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|
- 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 losses 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
- import logging
- from accelerate.logging import get_logger
- logger = get_logger(__name__, log_level="DEBUG")
- @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.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)
- log_interval = config.get('log_interval', 10)
- saving_epoch = config.get('save_freq', 2)
-
- data_params = config.get('data_params', None)
- sr = config['preprocess_params'].get('sr', 24000)
- train_path = data_params['train_data']
- val_path = data_params['val_data']
- root_path = data_params['root_path']
- min_length = data_params['min_length']
- OOD_data = data_params['OOD_data']
-
- max_len = config.get('max_len', 200)
-
- # load data
- train_list, val_list = get_data_path_list(train_path, val_path)
- train_dataloader = build_dataloader(train_list,
- root_path,
- OOD_data=OOD_data,
- min_length=min_length,
- batch_size=batch_size,
- num_workers=2,
- dataset_config={"sr": sr},
- device=device)
- val_dataloader = build_dataloader(val_list,
- root_path,
- OOD_data=OOD_data,
- min_length=min_length,
- batch_size=batch_size,
- validation=True,
- num_workers=0,
- device=device,
- dataset_config={"sr": sr})
-
- 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)
- # load BERT model
- from Utils.PLBERT.util import load_plbert
- BERT_path = config.get('PLBERT_dir', False)
- plbert = load_plbert(BERT_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, plbert)
- 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 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]
- # initialize optimizers after preparing models for compatibility with FSDP
- optimizer = build_optimizer({key: model[key].parameters() for key in model},
- scheduler_params_dict= {key: scheduler_params.copy() for key in model},
- lr=float(config['optimizer_params'].get('lr', 1e-4)))
-
- for k, v in optimizer.optimizers.items():
- optimizer.optimizers[k] = accelerator.prepare(optimizer.optimizers[k])
- optimizer.schedulers[k] = accelerator.prepare(optimizer.schedulers[k])
-
- 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
-
- # in case not distributed
- try:
- n_down = model.text_aligner.module.n_down
- except:
- n_down = model.text_aligner.n_down
-
- # wrapped losses for compatibility with mixed precision
- stft_loss = MultiResolutionSTFTLoss().to(device)
- gl = GeneratorLoss(model.mpd, model.msd).to(device)
- dl = DiscriminatorLoss(model.mpd, model.msd).to(device)
- wl = WavLMLoss(model_params.slm.model,
- model.wd,
- sr,
- model_params.slm.sr).to(device)
- for epoch in range(start_epoch, epochs):
- running_loss = 0
- start_time = time.time()
- _ = [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
-
- with torch.no_grad():
- mask = length_to_mask(mel_input_length // (2 ** n_down)).to('cuda')
- 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():
- 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, 0.0)
-
- with torch.no_grad():
- mask_ST = mask_from_lens(s2s_attn, input_lengths, mel_input_length // (2 ** n_down))
- s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
- # encode
- t_en = model.text_encoder(texts, input_lengths, text_mask)
- # 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 = []
- wav = []
- st = []
-
- 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)])
- y = waves[bib][(random_start * 2) * 300:((random_start+mel_len) * 2) * 300]
- wav.append(torch.from_numpy(y).to(device))
-
- # 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()
- wav = torch.stack(wav).float().detach()
- # clip too short to be used by the style encoder
- if gt.shape[-1] < 80:
- continue
-
- with torch.no_grad():
- real_norm = log_norm(gt.unsqueeze(1)).squeeze(1).detach()
- F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
-
- s = model.style_encoder(st.unsqueeze(1) if multispeaker else gt.unsqueeze(1))
-
- y_rec = model.decoder(en, F0_real, real_norm, s)
-
- # discriminator loss
-
- if epoch >= TMA_epoch:
- optimizer.zero_grad()
- d_loss = dl(wav.detach().unsqueeze(1).float(), y_rec.detach()).mean()
- accelerator.backward(d_loss)
- optimizer.step('msd')
- optimizer.step('mpd')
- else:
- d_loss = 0
- # generator loss
- optimizer.zero_grad()
- loss_mel = stft_loss(y_rec.squeeze(), wav.detach())
-
- 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)
- loss_mono = F.l1_loss(s2s_attn, s2s_attn_mono) * 10
-
- loss_gen_all = gl(wav.detach().unsqueeze(1).float(), y_rec).mean()
- loss_slm = wl(wav.detach(), y_rec).mean()
-
- g_loss = loss_params.lambda_mel * loss_mel + \
- loss_params.lambda_mono * loss_mono + \
- loss_params.lambda_s2s * loss_s2s + \
- loss_params.lambda_gen * loss_gen_all + \
- loss_params.lambda_slm * loss_slm
- else:
- loss_s2s = 0
- loss_mono = 0
- loss_gen_all = 0
- loss_slm = 0
- g_loss = loss_mel
-
- 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:
- log_print ('Epoch [%d/%d], Step [%d/%d], Mel Loss: %.5f, Gen Loss: %.5f, Disc Loss: %.5f, Mono Loss: %.5f, S2S Loss: %.5f, SLM Loss: %.5f'
- %(epoch+1, epochs, i+1, len(train_list)//batch_size, running_loss / log_interval, loss_gen_all, d_loss, loss_mono, loss_s2s, loss_slm), logger)
-
- writer.add_scalar('train/mel_loss', running_loss / log_interval, iters)
- writer.add_scalar('train/gen_loss', loss_gen_all, iters)
- writer.add_scalar('train/d_loss', d_loss, iters)
- writer.add_scalar('train/mono_loss', loss_mono, iters)
- writer.add_scalar('train/s2s_loss', loss_s2s, iters)
- writer.add_scalar('train/slm_loss', loss_slm, 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 ** n_down)).to('cuda')
- 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)
- 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, 0.0)
- # encode
- t_en = model.text_encoder(texts, input_lengths, text_mask)
-
- asr = (t_en @ s2s_attn)
- # 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 = []
- wav = []
- 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)])
- y = waves[bib][(random_start * 2) * 300:((random_start+mel_len) * 2) * 300]
- wav.append(torch.from_numpy(y).to('cuda'))
- wav = torch.stack(wav).float().detach()
- en = torch.stack(en)
- gt = torch.stack(gt).detach()
- F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
- s = model.style_encoder(gt.unsqueeze(1))
- real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
- y_rec = model.decoder(en, F0_real, real_norm, s)
- loss_mel = stft_loss(y_rec.squeeze(), wav.detach())
- loss_test += accelerator.gather(loss_mel).mean().item()
- iters_test += 1
- if accelerator.is_main_process:
- print('Epochs:', epoch + 1)
- log_print('Validation loss: %.3f' % (loss_test / iters_test) + '\n\n\n\n', logger)
- 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)
-
- with torch.no_grad():
- for bib in range(len(asr)):
- mel_length = int(mel_input_length[bib].item())
- gt = mels[bib, :, :mel_length].unsqueeze(0)
- en = asr[bib, :, :mel_length // 2].unsqueeze(0)
-
- F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
- s = model.style_encoder(gt.unsqueeze(1))
- real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
-
- y_rec = model.decoder(en, F0_real, real_norm, s)
-
- writer.add_audio('eval/y' + str(bib), y_rec.cpu().numpy().squeeze(), epoch, sample_rate=sr)
- if epoch == 0:
- writer.add_audio('gt/y' + str(bib), waves[bib].squeeze(), epoch, sample_rate=sr)
-
- if bib >= 6:
- break
- 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()
|