_lengths):
                    loss_s2s += F.cross_entropy(_s2s_pred[:_text_length], _text_input[:_text_length])
                loss_s2s /= texts.size(0)

                # L1 loss for monotonic alignment
                loss_mono = F.l1_loss(s2s_attn, s2s_attn_mono) * 10
            else:
                loss_s2s = 0
                loss_mono = 0

            # adversarial loss
            with torch.no_grad():
                _, f_real = model.discriminator(gt.unsqueeze(1))
            out_rec, f_fake = model.discriminator(mel_rec.unsqueeze(1))
            loss_adv = adv_loss(out_rec, 1)
            # feature matching loss
            loss_fm = 0
            for m in range(len(f_real)):
                for k in range(len(f_real[m])):
                    loss_fm += torch.mean(torch.abs(f_real[m][k] - f_fake[m][k])) 

            g_loss = loss_params.lambda_mel * loss_mel + \
                loss_params.lambda_adv * loss_adv + \
                loss_params.lambda_fm * loss_fm + \
                loss_params.lambda_mono * loss_mono + \
                loss_params.lambda_s2s * loss_s2s

            running_loss += accelerator.gather(loss_mel).mean().item()
            accelerator.backward(g_loss)

            optimizer.step('text_encoder')
            optimizer.step('style_encoder')
            optimizer.step('decoder')

            if epoch > TMA_epoch: 
                optimizer.step('text_aligner')
                optimizer.step('pitch_extractor')

            iters = iters + 1
            if (i+1)%log_interval == 0 and accelerator.is_main_process:
                logger.info ('Epoch [%d/%d], Step [%d/%d], Mel Loss: %.5f, Adv Loss: %.5f, Disc Loss: %.5f, Mono Loss: %.5f, S2S Loss: %.5f'
                        %(epoch+1, epochs, i+1, len(train_list)//batch_size, running_loss / log_interval, loss_adv.item(), d_loss.item(), loss_mono, loss_s2s))
                
                writer.add_scalar('train/mel_loss', running_loss / log_interval, iters)
                writer.add_scalar('train/adv_loss', loss_adv.item(), iters)
                writer.add_scalar('train/d_loss', d_loss.item(), iters)
                writer.add_scalar('train/mono_loss', loss_mono, iters)
                writer.add_scalar('train/s2s_loss', loss_s2s, iters)
                
                running_loss = 0
                print('Time elasped:', time.time()-start_time)

        loss_test = 0

        _ = [model[key].eval() for key in model]

        with torch.no_grad():
            iters_test = 0
            for batch_idx, batch in enumerate(val_dataloader):
                optimizer.zero_grad()

                waves = batch[0]
                batch = [b.to(device) for b in batch[1:]]
                texts, input_lengths, mels, mel_input_length, _ = batch

                with torch.no_grad():
                    mask = length_to_mask(mel_input_length // (2 ** model.text_aligner.n_down)).to('cuda')
                    m = length_to_mask(input_lengths)
                    ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)

                    s2s_attn = s2s_attn.transpose(-1, -2)
                    s2s_attn = s2s_attn[..., 1:]
                    s2s_attn = s2s_attn.transpose(-1, -2)

                    with torch.no_grad():
                        text_mask = length_to_mask(input_lengths).to(texts.device)
                        attn_mask = (~mask).unsqueeze(-1).expand(mask.shape[0], mask.shape[1], text_mask.shape[-1]).float().transpose(-1, -2)
                        attn_mask = attn_mask.float() * (~text_mask).unsqueeze(-1).expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1]).float()
                        attn_mask = (attn_mask < 1)

                    s2s_attn.masked_fill_(attn_mask, -float("inf"))

                    # get monotonic version 
                    with torch.no_grad():
                        mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** model.text_aligner.n_down))
                        s2s_attn_mono = maximum_path(s2s_attn, mask_ST)

                    s2s_attn = torch.nan_to_num(s2s_attn)

                # encode
                t_en = model.text_encoder(texts, input_lengths, m)
                asr = (t_en @ s2s_attn_mono)

                # get clips
                mel_input_length_all = accelerator.gather(mel_input_length)  # for balanced load
                mel_len = min([int(mel_input_length.min().item() / 2 - 1), max_len // 2])

                en = []
                gt = []
                for bib in range(len(mel_input_length)):
                    mel_length = int(mel_input_length[bib].item() / 2)

                    random_start = np.random.randint(0, mel_length - mel_len)
                    en.append(asr[bib, :, random_start:random_start+mel_len])
                    gt.append(mels[bib, :, (random_start * 2):((random_start+mel_len) * 2)])

                en = torch.stack(en)
                gt = torch.stack(gt).detach()

                with torch.no_grad():
                    F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
                    F0 = F0.reshape(F0.shape[0], F0.shape[1] * 2, F0.shape[2], 1).squeeze()

                # reconstruct
                s = model.style_encoder(gt.unsqueeze(1))
                real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
                mel_rec = model.decoder(en, F0_real, real_norm, s)
                mel_rec = mel_rec[..., :gt.shape[-1]]

                loss_mel = criterion(mel_rec, gt)

                loss_test += accelerator.gather(loss_mel).mean().item()
                iters_test += 1

        if accelerator.is_main_process:
          print('Epochs:', epoch + 1)
          logger.info('Validation mel loss: %.3f' % (loss_test / iters_test))
          print('\n\n\n')

          writer.add_scalar('eval/mel_loss', loss_test / iters_test, epoch + 1)
          attn_image = get_image(s2s_attn[0].cpu().numpy().squeeze())
          writer.add_figure('eval/attn', attn_image, epoch)
          mel_image = get_image(mel_rec[0].cpu().numpy().squeeze())
          writer.add_figure('eval/mel_rec', mel_image, epoch)

          if epoch % saving_epoch == 0:
              if (loss_test / iters_test) < best_loss:
                  best_loss = loss_test / iters_test
              print('Saving..')
              state = {
                  'net':  {key: model[key].state_dict() for key in model}, 
                  'optimizer': optimizer.state_dict(),
                  'iters': iters,
                  'val_loss': loss_test / iters_test,
                  'epoch': epoch,
              }
              save_path = osp.join(log_dir, 'epoch_1st_%05d.pth' % epoch)
              torch.save(state, save_path)            
            
    if accelerator.is_main_process:
        print('Saving..')
        state = {
            'net':  {key: model[key].state_dict() for key in model}, 
            'optimizer': optimizer.state_dict(),
            'iters': iters,
            'val_loss': loss_test / iters_test,
            'epoch': epoch,
        }
        save_path = osp.join(log_dir, config.get('first_stage_path', 'first_stage.pth'))
        torch.save(state, save_path)
    
if __name__=="__main__":
    main()