Support train hifigan (#83)

* support train hifigan

.gitignore

@@ -17,4 +17,5 @@
 *.sh
 synthesizer/saved_models/*
 vocoder/saved_models/*
+cp_hifigan/*
 !vocoder/saved_models/pretrained/*

README-CN.md

@@ -58,9 +58,12 @@
 * 预处理数据:
 `python vocoder_preprocess.py <datasets_root>`
 
-* 训练声码器:
+* 训练wavernn声码器:
 `python vocoder_train.py mandarin <datasets_root>`
 
+* 训练hifigan声码器:
+`python vocoder_train.py mandarin <datasets_root> hifigan`
+
 ### 3. 启动工具箱
 然后您可以尝试使用工具箱：
 `python demo_toolbox.py -d <datasets_root>`

README.md

@@ -61,9 +61,12 @@ Code：aid4
 * Preprocess the data:
 `python vocoder_preprocess.py <datasets_root>`
 
-* Train the vocoder:
+* Train the wavernn vocoder:
 `python vocoder_train.py mandarin <datasets_root>`
 
+* Train the hifigan vocoder
+`python vocoder_train.py mandarin <datasets_root> hifigan`
+
 ### 3. Launch the Toolbox
 You can then try the toolbox:
 

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)

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
 

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)))

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
 

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 <hifigan> and <wavernn> 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 <datasets_root>/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))
-    
+
+    # 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)
+
+