Develop

__init__.py

@@ -0,0 +1,4 @@
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self

inference.py

@@ -6,9 +6,9 @@
 import json
 import torch
 from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
-from models import Generator
+from .env import AttrDict
+from .meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
+from .models import Generator
 
 h = None
 device = None

inference_e2e.py

@@ -7,9 +7,9 @@
 import json
 import torch
 from scipy.io.wavfile import write
-from env import AttrDict
-from meldataset import MAX_WAV_VALUE
-from models import Generator
+from .env import AttrDict
+from .meldataset import MAX_WAV_VALUE
+from .models import Generator
 
 h = None
 device = None

meldataset.py

@@ -74,11 +74,11 @@ def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin,
 
 def get_dataset_filelist(a):
     with open(a.input_training_file, 'r', encoding='utf-8') as fi:
-        training_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')
+        training_files = [os.path.join(a.input_wavs_dir, x.split('|')[1] + '.wav')
                           for x in fi.read().split('\n') if len(x) > 0]
 
     with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
-        validation_files = [os.path.join(a.input_wavs_dir, x.split('|')[0] + '.wav')
+        validation_files = [os.path.join(a.input_wavs_dir, x.split('|')[1] + '.wav')
                             for x in fi.read().split('\n') if len(x) > 0]
     return training_files, validation_files
 

models.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from utils import init_weights, get_padding
+from .utils import init_weights, get_padding
 
 LRELU_SLOPE = 0.1
 

train.py

@@ -12,11 +12,11 @@
 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, MultiScaleDiscriminator, feature_loss, generator_loss,\
+from .env import AttrDict, build_env
+from .meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
+from .models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
     discriminator_loss
-from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
+from .utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
 
 torch.backends.cudnn.benchmark = True
 
@@ -196,7 +196,8 @@ def train(rank, a, h):
                             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()
+                            # val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item()
+                            val_err_tot += F.l1_loss(y_mel, y_g_hat_mel[:,:,:y_mel.size(2)]).item()
 
                             if j <= 4:
                                 if steps == 0:
@@ -242,6 +243,7 @@ def main():
     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)
+    parser.add_argument('--batch_size', default=8, type=int)
 
     a = parser.parse_args()
 
@@ -255,6 +257,7 @@ def main():
     torch.manual_seed(h.seed)
     if torch.cuda.is_available():
         torch.cuda.manual_seed(h.seed)
+        h.batch_size = a.batch_size
         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)