diff --git a/.gitignore b/.gitignore index fd0eb00..486b8fd 100644 --- a/.gitignore +++ b/.gitignore @@ -17,4 +17,5 @@ *.sh synthesizer/saved_models/* vocoder/saved_models/* +cp_hifigan/* !vocoder/saved_models/pretrained/* \ No newline at end of file diff --git a/README-CN.md b/README-CN.md index b741ab1..ca22f4f 100644 --- a/README-CN.md +++ b/README-CN.md @@ -58,9 +58,12 @@ * 预处理数据: `python vocoder_preprocess.py ` -* 训练声码器: +* 训练wavernn声码器: `python vocoder_train.py mandarin ` +* 训练hifigan声码器: +`python vocoder_train.py mandarin hifigan` + ### 3. 启动工具箱 然后您可以尝试使用工具箱： `python demo_toolbox.py -d ` diff --git a/README.md b/README.md index 36678d1..287bba0 100644 --- a/README.md +++ b/README.md @@ -61,9 +61,12 @@ Code：aid4 * Preprocess the data: `python vocoder_preprocess.py ` -* Train the vocoder: +* Train the wavernn vocoder: `python vocoder_train.py mandarin ` +* Train the hifigan vocoder +`python vocoder_train.py mandarin hifigan` + ### 3. Launch the Toolbox You can then try the toolbox: diff --git a/toolbox/__init__.py b/toolbox/__init__.py index 9f7c74e..08994b6 100644 --- a/toolbox/__init__.py +++ b/toolbox/__init__.py @@ -361,9 +361,10 @@ class Toolbox: # Sekect vocoder based on model name if model_fpath.name[0] == "g": vocoder = gan_vocoder - self.ui.log("vocoder is hifigan") + self.ui.log("set hifigan as vocoder") else: vocoder = rnn_vocoder + self.ui.log("set wavernn as vocoder") self.ui.log("Loading the vocoder %s... " % model_fpath) self.ui.set_loading(1) diff --git a/vocoder/hifigan/meldataset.py b/vocoder/hifigan/meldataset.py index 54a6a88..eb0682b 100644 --- a/vocoder/hifigan/meldataset.py +++ b/vocoder/hifigan/meldataset.py @@ -84,8 +84,8 @@ def get_dataset_filelist(a): files = os.listdir(a.input_wavs_dir) random.shuffle(files) files = [os.path.join(a.input_wavs_dir, f) for f in files] - training_files = files[: -500] - validation_files = files[-500: ] + training_files = files[: -int(len(files)*0.05)] + validation_files = files[-int(len(files)*0.05): ] return training_files, validation_files diff --git a/vocoder/hifigan/train.py b/vocoder/hifigan/train.py new file mode 100644 index 0000000..1914b27 --- /dev/null +++ b/vocoder/hifigan/train.py @@ -0,0 +1,240 @@ +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 vocoder.hifigan.env import AttrDict, build_env +from vocoder.hifigan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist +from vocoder.hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\ + discriminator_loss +from vocoder.hifigan.utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint + +torch.backends.cudnn.benchmark = True + + +def train(rank, a, h): + + a.checkpoint_path = a.models_dir.joinpath(a.run_id+'_hifigan') + a.checkpoint_path.mkdir(exist_ok=True) + a.training_epochs = 3100 + a.stdout_interval = 5 + a.checkpoint_interval = 25000 + a.summary_interval = 5000 + a.validation_interval = 1000 + a.fine_tuning = True + + a.input_wavs_dir = a.syn_dir.joinpath("audio") + a.input_mels_dir = a.syn_dir.joinpath("mels") + + 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)) + + generator = Generator(h).to(device) + mpd = MultiPeriodDiscriminator().to(device) + msd = MultiScaleDiscriminator().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]).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) + + # print(training_filelist) + # exit() + + 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) + 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) + + # 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_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_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}.pt".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)) + 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))) diff --git a/vocoder/vocoder_dataset.py b/vocoder/vocoder_dataset.py index 9eae1b5..3aedb09 100644 --- a/vocoder/vocoder_dataset.py +++ b/vocoder/vocoder_dataset.py @@ -1,7 +1,7 @@ from torch.utils.data import Dataset from pathlib import Path -from vocoder import audio -import vocoder.hparams as hp +from vocoder.wavernn import audio +import vocoder.wavernn.hparams as hp import numpy as np import torch diff --git a/vocoder_train.py b/vocoder_train.py index 224a65e..d3ad0f5 100644 --- a/vocoder_train.py +++ b/vocoder_train.py @@ -1,7 +1,10 @@ from utils.argutils import print_args from vocoder.wavernn.train import train +from vocoder.hifigan.train import train as train_hifigan +from vocoder.hifigan.env import AttrDict from pathlib import Path import argparse +import json if __name__ == "__main__": @@ -18,6 +21,9 @@ if __name__ == "__main__": parser.add_argument("datasets_root", type=str, help= \ "Path to the directory containing your SV2TTS directory. Specifying --syn_dir or --voc_dir " "will take priority over this argument.") + parser.add_argument("vocoder_type", type=str, default="wavernn", help= \ + "Choose the vocoder type for train. Defaults to wavernn" + "Now, Support and for choose") parser.add_argument("--syn_dir", type=str, default=argparse.SUPPRESS, help= \ "Path to the synthesizer directory that contains the ground truth mel spectrograms, " "the wavs and the embeds. Defaults to /SV2TTS/synthesizer/.") @@ -37,9 +43,9 @@ if __name__ == "__main__": "model.") parser.add_argument("-f", "--force_restart", action="store_true", help= \ "Do not load any saved model and restart from scratch.") + parser.add_argument("--config", type=str, default="vocoder/hifigan/config_16k_.json") args = parser.parse_args() - # Process the arguments if not hasattr(args, "syn_dir"): args.syn_dir = Path(args.datasets_root, "SV2TTS", "synthesizer") args.syn_dir = Path(args.syn_dir) @@ -50,7 +56,16 @@ if __name__ == "__main__": args.models_dir = Path(args.models_dir) args.models_dir.mkdir(exist_ok=True) - # Run the training print_args(args, parser) - train(**vars(args)) - \ No newline at end of file + + # Process the arguments + if args.vocoder_type == "wavernn": + # Run the training wavernn + train(**vars(args)) + elif args.vocoder_type == "hifigan": + with open(args.config) as f: + json_config = json.load(f) + h = AttrDict(json_config) + train_hifigan(0, args, h) + + \ No newline at end of file